33rd European conference

on Operational
Research
30th June – 3rd July 2024

Conference
PROGRAMConfirmed Exhibitors & Sponsors
PLATINUM SPONSORSGOLD SPONSORSSILVER SPONSORSOTHER SPONSORSOTHER EXHIBITORSISBN 978-87-93458-26-0
Contact Information
E-mail: euro2024cph@discongress.com
Website: euro2024cph.dk
Tag: #euro2024cph

Technical Program
Sunday, 16:30-17:30
 SF-01
Sunday, 16:30-17:30 - Room: Sportshallen (building: 101)

Opening Session
Stream: Opening and Closing
Plenary session3

MA-01EURO 2024 - Copenhagen

Monday, 8:30-10:00
 MA-01
Monday, 8:30-10:00 - Room: Sportshallen (building: 101)

Graham Rand
Stream: Keynotes
Keynote session
Chair: Gerhard-Wilhelm Weber
1 - To be OR - key Danish contributors to the history of OR,
IFORS and EURO

Graham Rand
The surprising fact that EURO24, the 33rd EURO conference, is thefirst hosted by Denmark, provides the opportunity to give a personalreflection on the Danish contribution to OR history. There will be anemphasis on four individuals who have played a significant role in thedevelopment of OR theory and professional infrastructure. Three ofthese individuals will be revealed at the lecture. For now, it is sufficient to say that the lecture is in honour of my dear friend Jakob
Krarup, who passed away last year. He was greatly looking forward to
the conference being held in his own city.
From this description it will be obvious that this will not be a standard keynote address. However, there may be one, or three, equations
included. That is a clue to one of the other three individuals whose
contribution will be noted.

result for a primal-dual regularized IPM. By inheriting the polynomial
complexity of IPMs and the numerical stability of PMMs, IP-PMM
offers a promising solution.
To enhance the applicability of our approach, we discuss generalpurpose preconditioning strategies for efficiently solving the associated linear systems within IP-PMM. Subsequently, we present numerical results spanning a diverse range of convex programming problems,
showcasing the benefits of regularization in IPMs and affirming thereliability and efficiency of the proposed IP-PMM algorithm.

2 - Demand Management in Shared Mobility Systems

Matthias Soppert
Shared mobility systems like car sharing and bike sharing have become an attractive and wide-spread type of urban mobility over the
past decades. The biggest challenge regarding the profitable operationof such systems is the occurring dynamic imbalance between supply
and demand, which stems from fluctuating demand patterns and spatially unbalanced vehicle movements. To counter these imbalances,
the scientific literature so far has focused on the supply-sided control
approach by means of active vehicle relocation. In this dissertation, demand management is proposed as a cost-efficient alternative, in which
the system’s demand side is influenced through pricing and availability control. On the one hand, specific practice-relevant problems are
addressed and solved. On the other hand, general modeling and solution approaches are developed, which can be transferred to related
optimization problems for tactical and operational control of sharedmobility systems. Extensive numerical studies, including case studies of Europe’s largest car sharing company Share Now, demonstratethat demand management can be implemented successfully in sharedmobility systems.

3 - Enhancing classification and regression trees. A mathematical optimization approach

Cristina Molero-Río

 MA-02
Monday, 8:30-10:00 - Room: Glassalen (building: 101)

EDDA
Stream: EURO Doctoral Dissertation Award
Award Competition session
Chair: Laura Palagi
1 - Regularized Interior Point Methods for Convex Programming

Spyridon Pougkakiotis
Large-scale convex optimization is integral to several application domains, given its amenability to efficient solution methods and its versatile modeling capabilities. Interior Point Methods (IPMs) represent
a widely adopted class of optimization techniques for solving convex
problems due to their ability to yield accurate solutions with a polynomial complexity guarantee. However, challenges such as numericalinaccuracy and ill-posedness of the underlying optimization problem
can impede their performance.
To address these challenges, researchers have explored regularized versions of IPMs, which exhibit improved robustness in practical scenarios. Despite the practical success of regularized IPMs, a comprehensive theoretical understanding has been lacking until recently.
In this talk, we present an infeasible IPM combined with the Proximal
Method of Multipliers (PMM). The resulting algorithm (IP-PMM) isinterpreted as a primal-dual regularized IPM, suitable for solving convex programming problems. We apply few iterations of the interior
point method to each sub-problem of the proximal method of multipliers. Once a satisfactory solution of the PMM sub-problem is found,
we update the PMM parameters, form a new IPM neighbourhood, andrepeat this process.
Crucially, we demonstrate the polynomial complexity of the algorithm
for a broad class of convex problems under standard assumptions,marking a significant advancement as the first polynomial complexity

4

Contrary to classic classification and regression trees, built in a greedy
heuristic manner, designing the tree model through an optimization
problem allows us to easily include desirable properties in Machine
Learning in addition to prediction accuracy. We present a Non-Linear
Optimization approach that is scalable with respect to the size of the
training sample, and illustrate this flexibility to model several important issues in Explainable and Fair Machine Learning. These include
sparsity, as a proxy for interpretability, by reducing the amount of information necessary to predict well; fairness, by aiming to avoid predictions that discriminate against sensitive features such as gender orrace; the cost-sensitivity for groups of individuals in which prediction
errors are more critical, such as patients of a disease, by ensuring an
acceptable accuracy performance for them; local explainability, where
the goal is to identify the predictor variables that have the largest impact on the individual predictions; as well as data complexity in theform of observations of functional nature. The performance of our approach is illustrated on real and synthetic data sets.

 MA-03
Monday, 8:30-10:00 - Room: 1005 (building: 202)

Industrial Optimization
Stream: Data Science Meets Optimization
Invited session
Chair: Grzegorz Pawlak
1 - Research on Optimization of Air Material Joint Allocation under Multi-level Inventory Model

Chenyang Wang, Jianwu Xue, Jiawei Tuo
With the rapid development of the aviation industry, airlines of various countries have to store a large amount of aviation materials at
airports and maintenance bases in order to ensure the normal operationof flights. However, this has resulted in a backlog of aircraft inventory,
which undoubtedly imposes significant depreciation and administrative costs on airlines. In this paper, we address the aircraft material

EURO 2024 - Copenhagenallocation problem faced by airlines under the multi-base and multitier aircraft material inventory distribution model, and mathematicallyabstract the factors affecting the allocation of aircraft material. By
constructing a mathematical optimization model with the lowest total
avionics management cost of the whole inventory system as the objective function, and the avionics guarantee rate and downtime as theconstraints, we aim to optimize the number of avionics allocation of
the avionics base as a whole, in order to reduce the amount of avionics
inventory, so as to achieve the purpose of reducing the inventory cost.
Taking China Eastern Airlines, one of the three major airlines in China,
the high-priced turnover parts of aviation materials as the research object, the mathematical simulation model is prepared using Matlab software. It confirms that the two-level inventory avionics management
model proposed in this paper has certain advantages and provides a
new solution for airlines in finding the balance between avionics protection and avionics management cost.

2 - Classifying import containers based on cargo contents:
an unsupervised text classification

Ying Xie, Dongping Song
Post unloading from vessels, import containers are temporarily stacked
at seaport yards before being transported to inland customers. A significant challenge faced at container ports is the optimal storage of import containers, including proximity to out-terminals and minimizing
reshuffling needs. However, uncertainties surrounding collection times
and transportation mode pose challenges for port operators. Nevertheless, port operators possess knowledge of each import container’s contents upon unloading from vessels. An interesting research question
arises:can cargo content information be leveraged to enhance container
storage management? Addressing this question entails categorising
import containers into manageable classes based on cargo contents.
This study tackles the challenge of categorising a high variety of cargo
contents, often exceeding 100K unique items, using the Standard International Trade Classification as labels. Our novel unsupervised
text classification approach employs pretrained Glove Word Embeddings and Cosine Similarity for label assignment. This integration offers an efficient approach to generate training datasets in data-scarcescenarios. Additionally, we address data imbalance through threestrategies:upsampling/downsampling, class weights, and weighted loss
function. Leveraging the Transformers based neural-network models
on enriched and balanced datasets, we achieve promising results in accurately classifying container content.

3 - Long-term maintenance optimization for integrated
mining operations

Yingying Yang
Maintenance activities are inevitable and costly in integrated mining
operations. Conducting maintenance may require the whole system,or sub-units of the system, to be shut down temporarily. These maintenance activities not only disrupt the unit being shut down, but theyalso have consequences for inventory levels and product flow downstream. In this paper, we consider an interconnected mining system inwhich there are complicated maintenance relationships and stock accumulation at intermediate nodes. We propose a time-indexed mixedinteger linear programming formulation to optimize the long-term integrated maintenance plan and maximize the total throughput. We also
devise an algorithm, which combines Benders decomposition and Lagrangian relaxation, to accelerate the computational speed. To validate
our mathematical model, we perform simulations for a real-world casestudy in the iron ore industry. The results show that our method canyield better solutions than CPLEX optimization solver alone in fastertime.

4 - Approximate dynamic programming for inland empty
container inventory management

Sangmin Lee, Trine Krogh Boomsma
Empty container repositioning (ECR) is crucial in handling global
trade imbalances by managing the flow and storage of empty containers to effectively accommodate customer demands and returns. We formulate a Markov decision process that accounts for practical characteristics of ECR, including multiple transportation modes and lead times,

MA-04

and uncertainty and serial correlation in net container inflows. To determine a cost-minimising repositioning policy for real-life scenarios,
we employ a stochastic approximate dynamic programming approach,integrated with statistical techniques, such as convex regression and
Latin hyper cube sampling. We highlight the advantages of incorporating exogenous variables into the approximation model. A case study
with historical daily data on empty container in- and out-flows demonstrates the effective control of on-hand inventory levels while optimising holding and leasing costs. Moreover, we quantify the benefits ofleveraging all transportation modes, with cost reduction potentials of
up to 26.05%. Finally, we evaluate the robustness of our algorithm
under variations in key parameters.

 MA-04
Monday, 8:30-10:00 - Room: 1001 (building: 202)

Recent Methodologies in Explainable AI(XAI) 1
Stream: Recent Advancements in AI
Invited session
Chair: Erkut Arican
1 - Mixture of Experts-based Coherent Probabilistic Forecasts for Hierarchically related Time Series

Junyong Lee, Jun-Geol Baek
Coherent probabilistic forecasting, where the goal is to forecast multivariate time series that have hierarchical aggregation is important for
accurate decision making in real world application. Recent work has
focused on end-to-end fashion that simultaneously learns from all timeseries in the hierarchy and comprise the reconciliation step in single
trainable model. However, this approach often neglects that distinct
advantages of several reconciliation methods, where is adept to identifying temporal characteristics. To address this gap, our study proposesa novel coherent probabilistic forecasting framework with mixture-ofexperts (MoE). Employing the MoE framework, our approach effectively captures both regular and irregular temporal dependencies tointegrate three diverse strategies: top-down, bottom-up and fully coherent. By introducing different experts, this model can generate forecasts that are probabilistically coherent as well as produce sophisticated forecasts that encompass diverse temporal dynamics. An empirical evaluation on real world hierarchical time series datasets demonstrates comparable advantages of the proposed approach.

2 - Using long short-term memory network for human body
depth image motion recognition and abnormal behavior
analysis in long-term care environment

JongChen Chen
Long-term care has become one of the problems that must be addressedin today’s society, and falls are one of the problems often encountered in long-term care. However, most previous studies have used
accelerometers to detect falls in older adults. Since this sensor must
be placed on the body of the elderly, its effect is quite limited in situations where the willingness to wear it is extremely low. This study uses
deep image recognition technology to conveniently track the real-time
behavior of the elderly and reduce the problems caused by accidents
under the appropriate protection of the subjects’ privacy. This researchuses a Long Short-Term Memory Network (LSTM) to identify daily
human movements and detect abnormal behaviors (such as falls). In
this study, two scenarios were tested. The first is to invite ten healthypeople to perform in a laboratory that simulates long-term exposure.
Each subject performed nine actions that might occur in the long-termcare environment. The second scenario is to apply the database established in the first scenario to an actual long-term photo field. The experimental results show that under the simulated laboratory, the overall
performance is quite good. In contrast, in the actual long-term photofield, the performance of motion recognition will be reduced due to the
influence of occlusions5

MA-07EURO 2024 - Copenhagen

3 - Contrastive Transfer Learning Based on Damage Propagation for Remaining Useful Life Prediction

Seunghwan Song, Jeena Son, Junyong Lee, Jun-Geol Baek
Remaining useful life (RUL) prediction is a crucial task in prognostics and health management (PHM), gaining increasing importance inindustrial applications. However, the accuracy of RUL predictions
in real-world industrial can be compromised due to data imbalanceand discrepancies in data characteristics caused by varying operational
conditions. Inspired by the idea of transfer learning, this study proposes a novel RUL prediction framework that utilizes damage propagation and contrastive transfer learning. At first, the proposed method
differentiates between health and degradation states within source domain data on a window basis, employing the augmented Dickey-Fuller(ADF) test and acceleration graphs. Then, it predicts the RUL by
training on the history subset of degradation state data to maximize
the similarity with the multi-scale feature representation of future subset. In the target domain, a comparable strategy is employed but withadjusted sensitivity towards the acceleration graph for more precise
state differentiation, enabling the application of the model learned in
the source domain to predict the RUL in the target domain. Finally,
the proposed method is proved to be effective through two public runto-failure datasets and one real-world dataset. Demonstrated throughexperimental results, the proposed method shows superior predictive
performance and high transferability, indicating its potential to address
the challenges in industrial environments.

 MA-07
Monday, 8:30-10:00 - Room: 1019 (building: 202)

Cutting and Packing 1 - 2D rectangularStream: Cutting and Packing (ESICUP)
Invited session
Chair: Sergey Polyakovskiy
1 - A Constraint Programming Solution to the Guillotine
Rectangular Cutting Problem

Sergey Polyakovskiy, Peter Stuckey
The guillotine rectangular cutting problem deals with a single rectangular plate of raw material and a collection of rectangular items to be
cut from the plate. Each item is associated with a profit and a demand.
The problem searches for a feasible layout of a subset of items on theplate so as to maximize the total profit of the selected items. The guillotine constraint restricts feasible layouts to those that can be obtained
via guillotine edge-to-edge cuts that run parallel to an edge of the plate.
We propose a novel constraint programming model that is suitable for
guillotine cutting with an arbitrary number of stages of alternating horizontal and vertical guillotine cuts. This is an assignment-based modelthat models guillotine cuts using a constant number of rectangular regions, with some regions allocated to items. It treats the entire plate asa primary region and decides on the guillotine cuts required to split theregions recursively till they produce space for the items. To speed thesearch, the model explores the strength of cumulative scheduling relaxations of the cutting problem. Our model is a successful alternative
to more traditional mixed-integer linear programming (MIP) models.
It outperforms a number of state-of-the-art MIPs on a set of small- andmoderate-sized benchmark instances and proves optimality for several
instances that remain challenging for these MIPs.

2 - Exact Mixed-Integer and Constraint Programming solutions to the two-dimensional bin packing problem withdue dates

Milad Dehghan, Sergey Polyakovskiy
The two-dimensional non-oriented bin packing problem with due dates
consists in packing a set of rectangular items, which may be rotated by
90 degrees, into identical rectangular bins. The bins have equal processing times. The problem searches for a feasible layout of items so

6

as to minimize the maximum lateness, with the lateness of each item
being the difference between its due date and the completion time of
its assigned bin. This minimax problem is computationally challenging: It has a huge solution space with many alternative solutions whose
packing configurations are either symmetric or lead to the same solution cost.
We propose two exact approaches. First, we solve the problem as amixed-integer program (MIP), which we strengthen with a set of feasibility and symmetry-breaking constraints. Our second solution employs constraint programming (CP), and thus benefits from the strengthof cumulative scheduling relaxations of the packing problem. Our extensive computational experiments show that MIP, which stands as amore traditional modelling technique within the cutting and packing
community, is only successful on small-sized benchmark instanceswith as few as 60 items. It is outperformed by CP that can solve
medium and large instances with up to 100 items in a reasonable time.

3 - Primal and Dual Bounds to the Variable-Sized TwoDimensional Guillotine Bin Packing Problem

Adam McGregor, Sergey Polyakovskiy
Given a set of small rectangular items and large non-identical rectangular bins, the variable-sized two-dimensional guillotine bin packing
problem searches for a feasible packing of the items into the bins, with
the items obtained via guillotine cuts. It minimises the total cost of theused bins. We propose a hybrid column generation based matheuristicthat solves the problem approximately. Its reduced master problem,
which is solved as an integer program, is the classical Dantzig-Wolfe
formulation of the VS2BP. The master problem uses a collection offeasible packings that are constructed heuristically during the search
process. The initial set of packings is obtained via a heuristic, which
selects and packs bins sequentially. This warm-start heuristic employsa look-ahead mechanism that prohibits the search of infeasible directions and enforces directions of likely advance, halting only after the
allocated time expires. After this, the incumbent is enhanced by solving a series of pricing problems for each bin type by finding a feasible
packing with the largest reduced cost, providing extra columns for the
master problem. The search ends either when no solution to the pricing
problem is found or when time expires. The packing process is done
approximately via an algorithm that hybridizes constraint programming with a heuristic search. Extensive computational experiments
provide evidence of good performance of the proposed approach, whencompared to the state of the art.

4 - Two-Phase Matheuristic for Assignment and Packing

Jakob Schulte, Daniel Wetzel
We introduce a novel two-phase matheuristic for assignment and packing (2P-MAP) designed to address large-scale assignment and truck
loading problems. The scope of our approach involves scenarios withan extensive amount of items requiring assignment to a heterogeneous
fleet of trucks and subsequent transportation. The proposed matheuristic strategically tackles the assignment problem in its first phase, followed by a complex truck loading algorithm to validate the assignment
solution in the second phase. A distinguishing feature of our heuristiclies in its comprehensive exploration of the solution space, enabling theidentification of optimal solutions. Moreover, our approach exhibits
seamless adaptability to diverse use cases, given that subproblems are
optimized in isolation before being integrated. This approach is motivated by the ROADEF/EURO challenge 2022, where we demonstratesuperior performance on select instances, showcasing the potential forsignificant cost reduction in Renault’s Supply Chains.

 MA-08
Monday, 8:30-10:00 - Room: 1020 (building: 202)

Portfolio optimization and sustainability
Stream: AI & Innovation in Sustainable FinanceInvited session
Chair: Filippo PiccottoEURO 2024 - Copenhagen
1 - Assessing the impact of Sustainability disclosure incrowdfunding: a Natural Language Processing analysis.

Simona Barone
This paper aims to initiate a dialogue between fintech and sustainability by evaluating the impact of sustainability disclosure on theoutcomes of 370 Italian crowdfunding campaigns, collected from theKickstarter platform between 2015 and 2024. Utilizing an NLP technique called zero-shot classification, we analyzed and classified the
blurbs of the campaigns as sustainable, employing a rigorous decisionframework. The model utilized was the BART-large model, trainedwith the MultiNLI dataset. The blurbs classified as sustainable by
the model were further validated through human supervision to ensure
accuracy. Furthermore, leveraging this classification, we constructeda measure of sustainability, and with a probit regression model, we
assessed the impact of sustainability on the success of crowdfunding
rounds. Several control variables were included such as the funding
goal, campaign duration, blurb length, and the number of investors.
The findings indicate that sustainability has a positive and significant
impact. Disclosing sustainability information increases the probabilityof success by 7.73%. Additionally, the number of investors also has apositive and significant effect, while the funding goal has a negative effect. This paper contributes to the literature by providing evidence thatsustainability acts as a signal of quality for investors, who are more inclined to invest in sustainable projects, resulting in a higher probabilityof success.

2 - Enhancing portfolio optimization: an ADMM approachwith embedded splitting for scenario-based model predictive control

Marc Weibel
This paper contributes towards developing an efficient algorithm, relying on the Alternating-Direction of Multipliers (ADMM), for solving
scenario-based Model Predictive Control arising in multi-period portfolio optimization problems.
We enhance the standard two-set splitting algorithm of the ADMM
method by including inequality constraints through a so-called embedded splitting without recourse to an additional splitting set.
We derive an alteration of the termination criterion using the probabilities assigned to the scenarios and provide a convergence analysis.

3 - Behavioural portfolio optimization with sustainable attitudes: an application to the STOXX Europe 600

Filippo Piccotto, Massimiliano Kaucic
Since its introduction, cumulative prospect theory (CPT) has emerged
as a significant approach for modeling the irrationality of the decision
makers’ behavior in complex real situations. Moreover, the interplay
between rational finance theories and behavioral finance framework is
becoming an intriguing stream of research. To harmonize these twoparadigms, a recent contribution in the literature proposed a modification of the PT value function and several alternative probability weighting functions to extend the classical CPT model. The contribution ofthis paper is twofold. On the one hand, our study delves into these alternative value functions and proposes a CPT-based portfolio optimization model that aligns with rational dynamic asset pricing models while
ensuring Environmental, Social, and Governance (ESG) compliance in
the investment process. Furthermore, our portfolio design integrates
cardinality, box, and budget constraints, while an upper bound for the
portfolio turnover maintains control of the transaction costs during therebalancing phases. On the other hand, to tackle the ensuing optimization problem, we propose a novel hybrid constraint-handling techniqueembedded into a level-based learning swarm optimizer. Finally, using
data from the STOXX Europe 600 Index, we test the profitability ofthe proposed portfolio optimization strategy. This is a joint work withProfessor Massimiliano Kaucic, who will be co-presenter of the paper
at the Conference.

MA-09

 MA-09
Monday, 8:30-10:00 - Room: 10 (building: 116)

Modelling and Economics of Storage
Technologies in Energy Markets
Stream: Energy Markets
Invited session
Chair: Valentin Bertsch
1 - On the potential of arbitrage trading on the German intraday power market

Neele Leithäuser, Till Heller, Elisabeth Finhold
Our study focused on the potential of a simple and common risk-averse
strategy called greedy pair trading on the German intraday power market. This strategy aims to monetize the flexibility potential of a battery
or similar assets. We analyzed real-world order book data from 15 selected days between 2020 and 2022 to estimate the theoretical potentialof this strategy when optimal pairs are selected. We also investigated
the impact of unbalanced auction wins, which can be a challenge forpractical implementation. In our analysis, we compared the greedy
approach to the ex post optimal strategy and examined the effect ofdifferent frequencies of order book accesses. We found that increasing the frequency of optimization points in the greedy approach did
not significantly improve the profit. However, in the ex post optimal
strategy, halving the time between trading points resulted in an average profit increase of 30%. Additionally, we observed that a loss of up
to 10% of the bids had a negligible impact on the overall profit. Ourfindings contribute to a better understanding of arbitrage trading on theGerman intraday market and highlight the advantages of incorporating
predictive information and frequent optimization updates.

2 - Extending Least-Squares Monte Carlo to a SystemOriented Study on Storage Operation

Benjamin Böcker, Maike Spilger, Christoph Weber
In energy systems dominated by renewables, the high share of volatileand uncertain electricity generation raises the need for flexibility forbalancing supply and demand. Operating a substantial share of energy storage capacities increases the smoothing impacts on electricity
prices. Therefore, a system-oriented approach considering uncertainties as well as endogenous price effects is crucial to optimize the operation of energy storage systems in renewable-dominated energy systems. The valuation of energy storage systems under consideration of
uncertainties is usually solved by determining optimal operating decisions for given price realizations. Current state-of-the-art approaches,such as least squares Monte Carlo, approximate value functions to recursively determine a dynamic decisions considering the uncertainty ofexogenous electricity prices. Our work introduces a hybrid model thatcombines a least squares Monte Carlo approach with a fundamental
model for determining endogenous electricity prices based on a piecewise linear merit order. Here, interactions between storage operations
on price determination are explicitly depicted. The developed model
thus enables the stochastic valuation of storage operation in the systemcontext and considers the uncertainties of renewable energies, electricity demand, and the technical availability of generation capacities.

3 - Electrical energy storage investment requirements andoperating characteristics in a gradually defossilised European power system

Valentin Bertsch, Leonie Sara Plaga, Jonas Finke, Katharina
Esser
With the ongoing energy transition in Europe, the share of variable
renewable energies (vRE) in the power system increases steadily anddifferent flexibility options gain importance. This work investigates
the deployment of supply side flexibility options in the future European power system. We use a cost-minimising energy system modelthat endogenously optimises the energy-to-power (E2P) ratio and particularly focus on lithium-ion batteries, vanadium redox flow batteries,
adiabatic compressed air energy storage and hydrogen storage. We
explore the competition of these flexibility options across a broad defossilisation spectrum. Starting at vRE shares of around 50%, we find7

MA-10EURO 2024 - Copenhagen

non-linearly increasing storage requirements and observe a leap whenapproaching a zero-emission system, which is mainly driven by hydrogen storage investments. We also find that lithium-ion batteries with
E2P ratios between 2 and 6 hours are particularly deployed in countries with high levels of solar power, whereas hydrogen storage with
E2P ratios between 300 and 1000 hours is mainly deployed in countries with high levels of wind power. Adiabatic compressed air energy
storage with E2P ratios between 15 and 20 hours are only deployed
in a completely defossilised system. In addition, we analyse storage
operating requirements including operating hours and ramp rates fordifferent defossilisation levels.

4 - Multi-Objective Optimisation for the Inverse Analysis of
Design Requirements for Low Technology Readiness
Level technologies from an Energy System Perspective

Katharina Esser, Jonas Finke, Valentin Bertsch
The energy sector is transitioning towards a climate-neutral future
based on renewable energy sources. Besides increasing demands
for decentralised, flexible, and environmentally friendly technologies,
technology innovation will be imperative for this transition, offering
unknown potentials. Energy system models are an important tool toaddress these challenges, using various input parameters for all technologies considered. However, low technology readiness level (TRL)
technologies require estimates for those parameters which are usually
not possible or poorly verified at an early stage of development. Thus,
including low TRL technologies in energy models is associated withhigh uncertainty and lacking in current research. We develop a new,inverted approach for energy system modelling that considers both thecommon central-planning perspective of optimal system design andthe technical design perspective of technology development. By turning model input parameters such as technology cost or efficiencies into
decision variables, these can be used as additional optimisation objectives. A multi-objective optimisation then enables trade-off analyses
between overall system cost and requirements for technology development, e.g. the least requirements that would still lead to the use ofa technology in a cost-efficient system. We demonstrate the inverted
method using the modelling framework Backbone and Carnot Batteries as location-independent energy storage with low TRL.

 MA-10
Monday, 8:30-10:00 - Room: 11 (building: 116)

EMS logistics
Stream: OR in Health Services (ORAHS)
Invited session
Chair: Melanie Reuter-Oppermann
1 - Operational planning of medical vehicle routes

Nicolas Blandamour
Medical transport is increasingly important in ensuring access to carefor patients unable to travel independently. The patient transport business comprises many aspects in which optimization needs can arise.
For example, it could benefit from optimized vehicle routes, improved
and more reliable access to patient care, and improved working conditions for ambulance drivers thanks to more predictable schedules.
This presentation will detail an industrial application developed by
Hexaly for a French leading healthcare company. This tool optimizes
the operational planning of non-emergency patient transport carriedout the following day.
The problem consists in assigning vehicles to employees and creating mission rounds for each employee. The underlying optimization
problem is related to the Dial-A-Ride Problem. Indeed, one of the key
features of non-emergency patient transport is the possibility of transporting several people in the same vehicle at the same time. Employees’ schedules are also subject to numerous contractual and regulatory
constraints such as breaks.

8

A typical dataset of the problem corresponds to a daily volume of 500
missions to be planned, with around 50 employees and vehicles available.
The optimization tool based on Hexaly Optimizer brought significant
gains when it moved into production. The quality of the employee
schedules and vehicle tours has improved, with gains of around 10%
in terms of revenue per hour and distance traveled.

2 - Modeling Fairness in Covering Problems of Emergency
Medical Service Location Planning

Isabel Wiemer, Jutta Geldermann
Emergency medical service (EMS) has to respond quickly and efficiently to all emergencies within a considered area. However, especially in areas with heterogeneous demand distribution, like urban,
mixed and rural areas, the level of coverage can vary widely. To reduce inequalities in coverage, many approaches take into account fairness as model objective by explicitly addressing the coverage of theworst-covered area. Thereby, the coverage levels of the second, third
etc. worst-covered areas are not directly addressed. Therefore, we propose to maximize the average expected coverage of the set p of worstcovered areas. Our model explicitly considers the second, third etc.
worst-covered area and aims to improve not only the coverage level of
the worst-covered area, but the average coverage level of the set p ofworst-covered areas. Our fairness objective’s applicability is analyzed
at hand of a real-world case study for the city of Duisburg (Germany).
We examine different sets p of worst-covered areas to analyze the influence on the individual coverage levels of the different areas. First
results show that the proposed fairness objective can improve the average coverage of the set p of worst-covered areas in EMS location
planning context. At the same time, computational complexity of ourmodel is low enough to maintain solution quality.

3 - Optimal decision control for a mixed fleet emergency
medical services system with dispatch protocols for
ALS ambulances using semi-Markov decision processes.

Stelios Koukoumialos, Giannis Kechagias, Alexandros
Diamantidis, Theodosis Dimitrakos
A mathematical model for the analysis of an Emergency Medical Service (EMS) system with a specific number of advanced life support
ambulances (ALS) and a specific number of basic life support ambulances (BLS) is presented in this work. The system admits incoming
emergency calls which are divided into two classes: (i) urgent, highpriority calls for which the patient’s life is potentially at risk and (ii)less urgent, low-priority calls. Advanced life support units can serveboth calls, while basic life support units handle only low-priority calls.
The system either serves the incoming calls or forwards them to othersystems if there are not enough ambulances for serving. Under a suitable cost structure, the system is modeled using an appropriate Markov
decision process in continuous time for which we seek a stationary policy that minimizes a predefined optimality criterion for vehicle mixes
over a set of candidate ambulance fleets. Based on this formulation, it
is possible to implement standard Markov decision algorithms, such as
the standard value-iteration algorithm and the standard policy- iteration
algorithm.

4 - Rendezvous Strategy for Emergency Logistics

Changle Song, David Levinson, Emily Moylan
An essential aspect of Emergency Medical Services (EMS) is the effective allocation of ambulance resources, which can greatly influencepatient outcomes. Key to this is the dispatch policy for ambulance deployment and the question of whether specialised ambulances, specifically designed for certain types of incidents, should be assigned toother types of emergencies when available. This research uses queuing models to represent the different dispatching policies and implements simulations to identify the most efficient policy for ambulance
resource allocation that would yield optimal patient outcomes when using specialised vehicles. This study proposes a simulation frameworkand methodology that could be adapted to other geographical regions,thus offering a versatile tool for evaluating the potential benefits ofEURO 2024 - Copenhagen
deploying specialised emergency vehicles. The model considers multiple types of emergency vehicles, the distribution of patient demand,and the EMS response process. The experimental results demonstratethat the deployment of advanced specialised ambulance vehicles canenhance the survival rates of emergency incidents, particularly thosecorresponding to the vehicles’ intended function.

5 - Ambulance routing problem for emergency medical services

Issam Nouaouri, Hamid Allaoui
In recent years, and particularly following the Covid 19 pandemic, anincrease in the number of patients in hospital has been noted. Medical transportation is essential for an effective and efficient healthcare
system. Demand continues to grow with the increase in needs frommedicine (city or country medicine) and healthcare structures. The
costs linked to medical transport are constantly increasing. Also, during an emergency, transport can be a determining factor in saving human lives. In this context, the problem is to reduce cost with better
regulation and efficient use of resources, while guaranteeing the quality of service to patients. Medical transportation problem is known in
the literature as the Ambulance Routing Problem (ARP). To deal withthis problem, we propose two approaches: a Mixed Linear Programming (MILP) and a meta-heuristics based on evolutionary and local
search algorithms. The goal is to minimize patient waiting time, travel
time, and travel cost. This involves developing a decision support tool
for planning and optimizing ambulance routes. The first results of theexperimental study show that the proposed approaches improve the objective function.

 MA-11
Monday, 8:30-10:00 - Room: 12 (building: 116)

Choice behavior
Stream: Behavioural OR
Invited session
Chair: Jyrki Wallenius
1 - Empirical studies for comparing interactive multiobjective optimization methods with human participants

Giovanni Misitano, Bekir Afsar, Johanna Silvennoinen, Ana
Belen Ruiz, Francisco Ruiz, Kaisa Miettinen
Applying interactive multiobjective optimization (MOO) methods necessitates preference information from a decision maker (DM). Because of the active role of the DM, comparison of interactive methods
must be planned carefully. Previous comparisons have been rare andirreproducible. We have developed a new, reproducible experimental
design to compare interactive MOO methods with human participants.
Our design utilizes a novel questionnaire, which can measure the cognitive load experienced by DMs, their satisfaction with the solution
process and the solution finally obtained. We can also measure themethods’ ability to reflect preferences and responsiveness to changes
in the preferences. Moreover, we have developed a web-based userinterface to allow participants to access and utilize interactive methods, and to answer the questionnaire interactively. Our design enables
a transparent and reproducible comparison of interactive MOO methods.

2 - Random Preference Model

Moha Ghaderi, Kamel Jedidi, Milosz Kadzinski
Modeling preferences from observed choices is a pivotal theme inmainstream economics and decision theory with expansive applications in policymaking, marketing, and transportation. The standard approach in modeling preferences is to attach utility scores to the choice
options consistent with the order defined by the preferences. Nevertheless, the utility scores are merely artifacts or mediums to represent

MA-11

preferences, whereas preferences are the underlying construct generating choices. We aim to develop a framework faithful exactly to thisview. Our model takes preferences as the central primitive. Preferences generate choice data and are represented by analytically convenient mediums called utility functions. We argue that contrary to
our approach, defining preferences in terms of utilities and choices canpose severe challenges for nonparametrically inferring the underlying
choice process. To address this shortcoming, we model preferences directly by a probability distribution over the set of strict linear orderingsof alternatives instead of an underlying utility. We build on the theoretical ground of the random preference literature and use the distributionally robust nonparametric approach to minimize the specificationand distributional assumptions required to derive robust conclusions.
We address the computational challenges and demonstrate the applicability of our model through an empirical application to a conjoint
setting.

3 - Evaluating actor behavior under uncertainty for participatory interventions

Ruben Akse
Multi-actor decision-making for transport innovations is complex,
among others because of the uncertainty experienced by decisionmakers about other actors’ behavior. Reducing this uncertainty bygathering more knowledge is insufficient and often impossible. Therefore, interventions are needed that assist actors in stimulating adaptive
and collaborative thinking. The research gap this study aims to address
is how to assess actor behavior under uncertainty for participatory interventions. Previous studies show that actors in the transport domain
experience more uncertainty about governance than system problems
regarding technology and sustainability. Also, actors treat long-term
uncertainties as short-term risks, and are focused on reducing the latterby project management strategies. In this study, we intend to validate
these findings, and how this is conditioned by individual and organizational variables. A stated preference survey has been performed inwhich mobility professionals had to indicate their strategy in alternative transport innovation scenarios. Scenarios include governance, system, long- and short-term uncertainties. Respondents are asked to state
their preference for a strategy, given the scenario: Stop the project, do arisk-assessment for their own organization, or explore the possibilities
collaboratively. The results of this study provide a theoretical basis foractor behavior under uncertainty and are illustrated by an intervention
in a serious game.

4 - Multiple conflicting objectives cause psychological burden on decision-makers and lowers decision quality

Jyrki Wallenius, Matias Kivikangas, Eeva Vilkkumaa, Julian
Blank, Ville Harjunen, Pekka Malo, Kalyanmoy Deb, Niklas
RavajaPractical planning and decision-making problems are often better andmore accurately formulated with multiple conflicting objectives rather
than a single objective. This study investigates a situation relevant forMultiple Criteria Decision Making (MCDM) as well as Evolutionary
Multi- objective Optimization (EMO), where the decision-maker needs
to make a series of choices between nondominated options characterized by multiple objectives (criteria, attributes, dimensions). The cognitive capacity of humans is limited, which leads to "cognitive burden"that influences human decision-makers’ decisions. We measure how
different levels of decision difficulty - the number of decision-making
dimensions (attributes, criteria, objectives) - influence the cognitive
burden in a laboratory study, and the impacts that this burden has on thedecision-makers’ behavior and the quality of their decisions. We use
psychophysiological, behavioral, and self-report methods. Our results
suggest that a higher number of decision-making dimensions (i) increases cognitive burden significantly, (ii) leads to adopting satisficing
strategies in which only a limited number of dimensions is considered,and (iii) decreases decision quality.

9MA-12EURO 2024 - Copenhagen MA-12
Monday, 8:30-10:00 - Room: 13 (building: 116)

Optimization in Agriculture
Stream: OR in Agriculture and Forestry
Invited session
Chair: Francisca Rosell
1 - Modeling a harvest planning and scheduling problem ina hierarchical agrifood supply chain by bilevel programming

Victor M. Albornoz
The contribution addresses a harvest planning problem in the context
of a hierarchical agrifood supply chain, which integrates the definitionof management zones for harvesting, the harvest scheduling problem
and the coordination between the producer and the wholesaler. The
problem is represented through bilevel programming models that allow the representation of the hierarchy between the producer (leader)and a wholesaler (follower). The producer plans and schedules theharvest of the different homogeneous management zones into the resulting partition of the farm and the production levels to be offered toeach wholesaler, while the wholesaler decides the quantity of product to be purchased to satisfy the demand requirements. First of all,a mixed-integer bilevel program is proposed and solved alternatively
using an algorithmic strategy based on the Benders decomposition
method. Then, a stochastic bilevel program is also proposed for representing the uncertainty in future crop yields, prices, and demands,
using a finite set of scenarios. The bilevel optimization models considered are reformulated into mixed-integer linear programs using the
Karush-Kuhn-Tucker conditions and replacing the nonlinear complementary constraints by the big-M method. The models were appliedin a Case Study for selective harvesting of grapes with data collectedfrom a farm. The results obtained when solving a set of instances highlight the importance of the problem and the proposed methodology.

2 - Model-Based Optimization of Irrigation Water Allocation
at the Watershed Scale

Noam Ouzana, Isaya Kisekka, Raphael Linker
Efficient water resource management in agricultural contexts is a major challenge. The present study focuses on the allocation of irrigationwater between competing crops at a watershed scale. The first step of
the optimization process includes the division of the area into responseunits, which assists in maintaining complex modeling with high spatial
resolution, while reducing the computation times by avoiding calculations over every pixel. This is done using the well-established SWAT+
model, which predicts, among others , the impact of different management decisions on fields within the watershed. A model-based, multiobjective optimization approach, which seeks to maximize the yieldand to minimize the irrigation in each unit, is implemented on each of
the response units. A curve is then fitted to the resulting Pareto front
providing an estimate of the unit water productivity function (WPF).
The estimated WPFs are then used for determining the amount of water that should be allocated to each crop for the next irrigation season through a single-objective optimization process, where the goal is
maximizing the economic profit. This optimization framework is applied to the San Joaquin watershed, which is predominantly occupied
by agriculture fields and orchards, and relies heavily on irrigation.

3 - Robust Network-based Optimization for Multi-Period
Fuel Treatment

Tomas Lagos, Nam Ho-Nguyen, Dmytro Matsypura, Oleg
Prokopyev
Wildfires pose grave risks to human life, health, and infrastructure. To
address these challenges, proactive fuel treatment strategies are crucial
before each fire season. However, managing fuel treatment resourcesin south-east Australia becomes problematic due to species protection
and forest maturity considerations. Existing models to tackle the multiperiod problem consider inaccurate approximations based on estimated10

parameters in order to design treatment plan schedules. Our study extends previous work and introduces a robust modeling approach fordevising multi-year treatment strategies. Initially, we introduce a new,more efficient formulation to solve the deterministic counterpart of the
problem, which is then extended to account for a given fixed noisethat surges the amount of fuel load due to uncertainties in vegetation
growth and treatment effect. We then expand this formulation to accommodate a fixed noise factor, representing uncertainties in vegetation growth and treatment effects. Subsequently, we adapt the model
to a robust setting, allowing “nature” to select values from a decisiondependent uncertainty set. Our polynomial size robust mixed-integer
optimization model incorporates worst-case scenarios from the planner’s objective perspective of fuel growth and treatment effects, offering adjustable uncertainty levels. Moreover, our modeling approachprovides flexibility for planners, enabling them to minimize the total
fuel load or optimize network connect

4 - Optimizing Winery Production Transfers using a MixedInteger Model to Reduce Water and Energy Consumption

Francisca Rosell
This work focuses on tanks’ wine transfers in a winery that are part ofthe production process of the wine before being bottled. During peak
production periods, the winery may face a shortage of available tanks,
resulting in unnecessary transfers and increased water usage for tankcleaning as well as higher energy consumption. To address this issue, a mixed integer optimization model has been developed to reducewater and energy consumption by making only the necessary movements, using the minimum number of tanks, and covering the shortest
distance. The model is based on three critical elements: the production planning, the tanks, and the route for the wine production. The
production planning defines what and when must be produced and theinitial mixtures for each class of wine. The ordered sequence of stages
to produce each type of wine defines a route. Each stage requires tanks
to keep the source and the resulting product. The model ensures eachproduct follows the defined order, using only the necessary tanks andcovering the shortest distance. The non-overlapping of tanks is guaranteed, which can be reused once they are empty and clean. The durationof each operation is considered, including transfer time and cleaning
after each use. Furthermore, the model ensures that tanks are filled up
following wine requirements at each stage to guarantee the quality of
the final product. The application of the model is demonstrated in avariety of realistic examples.

 MA-13
Monday, 8:30-10:00 - Room: 15 (building: 116)

Supporting Planning and Sustainability
Stream: Soft OR and Problem Structuring Methods
Invited session
Chair: Isabella Lami
Chair: Mike Yearworth
1 - Exploring the decision space to support the decision
maker’s reflection on strong or weak urban sustainability

Beatrice Mecca, Matteo Brunelli, Isabella Lami
The sustainable development of cities requires a focus on what is essential for the global ecosystems and economies. In this sense, it may
be useful to introduce the concept of weak and strong sustainability(WS and SS) in urban contexts. Indeed, it is well known that environmental problems have a local origin, even though they manifest
themselves globally. The concept of WS and SS involves a different
consideration of the relationship between natural and human capital,
which can be interpreted in different ways of translating sustainability to decision-making processes. In evaluation, the choice towards

EURO 2024 - Copenhagen
one type of sustainability entails a different process of aggregating indicators: this element is identified as the one to be paid the most attention when conducting an evaluation towards SS. To stimulate thedecision-makers reflection on which type of sustainability is supporting his/her urban transformation, this paper intends to contribute toarticulate the reasoning and explication of the negative and positive elements of the project alternatives. Specifically, we employ the p-ary
Choquet integral to represent various strategies across distinct regionsof the decision space. To manage the extensive preference informationthat needs to be gathered, we adopt a parsimonious elicitation method
introduced by Labreuche and Grabisch, ensuring the problem remains
manageable. The advantages of this method are illustrated through theapplication to an urban case study.

MA-14

 MA-14
Monday, 8:30-10:00 - Room: 16 (building: 116)

Impacts of transitioning to green gases
Stream: Energy Markets
Invited session
Chair: Lars-Peter Lauven
1 - Decommissioning and Conversion Optimization for Automated Natural Gas Distribution Grid Planning

Moritz Franz, Florian Hirschmann, Lars-Peter Lauven, Khalil
Ben Safta, Anton Bergmann
2 - System maps to foster a multi-perspective view on theenergy transition

Yvonne Beck

Modelling, quantitative or qualitative, is part of many study programs.
However, transferring trained methodology to application may dependon skills to communicate models in a multi-perspective context. Basedon multi-perspectivity in technology (Ropohl 2009), the study investigates how system mapping with causal loop diagrams supports a multiperspective view on the energy transition. The study was conductedwith five study groups and analysed builiding a typology based on
qualitative content analysis (Kuckartz 2018). Five types were identified along the typology’s axes "modelling preconceptions" and "linkage of perspectives": The two central types group participants, who,
despite lower modelling experience, connected several perspectives using the mapping methodology. Communicating the models to peers enhanced their learning. The "explorative type" transcended perspectives
but did not follow the mapping approach closely, while the "trained
methodologist" linked qualitative to quantitative modelling, remaining in a preferred perspective. The "multi-learner" was able to holistically integrate modelling approaches and perspectives. The findings
suggest that short sessions combining soft model building techniqueswith phases of interaction may be suitable to develop a common-sense
understanding for complex questions e.g. concerning sustainability
transitions. Possible applications for interdisciplinary stakeholder dialogues are presented.

3 - Crafting Consensus: A conceptual model for enhancing
urban infrastructure maintenance decisions

Roya Meydani, John Leander

Infrastructure owners face challenges in maintenance decision-making
due to the process’s multidisciplinary nature, spanning mathematics tocognitive science. This study optimizes maintenance for complex infrastructure systems, specifically in scenarios where a single primaryowner must consider the preferences and requisites of multiple stakeholders. We propose a generic conceptual model facilitating sharedviews, eliciting objectives early, and aligning agendas with desired outcomes. It offers valuable guidance for decision modelers, reinforcedby case studies. The decision-making process in infrastructure management varies based on industry standards, organization, and public
expectations. Evolving approaches recognize infrastructure as part ofa complex process, intensifying challenges such as multi-disciplinary
issues and stakeholder conflicts. In this matter, effective management
involves understanding uncertainty, navigating complex systems, andestablishing optimal solutions. Moreover, social complexity emerges
when stakeholders hinder collective inquiry due to singular problem
perceptions. In response, decision agents play crucial roles, facilitating consensus and commitment while promoting shared understanding
and restructuring education for a complexity mindset.

Due to the energy transition towards renewables, renewable alternatives will likely replace natural gas-based heating in the medium term.
Such alternatives include renewable methane and hydrogen as well asheat pumps. Currently, the expectation is that natural gas distribution
grids will therefore face declining usage in many areas.
To consider this development in automated energy grid-planning approaches, an existing, pandapower-based power grid expansion algorithm is extended to natural gas networks using pandapipes and enhanced to also cover partial decommissioning of natural gas pipelines.
The decline of gas consumers in the gas grid reduces the flow rate
in pipelines in some areas, possibly leading to violations of the network operational constraints while also rendering a number of assets
redundant. The algorithm uses Iterated Local Search with either Hill
Climbing or Late Acceptance Hill Climbing and cures violations by
shutting down or adding selected pipelines or pressure controller to thegrid model. If a pipe shut down results in unsupplied sinks, the algorithm uses the Delauney Triangulation to generate potential new paths
to reconnect the sink.
A special optimization problem occurs if numerous natural gas consumers intend to switch to hydrogen instead. In this case, the algorithm’s task is to design a hydrogen network while leveraging existing
pipelines through conversion and observing all relevant operational restrictions.

2 - Modeling of a Hydrogen Economy in Integrated Assessment Models: A Review

Olivier Bahn, Kamyar Amirmoeini, Sara Ghaboulian Zare,
Normand Mousseau, Najmeh Neshat, Martin TrépanierWithin the needed global energy transition, hydrogen presents a potential avenue for achieving deep decarbonization. The expanding role ofhydrogen in the global energy landscape is underscored by governmental interest. Recently, new national strategies have been adopted, taking
the total number of countries with hydrogen strategies to 41 countries
in 2022. To assess the decarbonization potential of a hydrogen economy, one may use an integrated assessment approach, a comprehensive framework that incorporates insights across diverse sectors likeenergy, land use, and the broader economy, along with their associatedgreenhouse gas emissions. Integrated assessment models (IAM) aremathematical tools to conduct an integrated assessment. This presentation proposes a survey of 12 distinct family of IAM from 50 different
studies, to assess the potential of a hydrogen economy for different
scenarios developed within the IPCC Sixth Assessment Report WGIII.
This presentation also reports on our ongoing efforts at GERAD tomodel hydrogen production pathways in our AD-MERGE integrated
assessment model.

3 - Optimal operation of a grid connected electrolyzer andgreenhouse gases emission accounting

Giulia Montanari, Nicolas Campion
This analysis utilizes a deterministic optimization model to determine
the optimal operational production of electrolytic hydrogen. The objective is to compare different operational strategies for electrolyzers
considering electricity prices, subsidies for certified renewable hydrogen production, and regulatory constraints on GHG emissions. Using electricity grid data from Northern Italy, hourly grid emission factors are calculated through various methodologies, including European

11MA-15EURO 2024 - Copenhagen

regulations for Renewable Fuels of Non-Biological Origin production,
the IPCC approach, and short-run marginal emission factors. Different emission accounting methods, such as Scope 2 and 3 from theGreenhouse Gas Protocol and the EU methodology are also considered. The comparison between different emission accounting methods
shows that emissions calculated with Scope 3 are around 38% higher
than those calculated considering Scope 2, and time-varying emissions
calculations show a significant difference from yearly average methods, particularly when applied to a flexible load. The study highlights
the temporal alignment of low prices and high emission factors; therefore, emission accounting methodologies that promote hydrogen production during low-price hours do not meet the need for GHG emission
reduction. The findings indicate that different accounting methods impact the optimal operational behaviour of the electrolyzer and suggest
the need of different strategies to reduce emissions.

4 - Quantifying the potential of curtailed electricity to produce green hydrogen

Alissa Ganter, Paolo Gabrielli, Tyler H Ruggles, Giovanni
Sansavini, Ken Caldeira
Hydrogen produced with low-carbon emissions plays a central role inEuropean decarbonization strategies. Scaling up renewable hydrogenproduction is envisioned to speed up the energy system transition andreduce dependency on fossil fuel imports. To facilitate this scale-up,
the European Union introduced guidelines outlining the conditions under which hydrogen is considered a renewable fuel. These guidelines
specify that electrolyzers should only be powered by grid electricity
during hours with excess renewables, or by electricity from dedicatedrenewable assets. With increasing shares of renewables in Europe, inrecent years, about 85-100 TWh of renewable electricity has been curtailed annually. In this study, we use a linear optimization model todetermine the optimal capacity and operation of electrolyzers for varying hydrogen prices and investigate the untapped potential of utilizing
this excess renewable electricity for hydrogen production. While theoption to deploy battery storage is included to store otherwise curtailed
power for later use, it is not deployed in any scenarios considered due
to high up-front investment costs. We show that by utilizing otherwise
curtailed renewable electricity, about 30% of today’s European hydrogen demands from chemical industry and refineries could be met. The
highest potentials are found in Germany, Denmark, and the Netherlands, where electrolytic hydrogen could displace over 60% of existingfossil hydrogen production.

 MA-15
Monday, 8:30-10:00 - Room: 18 (building: 116)

Machine learning and game theory inhealthcare
Stream: OR in Health Services (ORAHS)
Invited session
Chair: Majid Taghavi
1 - Peak prediction of pediatric hospitalizations due to respiratory diseases

Gloria Henríquez Díaz, Samuel Marin-Navarro
INTRODUCTION In Chile, every year in fall and winter, respiratory
diseases increase in pediatric population (due to different factors) andthis produces an increase in the number of hospitalizations, sometimes
collapsing the health system. To anticipate, a system has been created
to predict when the peak will be.
METHOD Data from Luis Calvo Mackenna Hospital were obtained
from the open access database DEIS of the Ministry of Health. The
created system was used to predict the peak date of pediatric hospitalizations from 2017 to 2023, except 2020 and 2021 due to the pandemic.
The curve produced were smoothed by moving averages. As the curve

12

ascends, alerts created with machine learning begin to appear. The
model was tested using antecedents from 2 to 7 previous years.
RESULTS Tests performed with 7 and 6 years of historical data had
100% results within the prediction interval (RMSE = 7.0 with respect
to the exact day), while with 5 and 4 years were 67% (RMSE = 10.2)
and 50% (RMSE = 11.3), respectively. Finally, with 3 years it was
20% (RMSE = 11.4) and with 2 it was 0% (RMSE = 12.6). In allcases, the prediction was approximately one month in advance.
CONCLUSIONS The system created has promising results if sufficienthistorical data is added to train the model. A future challenge is to adjust it for other pediatric hospitals.
ACKNOWLEDGMENTS This work was funded by the ANIDFONDEF IDeA I+D 2023 ID23I10423.

2 - Predicting delayed discharge from hospitals: A Decision support system using machine learning

Majid Taghavi, Mahsa Pahlevani, Peter Vanberkel
The increasing demand for healthcare services poses significant challenges in effectively managing patient flow, particularly concerning patients classified as Alternative Level of Care (ALC). These patients,
although no longer in need of acute care, often encounter obstacles todischarge and cause several issues such as hospital overcrowding andcompromised health outcomes. This study uses administrative healthdata from Canadian hospitals and proposes using machine learningmodels to identify potential ALC patients and estimate their hospital length of stay as early as their admission time. The findings show
the efficacy of the eXtreme Gradient Boosting algorithm in accurately
predicting potential ALC patients, while the Random Forest regression
model surpasses others in forecasting the length of stay for ALC patients. To understand how the predictions are made from the featuresof the dataset, the Shapley values were analyzed and used to identify
the most important features of the dataset and their impact on machinelearning outcomes. Using the most important features, two sets of userfriendly and easy-to-follow guidelines were developed for the hospital
staff to proactively identify the ALC patients and estimate their lengthof stay, and mitigate the patient flow challenges posed by the ALC
patients. Hospitals can use such decision-making tools to optimize
resource allocation, enhance operational efficiency, and ultimately improve patient care outcomes.

3 - Balancing Cost and Profit in the Public Sector Vaccine
MarketBanafsheh BehzadThis study addresses how the government can ensure the cost-effective
procurement of vaccines from private manufacturers. We combine optimization and game theoretic techniques to address cost-effective immunization in the United States. Our optimization model from the government’s perspective minimizes negotiated government costs, while
ensuring adequate national vaccination levels, linking dynamics inpublic and private sectors and incorporating competitive manufacturer
behavior. The optimization model embeds an extant game theoretic
price model to capture competitive interactions among manufacturers
in the private sector. The model is applied to two different case studiesof the US pediatric vaccine market and the COVID-19 vaccine market.
The results indicate that markets are at lower risk when high capacity
manufacturers have moderate target profits, especially in cases of highdemand and asymmetry. Our model can help restabilize a market thatexperiences a vaccine shortage We also underline scenarios in which
the government may be able to prevent monopolies through financial
incentives to manufacturers. The results support a paradigm shift fromannual contracts to ongoing negotiations, which would enable the government to exercise control over high-risk markets. Our study demonstrates an analytical approach for managerial government officials to
influence vaccine prices via the procurement of public-sector goods.

EURO 2024 - Copenhagen MA-16
Monday, 8:30-10:00 - Room: 19 (building: 116)

OR Education I
Stream: OR Education
Invited session

MA-17

During the talk, I will present both the conditions we have to take
into account in selecting call topics for our funding effort, and present
some of our funding opportunities, such as the mobility and network
programme for PhD students and researchers, our Hydrogen Valleys
programme and our Nordic Grand Solutions programme (Technology
agnostic, must meet impact goals of importance for the green transition).

Chair: Ken Murphy
1 - Educational Admissions Transformed: Navigating the
Future with Intelligent Screening

Pin-Rui Hwang
Integrating intelligent screening technologies into educational admissions systems fundamentally transforms the role of educators in selecting candidates. By employing advanced AI-based techniques, thissystem enhances the decision-making capabilities of educators in thecandidate selection process. The system assists educators in identifying promising candidates through the analysis of applicant data,
including academic achievements, extracurricular activities, and personal statements. The theoretical foundation is rooted in predictive
analytics, which assesses the content presented in applicants’ documents and their compatibility with the program’s development to estimate candidates’ suitability and potential success. This adjustment
more accurately reflects the review and evaluation of student data inthe university admissions process, particularly regarding the alignmentof students’ demonstrated content with the objectives of their chosen
department.

2 - The Role of the Multi-dimensional Learning Environment in the Context of Responsible Management Education

Johanna Oster
The need for a socio-ecological transformation of society is being emphasised in different contexts. Tomorrow’s business leaders will playa key role in this transformation. Therefore the call for more Responsible Management Education (RME) is becoming steadily louder. In
response, the United Nations-backed Principles for Responsible Management Education (PRME) initiative was founded, aiming to provide
future leaders with the skills they need to contribute to a sustainable
development. Three curricular dimensions influence, whether students
gain these skills: the formal, the informal and the hidden curriculum.
Aims of RME can only be reached if all three dimensions are aligned.
However, currently scholars pay little attention to the hidden and informal curriculum. This research aims to generate knowledge about thehidden curriculum. Moreover, connections between formal and hidden
curricula shall be researched. The hidden curriculum will be examinedthrough questionnaires and group discussions, including stimulated recalls. The formal curriculum will be raised through a documentary
analysis. Discussions and documents will be evaluated with qualitative content analysis. The results will presumably provide profoundknowledge concerning the interaction mechanisms of formal and hidden curricula. The outcome can help researchers develop RME implementation models. Moreover, this knowledge can support lecturers inquestioning and improving RME in their institutions.

3 - Towards Equity in Teaching Management Science

Ken Murphy
Student outcomes in an undergraduate management science course are
hypothesized to depend on prior preparation and student engagement.
A predictive model is presented that combines these factors using data
gathered from large, online course sections of business students. The
results help to inform instructors of actions to take to create equity ofopportunity in the STEM classroom.

4 - Nordic Project Funding as a Means to Create Impact
with Your Research MA-17
Monday, 8:30-10:00 - Room: 40 (building: 116)

Decision support in healthcare
Stream: OR in Health Services (ORAHS)
Invited session
Chair: Christos Vasilakis
1 - A Patient-Centered Hospital Procurement of Physician
Preferred Implants

Renato de Matta
We study the procurement of physician-preferred implants where boththe hospital and patient surplus are considered and optimized. For
better flexibility, we introduce the use of alternate devices instead ofpreferred ones. Considering alternate implants allows for improving
the hospital surplus through larger revenues and volume discounts, although it implies an additional cost to cross train physicians who mayneed to use implants and associated surgical procedures they are lessfamiliar with. The problem is NP-hard. We propose two heuristic
procedures for quickly finding nearly optimal solutions and for estimating the optimality bound. We demonstrated results of a series ofexperiments where we considered different scenarios corresponding toseveral patient demand distributions across patient-types and emphasized the benefits of alternate implants and physician cross-training.
Using patient-reported costs and total hip replacement outcomes fromthe medical literature in the experiments, we show that the increased
flexibility physicians acquire from cross-training: (1) Improves patient outcomes; (2) Improves the hospital’s financial performance byincreasing hospital revenues and cost savings from volume discounts
when larger orders of certain medical devices are placed; (3) Improves
patient and physician satisfaction; (4) Reduces the negative impact ofdemand uncertainty on a hospital’s financial performance.

2 - Selection and prioritisation of medical devices for HTA
evaluation: Towards a socio-technical framework basedon multiple criteria decision analysis

Ana Vieira, João Pimenta, Lilite Loição, Helena Monteiro
Given the diversity and rapid pace of innovation in the medical device (MD) industry, there is an increasing need for Health Technology Assessment (HTA) agencies to prioritise which MDs to evaluate. The Portuguese HTA agency, INFARMED, National Authorityof Medicines and Health Products, I.P. -, intends to develop a sociotechnical framework to address such a challenge. Taking a step towards
this goal, the present study develops an approach for structuring a multiple criteria decision analysis (MCDA) model to measure the value of
MD assessments. More concretely, it arrives at a robust value tree that
represents the relevant value dimensions to consider when prioritising
MDs for HTA evaluation at INFARMED. The proposed methodological approach follows a top-down ("value-focused thinking") approach,entailing a systematic literature review and social methods, such as a
web survey and a workshop. Causal mapping, as a powerful visual
tool capable of supporting structuring tasks, was applied as an intermediary method to build the final value tree. The resulting tree is
structured into three levels, comprising criteria and attributes within
two domains - disease-related and device-related dimensions. Looking
forward to operationalising this structure in the future, selected MCDA
techniques are discussed. All in all, the present study provides a soundoperational basis for the framework sought by INFARMED, with potential relevance for future studies on HTA priority-setting.

Lise Nielson13MA-18EURO 2024 - Copenhagen

3 - Site selection problems for clinical trial supply chains

Anh Ninh, Yunhong Bao, Daniel Mcgibney, Tuan Nguyen
Recruiting candidates globally and across multiple sites in different
geographic regions is necessary to speed up the enrollment of clinical trials. While patient enrollment can benefit from this globalization,
initiating clinical trials has become much more complicated. In thestart-up stage, the sites must be selected out of a set of potential candidates around the globe based on the specifics of those clinical trials,
such as protocols, operational costs, and recruitment deadlines. Sites
in one region can be very distinct from sites in another area. Yet, acommon mistake in selecting sites is to rely on too little knowledge
or subjective data. Poor selection decisions can lead to study delays
and prolong the time to market for life-saving treatments. Thus, we
propose a framework to aid the decision-making in global site selection problem. To ensure that our framework accurately captures theuncertainty in recruitment time, we adopt a risk-based constraint that
accounts for random patient enrollment. The extensive computational
studies help quantify significant time-cost trade-offs as a potential solution to control the costs of conducting a trial.

4 - Supporting long-term decision making at regional level
through modelling long-term changes in population
health state and associated healthcare resource usage

Christos Vasilakis, Zehra Önen-Dumlu, Luke Shaw, Richard
Wood
Decisions around medium and long-term allocation of healthcare resources are fraught with challenges arising from the inherent uncertainties in population growth as well as developments in the economy,society and technology. Against this background, health and care planners are constantly facing challenges on how to allocate healthcare resources with limited budget. The aim of this paper is to describe the development and early use of an innovative mathematical model and accompanying tool that captures the likely impact of long-term changes
in a regional population and associated healthcare resource requirements. The modelling tool works in two stages: first, it derives 20-year
population projections based on a Markov chain; second, it calculates
healthcare activity and related costs for same time horizon. Population
projections are based on segmentation of the population with respect
to the Cambridge Multimorbidity Score derived from a state-of-the-art
system wide dataset that provides patient-level linkable data for the entire population of a region in England of about 1 million people. Ourinnovative model and tool, which is already informing decisions in thecollaborating health care system, offers planning support by generating long-term population projections as well as provides capacity forrunning scenarios that flex the ’do nothing’ (baseline) scenario by considering various mitigations.

 MA-18
Monday, 8:30-10:00 - Room: 42 (building: 116)

Assessment Methods for Shaping theGreen, Inclusive, and Digital Cities I
Stream: Sustainable Cities
Invited session
Chair: Sara Torabi
Chair: Patrizia Lombardi

climate change including heat and flood while they continue to produce
greenhouse gases that contribute to climate change. A city is composedof diverse districts. While urban structure, land use and form matter at
the city or regional scale, physical environment of districts should be
improved to make them inclusive, safe, resilient and sustainable. The
district scale is said to be an appropriate scale for climate actions as it
is small enough to innovate quickly and large enough to have a significant impact. Participatory urban design methodology is needed at the
district scale as there are various stakeholders that shape the physicalenvironment composed of buildings, open spaces and public spaces.
Based on the history of participatory urban design, or Machizukuri
in Japan, we are starting to incorporate digital tools such as thermal
simulation and carbon mapping into stakeholder workshops to initiate
place-based climate change actions. Both tools are developed on a publicly available three-dimensional urban model. We will present several
cases of participatory urban design practices in Japan using these tools
and discuss about the achievements and issues.

2 - Optimal Placement of Nature Based Solutions in Urban
Greening

Diego Maria Pinto, Davide Donato Russo, Antonio M. Sudoso
This paper addresses the strategic placement of nature-based solutions(NBSs) to enhance sustainability and resilience in cities. Leveraging
Operations Research (OR) tools and convolution theory, we propose acomprehensive and flexible approach to maximize the benefits of urban
green infrastructure while ensuring equitable distribution across urban
areas. Our formulation integrates multiple variables, benefits, and constraints, including fairness considerations, to identify optimal locations
of NBSs such as parks, green walls, green walls, and tree plantings.
Through several case study analyses, we demonstrate the effectivenessof our approach in improving environmental and social indicators. This
research contributes to advancing the understanding and implementation of NBSs in urban planning, offering valuable insights and decision
support for re-designing resilient and equitable cities.

3 - A Framework to Identify and Prioritize Key Performance
Indicators: Assessment of the Effectiveness of Inclusive Climate Actions for Nature-based Solutions

Virginia Pellerey, Sara Torabi
Nature-based Solutions (NbS) are increasingly acknowledged as vital
strategies against climate change. However, citizens and ecosystems
benefit differently from NbS. To account for NbS trade-offs, theory andpractice have embraced Inclusive Climate Actions (ICAs) that simultaneously tackle climate change and urban inequalities. Currently there
is a notable gap in the assessment of environmental, economic, andsocial implications of ICAs for NbS. This research develops an evaluation and monitoring (E&M) framework to identify Key Performance
Indicators (KPIs) for evaluating the effectiveness of ICAs for NbS. The
methodology is structured in three phases: identification, refinement,
and prioritization. This approach integrates quantitative and qualitative techniques and involves stakeholders in the process. A set of 13
KPIs is proposed. These results can be further enhanced by engaging
local stakeholders to select supporting indicators tailored to addresscontextual challenges. The E&M framework assesses the effectivenessof completed or ongoing projects, or evaluates future scenarios. This
step serves for empirical assessment in five European cities, and for thedesign and planning of future scenarios in five Urban Living Labs. Results will help cities to implement ICAs that incorporate fairness anddistribute NbS impacts equitably. This study is part of the DUT project
GREEN-INC, growing effective and equitable nature-based solutions
through inclusive climate actions.

4 - Social impact assessment using ES through socioecological system dynamics digital simulation

Asef Ayatollahi, Eugenio Morello
1 - Participatory Urban Design Methodology for Climate
Actions at the District Scale: Thermal Simulation andCarbon Mapping

Akito Murayama, Junya Yamasaki, Takahiro Yoshida, Ryoichi
Nitanai, Rikutaro Manabe
Implementation of climate change mitigation and adaptation measures
are urgently needed in cities as they experience severe impacts from

14

Social impact assessment (SIA) of re-naturing measures in urban areas
offers an exclusive perspective on complex interactions and trade-offs
between humans and nature. Despite advancements in methodologies
for short-term assessment, long-term assessments, and evaluations stillrequire a concrete framework. The main challenges in long-term SIA
procedures are the high volume of complexities and interconnectivity of interaction components and difficulties in citizens’ engagements.
Focusing on these issues by utilizing digital platforms, this research
proposes a long-term SIA procedure based on the system dynamics

EURO 2024 - Copenhagen(SD) framework and co-monitored parameters to understand the valueof re-naturing measures and biodiversity in urban areas. A systematic review collects direct and indirect social indicators, impacts, andfactors of re-naturing through ecosystem services (ES). After contextualization at Giardino San Faustino, Milan, through consultation withactors, the results shape causal loop diagrams in the system dynamics
framework. To comprehensively cover the extensivity of interactionsand variable relations, the simulation tool of AnyLogic is used for fullcoverage of relations. This digital tool enables a complete constructionof relations and simulates interactions for SIA in defined timelines reflecting limitations and advantages in each scenario. The results rendera spectrum of interaction dynamics aiding decision-makers in better
evaluating human and nature interactions.

 MA-19
Monday, 8:30-10:00 - Room: 44 (building: 116)

Learning-assisted Optimization in Energy
Problems
Stream: OR in Energy
Invited session
Chair: Salvador Pineda Morente
Chair: Juan Miguel Morales
1 - Machine Learning based surrogate models for largescale sector-coupled energy systems

Tim Graulich, Marie Münster, Bissan Ghaddar, Rasmus
Bramstoft
Surrogate modeling is a well-established field within engineering sciences, where computational simulation and optimization models are
becoming increasingly complex. Instead of running costly highfidelity models, surrogate models can provide accurate estimations ,while reducing computation times. Currently, the energy system is becoming increasingly integrated across the energy sectors and vectors,
leading to a growing demand for sector-coupled large-scale energy system models. At the same time higher shares of variable renewableenergy sources require higher temporal and spatial resolutions when
performing the energy system modeling. This development results in
an increasing complexity of these models. One possible method to deal
with this growing complexity is to build surrogate models representinga specific energy sector (e.g. hydrogen), which can then be coupled
to another operational model (e.g. power system model). However,while surrogate models have been used for various applications in theenergy domain, ranging from building energy performance simulation
to power grid optimization, no surrogate models have been developed
to represent large-scale sectoral energy system models. A big challenge of these model is the large number of input and output parameters. This talk will present ongoing research on how to use sensitivity
analysis to reduce the models complexity for the surrogate model.

2 - A Quantile Neural Network Framework for Two-stage
Stochastic Optimization: Application to Power Systems
Operation

Antonio Alcántara, Carlos Ruiz, Calvin Tsay
Two-stage stochastic programming is a popular framework for optimization under uncertainty, where decision variables are split between
first-stage decisions, and second-stage (or recourse) decisions, where
the latter can be adjusted after uncertainty is realized. These problems
are often formulated using Sample Average Approximation (SAA),
where uncertainty is modeled as a finite set of scenarios, resulting ina large deterministic problem, i.e., where the model is repeated foreach scenario. The resulting problems can be challenging to solve,
and several decomposition approaches have been proposed. More recently, Patel et al. (2022) approximated the expected second-stage objective value for a set of scenarios using a neural network, which canthen be embedded in the first-stage problem to produce good heuristic

MA-20

solutions. In this work, we propose approximating the second-stage
objective value with a quantile neural network, which can capture uncertainty and is not limited to expected-value optimization, e.g., to optimize the Conditional Value at Risk (CVaR). We discuss optimization
formulations for embedding the quantile neural network and demonstrate the effectiveness of the proposed framework using several computational case studies including mixed-integer and nonlinear problems. Particularly, we test this novel methodology in relevant powersystems’ operation problems with high renewable energy penetration.

3 - Learning-Assisted
Switching

Optimization

forTransmission

Salvador Pineda Morente, Juan Miguel Morales, M. Asuncion
Jimenez-Cordero
We propose a novel learning procedure to assist in the solution of awell-known computationally difficult optimization problem in powersystems: The Direct Current Optimal Transmission Switching (DCOTS). This model consists in finding the configuration of the power
network that results in the cheapest dispatch of the power generating
units. The DC-OTS problem takes the form of a mixed-integer program, which is NP-hard in general. The proposed approach leverages
known solutions to past instances of the DC-OTS problem to speed up
the mixed-integer optimization of a new unseen model. Although it
does not offer optimality guarantees, a series of numerical experiments
run on a real-life power system dataset show that it features a very
high success rate in identifying the optimal grid topology (especiallywhen compared to alternative competing heuristics), while rendering
remarkable speed-up factors.

4 - Learning-based State Estimation in Distribution Systems with Asynchronized Measurements

Jose Gomez de la Varga, Salvador Pineda Morente, Juan
Miguel Morales, Álvaro Porras Cabrera
The task of state estimation in distribution systems faces a major challenge due to the integration of different measurements with multiple
reporting rates and asynchronization. As a result, distribution powersystems are essentially unobservable in real time, indicating the existence of multiple states that result in identical values for the available
measurements. Certain existing approaches utilize historical data to infer the relationship between real-time available measurements and thestate. Other learning-based methods aim to estimate the measurements
acquired with a delay, generating pseudo-measurements. Our paper
presents a methodology that utilizes the outcome of an unobservablestate estimator to exploit information on the joint probability distribution between real-time available measurements and delayed ones.
Through numerical simulations conducted on a realistic electricity
network with asynchronized measurements, the proposed procedure
showcases superior performance compared to existing state forecasting approaches and those relying on inferred pseudo-measurements.

 MA-20
Monday, 8:30-10:00 - Room: 45 (building: 116)

Ethics of OR and artificial intelligence
Stream: OR and Ethics
Invited session
Chair: Ulrike Reisach
Chair: Cathal MacSwiney Brugha
1 - A granular approach to optimal and fair patient placement in hospital emergency departments

Dessi Pachamanova, Maureen Canellas, Georgia Perakis,
Omar Skali Lami, Asterios Tsiourvas

15

MA-21EURO 2024 - Copenhagen

Prolonged emergency department (ED) length of stay (LOS) is associated with detrimental effects on patient care and quality, including
increased mortality, increased risk of hospital-acquired infections, anddisrupted patient flow. There is also evidence that certain groups of patients experience longer LOS based on their gender or race, especiallywith regard to the part of LOS that is attributable to waiting to be seen
by a clinician. This work tackles the patient prioritization and placement aspects of ED operations with the goal of improving throughput
and wait time in a fair, equitable way. We present a novel Mixed Integer Linear Programming predictive-prescriptive formulation that incorporates a breakdown of predicted patient ED LOS into actionable
pieces and allows for a more granular model of ED operations. We
show how to incorporate considerations for fairness and reformulate
the MILP formulation into a compact and computationally tractable
formulation that can be solved efficiently in real time. This work was
conducted in collaboration with a large US academic medical center.
Data from more than 40000 patient visits were used to shape and evaluate the models. We provide an interpretable metamodel trained on thecomplex model’s recommendations in order to help with the operationalization of the algorithm. The method will be used by the hospital
to improve patient flow and quality of care as well as to support morefair and consistent bed allocation decisions.

2 - An Ethical compass for Artificial Intelligence (AI) and
Chat CPT for students’ and universities

Ulrike ReisachThis abstract focusses on ethical concerns for students’ usage of algorithms AI and Chat CPT in different nations, laws and decision making.
Depending on care takers, universities and teachers’, the usage of thosenovel tools is quite different. For example, students might (a) be able
or not able to (mis)use the tools, (b) find or lack access to true or false
information, (c) loss of intellectual property rights, whether they andtheir parents or teachers are aware or not aware. This causes a discussion on ethical issues along the previously mentioned stakeholders andtheir complex societal or economic backgrounds. Using cases from
Generative AI which describes algorithms such as Chat GPT, we will
see what ethical issues mean for the students and their care takers andother stakeholders: Students who have access, usually like Chat CPT(Version 3, they cannot afford level 4) and they use it and their results
sound good, but also lowers their own creativity as well as their analytical and critical thinking. This sample shows ethical concerns acrossbiases, cultural/individual identities, autonomy, social justice, safety
and accountability. In a nutshell: The existence of novel tools allows
well-to- do users or universities to access new content and achieve support for various purposes like writing, speaking, marketing, etc., whileissues like inclusion/social cohesion might be left, while commercial
tool providers benefit from new customers and revenue.

3 - AI ethics-in-the-making: a situated perspective

Sabine Amend
In focus is a smaller Software-as-a-Software (SaaS) enterprise that is
pursuing AI-led innovation as a key strategy. Get a front-row view ofleader decision-making and organizational behavior as this enterpriseis operating in turbulent market conditions. How are AI ethics madeduring an industry-critical 6-month period (2023/24)?
A contemporary interpretation of Aristotelian phronesis - the ethics of"practical wisdom" - and an adaptive systems lens form the basis of inquiry into the situated action at this SaaS business: How are AI ethics
included, excluded, centralized, or marginalized while the technological landscape changes rapidly? Drivers for decision-making, such as
the tangle of stakeholder relationships, the evolving AI-based products
themselves, leader mindsets, policies, reputation management, and design processes come into view.
The situated exploration is set in the context of broadly discussed AI
ethics frameworks, such as the EU guidelines, UNESCO recommendations, and current academic research. Together, they outline a mapof gaps and blind spots, as well as effective practices. This map informs a discussion of the opportunities and constraints for embedding
greater AI-ethics "practical wisdom" into turbulent organizational life
- and for strengthening positive trajectories with AI technologies.

16

 MA-21
Monday, 8:30-10:00 - Room: 49 (building: 116)

Planning problems in electrical energy
systems
Stream: OR in Energy
Invited session
Chair: Natalia Alguacil
1 - Dynamic Expansion Planning of a Commercial Virtual
Power Plant through Coalition with Distributed Energy
Resources considering Rival Competitors

Santiago Maiz, Raquel García-Bertrand, Luis Baringo
We consider a virtual power plant (VPP) that expands its capacity byforming a mid-term coalition with distributed energy resources (DERs)
such as conventional and renewable power plants, as well as with energy storage systems and flexible demands. The VPP competes withrival VPPs to aggregate the DERs to its own portfolio. This problem is formulated as a three-stage stochastic bi-level model, where theexpected profit of the VPP is maximized in the upper-level problem,
while the lower-level problems deal with the decisions of each DER
regarding the selection of VPP. In the first stage, the VPP manager
places bids to secure each DER auction. The second stage involves
decisions to determine the DER auctions, forming the VPP coalition
and the procurement of power from the day-ahead market. Uncertainties in this stage include bid prices of rival VPPs and the minimum
selling price of DERs. Finally, in the third stage, the expanded VPP
determines its optimal operation and manages uncertainties related torenewable energy production levels and market prices. Variability ofparameters such as market prices and renewable energy production levels (both solar and wind) is modeled using representative days generated by a clustering K-medoids method. The model incorporates Conditional Value-at-Risk (CVaR) as a risk metric. Through a case study
using real data, it is demonstrated that weather conditions and electricity demand significantly influence the coalition formation.

2 - Strategic Planning for Transition to Next Generation
Clean Energy Technologies using Stochastic Programming

Burak Kocuk, Neman Karimi
In recent years, the transition from fossil fuels to clean energy technologies has accelerated. Although this transition might bring environmental and economic benefits, it requires a long-term strategic plan due
to the large investment costs involved. In addition, the uncertainties inthe technical and economic development of next generation clean energy technologies should be included in this plan. Our research focuseson optimizing strategic plans for the transition to next generation clean
energy technologies using stochastic programming. As a case study,
we examine the Machine Replacement Problem under Technological
Advances. Although it is common to periodically replace machinesthat completed their economic lifetimes, the exact times of replacement should be optimally determined. For example, in the short-term,
it might seem beneficial to replace a conventional machine with anexisting clean technology whereas in the long-term, it might be more
beneficial to wait and replace it with a future technology that is currently evolving. We formulate this problem using multistage stochastic programming by creating technology advancement scenarios. We
analyze the underlying problem structure and the computational complexity of deterministic and stochastic versions. We also present how
to solve large-scale problems more efficiently. Finally, we discuss potential applications of our approach to real-life problems such as clean
energy transitions for fleets and campuses.

3 - Coordinated Generation Expansion Planning for Transmission and Distribution Systems via ADMM

Natalia Alguacil, Nahid-Ur-Rahman Chowdhury, Gregorio
Muñoz-Delgado, Javier Contreras

EURO 2024 - Copenhagen
This work provides a practical algorithm for solving the generation expansion planning problem for both transmission and distribution systems. Conventional and wind-based generators are eligible to be installed under a set of scenarios that captures the uncertainty arisingfrom different operating conditions, demand fluctuations and wind dynamics. Assuming there is one planner at the transmission level anddistinct planners for each of the analyzed distribution systems, an optimal expansion plan for the whole power system is obtained. The
resulting problem, which is formulated as an instance of scenariobased linear programming, is solved here in a coordinated but distributed fashion by using the Alternating Direction Method of Multipliers (ADMM). Unlike other works that also facilitate transmission
and distribution planners’ coordination, the proposed solution methodology not only guarantees the finite convergence to optimality but also
the privacy of information related to each planner’s assets. Privacy
is achieved by solely exchanging information on the injections at theinterface nodes among neighboring transmission and distribution systems. The algorithm’s performance is tested on small- and mediumsized systems, revealing a reasonable tradeoff between solution quality
and computation times for a practical setting.

4 - Enhancing electricity system robustness through theapplication of a new time series cluster algorithm

Leonie Sara Plaga, Valentin Bertsch
The ongoing evolution of climate change presents unprecedented challenges to electricity system performance and reliability, which are inherently sensitive to weather conditions affecting both energy demandand generation capacities. To aid decision-making in energy systems
under climate change influence, energy system optimization models
can be combined with climate model outcomes. As future climate development is highly uncertain, there are many different climate models
and human greenhouse gas emission scenarios projecting the future.
Thus, we propose a new time series clustering algorithm. This algorithm allows to incorporate various climate models and emission scenarios into energy system optimization while focusing on providing arobust solution, meaning that the amount of unmet demand is low nomatter which climate scenario is realized. The algorithm operates by
first splitting the time series into single days which are optimized individually. The investment results of the single days are then used to
calculate the amount of unmet demand which occurs when the singleday investment decsisions are used with the complete time series. Thisdata informs the clustering process, prioritizing days with high unmet
demand. Our results demonstrate that this new clustering algorithm
can reduce unmet demand compared to traditional cluster algorithms
like k-means. In further research we plan to compare our approach to
traditional robust optimization approaches.

MA-23

parts are a significant threat to the reliability and safety of electronicequipment, highlighting the need for traceable systems to prevent anddetect fraudulent activities and protect consumers. Blockchain technology (BCT) offers several characteristics to address these issues
while bringing several challenges. It embraces substantial investments,
encompassing technological assets and the development of requisite
infrastructure. Cultural and governance issues also influence the willingness of all supply chain parties to collaborate on BCT investment.
To address these challenges, we model an evolutionary game to investigate the following research questions: how does parties’ adoption tendency in the semiconductor industry evolve, considering technical cost,
partners’ collaboration, and the ultimate goal of achieving complete
transparency through BCT adoption? What is the impact of external
factors, including cryptocurrency fluctuations and evolving consumer
behaviors, on parties’ decision-making processes in RSC, particularly
concerning their adoption or non-adoption of Blockchain technology?

2 - E-waste reverse logistics in India: Collaborative collection and processing

Aditya Prabhakar
One of the widely adopted practices for e-waste reverse logistics is thesealed bid process. In this process, the seller first invites quotations forselling a set of e-waste. The recyclers then send their bids/ quotations
in sealed envelopes which are opened only on the prescribed date. The
recycler with the highest bid wins the contract. In the above process,a seller may sell a particular type of e-waste or a combination of different types of e-waste. However, the recycler may or may not haveadequate infrastructure to process each type of e-waste offered by theseller. In such situations, the recycler may collaborate with anotherrecycler(s) to ensure better efficiency and make a higher profit. However, there are various uncertainties faced by the recyclers in terms of
the quantity, quality, recovery rate, procurement cost, transportation
cost, and processing cost of the e-waste. This paper tries to address
the uncertainty faced by the recycler in terms of the procurement cost
during a sealed bid process. While deciding on whether to bid or not ina sealed bid process, the recycler faces a tradeoff between profit margin and the quantity of e-waste collection. A stochastic integer linear
programming model is proposed that would help recyclers decide an
optimal bidding price for a certain set of e-waste to maximize theirprofit. Further, this paper analyses the impact of a coalition and thecapacity allocation on the profit shares of the partner firms.

3 - How do past repair experiences affect expectations
about right-to-repair legislation?

Weihua Zhang, Marc Reimann, Sadaat Yawar, Ines
Güsser-Fachbach MA-23
Monday, 8:30-10:00 - Room: 82 (building: 116)

Circular Economy, Remanufacturing andRecycling
Stream: Circular Economy, Remanufacturing and Recycling
Invited session
Chair: Sara Saberi
1 - Reverse Supply Chain in the Semiconductor Industry:
Examining the Blockchain Implementation

Davoud Hosseinnezhad, Sara Saberi, Sahar Ebrahimi Bajgani,
Fuminori Toyasaki, Cathal Heavey
In response to recent global fluctuations, companies are turning to reverse logistics to minimize waste and increase production capacity.
Implementing a reverse supply chain (RSC) is not without challenges,
including obtaining e-waste with the right quality, preventing fraud,and disposing of sensitive information securely. Counterfeit electronic

In today’s consumer-driven society, the issue of product repairability
and the Right-to-Repair (RTR) movement have seen significant attention and debate. As these RTR laws and regulations are very recent,there is no evidence of their impact on the desired issues associatedwith the repair. It is not even clear how well known RTR legislationis among the general public, and what consumers expect from such
legislation. Those expectations and whether as well as how they are
eventually satisfied will shape the effectiveness of the legislation. To
provide a comprehensive insight into the factors influencing expectations of RTR legislation, we conducted focus group interviews and aquantitative online survey with participants from both Austria and theUnited Kingdom (UK). Our main finding is that (in general) past experiences only indirectly affect the expectations about RTR legislation,
namely via repair intention. These results are surprisingly robust when
we zoom into the different types of repair scenarios, DIY and use ofa repair service. Finally, comparing Austria and the UK, the results
are strikingly robust. This research contributes to the growing bodyof knowledge on the RTR legislation and its implications for consumer
behaviour, sustainability, and public policy. Moreover, this study offers
guidance for policy-makers and repair companies in understanding thefactors that influence consumer expectations of RTR legislation.

17

MA-24EURO 2024 - Copenhagen MA-24
Monday, 8:30-10:00 - Room: 83 (building: 116)

Sustainable Supply Chain Design
Stream: Sustainable Supply Chains
Invited session
Chair: Grit Walther
1 - Green supply network design with carbon tariffs andlocation-specific carbon policies

Jens Christian, Florian Sahling
Carbon policies are often limited to specific regions. Therefore, carbon
leakage leads to the relocation of production to regions without carbon
policies. To prevent carbon leakage, the European Commission recently adopted the Carbon Border Adjustment Mechanism. It imposescarbon tariffs on carbon emissions imported into regulated regions. We
present a new mixed-integer model formulation for global and multiperiod supply network design subject to carbon tariffs and locationspecific carbon policies (SND-CT-CP). Consequently, locations differ
in whether and how carbon policies are active at the respective location. Here, we include carbon tax, carbon cap-and-trade and carbon
caps as location-specific carbon policies. In contrast, carbon tariffs are
imposed on emissions imported from unregulated locations. As wepropose a strategic planning problem with a long-term planning horizon, we include uncertainties in the SND-CT-CP. These uncertainties
focus on stochastic carbon caps, which have a particular influence on
the supply network design. Within the presented SND-CT-CP, we consider a four-layer supply network. Accordingly, the locations of manufacturing plants and distribution centers are planned. Suppliers are
selected based on resilience considerations. In addition, the demand
includes price-dependent levels. Thus, the objective of the SND-CTCP is to generate a supply network configuration that maximizes the
expected net present value.

2 - Bi-Objective Supply Chain Network Design with Transport Mode Selection

Eberhard Schmid
In Supply Chain Network Design (SCND), the decision maker’s task is
to decide upon locations for production or distribution facilities, product allocations to warehouses and factories, customer allocations andthe choice of the transport modes. These decisions do not only have an
impact on the economic performance of the supply chain but also on
environmental indicators like the emission of CO2. To account for
trade-offs, multi criteria SCND-modelling approaches are discussed
increasingly. In this paper, we will present a bi-objective SCND modelling approach which explicitly considers the selection of the mode oftransport, as one possible leaver in the reduction of CO2. We model
specific characteristics of transport modes, like the lead time and theshipment size and their influence on safety stock and cycle stocks.
Through this, we are able to model trade-offs, which so far are not considered in the literature on multi criteria SCND-models. We describea solution procedure, which includes the conversion into a mixed integer conic quadratic optimisation model, demonstrate the applicability
within a case study and discuss computational and managerial insights.
Further we give an outlook on the possible error supply chain decision
makers can make, when ignoring those effects in the application ofmulti criteria SCND models.

3 - A stochastic mixed-integer programming approach to
optimize a renewable fuel supply chain under uncertainty of renewable electricity supply

Mina Farajiamiri, Jörn Meyer, Grit Walther
Addressing climate change requires the transportation sector to adopt
renewable hydrocarbon fuels, particularly in aviation, maritime, andheavy-duty road transport, where electrification faces challenges. Thistransition necessitates the design of new production networks and supply chains, yet it faces uncertainties related to resources, technologies,
and demand. It is vital to ensure that these renewable fuel supply

18

chains are flexible and robust to uncertainties, such as supply and demand fluctuations or technological changes. We explore the impactof uncertainty on the design of renewable fuel supply chains, focusing
on the variable capacity factors of renewable energy sources and theirinfluence on electricity supply. We propose a multi-stage stochastic
programming approach to optimize the renewable fuel supply chain
design, taking into account seasonal and geographical variations inresource availability. We aim to minimize the expected total systemcost while accommodating the uncertainty in the capacity factor of renewable electricity which will affect its supply. Scenario generation isbased on 30 years of hourly data, using Latin Hypercube Sampling andIman-Conover transformation to incorporate correlation among uncertain parameters. A fast-forward heuristic algorithm is developed tosolve the model efficiently. Applied to a case study on the EU, results
show promise for this approach in designing multi-period supply chain
networks that work well under each scenario

4 - Green Hydrogen Supply Chain Network Design for Aviation: Multi-Period Optimization Model with Seasonal
Temporal Resolution

Akin Ögrük, Christian Thies
Hydrogen-based propulsion concepts for aircraft are considered apromising technology towards the decarbonization of aviation. Whilethe development of respective aircraft models is in progress, questions regarding the supply network of green hydrogen are arising. We
present a multi-period mixed-integer programming model for the hydrogen supply chain network design problem focusing on the aviation
sector. The model minimizes the total network cost by making strategic decisions (e.g. suppliers, locations, capacities, transportation infrastructure) and tactical decisions (e.g. hydrogen flows, storage quantities) at different temporal resolutions. Our model formulation considers the spatially and temporally varying supply and demand of hydrogen, the techno-economic characteristics of hydrogen storage, liquefaction and transportation (e.g., economies of scale), as well as the
specific requirements of hydrogen handling (e.g., losses). Model application is illustrated for German airports with local production andhydrogen import options, considering the projected development of
the European Hydrogen Backbone pipeline infrastructure. Optimal
network designs and results are presented and analyzed for differenthydrogen supply and demand scenarios.

 MA-25
Monday, 8:30-10:00 - Room: 011 (building: 208)

Discrete, continuous or stochastic
optimization and control in networks,transportation and design I
Stream: Combinatorial Optimization
Invited session
Chair: Gerhard-Wilhelm Weber
Chair: Antonio Mauttone
Chair: Jinal Parikh
1 - Optimizing with attractor: combinatorial complexity ofthe traveling salesman problem

Weiqi Li
Although TSP has been placed in the subject of NP-completeness, it
might not be as complex as expected. Often the seed of a solution
is in the problem structure that creates the complexity. The intrinsic
difficulty of TSP is due to the combinatorial explosion of tours in thesolution space S, which increases in (n-1)! as TSP size n increases. In
TSP, a small set of n(n-1) edges make up a large set of (n-1)! tours in
S. Let S’ be a subset of S, if we can exponentially reduce S to S’ usinga polynomial-time local search algorithm, and S’ can be polynomiallysearched by an exhaustive search algorithm, there is a polynomial algorithm for TSP. A local search can reduce S to S’ quickly using theEURO 2024 - Copenhagenedge matrix E of TSP as a reduction mechanism, because if one edge
is removed in E, (n-2)! tours are removed in S. Instead of searching for
better tours in S, our effort is to remove bad edges in E. In a local searchsystem, an attractor S’ drives search trajectories into the vicinity of a
globally optimal point in S. S’ contains a small set of the most promising tours and is exponentially smaller than S, thus make an exhaustive
search feasible. This new search paradigm is called optimizing withattractor. A new search algorithm consists of two search phases: local
search phase quickly construct S’ in S, and exhaustive search phase examines S’ completely to find the optimal tour. TSP size can be reduced
through its own data structure, thus it is a self-reducible problem.

2 - Exact and approximation algorithms for routing a convoy through a graphMartijn van Ee, Tim Oosterwijk, René Sitters, Andreas Wiese
We study routing problems of a convoy in a graph, generalizing theshortest path problem (SPP), the travelling salesperson problem (TSP),
and the Chinese postman problem (CPP) which are all well-studied in
the classical (non-convoy) setting. We assume that each edge in thegraph has a length and a speed at which it can be traversed and that our
convoy has a given length. While the convoy moves through the graph,parts of it can be located on different edges. For safety requirements,
at all time the whole convoy needs to travel at the same speed which
is dictated by the slowest edge on which currently a part of the convoy
is located. For Convoy-SPP, we give a strongly polynomial time exact
algorithm using dynamic programming. For Convoy-TSP, we providean O(log n)-approximation algorithm and an O(1)-approximation algorithm for trees. Both results carry over to Convoy- CPP which - maybe
surprisingly - we prove to be NP-hard in the convoy setting. This contrasts the non-convoy setting in which the problem is polynomial timesolvable.

3 - A new approach for inter-sample avoidance using an integer polytope formulation

Johannes Schmidt, Armin Fügenschuh
To model the physics of the used vehicles in trajectory planning problems, Newton’s equations of motion must be incorporated, discretizedby numerical solution schemes. Thus, the position of the vehicle isin the model known only at a discrete set of points. Due to this lackof information, corner cutting can occur, i.e., the vehicle can abbreviate through an obstacle if it remains outside at every evaluation point
again. Increasing the number of discrete time steps cannot resolve thisproblem completely and leads to higher computation times. To obtainalways realistic solutions, corner cutting must be restricted, yielding
so called inter-sample avoidance methods. We present a new approachto ensure inter-sample avoidance for polyhedral obstacles. The hyperplanes describing the obstacle divide the mission space into a set ofsubareas which can be encoded by binary vectors. These are used toensure feasible movements without requiring more evaluation points oradditional variables. We give a procedure to generate an integer polytope containing the feasible movements and derive an upper bound on
the number of necessary constraints to describe it. The new approach isqualitatively tested against other inter-sample avoidance methods fromthe literature and its computational efficiency is studied in numerical experiments, using a mixed-integer linear program for trajectory
planning of unmanned aerial vehicles and the state-of-the-art solver
Gurobi.

4 - Modeling equipment scales of catering trolleys for flight
operations

Hua-An Lu
Catering trolleys are one of the aircraft’s standard load equipment for
food-and-drink services in the cabin. Airlines must prepare sufficientscales to cope with all their passenger flight operations. The catering
work must start a few hours before the flight departs, and loaded onboard trolleys cannot be directly reused after arrival before the cleaning process. Trolley types can be divided into regular and half carts
by size and volume and are versatilely used on every aircraft type.
Aircraft galleys have specific areas with multiple blocks for the sideby-side storage of trolleys, with the principle of "all carts or no cart"
for a block, even storing empty ones, to avoid trolleys arbitrarily moving while flying. This study exploited a time-space network graph toMA-26

express the movement of trolleys between airports and airports. An integer programming model based on this graph was proposed with thedecision variables for the numbers of full and half trolleys on the arcs.
The objective minimizes the total number of equipment scales subject
to the flow conservation and constraints in handling catering trolleys
with a safety stock level at each airport. This preliminary research reported the numerical experiments for a Taiwanese airline operating international flights. Tested cases consisted of 43 airports and 643 flights
operated with four kinds of fleet and were solved within a reasonabletime.

 MA-26
Monday, 8:30-10:00 - Room: 012 (building: 208)

Combinatorial optimization topics intransportation
Stream: Combinatorial Optimization
Invited session
Chair: David Pisinger
Chair: Francisco Canas
1 - A consensus fixing heuristic for the stochastic prizecollecting TSPDavid Pisinger
We consider the two-stage stochastic prize collecting traveling salesman problem, in which the objective is to maximize profit of served
customers minus driving costs. The first-stage customers are to beserved every day, while the second-stage customers fluctuate from day
to day. The task is to select a subset of the first-stage customers, suchthat the expected earning is maximized. We present a highly parallel heuristics based on consensus fixing: The problems are solved independently for each scenario using a variable neighborhood searchheuristic. Then, the scenarios try to reach consensus about fixing asingle first-stage variable, using various score functions. The processof alternating between heuristic solution and variable fixing is repeateduntil all first-stage variables have been fixed. Computational results arereported for instances having up to 500 first-state and 500 second-stage
customers.

2 - Passenger and parcel share-a-ride problem withdrones: Models and solutions

Rong Cheng, Yu Jiang, Otto Anker Nielsen, David Pisinger,
Tom van Woensel
Growing concerns about traffic congestion and environmental pollution necessitate innovative solutions to enhance urban mobility for bothpeople and goods. A novel approach involves combining passenger
and parcel delivery services using demand-responsive buses (DRBs)
and drones. This integration aims to reduce the number of vehicles
on the road by combining the movement of passengers and parcels.
In this scheme, each DRB is paired with a drone to work together.
While DRBs accommodate both passengers and parcels, drones areused solely for delivering parcels. We introduce the concept of theinduced Route Planning Problem for DRBs and drones, termed theShare-A-Ride Problem with Drones (SARP-D). We first developed an
arc-based mixed integer programming model to formulate this problem, which can be solved by CPLEX for instances with up to 12 nodes.
Then, we developed a path-based model and a column generation approach to solve medium instances with up to 50 nodes. The column
generation approach achieves either optimal solutions or solutions very
close to optimality within a 3-hour timeframe. Large instances withup to 200 nodes are solved by an adaptive large-neighborhood searchmetaheuristic, which can also efficiently solve vehicle routing problems with drones. Our experiments showed that SARP-D could reduce
the number of used DRBs and DRB-traveled miles. We offer several
key insights for management based on our research outcomes.

19MA-28EURO 2024 - Copenhagen

3 - Solving the Rainbow Steiner Tree Problem using Kernel
SearchLorenzo Tomasetti, Francesco Carrabs, Renata Mansini,
Lorenzo Moreschini
Given an undirected edge-colored graph with non-negative edge
length, the aim is to find a minimum Steiner Tree that uses at mostone edge for each color. The problem, called Rainbow Steiner Tree
Problem (RSTP), is known to be NP-hard and can be used to model different real-world applications such as in wireless connectivity. Given
the complexity of the underlying problem, we develop a simple variant of the Kernel Search algorithm based on the multicommodity flow
formulation with rainbow constraints. Comprehensive work has been
done on the Kernel search framework to enhance its effectiveness overthe discussed problem. We test our approach on benchmark instancesand compare its performance with both state-of-the-art algorithms and
MIP solvers. Preliminary results are very promising.

4 - Extensions of Node-Depot Assignment Formulations
for the Hamiltonian p-Median Problem

Francisco Canas, Luís Gouveia
Given a weighted complete undirected graph with weights on the edges
and a positive integer p, the symmetric Hamiltonian p-Median Problem(HpMP) is to find a minimum weight set of p elementary cycles partitioning the vertices of the graph. We focus on a variant of the HpMP(the HpMP>=) where solutions with strictly more than p cycles arealso feasible.
We present new extensions of known node-depot assignment (NDA)compact formulations. NDA models are characterized by the inclusion
of NDA variables, which are 1 if and only if node d acts as the depot of the cycle node i is in (or alternatively, if node i is “assigned" tonode d), in addition to edge variables, which are 1 if and only if thecorresponding edge is in some cycle. We extend these formulations
with edge-depot assignment (EDA) variables, which are 1 if and only
if node d acts as the depot of the cycle edge i,j is in. In this talk, we:
1 - Propose new formulations which use the EDA variables; 2 - Relatethese models with formulations including only the edge and NDA variables; 3 - Present new inequalities in the space of the edge and NDA
variables that are derived from the new models using EDA variables; 4- Present computational results obtained with the new models.
Preliminary computational results show that the new models produce
very strong LP relaxation bounds and, when used in combination witha modern ILP solver, allow for the resolution of instances with over
one-hundred nodes.

 MA-28
Monday, 8:30-10:00 - Room: 065 (building: 208)

Advancements of OR-analytics in statistics,
machine learning and data science 1
Stream: Advancements of OR-analytics in statistics, machine learning and data science
Invited session
Chair: Gerhard-Wilhelm Weber
Chair: Olabode Adewoye
1 - Preventive maintenance for computerised tomography(CT) scan machines: an anomaly detection approachbased on log-data.

Felipe Maldonado
Preventive maintenance is a very critical topic for many industries.
Particularly in healthcare, often the associated costs (monetary andsocial) of unplanned downtimes are much higher than those coming

20

from scheduled maintenance. However, deciding upon optimal schedules is a very complex task. Ideally, maintenance should be performed
only when it is absolutely needed, minimising potential disruptions.
In this paper we collaborate with Siemens to analyse a large data set
consisting in log-data obtained from Computerised Tomography (CT)scan machines, with the objective of developing preventive maintenance based on anomaly detection. Here, the end-user (e.g., Hospitals) will have to make the decision of performing such maintenancebased on the model predictions (and therefore, having to re-schedulepatient’s appointments). Having this into consideration we further develop explainable analytics that provide a visual representation containing warning time windows for future potential machine failures,
and an explanation of why our model classifies them in that way.

2 - Research on profit control strategies of remanufacturing supply chain considering dual sales effort

Juhong Gao
Considering the consumption preference and fairness concern, this paper constructs a sub-channel remanufacturing supply chain in which
manufacturer sell new products through retail channel and remanufactured products through direct channel. The optimal profit equilibrium
strategy in the centralized and decentralized decision making of subchannel remanufacturing supply chain with or without sales effort isstudied by means of differential game analysis, considering the effectof time attenuation of advertising effect generated by dual sales effort
of manufacturer and retailer. It is found that regardless of the initialadvertising effect size of new products and remanufactured products,
both manufacturer and retailer must take sales efforts in a certain period
to stabilize the supply chain profits[U+FF0C]and there exists a criticalvalue for the time to take sales efforts. In addition, it is revealed thatthe changing trend and the final stable value of the advertising effectof products depend on the optimal sales effort level and the effect ofsales effort on the advertising effect, thus finding the changing law of
the profit of the members of the supply chain and the total profit overtime. This study provides a theoretical basis for supply chain member
enterprises to effectively implement sales efforts on the profit control
strategy of the sub-channel remanufacturing supply chain.

3 - Learning Decision-Focused Surrogate for DecisionDependent Problems with Samples

Zhuojun Xie, Yiping Fang, Adam Abdin
Decision-Focused Surrogate (DFS) framework has been recently proposed to tailor parametrically tractable proxies for intricate optimization problems with nonlinear and/or nonconvex nature. In this work,
we extend the frontier of DFS to a data-driven contextual stochastic
decision-dependent setting where the endogenous uncertainty is affected by decision-dependent features. Moreover, the conditional distribution remains unknown, yet historical observations are available.
Specifically, we substitute the unknown conditional distribution witha parametric linear model. To ensure the decision quality for specific tasks, we employ techniques from Decision-Focused Learning(DFL), including optimization differentiation and surrogate loss construction, to acquire gradient of decision quality concerning model’s
parameters. We examine the performance of DFS in a battery energy
management problem within a renewable power system considering
decision-dependent degradation. Our results demonstrate that compared to a surrogate model trained using standard regression loss, DFS
can achieve better decision quality, especially when the model is misspecified. Despite the higher computational expense of learning DFS,
our empirical analysis reveals that the trained surrogate can generate
high-quality solutions for instances under similar conditions.

4 - Towards analyzing DNNs by robust adversarial examples created with MILPs

Rónán Rian Carl Richter, Jörg Rambau
The interest in the use of Deep Neutral Networks (DNNs) has grown
rapidly over the last few years. As an increasing number of people
and businesses are using DNN-based systems and governments start
to take actions to regulate the use of artificial intelligence, there is a
growing demand for methods to analyze the trustworthiness of a DNN
and the limits of its application. One classical illustration for showing weaknesses of DNNs, especially in the context of image recognition, are Adversarial Examples. These are slightly modified versions

EURO 2024 - Copenhagenof input data, that lead a DNN into wrong classifications. As Fischetti
and Jo (2018) have shown, Adversarial Examples can be generated byusing mathematical programming methods. Thus, these Adversarial
Examples are provably optimal in respect to a given criterion, e.g. thedistance to some given input data. However, the structure of these examples highly depends on the parameters of the network. To address
this point, we will present a mixed-integer programming model for
generating Adversarial Examples, that are robust with respect to smallchanges in the weights and biases of a DNN. For relaxations of themodel, we will illustrate the impact of robustification on Adversarial
Examples. Furthermore, we present experimental results on the influence of training data on the distance of Adversarial Examples and on
the transferability of our examples.

 MA-29
Monday, 8:30-10:00 - Room: 157 (building: 208)

Combinatorial Optimization models andapplications in Logistics and Transportation
I
Stream: Combinatorial Optimization
Invited session
Chair: Ioannis Fragkos
1 - Dynamic Berth Allocation Policies at Deep-sea Terminals

Orkun Tunay, Pieter van den Berg, Rob Zuidwijk, Debjit Roy
While deep-sea vessels play an important role for the revenue streams
of container terminals, barges and feeders play an important role inensuring the functioning of the transshipment and hinterland transportation operations. Besides, the roles of feeders and barges are further leveraged as a cost and emission-efficient alternative to long-haultrucking. These different classes of vessels compete for the same berthspace in container terminals. The terminal operators conventionallyhave the tendency to fully prioritize deep-sea vessels. However, with
the increasing incentives and regulatory pressure for the increase ofbarge and feeder throughput, some terminal operators started to allocate dedicated berths for barges and feeders. In this paper we will
study the berth allocation problem in order to find a pareto-optimality
frontier that balances barge throughput and deep-sea vessel waitingtimes. Barges and feeders which are of smaller volume, occupy asmaller space and have a shorter expected handling time. By using
analytical methods from Queuing Theory, we will first demonstrate
the pareto-efficiency of different heuristics of berth allocation policies,
i.e. dedicated terminal spaces, barge limits, motivated by the current
industry practices. Based on the insights that we receive from analytically tractable systems, we will design a Markov Decision Process topropose a better pareto-optimality frontier with dynamic policies.

2 - Data-driven integer programming models for installation of offshore wind turbines.

Charles Emeka Onyi, Mahdi Doostmohammadi, Kerem
Akartunali
Millions of pounds are involved in the construction of offshore windturbines. Therefore, there is a need to optimize the whole process to
reduce the cost and duration of the final procedure involved in windenergy generation. Specifically, this project focuses on modeling theprocedure for the installation of offshore wind turbines as a mixed integer linear programming and the use of exact methods to solve thosemodels. The objective is to minimize the total cost used in the installation procedure. To obtain an optimal solution, we devise a shortest
path-like formulation using strong multicommodity flow models of the
network. We show that this new formulation is NP-Hard. We propose a polyhedral analysis of the set of solutions by identifying novel
problem-specific valid inequalities required to characterize the convex
hull of feasible solutions. Also, we design an efficient cutting plane

MA-30

algorithm that identifies the optimal configuration of vessel schedules
that minimizes the installation duration and cost. We present preliminary computational results on the problem specific and the literature
instances that are related to the problem formulation. Also, computational instances will be tested by real-world data collected from offshore wind farm installations.

3 - Strategic capacity planning for logistics service
providers

Ioannis Fragkos
Logistics service providers (LSPs) employ transportation assets likecar wagons and containers to meet customer needs. To adequately service these needs, it is essential to use assets with the right capacity foreach transportation task. The time needed for these operations can vary
due to disruptions in the network, which introduce the need to haveadditional resources. This situation leads to a complex version of the
newsvendor problem, where the LSP must decide the number of assets
to acquire and plan their deployment to cater to demands that specify
a certain capacity but have an uncertain duration. Any demand thatexceeds the planned capacity can be met by using spot assets, although
at a higher expense. This concept is applied to an LSP that leases train
wagons yearly and manages demand fluctuations through occasional
spot train rentals. We formulate a two-stage stochastic program to represent this problem. The formulation’s structural features, combinedwith column generation, lead to an efficient solution method. The effectiveness of our method is demonstrated through computational studies.

4 - A rich model for the tramp ship routing and schedulingproblem — solved through column generation

Alberto Tamburini, David Pisinger, Nina Lange
We consider the Tramp Ship Routing and Scheduling Problem(TSRSP) in which we plan routes for a homogeneous fleet of tramp
shipping vessels operating on a combined contract and spot market.
Earlier research has been fragmented due to variations in the side constraints studied, hence we present the first unified model that can handle speed optimization, chartering costs, bunker planning, and hullcleaning. The model is solved by column generation, where thecolumns represent the possible routing of a vessel while the masterproblem keeps track of the binding constraints. The pricing problem issolved efficiently using a time-space graph, making it possible to solve
relatively large instances of the TSRSP in a very short time. Detailed
computational results are reported giving insight into both the algorithmic performance but also answering operational and tactical questions
for the tramp shipping company and the tramp shipping sector.

 MA-30
Monday, 8:30-10:00 - Room: 064 (building: 208)

MIP Solvers
Stream: Software for Optimization
Invited session
Chair: Benjamin Müller
1 - What’s New in FICO Xpress Solver?

Timo Berthold
We will give an overview of the latest enhancements, the newest features, and the most recent performance improvements in the FICO
Xpress Solver for mixed-integer linear and nonlinear optimization
problems. These include new heuristics, cutting and branching techniques, an augmented API, and updates to our global MINLP solver.

2 - Whats new in Gurobi 11

Michael Winkler
We will give an overview about recent developments and performance
improvements within Gurobi 11.

21

MA-31EURO 2024 - Copenhagen

3 - Recent Advances in the SCIP Optimization Solver

Suresh Bolusani
SCIP is an open-source optimization solver for mixed-integer linear
and nonlinear optimization problems and a framework for constraint
integer programming and branch-cut-and-price implementations. This
talk will highlight the latest developments in SCIP 9.0, released in
February 2024, including symmetry handling, constraint handlers, primal heuristics, cut generation and selection techniques, branchingrules, and LP interfaces. SCIP 9.0 has an improved overall performance in solving time, the number of nodes in the branch-and-boundtree, and the solver’s reliability.

4 - Recent advances in the Cardinal Optimizer

data. One of the markers adapts counting the number of price changes
by instead computing the average rate of price change per retailer over
all its products both within and across countries. The other, novel,
marker computes the persistence of a retailer in the marketplace. Using robust standardization of the two markers and applying a heuristic based on appropriate thresholds of these standardized markers, wehave identified varying numbers of retailers within and between countries for the product categories employing algorithmic pricing. We
have therefore created a new heuristic for identifying pricing algorithms in low-resolution data sets. Furthermore, this heuristic allows
for further research in empirically identifying whether or not tacit collusion exists among algorithms and the effects such pricing has on the
profitability of the retailers who employ such technology compared tothose who do not.

Benjamin Müller
In this talk, we present the recent developments in the Cardinal Optimizer (COPT). We discuss some key techniques that contributed to theperformance improvements of our MIP solver and present performance
numbers of the latest COPT release for all problem classes.

 MA-31
Monday, 8:30-10:00 - Room: 046 (building: 208)

Recommender systems
Stream: Analytics
Invited session
Chair: Dries Benoit
1 - Estimation of Online Advertising Effectiveness using a
Coarsened Exact Matching Framework

Vamanie Perumal, Palaniappan Ramu
Geo experiments are a method of choice to estimate online advertising
effectiveness. It is challenging to relate ad spend (treatment) in eachgeo and the ensuing time series responses, such as sales. The return on
ad spends (ROAS) is the difference between responses obtained during treatment (after-data) and the counterfactual responses if treatment
had not occurred (before-data). Since we obtain only after-data fortreated geos, evaluating the ROAS depends on how well the beforedata are modelled. A typical approach for modelling before-data includes estimating the behavior of treated geos using the behavior ofcontrol geos. An inevitable problem is to find control geos homogeneous to treated geos so that any difference can be directly attributed tothe treatment. To tackle the challenges mentioned above, we propose atime series based statistical matching framework. The selected time series features used as covariates vary naturally among all geos without
being influenced by the treatment. We achieve a balanced covariate
distribution between control and treated geos using Coarsened Exact
Matching (CEM). The weights obtained from CEM are used to build atime-varying negative binomial regression model for counterfactual estimation of before-data for treated geos. We demonstrate results using
real-time online advertising data. We observe significant improvement
in homogeneity between control and treated geos, thereby improving
the accuracy of incremental impact measurement.

2 - Algorithmic pricing and collusion on a price comparison website

Charlie Lindgren, Kenneth Carling, Ross May, Niklas
Rudholm
Empirical evidence identifies a large proportion of retailers using algorithmic pricing for making price decisions. This opens several questions surrounding tacit collusion and its detrimental effects on competition and consequently consumers in the form of supracompetitive
prices. As an initial step towards answering whether or not tacit collusion is happening among pricing algorithms, one need to first identify
which retailers are using such technology. We have defined two newstatistical markers for identifying pricing algorithms in low-resolution

22

3 - A location and assortment problem for e-commerce distribution with returns and transshipments

Joris van Oers, Quang-Vinh Dang, Zumbul Atan, Ivo Adan
As customer demand changes over time, reevaluation of strategic decisions concerning network design becomes imperative. However, thisentails significant investment in capital goods, necessitating resilience
to future disruptions. Consequently, this crucial decision often getsdeferred in practice, leading to inefficient distribution networks. To
address this challenge, we introduce a robust decision-support tool forevaluating strategic choices under varying scenarios. Specifically, ourfocus lies on the optimization problem of facility location and network
design (FLNDP) within a distribution network. This involves selecting
the optimal subset of new warehouses to open from a set of potential
locations, taking into account the significant return flow of items. In
addition, a strategic planner can opt to equip the opened warehouses
with facilities for inspecting returned items at fixed costs. Our contribution is twofold. First, we develop a mathematical model to solve the
FLNDP that allows for transshipments between warehouses, in contrast to earlier literature. This results in notable differences in the optimal solution of the network design, due to the imposition of fixed
setup costs for transportation. Second, we provide managerial insights
by evaluating a use case with real-world data, featuring disruptions
from a pandemic and geopolitical shifts within Europe.

4 - Towards effective and efficient personalized ranking innews article recommender systems: balancing clicks
and diversity

Lukas De Kerpel, Dries Benoit
As the consumption of news articles continues to shift towards online
platforms, personalized news recommendation engines have become
an indispensable asset for news providers seeking to enhance users’
reading experience.
However, as the shelf life of news items is limited, traditional collaborative filtering approaches fall short to provide accurate recommendations because they heavily rely on user signals, which may not bereadily available for newly introduced news articles. Contextual multiarmed bandits (CMAB) emerge as a promising alternative by leveraging intrinsic characteristics and attributes of both news items and users.
While CMAB systems predominantly optimize for clicks, this researchargues for a more comprehensive evaluation framework that also promotes the diversity of recommended items, as diversity-aware recommenders are able to respond to news readers’ needs on information
variety and to expose users to counter-attitudinal behavior.
By conducting a benchmark study on a large-scale proprietary dataset
obtained from an international news provider, we demonstrate the feasibility of creating an effective and efficient personalized news article recommendation system based on CMAB models while simultaneously addressing the multifaceted nature of news article recommendation quality. In particular, this study highlights the necessity of considering diversity alongside clicks to enhance user satisfaction and engagement.

EURO 2024 - Copenhagen MA-32

4 - Estimation of Hydraulic Parameters Using the Augmented Lagrange Method

Monday, 8:30-10:00 - Room: 41 (building: 303A)

Fabio Fortunato Filho, José Mario Martínez

Large Scale Constrained Optimization:
Algorithms and Applications
Stream: Advances in large scale nonlinear optimization
Invited session
Chair: Matteo Lapucci
Chair: Marianna De Santis
1 - Feature selection in linear SVMs: a scalable SDP decomposition approach using a hard cardinality constraint

Bo Peng, Immanuel Bomze, Laura Palagi
In this talk, we focus on the feature selection problem in linear SVMs
by using a hard cardinality constraint. The problem is first reformulated into mixed-integer form, for which a novel SDP relaxation is proposed. Exploiting the sparse pattern of the relaxation, we decompose
it and obtain an equivalent SDP relaxation in a three-dimensional positive semi-definite cone. Based on the decomposed SDP relaxation,
we propose heuristics using the information of its optimal solution.
Moreover, an exact procedure is proposed by solving a sequence of
mixed-integer decomposed SDPs. Numerical results on classical online datasets are reported.

2 - A branch-and-cut algorithm
semidefinite programmingforbiclustering

MA-33

via

Antonio M. Sudoso
Biclustering, also called co-clustering, block clustering, or two-way
clustering, involves the simultaneous clustering of both the rows andcolumns of a data matrix into distinct groups, such that the rows andcolumns within a group display similar patterns. Focusing on the densest k-disjoint-biclique problem as a biclustering model, the objective
is to identify a set of k disjoint bicliques within a weighted complete
bipartite graph such that the sum of the densities of the complete subgraphs induced by these bicliques is maximized. A branch-and-cut
method is proposed to achieve global optimality. The upper bound routine involves solving a semidefinite programming relaxation, employing a cutting-plane approach to strengthen the bound. For the lower
bound, a maximum weight matching rounding procedure is designed,
leveraging the solution of the relaxation solved at each node. Computational results on both synthetic and real-world instances show thatthe proposed algorithm is capable of solving instances with a size tentimes larger than general-purpose solvers.

3 - Combining interior and exterior penalty for nonlinear
costrained black-box optimization problems

Andrea Brilli, Everton Silva, Ana Luisa Custodio, Giampaolo
LiuzziThis research explores an approach to addressing mathematical optimization problems characterized by nonlinear objective functions andconstraints, particularly in situations where derivatives are unavailable.
A Sequential Penalty Approach is introduced, incorporating a Logarithmic Barrier for managing inequality constraints and an Exterior
Penalty for equality constraints. Our proposed method aims to providing a comprehensive solution for a wide range of nonlinear optimization challenges. This strategy, proposed for the first time ina derivative-free setting in the framework of linesearch methods, isadapted and incorporated into SID-PSM algorithm, a generalized pattern search method, allowing to effectively handle general nonlinear
constraints. Under reasonable assumptions regarding the smoothnessof the functions, convergence is established, without any convexity assumption. Empirical validation is conducted through experimentation
on a diverse set of optimization problems. The results corroborate not
only the efficacy of the proposed approach but also its practical utility.
The direct search algorithm, when complemented by the Logarithmic
Barrier and Exterior Penalty, consistently exhibits promising numerical results, demonstrating the potential of the techinque.

The importance of hydraulic studies is undeniable in understanding
the behavior of fluids in natural and constructed environments, such as
channels, rivers, and streams. The Saint-Venant equations, a widelyadopted one-dimensional model, are crucial for this understanding.
However, ensuring the representativeness of these equations requiresconsidering all boundary conditions, which includes estimating theManning roughness coefficient. This estimation, essential for accounting for fluid friction with different surfaces, is a notable challenge dueto the coefficient’s variability along the channel. In this context, we
propose an approach that utilizes the Augmented Lagrange Method
to minimize errors in solving differential equations at different points.
Additionally, we employ the PRIMA software (BOBYQA) with space
reduction based on interpolation to estimate the Manning coefficient.
This methodology was applied to the East Fork river, and even withfew days of training data, we achieved relevant results in describing
the behavior of this river.

 MA-33
Monday, 8:30-10:00 - Room: 42 (building: 303A)

Optimal control in supply chain
management
Stream: Optimal Control Theory and Applications
Invited session
Chair: Edson Souza
1 - Joint production and human replacement optimization
policy for a deteriorating manufacturing system

Jean-Pierre Kenne
This paper tackles the integration of production and human resource
management planning, which considers the operator as a production
unit subject to degradation according to age, in the context of an unreliable production system in a considerable state of deterioration. The
deterioration of the production unit process has a twofold effect on its
reliability and continuity, and therefore, to moderate the effects of thisdeterioration, a replacement action can be conducted based on the stateof the system. The objective of the study is to determine an appropriate
production policy and replacement strategy in order to meet customer
demand. A combination of stochastic dynamic programming and numerical methods are used to solve this optimal control problem. In
addition, a numerical example is provided to illustrate the usefulnessof the proposed approach, as well as to study the interaction between agiven production strategy and human resource management. The effectof the new approach based on a logical implementation is presented
following a sensitivity analysis of the numerical example. Regarding
the results, a comparative study between recent research and the proposed policy is presented. Finally, an implementation chart is drawn
up to guide decision-makers in setting production rates and managing
human resources to meet customer demand.

2 - Non-committed cost auditing in supply chain contracts

Zhengping Wu, Jing Li, Wanshan Zhu
As a widely used tool in supply chain practice, audits can alleviate
the supply chain inefficiencies under asymmetric information. In thisstudy, we investigate the impact of the commitment to a cost audit on
the optimal supply chain contract design, and the corresponding audit
efficiency.

23

MA-34EURO 2024 - Copenhagen3 - Optimal control of one economic differential model ofmarket relations

Phridon Dvalishvili, Aleksandre Mosidze, Tamaz Tadumadze
For market relations, a controlled differential model with supplydemand functions and with delay parameter in control is constructed.
We call the difference between supply and demand functions the disbalance index. It is assumed that consumer demand at any point in time
will be satisfied through pre-ordering. To describe the development of
the market equilibrium process over time, a dynamic model with an
integral disbalance index is considered. For the proposed model an optimal control problem is studied. Namely, the existence theorem of an
optimal control and the necessary conditions for optimality are formulated on the basis of results obtained in [1, 2]. In the case of a linear
function of supply and demand, all controls that are doubtful of optimality are detected. An example is discussed at the end. Another caseof optimal control of market relations was studied in [3].
References:[1] Kharatishvili G. Tadumadze T. Formulas for the variation of a solution and optimal control problems for differential equations with retarded arguments. J. Math. Sci., 140 (1) (2007), 1-175.
[2] Tadumadze T. Variation formulas of solutions for functional differential equations with several constant delays and their applicationsin optimal control problems. Mem . Differ. Equ. Math . Phys., 70(2017), 7-97.
[3] Dvalishvili Ph. Tadumadze T. Optimization of market relation
model with delay. Journal of Modern Technology and Engineering
4(1) (2019) 5-10

4 - Dynamic Programming-based formulations and algorithms applied to inventory management: A systematic
review

Edson Souza, Stefan Creemers, Tanja Mlinar, Maud Van den
BroekeDynamic programming (DP) has been consistently employed in inventory management, especially in multi-period formulations, contributing to our understanding of the field. Despite the challenges posed by
the curse of dimensionality, DP-based formulations have evolved overtime to alleviate its effects, especially as inventory problems become
more complex. This review aims to analyze the use and evolution of
DP-based methods in inventory management, encompassing 287 articles. To provide a comprehensive overview of DP-based formulations
for inventory problems, we propose a typology classification, centeredinventory- and DP-based criteria, by categorizing inventory problems
based on the types of decisions explored and the main characteristicsof the problem investigated, and by identifying DP approaches employed. We emphasize the practical implications of DP approaches inindustry, specify limitations in existing studies, and suggest future research directions. In particular, we stress the relevance of considering
uncertainties beyond demand-related factors, integrating sustainability aspects into DP approaches such as incorporation of government
subsidy levels and regulations, and exploring inventory problems inreverse logistics and remanufacturing. Furthermore, we highlight theneed for further development of applied studies utilizing Approximate
and Hybrid DP in combination with data-driven methodologies to address challenges in real-life inventory scenarios.

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Monday, 8:30-10:00 - Room: 43 (building: 303A)

Optimization and learning for data scienceand imaging (Part I)
Stream: Advances in large scale nonlinear optimization
Invited session
Chair: Simone Rebegoldi

24

Chair: Federica Porta
Chair: Elena Morotti
Chair: Alessandro Benfenati
1 - Alternating Projections Methods for Matrix Completion:
Regularized and Inexact Projections

Mattia Silei, Stefania Bellavia, Simone Rebegoldi
Matrix Completion (MC) involves reconstructing the missing entries
of a partially observed matrix M by leveraging the assumption that M
has low rank [1]. Problems that can be approached with MC arise
in various fields, such as recommendation systems, image inpainting,and compressed sensing. Our work starts from the idea of reformulating MC as the problem of finding a matrix in the intersection of two
sets: the set of matrices with the same observed entries as M, and thenonconvex set of matrices of rank less than or equal to r. This is afeasibility problem (FP), which generally involves finding a point x
in the intersection of two sets A and B. Since the 1950s [2], the Alternating Projection Method (APM) has been proposed to solve FP.
Starting from a point in B, APM generates a sequence of points by alternately projecting onto A and B. We will discuss two theoreticallywell founded regularized variants of the method, employing exact projections (RAPM) and inexact projection onto the set B (iRAPM). The
obtained theoretical results guided us to the definition of a new stopping criterion for the Krylov iterative method employed for computing
the approximate truncated SVD (nedeed to compute the inexact projection onto the rank set). Some numerical experiments to validate theconvergence results and the performance of the studied methods willbe also shown.
[1] Candès E. J. et al., Found. Comput. Math., 9, 717–772, 2009 [2]Von Neumann J., Functional Operators, 1951

2 - Fractional graph Laplacian for image reconstruction

Marco Donatelli, Stefano Aleotti, Alessandro Buccini
A popular approach for regularization involves replacing the original
problem with an optimization problem that minimizes the sum of two
terms: an l2 term and an lq term with q smaller than or equal toone. The first penalizes the distance between the measured and reconstructed data, while the second imposes sparsity on certain coefficientsof the computed solution.
In [1], we propose to use the fractional Laplacian of a properly constructed graph in the lq term to compute extremely accurate reconstructions of the desired images. Furthermore, we propose automatic
approaches to determine the involved parameters so that the proposed
method is completely plug-and-play. We show that the algorithm, under some reasonable assumptions, is a regularization method. Someselected numerical examples show the performances of our proposal.
[1] S. Aleotti, A. Buccini, M. Donatelli, Fractional graph Laplacian forimage reconstruction, Applied Numerical Mathematics, 2023.

3 - A multiplicative components framework for joint correction and segmentation of magnetic resonance images

Marco Viola, Laura Antonelli, Valentina De Simone
The segmentation of Magnetic Resonance Images (MRIs) is a challenging task due to the artifacts introduced by the acquisition process,namely intensity inhomogeneity (also known as bias field) and noise(which follows a Rician distribution). In this work we introduce amodel called MICCT, based on the Multiplicative Intrinsic Components framework and Cartoon-Texture decomposition techniques, toperform simultaneous denoising and bias-field correction of an MRI.
The output of MICCT can be then be segmented with any state-of-theart segmentation strategy. We introduce an ADMM strategy to solve
the nonlinear and nonconvex model associated with MICCT and analyse its theoretical convergence properties. Finally, we present somenumerical experiments showing the effectiveness and the competitiveness of the proposed approach (combined with a K-means strategy) inthe segmentation of noisy MRI from the BrainWeb database.

EURO 2024 - Copenhagen
4 - Convergence analysis of optimization-by-continuation
proximal gradient algorithm and some primal-dual extensions

Ignace Loris
In this work we focus on the concept of optimization-by-continuation
as a strategy for solving a set of optimization problems in about thetime it would take to solve a single instance of them. Each cost function is a different linear combination of two convex terms, one differentiable and the other prox-simple. Such optimization problems occur
frequently in the numerical solution of inverse problems (data misfit
term plus penalty or constraint term), and the relative weight of bothterms is often not known in advance. The algorithm’s special featurelies in its ability to approximate, in a single iteration run, the minimizers of the cost function for many different values of the parameters
determining the relative weight of the two terms. We also discuss thesame problem in the presence of a third term in the cost function whichis the combination of a prox-simple function and a linear map. As a
special case, one recovers a generalization of the primal-dual algorithmof Chambolle and Pock.

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Monday, 8:30-10:00 - Room: 44 (building: 303A)

Robust and Stochastic Routing Problems
Stream: Stochastic, Robust and Distributionally Robust
Optimization
Invited session
Chair: Francesca Maggioni
Chair: Daniele Manerba
Chair: Francesca Vocaturo
1 - The Robust Bike Sharing Rebalancing Problem: Formulations and a Branch-and-Cut Algorithm

Bruno Bruck, Walton Pereira Coutinho, Pedro Munari
Bike sharing systems (BSSs) are an important component of sustainable urban transportation, providing flexible and eco-friendly alternatives for city logistics. However, these systems often face challenges
with unbalanced bike distribution among stations, requiring rebalancing operations to ensure efficient operations. Adding to this complexity is the uncertain demand at stations, which can further complicate
rebalancing efforts even during off-peak hours. This work introduces
the Robust Bike Sharing Rebalancing Problem (RBRP), which employs robust optimization to improve rebalancing operations in BSSs.
Despite the significant impact of uncertainty on system performance,
few studies have addressed this aspect in the literature. We propose
two new formulations and a tailored branch-and-cut algorithm for the
RBRP. The first formulation is compact, leveraging the linearizationof recursive equations, while the second relies on robust rounded capacity inequalities and feasibility cuts. Computational results based onbenchmark instances demonstrate the effectiveness of our approaches,
highlighting the benefits of robust solutions in supporting decisionmaking for BSSs.

2 - A rolling horizon heuristic approach for a multi-stage
stochastic waste collection problem

Andrea Spinelli, Francesca Maggioni, Tania Ramos, Ana
Barbosa-Povoa, Daniele Vigo
We present a multi-stage stochastic optimization model for a waste
collection routing problem. The problem is modeled as an inventory
routing problem where decisions are related to the selection of bins
to be visited and the corresponding visiting sequence in a predefinedtime horizon. The aim is the maximization of the total expected waste
collection at lowest transportation cost, considering uncertainty in thewaste accumulation rate in the network bins. Stochasticity in waste

MA-36

accumulation is modeled through scenario trees generated via conditional density estimation and dynamic stochastic approximation techniques. The model is solved through a rolling horizon approach, providing a worst-case analysis on its performance. Computational experiments are carried out on instances based on real data of a large
Portuguese waste collection company. The impact of stochasticity on
waste generation is examined through stochastic measures, and the performance of the rolling horizon approach is evaluated. Some managerial insights are finally discussed.

3 - Models and solution approaches for the consistent vehicle routing problem under stochastic travel and service times

Yossiri Adulyasak, Seyede Saeede Hosseini, Louis-Martin
Rousseau
We explore a consistent vehicle routing problem for home health care
under uncertain travel and service times. In this problem, the planner must schedule and route a group of care workers to visit patients
multiple times over a planning horizon while ensuring service consistency so that each patient will be serviced by the same care worker
at the same time slot every time the service is provided. This problem variant extends the home health care routing and scheduling problem (HHCRSP) to include service level constraints under stochastic
travel and service times. We propose a stochastic solution framework where the uncertainty is represented by (i) a discrete scenario set
and (ii) an extreme value theory-based (EVT-based) approximation. A
unified branch-and-check algorithm that leverages constraint programming (CP) to handle the subproblem is developed to efficiently solve
the stochastic models with these uncertainty representations. Results
show that the stochastic optimization framework can efficiently handle practical benchmark instances and produce schedules with significantly improved service levels than the deterministic model.

4 - The Waste Collection Vehicle Routing Problem with
Stochastic Time Dependent Demand Increments

Giuseppe Stecca, Simona Mancini, Francesca Vocaturo
We focus on a vehicle routing problem with stochastic time-dependent
demand increments. In this problem, a homogeneous fleet of vehicles
must visit a set of collection points with the aim of removing waste.
We divide the planning horizon (day) in not-overlapping time slots.
Each collection point must be visited exactly once during the planninghorizon and it has a known initial demand which may increase along
the day. In fact, for each collection point and for each time slot, additional demand may arise. The (uncertain) demand increments are
revealed upon arrival at the collection points. The goal is to minimize
the expected total cost which also considers a penalty for the residual
quantity of waste accumulated at the collection points after they havebeen visited. We present a two-stage stochastic program and discussspecific recourse policies for the problem under study.

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Monday, 8:30-10:00 - Room: 32 (building: 306)

Game Theory, Solutions and Structures I
Stream: Game Theory, Solutions and Structures
Invited session
Chair: Encarnación Algaba
Chair: Tamás Solymosi
1 - TU-games with utility: characterization sets for the uprenucleolus

Zsófia Dornai, Miklós Pintér
The u-prenucleolus is a generalization of the prenucleolus using utility
functions. The u-prenucleolus can also be considered as a generalization of the per-capita prenucleolus. We prove the generalizations ofsome important theorems about the prenucleolus to the u-prenucleolus,
such as Kohlberg’s theorem and the theorem of Katsev and Yanovskaya25

MA-41EURO 2024 - Copenhagen

about a sufficient and necessary condition on the unicity of the uprenucleolus. We also prove a generalization of Huberman’s theorem
by defining the u-essential coalitions and showing that these coalitions
characterize the u-prenucleolus of u-balanced games. Considering thedual of the game, we define the u-anti-prenucleolus, and show that theu-anti-essential coalitions characterize it. Using these results, we getthat in the primal game the u-dually-essential coalitions - which generalize the dually essential coalitions defined by Solymosi and Sziklai characterize the u-prenucleolus. This way, we get a characterization set
for the u-prenucleolus that differs from the set of u-essential coalitions.

2 - Random partitions, potential of the Shapley value, andgames with externalities

André Casajus, Yukihiko Funaki, Frank Huettner
The Shapley value equals a player’s contribution to the potential of agame. The potential is a most natural one-number summary of a game,
which can be computed as the expected accumulated worth of a random partition of the players. This computation integrates the coalition
formation of all players and readily extends to games with externalities. We investigate those potential functions for games with externalities that can be computed this way. It turns out that the potential that
corresponds to the MPW solution introduced by Macho-Stadler et al.
(2007, J. Econ. Theory 135, 339-356) is unique in the following sense.
It is obtained as a the expected accumulated worth of a random partition, it generalizes the potential for games without externalities, andit induces a solution that satisfies the null player property even in thepresence of externalities.

3 - Coalitional Manipulations and Immunity of the Shapley
Value

Frank Huettner, Christian Basteck
We consider manipulations in the context of coalitional games, wherea coalition aims to increase the total payoff of its members. An allocation rule is immune to coalitional manipulation if no coalition canbenefit from internal reallocation of worth on the level of its subcoalitions (reallocation-proofness), and if no coalition benefits from a lower
worth while all else remains the same (weak coalitional monotonicity).
Replacing additivity in Shapley’s original characterization by these requirements yields a new foundation of the Shapley value, i.e., it is theunique efficient and symmetric allocation rule that awards nothing toa null player and is immune to coalitional manipulations. We further
find that for efficient allocation rules, reallocation-proofness is equivalent to constrained marginality, a weaker variant of Young’s marginality axiom. Our second characterization improves upon Young’s characterization by weakening the independence requirement intrinsic tomarginality.

4 - On the Owen core of many-to-one assignment games

Tamás Solymosi, Regina Radanovits
Many-to-one assignment games (Sotomayor, 1992) are extensions of
one-to-one assignment games (Shapley and Shubik, 1972). It is knowthat the core of many-to-one assignment games is non-empty and has
the two side-optimal special vertices. We consider many-to-one assignment games as special linear production games (Owen, 1975) andinvestigate the Owen core (the set of core allocations obtained fromthe dual optimal solutions of the LP for the grand coalition). Unlike
in assignment games where the two sets coincide, in many-to-one assignment games the dimension of the Owen core is at most the number of players allowed to be assigned to several players of the othertype, while the dimension of the core is at most the number of the
1-capacity players. We show that the Owen core of a many-to-one
assignment game can be described as the core of an associated assignment game. This facilitates the application of various results known
for the core of assignment games to the Owen core of many-to-one
assignment games. For example, based on the graph-theoretic characterization of the two side-optimal core vertices (Solymosi, 2023), weshow that the same allocation maximizes the payoffs for all 1-capacity
players both in the Owen core and in the core, but the allocation which
minimizes all these payoffs in the Owen core need not be an extreme
point of the core. We also provide conditions for the coincidence of
these minimizing allocations.

26

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Monday, 8:30-10:00 - Room: 97 (building: 306)

Vector and Set Optimization I
Stream: Vector and Set Optimization
Invited session
Chair: Miguel Sama
1 - Cone-compactness and applications in set optimization

Marius Durea, Elena-Andreea Florea
We study the concept of cone-compactness for a subset of a normed
vector space from the perspective of a sequential characterization under a separability assumption. Then we show that this concept is naturally involved in the investigation of several issues related to set optimization such as stability problems, conic cancellation laws, subdifferential calculus and optimality conditions.

2 - Vector optimization with variable ordering structure: aunifying approach

Radu Strugariu
We investigate the possibility to unify the different approaches considered in the framework of vector optimization with variable ordering
structures. As consequence, we analyze the incompatibility principle
between optimality and openness and deduce new optimality conditions in this context.

3 - The Interval-valued optimization problem through variational inequality problems on Hadamard manifolds

Gabriel Ruiz-Garzón, Rafaela Osuna-Gómez, Antonio
Rufián-Lizana, Antonio Beato-Moreno
The aim of this work is to find the solutions of the Interval-valued Optimization Problem through the Variational Interval-valued Problem on
Hadamard manifolds. We will use the gH-differentiability for intervalvalued functions on Hadamard manifolds and we will extend the Stampacchia and Minty versions of these variational problems given by Euclidean spaces and real valued functions to interval-valued functions
on Hadamard manifolds.

4 - Stability analysis of state constrained multiobjective elliptic control problems

Miguel Sama, Akhtar Khan
This presentation focuses on a multiobjective elliptic control problem
with state constraints. Specifically, we show how we can apply recent
results on stability estimates for these kinds of problems (Khan, Sama.
Optimization 72(4), 1009-1036, 2023) to obtain numerical error estimates for different discretization schemes.

 MA-42
Monday, 8:30-10:00 - Room: 98 (building: 306)

Hybrid Classical-Quantum Algorithms
Stream: Quantum Computing Optimization
Invited session
Chair: Caroline Prodhon
Chair: Wesley Coelho

EURO 2024 - Copenhagen
1 - Quantum pricing-based column-generation frameworkfor hard combinatorial problems

Wesley Coelho
In this work we present a complete hybrid classical-quantum algorithm
involving a quantum sampler based on neutral-atom platforms. Thisapproach is inspired by classical column-generation frameworks developed in the field of operations research and shows how quantum
procedures can assist classical solvers in addressing hard combinatorial problems. We benchmark our method on the minimum vertex coloring problem and show that the proposed hybrid quantum-classicalcolumn-generation algorithm can yield good solutions in relatively few
iterations. We compare our results with state-of-the-art classical andquantum approaches.

2 - Mixed Integer Linear Programming Solver Using Benders Decomposition Assisted by Neutral Atom Quantum Processor

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In this communication, we aim to present the solving of an MBDA’s
use case related to optimal assignment, onto IBM online QPUs. The
Quantum Approximate Optimization Algorithm (QAOA) (Farhi et al.
2014) is the base of our Variational Quantum Algorithm developed.
We compare two methods to account for constraints, first primarily byintegrating them into the Cost Hamiltonian with Lagrangian multipliers and second, by adapting the Mixer Hamiltonian according to (Wanget al. 2022) and (Fuchs et al. 2022). For the former, determining the
optimal Lagrangian multipliers is generally a challenging task and theintegration of constraints into the Cost Hamiltonian can significantly
increase the associated circuit depth. The latter method aims to reduce
the overall Hilbert space to only feasible solutions, which lets get rid
of Lagrangian multipliers but may significantly enlarge the circuit associated to the Mixer Hamiltonian and make the initial state harder. It
is then interesting to compare the circuit depth of both methods withrespect to how well they are able to statistically put forward optimal
solutions against non-optimal and non-feasible ones, still for relatively
small sized instances, to fit on current QPUs.

Anna Joliot, Wesley Coelho, M. Yassine Naghmouchi
This work presents a new hybrid classical-quantum approach to solve
Mixed Integer Linear Programming (MILP) using neutral atom quantum computations. We apply Benders Decomposition (BD) to segment
MILPs into a master problem (MP) and a subproblem, where the MP
is addressed using a neutral-atom device, after being transformed intoa Quadratic Unconstrained Binary Optimization (QUBO) model. To
solve the QUBO, we develop a heuristic for atom register embedding
and apply Quantum Approximate Optimization Algorithm for pulse
shaping. In addition, we implement a Proof of Concept that outperforms existing solutions. We also conduct preliminary numerical results, outperforming classical BD approaches where the MP is solved
using simulated annealing. To the best of our knowledge, this work is
the first to utilize a neutral atom quantum processor in developing an
automated, problem-agnostic framework for solving MILPs throughBD. Looking to the future, our objective is to scale and diversify our
instances. This presents difficulties related to the limits on the numberof qubits and the convergence of the algorithm. To overcome resource
limitations, one can set a maximum number of qubits and implement
an iterative process that guarantees the quality of the solution. Whenfacing issues with convergence, we can consider multi-cuts, which involves generating multiple solutions for the MP and selecting a specificsubset of Benders cuts to not surcharge the MP.

3 - Application of quantum computing in securities transactions settlement framework

Ekaterina Alekseeva, Nicolas Wolters
A securities transaction is an exchange of securities versus payment
in currency between two financial actors. The transaction is settled,
if, inter alia, there are sufficient amounts of resources on the corresponding cash and securities accounts. Each night more than four hundred thousand transactions over a billion euro are settled in batches.
Banque de France is in charge of Mathematical Optimization Module(MOM), which is a component of a large European transactions settlement platform named T2S. Given a batch and the limited time, MOM
looks for a subset of transactions to settle whose size is as large as possible respecting all business constraints and taking advantage of somefinancial features reducing the number of failed transactions. It is composed of preparatory, construction and improvement solution phases.
Since the batch size is too large, no algorithm can find neither optimal nor approximate solution for the entire batch on a conventional
computer. Thus, MOM’s preparatory phase splits a batch of transactions into the subsets to address smaller problems. The current splitting
procedure has long runtime and its quality affects the final number ofsettled transactions. In this work, we present an approach that would
improve the splitting results and the MOM’s outcome. It is based ona Quadratic Unconstrained Binary Optimization solved by a Quantum
Approximate Optimization Algorithm and implemented on the available quantum devices via Qiskit platform.

4 - Solving an MBDA’s use case related to optimal assignment on current IBM Quantum Computers

Edouard Debry, Davide Boschetto, Rachel Roux, Janis Aiad,
Alexandre Kotenkoff

 MA-43
Monday, 8:30-10:00 - Room: 99 (building: 306)

Simulation of organizations I
Stream: Agent-based Models in Management, Economic
and Organisation Sciences
Invited session
Chair: Friederike Wall
1 - A Computational Account of Complex Systems

Davide Secchi, Martin Neumann
This study is concerned with understanding and classifying the variety
and difference among complex systems. Definitional differences usually stem from the disciplines where complexity was first detected andanalyzed. They are also characterized by emphasis on one specific element of the system, being its composing entities, their interrelations,entropy, or emergence, to name a few of the most popular. These classifications generate a broad definitional space for complexity, leading
to large differences among types of complexity. In other words, not
all complexity is made equal. The issue of concern for this paper is
to formally qualify these differences by using a series of agent-based
computational simulation models where complexity is manipulated bymodifying one component at the time. This should be able to provide a map of complexity, from more to less intuitive, from more toincreasingly less predictable, and from single- to multi-layered. The
research is structured around a model grounded in the social sciences,more specifically it illustrates intra-organizational dynamics as an exemplar of complexity in social systems. This research is aimed at improving our understanding and appreciation of complex systems in a
way to guide the analytical tools and strategies that should accompany
researchers when confronted with difference systems. The study concludes with a proposal for terminology to use in relation to different
complex systems.

2 - Distributed Competence: How Team Structure Affects
Task Performance

Martin Neumann
This paper is concerned with how different team structures affect task
performance. By using a Case Study of the maintenance department ofa large utility company as a starting point, the study creates an agentbased computational simulation model to compare four different structures: (a) strict hierarchy, (b) relaxed (or loose) hierarchy, (c) anarchy,and (d) hybrid (or baseline). The model is built by taking ethnographic
observations from the Case Study to develop the basic layout for themodel. The baseline case is the hybrid structure, from which the otherstructures have been created by reference to the literature. In accordance with the literature on distributed cognition in organizations, themain characteristic of the agents in the model is competence - i.e., theirknowledge and practical expertise as they are functionally constituted

27

MA-44EURO 2024 - Copenhagenin relation to their taking on of tasks as well as their subsequent completion. The simulation reveals that the effect of competence changes
depending on the network of relations (relationship range in the model)
among employees allowed by the team structure as well as according
to cognitive dispositions (docility in the model). These findings indicate that, despite an overwhelming individualistic characterization ofcompetence, a more accurate representation would be for it to be considered distributed rather than solely attributable to the individual.

3 - When Reciprocity of Trust Backfires: Results of an
Agent-based Simulation of Incomplete Incentive Contracts

Friederike Wall
Incentive contracts are a primary means to align subordinates’ behavior with the organization’s objectives. Among the difficulties incentive contracting encounters is measuring the performance in all taskelements assigned to a subordinate, which may result in incomplete
contracts. Then, mutual trust among the contracting parties is crucial:with an incomplete contract, the superior has some discretion over thesubordinate’s rewards. Hence, the subordinate’s trust in the superior
affects the willingness to incur effort related to the not-contracted taskelements. On the other hand, the superior’s trust in the subordinate isrelevant to the rewards provided.
Building on Brower et al.’s (2000) theoretical model of trust in organizations, the paper translates the situation above of incomplete incentive
contracts into an agent-based simulation model to study the emergenceof reciprocal trust and the performance effects thereof.
The results suggest that neither the contracting parties’ trust universally increases with the superior’s propensity to reciprocate the perceived subordinates’ trust, nor does performance generally increase
with higher trust levels. Among the reasons is that the superior’s trustin the subordinate might be "unjustified" from the subordinate’s perspective. The results indicate that intra-organizational interdependencies, in conjunction with the superior’s propensity to trust, subtly affectthe trust-building of the contracting parties.

 MA-44
Monday, 8:30-10:00 - Room: 20 (building: 324)

Preference Learning 1
Stream: Multiple Criteria Decision Analysis
Invited session

estimation problem is frequently non-unique although representative
solutions have been used in the numerical experiments. In this presentation, we show first that the set of all rankings of alternatives underthe estimated interval priority weights can be obtained easily. Using
the set of all rankings in the numerical experiments, we confirm theadvantages of several interval priority weight estimation methods.

2 - Stochastic Multicriteria Acceptability Analysis with
Nonadditive Probability

Sally Giuseppe Arcidiacono, Salvatore Corrente, Salvatore
Greco
We consider a nonadditive probability in the weight vector space considered by Stochastic Multicriteria Acceptability Analysis. We showthat this allows us to represent uncertainty with respect to the weights
to be assigned to the considered criteria as well as the Decision Maker’s(DM’s) optimism or pessimism in evaluating alternatives. After a motivating didactic example, we introduce our methodology, which isbased on the definition of a nonadditive probability as a transformation of an additive probability in the weight vector space. To this end,
we consider specific families of probability distributions and transformation functions discussing the results they provide, proposing also amethodology to induce them from DM’s preference information. We
discuss the results obtained through our methodology in the domain ofcomposite indicators, considering an application in the framework ofsustainable development of European Countries.

3 - A Seven-Valued Logic for Multicriteria Decision Aiding

Roman Slowinski, Salvatore Greco
In recent discussions on reasoning about data using the rough set concept, a class of many-valued logics has emerged to differentiate between vagueness resulting from imprecision and ambiguity arisingfrom coarseness. These logics, including the renowned Belnap fourvalued logic, stem from a comprehensive seven-valued logic framework. This study explores the application of the seven-valued logic andits derivatives in multicriteria decision aiding (MCDA). Specifically,
we demonstrate how our approach effectively addresses common challenges in MCDA, such as uncertainty, imprecision, ill-determination,and related robustness concerns.

4 - XOR Numbers: Theory and Application

Amin Hocine, Sergio Ortobelli Lozza

Chair: Roman Slowinski
Chair: Salvatore Greco
1 - Comparing Interval Priority Estimation Methods by Estimation Accuracy of Solution Set in Terms of Ranking
Alternatives

Masahiro Inuiguchi, Akiko Hayashi, Shigeaki Innan
The analytic hierarchy process (AHP) is a useful method for multiple criteria decision making. It provides a priority weight estimation
method from a pairwise comparison matrix (PCM). A PCM evaluatedby a decision maker (DM) is frequently inconsistent. The inconsistency is assumed to come from the evaluation errors. The priority
weights have been estimated by minimizing the errors. However, human perception is vague. Assuming that the inconsistency comes fromthe vagueness, estimating interval propriety weights instead of crisp
priority weights from a PCM was proposed. Many interval priority
weight estimation methods have been proposed because the original
interval priority weights estimation method tends to estimate insufficiently wide interval priority weights. Numerical experiments showedthat ranking alternatives by interval priority weights estimated by several methods performs better than the crisp priority weights estimatedby the conventional methods when we assume the DM’s vague evaluation. It is shown that the solution to the interval priority weight28

We often find ourselves struggling to determine the truth, hesitating
due to our ignorance. In such circumstances, the exclusive disjunction(XOR, for short) is commonly used to express our uncertainty, in contexts ranging from daily life, like "It was either Adam or John—but
I couldn’t determine which," to more professional scenarios, such as"Global growth is projected to be 3% in 2024, or it may weaken to
2.7% depending on prevailing economic conditions." The primary objective of this paper is to propose a framework to address the uncertainty inherent in such expressions by raising a pivotal inquiry: how
to quantify this uncertainty without resorting to oversimplification that
leads to the loss of crucial details essential for understanding the problem under consideration? Attempting to address this question leads tothe concept of ’xorness’ and the ’XOR number.’ In particularly, xorness is defined as a state of uncertainty stemming from hesitation, in
which multiple objects (such as numbers in our context) are mutually
exclusive, indeterminate, and none of them is dominating. This exploration proposes the ’XOR number’ as an approach to quantify xorness,
recognizing that the multiple options itself represents a choice. This
investigation extends to arithmetic operations, metric spaces, and theordering of XOR numbers. Multiple criteria decision analysis is employed to demonstrate the applicability of the proposed framework.

EURO 2024 - Copenhagen MA-46
Monday, 8:30-10:00 - Room: 40 (building: 324)

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2 - Applying System Thinking Approaches for Supporting
the Criteria Modelling Process in MCDA

Athanasios Spyridakos, Konstantina Theona

Fast And Furious: Lightning Talks
Stream: Making an Impact
Invited session
Chair: Ruth Kaufman
1 - Fast And Furious: Lightning Talks

Paul Bouman, Gökhan Ceyhan, Laura Albert, Hans
Schwartzlose
At a conference like EURO, it is impossible to catch up with everything; and all too easy to stick to a single stream within your usual
professional specialism. This session showcases some of the breadthof OR in action, with a set of twelve 5-minute presentations handpicked to inspire, interest and extend your knowledge. The Fast andFurious session is always intense, enlightening and fun for audienceand speakers alike, so a great way to start the conference.
Some of the presentations will be designed for this session only, others
will be abbreviated versions of full talks appearing elsewhere in theconference. We are holding this session early on in the conference togive the audience the best chance of catching up with the speaker lateron.
The talks cover a wide range of OR methodology across a great variety of practical application areas. Speakers include academics acrossthe range from eminent to early career, as well as practitioners from avariety of industries.
The latest details of speakers can be found on the Making an Impact page on the main conference website(euro2024cph.dk/programme/making-an-impact-2024).

 MA-47
Monday, 8:30-10:00 - Room: 50 (building: 324)

MCDA applications in Engineering andManagement 1
Stream: Multiple Criteria Decision Analysis
Invited session

The criteria modelling stage of the MCDA approaches constitute fundamental and critical process for the effective implementation and supporting the decision making. The level of consistency of the criteria family affects the accuracy and reliability of the application of themethodological approach used and the final acceptance of the assessed
preference model. This research work is focused on the applicationof system dynamics methodological tools for the analysis of the decision problem statement and the identification of the relative features ina detailed manner. The tools of the system thinking approach and thetechniques of the archetypes are employed in order to picture the pointsof view of the decision problem, their interrelations as well as their impacts. Exploiting the analytical features of the above-mentioned tools
a comprehensive and easy to use approach for the analysis and presentation of the decision problem is available. The methodology is illustrated through a case study concerning the location selection problem
for the establishment of Educational Units.

3 - Classification of Students Based on the Risk of Dropping out of University Degrees

Marina Segura, Ana Maria Sanchez Sanchez, Adolfo
Hernandez
Dropping out of university degrees is a problem with a considerable
negative impact not only academically but also economically. The objective of this work is to design a multicriteria model that allows classifying students based on the risk of dropping out of the degree they
are pursuing after the first year. Ordered categories of higher to lower
risk are generated using the Global Local Net Flow Sorting (GLNF
Sorting) algorithm, and the quality of student assignments to a risk
group is evaluated using the Silhouette for Sorting (SILS) quality indicator. To quantify the risk of dropping out, criteria both before accessto higher education and related to the context and results of students
in the first semester have been used. The model is validated with real
data from students in various degrees at Complutense University of
Madrid. The obtained classification is compared with those obtainedusing traditional classification techniques and machine learning.

4 - A Multicriteria Model for Sustainable Customer Segmentation using a Sorting Outranking Method

Chair: Michalis Doumpos
Chair: Constantin Zopounidis

Concepción Maroto, Ivan Felipe Barrera, Marina Segura,Baldomero Segura

1 - A Multicriteria Framework for Evaluating the Sustainability Performance of Local Governments: Application
to French Municipalities

Customer segmentation plays a key role in improving supply chain
management by implementing appropriate marketing strategies. The
objectives of this research are to design and validate a multicriteria
model to support decision making for sustainable customer segmentation in a business to business context. First, the model based on thetransactional customer behaviour is extended by a hierarchy with threemain criteria: Recency, Frequency and Monetary (RFM), customer
collaboration and growth rates. Customer collaboration includes quota
compliance, variety of products and customer commitment to sustainability (reverse logistics and shared information). Second, the Global
Local Net Flow Sorting (GLNF sorting) algorithm is implemented andvalidated using real company data to classify 8,157 customers of amultinational healthcare company. Third, the SILS quality indicator
has been implemented and validated to assess the quality of preferenceordered customer groups. The results are also compared with an alternative model based on data mining (K-means). The multicriteria
system proposed allows to segment thousands of customers in orderedcategories by preferences according to company strategies. The segments generated are more homogeneous, robust and understandable bymanagers than those from alternative methods. These advantages represent a relevant contribution to automating supply chain management
while providing detailed analysis tools for decision making.

Michalis Doumpos, Alexis Guyot, Emilios Galariotis,
Constantin Zopounidis
Sustainable development has become a central issue for policy decisions by governments and international organizations. The core framework of the UN’s 2030 Agenda for Sustainable Development defines
17 Sustainable Development Goals (SDGs), which set the basis for operationalizing specific metrics to evaluate and monitor the actions andpolicies that countries take to balance social, economic, and environmental sustainability. Towards achieving the goals of the UN Agenda,
the role of local governments is crucial. In this context, the objective of this study is to describe the development and implementationof a multicriteria framework for assessing the performance of local
governments from the perspective of the UN’s SDGs. The proposed
framework is based on a comprehensive set of indicators that are aggregated through an outranking multicriteria decision aiding (MCDA)
approach. The MCDA approach relies on a simulation process to consider the uncertainty and vagueness in the importance of the indicators
across the various SDGs. Results are presented for a large sample ofFrench municipalities, based on the most recent available data.

29

MA-48EURO 2024 - Copenhagen MA-48
Monday, 8:30-10:00 - Room: 60 (building: 324)

DEA and its application
Stream: Data Envelopment Analysis and its Application
Invited session
Chair: Víctor Javier España
Chair: Ana Camanho
1 - Modeling Data Sovereignty in Public Cloud - A Comparison of Existing Solutions

Stanisław Galij, Grzegorz Pawlak, Slawomir Grzyb
Data sovereignty has long been a focal point for enterprise customers,
cloud service providers (hyperscalers), end-users, and national andinternational policymakers. The cloud distributed computing model
has been presented as a means to control and apply data sovereignty
aspects. This presentation delivers a comprehensive review of data
sovereignty aspects within the context of public cloud computing environments. While multiple technologies supporting data sovereignty inthe public cloud already exist, they also impose certain limitations andincreased costs due to the necessity of adhering to specific frameworks
and leveraging additional cloud cryptography, thereby increasing computational overhead. This paper provides a comparison of reference architectures for solutions reflecting data sovereignty in the public cloud.
Appropriate computational experiments were conducted, and conclusions have been formulated.

2 - DEA in Environmental Performance Evaluation for theTransport Sector: A Survey

Adel Hatamimarbini, Pegah Khoshnevis
The ITF Transport Outlook (2021) projects a 60% increase in carbon dioxide emissions (CO2) by 2050, primarily from the transport
sector. Emission reduction, crucial for sustainable development goals(SDG13), is emphasised by the UK Department for Business Energy &Industrial Strategy (2021) reporting a swift rise in CO2 emissions from97.7 MtCO2 in 2020 to 107 MTCO2 in 2021. Urgency is stressedby the UK Department of Transport (2021), urging a shift to a lowemission transport system with a 50% reduction by 2030. Despite this,EU transport strategies, as per the European Union’s examples, havenot effectively reduced CO2 emissions. Researchers focus on methods like Data Envelopment Analysis (DEA) to assess transport performance, revealing a literature emphasis on road and passenger car CO2
emissions. The study identifies gaps, suggests future directions, andrecommends assessing maritime and aviation sectors.

3 - Measuring and managing (un)sustainable workload
under digitization: A frontier-based job demandsresources model

Ahmed-Youssef Oukassou, Marijn Verschelde
In this paper, we bridge the psychological Job Demands & Resources(JD-R) literature with the operations literature on activity analysis. The
JD-R model provides a comprehensive framework, categorizing risk
factors associated with different occupations. On one hand, resources
positively affect performance, while on the other hand, demands relate to work conditions. Understanding the health impairment process (strain due to excessive demands) and the motivational process(resource-driven well-being enhancement) is essential in defining asustainable level of workload. We apply a multidimensional nonparametric activity analysis on the JD-R model, to assess the relation between resources, demands, and mental well-being of employees. Using event-level data of railway control room operators, we construct a
multidimensional JDR indicator for digital production, which we empirically validate by using proxies for operational risk and employee
well-being.

4 - Adaptive Constrained Enveloping Splines and Random
Forest for Technical Efficiency Measurement

Víctor Javier España, Juan Aparicio, Josep Xavier Barber
In various research fields, understanding the relationship between predictors and a response variable involves curve estimation with specific characteristics. For instance, isotonic regression estimates mortality rates, assuming an increasing relationship between older age
groups and mortality risk. Similarly, efficiency analysis can be reframed as a shape-restricted regression problem, aiming to estimate a
non-decreasing and concave function that envelopes the observed data
points. In this context, Data Envelopment Analysis (DEA) is commonly used for nonparametric production frontier estimation. However, DEA is susceptible to overfitting, resulting in overly optimistic
efficiency estimates.
Recently, an adaptation of the Multivariate Adaptive Regression
Splines (MARS) algorithm was introduced for production function
estimation, addressing overfitting concerns. Our work builds uponthis methodology, with three primary objectives. First, we propose
a method to incorporate variable interaction during model fitting while
maintaining shape constraints for production functions, enhancing predictive capacity. Second, we enhance robustness by randomizing dataand input variables during model construction, drawing inspirationfrom the Random Forest (RF) methodology. Finally, within the RF
framework, we can identify the most relevant inputs related to output
prediction.

 MA-49
Monday, 8:30-10:00 - Room: M1 (building: 101)

Lot-sizing with joint replenishment androuting decisions
Stream: Lot Sizing, Lot Scheduling and Production Planning
Invited session
Chair: Xinyi Ye
1 - Joint Replenishment and Heterogeneous Vehicle Routing Problem with Cyclical Schedule

Ming-Jong Yao, Chin Sum Shui, Chih-Han Wang
This study is based on a decision scenario of an industrial gas company that applies the Vendor Managed Inventory (VMI) model andsupplies customers with liquid oxygen using a self-operated heterogeneous vehicle fleet. We name it a Joint Replenishment and Heterogeneous Vehicle Routing Problem with Cyclical Schedule. To solve thisproblem, we formulated it as a non-linear mixed-integer programming
model that simultaneously determines the length of the planning cycle(PC), the length of the replenishment cycle, the dates to replenish eachcustomer, and the vehicle routes of each day within PC, such that the
average total cost within PC is minimized where the total cost includes
inventory holding cost, setup cost, transportation cost, and overtime
labor cost. We proposed a genetic algorithm (GA), which is embeddedwith an innovative encoding and decoding mechanism, local search
operators, and a hash function to avoid repetitive fitness evaluation for
identical solutions. Our numerical experiments demonstrate that the
proposed GA can effectively solve the problem under different lengthsof PC and number of customers. Also, when the demand of a customer increases, it can be applied to determine whether the company
should expand the capacity of storage installed at the customer. Sensitivity analysis of the vehicle fleet composition showed that deploying
a mixed fleet can reduce the average total cost.

2 - The Joint Replenishment Problem with Permissible
Delay in Payments and Resource Constraints underPower-of-Two Policy

Jen-Yen Lin

30

EURO 2024 - Copenhagen
The Jointly Replenishment Problem (JRP) is concerned with determining lot sizes and scheduling replenishment times for $n$ products tominimize total costs per unit time. In practice, consolidating replenishments to share common setup costs is crucial. Additionally, researchers often introduce constraints to enhance the applicability of
inventory systems. Examples include the adoption of a power-of-two(PoT) policy for streamlined scheduling, imposing minimum order
quantities for commitment, and enforcing maximum shipment sizes
based on capacity constraints.
In this paper, we explore JRP models incorporating the PoT policy andbounds for replenishment time. We provide a theoretical analysis ofthese models, propose a search algorithm to identify global optimal
solutions, analyze the complexity of the algorithm, and conduct randomized experiments to validate our approach.
By addressing these issues, our work contributes to the advancementof JRP models, offering practical insights and solutions that accommodate real-world constraints and enhance operational efficiency.

3 - On Static and Dynamic Inventory Rebalancing in Bike
Sharing

Haoxiang Wang, Dimitrios Letsios, Xinyi Ye
We elaborate on inventory rebalancing in bike sharing. The goal is todecide how to reposition bikes between the stations of a bike sharing
network so as to optimize service loss, based on the dynamic lot sizing model. Previous work computationally demonstrates the benefitsof dynamic compared to static rebalancing. We propose new optimalalgorithms for both problem variants. On the negative side, we showthat static rebalancing results in O(1/epsilon) proportion of successful requests. On the positive side, we prove that dynamic rebalancing
achieves an Omega(1-epsilon) performance guarantee. To our knowledge, this is the first theoretical characterization of the dynamic rebalancing benefits compared to static rebalancing. Next, we consider
the more general problem of optimizing service loss with bounded
rebalancing costs. For both the static and dynamic variants, we develop new scalable mixed-integer approaches to practically relevant
instances. We computationally substantiate the proposed approaches
using data from the London bike sharing network.

4 - Optimizing the Work-in-Progress Area Allocation of
pallet-like production process: A Mixed-Integer Linear
Programming Approach

Katharina Eibler
An efficient allocation of space in Work-in-Progress (WIP) areas is a
key factor for a smooth flow in production, as inadequate planning canlead to bottlenecks, machine idle time and delays. This is especiallycritical in situations of a high occupation rate as well as limited space.
We present an optimization approach for pallet-like production processes illustrated by the working flow in a plant. Our approach is basedon mixed-integer linear programming (MILP) and the model allocatesorder blocks to lanes of varying sizes, while considering the production plan of machines before and after the WIP areas, coupling possibilities of lanes as well as different sequence requirements on lanes.
The allocation is optimized according to plant-specific needs, including suitability of the lanes for the orders, lanes kept free for potentialchanges and blocks of the same order being assigned near each other.
We illustrate our approach using synthetic data to mimic the production process of a real plant.

MA-50

1 - The Impact of Customer Picking on Retail Food Waste

Alexander Hübner, Fabian Schäfer, Tobias Winkler, Kai
HobergThis paper investigates the impact of customer withdrawal on retailfood waste. Customer picking for the freshest items (called last-infirst withdrawal) leaves products with shorter expiration dates on the
shelves. However, currently applied barcodes do not include the expiration date. We partnered with a retailer to collect missing expiration
data. We implemented a process adaption in a pilot warehouse and develop a novel method to quantify waste caused by customer picking inpanel data. Our findings show that, on average, 45% of the food waste
in our sample is caused by customer picking. Building on the resultsof the customer picking waste quantification, an exploratory approach
is applied to identify product and store-related drivers.

2 - Quantifying the potential to reduce food waste andincrease freshness in a two-echelon divergent single
product supply chain.

Rob Broekmeulen, Karel van Donselaar
Our study investigates allocation strategies at a retailer warehouse that
replenishes a multi-period perishable product in case packs to a set ofdifferent stores that face stochastic demand. The objective of the retail chain is to reduce food waste and increase freshness, while maintaining an agreed service level in the stores. Since the warehouse hasa high service level target for replenishing the stores, it has to keep
safety stock, which results in batches with different ages at the warehouse. We showed in a previous study that the waste at the stores canbe significantly reduced by unpacking the case packs at the warehouse
and/or increasing the remaining shelf life, especially for stores witha low demand. Unpacking at the warehouse allows the stores to order in smaller quantities, but this increases the order picking costs forthe warehouse. We quantified in our new research the potential improvements of allocating only a fraction of the stores with unpacked
and/or fresher products to limit the additional supply chain costs resulting from applying these strategies.

3 - Dynamic pricing strategy for substitute perishable
products

Mariana Sousa, Sara Martins, Maria João Martins dos Santos,
Pedro Amorim
Dynamic pricing is a widely used strategy for adjusting the price ofgoods and services in response to changing and demanding consumer
behavior. In grocery retailing, this approach is often applied to perishable products to encourage consumers to purchase items with limited
remaining shelf life. However, existing pricing strategies often overlook the impact of sales cannibalization among similar products andlack differentiation in discounts across the product spectrum. In thisstudy, we develop a dynamic pricing approach for a variety of perishable items with a focus on optimizing retail profits and reducing
food waste. We first test assumptions about substitution effects among
similar products using historical data from a European retailer. We
then develop a demand forecasting model for a subset of the products
and incorporate it into a reinforcement learning algorithm. This algorithm determines the pricing strategy for the selected set of products,
establishing continuous discounts throughout their shelf life. Finally,
we analyze the impact of considering multiple products rather than asingle product at a time to develop the pricing policy. This research
provides insights into a methodology for constructing dynamic pricing
strategies and uncovers practical implications.

4 - Optimizing assortments with age-dependent inventory

Manuel Ostermeier, Alexander Hübner, Lena Riesenegger

 MA-50
Monday, 8:30-10:00 - Room: M2 (building: 101)

Food Waste
Stream: Retail Operations
Invited session
Chair: Manuel Ostermeier

Determining the assortment and inventory levels based on their shelf
life is essential for retailers to maximize profits while avoiding foodwaste. The limited shelf space and the handling of perishable products with limited shelf lives interrelate assortment and inventory decisions. A joint approach is needed that defines the assortment size
and the maximum possible inventory levels while considering differing product ages and their implications on the planning. The productages on the shelves are further impacted by the customers’ withdrawal
behavior. For instance, customers intentionally selecting products withlonger expiration dates change the order of depletion as older products
are left on the shelf. These interferences need to be considered when31

MA-51EURO 2024 - Copenhagen

deciding on the products within the assortment and their corresponding
inventory levels. We develop the first multi-period approach to address
the interrelation between assortment planning and inventory management by integrating product shelf life and outdating. The resulting
planning problem is solved using an iterative approach that considers
different customer withdrawal behaviors and established demand effects.

 MA-51
Monday, 8:30-10:00 - Room: M5 (building: 101)

Risk management in financeStream: Risk management in financeInvited session
Chair: David Neděla
1 - Firm Zombification & Corporate Policy: Empirical examination of Indian Firms

S Yesaswi Dora, Ajay Kumar Mishra
This study addresses the escalating concern of zombie firms in the
Indian context, employing a unique methodology to identify and analyze their characteristics. Utilizing a dataset encompassing 45,867
firm-year observations from 2009-2021, our approach identifies 1,199
zombie firms, comprising 29.35% of the sample. Regression analysis
reveals a positive link between high reliance on debt capital and thelikelihood of zombification. These firms exhibit characteristics suchas being relatively new, possessing small asset sizes, higher leverage,
and poor performance. Additionally, they maintain lower cash holdings, higher leverage ratios, and lower capital intensity. Our findings
indicate that zombie firms adopt a conservative cash policy and aggressive financing, coupled with a conservative investment approach.
This study contributes to the existing literature, shedding light on theimplications of zombification for corporate policies in the Indian business landscape. The research emphasizes the potential misallocation offunds, posing a financial burden for both companies and the economy.
As the first of its kind in the Indian context, this study holds critical implications for corporates, creditors, and policymakers, providing
valuable insights for enhancing economic well-being and long-term
viability.

2 - Robust Asset Liquidation Strategies in Financial Systems

32

3 - Portfolio selection with two risk sources and inverse optimization

Hanna Zhurba, Sergio Ortobelli Lozza
In this work, we first discuss how evaluating two sources of risk (market risk and trend risk) using a unique measure of risk. In particular, we
combine two risk measures with alternative correlation matrix in orderto get a unique risk measure. Then we extend the inverse optimization problem proposed by Black and Litterman (1992) adapted to thiscontext. Finally, we propose an ex post empirical analysis, where wecompare the proposed models with other different portfolio selection
models.

4 - Return and volatility spillover among the global private
equity markets

Antonio Díaz, Carlos Esparcia Sanchís, Lars TegtmeierThis study investigates return and volatility spillovers between different regional private equity markets and investment styles to analyze thedynamics of interconnectedness. This is done using the LPX Group’s
listed private equity indices for the period January 2004 to September
2023. The results show that the LPX America index is a net transmitter for the LPX Europe index. The transmission of volatility spillovers
from the US to Europe precedes the impact of return spillovers by several months. Furthermore, Europe only becomes a transmitter for the
US in a few specific European events. For the investment style indices,the LPX Buyout index consistently acts as a net return spillover transmitter throughout the sample period, while the LPX Mezzanine index
consistently exhibits higher net volatility spillovers. The LPX Ventureindex consistently acts as a net receiver. The catalysts for risk contagion within the LPX indices are explicitly stated to be the differential
impact of major economic and financial events. Another reason forthe observed spillover effects could be herd behavior as a transmission
channel.

 MA-52
Monday, 8:30-10:00 - Room: 8003 (building: 202)

Combinatorial optimization approaches for
freight deliveries and home services
Stream: Combinatorial Optimization
Invited session
Chair: Claudia Archetti
1 - A Triple Bottom Line optimization model for assignmentand routing of on-demand home services

Hongyi Jiang, Dohyun Ahn

Debajyoti Biswas, Laurent Alfandari, Claudia Archetti

In this paper, we study the problem of liquidation strategies in financial networks over two periods. Financial contagion arises when substantial overlapping illiquid assets are sold at significantly depressed
prices. We present a novel strategic liquidation model for a bank in financial networks, allowing for preemptive liquidation before maturity,
where the uncertainty of other banks’ two-period liquidation decisions
poses a challenge to risk evaluation. To address the issue, we propose
a robust quantification for the liquidation income and develop the optimal liquidation strategy for maximizing the bank’s cash after liquidation under the worst-case scenario, which we refer to as the ’maximin
strategy.’ We prove that the maxmin strategy is unique and remains invariant in the presence of interbank liabilities. We further demonstratethat with the information on all banks’ liquidation strategies available,
the strategic liquidation problem becomes a liquidation game whose
unique Nash equilibrium is that all banks adopt their maxmin strategy.
In addition, with only aggregate information on interbank liabilities,
we provide a modified near-optimal strategy for weakly interconnectedfinancial networks. Our results provide guidelines for developing robust liquidation strategies that alleviate losses from financial contagion
and build insights into evaluating liquidation effects in the financial
network for decision-makers.

In this paper, we study the assignment and routing of service professionals for serving customers of an on-demand home services platform considering the Triple Bottom Line (TBL) criteria for ensuring
sustainability in operations. We characterise this as the Home Services Assignment and Routing Problem with the Triple Bottom Line
(HSARP-TBL) and implement a Mixed Integer Linear Programming(MILP) model for solving it. We assign service professionals to customers based on their desired time slots and also transport modes foreach customer visit by a professional, considering either combinationsof public transport or a personal vehicle for each professional’s tour.
The objective is to minimize costs due to time window violations anduncovered customers, catering to the economic pillar of the TBL. We
incorporate additional constraints based on the TBL by - improving
customer satisfaction based on the ratings of assigned professionals
to customers, with and without subscription (economic), controllingemissions due to transportation of professionals (environmental) andensuring equity in service allocation and net earnings between professionals (social). For tackling large instances we implement a Hybrid
Genetic Search (HGS) algorithm adapting it to our problem setting.
We demonstrate that the HGS outperforms the MILP model systematically for large instances in terms of solution value and computationaltime.

EURO 2024 - Copenhagen
2 - Stochastic Scheduling and Routing Decisions in Online
Meal Delivery Platforms with Mixed Force

Claudia Archetti, Laurent Alfandari, Yanlu Zhao
This paper investigates stochastic scheduling and routing problems
in the online meal delivery (OMD) service. The huge increase inmeal delivery demand requires the service providers to construct ahighly efficient logistics network to deal with a large-volume of timesensitive and fluctuating fulfillment, often using inhouse and crowdsourced drivers to secure the ambitious service quality. We aim to address the problem of developping an effective scheduling and routing
policy that can handle real-life situations. To this end, we first modelthe dynamic problem as a Markov Decision Process (MDP) and analyze the structural properties of the optimal policy. Then we propose
four integrated approaches to solve the operational level schedulingand routing problem. In addition, we provide a continuous approximation formula to estimate the bounds of required fleet size for the
inhouse drivers. Numerical experiments based on a real dataset show
the effectiveness of the proposed solution approaches. We also obtainseveral managerial insights that can help decision makers in solving
similar resource allocation problems in real-time.

3 - Dynamic programming state space restrictions to solve
the joint order batching and picker routing problem via
column generation

Maxime Ogier, Hadrien Cambazard, Nicolas Catusse
We address the joint order batching and picker routing problem(JOBPRP). Given a set of orders to collect, each order being composed of several items located in a warehouse, the JOBPRP consists
in batching orders into capacitated trolleys such that the total travelled
distance to collect all the items of the orders is minimized.
We are interested in algorithms based on column generation. A bottleneck of such approaches is to efficiently solve the pricing problem,that is a profitable order picking problem where all products of an order must be collected together in the same tour and a price is gainedfor collecting an order. At the core of this pricing problem lies a profitable traveling salesman problem (TSP) in a rectangular warehouse.
Dynamic programming (DP) approaches can solve efficiently this TSP
for such layouts.
In this work, we extend the DP approach to the profitable variant. The
directed acyclic graph underlying the DP can be used to provide a powerful Mixed Integer Programming (MIP) formulation where the order
requirements (all products of an order must be collected together) canbe taken into account with linking constraints. Such MIP formulation
has been studied for the special case of warehouses with a single blocof aisles. We generalize to the case with several blocks, and propose
state space restrictions of the DP to heuristically solve the pricing problem.
The proposed approach is validated on instances from the literature.

 MA-54
Monday, 8:30-10:00 - Room: S01 (building: 101)

Disruption management in passenger
railways
Stream: Public Transport Optimization
Invited session
Chair: Joris Wagenaar
1 - An Iterative Framework for Rolling Stock Reschedulingwith Railway Infrastructure Availability Constraints

Jia Hui Zhu, Twan Dollevoet, Dennis Huisman
Disruptions on the railway network lead to reduced availability of therailway infrastructure. In the face of such disruptions, rolling stock

MA-54

dispatchers are tasked with adjusting the rolling stock schedule. In thispaper, we develop a rolling stock rescheduling method which ensures
feasibility with respect to the availability of the railway infrastructure.
In particular, we explore the option of performing shunting movements
at stations where shunting is not allowed in current practice, due to thelarge number of trains that pass through or due to the complexity ofthe station layout. We introduce an iterative rolling stock reschedulingalgorithm which alternates between two mathematical formulations,
namely one that creates an interim rolling stock schedule and one thattries to fit the suggested shunting movements between the remaining
railway traffic. We test our solution approach with instances that contain complete railway blockages on the Dutch railway network. We
allow for shunting at some of the busiest stations in the country andmodel the infrastructure of these stations to evaluate the feasibility ofthe suggested shunting movements. Our algorithm succeeds in adjusting the rolling stock schedule within running times of around a few
minutes. We successfully identify feasible shunting movements andtherefore improve upon the rolling stock schedule that would otherwise be obtained if performing shunting movements at the considered
stations is prohibited.

2 - Extension to RECIFE-MILP Train Rescheduling

Valentina Cacchiani, Andrea Bettinelli
Real-time Train Rescheduling is applied to obtain a new feasible
schedule when unexpected delays occur causing train conflicts and delay propagation. Train Rescheduling consists of train reordering, retiming, and rerouting. We consider the well-established approach forTrain Rescheduling, called RECIFE-MILP: it consists of first solving a
microscopic Mixed Integer Linear Programming (MILP) model withina given time limit by neglecting train rerouting and then solving thecomplete model with the computed solution as a warm-start.
In this work, we extend RECIFE-MILP by including additional constraints on (mandatory or soft) train connections and restrictions on
the access or speed to utilize the available infrastructure resources, andan objective function that penalizes, through a step-function, trains arriving late or early and differences from the desired travel time, the useof detours, and the violation of soft train connections. Computational
results are reported on realistic instances to show the performance ofthe proposed approach.

3 - Robust Rolling Stock Rescheduling

Evelien van der Hurk
In railway planning, effective disruption management tools are necessary to limit the impact of a disruption. The duration of a disruption plays a central role in this process and is typically assumed to be
known with certainty. However, in practice the duration is uncertain,
and neglecting this aspect may lead to inefficiencies. We propose twodifferent approaches for incorporating robustness when reschedulingrolling stock units while considering a disruption of uncertain duration. The first, termed strict composition robustness, obtains a schedulethat is feasible for all possible realizations of the disruption duration
and, therefore, can be easily modified in the case that the disruption
lasts longer than expected. The second, termed light trip robustness,
focuses on the notion of “trip robustness”, and requires that out of aset of critical trips a pre-specified percentage should be robust withrespect to their incoming composition across different disruption durations. We propose extensions of a well-known rolling stock schedulingmodel to respectively capture the different robustness criteria and, on
a diverse set of real-life instances from the Dutch railway network,
compare their performance to a naive approach that assumes a given
disruption duration. The results show that all methods are worthwhile
and that the choice of which to apply is highly dependent on the likelihood that the disruption will last longer than anticipated.

4 - A Heuristic Approach for Real-Time Adjustment of Disruption Programs - Applied to the Context of Commuter
Railway Systems

Cedric Steinbach
Disruption on commuter railway systems with significant capacity restrictions (e.g. line closures) lead to substantial effects on the planned
operations. One strategy to manage occurred disruptions is based on
the use of pre-structured operating concepts, prepared to deal withgeneric disruption scenarios, so-called disruption programs (DRP).
Since DRPs are planned offline for specific scenarios (e.g. failure of33

MA-55EURO 2024 - Copenhagena certain group of infrastructure elements, like signal boxes), they allow dispatchers to react more effectively to the occurred event. If a
disruption affects a combination of infrastructure elements that are notexplicitly considered within an existing DRP, the full potential of the
DRP cannot be utilized. To support dispatchers in responding to disruptions, an ad-hoc adjustment of the DRPs to the availability of theinfrastructure has been developed. The approach determines the need,develops and introduces necessary adjustments to existing DRPs. At
its core, the adjustment is a problem of combinatorial optimization,
which is solved using a heuristic method. By combining components
of different metaheuristics, a problem-specific solution approach basedon a local search was developed. The approach assesses the adjustments based on their impact on the resistance of the passengers’ connections and ensures the operational feasibility of the adjusted DRP.
To illustrate the approach, it is applied to an example of a disruption in
a commuter railway system MA-55
Monday, 8:30-10:00 - Room: S02 (building: 101)

Transportation Network Modelling andOptimization I
Stream: Transportation
Invited session
Chair: Natsumi Takahashi
1 - A Queueing-based Throughput Analysis of the Mixed
Traffic with Autonomous Vehicles

Xiangdong Chen, Hao Guan, Qiang Meng
In recent years, the automotive industry and transportation systems
have seen a remarkable surge in the development of autonomous vehicles (AVs), driven by advancements in automation and vehicle-toeverything (V2X) communication technologies. In the foreseeable future, when AVs come into our society, it is evident that semi-AVs willcontinue to play a predominant role in the market due to safety concerns and cost considerations. This study aims to provide models toderive and analyze the throughput and travel time of the transitional
mixed traffic with semi- and fully-AVs. To accurately capture the dynamics of different platooning behaviors of fully- and semi-AVs andtheir impact on travel efficiency, we employ a Markov chain model
to derive headway distributions under varying traffic densities, and integrate the Markov chain with a state-dependent queueing model tocharacterize the mixed traffic flow. The derived mean headways arethen utilized to compute the service rate of a state-dependent queueing
system, enabling the calculation of long-term throughput and mean
travel time through a highway segment. This study reinforces the research on transitional stages of mixed traffic with semi-AVs, revealing the potential for semi-AVs to enhance traffic efficiency. Moreover,it provides valuable insights for the development of management andcontrol strategies in the transitional mixed traffic stages, guiding future
designs and implementations.

2 - An active learning-based optimization for road traffic
networks with uncertain service

Suh-Wen Chiou
For a road traffic network with uncertain service, a learning-based optimization is presented. In order to appropriately address spatial evolution of traffic congestion inside road links, a time-varying traffic modelwith uncertain capacity at links downstream is considered. Accounting for road users’ behavior, a learning-based bilevel program can beproposed. A learning-based optimal signal settings can be determinedat the upper level via reinforcement learning and users’ equilibrium
traffic flow can be decided at the lower level. In order to effectively
solve the proposed bilevel program, a learning-based optimization canbe decomposed into two sub-problems. A Quasi-Newton update is introduced to find solution with globally asymptotical convergence. In

34order to ensure feasibility of solution found against high-consequence
realization of stochastic capacity, a learning-based robust model is proposed. Numerical experiments are performed at a moderate traffic
grid. As compared to conventional approach, obtained results obviously showed that the proposed model can exhibit sufficient gain ofachieving effectiveness while attenuating time-varying congestion in
the presence of stochastic capacity at links downstream.

3 - A link-based traffic equilibrium model for multi-modal
network

Weizhao Su, Selin Ahipasaoglu, Edilson Arruda
This study introduces a link-based traffic assignment and equilibrium
model for multi-modal networks considering congestion effects on
travel times. To seamlessly enable the mode switch at any step of
the journey, we employ an intervention control modelling techniquethat utilizes a multi-layer network wherein each layer corresponds toa feasible mode choice. The approach also enables us to prescribe
node-dependent mode switching costs. The solution technique relies
on dynamic programming and allows us to implicitly consider all possible route and mode choice combinations without enumerating all ofthem explicitly. This model can be used to design, analyze, and operatetransportation systems that include multiple modes such as a variety ofsustainable options.

4 - Exploring Strategy for Stochastic Maximum Flow Network Interdiction Problem

Natsumi Takahashi, Tetsushi Yuge
In this paper, we consider the network interdiction problem using the
network flow as the performance measure. An evader wishes to maximize the expected flow of some illegal commodities such as drugs
without being detected. On the other hand, a defender aims to minimize the objective of the evader by interdiction activities, such asadding some security controls, that can alter the probability measures.
In this study, flows are intercepted stochastically at each arc, and the
expected flow is calculated by considering the penalty cost when the
network flow is detected. We need to determine which path from thesource to the sink in the network can most effectively maximize theexpected flow considering the detection probability, and to determine
which arc is the best to interdict. To minimize flow, the defender increases the detection probabilities of arc with a cost by interdiction
activities. Under the total interdiction cost constraint, whether eacharc is to be interdicted or not is the decision to be made. This paper
proposes an efficient search method for optimal interdicted arcs in suchsituations.

 MA-56
Monday, 8:30-10:00 - Room: S04 (building: 101)

Last mile delivery modeling
Stream: Transportation
Invited session
Chair: Alessandro Farina
1 - Characterizing last-mile freight transport using mobile
phone data: The case of Santiago, Chile

Raul Pezoa
Understanding the mobility of ground freight transport is critical in urban planning and for developing public policies. Literature shows thatmost of the previous studies on this topic rely on surveys and limited
data, hindering the accuracy of the analysis. To cope with this drawback, in this paper, we present an innovative methodology for characterizing last-mile freight transport that uses a novel data source: mobile
phone data, which offers the advantage of a broader coverage compared to data sources used in previous contributions. To the best of ourknowledge, no prior articles have employed mobile phone data sources
to study freight transport. To bridge this gap, we propose a methodology that calibrates supervised machine learning models to determine
which cell phones correspond to truck drivers. To do so, we construct

EURO 2024 - Copenhagen
input variables that seek to track the daily movement patterns of mobile phones, including traveled distances, interactions with highway
networks, and land use variables. We test our approach by conducting
a case study in Santiago, Chile, for which we analyze mobility patterns and logistic indicators disaggregated by day, hour, and zoning.
Our results show high activity in Santiago’s outskirts, especially in thenorthwest and southwest areas, with many interactions due to logistic
centers and industrial zones. Similarly, central and eastern Santiago
also show high stop density, reflecting intense commercial activity.

2 - A Real-Life Order Allocation and Dispatching Problem
in Megacities

Muchammad Arya Zamal, Albert Schrotenboer, Tom van
WoenselThis study addresses the complexities of two-echelon distribution systems in modern city logistics, highlighting the challenges faced bymegacities like Jakarta, Indonesia. In collaboration with an industry
partner, our research explores a logistics platform designed for efficientsame- and next-day deliveries between local shops and consumers. In
this context, we introduce the stochastic and dynamic order allocation and dispatching problem (SDOA-DP) aimed at minimizing costsby optimizing order allocations to linehauls between two-echelon systems and dispatching strategies within them. This paper makes the following contribution: advancing corridor-based logistics by explicitly
integrating efficient linehaul planning with first and last-mile routing,
employing a cost-function approximation within a two-stage stochastic
programming formulation for dynamic order assignment, and leveraging a parameterized Adaptive Large Neighborhood Search algorithm
for routing. Finally, our model was implemented using real-life data,
demonstrating the approach’s effectiveness in reducing logistics costs.

3 - Sharing e-bikes between multiple depots

Dmitry Krushinsky
Electric bikes have their niche in the last mile logistics. They offer
certain advantages in terms of reduced emissions and infrastructure
congestion. Sharing bikes between multiple depots provides for extra
route flexibility and improved performance of the logistics system. In
this talk, we address route optimisation for electric bikes in a sharedmulti-depot setting. We discuss modelling issues and present insightsfrom computational experiments a.o. on the interplay between the locations of the depots and the performance of the transportation system.

4 - Modelling and optimization of a two-echelon last mileurban freight distribution system.

Alessandro Farina, Daniele Conte, Giada Marconi, Marino
Lupi
In this paper, a new methodology for the modelling and optimizationof a two-echelon last mile freight transport system for urban areas is
proposed. Last mile deliveries are managed by means of an UDC (Urban Distribution Centre) located on the border of the urban area. At
the UDC, freight is consolidated on small load units, for example pallets or small swap bodies, that are loaded on train wagons. Freightis then transported by train to some places on the border of the city
centre, namely cross-docking points, where it is transhipped to small
electric vans that carry out the last mile distribution. This problem has
been addressed as a two-echelon urban freight distribution system with
fixed satellites, where the UDC plays the role of the depot and crossdocking points play the role of satellites. A micro simulator has beendeveloped to represent the second by second activity of each vehicle, of
each train, and of each load unit. The micro simulator represents: thelast part of the delivery trips, from cross docking points to receivers,
but also the part of delivery trip carried out by train and the transhipment operations at cross-docking points. The simulator embeds a genetic algorithm that optimizes the vehicle routes from cross docking
points (satellites) to final receivers. The proposed transport system has
been applied in simulation to the city of Livorno, Tuscany, Italy.

MA-57

 MA-57
Monday, 8:30-10:00 - Room: S06 (building: 101)

Modelling commodity markets dynamics
Stream: Modern Decision Making in Finance and Insurance
Invited session
Chair: Lorenz Schneider
1 - Assessing the Profitability of Large-Scale Batteries in
the UK Electricity Market

Nina Lange, Stefan Ropke
The transition towards an electricity market with a high penetration ofrenewable energy is characterized by higher price volatility comparedto the more traditional electricity market dominated by conventional
generation. The increasing share of intermittent generation requires amethod of securing supply and matching demand with supply. Integrating batteries as fully controllable energy storage into the system isa solution to handling this issue. The inclusion and operation of batteries can contribute to the green transition if market participants canoperate them profitably. This project aims to quantify these batteries’
economic viability, increasing their incentive to be invested in and participate in the electricity markets.
In this paper, we run an optimization model based on forecasts of electricity prices in the day-ahead and in the intraday market. The model’s
performance relies on forecasts of the electricity prices.
We compute the profit from operating the batteries according to ouralgorithm during 2020 and 2021 in the UK electricity market. The
chosen forecast methods can obtain profits in the range of 30-40%
compared to the profit generated from perfect foresight of price developments, i.e., supplying the models with realized prices. In addition,
we investigate how participating in both the day-ahead and the intraday markets yields superior results compared to focusing on just oneof the markets.

2 - Commodity Forward Curves with Stochastic Time
ChangeMaren Diane Schmeck
Using powerful technique of stochastic time change, we introduce a
new two-factor commodity price model, where one of the fundamental
factors is the activity rate. This factor implicitly introduces stochastic
volatility into the model. The model is developed under both physicaland risk neutral probability measures, which allows for a wide range ofapplications ranging from derivatives pricing to risk management. We
derive forward prices and forward curve evolution within the model’sframework and develop an ingenious calibration procedure, which allows us to filter out the activity rate from daily observed price data. We
apply the model to the rich dataset of daily crude oil and natural gas
spot and futures prices and demonstrate its versatility and excellent fit
to the historical forward curves

3 - Calendar Spread Options and the Term Structure of
Volatility

Malthe Rauh Johansen, Nina Lange
We introduce a multi-factor stochastic volatility model aimed at describing the combined dynamics of both futures and option prices incommodity markets. The model is arithmetic, meaning that futures
prices are affine functions of a finite set of state variables, which allows
us to derive tractable formulas for vanilla options, and importantly, also
for spread options. This dual tractability is a distinguishing feature ofarithmetic models when compared to their multiplicate counterparts,and makes the model parameters amenable to efficient estimation using the Kalman filtering methodology on time series data consisting of
futures and both types of options.
As an empirical application of the model, we apply the unscented
Kalman filter on a data panel consisting of WTI crude oil futures,
vanilla options, and calendar spread options. We demonstrate that the35

MA-58EURO 2024 - Copenhagenmodel can achieve a good fit to the data and describe how the inclusion of calendar spread options in the estimation provides additional
information on the term structure of volatility and correlation.

4 - Revisiting the Gibson-Schwartz and Schwartz-Smith
Commodity Models

historical data to demonstrate the quality of priori decisions regarding
shift and break timing, staff allocation, and long-term workforce planning. Based on this, we offer insights into the significance of diverse
uncertainty types, their magnitudes, and correlations, demonstrating
how planning decisions can be adjusted without sacrificing computational tractability.

Lorenz Schneider, Bertrand TavinWe extend the Gibson-Schwartz (1990) and Schwartz-Smith (2000)
models to include stochastic volatility and correlation based on thegeneralized Wishart variance-covariance matrix process. We study European option pricing via characteristic functions. Furthermore, we
perform parameter estimation via maximum likelihood based on thestate space formulation of the models.

 MA-58
Monday, 8:30-10:00 - Room: S07 (building: 101)

Automated Timetabling
Stream: Automated Timetabling
Invited session
Chair: João Almeida
1 - Service-oriented approaches to automated timetabling

Alexander Kuckelberg
The optimal creation of timetables under various aspects is a topic inmany research and implementation projects. Various procedures andmathematical approaches are used as well as modelling, target functions and perspectives. For example, timetables can be optimized froma railway undertaking’s perspective in terms of resource and circulation planning, from an infrastructure operator’s perspective focussing
capacity perspective, or from a transport association’s perspective fromtraffic and stream considerations. On European main railways, it became practice for a separated, independent company - usually infrastructure manager - to be responsible for coordinating and synchronizing the various requirements and wishes of the various parties involved
and bringing them together into a common timetable. The timetableprocesses are generally subject to predetermined agendas, phases andconditions, e.g. early route registrations, international traffic, route
prices and priorities, framework agreements, etc. As a practical report,
the article presents an approach to making this timetable planning process available as a usable IT service through a service interface andgives a brief insight into the scenarios, usages and working methods
as well as results of such service components and organizes them into
the practical process and the framework conditions of practice-relevant
systems automated timetable planning.

2 - Analyzing the impact of operational disruptions on airline ground staff planning

Mahekha Dahanayaka, Dennis Prak, Rohit Gupta, Martijn
Mes
Staff planning for airport ground operations usually starts well ahead ofthe day of operation. Aircraft delays, caused by factors such as weather
and technical issues, subsequent cascading delays on the ground, as
well as workforce uncertainties, are all unknown at the moment that
staff plans are made and are typically disregarded. In practice, airlines
have traditionally prioritized equipment over staff as the primary constraint, thus devoting less attention to the latter. Moreover, the computational complexity associated with staff shift planning problems has
led the prevailing approach to be deterministic, assuming complete
knowledge of the specific tasks and their respective timings. However, recently, the labour market has tightened significantly, causing a
focal shift toward staffing constraints. In this study, we explore the impact of uncertainty on schedule performance, drawing inspiration fromthe practical operations of a major European hub-and spoke carrier,with a particular focus on the planning of baggage (un)loading staff.
We simulate disruptions and the consequent recovery strategies using

36

3 - Using predictions on employee absenteeism to generate robust personnel rosters

Pieter Smet, Martina Doneda, Giuliana Carello, Ettore
Lanzarone, Greet Vanden Berghe
Employee absenteeism occurs when an employee is not present at work
during their scheduled hours. When this situation occurs, the absent
employee’s shift must be covered by another employee. This in turn
may affect others in the organisation, creating an undesirable ripple effect that can propagate throughout the entire roster. Generating robust
personnel rosters can proactively mitigate the negative consequences
when employees are absent from work. One way of increasing roster robustness is by scheduling special on-call duties that can be converted into regular working duties whenever necessary. Machine learning models that predict when employee absences will occur provide
an intuitive and regularly employed approach to determine where best
to position on-call duties in a roster. In this talk we will propose amethodology to evaluate the circumstances under which such predictions can actually increase roster robustness. More specifically, we will
analyze the results of a series of computational experiments to determine the prediction performance levels needed by a machine learningmodel to outperform a non-data-driven robust rostering method.

4 - A hybrid meta-heuristic for the generation of feasible
large-scale course timetables using instance decompositionJoão Almeida, José Rui Figueira, Daniel Santos
We propose a hybrid meta-heuristic for generating feasible course
timetables for large-scale problems. A novel instance decomposition
technique is introduced. Different types of constraint violations are regarded as objective functions to be minimised. The search targets slots
where the most violations are identified. After some iterations without
improvements, the most challenging constraint groups are given new
weights, guiding the search towards non-dominated solutions, which
improve the performance of these objectives, even if the total sum of
the violations increases. If this mechanism fails to escape these local
optima, a shaking phase is conducted. The decomposition mechanism
operates as follows: curricula are iteratively introduced to the problem,and new feasible solutions are found considering the increasing set of
lectures. The assignments from each iteration can be modified in subsequent iterations. We tested real-world instances from our university
and random sub-divisions. For sub-divisions with 400 curricula, decomposition reduces the solution times to up to 27%. For real-world
instances, with 1288 curricula, the reduction is 18%. Clustering curricula with common characteristics when incrementing the instances
improved the solution times by 18% more than random increments.
Feasible solutions for the real-world instance are found in 21 minutes,
whereas the commercial software takes several hours, and can fail tocomply with some constraints.

 MA-59
Monday, 8:30-10:00 - Room: S08 (building: 101)

Pricing and applications 3
Stream: Pricing and Revenue Management
Invited session
Chair: Joshua Foster

EURO 2024 - Copenhagen
1 - Advertising by Recruiting Influencers

Maya Jalloul, Vasiliki Kostami
The growth of social media platforms allowed customers to rely on influencers and past consumers’ experiences for their purchase decisions.
In this paper, we address some of the firm’s challenges of influencer
marketing by considering different types of influencers and partnership
schemes with them and we address the effect of social learning. We introduce a two-period model. In the first period, the firm launches a new
product and an influencer marketing campaign. The campaign can relyon two types of influencers that differ in popularity and credibility andtwo potential partnerships; sponsored advertising and public relations.
Customers make their purchase decisions using their original beliefs,
the information obtained via the influencers they follow, and reviewsof early adopters as they become available in the second period. Whencustomers are equally likely to follow the same influencer, the firmshould partner with a single influencer type through a single type ofpartnership. Furthermore, social learning is highly beneficial for anundervalued product when supplemented by advertising. Finally, in asociety formed by classes where followers and influencers usually belong to the same class, a diverse mix of influencer advertising methods
can be optimal. We suggest the strategic partnerships a firm should
establish with social media influencers depending on their credibility
and popularity, the market composition, and the characteristics of thesocial media platform.

2 - The Power of Perceived Value and Transformative Pricing in Online Food Delivery Platforms

Arvind Shroff, Bhavin ShahCurrently, Online Food Delivery (OFD) platforms like DoorDash,
Zomato and UberEats dictate the delivery fee charged to customers
for orders placed through them. This delivery fee is based on the order value and the distance traveled by the driver to complete the delivery. Pay-As-Asked (PAA) delivery pricing strategy is the name ofthis tactic. On the contrary, Pay-What-You-Want (PWYW) is an innovative pricing strategy that gives customers the final say over the
price they are willing to pay based on how valuable they perceive
the service. To investigate how the adoption of pay-what-you-want(PWYW) for delivery fee pricing affects the financial viability of OFD
platforms, we present an analytical model. By dividing consumers into
three groups—free riders, fair-minded consumers, and generous consumers—we are able to identify their social preferences for things likefairness and reciprocity. This allows us to determine the conditions
under which PWYW might be more profitable for the platform than
the typical PAA delivery pricing strategy. Our research suggests thatto maximize platform profits when implementing a PWYW delivery
fee, the OFD platform managers should work to reduce delivery costs
as much as they can. Furthermore, we propose that platforms impose aminimum delivery fee that customers may use as a guideline when determining the fee they want to pay to prevent free riders from reducing
profits under PWYW.

3 - Disruption Mitigation and Pricing Flexibility

Oben Ceryan, Florian Lücker
We study how a firm’s pricing flexibility to potentially increase its
prices during a disruption impacts its decisions on the level of reserve
inventory or reserve capacity to carry in anticipation of disruptions.
While the reserve inventory provides a certain quantity of inventory
that the firm can continue to sell during a disruption, reserve capacity
allows the firm to maintain production during a disruption at a certain
production rate. Specifically, we consider a firm producing a single
product and that is exposed to random disruptions. The firm first decides on the level of reserve inventory or reserve capacity to carry inanticipation of disruptions. When a disruption does occur, it may thenchoose to adjust its price to better align demand with the supply restrictions during the disruption. We characterize the firm’s optimal pricing
decisions during disruptions, and the level of reserve inventory or reserve capacity to hold in anticipation of disruptions. We find that acertain level of pricing flexibility may be required to justify holding
reserve inventory or reserve capacity. Further, we also show that whilea firm’s pricing flexibility during a disruption and reserve capacity decisions are substitutes, pricing flexibility and reserve inventory may act
either as substitutes or complements, that is, a firm may be incentivized

MA-60

to carry a lower or higher amount of reserve inventory if it possesses
further flexibility to increase its prices during a disruption.

4 - Giving Deep Attention to Consumer Preferences with
Large Language Models

Joshua Foster, Fredrik Odegaard
We use the natural language descriptions of exotic and collectible vehicles from online auction markets to semi-nonparametrically estimate
the primitives of demand (private valuations, number of potential bidders) for each individual vehicle in our dataset. The first stage of ourestimation method fine-tunes a large language model (RoBERTa, Liuet al., 2019) to predict the inputs of our demand estimators using thedescriptions provided for each vehicle. We then append a multi-layer
perceptron to the tuned language model that projects into the parameter space of these estimators so that a second stage training can recover
the primitives of interest. Our identification method relies on the structural features of specific bids submitted in these markets, which can
proxy for valuations under our data generation assumptions. Finally,
we demonstrate how this model can generate counterfactual analyses
using new natural language descriptions within the defined productspace.

 MA-60
Monday, 8:30-10:00 - Room: S09 (building: 101)

Vehicle routing I
Stream: Transportation
Invited session
Chair: Sara Charaf
1 - A Black Widow Optimization for The Time-dependent
Vehicle Routing Problem with Fleet and Crew Assignment

Antonia Ilabaca, Germán Paredes-BelmarThis paper focuses on the efficient distribution of goods in urban areas by using the time-dependent vehicle routing problem with fleet
and crew assignment (TD-VRPFCA). The objective is to find the bestroutes for a group of vehicles to deliver goods to a set of destinations,considering various factors that are specific to city logistics, such as
traffic congestion, the locations of different buildings, and the difficulties that delivery workers may face in accessing these places. Unlike
the traditional vehicle routing problem, our focus is on the efficientcrew assignment for delivery vehicles. The crew composition can consist of different roles, such as drivers, delivery workers, or a multifunctional worker who perform both driving and delivery tasks. The crew
assignment aims to minimize the total distribution time, consideringthat service and travel time depend on the time of day and the locationof each vehicle due to the traffic flow of people in the buildings andcongestion on the streets.
To solve this problem, we propose a new algorithm called Black
Widow Optimization (BWO) for Vehicle Routing Problems. BWO
is an efficient metaheuristics algorithm proposed for continuous variables, but in this paper, we propose an algorithm for mixed integer
variables. The resulting set of efficient routes and crew vehicle assignments can support decision-making and improve customer service.

2 - A Multi-Trip Periodic Vehicle Routing Problem with Time
Windows, Split Delivery for Simultaneous Pick-Up and
Delivery

Gorkem Yilmaz
Efficient vehicle routing and delivery planning are critical challenges
in logistics systems, impacting operational effectiveness and cost efficiency. Planners are tasked with devising effective delivery and pickup schedules to meet customers’ needs, considering factors such as
pick-up and demand quantities per customer, available working hours,
shift times, total distances between customer locations, cost parameters, and various transportation constraints. Delivery planners allocate

37MA-61EURO 2024 - Copenhagenvehicles, determine the start time of service, optimize vehicle routes
within specified time windows, schedule shifts, and decide on the number of tours for each vehicle. These actions aim to maximize vehicle
utilization and meet requirements at minimum cost. We propose mixed
integer programming models that reduce the logistic costs and numberof vehicle traffic by combining shipments from different customers.
To solve large problems, we propose a decomposition-based heuristics
that separates periods by considering time windows and setting an objective that first minimizes the number of used vehicles in each period.
The results show that inefficient transportation between two nodes isavoided, the numbers of vehicles are decreased, the utilization rate ofeach vehicle is increased, and vehicle traffic is reduced in each customer area.

3 - A Local Search metaheuristic for the last-mile Vehicle
Routing Problem with Delivery Options

Eleftherios Manousakis, Ioannis Kitsos Kalyvianakis,
Angelos Omirolis, Emmanouil Zachariadis
The Vehicle Routing Problem with Delivery Options (VRPDO) extends the classical Vehicle Routing Problem (VRP) by incorporating
multiple alternative delivery locations per customer with potentially
distinct time windows, ordered according to customer preferences.
These alternative destinations, such as homes, workplaces, or lockers, introduce additional complexities, including varying capacity andtime window constraints at shared delivery points. Ensuring customer
satisfaction involves adhering to minimal service level requirements,
reflecting preferences for alternative delivery locations. The VRPDO
calls for determining optimal routes and delivery locations while minimizing total routing costs and satisfying all time window, capacity andservice level constraints. We propose a minimum insertion heuristic
for generating initial solutions, followed by a local search framework
to refine these solutions. The framework incorporates traditional routing operators alongside a novel service level-related operator capableof adjusting delivery locations to satisfy customer preferences. Initial
findings demonstrate the effectiveness of our approach in achieving
high solution quality while maintaining computational efficiency.

4 - The Two-Echelon Inventory-Routing Problem:
Matheuristic ApproachA

Sara Charaf, Duygu Tas, Simme Douwe Flapper, Tom van
Woensel
The two-echelon inventory-routing problem (2E-IRP) addresses thecoordination of vehicle routing and inventory management throughout a two-echelon supply network. The latter consists of intermediate
facilities that are located in the city outskirt, supplied from distant depots, and serve a set of geographically widespread customers. The
customers’ demand is met from either their local inventory or intermediate facilities’ inventory. The aim is to minimize the transportationand inventory costs while meeting customers’ demands over a finite
discrete planning horizon. We introduce an effective tabu search-based
matheuristic approach to solve it. Computational experiments showthat our approach achieves good results regarding solution quality andcomputational time. For small instances, the matheuristic finds 99 optimal solutions out of 165 known optimal solutions and achieves anaverage gap of (-2.32%) over 235 instances with a known best upper bound only. Our approach also solves larger instances within areasonable computational time and provides upper bounds for all 400
large-sized instances for the first time to the literature.

 MA-61
Monday, 8:30-10:00 - Room: S10 (building: 101)

Advances in Location Analysis
Stream: Locational Analysis
Invited session
Chair: Vladimir Marianov

38

1 - Exact solution approaches for the nested p-center problem

Christof Brandstetter, Markus Sinnl
Consistent solutions are vital in multi-period facility location problems, especially for long-term planning. In this work, we present various exact solution approaches on the nested p-center problem, a recently introduced variation of the well-known (vertex) p-center problem, which can be defined as follows: Given a set of locations and afinite time horizon, in each time period, a set of open facilities needs tobe opened, where the set of a time period, needs to be a subset of theset of open facilities in the following period. The goal is to minimize
the sum of the maximal distance between any customer and its nearest
open facility over the time horizon.
In our work, we present different mixed integer linear programming
formulations on the nested p-center problem, which are solved using exact solution methods like Bender’s Decomposition or branchand-bound. Furthermore, we have developed a solution framework toincrease the performance in terms of runtime on all formulations byadopting a diverse set of methods, like cut separation, lifted optimality
cuts or preprocessing.
The different formulations and methods used are analyzed in a computational study on benchmark instances, known from previous literatureand the managerial implications of the nesting property are analyzed.

2 - Variable aggregation in a Benders decomposition forthe p-median problem

Rick Willemsen, Daniel Aloise, Raf Jans
The p-median problem is a classical discrete location problem and isequivalent to the well-known k-medoids problem in the unsupervised
clustering literature. The aim is to select p centers while minimizing
the sum of distances from each customer to its nearest center. Recent advancements in solving the p-median and related problems havesuccessfully leveraged Benders decomposition methods. In order toreduce the number of variables and possibly the number of Benders
cuts in these models, it is possible to aggregate distance variables corresponding to customers. We propose to partially aggregate the distance variables based on an initial solution: aggregation occurs only
when the corresponding customers are assigned to the same center in
the initial solution. In addition, we propose a set of tailored valid inequalities for these aggregated variables. Initial experiments indicatethat our model, post-initialization, provides a stronger lower bound,
thereby accelerating the resolution of the root node. Furthermore, thisapproach seems to positively impact the branching procedure, leading
to an overall faster Benders decomposition method.

3 - Capacitated service location when fairness principles
are significant

Inigo Martin Melero, Mercedes Landete, Juan Carlos
Gonçalves-Dosantos, Joaquin Sánchez-Soriano
In this work we address the problem of deciding where to install service points when it is certain that the total demand is greater than thecapacity that the installed system can achieve. The starting point isa set of potential locations for service points and a set of locations
where customers with known demands are. There is a budget to install
p service centers with different capacities, although the sum of thesecapacities is less than the sum of the demands. The problem is deciding where to install these p centers using allocation criteria from conflicting claims problems. Equal losses, equal awards or proportional
allocation approaches are considered. All the optimization models we
introduce are mixed linear, their optimal solutions illustrate the effectof scarce resource management on the service centers location. Likewise, in addition to including different fairness criteria in the objectivefunction of the optimization models, we also analyze different families
of constraints that allow us to guarantee other desirable properties suchas that two customers with the same demand have similar services. We
check the conditions under which the solutions of our models are efficient in the sense that all capacity is allocated. Finally, we illustrate thebehavior of the optimization models by solving instances of different
sizes.

EURO 2024 - Copenhagen
4 - Facility location under agglomeration and two-stage
customers’ spatial choice

Vladimir Marianov, Gonzalo Mendez-Vogel, Armin
Lüer-Villagra, H.a. Eiselt
Most stores offering clothing, electronics, phones, and similar shopping goods, agglomerate in clusters —malls or shopping districts. Thislocation pattern facilitates multipurpose shopping, which consists ofpurchasing more than one good during the same trip, and comparison
shopping, the process of comparing products of different stores, but
serving the same purpose, before purchasing. Clustering leads to a
two-stage (or sequential) destination choice by customers. They decide first on a cluster to visit, and then, the stores to visit within thecluster. The two stages are not independent, as the first decision depends on the stores in the cluster: if a consumer is in search of a product serving a purpose, the number of alternative stores that fulfill that
purpose in the cluster, and the number of stores offering other products in the same cluster, affect the selection of the chosen cluster. This
agglomeration and the correlation are not represented in the customer
choice rules common in location models (Multinomial Logit, Gravitational rules), as they do not take into account the spatial distributionof stores, although modifications to gravitational and Logit rules havebeen suggested by marketing researchers. We explore a Nested Logit
model fit for the spatial choice process and later use it to formulate,
for the first time, a location model that includes the two-stage process
and the spatial agglomeration. Preliminary computational experienceis also presented.

 MA-62
Monday, 8:30-10:00 - Room: S12 (building: 101)

Seaside Planning I
Stream: OR in Port Operations
Invited session
Chair: Frank Meisel
Chair: Arne Heinold
1 - Optimizing RoRo Stevedoring Operations: Dual Cycling
for Vessel Turnaround Efficiency

Teresa Marquardt, Arne Heinold, Catherine Cleophas, Frank
MeiselThis work addresses the challenge of decreasing vessel turnaroundtime in Roll-on Roll-off (RoRo) shipping as a means to achieve emission reduction by enabling time savings that allow for slower sailing.
RoRo shipping has received comparatively less attention than container shipping by research, yet it is of high relevance, especially inEurope with its long shorelines. The study investigates the potentialof stevedoring optimization by extending the Dual Cycling Problem
from the literature, where on- and off-loading operations happen simultaneously. To that end, we model a continuous time horizon, differentiate between driver-handled cargo and stevedore-handled cargowith heterogeneous processing times, and solve the assignment andsequencing of operations for the stevedores. We propose a mixed integer linear programming model to solve the resulting problem. Givenhigh computational effort for instances of realistic size, we further propose a biased random-key genetic algorithm, a random construction
heuristic, and rule-based heuristics to find good solutions in short runtime. We present computational results demonstrating the effectiveness of the proposed approaches under varying conditions. Additionally, managerial insights clarify the superiority of stevedoring with adual cycling strategy over single cycling, the impact of different cargohandling policies, and the effects of different stowage policies on theturnaround time.

2 - Comparison of 3 modelling approaches for stowage ofheterogeneous cargo on RoRo ships with stability requirements

MA-62

Oliver Rise Thomsen, Magnus Vilhelm Barrett Levinsen,
Alastair Main, Line Reinhardt, Dario Pacino
This study evaluates the three main representations methods of the
RoRo stowage planning problem for heterogeneous cargo and stability constraints. The lane, grid and hybrid-slot model are formulated
and implemented in an equal manner to compare them. All formulations accounts for vessel stability by ensuring that the vertical centersof gravity, shear force and bending moment are within their respective
limits. The results show the hybrid-slot approach create the best solutions on all instances across test on a small, medium and large ship.

3 - A rolling horizon approach to a two-stage stochastic
model for dual cycling under uncertainty in RoRo terminals

Line Reinhardt, Asefe Forghani, Dario Pacino
Dual cycling, which executes loading and unloading simultaneously,is acknowledged for reducing handling time at the port, thus enablingslow steaming and GHG reduction. Previous studies on dual cycling
mainly focused on container shipping with deterministic cargo arrivals,
which are unsuitable for the RoRo (Roll-on/Roll-off) sector’s inherent
uncertainties. Our research bridges this gap by proposing a two-stage
stochastic model that aims to minimise the total discharging and loading operation time under uncertain cargo arrivals. The model is basedon several assumptions: cargo is distinguished as unaccompanied trailers and classified by weight, and it is assumed that the ship’s space isdivided into slots for distinct cargo types based on a predeterminedweight-based plan; cargo arrivals follow a stochastic pattern, and theship should wait until all planned cargoes have arrived and are loaded.
Loading and unloading follow specific precedence rules reflecting thedeck layout and safety requirements, and the number of tugs during
the entire operations remains constant. Addressing the computational
challenge, we propose a heuristic rolling horizon approach and a scenario reduction framework. As a case study, the proposed model was
implemented on a shuttle vessel’s tank deck. The results demonstrated
that, compared to the classical discharge-then-load policy, dual cycling
achieved an 11.11% turnaround time reduction and a 38% tug utilisation improvement.

4 - Scheduling and conflict-free routing of automatedguided vehicles in container terminals

Mohamed Ben Ahmed, Steffen Bakker, Kjetil Fagerholt
In automated container terminals, automated guided vehicles (AGVs)
are used to transport containers from the quayside to their locationin the storage area. AGVs navigate between key terminal locations,following predefined guide paths. They have bidirectional movement
capabilities that enable forward and backward travel, and their movements remain predominantly linear, aligning with the terminal’s layout.
Limited buffer space at pickup and drop-off points causes AGVs to wait
until ongoing operations conclude, creating delays. Delays may also
occur when a quay crane must wait for an AGV. Therefore, minimizing
both the delay times of QCs and the travel time of AGVs is vital for
reducing vessel dwell time and enhancing operational efficiency. AGV
conflicts, like deadlocks and collisions, worsen delays, causing congestion and decreased throughput. Implementing advanced routing algorithms and control systems is thereby essential to optimize AGV movements and enhance operational efficiency. This work presents a timespace network formulation for the problem scheduling and routing of
AGVs in container terminals where conflict avoidance constraints are
explicitly modeled. The problem’s objective is to minimize the vessel’s dwell time while ensuring the smooth flow of container handling
operations. A solution method that combines a mathematical formulation and an insertion heuristic is implemented. Testing and validation
are conducted using real data from Norwegian port cases.

 MA-63
Monday, 8:30-10:00 - Room: S14 (building: 101)

Applications in Finance and Economics
Stream: OR in Banking, Finance and Insurance: New39

MA-63EURO 2024 - Copenhagen

Tools for Risk Management
Invited session
Chair: Francesco Biancalani
1 - Effect of the Inflation Reduction Act on Equity Raised
Values in the US Healthcare Macro-Sector: A MatrixCompletion ApproachFrancesco Biancalani, Giorgio Gnecco, Andrea Signori,
Silvio Vismara
We investigate the effect of the recent Inflation Reduction Act (IRA)- introduced in August 2022 - on equity raised in the US healthcare
macro-sector. For the data analysis, we employ a panel approach, inwhich a subset of entries of a matrix of new equity raised values is observed, and one aims to predict counterfactual values for the missing
entries (i.e., the equity raised values in the US healthcare macro- sector
that would have been obtained after August 2022, in the absence of theIRA). Rows of the data matrix refer to specific pairs having the form
region/macro-sector (for a total of 3 x 5=15 rows), and their columns
to specific quarters of the year (for a total of 2 x 4=8 quarters, 4 before
the adoption of the policy, and 4 after that event). Such predictions are
obtained by employing a supervised machine-learning method named
matrix completion, whose use for causal panel data analysis has been
recently advocated in a celebrated work by Athey et al. in 2021. According to the results of our analysis, the effect of the IRA was toincrease equity raised values in the US healthcare macro-sector during the treatment period, as compared to their predicted counterfactual
values.

2 - Impact of Sentiment analysis on Energy Sector Stock
Prices : A FinBERT Approach

Rania Hentati Kaffel, Sarra Ben Yahia, José Ángel GarcÍa
SÁnchezThis paper provides accurate estimations of the impact of sentiment
analysis on forecasting trends in stock prices. We use FinBERT, a
pre-trained and fine-tuned NLP model to label and to score a large
web-scrapped data (tweets posts and Reddit discussions) of 10 selectedenergy stock daily returns and the S&P Energy Index spanning from2018 to 2023. Our approach confirms: i) the accuracy of stock pricepredictions going up top 85%. ii) Results are highly sensitive to thetest period and confirm the link between risk aversion and negatif or
positif score. Also, a novel contribution is provided and consist on employing time series model to capture the temporal dependencies andautocorrelation structures within both the sentiment scores and stock
returns data. Our study reinforces the concept of market efficiency andoffers empirical evidence regarding the delayed influence of emotional
states on stock returns.

3 - Copula-based synthetic networks generation

Giulia Rotundo, Roy Cerqueti
In this work, we elaborate on the concept of assortative mixing. The
role of assortativity contagions has been widely studied: in the spreadof epidemics, in systemic risk, and in the resilience of networks, justto point out a few. The classic definition of assortativity through thePearson correlation coefficient among the degrees of the nodes at theend of each edge captures the linear dependencies among the degreesof the connected nodes. We are going to push forward the concept
of assortative mixing allowing for non linear dependencies. In orderto achieve this task, we fix the marginal distributions as the most detected from the empirical data sets and we base on the generation ofcomplex networks through copulas. The approach is different from theclassic configuration model and its extensions which allow to build a
network with a target level of correlation. It is worth mentioning thatthe application of the technique goes beyond the mere result of simulating networks. Actually, the possibility to generate networks withspecific properties allows to have a null model for investigating the roleof topological structures in specific problems. For instance, the generation of synthetic data keeping the statistical distribution of real data
sets is currently successfully used in machine learning to enlarge thetraining set.

40

4 - An analytical study of variable annuities with surrender
optionGabriele Stabile, Alessandro Milazzo, Tiziano De Angelis
Variable annuities (VAs) are insurance contracts designed to meet retirement and long-term investment goals. These products combine theparticipation in equity performance with insurance coverages. The
subscriber of a VA (policyholder) enters the contract by paying a premium, and chooses to invest it from a selection of investment funds.
Besides the life insurance protection, VAs provide a number of financial guarantees and options. A minimum rate is guaranteed by the
insurer in order to protect the policyholder’s capital against market
downturns. Moreover, the policyholder has the right to early terminate the contract (surrender option) and to receive the account value.
In general, a penalty is applied by the insurer in case of early surrender, which decreases in time. These financial guarantees are financed
by a fee paid by the policyholder, typically in the form of a fixed proportion of the account value. At maturity, the policyholder can choose
to obtain the account value or convert it into an annuity stream. Thesefeatures of the VAs contracts are liabilities to the issuer and constitute a
potential hazard to company solvency. In this paper we provide an analytical study of the pricing formula for the VAs and a characterisationof the optimal exercise strategy for the surrender option.

 MA-64
Monday, 8:30-10:00 - Room: S16 (building: 101)

Routing in Warehouses
Stream: VeRoLog - Vehicle Routing and Logistics
Invited session
Chair: Felix Weidinger
1 - Integrating Order Picking and Delivery Decisions in a
Dynamic Setting

Ruben D’Haen, Katrien Ramaekers, Claudia Archetti, Kris
Braekers
The growing importance of e-commerce retail, accompanied with theneed of offering a high customer service level, requires companies tooptimise their operations. One possible area of improvement is the integration of order picking and delivery decisions. If a new customer
order arrives, the requested items first need to be picked in the warehouse. Afterwards, the items are delivered to the customer’s location.
Traditionally, the picking and delivery operations are considered individually and in a sequential manner. By using integrated decision
making, however, more efficient operations can be obtained.
Since customers demand very fast deliveries, the use of an online optimisation strategy is preferred, as it allows to consider new customer
orders very quickly in the current operating schedule. While previous
research considered the integration of picking and delivery in a static
problem setting, its impact in a dynamic setting is yet unstudied.
We propose new metaheuristic algorithms to solve the integrated picking and delivery problem in a dynamic setting. The performance ofseveral algorithms, i.e., a simple sequential algorithm, an iterative algorithm and two integrated algorithms, is compared to show the benefits of using a more involved optimisation strategy.

2 - Synchronizing Human-Robot Cooperation in Warehouses: Integrated Order Batching and Routing Problem in AMR-Assisted Picking Systems

Sina Khodaee, Melih Celik, Vaggelis Giannikas
With technological advancements and the growth of e-commerce industries, warehouses are taking advantage of robots’ assistance to gain
more productivity. This increasing usage, however, has directed them
to face more challenges in terms of human-robot collaboration, specifically in the order-picking process. In this study, we address the problem of human-robot coordination in a picker-to-part picking system,EURO 2024 - Copenhagen
where automated mobile robots (AMRs) that are equipped with bins
move through the warehouse to collect the items on the batch lists.
Human pickers, on the other hand, travel between item locations toload the AMRs. As there are fewer human pickers than AMRs, efficient routing of both human and robot pickers plays a pivotal rolein determining the productivity of the system. Hence, the main decisions of the problem are the batching of orders for the AMRs and theroutes of all pickers. To address this problem, we propose a mathematical formulation and a novel two-stage decomposition-based heuristic
approach to minimise the makespan. The first stage of our heuristic
solves the integrated batching-routing problem for the AMRs using avariable neighbourhood search approach, and then, in the second stage,by setting AMRs’ arrival times at each location as due dates for the human pickers, the algorithm searches for their routes. We also test theperformance of the heuristic and generate managerial insights using
randomly generated instances on different warehouse structures.

3 - Multi-Depot Dynamic Routing of Multiple Pickers With
Order Priorities and Due Dates in Smart Warehouses

Xiaochen Feng, Arsham Atashi Khoei, Melih Celik, Vaggelis
Giannikas, Niccolò Corsini
The fast development of e-commerce and higher customer expectations for quicker order delivery emphasise the importance of efficientmanagement in urban warehouses, which operate in a setting with dynamic order patterns and stringent order lead time. Dynamic orderpicking strategies became increasingly important as they allow for the
dynamic assigning of incoming orders to pickers during each pick cycle. Collaborating with an industrial partner that develops cutting-edge
Internet-of-Things technologies and platforms for smart warehousing,our proposed model extends the original dynamic order-picking problem by considering the due date and the order priority. Given the setof pickers and the sets of continuously coming new orders, our model
focuses on assigning incoming orders to pickers and redesigning theroutes of each picker on the sequence of item locations they will visit
and the depot they will return to while meeting the time frame requirements for each order and considering the order priority in order to ensure a more efficient order-picking process in smart warehouses. Dueto the time constraints and the need for quick response to the complex
order fulfilment processes, the efficiency of the algorithm is as important as the solution quality. We propose a problem-tailored heuristic
solution approach by extending the well-known heuristics, which canefficiently deal with the decisions of picklist updating, picker assignment, and rerouting.

4 - A Flexible Procedure for the Single Picker Routing Problem With Scattered Storage

Felix Weidinger, Constantin Wildt
E-commerce retailers have substantially reinvented warehousing. To
pick small, heterogeneous orders in a short period of time, novel warehousing approaches have been developed. The equivalent of classicalmanual warehousing is, hereby, mixed-shelf storage; where pieces ofthe same SKU can be found at many storage positions. This, however,makes the picker routing problem, which aims to find a shortest picking tour for collecting all items on the current pick-list, a combined
selection and sequencing problem. The storage positions to be visited must be selected, and a tour through the selected positions must
be planned. The resulting problem, often referred to as the Single
Picker Routing Problem with Scattered Storage (SPRP-SS), has gainedmuch attention recently. Most of the proposed procedures, however,assume a rectangular layout of warehouses, such that the subproblem,
once the positions to be visited are selected, is solvable in polynomial
time. However, for warehouses in existing dense inner-city buildings
or multi-level mezzanine systems, the property of rectangular layouts
is not met. In the talk, an exact and heuristic solution approach forsolving the SPRP-SS for arbitrary distance matrices is presented anddiscussed.

MB-02

Monday, 10:30-12:00
 MB-01
Monday, 10:30-12:00 - Room: Sportshallen (building: 101)

Immanuel M. Bomze
Stream: Keynotes
Keynote session
Chair: Emilio Carrizosa
1 - Need to relax - but perhaps later?

Immanuel Bomze
In some ML communities, researchers claim that obtaining local solutions of optimality criteria is often sufficient to provide a meaningful and accurate data model in real-world analytics. However, this issimply incorrect and sometimes dangerously misleading, particularly
when it comes to highly structured problems involving non-convexity
such as discrete decisions (binary variables). This talk will advocate
the necessity of research efforts in the quest for global solutions andstrong rigorous bounds for quality guarantees, showcased on one of
the nowadays most popular domains – cardinality-constrained models.
These models try to achieve fairness, transparency and explainability
in AI applications, ranging from Math.Finance/Economics to social
and life sciences.
From a computational viewpoint, it may be tempting to replace thezero-norm (number of nonzero variables) with surrogates, for the benefit of tractability. We argue that these relaxations come too early.
Instead, we propose to incorporate the true zero-norm into the basemodel and treat this either by MILP relaxations or else by lifting totractable conic optimization models. Both in practice and in theory,these have proved to achieve much stronger bounds than the usual LPbased ones, and therefore they may, more reliably and based upon exact arguments, assess the quality of proposals coming from other techniques in a more precise way. With some effort invested in the theory(aka later relaxations), the resulting models are still scalable and would
guarantee computational performance closer to reality and/or optimality.

 MB-02
Monday, 10:30-12:00 - Room: Glassalen (building: 101)

EDDA2
Stream: EURO Doctoral Dissertation Award
Invited session
Chair: Laura Palagi
1 - Advanced Demand-Capacity Balancing Mechanisms to
Improve Performance of European Air Traffic Networks

Jan-Rasmus Kuennen
Air travel disruptions due to Covid recovery, strikes, and military presence highlight the need for efficient airspace management. The dissertation presents demand-capacity balancing mechanisms aimed at improving European air traffic management (ATM) performance. The
first paper introduces dynamically priced flexible trajectory products
to manage demand, based on an optimizing routing under capacity
constraints. The second paper proposes a simulation-optimization approach for cross-border capacity planning, showing potential for significant delay reductions. Finally, the third paper analyzes measures toreduce aviation emissions through capacity planning and pricing initiatives. Impacting both research and practice, the findings offer innovative solutions for managing demand-capacity imbalances post-Covid,
potentially reshaping European airspace management.

41

MB-03EURO 2024 - Copenhagen

2 - Adjustable Robust Optimization with Non-Linear Recourse

Henri Lefebvre
Over the last century, mathematical optimization has become a prominent tool for decision making. Its systematic application in practicalfields such as economics, logistics or defense led to the developmentof algorithmic methods with ever increasing efficiency. Indeed, fora variety of real-world problems, finding an optimal decision among a
set of (implicitly or explicitly) predefined alternatives has become conceivable in reasonable time. In the last decades, however, the researchcommunity raised more and more attention to the role of uncertainty inthe optimization process. In particular, one may question the notion of
optimality, and even feasibility, when studying decision problems withunknown or imprecise input parameters.
In this talk, we study a class of optimization problems which sufferfrom imprecise input data and feature a two-stage decision process, i.e.,
where decisions are made in a sequential order and where unknown parameters are revealed throughout the stages. The applications of suchproblems are plethora in practical fields such as, e.g., facility location
problems with uncertain demands, transportation problems with uncertain costs or scheduling under uncertain processing times. The uncertainty is dealt with a robust optimization (RO) viewpoint (also known
as "worst-case perspective") and we present original contributions to
the RO literature on both the theoretical and practical side.

 MB-03
Monday, 10:30-12:00 - Room: 1005 (building: 202)

Optimization in Online Environments
Stream: Data Science Meets Optimization
Invited session
Chair: Grzegorz Pawlak
1 - Multi-objective optimal recommender systems

challenging problem for advertisers. In our work, we first propose anovel definition of impression value under the background of the mobile gaming market, which takes into account players’ in-game purchase, conversion revenue, and publisher characteristics. In addition,
to enhance the interpretability of the strategy, we introduce a two-part
method: the first part employs reinforcement learning techniques toobtain a two-dimensional vector based on an optimization problem,
and the second part utilizes this vector to guide modifications to existing generic bidding paradigms in the industry. Real-world experiments
demonstrate an increase in return on investment by over 80% comparedto current practices after applying our method.

3 - An Algorithmic Approach to Managing Supply Chain
Data Security: The Differentially Private Newsvendor

Du Chen, Geoffrey A. Chua

Data is now unanimously considered a key firm asset for enabling better operational decisions. However, data-driven decisions can inadvertently expose private data, leaving firms vulnerable to unforeseen
danger. How to manage data security risks by protecting data frombeing inferred from observable decisions thus becomes an importantquestion. In this paper, we focus on data security in supply chainsdue to their data-intensive nature. Specifically, we examine a datadriven contextual newsvendor problem. To quantify and ensure data
security, we adopt the notion of differential privacy, a mathematicallyrigorous measure of data security that limits an attacker’s inference
accuracy. Employing convolution smoothing and noise injection, we
propose several differentially private algorithms that provably guarantee both data security and asymptotic optimality with (near) optimal
rates. In the non-asymptotic regime, we further identify three driversof the cost of data security; namely, dataset size, context, and numberof products. This finding suggests that gathering more data, collecting
detailed context, and pooling data from multiple products can lower
data security cost. Lastly, we examine the impact of a newsvendor’s
private algorithms on supply chain partners. We discover additional
distortion to the demand signaling process and lower profit share foran upstream supplier.

Elaheh Lotfian, Alireza Kabgani
In this talk, we introduce a multi-objective recommender system aimed
at enhancing both the accuracy and diversity of top-n recommendation
lists. To address this optimization challenge, we present a customized
hybrid AMOSA_NSGA-II (HAN) algorithm, facilitating the creationof a Pareto set for top-n lists. Additionally, we provide a methodology for selecting the optimal list for each user within this Pareto
set. Initially, we generate preliminary top-n lists through item-based
collaborative filtering. Subsequently, the second stage addresses a biobjective optimization problem related to recommendation lists, utilizing the customized HAN algorithm. Finally, the third stage focuseson producing optimal personalized top-n lists for individual users. To
assess the effectiveness of our method, we implement it on real-world
datasets, conduct a thorough performance evaluation, and compare its
results with existing approaches in the literature.

2 - Bidding in Online Display Advertising: A Deep Reinforcement Learning Model for Mobile Gaming Market

Qirui Yang, Frank Chen, Mengzhuo Guo, Houmin Yan,
Qingpeng Zhang
When a user opens a website or app, there are often several advertisements interspersed. Generally, whenever an advertisement appears,
loads, and is seen on a user’s screen, it is referred to as an impression. For advertisers, an impression signifies an opportunity for theirads to be viewed. Therefore, real-time bidding (RTB) has emerged
as a prominent paradigm, allowing advertisers to procure impressions
through instantaneous automatic auctions. In RTB, the goal for advertisers is to maximize the total revenue generated from the impressions
they win while considering constraints such as budget. Due to thedifficulty in estimating the value of impressions and the bids of competitors, determining the optimal bid for each impression becomes a

42

4 - Applying Mathematical Programming to Visualize Text
Summarization from Multiple Perspectives

Li-Ching Ma

The widespread adoption of the Internet and social networks has resulted in an explosion of data from diverse sources, often causing information overload. Consequently, a growing need for text summarization has emerged to assist users in quickly digesting information.
Additionally, many practical scenarios require comparative opinionsfrom multiple perspectives. However, most research tends to address
issues from a single viewpoint. The aim of this study is to proposea graphical text summarization approach that integrates multiple perspectives through the utilization of text mining and goal-programming
optimization methods. From the idea of a data warehouse schema, thisstudy constructs three-layer heterogeneous graphs to represent entities,
nouns, adjectives, and their interrelationships. Star and Snowflake diagrams are constructed to show relationships between nouns and adjectives. Additionally, a constellation diagram is introduced to visualize
the relationships among different entities. The proposed approach offers several significant advantages. It provides a straightforward andeasily understandable method to summarize a large amount of text
content on a single graph. Users can, therefore, significantly reduce
the time spent individually browsing numerous documents or reviews.
Decision-makers can compare the strengths and weaknesses of different entities from an integrated graph or understand the differences inuser opinions from various perspectives.

EURO 2024 - Copenhagen MB-04
Monday, 10:30-12:00 - Room: 1001 (building: 202)

MB-07

order) assuming that the samples are randomly shuffled. This meansthat the Dutch Draw baseline is the optimal baseline under these intuitive requirements and should therefore be used in practice.

Recent Methodologies in Explainable AI(XAI) 2
Stream: Recent Advancements in AI
Invited session
Chair: Sureyya Ozogur-Akyuz
1 - A Novel Ensemble Framework for XAI-Based Feature
Selection in Machine Learning Models

Sureyya Ozogur-Akyuz, Halil ibrahim DemirelThis study introduces an innovative ensemble framework that leverages Explainable Artificial Intelligence (XAI) to enhance feature selection processes in machine learning (ML) models. The primary aim
is to improve the interpretability and predictive performance of thesemodels. Our approach integrates multiple ensemble learning algorithms, including Gradient Boosting Machines and Random Forests,
with XAI techniques like SHAP to create a transparent, interpretablemodel. We evaluate our framework on various datasets, demonstrating its ability to maintain high predictive accuracy while providing insights into the feature selection process. The results indicate that ourensemble framework significantly enhances model transparency andinterpretability without compromising on performance.

2 - Learning a Policy for the Real-Time Inventory Rack Storage Assignment and Replenishment Problem

Sander Teck, San Tu Pham, Louis-Martin Rousseau, Pieter
Vansteenwegen
The e-commerce sector is rapidly automating warehouse operations,necessitating efficient control systems to manage this growing complexity. This study investigates Robotic Mobile Fulfillment Systems(RMFS), employing autonomous mobile robots (AMRs) for inventory
rack management. These systems eliminate the need for human operators within the storage area, with AMRs responsible for both storing
and retrieving movable inventory racks. Human operators stationed atworkstations alongside the storage area fulfill customer orders by picking goods from these racks. RMFS dynamically adjusts rack positions
based on usage frequency. Furthermore, effective restocking timing is
crucial to prevent operation delays. Real-time scheduling, especiallyin inventory rack storage and replenishment, is essential for optimal
performance.
To minimize cycle time, a deep reinforcement learning (DRL) approach is proposed. The storage area is divided into zones to facilitate efficient decision-making. The learning agent interacts with theenvironment, observes changes in its state, and learns through trialand-error from these interactions. Thereby, it is able to construct a
policy by mapping environment states to actions. Experimental results
demonstrate significant cycle time improvements compared to traditional decision rules. The study underscores the importance of realtime decision-making for the storage assignment and replenishment
problem.

3 - The optimal input-independent baseline for binary classification

Sandjai Bhulai, Joris Pries, Etienne van de Bijl, Jan Klein,
Rob van der Mei
Before any binary classification model is taken into practice, it is important to validate its performance on a proper test set. Without a frameof reference given by a baseline method, it is impossible to determine
if a score is "good" or "bad." The goal of this paper is to examine allbaseline methods that are independent of feature values and determine
which model is the "best" and why. By identifying which baselinemodels are optimal, a crucial selection decision in the evaluation process is simplified. We prove that the recently proposed Dutch Draw
baseline is the best input-independent classifier (independent of feature values) for all order-invariant measures (independent of sequence MB-07
Monday, 10:30-12:00 - Room: 1019 (building: 202)

Cutting and Packing 2 - 2D irregularStream: Cutting and Packing (ESICUP)
Invited session
Chair: José Fernando Oliveira
1 - A heuristic for packing weighted unequal circles in circular containers

Yun Yang, Somayeh Allahyari, Gu Pang, Yong Lin
The Weighted Circles Packing Problem (WCPP) is an extension of theclassical circle packing problem with practical applications across various industries, including logistics, facility layout, automotive, manufacturing, telecommunications, and materials science. In WCPP, eachcircle is characterized not only by its size (i.e., radius) but also by aweight value, representing cost, value, priority, or other relevant metrics. This introduces complexity to the problem, as algorithms need tosimultaneously consider the physical arrangement of circles and optimize their total weighted configuration. This study aims to maximize
both the number of unequally sized circles packed in a container andthe total layout score of the packed weighted circles while adhering to
the constraint of non-overlapping circles or between each circle and thecontainer. For this problem, we develop an Iterated Local Search metaheuristic algorithm where the initial feasible solution generated using
polar coordinates and geometric methods is improved iteratively by local search and shaking procedures. Computational tests on a relaxedversion of this problem using the benchmark published in López and
Beasley (EJOR, 2016) clearly indicate the effectiveness and significant
time-efficiency of the develop

2 - Decoupling Geometry from Optimization in 2D irregularCutting and Packing problems: an Open-Source Collision Detection Engine

Jeroen Gardeyn, Greet Vanden Berghe, Tony Wauters
While the objectives and particulars of irregular C&P problems can differ, they all share a common feasibility check: whether or not an item
can be placed at a certain position. This check is particularly complex
due to the geometric irregularity of the items and containers. For C&P
problems, there is currently no sufficiently general approach to tackle
this task in an easy and efficient manner. This not only heightens thebarrier to entry, but also results in researchers having to continuously
reinvent the wheel whenever they are addressing new problems or developing new approaches for existing problems. This work aims todecouple geometry from optimization and develop a high-performing
adaptable engine, capable of efficiently handing the geometric component of irregular C&P problems. We envisage two target audiences.
First, there are those who simply want to focus on their optimization
problem at hand and who would therefore greatly benefit from having
an engine they can incorporate into their own methodology. Such anengine would essentially outsource the geometric challenge and enable
them to focus their efforts on developing smart solution methods. The
second target audience are those who, rather than solving problems
themselves, might have good ideas concerning how to further improveand refine this open- source engine. The project is called "jagua-rs"
and is publicly available at: https://github.com/JeroenGar/jagua-rs

3 - Extreme Points-Based Heuristic for Irregular Panel 2DBin Packing with Defects

Elsa Silva, Tiago Silveira, Maria Antónia Carravilla, José
Fernando Oliveira, Matheus Campinho

43MB-08EURO 2024 - Copenhagen

The 2-dimensional Bin Packing Problem addressed in this presentationarises within the ornamental stone industry, where heavy stone panelsof irregular shapes, often with defects, must be cut to meet the demand
for rectangular items. The cutting process in this industry is subject
to practical constraints, primarily influenced by the thickness of thestone panels and the cutting technology employed, resulting in an effect known as overcut. Overcutting involves cutting the raw material
beyond the edge of the intended piece. This effect can damage pieces
placed on top of the cut unless there is sufficient separation between
the pieces or alignment with another cut in the same direction.
To the best of our knowledge, this constraint has never been explicitly
addressed in the literature, making it a non-trivial challenge to overcome.
In response to these challenges, a novel approach is proposed. An
extreme point-based heuristic has been developed to solve the problem, enhanced by a local search procedure based on the two-exchange
heuristic. The irregularity of the panels is managed through a transformation process that converts irregular shapes into regular ones, simplifying the structure. Computational experiments using real-world data
have been conducted to validate the effectiveness of the proposed approach, and the results and practical applications are thoroughly discussed.

 MB-08
Monday, 10:30-12:00 - Room: 1020 (building: 202)

AI in Eco-Finance: Time, Space, andNetworks
Stream: AI & Innovation in Sustainable FinanceInvited session
Chair: Barbara Bedowska-Sójka
˛
1 - Green Bonds and Environmental Scores: analysing impact and factors of environmental performance

Christina Erlwein-Sayer
Green bonds are widely utilized financial instruments designed for the
purpose of funding environmentally friendly projects. Positive environmental effects of green bonds have been analyzed previously. These
investigations largely rely on the Difference-in-difference models togauge the overall impacts. We investigate the influence of corporate
green bonds on the environmental performance of issuers at an individual level and employ a Controlled Interrupted Time Series Model.
When statistically significant effects on the issuers’ environmental performance, measured by the E score of the issuers’ ESG, are determined, two sequential experiments are performed: firstly, we probe
into the factors that can influence the issuance of green bonds. Secondly, we examine the interrelations between company characteristics, issuer characteristics, and the magnitude of the effects released
by green bonds. To address the first and second experiment, we builda random forest and a generalized additive model, respectively. Ourfindings indicate that the environmental performance of most issuers
improves following the issuance of green bonds. While both bond andcompany characteristics influence the impact of green bonds, it is thecompany’s characteristics that play a more pivotal role.

2 - Day-ahead probability forecasting for redispatch 2.0
measures

Vlad Bolovaneanu
The purpose is to advance a data-driven, day-ahead forecasting model
for assessing the probability, direction, and scale (load) of electricalcongestions within Germany’s complex electrical power grid. Utilizing
state-of-the-art machine learning algorithms, the model is specificallydesigned to operate on an hourly basis, thereby offering timely insights
for grid management. The analysis uncovers compelling evidence that
key exogenous variables, such as real-time meteorological conditions,electricity supply-demand indicators, and Brent oil price fluctuations,44

can be harnessed to make highly reliable predictions concerning gridcongestion events. Also, seasonal patterns are uncovered. Our contribution has the potential to serve as a useful resource for transmission
system operators (TSOs) and policymakers interested in grid management and cost mitigation efforts.

3 - Spillovers between risk indices and the cryptocurrency
marketBarbara Bedowska-Sójka,˛
Joanna Górka
The aim of the paper is to examine the dependence between uncertainty indices and the uncertainty of cryptocurrencies. The focus ison volatility spillovers between different risk proxy indices: geopolitical events, economic policy, commodity, equity and bond markets,and cryptocurrency uncertainty indices. The spillovers are used in networks, and various centrality measures are calculated to test whichrisk index or cryptocurrency is leading the market changes. Based on
weekly data from April 2014 to March 2024, covering several market ups and downs, it is found that the transmission from uncertainty
indices to cryptocurrency uncertainties is rather weak on average, but
accelerates during turbulent periods such as the Covid-19 outbreak or
the start of the Ukraine war. Overall, we conclude that while cryptocurrency pricing is largely decoupled from economic risk, the abilityof the asset class to serve as a portfolio hedge may be limited.

 MB-09
Monday, 10:30-12:00 - Room: 10 (building: 116)

Electricity Market Design
Stream: Energy Markets
Invited session
Chair: Dogan Keles
1 - Hybrid Electricity Market Models: Combining Zonal andNodal Pricing for Congestion Management

Endre Bjørndal, Benjamin Fram, Mette Bjørndal
Regulators in Europe are pushing for increased synchronization between European electricity markets. While this market couplingpromises higher market efficiency and welfare gains, it also presents
challenges for market administrators and grid operators who must
manage congestion between market areas.
In this paper, we propose several novel hybrid pricing models for congestion management in electricity markets. Hybrid models manage
congestion by allowing certain market areas to use nodal pricing andothers to use zonal pricing and have been previously shown to offer significant welfare improvements over zonal pricing models. We present
two classes of hybrid models: simultaneous and sequential. Simultaneous models clear the market in a single step where nodal and zonal
pricing areas are both cleared at once. Sequential models first clear
a fully zonal model, fix the resulting power flow quantities between
market areas, and then re-clear the nodal pricing areas in a second
modelling step. We also present several subvariants of these models
where we aggregate, partially disaggregate, and fully disaggregate thetransfer capacities of lines connecting market areas.

2 - The Effects of Multi-Market Participation on Day-Ahead
Trading of an Electricity Producer

Helmi Hankimaa, Bruno Fanzeres, Fabricio Oliveira
The green transition has accelerated the expansion of variable renewable energy generation, most prominently wind generation. This significant penetration of wind power amplifies the need for reserve capacity and adjusting commitments close to dispatch. While these needs
are recognised, whether current market prices incentivise generationcompanies to participate in reserve and intraday markets is unclear.
This work investigates whether price signals provide adequate incentives in these markets and how multi-market participation affects trading on the primary market for electricity, the day-ahead market, from asingle-agent perspective. To that end, we propose a profit-maximisingEURO 2024 - Copenhagenoffer-strategy optimisation model for a price-taker in the Nordic dayahead market and a reserve market. Uncertainties in market outcomes,
intraday trading opportunities, renewable generation, and imbalancesettlement are accounted for.
Incentives for and the effects of multi-market participation were assessed in a case study. Our results indicate that current price signals
incentivise multi-market participation, which is seen from an increasein expected profits. We also observed that anticipating reserve and intraday market opportunities had an impact on day-ahead trading. Furthermore, a bias according to expectations regarding the system imbalance was identified. In fact, under certain circumstances, day-aheadoffer curves in multi-market offer strategies resembled the use of market power.

3 - Bidding Behavior Analysis in Electricity Markets Using
Inverse Reinforcement Learning

Ezgi Polat, Mehmet Güray Güler, Mehmet Yasin Ulukus
In electricity markets, participants employ diverse strategies when submitting their bids. From a realistic perspective, it can be consideredthat participants aim to increase profitability and thus maintain theirmarket presence. However, unlike profit maximization, participants’bidding behaviors are shaped by considerations like available capacity,
market prices, market risk, etc. This research aims to investigate thebidding behaviors of electricity market participants using Inverse Reinforcement Learning (IRL). IRL aims to identify an objective function(reward function) from the historical bidding behaviors of an optimally
behaving market participant. In this study, different IRL structures are
utilized for linear or nonlinear reward function assumptions. In addition, the bidding behaviors of power plants using different energy
sources, such as wind, gas, coal, etc., are comparatively analyzed. The
research relies on data sourced from the Australian electricity market.

4 - Modeling and solving the first pan-European Guarantees of Origin market with Artelys Knitro

Stefania Pan, Michaël Gabay
Guarantees of origin (GOs) are a tool to trace and guarantee the renewable origin of electricity. Using GOs enable electricity suppliers toprovide specific contracts to customers such as renewable energy contracts or even more specific contract such as German non-subsidizedwind electricity. This also encourages investments in renewable energy technologies and provides an additional source of revenues forenergy producers. The European Power Exchange (EPEX SPOT) andEuropean Energy Exchange (EEX) have launched in 2022 the first panEuropean spot auction for GOs. GOs may differ in issuing country,
production technology, subsidy schemes or production month. Thisauction is uncommon as it offers market participants with the option
to bid for products which are either specific or generic, enabling purchasers to express precise requests, stimulate competition and create
reliable price indices. Artelys has worked closely with EPEX and EEX
in the auction design and was in charge of the development of the market clearing engine. The algorithm has to ensure that various businessrules are respected, and to strike the right balance between flexibility,
complexity, fairness and intuitiveness. The algorithm objectives are to
maximize the social welfare, the traded volume, and to compute all
specific and generic market clearing prices. The problem consists ina multi-staged mathematical optimization algorithm which is solved
using the state-of-the-art numerical solver Artelys Knitro.

 MB-10
Monday, 10:30-12:00 - Room: 11 (building: 116)

MB-10

1 - Optimized planning of volunteer first response resources to emergencies

Tobias Andersson Granberg
The number of initiatives involving volunteers contributing to initial
response in emergency situations is increasing. Determining which
volunteers to alert, and assigning tasks to them, is not a simple question to answer, especially when there are many different types of tasks.
Therefore, we develop dynamic models and strategies for task assignment and alerting of volunteer responders. Additionally, we evaluate new alerting strategies using simulation (computer simulation andreal-world simulation) to better understand their impact, enabling us
to enhance the developed strategies. Drones can also be used to transport equipment to an incident site; for example, drones are used totransport defibrillators to suspected cardiac arrest cases in the Västra
Götaland region in Sweden. Since it is crucial for the operation where
drones start their journey and that someone is available to use the defibrillator when it reaches the patient, we have developed models forlocating drone stations, combining them with models for alerting volunteers. The results show that by joint planning of drones and volunteers, it may be possible to improve the survival probability fromout-of-hospital cardiac arrest.

2 - Optimizing the training of community first responders

Brian Overbeek, Rob van der Mei, Caroline Jagtenberg, Pieter
van den Berg
Traditional emergency medical services can be expanded by Community First (CFR) systems. In such CFR systems, trained volunteers
near the incident location can be dispatched, potentially reducing the
emergency response time.
LIVES is an example of such a CFR system; LIVES dispatches volunteers to medical emergencies in (mostly) rural areas of the UK, where
the response time of EMS can be rather long. The volunteers of LIVES
respond to a wide variety of medical emergencies. However, not allvolunteers are qualified to handle all emergencies. Volunteers are classified into different categories based on their medical skills. LIVES
provides training to its volunteers to increase their skill level, allowing
volunteers to be dispatched to more emergencies as a result. LIVES has
a fixed training budget and would like to know how to divide it overits volunteer base. We leverage historical data on incidents as well as
volunteer availability, derived from individual sessions in which a volunteer was logged on within the system. Their stochastic availability,
combined with a geographically diverse volunteer set, translates into a
unique optimization problem: how to spend the training budget in sucha way that response times are minimized. In this talk, we present ourongoing work on how to tackle this optimization problem.

3 - Optimizing Emergency Medical Service Capacity to a
Mass Casualty Incident: Balancing with Routine Emergency Medical Service Demands

Inkyung Sung, Peter NielsenThis study addresses the dynamics of an emergency medical service(EMS) system during the response to mass casualty incidents (MCIs),
where timely and efficient decision-making is crucial to save more
lives. The key decisions to efficiently respond to an MCI with limited
EMS resources, such as ambulances and hospitals, include ambulance
allocation to the MCI, balancing with response capacity for routine
EMS demands, and hospital transportation for MCI victims, thereby
avoiding congestion at the hospitals. A mathematical model is developed to represent the relationships between the decisions involved in
the MCI response phase and the relevant EMS performance metrics.
Based on the model, numerical analysis is conducted with the aim ofoffering insights into protocol and policy design for EMS systems during the MCI response.

4 - Towards a repository for datasets of mass casualty incidents

EMS and crisis logistics

Melanie Reuter-Oppermann, Florentina Hager

Stream: OR in Health Services (ORAHS)
Invited session

Many mathematical models and algorithms to address logistics aroundmass casualty incidents (MCI) have been proposed in the literature.
However, hardly any data sets exist that can be used to analyse, validate
and evaluate approaches and solutions. As MCI are comparably rare,real-world data is scarce and often very sensitive, making it difficult for

Chair: Rob van der Mei

45

MB-11EURO 2024 - Copenhagenresearchers to get access and build test datasets. In this talk, we therefore present the first steps towards a repository to share datasets to facilitate validation and comparison. We first provide an overview of potential stakeholders such as working groups, review existing disasterrelated databases and existing repositories for other fields followed bya preliminary taxonomy to identify and structure the relevant entities
and their characteristics within models for disaster management.

 MB-11
Monday, 10:30-12:00 - Room: 12 (building: 116)

Behavioral Decision Analysis I
Stream: Behavioural OR
Invited session
Chair: Gilberto Montibeller
1 - Risk, Uncertainty and AI: non-probabilistic methods for
anticipating and preventing AI risks

3 - Multivariate global sensitivity analysis of a coupledclimate-economy model

Leonardo Chiani, Emanuele Borgonovo, Elmar Plischke,
Massimo Tavoni
We use a novel multivariate sensitivity analysis technique based on
Optimal Transport to quantify the sources of uncertainty in future CO2
emissions to help support robust decision-making in climate policy.
We apply these methods to an open-source climate-economy model(RICE50+), using the most up-to-date input distributions and longterm projections of key demographic and socio-economic drivers. We
assess different scenarios to address different possible futures. We performed the same analysis on the global and regional levels, finding
similar patterns but also regional specificities. We show the feasibilityof applying new indices to a model with many correlated inputs and atime and region dependent output such as CO2 emissions pathways.

4 - Global Sensitivity Analysis with Optimal Transport

Emanuele Borgonovo
In this work, we present a review of global sensitivity analysis using
optimal transport. We describe the properties of the method. The interpretation of the results and the decision analysis implications are thoroughly discussed, also in comparison with other methods developed in
the management sciences.

Vicki Bier, Alexander Gutfraind
The rapid advance of AI has created risks that are difficult to foresee,
let alone quantify, making AI risks an area of deep uncertainty. Despite
this, researchers have begun applying probabilistic methods to AI risks
at a coarse level; e.g., existential catastrophe. This type of analysis may
be useful for supporting strategic policy-level considerations, but is notparticularly useful for design or operational decisions.

 MB-12We argue that many practical AI problems could be addressed bydrawing from a toolkit of non-probabilistic risk-management methods.
There exists a large class of strategies for managing risks that could be
utilized to improve AI safety, using methods from fields such as safetyengineering, product management, and military planning. These techniques could be used both to anticipate AI risks, and to mitigate AI
risks even when they are imperfectly understood or cannot be quantified. These methods range from qualitative fault trees and event trees,to simple but effective solutions like checklists, what-if thinking, andpre-deployment testing (even by non-expert users).

Stream: OR in Agriculture and Forestry
Invited session

We distinguish between safe design vs. rapid reaction to undesired behaviors. Reactive solutions include contingency planning, monitoring,
and anomaly detection. These strategies can also operate in parallel,creating defense in depth. Drawing on these non-probabilistic methods should make it possible to develop safer AI applications, whileallowing the field to advance.

2 - A Behavioural Investigation of Feasibility and Demand
Uncertainty in Production Planning Systems

Maryam Azani, Lijia Tan, Rob Basten, Ton de Kok
Production planning systems are an integral component in human planners’ decisions and, ultimately, joint human-system performance. Although analytically superior systems are known to generate better suggestions, it remains unclear how human planners react to the suggestions and, thus, whether the resulting joint performance is enhanced
when using these systems. We examine the impact of different systemsuggestions on joint performance in a rolling horizon setting with two
products and three components under demand uncertainty. We test thisin a laboratory environment. We compare human planners’ judgemental adjustments and joint performance using system suggestions withdifferent analytical performance (control, Material Requirement Planning, Synchronized Base Stock). We understand that human planners’deluded self-confidence and a desire for control over the system resultin adjusting system suggestions regardless of the analytical superiority of the system, which constantly degrades performance. This effectis stronger for systems with superior analytical performance. Additionally, we understand that human planners’ judgemental adjustment
patterns are different across three systems. We replicate these findings
in a field experiment with actual planners.

46

Monday, 10:30-12:00 - Room: 13 (building: 116)

OR in Forestry I

Chair: Arturo Annunziata
1 - The Team Orienteering Problem with Variable Time Windows: An application for wildfire suppression

Begoña Vitoriano, Bibiana Granda
The Orienteering Problem (OP) is a well-known routing problem
within Operational Research, from which various variants haveemerged over time. This presentation introduces the Team Orienteering Problem with Variable Time Windows (TOPVTW) as a new variant based on the Team Orienteering Problem with Time Windows. The
distinguishing feature of TOPVTW lies in its adaptive time windows,
which evolve dynamically according to the solution. This problem
arises within the context of spreading processes that need to be contained. An application to wildfire suppression will be presented. In thisproblem, the spread of fire determines the time windows for brigades towork in a parcel of the landscape or cross through it. Simultaneously,
the brigades’ work controlling parcels modifies the spread of fire, preventing it from spreading through the controlled area. The aim is todetermine the location of the brigades’ work and their routes throughthe landscape to minimize the value of the burned area while maintaining safety measures for the teams. Two alternative mathematicalprogramming models will be shown for this problem, with computational results comparing the solutions.

2 - pyO3F - A python framework for wildfire related optimization

Filipe Alvelos, Marco Henrique Vieira Marto, Isabel Martins,
Susete Marques, André Bergsten Mendes
We present a python framework, pyO3F, which aims to support the development of optimization approaches for wildfire related problems.
We describe the pyO3F main modules, their interactions, and the information they use to model fire behaviour, fire spread scenarios, anddifferent types of firefighting resources. At the core of pyO3F is theuse of the minimum travel time principle in a network with nodes representing locations and arcs representing potential fire transmissions
between adjacent locations. Fire spread is the modelled as a set ofquickest paths with respect to the fire transmission times associatedwith the arcs (estimated with fire behaviour models). We exemplify

EURO 2024 - Copenhagenthe use of pyO3F in two types of problems: fire suppression and fireaware forest management. In the former, it is intended to manage aset of available firefighting resources to minimize the impact of fire(e.g. the burned area). In the latter, it is intended to select a set of prescriptions, one for each forest stand, to maximize the net present valueof the timber, taking into account bounds on other ecosystem services(e.g. carbon stock) and fire spread simulations.

3 - An extension of the Area Restriction Model

Arturo Annunziata, Matteo Lapucci
The problem of harvest scheduling consists in determining the optimalplanning solution selecting from different stands in a forest in a multiperiodal setting. The objective is to maximize the total revenues undersome economical and regulatory constraints. This problem involves
both financial and environmental considerations. In the latter the mainconcern is the one of the conservation of wildlife and natural habitats and it results in a restriction on the maximum contiguous area ofclearcut regions in a forest. One approach to address this problem is thearea restriction model (ARM) an Integer Linear Programming modelin which decisions variables are binary variables prescribing the cut ofa specific stand in a specific period. In particular the Path formulationof the ARM is an efficient way to manage the maximum clearcut area
constraint. In this work an extension of this formulation is presented
in which the planner is provided with an additional option to schedulethe harvesting. The consideration of some special areas between eachpairs of stands in the forest, called buffers, allows for considerable improvements in the optimal value of the objective function and makes
the resulting Mixed Integer Linear Programming model more flexible.
In fact the introduction of buffers often allows for feasible solutions in
scenarios in which the problem would be otherwise unfeasible.

 MB-13
Monday, 10:30-12:00 - Room: 15 (building: 116)

OR Innovations in Policy Making - A
Stream: Soft OR and Problem Structuring Methods
Invited session
Chair: Irene Pluchinotta
Chair: Ine Steenmans
1 - Integrating Socio-Emotional and Cognitive Perspectives in Causal Loop Diagrams Analysis

Ke (Koko) Zhou, Irene Pluchinotta, Nici Zimmermann
Research on behavior and soft systems emphasizes the role of ’socioemotional demands’ in decision-making processes. Causal loop diagrams, which represent the complex mental models of decisionmakers, play a crucial role in achieving a shared understanding of
the system elements and boundaries. However, it is not fully understood which elements might be overlooked in the process of reachinga common understanding, and why this occurs. Drawing from criticalsystems thinking, we propose that during the creation of causal loop
diagrams intended to aggregate diverse mental models, certain variables and links might be unintentionally neglected. We introduce theconcept of two types of boundaries in these diagrams: structural (theinterconnections between system elements) and emotional boundaries(how stakeholders perceive the variables with varying valence and activation). The complex interaction between these boundaries can lead toparts of the system being ignored. Through a case study on sustainableurban development, our research deepens the understanding of how
boundaries are compared and elements of the system are marginalized. Our goal is to encourage the integration of socio-emotional andcognitive perspectives in analyzing causal loop diagrams in complex
systems.

2 - From Cognitive Maps to Value Trees for Policy Design

Halil Berkay Tosunlu, Joseph Guillaume, Irene Pluchinotta,
Alexis Tsoukias

MB-13

Complex policy issues, decision aiding, and the intricate landscapeof conflict transformation and management demand meticulous attention. In this context, our study delineates a structured three-step approach. We first focus upon Conflict Transformation and Management,
improving Problem Structuring Methods, cognitive maps, and value
trees to address conflict situations and complex decision-making tasks.
We prioritize the transformation of subjective insights from Cognitive
Maps into structured Value Trees. We then move from decision theory
to design theory, where we aim to further enhance PSMs by introducing innovative and out-of-the-box alternatives, with a particular focus
on Concept-Knowledge (C-K) theory to create a meaningful connection between PSMs and C-K theory. Finally we discuss how thesefindings relate to policy design, linking our research to Conflict Transformation and Management within the sphere of public policy.

3 - Multi-objective optimization to support decision making
in policy interventions

Laura Selicato, Alessandro Pagano, Estrella Lopez Moya,
Manuel Bea Martínez, Andreas Panagopoulos, Vassilios
Pisinaras, Flavia Esposito, Raffaele Giordano
The aim of the Water-Energy-Food-Ecosystem Nexus (WEFE Nexus)
approach is to overcome the traditional "siloed" approach to natural resources management - i.e. based on sectorial perspectives - ultimately
achieving an integrated resources management. In this study, we particularly focus on the support to policy-making in complex Nexus
systems, proposing a highly participatory methodology to co-design
potential policy interventions capable of achieving sectoral security,
while optimizing the Nexus. A System Dynamics Model (stock andflow) that describes the security dimensions was built. We then selected the variables representative of the concept of security, and built
the underlying equations, with the idea of evaluating their variationin different political scenarios. Multi-objective optimization (MOO)
has been used for identifying and analyzing existing and potential implications and trade-offs in intervention policies. In our framework,
we compared two distinct a posteriori MOO methods - pattern search(PS) and the Evolutionary algorithms (EA) - to provide the Pareto front
for two intervention areas, i.e., Donana and Pinios (LENSES project).
Some hints are given on how this tool turns out to be very useful as asupport to decision-makers, to allow accounting for their preferencesin the process.

4 - Structuring decision problems and generating alternatives: report of a workshop

Irene Pluchinotta, Ke (Koko) Zhou, Marta Bottero, Alberto
Colorni, Chiara D’Alpaos, Alessandra Oppio, Alexis Tsoukias
Problem structuring and alternatives generation are important andhighly interconnected stages of decision-making and decision-aiding
processes. An ill-formed problem formulation or a limited set of alternatives have a significant impact on the quality of the final decision.
This is emphasised when complex multi-actor decisions or policy making need to be supported. Problem structuring methods are largely recognized for their valuable contribution to decision-and policy-making
processes; on the other side, the alternatives generation phase is often
overlooked in research and practice, leading to unsatisfying decisions.
Yet, alternatives are generated before being analysed, explicitly or implicitly, formally or informally.
Within this context, this talk presents the lessons learnt from a collaborative activity carried out with international Operational Research (OR)experts involved in an experimental workshop. The focus of this activity was the problem formulation and alternatives generation phases
occurring between a client and six analysts using different OR methods. The objectives of the experimental workshop were to observeand discuss how different approaches deal with structuring decision
problems and generating alternatives, and to share experience on procedures which might be transferable. In doing so, the talk catalyses

47MB-14EURO 2024 - Copenhagen MB-14
Monday, 10:30-12:00 - Room: 16 (building: 116)

Flexibility in future energy systems
Stream: Energy Markets
Invited session
Chair: Henrik Madsen
Chair: Mehrdad Pirnia
1 - Impact of storage flexibility in energy markets underrisk aversion

Sining Liu, Martin Densing, Russell McKenna, Giovanni
SansaviniIn this study, we explore the role of storage in multi-period risk-averse
energy markets. We consider market agents, namely energy producers and consumers, optimising profits across both energy spot andderivative markets. Each agent acts as a price-taker in the environment where the prices are endogenously determined. The energy producers have storage devices and manage energy flows through charging/discharging. We use coherent risk measurement in its dynamic
form to model risk aversion. To solve the agents’ stochastic optimisation problems, we adopt ADMM (alternating direction method of multipliers) algorithm through decomposition and extend the algorithm tothe multi-market framework. A case study is conducted involving conventional power plants, pumped hydropower and consumption, withthe focus on peak and based load scenarios. Specifically, we investigate
how storage flexibility, encompassing charge/discharge and boundary
conditions in storage and capacity constraints, influence agents’ behaviours and market outcomes. The relationship between optimal storage strategies and hedging activities in the derivative markets is also
examined. Furthermore, we analyse how varying levels of risk aversion
affect decision-making processes, along with their effects on prices andprofitability.

2 - Modeling electricity consumption flexibility to determine the optimal medium-term electricity procurement
for cement producers

Miguel Carrión, Jose Arellano, Álvaro García-CerezoWith the current economic and energy situation, it’s crucial for large
electricity consumers to thoughtfully consider their electricity options.
This work aims to determine the optimal approach for cement manufacturers to decide their electricity procurement sources. The available
options include grid electricity purchase, bilateral contracts, and theutilization of a solar photovoltaic installation and batteries. Furthermore, we analyze the flexibility in electricity consumption by modeling all stages of cement production. The proposed approach is a
mid-term decision-making tool considering different sources of uncertainties, formulated through a two-stage stochastic programming approach. To validate our approach, we have conducted several casestudies based on a real cement producer based in Spain.

3 - Addressing explicit demand response in a day-ahead
electricity market in Italy: Case study on the diving
equipment manufacturing sector

Ehsan Manafi, Eduard Bullich-Massagué, Bruno Domenech,
Marc Juanpera, Rafael Pastor, Matteo Ranaboldo
Maintaining a balance between electricity generation and consumption
is critical for grid stability. While not extensively studied in scheduling problems, explicit (incentive-based) demand response (DR) is aneffective strategy to tackle this concern. Here, the ancillary service
agrees to customers’ proposals to reduce energy consumption during
specific time periods. This paper aims to investigate the explicit demand response approach at an Italian company consisting of multiple press machines on the shop floor, specialized in producing diving
equipment. A heuristic procedure is developed to propose a baseline
production plan and multiple alternative plans, where a subset of machines are deactivated, to provide flexibility services to the market.

48

The several electricity consumptions profiles resulting from the different production plans will be submitted to the ancillary market. During
the production, the ancillary service may request the manufacturer fora transition from the baseline to an alternative production plan, in exchange for an agreed monetary incentive. Hence, the proposed heuristic is applied to assess the effectiveness of the incentive-based demand
response strategy in the Italian plant. The results indicate that the proposed heuristic amplifies profitability for both the service provider andthe manufacturing system.

4 - Chance Constraint Models for the Operations of Storage Systems in Energy and Reserve Markets

Mehrdad Pirnia, David Fuller, Hassan Shavandi
We present several analytical results for a chance constrained modelof electric energy storage participating in both the energy and reserve
markets, alongside generation, in the context of a day-ahead unit commitment model. The main source of uncertainty, and thus the reason
for a reserve market, is assumed to be imperfect forecasts of wind generation. Although some intuitions in deterministic models remain valid
under uncertainty but most of the intuitions and proofs become invalid
in the presence of uncertainty, like buying energy at the lowest priceand selling at the highest price by the battery when is available forreserves. Therefore, analyzing the unit commitment problem in thepresence of wind power uncertainty and inclusion of storage batteries
become crucial. We develop two chance constrained models with different features and applications. The results are discussed through a
numerical example.

 MB-15
Monday, 10:30-12:00 - Room: 18 (building: 116)

Machine learning and analytics in healthcare
Stream: OR in Health Services (ORAHS)
Invited session
Chair: Phillips Edomwonyi Obasohan
1 - Optimizing General Practitioner Appointments with Machine Learning: A Data-Driven Approach

Eric Leung
Global healthcare faces a significant challenge with missed appointments, leading to financial losses and decreased service efficiency. According to the UK’s National Health Service (NHS) reports over 300
million missed appointments annually, costing over ’216 million. This
paper introduces an innovative machine learning-based system for optimizing General Practitioner (GP) appointments, enhancing scheduling by analyzing patterns of cancellations and no-shows. Utilizing
both real and hypothetical data, we propose a highly applicable system which employs fuzzy logic algorithm to dynamically adjust GP
schedules, prioritizing urgent cases and allowing walk-ins. This approach significantly benefits healthcare providers by maximizing resource utilization and reducing idle time. From a patient’s perspective,
it ensures better access to care and shorter wait times for urgent needs.
Our findings demonstrate the system’s potential in offering practical
recommendations for managing walk-ins and unforeseen consultations
more efficiently, paving the way for improved patient satisfaction andoperational effectiveness in healthcare settings worldwide.

2 - Optimizing red blood cell matching policies to reducealloimmunization in regularly transfused patients

Merel L. Wemelsfelder, Folarin Oyebolu, Dick den Hertog,
William J. Astle, Mart P. Janssen
We propose a method for optimizing daily red blood cell (RBC) matching policies to minimize long-term alloimmunization rates in regularlytransfused patients. When antibodies are formed against an antigen,a risk that comes with RBC transfusions, the patient can never be
mismatched on that antigen again. Clinical guidelines recommend
extended preventive matching for patients at high risk of alloimmunization, such as sickle cell disease patients, who need a transfusion
every six weeks. They must be matched not only for major antigens

EURO 2024 - Copenhagen
A, B, and D, but for an additional 5 to 12 minor antigens as well. We
use linear optimization for daily RBC matching, including parameters
for weighting elements of the objective function. We train machinelearning models to predict the long-term outcomes of the matchingstrategy, then use these models to optimize parameter values. A wide
range of potentially optimal parameter values is found, likely a resultof the stochastic nature of real-world scenarios, combined with the interplay of factors influencing long-term outcomes. We implemented aselection of these for daily matching, and observed an increased performance for several long-term outcomes with respect to the baseline
setting without tuned parameters. The methodology developed in thispaper in the context of RBC matching, could also be used in applications where we optimize long-term performance via a parameterized
short-term optimization model.

3 - The intersection of sex, age, and household socioeconomic status in the multimorbidity of malaria, anaemia,
and malnutrition among children 6-59 months of age in
Nigeria

Phillips Edomwonyi Obasohan
Multimorbidity of malaria, anaemia, and malnutrition (MAMM) is acondition an individual cohabits with two or more of these health outcomes and is becoming an emergent public health concern in subSaharan African countries. In addition, the independent associationsof child’s sex, age, and socioeconomic disparities with child’s healthoutcomes and MAMM have been established in literature. But, theeffects of the intersection of these factors, while accounting for othercovariates have not been studied. Therefore, this study aimed to determine how child’s sex, age and socioeconomic status interact to explain
the variations in MAMM among children age 6-59 months in Nigeria. Data from the 2018 Nigeria Demographic and Health Survey wasused. A multilevel mixed effect ordinal logistic regression model wasused. Five models were created in this scenario. Model 1 is the interaction between child’s sex and wealth status; model 2, child’s sex andage; model 3 is between child’s age and wealth status; model 4 has
the three 2-way interactions of child’s sex, age, and household wealthstatus, and model 5 includes model 4 and the 3-way interactions between a child’s sex, age, and wealth quintiles. Model 3 was however
the model of best fit compared with other models, So, the two-way interaction effects of a child’s age and wealth status are relevant in themodel prediction. Thus, the variation in MAMM over a child’s age
differs depending on the household wealth quintile.

 MB-16
Monday, 10:30-12:00 - Room: 19 (building: 116)

OR Education II
Stream: OR Education
Invited session
Chair: Ana Raquel Xambre
1 - Development of simulation models in Excel for conducting inventory analyses in continuous time

Christian Larsen
When teaching a course in Inventory Management at the graduate level
it is inevitable that the lecturer needs to use simulations for pinpointing
some of the more complex matters that, cannot adequately be coveredby any analytical formulas. The issue can then be whether the simulation program used in the course, should be a specialized discrete
event simulation program like Arena, or it could be done in Excel, ormaybe using both tools in the course. When teaching business students
who are not that well acquainted with a program like Arena it can beof advantage to use Excel, entirely. However, Excel is at first glance
mostly geared for working in discrete time, as it does not have any
sort of event calendar file. The presentation will demonstrate how it ispossible, by using some simple modeling effort, to conduct inventory
analyses in Excel in continuous time.

MB-16

2 - Conceptual Simplification: A New Method for Analysis,
Learning and Memorisation of Post-Tonal Piano Music

Laura Farré Rozada
There is a gap in music performance, education and psychology interms of memorisation training for post-tonal piano music. Despite therepertoire spanning over 100 years, pedagogues and professionals stilllack effective tools for developing this skill. Existing research mostly
focused on observing practitioners’ behaviours during practice, to understand how these prepare for a memorised performance. However,a systematic method for effective memorisation is not provided. This
paper discusses Conceptual Simplification: a new method for analysis,
learning and memorisation of post-tonal piano music. This presents
a novel implementation to musical memorisation building on mathematics and computer science to improve human memory and musicalperformance. However, Conceptual Simplification does not requireprevious scientific training to be successfully implemented and works
for different learning styles and types of complexity. It could also be
adapted to other instrumentalists, singers and conductors; and musicalgenres; and presents enough flexibility for other practitioners to incorporate additional strategies. The method also assists in preventing
performance anxiety and reducing the potential for injuries that usually result from repeated practice. The method’s systematic approachengages conceptual memory and reasoning, leading to more confident
memorised performances, while needing less repetition during practice.

3 - Leveraging Virtual Reality for Soft OR Education

Katharina Burger
Traditionally, Soft OR is operationalised in in-person workshops,
where participants interact with a range of tools, which may include
modelling software but are often just flipcharts and sticky notes. Morerecently, Soft OR interventions have been conducted in 2D online
spaces (e.g. via Zoom and using Miro). However, considering thegrowing potential of embodied interaction in immersive virtual reality (VR) spaces, what are the opportunities and challenges of using
3D VR spaces as part of Soft OR education? Drawing on empirical
data from student participants interacting in immersive virtual environments (IVEs), we differentiate between challenges and opportunities pertaining to technology appropriation and in-IVE interaction forSoft OR. We conclude by reflecting on the broader implications forcreating inclusive hybrid futures of OR education.

4 - OR in an wind blade production unit: an assignment
problem formulation developed by undergraduate students

Ana Raquel Xambre, Carolina Correia, Hugo Malcato,
Mariana Pinto, Xavier Pinto
The Industrial Engineering and Management (IEM) undergraduate students, from the University of the Aveiro, have to develop a project intheir last semester. The purpose is for them to apply the knowledge
and skills acquire throughout the IEM program to a complex problem,
while also developing other skills like critical thinking, Problem solving and team work. One of the problems, addressed by a group of four
students, originated from a wind blade production unit, specifically inthe part of the process where the blade is molded using different sizedfibers. The fibers have to be transported in carts that are positioned
near the blade, so the goal is to assign them to the carts, as well as the
carts to the locations near the blade, so as to minimize the distance ofthe fiber to the respective point of use. The work developed allowed
the students to comprehend a complex industrial problem, while alsousing OR and thus understanding how it can be put in practice, in order to help organizations with their problems. The results of the work
were very useful for the company, helping them improve their performance in this process. It was also very positive for the students since
it contributed to a more practical vision of the use of OR. It is also
expected that this work, and other similar developed by undergraduate students, may become examples for future students, helping themcross that same bridge between theory and practice.

49

MB-17EURO 2024 - Copenhagen MB-17
Monday, 10:30-12:00 - Room: 40 (building: 116)

DEA in healthcare
Stream: OR in Health Services (ORAHS)
Invited session
Chair: Erhan Berk
1 - Performance of private healthcare sector and the role ofpublic policy: A study of India using Data Envelopment
Analysis and Gradient Boosted Trees

Aishna Sharma, Rahul Nair
The demand for healthcare services provided by Indian private hospitals has increased, which makes it imperative to assess their performances. We also assess the impact of public spending and governanceon their performances. We first estimate output oriented and input oriented technical efficiency scores of 163 private hospitals for the year2022-23 using Data Envelopment Analysis. Expenditure on compensation of employees of hospitals, on power, fuel & water, on repairs
and maintenance and Net Fixed Assets are inputs, with Total Income
as output. After this, we estimate Gradient-Boosted Tree Classifier(GBTC) taking output efficiency score as dependent variable and public spending and governance factors as independent variables. Results
show, on an average, the output oriented technical efficiency score is
0.66 and the input oriented score is 0.64. Big hospitals outperform
small hospitals by 16 percentage points. Extent of misutilisation orunderutilization of financial resources is greater than the extent of inadequacy of services delivered by the hospitals. In GBTC analysis,
with output oriented scores as dependent variable, the most important features with a Mean Decrease in Impurity greater than 0.1 are
Voice and Accountability score, Revenue Expenditure on Education,
Revenue Expenditure on Water supply and sanitation and Revenue Expenditure on Urban Development. We recommend better governancestructure; increased public spending and periodic audits of private hospitals

2 - A Four-Step Roadmap for Measuring and Maximizing
Patient Value in Healthcare Using Data Envelopment
Analysis

Joke Borzée, Brecht Cardoen, Filip Roodhooft, Laurens
Cherchye, Bram De Rock, Jo Lambert
Value-Based Healthcare (VBHC) is a framework that aims to shift
the focus of healthcare systems from increasing volume to enhancing
value, which is defined as the ratio of patient-relevant health outcomesto the costs of achieving them. This framework can help to address
the rising healthcare expenditures by encouraging health facilities toorganize around the patient and pursue the goal of maximizing patient
value. However, measuring patient value is still challenging due to
its multidimensional nature, impeding the implementation of VBHC.
In this research, we therefore propose a four-step value measurement
roadmap, which leverages operations research techniques to convert
the value equation into a single value score for each patient, using Data
Envelopment Analysis (DEA) as the main tool. This novel applicationof DEA enables a holistic benchmarking of patients by directly linking outcomes to costs, unveiling the relative value that is created. We
illustrate how the roadmap can be customized and applied to different health contexts, using the case of psoriasis as an example. After
collecting individual health outcomes and calculating treatment costs
for 109 psoriatic patients, we construct a value frontier which reveals
substantial differences in created value within the patient population.
Subsequent detailed analysis of the value scores offers insights into thedrivers of value creation, supporting health professionals to optimize
healthcare delivery.

3 - A new classification of data envelopment analysis applications in hospitals.

Frederic Vanswijgenhoven, Brecht Cardoen, Paul Gemmel

50

Healthcare providers are continuously striving to organize care andcure more efficiently while maximizing outcomes for the patient.
Benchmarking is seen as an important instrument to improve processes
and the organization of care on the level of hospitals and processes
within these hospitals. Data envelopment analysis (DEA) shows to
be a successful method to facilitate such benchmarking, thanks to its
ability to combine inputs and outputs into a single efficiency score.
However, the method might not have been used to its full advantage
given recent evolutions in the healthcare landscape. By means of asystematic literature review, including 498 articles for the period 1980
till now, we developed a classification system to position DEA benchmarks. Only a very small number of studies can be classified as DEA
applications aimed at investigating process flows within and between
hospitals, compared to a more traditional static or silo-oriented approach. This opens a future research agenda to investigate efficiency
when care processes extend beyond multiple units within a hospital, oreven across multiple hospitals which we now see emerging by formation of hospital networks and systems. In this presentation, we break
down the DEA literature on different new dimensions and show a clear
imbalance in problem settings studied by the academic community,
highlighting new opportunities towards problems practice is currently
challenged with.

4 - Evaluation of Healthcare Systems during COVID-19
Pandemic in Türkiye

Erhan Berk
COVID 19 infection spread to the whole world in a short time at theend of 2019 and affected the health system of many developed and developing world countries negatively. Türkiye was one of the most negatively affected countries in the world. In this paper we evaluated theperformance of 81 provinces and 7 regions of Türkiye against COVID19 disease using efficiency score which was calculated by data envelopment analysis (DEA) and compared the efficiency score with thedifferent models which are used to evaluate the efficiency of healthcare system. We took input parameters as public health expenditure
in a million, number of hospitals, number of hospital beds, percentage of health workers, population density, and number of infected. We
divided output parameters into good and bad categories such as thenumber of recovered are taken as good output. The number of deathis taken as bad outputs. Finally, the provinces and regions are ranked
with the super efficiency score aand evaluated benchmarking for eachstates and regions.

 MB-18
Monday, 10:30-12:00 - Room: 42 (building: 116)

Assessment Methods for Shaping theGreen, Inclusive, and Digital Cities II
Stream: Sustainable Cities
Invited session
Chair: Sara Torabi
1 - Optimising Food Bank Operations: A Multi-Objective
Approach for Improved Nutrition, Equity, and Variety

Pol Gil-Figuerola, Marc Juanpera, Albert Soler-Noguera, Laia
Ferrer-Martí, Rafael Pastor
Food banks play a crucial role in facing food insecurity that affects millions of people worldwide. These organisations receive donations anddistribute them to vulnerable families, usually through standardised
food baskets. Food banks increasingly prioritise nutritional adequacy,
food assortment diversity, and equitable product allocation. However,the food baskets may lack sufficient and balanced nutrients, and thefamily nutritional requirements, which depend on the sex and age ofits members, are not considered in detail. In this study, we propose
a multi-objective linear program to optimise the food allocation problem from food banks to beneficiaries. We employ a Thresholded Lexicographic Ordering approach to consider the following objectives, inEURO 2024 - Copenhagenorder of importance: 1) maximise the nutritional coverage of the least
covered nutrient-beneficiary pair, 2) maximise the variety of products
in food baskets, and 3) minimise the total inequity. The model also
considers the specific nutritional needs of the beneficiaries according
to their sex and age, as well as the incompatibility between certain foodproducts and beneficiaries due to dietary restrictions. We applied themodel to a Catalan food bank case study, and our solutions led to significant improvements in all three objectives compared to the baseline
solution.

2 - Developing a knowledge base for the Sustainability Assessment of urban projects: the GLOSSA projectFrancesca Abastante, Beatrice Mecca
In line with the 2030 Agenda, measurement through indicators is
recognised as a mean to overcome economic, social and environmental challenges in favour of sustainable development. In this context, theSDG11 with its indicators constitutes the starting point for monitoringand implementing urban sustainable development. However, there is aneed for measurements going beyond conventional statistics, including
data generated, collected and stored by governments as well as those
provided by external data sources. In this context, the present researchshows a literature review of indicators useful for measuring and evaluating urban sustainability. More specifically, this research aims to:i) build a framework of the official SDG11 indicators at a global, European and Italian national level; ii) identify innovative and operative
indicators related to SDG11 proposed in the scientific literature, in the
Sustainable Development Strategies produced at the Italian level andin the Sustainable Assessment Tools; iii) provide a set of indicators
relevant and useful both for monitoring urban areas and for constructing and evaluating projects. The research result constitutes the first
theoretical preliminary step of the research Project of Relevant Interest(PRIN) called GLOSSA (A GLOcal Knowledge system for the sustainability assessment of urban projects).

3 - Estimating Urban Solid Waste Generation for Collaborative Collection Routes using Spatial Analysis and Machine Learning

Carola Blazquez, Victor Silva
In the last decades, there has been an increasing generation of solid
waste in urban areas, imposing an environmental, economic, and social burden worldwide. Thus, there is a need to estimate solid waste
generation rates for obtaining efficient collection routes and itineraries,
particularly when two or more communes decide to collaborate theirresources (e.g., vehicles and staff), in order to reduce costs and CO2
emissions, and increase equitable workload. This study performs spatial analysis using census data, and historical data based on daily GPS
measurements and tare weights (tonnage) of collection vehicles in two
communes in Santiago, Chile to identify collection zones and estimate
initial solid waste generation rates at the street level. Machine learningtechniques are applied to predict daily solid waste generation rates using demographic characteristics, land-use, socioeconomic status, andweather information, which are subsequently tested with the initialsolid waste generation rates estimations. Prediction results are promising when compared with these initial estimations. Our results provide
important insights for supporting the optimization of collaborative collection truck routes among different communes in urban areas.

4 - Enhancing Infrastructure Decision-Making in Smart
Cities: A Support Methodology

Pedro Palominos, Diego Cabrera, Luis Quezada, Astrid
Oddershede, Valerio Salomon
The challenges posed by urban growth and development are indeed
significant world-wide. As cities expand, they grapple with issues
related to space, resources, and facilities. The migration from rural
to urban areas exacerbates these challenges, leading to increased demand for services such as energy, sanitation, and mobility. Pollution
and other environ-mental problems also arise due to rapid population
growth in urban centers. Traditionally, decision-making in municipalities followed top-down approaches, often overlooking the perspectives and desires of citizens. However, a paradigm shift toward Smart

MB-19

Cities (SC) emphasizes multi-stakeholder involvement and shared visions. To address this, a novel decision-making methodology is proposed, integrating the needs of various stakeholders, including citizens.
The stages of the methodology are a) SC Paradigm: Which emphasizes sustainable development, green growth, and collaboration among
stakeholders; b) Survey-Based Approach: The process begins with asurvey to capture community needs. This data collection phase is crucial for understanding the diverse perspectives within the community;c) ELECTRE: a decision-making technique, is employed to formalize and prioritize these needs. It balances different interests and helps
optimize project design. This methodology was applied to a neighborhood in the city of Santiago de Chile, generating solutions to the main
problems faced by residents of the sector.

 MB-19
Monday, 10:30-12:00 - Room: 44 (building: 116)

OR in Electricity Markets
Stream: OR in Energy
Invited session
Chair: Ruth Dominguez
1 - Analysing load curve variations in the energy transition- a simulation approach

Maritza Jimenez, Carlos Jaime Franco, Isaac Dyner
The energy transition has implied changes in the behavioural dynamics
of the electricity market participants, both from the energy supply anddemand perspective. In this regard, it is important to analyse the possible market effects of these changes in the medium and long term. Thus,
simulation is presented as a relevant tool for analysis as it allows us to
explore the effect of pattern changes based on possible future scenarios.
This paper uses the system dynamics simulation approach to evaluate
the effect of changes in demand-side consumption dynamics in developing economies by assessing the load curve. The results show thatsome demand-side management alternatives have greater impacts thanothers on the load curve behaviours and that the combination of different participation alternatives could have challenging contributions to
the technical performance of the intra-day market.

2 - Multiobjective approach for the design and operation ofenergy communities

Bruno Domenech, Ángel David Téllez-Macías, Marc
Juanpera, Antonin Ponsich, Matteo Ranaboldo
Energy communities (EC) using renewable sources are a way toachieve energy transition, assigning an active role to end-users (calledprosumers). Every prosumer is an electricity consumer and producer,
exchanging electricity with other prosumers and the grid. EC are gaining attention in Europe among researchers and the industry, thanks totheir economic and legal recognition within the national regulations.
The design and operation of EC is a complex combinatorial optimisation problem, where several objectives are to be accounted for: economic, to ensure cost savings, environmental, to take profit of renewable sources, and social, to achieve equity between prosumers. In thiswork, a multiobjective (MO) mathematical model is developed for thedesign and operation of EC, determining the location and size of equipment (renewable generators and batteries) as well as the electricity exchanges between prosumers and the grid. A correlation analysis between the solutions obtained from the individual optimisation of several economic, environmental and social objectives is performed in order to select a reduced set to be included in the MO model. Througha simple approach based on -constraints, an approximation of thePareto front can be identified, constituting a set of alternative configurations among which decision-makers may select their preferred one.

51

MB-20EURO 2024 - Copenhagen3 - Optimal participation of wind producers in the continuous intraday market: a multi-stage stochastic approach

Ruth Dominguez, Miguel Carrión, Giorgia Oggioni
Flexible mechanisms, in terms of both hardware and market rules, arerequired to ensure the economic, technical, and environmental sustainability of renewable-dominated electric energy systems. Short-term
markets such as intraday markets are very important for wind and solar
photovoltaic producers since the prediction of their power generation
is more trustable just a few hours ahead of the actual energy supply.
In this work, we tackle the problem of a wind power generator thataims at deciding the offers to be made in the continuous intraday market taking into account the possibility of participating in consecutive
trading floors, such as in the auction-based intraday market and thebalancing market. The model is formulated as a multi-stage stochastic
programming problem considering the uncertainty of the wind power
availability, the electricity prices in each trading floor, and the probability of acceptance of the offers in the continuous market. The CVaR
is introduced in the formulation to control the risk level of the solution.
The model is tested with real data from the Spanish power system. The
scenarios of wind power at each decision stage are generated considering the evolution of the forecast error distribution according to thetime spanning between the offering and the delivery time. Numericalresults of different sensitivity analyses will be presented.

4 - Modelling balancing service provision in 5th gen district
heating systems

Erik Fröhlich, Kerem Öncel, Annette Steingrube
5th gen district heating systems are bi-directional low temperature district heating and cooling networks with decentral generation units.
They are well suited for providing flexibility to the power grid. A casestudy has been performed to model the flexibility such a system can offer. The energy system is represented as a linear optimization problem,that has been extended to model bi-directional flows within the district
heating network in the form of energy balances. For the purpose ofmodeling flexibility provision in the form of balancing services, a twostage stochastic optimization approach has been developed. The firststage represents planned operation without offering flexibilities, in thesecond participation on the control reserve market is modeled. Callprobabilities for energy bids are implemented as randomized binary
series. This concept is aiming at ensuring that the optimization modelcannot apply perfect foresight of called balancing services, but a better representation of actual operation is achieved. Due to uncertainties
of price development in balancing markets a sensitivity analysis w.r.tbalancing service remuneration is conducted, yielding crucial tipping
points at which capacity planning and operation in the considered energy system is altered to reduce system costs through providing flexibilities. Challenges resulting from the simplified representation in a
linear optimization model are analyzed and suggestions for solution
approaches formulated.

 MB-20
Monday, 10:30-12:00 - Room: 45 (building: 116)

Just and ethical sustainability transitions
Stream: OR and Ethics
Invited session
Chair: R. M. Kazakov
1 - The Roof of Africa Project: scenario simulation for supporting sustainable development in Sub-Saharan Africa

Genuine Mwasha, Yavora Rosenova, Penka Petrova, R. M.
Kazakov
The Roof of Africa Project is an initiative aimed at promoting sustainable development in Tanzania, focusing on access to education,
food, health, and family well-being. Our methodological approach

52

employs system dynamics modelling and scenario simulation to comprehensively analyse the project’s contributions to key UN Sustainable
Development Goals related to developing access to education, food,health and supporting local families. This project will explore criticalfactors impacting the initiative’s success, including infrastructure development and educational resources, community livelihood uplifting,food and healthcare provision. Through system dynamics lenses, we
explore the complex interactions among these factors and their combined effect on elevating the local communities. Preliminary findings
suggest that the Roof of Africa Project offers a transformative andreplicable model for tackling the multi-dimensional challenges of sustainable development in impoverished communities.

2 - Can justice be profitable? The benefits of balancing equity and equality in optimization models

David Rea, Leonardo Lozano, Craig Froehle
Employees care about being treated fairly and will react strongly toperceived unfairness. As such, organizations are increasingly seeking ways to incorporate employee-centric principles in their decisionmaking processes. This presents the challenge of balancing two sets oftrade-offs: the organizations well-being versus their employees, andtheir employees’ desire for equality versus equity. Ensuring these
tradeoffs are consistently balanced according to the organization’s values at the scale and speed of modern business requires an algorithmic
approach. The increasing use of algorithms to guide such value-driven
decision-making necessitates a better understanding of how these elements should be integrated within decision-making models. This research explores these complex trade-offs computationally by modeling two representative case studies: a synthetic gig-economy package
delivery problem and an empirical interhospital patient transport problem, both of which are NP-hard. One key observation is that certain
combinations of functions in the model objective lead to opportunities for organizations to perform better while also representing fairness
more precisely. We also find that the effects of trade-off parameters
are partially determined by both their relative levels and the functions
used in the objective. These and other observations offer guidance forpractice while also motivating future research to help fully explore howthese concepts shape decision-making.

3 - Model Predictive Control to organize the first vaccination wave during an epidemics (Application to COVID-19
for Wallonia - Belgium)Morgane Dumont, Candy Sonveaux, Mirko Fiacchini
In case of a new epidemic, decision makers have huge responsibilities in terms of economic impact and public health security. When avaccine becomes available, they need to strategically define who is going to be vaccinated and when. Moreover, the strategy could need anadaptation after a few days or weeks to be as effective as possible. We
propose to use the Model Predictive Control method in order to have adynamical choice of who is vaccinated and at which moment (with amaximum number of total daily vaccinations). This method starts fromthe knowledge of the evolution of the disease without any vaccinationand proposes a strategy over a fixed prediction horizon by iteratively
adjusting the control inputs (vaccination) based on the ongoing system
feedback to optimize the performance (minimize the number of deadindividuals). To simulate the disease spread, we consider a compartment model including the susceptible, infected, recovered and deaths
compartments. The proposed method is illustrated thanks to data about
the COVID-19 in Wallonia (Belgium) and the resulting strategy campaign is compared to the strategy really adopted by the government(decreasing order of ages). We show that the two types of campaigns
gave similar results in terms of number of deaths and infected, but theModel Predictive Control approach consumes only 63.95% of the vaccines used by the decreasing age strategy. It is thus economically very
interesting.

4 - The Hybrid Hospital: Balancing On-Site and Remote
Hospitalization

Noa Zychlinski, Gal Mendelson, Andrew Daw
We study the dynamics of hybrid hospitals offering on-site and remotehospitalization through telemedicine. These new healthcare models
require efficient operational policies to balance costs, efficiency, andpatient well-being. Our study addresses two primary operational questions: (i) how to direct patient admission and call-in policies based on

EURO 2024 - Copenhagen
individual characteristics and proximity and (ii) how to determine theoptimal allocation of medical resources between these two hospitalization options. We develop a model that uses Brownian Motions tocapture the patient’s health evolution during remote/on-site hospitalization. By optimizing call-in policies, we find that remote hospitalization is cost-effective only for moderately distant patients, where thecall-in threshold has a non-monotonic relationship with travel time.
The impact of scarce resources is reflected through simultaneous increase of both remote and on-site costs by the same value, without
altering the solution structure under abundant resources. Contrary to
the widely held view that telemedicine can mitigate rural and non-rural
healthcare disparities, our research suggests that on-site care may actually be more cost-effective than remote hospitalization for patientsin distant locations, due to increased risks for remote patients who arecalled in to the hospital. This may be of particular concern in lightof the growing number of "hospital deserts" amid recent rural hospital
closures.

MB-23

or, more efficiently, a convex optimization solver. When studying suchgames, it is common to aggregate the consumers and substitute theirinverse demand function to eliminate the market price. We argue, however, that the market price should be treated as an explicit variable thatis central to the game. We show that the problem can be reformulated
as a fixed-point problem (FPP) for the price only, thereby greatly reducing its dimensionality. The FPP can therefore be solved even more
efficiently than the QP and also yields a more accurate solution. The
FPP can be generalized to spatial games and games involving multiple commodities. We envision that this could vastly improve solution
efficiency and accuracy when incorporated into dynamic or multilevel
games. We finally consider a Cournot game across the markets for two
commodities that may be converted into each other. The FPP formulation reveals the structure of the equilibrium and allows an analyticalstability and sensitivity analysis. We find that market imperfections ineither of the markets extend to the opposite market and may significantly reduce the welfare gains from market coupling.

4 - Benefits and challenges of soft-linking for large-scaleenergy system modelling MB-21
Monday, 10:30-12:00 - Room: 49 (building: 116)

Energy sector coupling, optimization andequilibrium
Stream: OR in Energy
Invited session
Chair: Trine Krogh Boomsma
1 - Solving Equilibrium Problems in Infrastructure using
the Difference of Convex Functions Algorithm

Steven Gabriel, Dominic Flocco, Trine Krogh Boomsma,
Martin Schmidt, Miguel Lejeune
We describe a novel application of the difference of convex function
algorithm (DCA) to a variety of equilibrium problems using the mixed
complementarity problem (MCP) format. These problems involve bilinear constraints, i.e., complementarity and can be approximated iteratively via convex subproblems using DCA. We develop the necessary
theory to make this possible and showcase how it works on several
MCPs in energy and other infrastructure.

2 - Addressing Non-Convexities in Sector Coupling Problems via the Difference of Convex Functions Algorithm

Dominic Flocco, Steven Gabriel, Trine Krogh Boomsma,
Martin Schmidt, Miguel Lejeune
We present the difference of convex functions algorithm (DCA) applied to a variety of non-convex optimization problems arising inenergy sector coupling and equilibrium problems. Non-convexities
emerge frequently in many energy models, such as though bilinear
terms, integer constrained variables and non-convex quadratic constraints. Recently, DCA has proven effective in solving such problems
by linearizing non-convex terms and converging to an optimal solution
iteratively by solving convex subproblems. In our work, we use DCA
to solve a wide range of non-convex problems, such as linear complementarity problems, mixed integer linear programs and quadratic
programs with non-convex quadratic constraints. We demonstrate theeffectiveness and versatility of DCA by applying it to a variety of energy models, such as the unit commitment problem, district heating
networks and energy market equilibrium problems.

3 - A New Strategy for Solving Cournot Games

Nena Batenburg, Trine Krogh Boomsma
It is well known that Cournot games involving heterogeneous playerswith affine and decreasing demand functions and convex quadratic production cost functions can equivalently be cast as a mixed linear complementarity problem or a quadratic optimization problem (QP). Market equilibria can thus be computed using a complementarity solver

Mathias Berg Rosendal, Jean-Marc Janin, David Pisinger,
Rasmus Bramstoft, Marie Münster
Computational tractability remains a significant challenge in modelling
large-scale, sector-coupled energy systems. This study investigatesthe soft-linking of models to broaden the analysis scope while maintaining practical computation times. A case study is defined becausesoft-linking applications are unique to the specific research question.
Resource adequacy assessment is therefore defined as the scope in abidirectional coupling of Balmorel (which performs investment anddispatch on time slices) and the stochastic dispatch model, Antares,
which optimises dispatch in multiple weather years with hourly resolution. The models are fully harmonised in terms of data, geographical granularity and level of sector-coupling in a pan-European context.
The bidirectional soft link is analysed by comparing computation times
at different temporal aggregation levels with the objective of finding
the least-cost system with the lowest loss of electricity and hydrogenload. A higher degree of aggregation leads to lower computation times
per iteration but more inadequate systems. Thus, more iterations are
required to adjust investments. The opposite is valid for a less aggregated approach. A discussion is carried out of the different methods
used to signal the need for more investment in carrier transmission,
generation capacity, etc., and a conclusion on the benefits and challenges of soft-linking is derived from these findings.

 MB-23
Monday, 10:30-12:00 - Room: 82 (building: 116)Managing product returns
Stream: Circular Economy, Remanufacturing and Recycling
Invited session
Chair: Gokce Esenduran
1 - An emergency supply policy for an inventory replenishment model with returns and partial backorders

Yonit Barron, Netanel Drezner
This paper studies a continuous-review inventory replenishment modelwith a limited storage capacity S in an uncertain environment. We assume that the demands and returns follow independent Poisson processes. We further assume a random shelf life, a random lead time,and early loss. The storage is managed according to the base-stock(S,s) policy for s0. In case of overstock, each returned item exceeding
S is transferred to a foreign facility. If during the lead time a demandreaches zero stock, we consider two alternatives: either allow partial
backordering up to L_B items, beyond which the unsatisfied demand islost, or call for an immediate and costly emergency supply up to level053MB-24EURO 2024 - Copenhagen2 - Inventory Management for an Omnichannel Retailer under Product Returns

Esra Gokbayrak, Berk Deniz, Enis Kayis, Refik Gullu
Fast fashion brands seamlessly integrate various sales channels to create a cohesive shopping experience. This seamless integration extends
to the handling of product returns, ensuring that customers experience
consistent policies across all channels, thereby enhancing overall satisfaction and retention. Particularly during peak shopping seasons andspecial occasions, consumers value the convenience of returning products to any store within the brand’s network. However, the growth ofapplying omnichannel retailing has also led to an increase in productreturns, posing challenges for inventory management and environmental sustainability due to unsold merchandise. This paper presents asolution for inventory management, considering the complex dynamics of omnichannel systems and the unique challenges generated byhigh return rates. Using dynamic programming, we formulate a modelthat optimizes pre-season order quantities for online warehouses andtransshipment quantities to brick-and-mortar stores during the sellingseason. Our sensitivity analysis offers valuable insights for retailers
seeking to balance profitability with environmental responsibility inthe face of evolving consumer preferences and market dynamics.

3 - A capacity reservation contract for WEEE reverse supply chains

Jenny Andris, Christoph Schwindt
We consider a reverse supply chain for waste from electrical and electronic equipment (WEEE). In Europe, the manufacturers of EEE products are in charge of collecting WEEE and feeding it back into the
production cycle. Manufacturers generally appoint collection companies to assume these duties. According to German legal regulations,the clearinghouse Stiftung Elektroaltgeräteregister EAR continuously
allocates returned WEEE to the manufacturers based on their registered sales tonnage. In this talk, we focus on long-term contracting
between a collector and a manufacturer. We propose a capacity reservation contract, which protects the collector’s investment in capacity
extension from the risk incurred by uncertain future order volumes.
The contract parameters of the collector, who is in the position of a
Stackelberg leader, include the capacity allocated to the manufacturer,
the option price, the exercise price upon activation of the option, andthe per-unit fee for extra short-term orders placed within the agreed
capacity. The manufacturer optimizes the number of options bought
subject to an upper bound on the value at risk and accepts the proposal
if it provides the reservation utility from competing offers. We showthat the contract is guaranteed to coordinate the dyadic supply chain
under plausible assumptions. Numerical examples illustrate that the
optimal exercise price can be negative, which due to EAR’s role as anintermediary, does not impair incentive compatibility.

4 - Optimal Product Return Insurance

Gokce Esenduran, Paolo Letizia, Anton Ovchinnikov

1 - Optimal design of socially sustainable raw material supply chains

Lea Franze, Karsten Kieckhäfer, Bruna Mota, Ana
Barbosa-Povoa
Driven by due diligence requirements, social sustainability aspects receive more attention in global supply chains. In the automotive industry, e.g., companies search for sustainable supply sources, engage insupplier development, or invest in raw material mining projects to meetsocial standards. In current research on strategic network planning,
however, social sustainability is rarely studied, and often approached
in a simplified manner. To address this gap, we develop a novel network planning approach that combines social life cycle assessment,
activity analysis, and mixed-integer linear programming. The model
particularly decides how much raw material to source from multiple
sites, which may operate at different levels of costs and social risks. To
meet certain social standards and fulfill demand, different measures areavailable: commitment to long-term contracts with suppliers, investment in mining projects, and implementation of social development
measures. The costs associated with these decisions are minimized inan economic objective function, while social risk is limited by additional constraints. The approach is applied to raw material extractionin the supply chain of lithium-ion batteries for electric vehicles. The
analyses show the trade-offs between the sourcing decisions to meetsocial standards. In cobalt mining, e.g., investing in mining projects
in Australia can reduce the social risks associated with sourcing cobalt
from long-term contracts in the Congo.

2 - A Model to Diversify the Import of Green Hydrogen

Louis Vincent Sroka, Frank Meisel
Hydrogen, particularly ’green hydrogen’ generated from renewable
feedstock, is a promising alternative to fossil fuels for decarbonizing
the transport and energy sectors. However, achieving a sustainable energy economy based on green hydrogen poses significant challenges
due to limited resources and production capacities in many countries.
The discrepancy between high demand and low self-supply has to becompensated through imports. Since overreliance on a single exporting nation can lead to geopolitical, financial, and moral complications,the import of green hydrogen should be diversified. In contrast to
only regarding parallel bilateral imports, a multinational transportation
network offers potential cost reduction through consolidations in production, conversion, and transportation. We propose an optimization
model for such a multinational import network for green hydrogen, thatconsiders import diversification as well as factors like transportation
losses and conversion efficiencies. Using Germany as a case study, wedemonstrate how enforced diversification impacts sourcing decisions
and overall costs to further emphasize the trade-off between resilienceand cost-effectiveness. Furthermore, we highlight how reducing lossesof liquid hydrogen during transportation (through advancing storage
technologies) could further diminish the hydrogen price.

3 - Sustainable Planning Optimization in Phosphate OpenPit Mines: Exploring Extraction Routing Impact

Bassma Azzamouri, Ahlam Azzamouri, Agnes Gorge
In response to the high cost of returns, some firms stop a free returns
policy, but give their customers the option to purchase return insurance,
which provides customers with the option to return items and receivea refund. We study how to design the return policy and insurance optimally.

 MB-24
Monday, 10:30-12:00 - Room: 83 (building: 116)

Raw Material Supply Chains
Stream: Sustainable Supply Chains
Invited session
Chair: Karsten Kieckhäfer

54This paper deals with sustainable production in the mining industry.
It aims at planning production activities in Open Pit Mines (OPM) of
phosphate while considering the impact of extraction routings (ER),in ensuring the necessary diversity of minerals while preserving rare
ones. Planning production activities in phosphate OPM for tactical/operational levels consists of selecting the extraction zones, andblocks, machine allocation, and ER choices. Several researchers haveworked on machine allocation and block and zone choices, but less attention was given to the ER impact, especially in ensuring the balance
between managing the Supply Chain (SC) efficiently and high-quality
phosphate preservation. These ERs consist of extracting non-mineral(waste) layers with mineral ones or independently, controlling minerals’ characteristics. We focus on a real case of a SC composed of several OPMs where a unique ER is adopted, making satisfying customer
requirements challenging with excessive use of high-quality minerals.
For this reason, we first conducted an exploratory study to understandthe impact of ERs on OPMs. Second, we developed a MILP model toselect the optimal ER to adopt. The model provides the corresponding
production program of those mines, defined by the quality, volumes,

EURO 2024 - Copenhagenand lead time of phosphate. The results shed light on the difference
between the production programs resulting from the use of each ER
and the balance ensured between efficiency and sustainability

4 - Multi-layer Supply Network Dynamics and Resilience in
the Copper Industry: A Complex Network Theory Analysis

Qiyan Liu, Peter Adriaens
Copper, an essential mineral for renewable energy technologies, faces
significant supply chain disruption risks due to its uneven global distribution. This research focuses on the vulnerabilities within the copperindustry’s supply chain, which is extensively interconnected from theextraction of raw materials to the production of finished goods.
This study leverages complex network theory to develop a weighted,
multi-layer supply chain network model at the firm level within thecopper industry, utilizing real-world data from the FactSet Supply
Chain Relationship database. It examines the network’s structuralcharacteristics and employs degree-based removal strategies to simulate targeted attacks on the network, offering insights into its robustness.
This study enriches the literature by providing an in-depth analysisof the copper supply chain at the firm level, employing dynamic risk
transmission modeling to explore supply risks across firm, industry,and regional levels. The study not only identifies vulnerabilities but
also proposes strategies to enhance network resilience. Through thiscomprehensive framework, we offer new insights into mitigating supply chain vulnerabilities in the copper industry, highlighting the critical role of network structure and interdependencies in supply chain
resilience.

 MB-25
Monday, 10:30-12:00 - Room: 011 (building: 208)

Discrete, continuous or stochastic
optimization and control in networks,transportation and design II
Stream: Combinatorial Optimization
Invited session
Chair: Bruno Kamdem
Chair: Gerhard-Wilhelm Weber
Chair: Gordon Dash
Chair: Nina Kajiji
1 - Solving a VRP in the energy sector through tabu searchwith a double neighborhood

Paula Amaral, Ana Mendes, Miguel Espinosa
This presentation deals with the distribution of products in the context of an energy company. This problem can be modeled as a Vehicle
Routing Problem (VRP) with several complexities like: time windows,
different vehicles, several products, among others. The urgency of
optimizing energy transportation cannot be overstated, given the rising global demand for energy resources. Tabu Search is a powerful
meta-heuristi algorithm commonly used for solving combinatorial optimization problems. Its adaptability and efficiency make it well-suited
for a variety of applications, as the VRP, for identifying good vehicle routes. However, like any optimization algorithm, Tabu Search
faces challenges, and one of the main issues is dealing with local optima where the algorithm might get stuck. In scenarios with numerous local optima, Tabu Search may struggle to escape these regions of
attraction efficiently, leading to slow convergence and potentially suboptima solutions. In response to this challenge, we propose a novel
double neighborhood strategy employing opposite optimization directions (minimization and maximization). The maximization component
is strategically employed to facilitate the escape from local minima.

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We showcase the application of this approach to a challenging and intricate VRP problem. Our results demonstrate the effectiveness of the
double neighborhood strategy in enhancing the performance of Tabu
Search, particularly in scenarios where traditional implementations ma

2 - Energy-constrained multi-drone covering tour problem
with visit durationsÖzge Şatir Akpunar, Şener Akpınar, Tolga Bektas, Cagatay
Iris, Grigorios Kasapidis
This study focuses on the deployment of a fleet of drones required toprovide temporary communication during emergencies. It is assumed
that drones are initially placed at a depot, while the area is divided
into subregions that require continuous communication services. In
addition, each drone is subject to battery limitations and thus, needs
to return to the depot to recharge its battery. As emergency communication is a continuous activity, drones need to visit the subregions
in a sequence. The aim here is to provide continuous coverage of thearea by utilizing drones and their limited batteries efficiently. The first
aspect of interest is how long a drone should hover over a subregion.
Hence, it is aimed to keep each drone as much as possible over every
visited subregion, to ensure maximum coverage. Decisions include
the selection of visited subregions, the optimal hovering time of eachdrone over the subregions, and the scheduling of the subregion visit
sequence, all aimed at maximizing temporal and spatial coverage. The
problem is formulated as a mixed-integer linear program (MILP) resembling the covering tour problem. This mathematical model enables
us to model the complex interplay between drone movements, area
coverage, and battery constraints. Furthermore, a constraint programming (CP) model is proposed to solve the problem and computational
experiments are conducted to assess the performance of the CP and
MILP models on randomly generated large-scale instances.

3 - Routing in offshore wind farms: A multi-period locationand maintenance problem with joint use of a service operation vessel and a safe transfer boat

Majid Eskandarpour, Chandra Irawan, Mohammad Reihaneh,
Stefano Starita
This paper studies an interesting logistical problem related to maintaining a set of selected turbines in offshore wind farms. A service
operation vessel (SOV), used to support maintenance activities, also
carries a safe transfer boat (STB), which is used to transport technicians, parts, and equipment to the turbines. The maintenance activities
are optimised over a planning horizon by simultaneously coordinating
the SOV and the STB. A mixed-integer linear programming model isdesigned, where the objective function is to minimise the total maintenance cost. As the proposed mathematical model is hard to solve witha commercial solver, a decomposition approach is developed. A twostage stochastic programming model is also designed to tackle uncertain conditions, including the duration of maintenance and the STB’s
travel time. The performance of the solution method is evaluated using the Thanet Offshore Wind Farm, in the southeast of the UK. The
results of our experiments show that joint use of an SOV and an STB
can yield a lower total maintenance cost than only using an SOV orcrew travel vessels (CTVs) that are currently the common practice inmaintaining offshore turbines.

4 - Four-echelon facility location supply chain design

Bahram Alidaee, Haibo Wang
At k-level uncapacitated facility location, ensuring each client is served
by a sequence of k facilities is common in complex logistical systems,
where central depots distribute goods through smaller depots. Dueto its complexity, especially when k > 1, approximation algorithms
are crucial for swiftly obtaining high-quality solutions. This study focuses on a four-echelon supply chain design facility location problem,
involving plants (P), ware-houses (W), distribution centers (D), andretail stores (S). The objective is to select locations of plants, retailstores, warehouses and distribution centers to maximize the total profit
of serving selected retail stores. We formulate the problem using a bipartite Boolean quadratic programming (BBQP) model. Its solution is
obtained through a heuristic approach that employs Tabu search with
an r-Opt sequence embedded diversification.

55

MB-26EURO 2024 - Copenhagen MB-26
Monday, 10:30-12:00 - Room: 012 (building: 208)

Novel topics and recent advances insolution approaches in scheduling
Stream: Combinatorial Optimization
Invited session
Chair: Alena Otto
Chair: Erwin Pesch
Chair: Amir Hosseini
1 - From catch to coast: An optimization approach for vessel routing and scheduling in the Nordic fishing industry

Ali Ghavmaifar, Allan Larsen
The European Union (EU) mandates environmentally friendly, economically viable, and socially sustainable fishing practices for longterm food security, as outlined in the Green Deal objectives. Despite
these aims, fisheries still heavily rely on fossil fuels, contributing significantly to global greenhouse gas emissions. Efficient routing canhelp vessels optimize their fuel consumption by considering the shortest distance to the fishing fields, while effective scheduling can prevent
over-fishing of specific areas by avoiding excessive fishing pressureduring certain times. Moreover, effective scheduling significantly impacts the quality of fish unloaded at the harbor. Motivated by a reallife case for a company operating in the Nordic fishing industry, wedeveloped a mixed-integer programming model for vessel routing andscheduling considering fuel consumption. The resulting optimization
problem is complex featuring various types of uncertainty, with theprimary source of uncertainty stemming from the variability of fish
population in fishing fields. To tackle this issue, we employ the Sample Average Approximation (SAA) approach. Additionally, a heuristic
approach has been devised to efficiently solve large-scale problems.
Finally, we examined our proposed approach using real data from thecorresponding company. Our results highlight how efficient vessel
routing and scheduling in upstream processes of the supply chain can
improve overall supply chain efficiency.

2 - An exact solution for the energy constrained AGV
scheduling problem

Amir Hosseini, Alena Otto
Automated Guided Vehicles (AGVs) play a pivotal role in moderntransportation systems, revolutionizing the movement of goods andmaterials in warehouses and production systems. Yet, exploiting theperformance of AGVs requires careful consideration of their operational constraints during the planning phase. In this study we consider
the AGVs scheduling problem, where a fleet of AGVs are used to perform a set of transport requests. The AGVs are energy constrained andtheir battery needs to be swapped regularly. The problem is to assign
transport activities to AGVs, sequence them, and determine optimalbattery swap times, such that the makespan is minimized. In essence,
this problem can also be viewed as a parallel machine scheduling problem with job-dependent activity cycles and constant maintenance time.
For this problem, we introduce a novel mixed-integer linear programming formulation that surpasses existing state-of-the-art models. Then,
we develop a logic-based Benders decomposition algorithm embedded in a single search tree framework and strengthened by new setsof lower bounds and valid inequalities. Preliminary computational results indicate the algorithm’s capability to solve previously unsolved
instances reported in the literature.

3 - Workforce scheduling and routing with substitution
skillsClara Chini Nielsen, David Pisinger
This study addresses the tactical planning aspect of a dynamic technician routing and scheduling problem spanning several days and withhome depots. The challenge involves pre-known tasks and dynamically arriving tasks, requiring efficient scheduling to minimize overall

56

driving distance. Technicians, each possessing specific skills, are dispatched to locations with tasks having time windows and skill requirements. The goal is to minimize driving distance while maximizing
task completion. The extended model introduces substitution skills,
allowing less qualified technicians to serve tasks with a time penalty.
The study presents both an MIP formulation and an ALNS algorithm
for optimization. Results, derived from real-life data, are comparedagainst routes executed in an actual company, where technicians canonly handle tasks matching their skill sets.

4 - Scheduling on parallel dedicated machines with job rejection

Baruch Mor, Gur Mosheiov
We study scheduling problems on parallel dedicated machines. Thus,
each job can be processed on one specific machine only. The optionof job-rejection is considered, and the total permitted rejection cost of
all the jobs is bounded. Six scheduling problems are solved: (i) minimizing makespan, (ii) minimizing makespan with release-dates, (iii)
minimizing total completion time, (iv) minimizing total weighted completion time, (v) minimizing total load, and (vi) minimizing maximum
tardiness. Pseudo-polynomial dynamic programming algorithms are
introduced for all these NP-hard problems.

 MB-27
Monday, 10:30-12:00 - Room: 047 (building: 208)

On Mathematical Optimization for
Explainable and Fair Machine Learning
Stream: Mathematical Optimization for XAI
Invited session
Chair: Kseniia Kurishchenko
1 - Making Black-Boxes Explainable and Fair ThroughMathematical Optimization

Thomas Halskov, Emilio Carrizosa, Dolores Romero Morales
Machine Learning has increasingly been used for algorithmic decisionmaking, often through what are considered "black-boxes", i.e., models
which do not provide an explanation for their outputs. In this talk, we
consider the task of explaining the output and decisions from a given"black-box" to provide an interpretable explanation. Additionally, wetackle fairness concerns that might arise from using "black-box" models, where discrimination can be present. Our approach aims to give a
local explanation for an output, while taking into account global fairness and accuracy.

2 - From fair predictions to just decisions

Elif Ersoy, Enis Kayis
Data-driven decisions often raise concerns about fairness and justice,which are frequently confused and used interchangeably. This study
investigates the relationship between these two definitions: Fairness isgenerally considered during the prediction phase of decision-making,yet justice mostly relates to the equality or equity of the resulting decisions. Thus fairness in predictions may not automatically yield justdecisions. We propose metrics for quantifying distributive justice indecision-making and investigate how fair predictions relate to just decisions. Two main questions guide our research: Do fair predictionstranslate into just decisions? Secondly, what is the financial impact
of making just decisions? We propose justice metrics and developed
mathematical models to improve the quality of the decisions from thisviewpoint. We focus on a credit lending application. Our results underscore the distinction between fairness and justice, demonstrating thatfair predictions do not guarantee just decisions and may even reduce
the overall profit for the lending firm.

EURO 2024 - Copenhagen3 - Fair treatment allocation via tree ensembles

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2 - Efficient forecast via transfer learning

Kseniia Kurishchenko

Shumin Ma

In the treatment allocation problem, a decision maker needs to decide
who will receive the treatment. To define the individuals that benefit the most, a machine learning procedure is often used to predict thetreatment effect. With these predictions, a model is built to make the allocation decisions. Unfortunately, the data used to build such a model
may be discriminating against a group defined by a sensitive attributesuch as gender or age. If not carefully trained, the model may provide
unfair results, unequally allocating treatment to individuals in the sensitive and non-sensitive groups. In this presentation, I introduce an unfairness measure that can be applied to treatment allocation problems.
I propose to measure unfairness as the difference between the averagetreatment effects in the sensitive group and the non-sensitive group. I
introduce a Mathematical Optimization model to have accurate treatment effect predictions and a good level of fairness, which will be thebasis for the treatment allocation in forthcoming individuals.

Classical Domain Adaptation methods acquire transferability by regularizing the overall distributional discrepancies between features in thesource domain (labeled) and features in the target domain (unlabeled).
They often do not differentiate whether the domain differences comefrom the marginals or the dependence structures. In many business andfinancial applications, the labeling function usually has different sensitivities to the changes in the marginals versus changes in the dependence structures. Measuring the overall distributional differences will
not be discriminative enough in acquiring transferability. Without theneeded structural resolution, the learned transfer is less optimal. This
paper proposes a new domain adaptation approach in which one canmeasure the differences in the internal dependence structure separatelyfrom those in the marginals. By optimizing the relative weights among
them, the new regularization strategy greatly relaxes the rigidness ofthe existing approaches. It allows a learning machine to pay special
attention to places where the differences matter the most. Experimentson three real-world datasets show that the improvements are quite notable and robust compared to various benchmark domain adaptation
models.

4 - Beyond Efficient Global Optimization: an Algorithm
Minimizing Expected Regret

Zizhou Ouyang, Belen Martin Barragan, Xuefei Lu
Efficient Global Optimization (EGO) has been widely applied to simulation optimization problems, where it uses the Expected Improvement(EI) criterion to navigate the search space for optimal solutions, traditionally relying on Kriging for surrogate modelling and uncertainty
estimation. Some existing efforts have attempted to broaden EGO’s
applicability by substituting Kriging with various machine learningmodels. However, approximating emulation uncertainty is limited in
the absence of Kriging. In this paper, we propose the Expected Regret(ER) criterion, which captures uncertainty through data-driven insights
without relying on Gaussian assumptions for uncertainty estimationand enables the direct utilization of machine learning regression models for surrogate modelling. We then apply the proposed algorithm
to various simulators of different dimensionalities. Comparative performance evaluations with the classical EGO method demonstrate that
our approach achieves competitive results while offering greater flexibility in selecting machine learning models, indicating its potential for
addressing high-dimensional optimization problems.

 MB-28
Monday, 10:30-12:00 - Room: 065 (building: 208)

Advancements of OR-analytics in statistics,
machine learning and data science 2
Stream: Advancements of OR-analytics in statistics, machine learning and data science
Invited session
Chair: Olabode Adewoye
Chair: Gerhard-Wilhelm Weber
Chair: Nahid Jafari

3 - Estimated economic impact of model drift

Andrew Clark
In the dynamic landscape of data-driven business operations, model
drift poses a significant challenge, often leading to substantial economic repercussions. Traditional approaches to quantifying the impact
of model drift have been rudimentary, frequently relying on simplistic,
ad-hoc calculations. Our paper introduces the Economic Impact Estimation Metric, a pioneering method designed to assign a quantifiable
fiat cost to the concept of model drift in a systematic and accessible
manner.

4 - Mixed-integer linear optimization for semi-supervised
optimal classification trees

Maria Eduarda Pinheiro, Jan Pablo Burgard, Martin Schmidt
Decision trees are one of the most famous methods for solving classification problems, mainly because of their good interpretability properties. Moreover, due to advances in recent years in mixed-integer optimization, several models have been proposed to formulate the problem
of computing optimal classification trees. The goal is, given a set oflabeled points, to split the feature space with hyperplanes and assign
a class to each partition. In certain scenarios, however, labels are exclusively accessible for a subset of the given points. Additionally, thissubset may be non-representative, such as in the case of self-selection
in a survey. Semi-supervised decision trees tackle the setting of labeled
and unlabeled data and often contribute to enhancing the reliability of
the results. Furthermore, undisclosed sources may provide extra information about the size of the classes. We propose a mixed-integer linear
optimization model for computing semi-supervised optimal classification trees that cover the setting of labeled and unlabeled data points aswell as the overall number of points in each class for a binary classification. Our numerical results show that our approach leads to a better
accuracy and a better Matthews correlation coefficient for biased samples compared to other optimal classification trees, even if only few
labeled points are available.

1 - Production, pricing and transshipment control on abacklogging manufacturing system

Jianjun Xu
We study a backlogging inventory model of a manufacturing systemwith two facilities that periodically produces, transfers and sells a product to stochastic and price-sensitive demands. By expounding an insightful difference in reactive transshipment decisions between lostsales and backlogging models, we characterize inefficient backordertransshipment around the facilities in the backlogging situations anddemonstrate, on account of the inefficient backorder transshipment,that optimal production, pricing, and transshipment decisions can retain complementarity for a group of variables, and behave monotonically. Further, we perform the monotone comparative statics of thecomplementary variables to establish sensitivities for substitutable demands to optima pricing decisions.

 MB-29
Monday, 10:30-12:00 - Room: 157 (building: 208)

Combinatorial Optimization models andapplications in Logistics and Transportation
II
Stream: Combinatorial Optimization
Invited session
Chair: Ioannis Fragkos
Chair: Liana van der Hagen57

MB-30EURO 2024 - Copenhagen

1 - Enhancing Last-Mile Delivery Efficiency: A Hybrid
Simulation-Optimization Approach for Automated Parcel Locker Network

Javier Faulin, Irene Izco, Adrian Serrano, Luis Cadarso,
Bartosz Sawik
The disruptions caused by the COVID-19 pandemic have revealed thenecessity for innovative last-mile delivery solutions in urban logistics.
Thus, automated parcel lockers offer benefits for courier companies
and customers by enhancing flexibility and reducing mileage, delivery
time, and emissions. This work introduces a novel approach by integrating a System Dynamics Simulation Model to forecast e-commerce
demand in Pamplona, Spain, and assesses its scalability for other cities.
Additionally, a bi-objective Facility Location Problem (FLP) is rooted
within the simulation environment to test various scenarios of demand
coverage. Simulation and demand forecasting are conducted using
Anylogic software with the CPLEX optimization solver for the FLP,
featuring an -constraint approach based on economic and demand
criteria. Results draw optimal APL networks, derived from the nondominated solution set of the bi-criteria mathematical programming
model through  parameterization. That leads to the identification of
the Pareto Frontier and the trade-offs between the number of lockers and parcel demand satisfied. Finally, we encourage the utility ofsimulation-optimization in addressing dynamic environments.

2 - The value of modular buses optimization

Carlo Filippi, Gianfranco Guastaroba, Lorenzo Peirano, M.
Grazia Speranza
Modular bus systems recently attracted attention due to their potential ability increase the flexibility and efficiency in transit networks
scheduling. In this work, we focus on the potential advantages of modular buses with respect to fixed-capacity buses. We develop a static optimization model to minimize the total number of modules flowing in agiven network of bus lines. The model assumes that for a set of origindestination pairs located at the stops of the bus network, a rate of transport demand is given and must be satisfied. Buses are formed by one ormore connected identical modules. Modules can be attached/detachedat the endpoints of a line and at intersections of lines. Empty modules can be transferred among different endpoints and intersections if
needed. The model establishes the rate of departure of modules fromeach endpoint and intersection along each line and the rate of transfer
of empty modules among endpoints and intersections that allow to satisfy the demand of transport using a minimum number of modules. By
analyzing two extreme cases, we bound the relative value of module
sharing among lines and the relative value of empty module rebalancing. Computational experiments on instances derived from real-worldtransportation networks show the validity of the model and the saving
in total transport capacity that can be obtained from a modular systemwith respect to a fixed-capacity system.

3 - Optimization of auto-carrier routes: a real-world casestudy in Italy

Andrea Bettinelli, Daniele Ferone
Bertani Trasporti Spa is one of Italy’s leading players for logistics
services in the automotive sector, delivering more than 5000 vehicles
daily. One of the critical tasks in their planning process is the definition of the delivery trips for their fleet of auto-carriers. The problem
consists in selecting the best subset of orders that will be served thenext day and building a set of optimal routes to deliver them. The routing of the auto-carriers must take into account constraints related to theloading of the vehicles on the carriers. The goal is to use the carriers in
the most efficient way (at present there is more demand than transportcapacity). This means that we must saturate auto-carriers capacity andcreate trips visiting few and geographically close delivery points. At
the same time we have to minimize the orders not delivered on time.
In this work we propose a heuristic method to build optimized routes,
based on an Adaptive Large Neighborhood Search (ALNS) scheme,
where loading constraints combine explicit rules with coefficients dynamically learned from historical data and feedback from expert planners.

58

4 - Feasibility Prediction in Attended Home Delivery with
Uncertainty: A Machine Learning Approach

Liana van der Hagen, Niels Agatz, Remy Spliet
In attended home delivery, retailers usually let customers select a delivery time slot for receiving their orders. The delivery capacity, i.e.,
the number of vehicles and drivers, is often fixed and inflexible in the
short term. To effectively use their delivery capacity, e-grocers may dynamically close time slots for certain new customers given the alreadyaccepted customer orders. One complicating factor is that for example online grocers allow customers to change their order basket at any
time before the cut-off. Consequently, the e-grocer is uncertain about
how much vehicle capacity should be reserved for each of the customers during the booking process. We study the challenges that arise
with this order size uncertainty and propose strategies to deal with it.
For each of the time slots to be evaluated, we use machine learning
to quickly predict the probability that a feasible route plan exists that
visits all accepted customers in their selected time slot.

 MB-30
Monday, 10:30-12:00 - Room: 064 (building: 208)

MINLP Solvers
Stream: Software for Optimization
Invited session
Chair: Julio González-Díaz
1 - Solving MINLPs to Global Optimality with FICO Xpress
SolverTristan GallyThis talk introduces the global optimization capability within FICO
Xpress Solver, which allows to solve general mixed-integer nonlinear
problems to global optimality. We will discuss how existing featuresof the MILP and local NLP solvers are used and explain several different extensions for, e.g., spatial branching and convexification cuts, andtheir performance implications.

2 - Global optimization of continuous and discrete nonlinear programs with BARONYi Zhang, Nikolaos Sahinidis
BARON, under development since the early 1990s, has evolved into
a robust computational system for solving continuous and discrete
optimization problems to global optimality. The current generationof BARON focuses on enhancements in hybrid relaxations, presolve
methods, convexification techniques, heuristics, and robustness improvements. Our experiments underscore the importance of all algorithmic components within the branch-and-reduce framework. Computational comparisons against state-of-the-art global and local solvers
highlight BARON’s superior performance.

3 - Global MINLP in Gurobi

Tobias Achterberg
With Gurobi 9.0 we introduced an API to define univariate nonlinear
convex and nonconvex constraints, which Gurobi would then automatically approximate using piece-wise linear functions. With Gurobi 11.0,
the user can now choose to solve models with nonlinear constraints directly in order to get a globally optimal solution to the mixed integer
nonlinear program (MINLP) at hand.
In this talk, we review the algorithmic technology employed in Gurobi
11 to address nonlinear constraints. We highlight the strengths and limitations of our approach and give an outlook on further developmentsthat will be part of Gurobi 12.

EURO 2024 - Copenhagen
4 - RAPOSa: A free global solver for mixed integer polynomial optimization problems

Julio González-Díaz, Brais González Rodríguez, Iria
Rodríguez-Acevedo, Ignacio Gómez-CasaresThis talk aims to showcase the latest developments in RAPOSa, aglobal solver for polynomial optimization problems, with a primary
focus on its extension to address mixed-integer problems. The talk will
delve into various facets of the key challenges encountered in this extension, along with some numerical experiments designed to assess theperformance of different approaches, both relative to one another andalso relative to other state-of-the-art global solvers for MINLP problems.
Regarding the extension to mixed integer problems, we will discussapproaches targeted to improve the quality of the final bounds returnedby the algorithm. In particular, for the upper bounds we will discuss
the integration of the calls to auxiliary local solvers (discrete and continuous) and the trade-offs that arise when trying to do it efficiently.

 MB-31
Monday, 10:30-12:00 - Room: 046 (building: 208)

Crime Analytics
Stream: Analytics
Invited session
Chair: Carla Vairetti
1 - Dealing with noisy labels in text analytics: an application in crime analytics for prioritizing reports

Carla Vairetti, Sebastian Maldonado, Richard Weber
Deep learning has emerged as the predominant method for text analytics owing to its capacity to learn language patterns from vast datasets
and subsequently apply this knowledge to specific tasks. In this study,
we leverage deep learning techniques to enhance the categorizationof public security reports submitted by ordinary individuals through amobile application in Chile. Users select a category to report an incident from a wide array of options, encompassing everything from lostpets and disturbing noises to accidents involving injuries or burglary.
They also have the option to provide a description of the incident. A
primary challenge arises from users often misreporting events in thefirst category presented in the app. While the app has proven invaluable for municipalities and police departments in efficiently deploying
resources and patrols to address incidents and prevent crime, its prioritization scheme relies on the category provided by the user. This study
introduces a machine learning solution employing BERT and otherTransformer architectures to learn from incident descriptions and accurately assign labels, thereby improving event prioritization. Our results show a positive predictive performance. Additionally, the modelshows potential to extract further insights from descriptions, augmenting prioritization by, for instance, assessing the severity of accidents orcrimes.

2 - Using crime report app data to predict burglary via machine learning

Sebastian Maldonado, Joaquín Roa, Carla Vairetti, Richard
Weber
Predictive policing represents a productive avenue of research utilizing analytics to anticipate potential criminal activities. This study aims
to develop a learning machine capable of forecasting the occurrenceof specific crime types in a given area based on historical data. The
primary objective of this model is to furnish municipalities and police departments with a tool to deploy patrols and allocate resources
efficiently. Data was collected from a Chilean crime report app, encompassing various criminal events and suspicious activities reportedby the public. Under the assumption that past reports serve as reliablepredictors of future criminal activities, we formulate a classification

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problem to forecast burglary and motor vehicle theft in a designatedarea. Promising outcomes were achieved using conventional statistical and machine learning methods such as logistic regression, decision
trees, and gradient boosting, attaining a balanced accuracy exceeding
80%. We also discuss the used of graph-based deep learning and othernetwork architectures to take advantage of the spatial structure of the
problem.

3 - A Systematic Review and Experimental Evaluation ofSocial Network Analytics for Anti-Money Laundering

Bruno Deprez, Toon Vanderschueren, Wouter Verbeke, Bart
Baesens, Tim Verdonck
Money laundering presents a pervasive global challenge, placing a burden on society through the financing of illegal activities. To combat
this phenomenon, the inclusion of network information in the modelling pipeline is increasingly being explored for more effective detection, exploiting the fact that money laundering necessarily interconnected parties. This evolution has resulted in a strong growth of theliterature on social network analytics (SNA) for anti-money laundering(AML) in the past few years. At the same time, the increasing attention for network approaches for AML in the literature has created important challenges for researchers and practitioners. There is no clear
overview of the existing work, there exist limited insights into how different methods compare, and it is not clear which approach works bestin practice.The main objective of this paper is to construct a taxonomy
of SNA for AML using a systematic literature review. This review is
supplemented by an extensive experimental evaluation of graph learning methods, going from manual feature engineering, over shallow representation learning to graph neural networks. These are evaluated on
two money laundering datasets, a crypto-currency and a proprietarytransaction data set. The results highlight the strengths and weaknessesof the different graph learning methods in the specific context of money
laundering.

4 - Optimization in Social Networks to Identify Related
CrimesRichard Weber, Carla Vairetti, Fredy Troncoso, Sebastian
Maldonado, Alex Barrales-Araneda, Valentina Reyes, Pablo
PincheiraWe introduce an innovative methodology for social network analysis
that clusters similar criminal activities without initially identifying theperpetrators involved. This approach leverages an optimization model
to facilitate the optimal grouping of past crimes, utilizing a newly developed similarity measure. Drawing from existing models where adelinquent is represented as a node and a mutual crime as a connecting link, we propose an "inverted social network" framework. In thisnovel network, each node symbolizes a crime event, and a link between
two nodes signifies a similarity exceeding a predetermined threshold.
This methodology employs a recently introduced similarity measure,specifically designed to encapsulate the unique characteristics inherent to this application domain. An optimization model is utilized toaggregate crimes based on their similarity, showcasing the method’s
applicability across various instances where the involved criminals are
yet to be identified. This technique proves invaluable for deciphering
patterns within crime databases and serves as an investigative tool for
examining recent crimes with minimal information on the culprits. We
will present the most recent findings from applying this methodology
in collaboration with the Chilean Prosecutor’s Office, illustrating its
potential and outlining avenues for future exploration.

 MB-32
Monday, 10:30-12:00 - Room: 41 (building: 303A)

Algorithmic Advances in Large Scale
Nonconvex Optimization
Stream: Advances in large scale nonlinear optimization
Invited session
Chair: Marianna De Santis

59MB-32EURO 2024 - Copenhagen

Chair: Matteo Lapucci

4 - A Block Cubic Newton method with Greedy Rule

Andrea Cristofari
1 - Don’t be so Monotone: Relaxing Stochastic Line Searchin Over-Parameterized Models

Leonardo GalliRecent works have shown that line search methods can speed up
Stochastic Gradient Descent (SGD) and Adam in modern overparameterized settings. However,existing line searches may take steps
that are smaller than necessary since they require a monotone decreaseof the (mini-)batch objective function. We explore nonmonotone line
search methods to relax this condition and possibly accept larger step
sizes. Despite the lack of a monotonic decrease, we prove the same fast
rates of convergence as in the monotone case. Our experiments showthat nonmonotone methods improve the speed of convergence and generalization properties of SGD/Adam even beyond the previous monotone line searches. We propose a POlyak NOnmonotone Stochastic(PoNoS) method, obtained by combining a nonmonotone line searchwith a Polyak initial step size. Furthermore, we develop a new resetting technique that in the majority of the iterations reduces the amountof backtracks to zero while still maintaining a large initial step size.
To the best of our knowledge, a first runtime comparison shows thatthe epoch-wise advantage of line-search-based methods gets reflectedin the overall computational time.

2 - A globally convergent gradient method with momentum

Matteo Lapucci, Giampaolo Liuzzi, Stefano Lucidi
Among many effective strategies to speed up first-order solvers forsmooth nonlinear optimization problems, Polyak’s momentum (orHeavy-Ball) certainly stands out. The idea of Polyak consists of exploiting information about past iterates, carrying out a step along a
direction which is a linear combination of a suitable gradient-related
descent direction and the previous step. The idea of this update rule isthat, partly following the direction of last iteration, oscillations can becontrolled and acceleration can be obtained in low curvature regions.
However, except for the quadratic case, the selection of the two parameters defining the update is not trivial; to this aim, we develop an
approach where these coefficients are simultaneously determined by asimple inexact bidimensional search. This strategy allows us to define
a class of gradient methods with momentum enjoying, under reasonably weak assumptions, global convergence guarantees and an optimal
worst-case complexity bound in the nonconvex setting. To the bestof our knowledge, these results are novel to the literature. Moreover,extensive computational experiments show that the gradient methods
with momentum here presented outperform conjugate gradient methods and are (at least) competitive with the state-of-art method for unconstrained optimization, i.e, L-BFGS method.

3 - Stochastic gradient descent with momentum and linesearches

Davide Pucci, Matteo Lapucci
In recent years, the adoption of line-search techniques within incremental gradient-based methods for finite-sums problems has gatheredconsiderable interest among researchers. These techniques enable theuse of substantially larger step-sizes while maintaining robust convergence properties and empirically showing a reduction in the number of
iterations required to achieve high-quality solutions. Most recent research focused on incorporating line-searches within Stochastic Gradient Descent (SGD), as the usage of a descent direction for the minibatch objective is essential to ensure the line-search terminates in a
finite number of steps.
In this talk we discuss the nontrivial integration of these line-searchtechniques within algorithmic frameworks employing Polyak’s momentum terms. In particular, we are interested in structured ways
to combine stochastic gradients and momentum to obtain a suitablesearch direction together with a proper initial step-size for the line
search. We then present a computational comparison, carried out on
both convex and nonconvex test problems, concerning different viableoptions to achieve this goal. We thus offer insights into the potential
strengths and drawbacks of the considered approaches, with respect toother state-of-the-art methods.

60

In this talk, a Block Cubic Newton method is proposed for the unconstrained minimization of a non-convex function with Lipschitz continuous Hessian matrix. At each iteration, the proposed method first selects a block of variables by means of a greedy rule, that is, on the basisof the optimality violation. Then, the next point is obtained by computing an approximate minimizer of the cubic Newton model. Global
convergence to stationary points is proved and complexity results are
given. Finally, some numerical results on optimization problems frommachine learning are presented.

 MB-33
Monday, 10:30-12:00 - Room: 42 (building: 303A)

Dynamics of the Firm I
Stream: Optimal Control Theory and Applications
Invited session
Chair: Peter Kort
1 - Perpetual American Options in Two-Dimensional Diffusion Models

Pavel Gapeev
We study the perpetual American options optimal stopping problem in
two-dimensional diffusion models with linear and multiplicative payoff structure. It is assumed that the risky asset prices are modelled asgeometric Brownian motions driven by constantly correlated standard
Brownian motions. We find closed formulas for the value functions
expressed in terms of the optimal stopping boundaries which in turnare shown to be unique solutions to nonlinear Fredholm integral equations. A key argument in the existence proof is played by pointwise
maximisations of the expression obtained by the change-of-measurearguments. These provide tight bounds on the optimal stopping boundaries as well as describes its shape and asymptotic behaviour for smallor large coordinate values of the risky asset prices. This is a joint workwith Goran Peskir (Manchester).

2 - Singular Control in Inventory Management with Smooth
Ambiguity

Arnon Archankul, Jacco Thijssen
We consider singular control in inventory management under Knightian uncertainty, where decision-makers have a smooth ambiguity preference over Gaussian-generated priors. We demonstratethat continuous-time smooth ambiguity is the infinitesimal limitof Kalman-Bucy filtering with recursive robust utility, heuristically
proposed by Hansen and Sargent (Journal of Economic Theory,
146(3):1195-1223, 2011). Additionally, we prove that the cost function can be determined by solving forward-backward stochastic differential equations with quadratic growth. Using variational inequalities
in a viscosity sense, we derive the value function and optimal control
policy. By a coordinated transformation, we simplify the problem into
two-dimensional singular control, offering insights into model learning and aligning with classical singular control free boundary problems. This study marks the first attempt, to our knowledge, to integrate
singular control with smooth ambiguity.

3 - Investment under Uncertainty: Abrupt Business Environment Changes and Time-to-Build

Nathan Bun, Kuno Huisman, Peter Kort
Recent geopolitical factors have a considerable effect on the economic
environment of firms. We study abrupt business environment changes
that affect demand growth and demand uncertainty in combination with
time-to-build in a dynamic investment model of the firm. The businessenvironment can either be a regime of relative prosperity or a regimeof geopolitical turmoil. The firm has to decide on the timing and sizeof the investment. We find that time-to-build by itself does not affectthe size and only delays the investment resulting in a constant projectEURO 2024 - Copenhagenvalue at the moment of the investment. Time-to-build also does not affect the size greatly if regime shifts are included. The effect of the shift
rates on the investment decision is twofold. The firm prioritizes thecurrent regime in the investment decision if the probabilities of regime
shifts occurring are small. If the probabilities of regime shifts occurring are large, the firm invests based on the weighted average of thetwo regimes.

4 - Optimal entry deterrence under uncertainty

Peter Kort, Richard Hartl, Stefan Wrzaczek
The objective of this paper is to study an incumbent-entrant model
under uncertainty. The entrant knows the realization of the randomvari- able(s) before it makes its decision on entry and eventual capacity
choice. So all the uncertainty is on the incumbent’s side. The sourcesof uncertainty consider the characteristics of the entrant’s product andthe entry cost the entrant needs to incur before becoming active. We
know from the literature that the incumbent-entrant setup could resultin three di’erent outcomes: blockaded entry, i.e., the incumbent behave
like a monopolist and the entrant does not enter, deterred entry, i.e., theincumbent overinvests to make the market unprofitable for the entrant,
and accommodated entry. The main result from our work is that under
uncertainty there can be four outcomes: apart from blockaded entry
and accommodated entry, it can be either 100% entry deterrence or
entry deterrence with a certain probability.

 MB-34
Monday, 10:30-12:00 - Room: 43 (building: 303A)

Optimization and learning for data scienceand imaging (Part II)
Stream: Advances in large scale nonlinear optimization
Invited session
Chair: Simone Rebegoldi
Chair: Federica Porta
Chair: Elena Morotti
Chair: Alessandro Benfenati
1 - A Nested Primal-Dual Iterated Tikhonov Method for Regularized Convex Optimization

MB-35

2 - Parameter-free FISTA

Luca Calatroni, Jean-François Aujol, Charles Dossal,
Hippolyte Labarrière, Aude Rondepierre
We propose an adaptive backtracking and restarting strategy to automate a variant of the Fast Iterative Soft-Thresholding Algorithm(FISTA) for structured optimization problems. Under a generalized (non-restrictive) strong convexity growth condition, we prove
the linear convergence of function values generated by the resulting
parameter-free algorithm. The algorithm’s performance and versatility
are demonstrated on exemplar imaging problems.

3 - Solving large-scale nonlinear least-squares with random Gauss-Newton models

Benedetta Morini, Stefania Bellavia, Greta Malaspina
We address the solution of large-scale nonlinear least-squares problems by stochastic Gauss-Newton methods combined with a linesearch strategy. The algorithms proposed have per-iteration computational complexity lower than classical deterministic methods, due tothe employment of random models inspired by randomized linear algebra tools. Under suitable assumptions, the stochastic optimization
procedures can achieve a desired level of accuracy in the first-order
optimality condition. We discuss the construction of the random models and the iteration complexity results to drive the gradient below aprescribed accuracy, then we present results from our computational
experience.

4 - Mean square convergence analysis of nonlinear distributed recursive estimation under heavy-tailed noise

Manojlo Vukovic, Dusan Jakovetic, Dragana Bajovic,
Soummya Kar
Distributed recursive estimation under heavy-tailed sensing and communication noises is considered. Therein, the sensing and communication noises can be mutually correlated while independent identically
distributed over iterations. A general setting is assumed where boththe sensing and communication noises may have infinite variances. A
consensus+innovations distributed estimator is presented, involving ageneral nonlinearity in both consensus and innovations update rules.
We present several results on the estimator performance. It is shownthat the mean squared error (MSE) of the estimation converges to zero,and moreover, an explicit MSE sublinear convergence rate is established. In addition, almost sure convergence and asymptotic normality results are derived. Analytical and numerical examples confirm
that the presented method converges under the simultaneous heavy-tailcommunication and sensing noises, in contrast with existing estimatorsthat break down under the same setting.

Stefano Aleotti, Silvia Bonettini, Marco Donatelli, Marco
Prato, Simone Rebegoldi
Proximal-gradient methods are iterative first-order techniques thatprove useful in various applications, such as image deblurring and denoising. Alongside the potential issue of slow convergence, one crucial
challenge is the assumption that the proximal operator is computablein closed form. To tackle this, adopting a variable metric approach andintegrating an extrapolation step can enhance the efficiency of thesemethods. However, a significant concern arises from the inexact computation of the proximal operator, often addressed through a nested
primal-dual solver.
In this work, we introduce a nested primal-dual method designed for
efficiently solving regularized convex optimization problems. Our proposed method approximates a variable metric proximal-gradient stepwith extrapolation by executing a predetermined number of primaldual iterates while adjusting the step length parameter through an appropriate backtracking procedure.
Furthermore, we investigate the numerical performance of our proposed method on an image deblurring problem, defining a scaling
matrix inspired by the Iterated Tikhonov method. The numerical results demonstrate that the combination of such scaling matrices and
Nesterov-like extrapolation parameters yields an effective acceleration
towards the solution of the initial problem.

 MB-35
Monday, 10:30-12:00 - Room: 44 (building: 303A)

Stochastic Optimization for Energy
Transition
Stream: Stochastic, Robust and Distributionally Robust
Optimization
Invited session
Chair: Patrizia Beraldi
1 - ML-Driven Day-ahead Offering for RES Generators in
Uncertain Balancing Markets

Wenxiu Feng, Carlos Ruiz
We examine a renewable energy source (RES) generator engaged in
short-term electricity market trading. Traditionally, variable RES generation has been exempted from balancing responsibilities; however,this exemption is no longer prevalent in many markets due to increasing penetration. We consider a generator participating in both a dayahead market and a subsequent balancing market. We seek the RES

61

MB-36EURO 2024 - Copenhagengenerator’s optimal day-ahead offerings in terms of energy volumes,
considering the impact of balancing market settlements. To this end,
we implement and analyze the accuracy of state-of-the-art Machine
Learning (ML) models in forecasting imbalance signs, prices, andother market outcomes, assessing the potential to enhance RES’s profitability. Additionally, we evaluate the impact of employing storage
techniques to mitigate the intermittency associated with RES capacity.
We develop a two-stage stochastic optimization model. The first stage
involves day-ahead decisions that anticipate scenario-dependent balancing settlements and real-time battery operation in the second stage.
The model uses improved forecasting scenarios generated by an ensemble of ML models, considering contextual and past historical market outcomes. We conduct an extensive set of out-of-sample simulations using real data from the Spanish market, and under the two main
financial settlement mechanisms available for balancing markets: single and dual pricing, each with distinct implications for the RES generator.

2 - Stochastic optimization for unit commitment applied tothe security of supply

Jonathan Dumas
Transmission system operators employ reserves to deal with unexpected variations of demand and generation to guarantee the securityof supply. The French transmission system operator dynamically sizes
the required margins using a probabilistic approach relying on continuous forecasts of the main drivers of the uncertainties of the system
imbalance and a 1 % risk threshold. However, it does not consider thecost of units and the lost load. In addition, it does not provide insight
into which means to activate upward/downward and when to face a
deficit of margins.
This work presents a unit commitment strategy using stochastic optimization, including i) the fixed and variable costs of power plants andthe costs of lost load and spillage and ii) uncertainties of the renewablegeneration, consumption, and technical failures of units.
A multi-stage stochastic program formulates this problem and is approximated with a rolling sequence of two-stage stochastic programs.
A simplified version that solves a single two-stage stochastic programis implemented on a case study comprising a few nuclear and fossilbased units due to tractability issues related to the technical constraintsof units. Extensions are required to: Better model the uncertainties.
Consider other risk-aversion approaches, such as using a CVaR orchance-constrained. Deal with the tractability issues. Model the lost
load cost according to the volume of not-served energy and outage duration.

3 - A robust optimization approach for the strategic designof EV charging stations

Giovanna Miglionico, Patrizia Beraldi, Giovanni
Giallombardo
Although the environmental policies have pushed for the introduction of electric vehicles (EVs), the development of the EV industry isslowed down by the issues related to short driving range and extended
recharging time. These challenges underscore the need for increased
investment in the charging infrastructure. We consider strategic andoperational planning decisions arising in managing plug-in electric vehicle (PEV) charging stations. At a strategic level, the station owner,makes decisions on the capacity of the station infrastructures (PEVcharging piles, renewable generators, and energy storages) and on theretail prices for PEV-charging energy. These decisions depend on theunknown values for the productivity of the renewable resources, forthe number of PEV clients and for the wholesale prices. Based on theworst possible outcome for these scenarios the PEV owner would liketo make the best operational decisions regarding the quantity of supplementary energy to be produced. We introduce a robust formulationof the problem and solve it by an iterative method designed for solving
linearly constrained optimization problems, whose non-smooth nonconvex objective function is defined as the pointwise maximum offinitely many concave functions. We provide some computational results to compare the adopted solution method with those derived fromthe literature.

62

4 - Optimizing System Sizing: A Stochastic Bi-Level Approach for Green Electricity Retailers

Patrizia Beraldi
We present a stochastic Bi-Level approach to support electricity retailers in making investment decisions in sustainable energy systems to
supply green electricity. The interaction between the retailer, acting
as leader, and the follower, representing a set of residential prosumers,
is modelled as a Stackelberg game. Both players solve an optimization problem subject to uncertainty related to fluctuating market prices,
weather variables and prosumer load. In particular, the leader solves a
strategic problem referring to the optimal sizing of the energy system
while conjecturing the possible reaction of the follower to the offeredtariffs. The follower reacts to the communicated prices defining the
procurement plan in terms of amount of electricity to purchase fromthe retailer and competitors so to minimize the expected electricity
bill. The follower’s reaction affects the retailer’s profit computed as
difference between the revenue from electricity selling and the total
cost. To account for the retailer’s attitude towards risk, the upper levelproblem includes a safety measure to control the average profit that canbe gained in a given percentage of unfavourable situations. A tailoredsolution approach is developed and tested on a realistic case study calibrated on the Italian electricity market. Numerical results demonstrate
the efficiency of the proposed approach and validate the significance of
explicitly dealing with uncertainty and integrating safety measures.

 MB-36
Monday, 10:30-12:00 - Room: 32 (building: 306)

Game Theory, Solutions and Structures II
Stream: Game Theory, Solutions and Structures
Invited session
Chair: Gustavo Bergantinos
1 - Water quality management based on proportionality inmulti-issue problems with crossed claims

Rick Acosta, Encarnación Algaba, Joaquin Sánchez-Soriano
Water pollutants can be classified into three categories, each of which
includes several classifications of substances. In this paper, we present
a methodology based on bankruptcy models to determine the emission
limits of polluting substances belonging to more than one category. We
model the problem as a multi-issue allocation problem with crossed
claims and introduce the constrained proportional awards rule to obtain
the emission limits. This rule is based on the concept of proportionality and extends the proportional rule for bankruptcy problems. We
also provide an axiomatic characterization of this rule. Moreover, thisallocation rule is illustrated by means of a numerical example basedon real-world data. Finally, managerial and policy implications of thisapproach for water pollution control are given.

2 - Bankruptcy Problems over Time

Maaike Vollebergh, Wout Dullaert, Arantza Estévez
Fernández, Yousef Ghiami
A bankruptcy problem occurs when several agents each claim a portion of an estate that is insufficient to satisfy all the claims, raising
the question of how to divide the estate equitably among the agents.
In the traditional bankruptcy problem, there is a single decision madeat a single point in time. Looking at the applications of bankruptcy,
e.g. water sharing or vaccine allocation, one can see that these are
actually problems where allocations have to be made over time. In
this presentation, we introduce the concept of bankruptcy over timein the Multi-period Bankruptcy Problem. We also present three different perspectives on bankruptcy over time. For each of these perspectives, we present mechanisms to generalize existing bankruptcy
rules to the multi-period setting. Moreover, we formulate propertiesof these multi-period bankruptcy rules, and prove that these properties
are inherited from the traditional bankruptcy rules. Finally, we present

EURO 2024 - Copenhagen
efficient algorithms to apply the bankruptcy rules using recursive methods. By introducing the Multi-period Bankruptcy Problem, presenting
and computing multi-period bankruptcy rules, we want to extend theusefulness of bankruptcy rules in practice and open avenues for further
research on multi-period bankruptcy problems.

3 - Revenue distribution in streaming platforms

Juan Carlos Gonçalves-Dosantos, Ricardo Martinez, Joaquin
Sánchez-Soriano
The streaming industry has experienced exponential growth over the
past decade. Streaming platforms provide subscribers with unlimitedaccess to a diverse range of services, including movies, TV shows, andmusic, in exchange for a subscription fee. We take an axiomatic approach to the problem of how to share the overall revenue obtained
from subscription sales among services or content producers. In doing
so, we provide normative justifications for several distribution rules.

4 - Revenue sharing at music streaming platforms

Gustavo Bergantinos, Juan D. Moreno-Ternero
We study the problem of sharing the revenues raised from subscriptions to music streaming platforms among content providers. We provide direct, axiomatic and game-theoretical foundations for two focal(and somewhat polar) methods widely used in practice: pro-rata anduser-centric. The former rewards artists proportionally to their number of total streams. With the latter, each user’s subscription fee is
proportionally divided among the artists streamed by that user. We
also provide foundations for a family of methods offering a compromise among the previous two, which addresses the rising concern inthe music industry to explore new streaming models that better alignthe interests of artists, fans and streaming services.

 MB-38
Monday, 10:30-12:00 - Room: 34 (building: 306)

Interior point methods
Stream: Conic Optimization: Theory, Algorithms, and Applications
Invited session
Chair: Marianna E.-Nagy

MB-40

3 - A predictor-corrector algorithm for semidefinite programming that uses the factor width cone

Etienne De Klerk
We propose an interior point method (IPM) for solving semidefinite
programming problems (SDPs). The standard interior point algorithms
used to solve SDPs work in the space of positive semidefinite matrices.
Contrary to that the proposed algorithm works in the cone of matricesof constant factor width. We prove global convergence and provide acomplexity analysis. Our work is inspired by a series of papers by Ahmadi, Dash, Majumdar and Hall, and builds upon a recent preprint by
Roig-Solvas and Sznaier [arXiv:2202.12374, 2022]. This talk is basedon joint work with Felix Kirschner.

4 - Simplified Analysis of Kernel-Based Interior-Point Methods for $P_*(kappa)$- Linear Complementarity Problems

Goran Lesaja, Zsolt Darvay, Marianna E.-Nagy, Petra Renáta
Rigó, Anita Varga
We consider kernel based Interior-point methods (IPMs) for$P_*(kappa)$- Linear Complementarity Problems (LCP) that are
based on the class of Eligible kernel functions (EKFs). The importanceof kernel-based IPMs stems from the fact that the iteration bounds oflarge-step IPMs is significantly improved for some instances of EKFs.
However, the derivation of the iteration bounds for particular EKFs isusually long and quite involved which was the motivation to investigate
whether this process can be simplified and under what conditions.
Hence, we introduce additional conditions on the class of EKFs, which
are not very restrictive, however, they allow for the significant simplification of the analysis and calculation of iteration bounds. We derivea new simplified scheme to calculate iteration bounds and illustrate itwith calculation of iteration bounds of most EKFs with polynomial andexponential barrier terms mentioned in the literature. In all cases wematch the complexity obtained using the classical scheme.

 MB-40
Monday, 10:30-12:00 - Room: 96 (building: 306)

1 - The size of the handicap

Marianna E.-Nagy, Tibor Illés, Laszlo Vegh
The handicap is a matrix parameter associated with the linear complementarity problem (LCP). While LCP is an NP-hard problem, there
are special cases, such as when the handicap of its coefficient matrix isfinite, where an interior point algorithm can efficiently solve the problem. However, the theoretical complexity of the algorithm is linearly
dependent on the handicap. Consequently, a key and relevant inquiry
is to give an upper bound on the size of the handicap. We show that,with a fixed matrix dimension, the handicap cannot be arbitrarily large,
more precisely, we prove the conjecture that it cannot be double exponential in the bit size of the matrix.

2 - Ai-Zhang-type interior-point algorithms for solving linear complementarity problems

Anita Varga, Marianna E.-Nagy
This talk investigates a long-step interior-point framework for solving
linear complementarity problems. The algorithmic framework combines the approach of Ai and Zhang and the algebraically equivalenttransformation technique proposed by Darvay. We investigate a set of
sufficient conditions on the transformation function applied in the algebraically equivalent transformation technique, under which the generalalgorithmic framework’s convergence and best known iteration complexity can be proved. We analyze the theoretical and practical role ofdifferent neighborhood definitions. As expected, the proposed conditions depend on the definition of the neighborhood of the central path.

Advances in Stochastic Modelling andApplied Probability Ι
Stream: Advances in Stochastic Modelling and Learning
Methods
Invited session
Chair: Ioannis Dimitriou
Chair: Tuan Phung-Duc
1 - A discrete choice model for multi-product pricing basedon the Markov Chain Choice Model combined withreservation prices

Ivo Adan, Laura Sprenkels, Zumbul Atan
A customer choice model, based on the Markov Chain Choice Modelwith reservation prices, is proposed to solve the problem of finding optimal selling prices for substitutable products. The model includes bothcustomers’ willingness to pay and substitution behavior explicitly, andcan handle any type of correlation between products. A discrete version of the model is formulated and solved to optimality. This discretemodel is instrumental in a simulation procedure to estimate optimalreservation prices in the original continuous model.

63MB-41EURO 2024 - Copenhagen

2 - Call center data analysis and model validation

Ger Koole, Siqiao Li, Sihan Ding
We analyze call center data on properties such as agent heterogeneity,
customer patience and breaks. Then we compare simulation models
that are different in the ways these properties are modeled. We classify
them according to the extend in which they approach the actual service
level and average waiting times. We obtain a theoretical understanding on how to distinguish between the model error and other aspects
such as random noise. We conclude that modeling explicitly breaks
and agent heterogeneity is crucial for obtaining a precise model.

3 - Analysis of Waiting Times in a Novel Two-Class Queueing Model Motivated by Road Traffic

Joris Walraevens, Sara Sasaninejad, Sabine Wittevrongel
Multiple vehicle types typically use the same roads simultaneously.
These different types do not only differ in attainable speeds but alsoin feasible accelerations and decelerations. For instance, freight vehicles accelerate at a slower pace than normal vehicles leading to slower
queue dissipation at for instance intersections and creating gaps inthese queues. In this paper, we propose a novel discrete-time FCFS
queueing model with two types of traffic (freight and regular vehicles
for instance) where service times depend on whether freight vehicles
are present or not at the start of these service times. We analyze thedelay of individual vehicles of both types and characterize gap lengths
created by different accelerations of different vehicle types.

4 - On dual risk models with dependence structure

Ioannis Dimitriou
In this work we consider generalizations of the dual risk model withproportional gains, constant expense rate, and innovations that arise
according to a renewal process. Among others, we consider thecase where the proportional parameter can be a constant or a random variable, as well as cases where several independence assumptions among gain/innovation sizes and the interrarrival times of thegains/innovations are lifted. More precisely, we consider the case withcausal dependence structure, as well as the case where the dependence
is based on the generalized Farlie-Gumbel-Morgenstern copula. We
also consider the case where the proportional parameter is randomly
chosen, as well as the case where we may have two-sided jumps. The
ruin probability and the distribution of the time to ruin are determined.

 MB-41
Monday, 10:30-12:00 - Room: 97 (building: 306)

2 - On optimality conditions for nonsmooth multiobjective
programming problems with vanishing constraints

Tadeusz Antczak
A particular form of extremum problems which attracted the attentionof the optimization community over more than the past decade are aso-called optimization problem with vanishing constraints. This follows from the fact that optimization problems of such a type have been
served as a model for many extremum problems from structural andtopology optimization. In most of works in the optimization literature, optimality and duality results have been proved only for differentiable scalar optimization problems with vanishing constraints. In ourconsiderations, we shall investigate optimality results for a new classof nondifferentiable multiobjective programming problems with vanishing constraints. Namely, we derive both necessary and sufficient
optimality conditions for a new class of such nonsmooth vector optimization problems.

3 - Robust games with vector payoffs

Elisa Caprari, Lorenzo Cerboni Baiardi, Elena Molho
In non-cooperative game theory, it may happen that players’ payoffs
are represented by vector parametric functions, with no information
available on the probability distribution of the parameters. A fruitful approach to this problem is to consider all the realizations of theparameters in a set-valued payoff. The worst case scenarios can beconsidered by the introduction of an appropriate quasi-order relation
between sets. We address the question whether scalarization and robustification can be commuted in a non componentwise framework.

4 - Games with incomplete preferences: a pointwise completion approach

Elena Molho, Elisa Caprari, Lorenzo Cerboni Baiardi
We introduce a pointwise axiomatic approach to completion of incomplete preference relations through appropriate value functions. We
consider an abstract scalarization framework, originally developed in
the field of vector optimization, in order to tackle generic partial quasi
orders. We apply this approach to study non cooperative games where
players have incomplete preferences. In this context, our results allow us to obtain both necessary and sufficient Nash equilibrium conditions. We compare our representation results with other approaches
to preference completion, in the special case where value functions
are Aumann utilities. In detail, we identify minimal sets of Aumannutilities that allow us to find all Nash equilibria of a game with incomplete preferences, with no need of additional assumptions. As a specialcase, we reframe our pointwise approach in vector games, where players have vector-valued payoffs and partial orders on players’ outcomesnaturally arise. We characterize Nash equilibria in vector games byscalarization, extending to the non convex case the results originallydeveloped by Shapley (1959).

Vector and Set Optimization II
Stream: Vector and Set Optimization
Invited session

 MB-42

Chair: Elena Molho

Monday, 10:30-12:00 - Room: 98 (building: 306)

1 - Existence and uniqueness of solutions to the Bellman
equation in stochastic dynamic programming

Decomposition methods for Quantum
Optimization

Juan Pablo Rincon-Zapatero
We obtain new results about the existence and uniqueness of solutions
to the Bellman equation of stationary stochastic dynamic optimization
problems in discrete time and infinite horizon. We extend previous results for weighted and local contractions, through a generalization of
the notion of Banach contraction which is especially well suited forproblem where the shock variables are exogenous and/or the discountfactor may depend on uncertain events. We apply the results obtained
to an endogenous growth model and compare our approach with otherwell known methods, such as the weighted contraction method, countable local contractions and the Q-transform.

64

Stream: Quantum Computing Optimization
Invited session
Chair: Philippe Lacomme
1 - SAT resolution with an Indirect Quantum based approach

Philippe Lacomme, Gérard Fleury
In recent years, there has been a significant increase in the adoptionof Boolean Satisfiability (SAT) models and algorithms to tackle various challenges in Engineering and Computer Science. This surge canEURO 2024 - Copenhagenbe credited to the notable advancements in SAT algorithms during thisperiod, allowing for the resolution of more complex problem instancesacross diverse application domains.
We present a highly specific modelization of SAT, with a resolution
approach based on Quantum Approximate Optimization Algorithm(QAOA). This particular methodology enables the solution of SAT
problems with 96 clauses, currently representing the largest problem
sizes solvable using quantum approaches.

2 - A quantum search algorithm exploiting specific time
windows structure. Application to production units
planning problems.

Rodolphe Griset
Quantum search, spurred by Grover’s algorithm in 1996, has long held
promise in quantum computing for its quadratic speedup over classicalalgorithms in unsorted set searches. However, demonstrating a quantum advantage on finding the best solution to a combinatorial problems
is not straightforward due to the existence of efficient classical heuristics, unsufficient speedup and inherent quantum noise. In this study, weinvestigate a novel approach: constructing a superposition of states of
reduced size by leveraging problem structure before employing quantum search algorithms. We apply this methodology to a multi-machine
job scheduling problem characterized by specific time window structures, local job constraints within machines, and resource limitations.
Our objective is to minimize costs associated with job dates, aiming to
identify solutions below a predefined threshold. This model is particularly suited for multi-year planning in power plant maintenance, wheremachines represent production units and jobs signify maintenance outages for specific units. We exploit the unique structure of spacing constraints between jobs and structured time windows to create a superposition of state subspaces. Subsequently, we apply amplitude amplification by designing two oracles to account for resource constraints
and the cost function. This innovative approach holds promise for improving the efficiency and effectiveness of quantum optimization techniques in real-world applications.

3 - Efficient Encoding for the Traveling Salesman Problem
using Quantum Variational Algorithm

Imran Meghazi, Eric Bourreau
The Traveling Salesman Problem (TSP) stands as a quintessential challenge in combinatorial optimization, with vast implications across industries. Given the absence of efficient classical algorithms to solve
this problem, there’s a great interest in quantum computing to address
such challenge. However, the current quantum computing landscape isdominated by Noisy Intermediate Scale Quantum (NISQ) computers.
Hence, the development of efficient encodings becomes paramount inharnessing the potential of these computers to tackle complex problems like the TSP. While previous encoding techniques have been introduced, they lacked the essential characteristic of proximity between
neighboring solutions. We propose an algorithm capable of efficiently
computing an encoding with proximity. Through experimentation, weshow that our encoding facilitates superior performance of variational
quantum algorithms compared to existing encodings. Furthermore, ourapproach requires fewer gates and qubits compared to conventional algorithms such as QAOA, highlighting its potential for practical quantum computing implementations.

4 - Solving Combinatorial Optimization Problems with
Quantum Annealers

Janez Povh
In this talk, we present a comprehensive analysis of solving two famous
NP-hard problems: the Max-Cut and the Stable Set Problem. While theformer is equivalent to Quadratic Unconstrained Binary Optimization(QUBO) problem, the latter needs to be reframed as a QUBO problem.
We solve both using exact solvers like GUROBI or BiqBin, and approximately by quantum annealing solvers such as the D-Wave quantum processing unit and D-Wave hybrid solver.
In cases where the capabilities of quantum annealing solvers are exceeded, we introduce for the Stable Set Problem a specialized decomposition technique, which effectively breaks down the input graph

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into smaller graphs, rendering them more manageable for quantum annealers. Finally, we conduct a thorough assessment of both quantum
solvers alongside a classical simulated annealing solver. Our extensive
numerical evaluation on various families of benchmark graphs demonstrates that the simulated annealing approach remains highly competitive. This competitiveness is primarily attributed to its simplicity andcomputational efficiency compared to quantum solvers.

5 - Quantum annealing approach for scheduling a job shopwith availability constraints

Riad Aggoune, Samuel DeleplanqueQuantum optimization represents a rapidly evolving domain of research. It involves leveraging quantum computing systems and algorithms to address NP-hard problems. It generally requires the modelingof these problems in the form of Quadratic Unconstrained Binary Optimization (QUBO) models. The limitations of current quantum computers, particularly in terms of number of available qubits and theirability to handle variables, make it significantly challenging to develop
optimization models that accurately reflect real-world constraints. In
this paper, we consider the job shop scheduling problem subject to machine availability constraints. We propose a quantum annealing solution adaptable to both scenarios where machine unavailability periods
are predetermined and when they are variable. It is based on a mathematical representation of the job shop scheduling problem formulated
as a QUBO model [1]. The latter is derived from the first quantum
annealing solution targeting the minimization of the makespan, introduced in [2].
[1] Aggoune, R. and S. Deleplanque. A Quantum Annealing Solution
to the Job Shop Scheduling Problem. ICCSA 2023. Lecture Notes in
Computer Science, vol 14104. Springer. https ://doi.org/10.1007/9783-031-37105-928[2] Venturelli, D. and Marchand, D. J. and Rojo, G. Quantum annealing
implementation of job-shop scheduling. arXiv preprint :1506.08479,
2015.

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Monday, 10:30-12:00 - Room: 99 (building: 306)

Simulation of organizations II
Stream: Agent-based Models in Management, Economic
and Organisation Sciences
Invited session
Chair: Stephan Leitner
1 - Designing Organizations for Resilience:
Making Modes and Incentive Mechanisms

Decision-

Stephan Leitner
In today’s rapidly changing world, organizations are confronted withan environment marked by constant shifts and uncertainty, driven byfactors like rapid technological progress and geopolitical instability. Insuch circumstances, the ability to adapt and thrive becomes crucial foran organization’s survival. While some organizations display impressive resilience in the face of challenges, others struggle to cope with
disruptions. Organizations that can swiftly respond to emerging challenges and seize new opportunities are often better equipped to navigate turbulent times successfully. However, building resilience is notjust about reacting to external pressures; it also involves proactively
shaping organizational structures and processes to enhance adaptability.
This study seeks to shed light on the mechanisms that drive organizational resilience, with a particular focus on key design elements suchas decision-making modes and incentive mechanisms. Using agentbased modeling and simulation, the study aims to explore how theseorganizational design factors interact and influence resilience. Preliminary findings suggest that these design factors significantly impact an65

MB-44EURO 2024 - Copenhagenorganization’s resilience, with the effects being moderated by the complexity of tasks. By exploring these dynamics, this research contributes
to our understanding of organizational resilience and provides insightsthat can inform strategies for mitigating vulnerability.

1 - Java Framework for Evolutionary Computation andMultiple-Criteria Decision Analysis

2 - Human Digital Twin for Improving Occupational Healthand Safety

We introduce a novel Java framework for Evolutionary Computationand multiple-criteria Decision Making named JECDM. It predominantly focuses on facilitating research on a joint area of preferencedriven evolutionary multi-objective optimization. From the programmer’s perspective, the main features of JECDM are as follows. Firstly,
we were focused on efficiency during the development to guarantee alow computational burden imposed when executing the code. Second,the code is highly object-oriented, facilitating readability, reusability,
modularity, and low code redundancy. Thirdly, we reduced the useof external libraries to a bare minimum, securing the framework’s selfsufficiency. From the practitioner’s viewpoint, our framework provides
a vast spectrum of well-organized functionalities. They are categorized
into several top-level modules dedicated to evolutionary computation,
multiple-criteria decision analysis, visualization, and experimental validation. The framework is available online and free to use for academic
purposes. We explain how to operate it in a series of extensive tutorials provided as PDF files accompanied by code examples that are
published along with the framework. These tutorials, e.g., discuss thegeneral structurization of the framework’s components, show how to
implement new algorithms by oneself or demonstrate how to design
experiments even of an extensive nature without much effort spent.

Hanane El RAOUI, Le Khanh Ngan Nguyen, Ian Belton,
Nathan Thompson, Mohamed Elawady
Human-centricity is central to the transition to Industry 5.0, prioritizing diverse human needs from health and safety to personal growth. In
Industry 5.0, digital twins (DT) play a key role in seamlessly bridging
the physical and virtual realms. While these virtual replicas enhance
process safety, they often short in providing a comprehensive overview
of safety issues. Various human factors contribute to the emergence ofrisky behaviours among workers, thereby posing significant threats tohealth and safety. Recognizing this reality underscores the imperative for integrating human elements within digital twins. In the industrial field, there’s a noticeable lack of comprehensive exploration into
the digital twins of workers, mainly stemming from a prioritization of
production efficiency over human needs. The proposal of the Human
Digital Twin (HDT) emerges as a critical approach to realizing humancentricity in Industry 5.0. HDTs, serving as digital counterparts of humans, aim to revolutionize human-system integration by establishinga direct connection between human characteristics and system design
and performance. However, the exploration into advancing HDT development to redefine human roles and unlock full potential remains
limited. This research aims to investigate the role of the human digitaltwin as an enabler of human-centricity in the industry. It proposes an
HDT framework from a human factors perspective, promoting humansafety and well-being in Industry 5.0.

3 - A model of burnout contagion in organizational settings

Claudia Estevez
Burnout is a syndrome recognized by the World Health Organization as
an occupational phenomenon that costs billions of dollars in healthcareeach year. Burnout manifests itself through symptoms of emotional
exhaustion, psychological distancing, and a lack of personal accomplishment, which have been related to a variety of negative individual,group, and organizational consequences. More recently research hasprovided empirical evidence that burnout can be contagious. Explanations behind this contagion suggest that the emotions of an employeetransfer via direct contact with other members of the group, a simple within-groups diffusion process. However, if this were the only
mechanism behind burnout contagion, burnout levels of work groupcolleagues should be similar, which is not what is seen in real data.
Given this contradiction, in this research, I build an agent-based modelof burnout contagion considering individual-level variables, both ingroup and outgroup interactions, and different mechanisms as possible
paths to the diffusion of the syndrome. Model results are then compared to real burnout data collected using an electronic survey from1800 employees in an organization. Results shed light on the underlying complexity of burnout contagion and give support to claims fora dynamic understanding of the phenomenon. This will mean movingfrom more traditional and individualistic research approaches to more
complex ones that match the nature of the phenomenon.

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Monday, 10:30-12:00 - Room: 20 (building: 324)

Preference Learning 2
Stream: Multiple Criteria Decision Analysis
Invited session
Chair: Salvatore Greco
Chair: Roman Slowinski

66

Michał Tomczyk, Milosz Kadzinski

2 - Better Decisions with Less Cognitive Load: The Parsimonious BWM

Salvatore Corrente, Salvatore Greco, Jafar Rezaei
Despite its recent introduction in literature, the Best-Worst Method(BWM) is among the most well-known and applied methods in Multicriteria Decision-Making. The method can be used to elicit the relative
importance of the criteria as well as to get the priorities of the alternatives on the criteria at hand. Here, we will present an extension of
the method, namely, the parsimonious Best-Worst-Method (P-BWM)
permitting to apply the BWM to get the priorities of the alternatives
in case they are in a large number. At first, the Decision-Maker (DM)is asked to give a rating to the alternatives under consideration; after, the classical BWM is applied to a set of reference alternatives to
get their priorities used to compute, then, the priorities of all the alternatives under consideration. We propose also a procedure to selectreference alternatives, possibly in cooperation with the DM, providing
a well-distributed coverage of the rating range. In addition to the standard approach, we propose one alternative way of inferring the priority
vectors in BWM and P-BWM based on the barycenter of the space ofalternative priorities compatible with the preferences given by the DM.
Finally, an experiment with university students has been conducted totest the new proposal. A comparison among BWM, P-BWM, the Analytic Hierarchy Process (AHP), and the parsimonious AHP in termsof the amount of preference information provided by the DM in eachmethod to apply it is also given.

3 - Preference Learning Approaches in Interactive Multiple Criteria Decision Making Using Augmented Training
Sets

Srinivas Prasad, Atilla Ay, Luis Novoa
We investigate machine learning based preference elicitation strategies in the context of Interactive Multiple Criteria Decision Making.
Specifically, such methods employ popular machine learning techniques such as neural networks among others. We study how to makethe training phase efficient in such an approach by employing certain
preference representation structures that provide for augmentation of
the training set. Such augmentations help accelerate the preference
learning phase by making maximum use of the elicited preferences in
eventually estimating the decision maker’s preference profile based onpartial preference information. We study different elicitation strategies
in combination with different levels of augmentation and provide some
empirical results illustrating their effectiveness.

EURO 2024 - Copenhagen
4 - Setwise Preferences: A Case Study in High-Resolution
EO Satellite SchedulingAlex Vasegaard
In Operations Research and specifically for the high-resolution Earth
Observation Satellite (EOS) scheduling problem, setwise preferences
introduce both a robustness to the automation of decisions, but also
a complexity that significantly expands the preference space making
the elicitation and utilization of these preferences a formidable challenge. In EOS scheduling, which is crucial for disaster management,sustainability, agriculture, and security, large areas of interest are often
subdivided into smaller image strips, where the value of obtaining adjacent image strips in close temporal proximity is significantly increased
due to temporal dependencies between images, and consequently thevalue of obtaining a particular set is of higher value than just the individual images. This study introduces a novel representative approach
designed to streamline the elicitation and integration process of setwise
preferences by interpolating outranking results from a sampling technique. The adoption of this approach addresses the challenges posedby the large preference space of setwise preferences, offering a pragmatic approach to incorporating these critical considerations into fully
automated decision-making systems. Consequently, this study contributes to the broader field of OR by providing a viable solution to theintricate problem of setwise preference elicitation and integration.

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MB-47

1 - Group MCDM Methodology for Cyber Attack Mitigation
and Response

Andrej Bregar, Jose Luis Flores, Anas Husseis
Due to the high complexity and relevance of cybersecurity, efficientmanagement of cyber attacks and threats is becoming a strategic technology trend. It elevates the resilience of ecosystems through the useof reactive and proactive strategies, which rely on standardized incident response procedures and the implementation of suitable mitigation countermeasures to prevent cyber attacks. The selection of actions
is a multi-criteria problem encompassing technical and organizational
aspects that must deal with benefits, costs, and the cascading effects between dependent assets. A group decision-making setting is required toincorporate the opinions of different experts on the information, business, and organizational levels. We hence introduce a comprehensive
group decision-making process that helps security operations centres
assess and choose cybersecurity actions. It applies the Delphi technique to unify the judgments of decision-makers and is aligned with
the MCDM model that equivalently uses the quantitative or qualitativevalue function based on the standard scoring system. The veto functionis also aggregated to prevent the selection of actions with an insufficient improvement in efficiency. The model considers criteria fromthe common CIA, ISO/IEC, and NESCOR standards. The framework
utilizes the mappings between compromised assets, vulnerabilities, attack techniques, and responses. It further enhances analytics throughexternal data sources and sensitivity analysis.

2 - Identifying Root Causes and Effects of RFID System
Failures with Integration of AHP, DEMATEL, TOPSIS,
DEA and FMCEA

Monday, 10:30-12:00 - Room: 40 (building: 324)

Sahand Daneshvar, Khaoula Chnina

Speed Networking

FMCEA (Failure Modes Causes and Effect Analysis) has long beenutilized as an effective risk management approach across various industries for examining and assessing the failure modes of diverse products and processes. Nonetheless, this method exhibits several shortcomings associated with traditional RPN (Risk Priority Number) calculations, such as duplicate identification, gaps, and equal weightingof experts, as well as the lack of consideration for various factors likerisk parameter weights, interrelationships, cost, and time and efficiency
notions in risk assessment.

Stream: Making an Impact
Invited session
Chair: Susanne Heipcke
Chair: Ruth Kaufman
1 - Speed Networking

Making An Impact
Meet your peers in a friendly, informal way, through discussions ingroups of 2-3 persons with pairings changing after a fixed time spanof a few minutes. OR is a team business, and knowing people you canturn to for ideas, feedback and support makes all the difference. But it
is not always easy building your network, especially if you are shy or
feel that you are an outsider. This welcoming session is a way of overcoming the barriers to networking, and enjoying yourself while you doit.
You may find it easier if you come with an idea of how you can introduce yourself to others in just two minutes. But don’t worry if you
don’t have time to prepare - you’ll soon pick it up.
If you have business cards, do bring them along; if not, we’ll haveplenty that you can use if you choose.

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Monday, 10:30-12:00 - Room: 50 (building: 324)

MCDA applications in Engineering andManagement 2
Stream: Multiple Criteria Decision Analysis
Invited session
Chair: Andrej Bregar

To overcome these limitations, a five-stage methodology integrating
Analytic Hierarchy Process (AHP), Technique for Order of Preferenceby Similarity to Ideal Solution (TOPSIS), Decision Making Trial and
Evaluation Laboratory (DEMATEL), and Data Envelopment Analysis(DEA) with FMCEA is proposed. First, AHP assigns weights to experts, while FMCEA assesses failure causes considering occurrence,
detection, severity, cost, and time. DEMATEL weights risk factors
and explores their interrelationships. TOPSIS calculates RPNs. DEA
measures efficiency and establishes interrelation matrices. DEMATEL
ranks sub-failure modes based on their interrelationships. Finally, anaggregation procedure combines related sub-failure modes. The RFID
system is used as an example to illustrate the methodology to show theadvantages of the proposed approach.

3 - An Enhanced Simulation-Based Approach for Multicriteria Evaluation Problems

Pappalardo Maria Rosaria, Silvia Angilella, Michalis
Doumpos, Constantin Zopounidis
The sigma-mu efficiency methodology, derived from Stochastic MultiAttribute Acceptability analysis (SMAA), addresses uncertainty in decision alternatives’ performance by constructing Pareto-Koopmans efficiency frontiers (Greco et al.,2019). These frontiers evaluate alternatives based on expected performance µ and variability σ, across diverse
criteria weights. In this paper, we assess alternatives’ performance bysynthesizing the distribution of composite indicator values, incorporating additional parameters beyond µ and σ, namely, skewness and kurtosis. These parameters offer valuable insights into the shape characteristics of the probability distribution of composite indicators, i.e. tailedness and symmetry. Therefore, in this study, we propose revisiting the
SMAA model by adopting the versatile Dirichlet distribution to theweights of the criteria (Saint-Hilary, et al.,2017; Jia et al.,1998). The
Dirichlet distribution, thanks to its flexibility in representing shapes,67MB-48EURO 2024 - Copenhagen

incorporating prior knowledge, and supporting the simplex, is deemedsuitable for modeling weights’ uncertainty without expert elicitationand capturing skewness and kurtosis based on shape parameters α andβ. The proposed approach is employed to evaluate the European Smalland Middle-Sized Enterprises’ (SMEs) performance from 2018-2022
using financial, qualitative, and ESG criteria, creating a comprehensive composite indicator aligned with the sustainable goals of Agenda
2030.

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Monday, 10:30-12:00 - Room: 60 (building: 324)

DEA and Machine Learning
Stream: Data Envelopment Analysis and its Application
Invited session
Chair: María D. Guillén
1 - Exploratory benchmarking with DEA and multidimensional scaling - process, reference model and application

3 - Improving the estimation of production functions
through machine learning: a gradient boosting approach

María D. Guillén, Juan Aparicio
Making accurate predictions of the true production frontier is critical
for reliable efficiency analysis. However, traditional deterministic nonparametric methods like Free Disposal Hull (FDH) or Data Envelopment Analysis (DEA) provide approximations of the production frontier that suffer from overfitting, systematically underestimating firms’
inefficiency and yielding inaccurate predictions of the output. In thiswork, we propose a new approach that, following the machine learning paradigm, provides a more accurate prediction of the underlying
true production frontier by adapting the Gradient Tree Boosting algorithm to the production context. We prove that the new estimatorsatisfies certain required regulatory conditions such as envelopment ofdata and monotonicity. The performance of the new models is evaluated through a computation experience that shows the outperformanceof the new approach in terms of mean squared error and bias in relation
to the standard techniques. Moreover, we show how to calculate different efficiency measures using the estimator determined through the
new algorithm. Nevertheless, from a computational point of view, the
new approach presents thousands of decision variables, making it computationally complex to solve. To tackle this problem, we also propose
and check a heuristic approximation for the exact measures.

Steffen Hoffmann
Benchmarking is a valuable process for improving strategic and operational decisions in both profit and non-profit organizations. An important sub-process of benchmarking is to measure performance gaps anduncover their causes. While Data Envelopment Analysis (DEA) canhandle the evaluation part, Data Mining tasks are suitable to explore thelink between computational results and additional knowledge. Therefore, a new Data Mining process called Exploratory Benchmarking is
proposed. The process integrates DEA and Multidimensional Scaling(MDS) as a reference model to structure decision support using wellresearched methodologies. First, the role of DEA is to evaluate therelated data in context. Next, MDS is used to visualize the results ofthe evaluation. Finally, a systematic analysis is performed to uncovermanifest and latent structures using a specially developed exploration
procedure. The entire process is exemplified by a real-world application in the field of air navigation service providers at airports. However, the approach can be applied far beyond this as a standard process
in business analysis and consulting.

2 - Simulating Data Envelopment Analysis with Machine
Learning: A Clustering-Based Data Preprocessing
Technique for Training Set Selection

Barbara Kaminska, Dimitrios-Georgios Sotiros
Data Envelopment Analysis (DEA) is a non-parametric technique formeasuring the relative efficiency of a set of decision making units(DMUs), on the basis of multiple inputs and multiple outputs. Performing a typical analysis with DEA requires to solve a series of linear
programs, one for each DMU. Therefore, DEA suffers from the curseof dimensionality, i.e., on big data the computational load is very high.
This issue is commonly treated in the literature with the adoption of
Machine Learning (ML) algorithms. Nevertheless, even though theselection of the training dataset is of crucial importance in such algorithms, in the DEA literature this factor is neglected and all methods
rely on random sampling. In this paper, we built on the existing literature and we introduce a clustering-based data preprocessing techniqueto select the training dataset in a way that it represents the entire dataset
as much as possible. We use simulated data to test this new techniqueagainst random sampling under different ML algorithms, number of
netputs and standard DEA models. We further test it on a network DEA
model for two-stage series structures in which the efficiency scores are
represented in a two-dimensional vector. In all cases, the results highlight that the proposed technique increases the accuracy of the ML
algorithms, whereas it may even decrease the required computational
load.

68

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Monday, 10:30-12:00 - Room: M1 (building: 101)

Integrated lot-sizing problems
Stream: Lot Sizing, Lot Scheduling and Production Planning
Invited session
Chair: Silvio Alexandre de Araujo
1 - Population-based search algorithms for an integrated
scheduling problem in the pharmaceutical two-stage
supply chain

Byung Soo Kim, Seung Jae Lee
This paper addresses an integrated scheduling problem in the pharmaceutical two-stage supply chain. The problem is composed of multiple pharmaceutical orders, multiple unrelated factories, and one distribution center. Each factory uses a distinct permutation flow shop
line consisting of hybrid batch/continuous processes and considers
sequence-dependent change-over time. The distribution center picks
up the products by truck shipment. The objective function is to minimize the makespan. Lot sizing, assigning, and production and truck
shipment sequencing processes are the main decisions for the addressed problem. A mixed integer linear program is developed, andtwo types of population-based search algorithms are proposed to effectively and efficiently find the near-optimal solutions for the large-sized
instances. In the small-sized experiments, the performances of the algorithms are verified by the absolute comparison with the mixed integer linear program. In the large-sized experiments, the performancesof the algorithms are evaluated by the relative comparison.

2 - Integrated Scheduling of Continuous Casting and Hot
Rolling in Steel Production: Modeling as a Two-stage
Lot Sizing/Assignment Problem

Hubert Missbauer, Alexander Lohr, Julian Fodor
Scheduling of the sequential production stages Continuous Casting and
Hot Rolling in steel production for a time horizon of about 7 days is
a complex problem that requires considering technical constraints at
both stages as well as limited capacities of upstream facilities (steelmaking and refining) and intermediate stages (slab processing and heat

EURO 2024 - Copenhagenpreservation pits). Due to this complexity, decomposition is required
to handle the problem. Based on the case of an Austrian steel plant
we elaborate on the decomposition principles applicable, mainly temporal decomposition and two-stage block planning, where the former
seems to be closer to practical applicability. In our research we pursue
the latter. We show that the technical characteristics of the two stages
lead to a combined two-stage lot sizing and slab assignment problem
that can be solved by a fix-and-relax heuristic using an MILP solver.
The sequencing problem on the continuous casters and the determination of start and completion times of the casts and of the rolling turns(timing) must be solved subsequently due to the problem decomposition. Numerical experiments indicate that the sequencing problem
can be solved well by established methods provided that appropriate
constraints are formulated at the lot sizing/assignment stage. The timing problem can be solved by resource-constrained project schedulingmethods, but the capacity constraint of slab processing requires extension of the scheduling method we selected.

3 - Hybrid Biased Random Key Genetic Algorithm For The
Capacited Multi-Period Cutting Stock Problem with Setups

Eduardo Silva, Silvio Alexandre de Araujo, Antonio Chaves,
Raf Jans
The Multi-Period Cutting Stock Problem (MPCSP) involves cutting
large stock objects into small items to satisfy demands for each period
while allowing for inventory. The MPCSP is a variation that integrates
the well-known Cutting Stock Problem (CSP) and the Lot-Sizing Problem. Practical applications are found in the furniture and paper industries.
In CSP, the primary goal is to minimize material costs. However, real
production involves auxiliary costs, like switching between cutting patterns, causing interruptions and setup costs. A desirable cutting plan
has fewer patterns. Literature on pattern setups in MPCSP is limited,
and when capacity constraints are considered, the problem becomeseven more challenging.
Pattern-based models and column generation are common approaches
for MPCSP along with another heuristics. A large number of columns
in practical problems is a challenge for pattern-based models, making
techniques like evolutionary algorithms an alternative approach.
This work proposes a Biased Random Key Genetic Algorithm(BRKGA) optimized by Q-Learning (BRKGA-QL) to solve MPCSP
with capacity constraints and pattern setups.
The BRKGA-QL is compared with a hybrid column generation approach from the literature using benchmark instances. Results show
BRKGA-QL consistently outperforms in terms of integer values acrossall instances.

4 - Integrated lot-sizing and cutting stock problem applied
to lattice slab production

Silvio Alexandre de Araujo, Caroline de Arruda Signorini,
Raf Jans
In the construction industry, lattice joists (referred to as an "item" inthis study) are precast structures that combine a steel truss with a concrete base. Their production is based on a client’s order that specifies
the quantity, length, and deadline for delivery. A process manager uses
this information to create a weekly production plan that determines thenumber and type of items to produce in each period. This study considers a real-world production planning problem in a Brazilian lattice slab
factory. The primary concerns of the factory include minimizing thewaste of steel trusses and optimizing setups and inventory. Decisions
regarding purchasing steel trusses (objects) and managing stocks indifferent sizes are also considered. We propose two alternative mathematical formulations for this integrated lot sizing and cutting stock
problem. Additionally, solution strategies are proposed to address theresulting mixed-integer programming models. Computational tests are
conducted to evaluate the effectiveness of the proposed model and thesolution approaches.

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Monday, 10:30-12:00 - Room: M2 (building: 101)

Retail Inventory Management I
Stream: Retail Operations
Invited session
Chair: Rob Broekmeulen
1 - Integrating time-temperature dependent deterioration in
an EOQ-based methodology for perishable products in
multi-echelon supply chains

G.D.H. (Frits) Claassen
Literature shows that order-level inventory systems for perishable
items are often modelled by parametric approaches e.g., a timedependent (Weibull) deterioration rate. In practice however, productdeterioration for perishable products is a complex process, highly product dependent and, crucially, depending on environmental conditions
from origin to final destination in supply chains. We develop a generalizable concept for order-level inventory systems in multi-echelon
supply chains in which product-specific deterioration is derived from
biological sciences. We take time-temperature-dependent deterioration, and multi-echelon supply chains into consideration and integrate
both aspects into a generically applicable EOQ-based methodology.
We demonstrate the concept from a real-life case study for cold chain
management in floriculture. Two common multi-echelon cold chainsfor cut roses in practice are considered and compared. The results showthat the optimal order levels for different actors in the supply chains aresubstantially different. In addition to the sojourn time, the temperature
has a major impact on order levels, the total supply chain costs, and theremaining shelf-life at retail level. We demonstrate that the proposed
concept for extending the EOQ-based model to time- and temperaturedependent deterioration can be easily generalized and applied to otherspecific causes of product-dependent deterioration.

2 - Shelf-life requirements and their impact on contractual
agreements in perishable-product supply chains

Sandra Transchel, Mahyar Tahery, Andreas Gernert
Two primary goals of grocery retailers when managing perishable
products (e.g., food) are (i) to offer products with a high remaining
shelf life to consumers, as those are perceived to be of high quality,and (ii) to minimize waste driven by products that exceed their maximum remaining shelf life. Both goals prompt retailers to integrate aminimum remaining shelf-life requirement, known as "Minimum Life
on Receipt" (MLOR), into the contracts with their suppliers. However, little is known about how such an MLOR requirement affects thecontractual agreement between retailers and food producers and how
it further affects replenishment and production decisions, profitability,
and waste of the retailer and the food producer. In this paper, we investigate how an MLOR requirement of the retailer affects the operational
performance of a wholesale price contact in a two-echelon perishableproduct supply chain. We consider that both the retailer and the foodproducer follow a basestock policy, where the basestock levels are optimized depending on the wholesale price set by the food producer andthe MLOR requirement. Our results challenge widely accepted findings that retailers benefit from organizing their inventory management
following a first-in-first-out (FIFO) issuing policy compared to a lastin-first-out (LIFO) policy.

3 - Definition of the MLOR rule through a bilevel optimization model

Maria João Santos, Sara Martins, Pedro Amorim
The Minimum Life On Receipt (MLOR) rule is established by retailers to impose the maximum age a perishable product can be accepted
upon reception. Such a rule tends to be rigid but allowing the rule’s
flexibility may bring benefits for both producer and retailer. The objective of this work is to investigate such benefits. The problem will
be represented by a bilevel optimization model where the retailer is theleader, and the producer is the follower. In this problem, the rational
response of the producer is to either comply with the fixed MLOR rule69MB-51EURO 2024 - Copenhagen

imposed by the retailer or offer a discount to the retailer’s orders whenever he/she is willing to adopt a flexible rule. An instance composed ofa perishable product with 3 days of shelf life and a time horizon of 3 periods is used to enumerate all possible solutions for the problem. The
solutions are examined in terms of profits and waste generated. Afterwards, an exact solution method is developed through single-level
reformulation aiming to solve the general problem.

4 - A novel competitive model for production and distribution planning of deteriorating products under uncertainty: a case study

Adel Aazami, Sebastian Kummer
This paper develops a new robust optimization model for production and distribution planning considering competition among supply
chains. The model maximizes the profit of a production and distribution corporation providing deteriorating products. The corporation’s
market share is enhanced by concentrating on freshness, some encouraging policies, and competitive characteristics. The model considers
price, distance, and service level the most critical competitive characteristics. Regarding these characteristics, Huff’s gravity model is developed to assess retailers’ behavior better and compute market sharerightly. Therefore, a mixture of compensatory and non-compensatory
approaches considering the comparative attractiveness associated witheach characteristic is devised to estimate the final utility of the regarded
corporation for the retailers. Also, a robust optimization method isused to cope with the uncertainty. Due to the NP-hardness of the problem, an adaptive large neighborhood search algorithm is suggested.
The model’s performance is analyzed by solving a real-world industrial case study. Based on the computational examination, employing
this study can seriously increase the corporation’s profit by improving
its market share.

introduce different values of the cardinality constraint and vary the period length used for parameter estimation in the portfolio optimisation
problem. This approach allows for a comprehensive comparison ofrisk-minimization portfolios, taking into account the selected risk measure, cardinality constraint, and period length simultaneously. Furthermore, we evaluate portfolio performance under various market conditions, specifically in the United States, Germany, and Chinese stock
markets. Evaluation metrics include the Sharpe ratio, the Sortino ratio,
and the Calmar ratio. The results reveal that no single risk measuredominates the others, and significant differences exist among them.
Although the durations of optimal estimation periods vary for different
risk measures, certain patterns emerge from the findings.

3 - Competition in Liquidity Provision: Analysis of HighFrequency Market Making and Policy Implications

Katsumasa Nishide, Takaki Hayashi
We construct a financial market model wherein market makers post
limit orders to compete for liquidity. We show that market makers may
relinquish liquidity provision because of competition if the fundamental value of the asset is volatile. Besides the general perception thatcancellation harms market liquidity, we find that cancellation can sustain liquidity and prevent depletion. Moreover, this finding, as well asothers, leads to an important policy implication that the cancellation
strategy, one of the most prominent characteristics of high-frequency
trading, can positively affect market liquidity and should not be strictly
regulated.

 MB-52
Monday, 10:30-12:00 - Room: 8003 (building: 202)

Combinatorial optimization approaches for
freight deliveries MB-51
Monday, 10:30-12:00 - Room: M5 (building: 101)

Stream: Combinatorial Optimization
Invited session

Market risk in a volatile world

Chair: Claudia Archetti
Chair: Yanlu Zhao

Stream: Risk management in financeInvited session

1 - Freight-on-Transit for urban last-mile deliveries withroad transportation and metro line

Chair: Aleš Kresta
1 - Sparse Vine copula-based portfolio optimization

Illia Kovalenko, Thomas Conlon, John Cotter
This paper studies the use of the Regular Vine copula models in thecontext of the construction of portfolios with varying sizes. We findthat copula-based portfolios outperform the naïve equally-weighted
benchmark before transaction costs. Significantly reduced tail risk
makes copula-based portfolios especially desirable for investors withhigh risk aversion. The superior performance is more pronounced during periods of high dependence asymmetry and high market volatility. Sparse vine models, in which independence pair-copulas prevail,provide significantly improved results for large portfolios across various performance measures, specifically reducing turnover. The improvement of portfolio performance, however, is attenuated as we taketransaction costs into account. Limiting large portfolio turnover by increasing investment horizon or rebalancing error tolerance restores theoutperformance of copula-based strategies.

2 - Comparison of risk-minimization portfolios with cardinality constraints and period lengths

Aleš Kresta, Qian Gao
In this study, we empirically compare the performance of portfolios
optimized to minimise selected risk measures under a cardinality constraint. The chosen risk measures include standard deviation, semideviation, minimax, mean absolute deviation, maximum drawdown,
conditional value at risk, and entropic value at risk. Additionally, we

70

Yanlu Zhao, Mingda Liu, Zibo Zhang, Yixiao Huang
In this study, we explore the integration of cargo delivery into public transportation as an innovative and eco-friendly approach for citybased last-mile deliveries. This freight-on-transit (FOT) study, isdriven by a prominent courier company’s (SF Express) vision for a
future where freight is transported via metro systems in Beijing, aiming to utilize the most of idle metro carriage space. The complexityof this hybrid road-subway delivery system, which needs to adhereto specific time schedules and split delivery, makes it a challenging
problem to solve. To tackle this, we formulate it as a mixed integer
programming model and develop an exact algorithm within a branchprice-and-cut framework. This method is capable of achieving nearoptimal solutions efficiently. Our real-world data testing demonstrates
that our strategy significantly outperforms the company’s existing delivery strategies. In addition, we conduct various policy tests to assess
the impact of dynamic dispatching and routing, considering changes inthe size of the delivery fleet and the frequency of dispatches.

2 - Pricing and bundling decisions considering driver behavior in crowdsourced delivery

Alim Buğra Çınar, Claudia Archetti, Wout Dullaert, Markus
Leitner, Stefan Waldherr
Crowdsourced delivery utilizes the services of independent actors. As
opposed to traditional modes of delivery, availability and acceptancedecisions of crowdshippers are uncertain and cannot be fully controlled
by an operator. We consider a setting in which an operator groups
tasks into bundles and in which the resulting bundles are offered to
crowdshippers in exchange for some compensation. Uncertainty in thecrowdshippers’ behavior who may accept or reject offers is consideredEURO 2024 - Copenhagen
via (individual) acceptance probabilities. We consider generic probability functions whose main parameters are the compensation offered,
the number of tasks in a bundle, and the total detour to deliver a bundle. The objective of the resulting optimization problem is to minimize
the expected total cost of delivery. We propose a mixed-integer nonlinear programming (MINLP) formulation that simultaneously decides
how to group tasks into bundles, which bundles are offered to which
crowdshipper, and the compensation offered for each bundle. We showthat this MINLP can be reformulated as a mixed-integer linear programwith an exponential number of variables. We present a column generation algorithm for solving instances of the latter whose pricing subproblem corresponds to an elementary shortest path problem with resource constraints (ESPPRC) and a nonlinear objective function. Our
experiments show that the algorithm is capable of solving large instances.

3 - Exact approaches for the green commodity-constrained
split delivery vehicle routing problem

Kamyla Ferreira, Claudia Archetti, Diego Delle Donne,
Reinaldo Morabito, Pedro Munari
We address the Green Commodity-Constrained Split Delivery Vehicle Routing Problem (GC-SDVRP), which involves designing efficientand environmentally friendly delivery routes that reduce the carbon
dioxide (CO2) emission associated with the transport of multiple commodities. The GC-SDVRP considers that different types of commodities are available at the depot, and each customer has specific demands
for different types of commodities. Multiple vehicles may visit a customer to deliver different types of commodities, as long as all commodities of the same type are delivered by the same vehicle, thus reducing customer inconvenience caused by split deliveries. Despite its
practical relevance, the green variant of this problem has not been studied in the literature so far. In this talk, we introduce the GC-SDVRP
and propose a branch-and-cut approach that is based on the solution ofa relaxed formulation that provides a lower bound on the optimal value.
Additionally, we adapt two other formulations from the literature. The
results of computational experiments using benchmark instances indicate the effectiveness of our proposed method, which outperforms theresults of other approaches. Our computational experiments show thatminimizing emissions does not necessarily imply minimizing the travel
costs, and vice versa. Moreover, it is computationally easier to solve
the problem with CO2 emissions than with the traditional objective ofminimizing travel costs.

 MB-54
Monday, 10:30-12:00 - Room: S01 (building: 101)

Crew Planning in Public Transport
Stream: Public Transport Optimization
Invited session
Chair: Bart van Rossum
1 - Integrated railway train unit and crew scheduling using
column pre-generation

MB-55

to auxiliary tasks like shunting and unit coupling/decoupling, which
are determined during the integrated process but unknown in advance.
Another challenge is the need to pre-generate deadheading trips andincorporate them into pre-generated shifts. This challenge does notexist during the separate procedure. In theory, there is a huge number of deadheading trips, leading to a severe increase on the numberof pre-generated shifts. We explore solutions to these issues throughboth synthetic and real-world examples, demonstrating our approach’s
effectiveness in solving these challenges.

2 - Robustness in railway crew schedulingPaul Päprer, Janis Sebastian Neufeld, Udo Buscher
Traction unit drivers have become an increasingly critical resource forrailway companies, making the railway crew scheduling problem an
essential step in the planning cascade. While efficiency has traditionally been the primary goal, the vulnerability of schedules to disruptions requires robust planning. Compared to other planning stages in
the railway sector, crew scheduling robustness has received less attention, although its counterparts in the bus and airline industries arethoroughly evaluated. We address this gap by investigating robustness
in railway crew scheduling problems and its impact on efficiency. Our
approach involves solving real-world problems using column generation. We assess existing robustness indicators and adapt a performanceindicator from timetabling called t-robustness. This criterion is new
for crew scheduling. On the one hand, t-robustness penalizes trainchanges, which are critical since delays are transferred to other circulations. On the other hand, buffers at important spots within a shift are
rewarded. We evaluate the dependencies between efficiency and robustness through empirical analysis of real-world planning problems,
highlighting the significance of our proposed t-robustness measure. By
integrating robustness considerations into crew scheduling, railway operators can achieve shift plans that not only optimize efficiency but also
better withstand unforeseen disruptions, ensuring resilient operations.

3 - Benders Decomposition for Robust Tactical Crew
Scheduling

Bart van Rossum, Twan Dollevoet, Dennis Huisman
We consider robust tactical crew scheduling for a large passenger railway operator, whose timetable is subject to minor but uncertain updates on a weekly basis. Before the start of the planning period
we must select a number of templates, specifying the time windows
in which crew is supposed to work. These templates must be costefficient whilst providing sufficient capacity for the realised updated
timetables. Moreover, the templates must be compatible with the rostering process, i.e., we wish to limit the number of different templates
used and ensure that there are not too many templates on early or latemoments of the day. Throughout the planning period, the templates
are assumed to be fixed and the updated timetable is released on a
weekly basis. All tasks in the timetable must be covered using thecapacity provided by the templates, or, if necessary, costly reserve capacity. We model the template selection problem as a scenario-based
robust optimisation model, for which we propose a Benders decomposition algorithm. We conduct computational experiments on real-life
instances from Netherlands Railways and show that our algorithm isable to solve instances featuring up to three scenarios and up to 1,000
tasks per scenario to optimality. Including more scenarios, allowing
for more flexibility in choosing templates, or enlarging the template
length all lead to reductions in reserve crew costs without increasing
template-based crew costs.

Chuhan Yin, Zhiyuan Lin, David Watling
In passenger rail operations, train unit and crew scheduling are key
processes. Train unit scheduling assigns rolling stock, while crewscheduling allocates personnel like drivers. Both tasks, critical for
optimizing vehicle and crew use to save costs, are complex NP-hardproblems traditionally solved separately as two stages. This sequential
approach limits achieving truly optimal schedules. An integrated approach, considering both simultaneously, promises more optimized solutions and has been successful in bus operations, reducing operationalcosts and crew usage, but is under-researched in rail planning. In thisstudy, we introduce an integrated approach for scheduling train units
and crews by utilizing pre-generated shifts (duties). This approach
faces unique obstacles in the rail sector, particularly when employing
the pre-generation strategy. A key challenge involves allocating staff

 MB-55
Monday, 10:30-12:00 - Room: S02 (building: 101)

Transportation Network Modelling andOptimization II
Stream: Transportation
Invited session
Chair: Luca D’Acierno

71MB-56EURO 2024 - Copenhagen

1 - Time-constrained hub-based networks: Designing beyond location-allocation

Ana María Anaya-Arenas, Esteban Ogazón, Angel Ruiz
Hub-and-spoke problems have been widely studied in the literature andas they are more of a strategic problem, little effort has been devoted
to incorporating service-related restrictions into them. However, current trends in transportation seek more flexibility and tighter service
schedules. In this talk we will discuss the nuances of integrating time
constraints into hub-and-spoke systems. Two alternative and efficient
formulations for hub network design with strict time restrictions andservice levels will be presented. To illustrate our contribution, a casestudy inspired from the healthcare network of Québec, Canada, willallow us to show the interest and importance of considering this restriction explicitly in the model.

2 - Coordinated Passenger Flow Control into Urban RailTransit Systems to Mitigate Overcrowding during largescale Events

Knut Haase
Responsible policymakers and transportation planners must ensure
safe transportation during large-scale events, especially in urban railtransit systems that are not designed to handle high demand, as overcrowding can lead to dangerous situations and even fatalities. This
paper presents an approach to optimize the number of passengers allowed into urban rail transit systems, considering the limitations of
trains and station platforms, while also ensuring that as many people
as possible can be transported. To achieve this, we developed an optimization model embedded into an iterative heuristic that incorporates
the predicted demands, real time-data and the capacity limitations of
the entire system. To ensure the accuracy and robustness of our model,
we evaluated our approach on a data set based on a comprehensive
transportation demand simulation. Our results indicate the effectiveness of our approach. By iteratively optimizing the allowance, we canachieve a good balance between the capacity utilization and the number of people transported. Our system can assist policymakers andtransportation planners to control the passenger flows and thereby prevent overcrowding and accidents.

3 - The influence of passenger weight on rail and metro
running times: Theoretical formulation and applications
to the case of Line 1 of the Naples metro system (Italy)

Luca D’Acierno, Luca De Matteis, Rosario Stefanelli,
Marilisa Botte
Analyses of transport systems require knowledge of the interactions
between travel demand and transport system performance. In general,
the travel demand depends on transport system performance, and inturn the transport system performance depends on how many users use
the same element at the same time. In the literature, rail and metro systems are generally considered uncongested (i.e. performance does notdepend on the travel demand) at least as regards train running times.
This contribution examines the dependence of train running times on
the number of transported users. Indeed, in rail and metro contexts,
the weight of carried passengers in the case of a full load has the sameorder of magnitude as the unloaded train weight. Hence, variation in
the maximum traction effort and the motion resistance between a fully
loaded and a completely empty condition implies variations in train
running times. Moreover, in frequency services, even in the case of
constant demand (i.e. rigid demand assumption), the number of users
boarding each train depends on the service frequency. Hence, sinceservice frequency depends on the running times through the cycle time,
it is possible to identify a fixed-point problem between the number ofboarding passengers and train running times. The application to thecase of Line 1 of the Naples metro system has shown the extent ofthese variations and the extension times conditions that allow defininga cycle time independent of demand variability.

4 - A Variable Fuel Consumption Curve in the Tramp Ship
Routing and Scheduling Problem

Sahar Moazzeni, Gabriel Fuentes, Julio C. Góez

72

This study explores tramp shipping operations, where efficient fuelmanagement is important for financial success, environmental responsibility, and global sustainability goals. The paper focuses on integrating speed-dependent non-linear fuel consumption curves into the basic
tramp ship routing and scheduling problem, recognizing the need for
precise fuel consumption estimation. A non-linear mixed integer programming model is proposed for routing, scheduling, and speed optimization. The model is to study the impact of variable elasticities
across different speed intervals, aligning closer to the complexities ofa per vessel-specific fuel consumption. Finally, we will examine theimpact of introducing speed and specially speed-dependent fuel-speed
elasticities and calculate the time charter equivalent as an economic
measure, based on the outputs of the model with constant elasticity
and the model with speed-dependent elasticity.

 MB-56
Monday, 10:30-12:00 - Room: S04 (building: 101)

Freight railway transportation
Stream: Transportation
Invited session
Chair: Ricardo Garcia-Rodenas
1 - Scheduling shared passenger and freight transport for
high-speed railway networks

Siqiao Li, Li Wang
This study investigates the potential for a shared freight and passenger
system to effectively utilize the remaining capacity of high-speed railway networks after serving passenger demand. Two sharing modes,
which determine the mixing of passenger and freight flows, are considered and jointly optimized. A space-time network is constructed tocapture the passenger/freight trajectories on an existing schedule. A
penalty cost based on train load factors is introduced for each trip torepresent the influence of shared transportation upon passenger satisfaction, which allows us to analyze the interplay between passenger
and freight flows. The model is first formulated as a mixed-integer
program that minimizes service and routing costs and then reformulated into a path-based model. A Benders decomposition approach is
proposed to decompose the problem into two subproblems. A columnpool-based approximation approach is proposed to efficiently generate
space-time paths and obtain good upper bounds. Two tailored acceleration techniques are presented to overcome the problems of slow iteration and wild oscillation. The developed algorithm is tested on 2
small-scale cases and 12 large-scale cases. The computational study
provides insight into how the optimal network is affected by the trainload factor, penalty cost, sharing mode, and commodity volume.

2 - Determining the optimal Point in Time and Duration of
Track Possession Times

Yannick Burmeister, Arturo Crespo Materna, Andreas Oetting
While track possession times (due to railway construction and maintenance works) for the most part render portions of the railway infrastructure temporarily unavailable, they are necessary to upkeep theits long-term serviceability. Therefore, possession times and the construction processes themselves must be planned and executed in sucha way as to minimize the impact on railway operations. This contribution proposes an approach to determine the optimal point in time andduration of track possessions with a focus on ETCS installation. The
approach utilizes mathematical modeling techniques and a hybrid between an exact and heuristic method while considering effects on therailway operations, construction management requirements, and production resource constraints like available labor force. The proposed
approach enables a better capacity consumption in the railway networkby allowing an optimized planning of possession times and a faster realization of railway construction works. To validate the approach, theresults are compared with real-world track possession time planning.

EURO 2024 - Copenhagen
3 - A Hybrid Benders Decomposition for Multi-stop Railway
Freight Loading and Unloading Problem

Chenhao Zhou, Xiaorong Ren, Li Xue
Railway freight transportation plays a crucial role in China’s economic
growth and has received substantial investment in railway infrastructure. The introduction of the high-speed freight train presents thelogistics market with a competitive alternative between air and landtransport. This new service enables cargo loading and unloading at
various stops along the journey in order to maximize carriage utilization. Given the tight transit time at each stop, the high-speed freight
service needs to allocate freights into different carriages without any
remarshalling — such as removing existing freights before putting everything back — between freights. As such, the freights must be handled in a First-In-Last-Out (FILO) order and requires a careful plan ofcarriage assignment and storage allocation for each freight, which motivates this multi-stop railway freight loading and unloading problem.
Considering the aforementioned operational features, a fresh variant of
the bin packing problem (BPP) can be defined, i.e., the Temporal BPP
with FILO policy (TBPP-FILO). This novel variant incorporates thesequential requirements of loading and unloading processes, presenting a unique and difficult challenge compared to existing BPP variants. A hybrid Benders decomposition approach is developed using
the technique of Lagrangian relaxation, which adjusts the allowance of
capacity violation through the update of Lagrangian multipliers.

4 - An Optimization Model for Determining Access Costs inRail Freight Transportation

Ricardo Garcia-Rodenas, Esteve Codina, Luis Cadarso, Maria
Luz Lopez, José Ángel Martín-Baos
The liberalization of European railway markets necessitates a verticalseparation governance framework, with Infrastructure Managers (IMs)providing resources and Freight Operating Companies (FOCs) utilizing them. Effective pricing of track access between these entities isvital for profitability and system efficiency. Additionally, achieving amodal shift of freight from road to rail is crucial for road safety and EU
objectives alignment. This paper introduces a novel pricing methodology for train paths, optimizing infrastructure utilization by IMs whileconsidering competitive dynamics and environmental impacts. It addresses challenges such as elastic demand, integrating network capacity into pricing strategies, and managing non-additive costs and path interdependencies. The proposed model incorporates dynamic demand
patterns, temporally variable capacities, and total revenue considerations across the planning horizon. A discretization-based approach, focusing on irregular time intervals and unit freight tonnage, is developed
for solving the continuous model. This involves linking freight tonnage
units to a prototype train, with a discrete event simulation model employed. The objective function is also discretized, yielding a finite setof optimization variables to be optimized subject to simulation model.
An application of this approach is demonstrated through a case studyof the Mediterranean Corridor.

 MB-57
Monday, 10:30-12:00 - Room: S06 (building: 101)

Decision making in Insurance and Pensions
Stream: Modern Decision Making in Finance and Insurance
Invited session
Chair: Peter Hieber
1 - Optimal consumption, investment, and insurance understate-dependent risk aversion

Mogens Steffensen
We consider classes of dynamic decision problems where an investor
maximizes utility but faces random preferences. We consider three versions of the problem. In one version, the investor optimizes expected

MB-57

utility where the expectation is taken with respect to both financial andpreference uncertainty. That is based on Steffensen and Søe (2023).
Another version is based on certainty equivalents. We tackle the timeconsistency issues arising from that formulation by applying the equilibrium theory approach. The case where the investor learns nothing
about his preferences as time passes is based on Desmettre and Steffensen (2023). We also discuss another case where risk aversion is anobserved stochastic process.
References:
Desmettre, S., & Steffensen, M. (2023). Equilibrium investment
with random risk aversion. Mathematical Finance, 33, 946-975.
https://doi.org/10.1111/mafi.12394
Steffensen, M., & Søe, J. (2023). Optimal consumption, investment,
and insurance under state-dependent risk aversion. ASTIN Bulletin:
The Journal of the IAA, 53(1), 104-128. doi:10.1017/asb.2022.25

2 - Value-at-Risk estimation in nested simulations

Alexander Strack
Since the ratification of Solvency II, the determination of the Valueat-Risk in the insurance industry has gained renewed importance. We
examine the convergence of estimators of the Value-at-Risk or, more
specifically, of quantiles within the nested Monte Carlo framework as
they occur in the internal models of (typical life) insurance companies. Due to the numerous model uncertainties, robust methods andmodel-free results are of particular importance. To this end, we provide sharp results regarding the deviation of the quantiles of a perturbed random variable to the quantile of the undisturbed random variable depending on the perturbation, while reducing the assumptions
to a minimum without sacrificing the sharpness of the results. These
results are combined with classical results from estimation theory toderive almost-sure convergence rates for the Value-at-Risk estimator
under rather weak assumptions.

3 - Catastrophe Risk Management

Tadashi Uratani
Serious disasters had occurred in Japan as Kobe earthquake, EastJapan Tsunami and Covid-19 and Noto earthquake of this year. The
economic burden to recover from disasters is expanding government
deficit further. Reinsurance and securitization such as catastrophe bondare analyzed for application to overcome future financial burden of disasters .

4 - The Value of Lender-Initiated Loan Credit Insurance in
Supplier Financing

Hechen Zhong, Nina Yan, Jizhou Lu, Kin Keung Lai
Supply chain finance (SCF) solutions facilitate access to financing forsmall and medium-sized suppliers. However, in volatile market conditions, payment defaults among supply chain participants can affectthe stability of SCF systems. To effectively mitigate default risks, financial service providers often purchase loan credit insurance (LCI) totransfer potential losses to insurers. When retailers cannot repay or default on their payment obligations, insurers will compensate the lender
within the policy terms. Using a game-theoretical approach, we assess
the value of LCI for the lender and the capital-constrained supplier.
Our analysis reveals that the lender can benefit from LCI when the insurance deductible is relatively high, and the insurer’s loading factor
is relatively low. The capital-constrained supplier with higher capital
investment and lower unit production cost can also benefit from LCI.
Furthermore, when the insurance scale and loading factor are low, thesupplier is more likely to increase profitability. However, the supplier
can achieve superior investment efficiency in scenarios with higher insurance scale and loading factor, suggesting increased capital investment to benefit from LCI better. These findings underscore the valuethat the lender-initiated LCI can bring to capital-constrained suppliers,
offering significant insights for small and medium-sized suppliers andfinancial service providers engaged in SCF practices.

73

MB-58EURO 2024 - Copenhagen MB-58
Monday, 10:30-12:00 - Room: S07 (building: 101)

Heuristics for Vehicle Routing 3
Stream: VeRoLog - Vehicle Routing and Logistics
Invited session
Chair: Lucas Contreras Rada
1 - A new Simuheuristic for Stochastic Cross-Dock
Scheduling Problem With Time Windows Under Uncertainty

Wei Yang, Yang Wang, Jin-Kao Hao
Cross-docking, a rapid logistics strategy, emerges not just as an option
but as a strategic necessity in the era of just-in-time inventory and manufacturing systems. In this context, we study a novel stochastic crossdock scheduling problem considering uncertain truck arrival times tominimize the total penalty of untransferred goods and truck departure
delay. A new hybrid genetic simheuristic framework is proposed thatembeds a simulation-based local search into a variable neighborhood
decent (VND) algorithm. VND optimizes on the deterministic version, in each iteration, if the current solution is identified as a promising solution (not necessarily a local optimal solution), SBLS, where
the neighborhood is evaluated by the simulation instead of the deterministic objective value, is triggered in the expectation of finding a
local optimal scheduling policy under the stochastic version. A fastevaluation technique for sample scenarios is designed, independent of
the objective function, which allows the expectation of the objective
under a given sample to be evaluated without simulation, thereby improving the efficiency of the search. And we extend the fast evaluation
technique to scheduling problems with a similar structure. Finally, systematic experiments and analysis prove the efficiency of our proposed
algorithmic framework and fast evaluation technique.

2 - Multi-Objective Shipment Consolidation and Dispatching Problem

Ozgur Ozpeynirci, Ozge Buyukdeveci, Selin Özpeynirci
In recent years, with the increase in global production and demand,transportation problems have become a widely studied area, and studies focus on providing high-quality service at the lowest cost. This
study considers a bi-objective shipment consolidation and dispatching problem with the objectives of minimizing the total cost and thetotal distance. In order to create a non-dominated solution set, amulti-objective mixed integer linear programming model is developed
and augmented epsilon constraint method is used to generate the nondominated frontier. However, this approach is not capable of finding
the non-dominated solution set in a reasonable time, even for smallsized instances, and therefore, we propose a multi-objective variableneighborhood search heuristic. To measure the performance of the
proposed approach, a computational experiment is conducted on randomly generated instances available in the literature. The experimental
results indicate that the multi-objective variable neighborhood searchheuristic performs efficiently in reasonable time.

3 - New Reinforcement Learning Algorithms for Pickup and
Delivery Routing Problems

King Tong Wong, Tsung-Sheng Chang, Jamal Ouenniche,
Joosung Lee
The Pickup and Delivery Routing Problems (PDRPs) are challenging
combinatorial optimization problems with significant real-world applications. This paper proposes new Reinforcement Learning (RL) methods to address this type of routing problems. The typical mathematical programming formulations and the existing solution methods are
first presented. Then, the concepts of Reinforcement Learning (RL)
and Deep Learning are introduced, and the PDRP is reformulated as a
RL problem. Several RL algorithms including new proposals are discussed and their performance is compared to an exact solution. This
research contributions include employing RL to solve PDRP and comparing various RL algorithms including new proposals.

74

4 - Hybrid Genetic Algorithm a new Perspective to Solving
Capable Vehicle Routing Problem (CPVPR)

Lucas Contreras Rada, Helman Enrique Hernandez Riaño,
Jorge Mario López PereiraThis study focuses on solving a capable vehicle routing problem(CVRP) using a genetic algorithm (GA). The CVRP is a complex optimization problem that often arises in supply chains when routing vehicles with different populations. The study proposes a new solution thatuses a metaheuristic to optimize the implementation between crossoverand mutation of new operators. These operators use different strategies
to search for better solutions generated from an initial population. The
CVRP is formulated as a mixed integer programming (MIP)problem,
where the genes represent decisions about which customers to visit andwhich vehicles to use for the route.
The existing literature provides ample support for this issue, with many
articles discussing the indicators and comparisons of experimental tests
post-realizations that show the efficacy of the proposed algorithm.
Through significant advances in the solution problem, the study obtained a GA-modified solution to the CVRP by adjusting different parameters to identify the most adaptable solution. The results were thencompared with other solution methods as solvers focus on the sameproblem.

 MB-59
Monday, 10:30-12:00 - Room: S08 (building: 101)

Revenue Management in Sharing/Platform
EconomyStream: Pricing and Revenue Management
Invited session
Chair: Dong Li
1 - Enhancing carsharing pricing and operations throughintegrated choice models

Selin Ahipasaoglu, Beatriz Brito Oliveira
Balancing supply and demand in free-floating one-way carsharing systems is a critical operational challenge. This paper presents a novel
approach that integrates a binary logit model into a mixed integer linear programming framework to optimize short-term pricing and fleet
relocation. Demand modeling, based on a binary logit model, aggregates different trips under a unified utility model and improves estimation by incorporating information from similar trips. To speed up
the estimation process, a binning approach is used, where variables
such as location and time are categorized into a few bins, which is particularly beneficial for trips with limited observations. The modelingframework adopts a dynamic structure where the binary logit model
estimates demand using accumulated observations from past iterations
at each decision point. This continuous learning environment allows
for dynamic improvement in estimation and decision making. At theframework’s core is a mathematical program that prescribes optimallevels of promotion and relocation. The framework then includes simulated market responses to the decisions, allowing for real-time adjustments to effectively balance supply and demand. Computational
experiments demonstrate the effectiveness of the proposed approachand highlight its potential for real-world applications. The continuous
learning environment, combining demand modeling and operational
decisions, opens avenues for future research in transportation systems.

2 - Enhancing the performance of carsharing systems by
optimizing multi-attribute pricing plans

Masoud Golalikhani, Beatriz Brito Oliveira, Gonçalo Correia,
José Fernando Oliveira, Maria Antónia Carravilla

EURO 2024 - Copenhagen
Carsharing operators commonly offer several pricing plans tailored for
different customer profiles, constructed from various attributes such as
registration, travel distance, and travel time fees. Each plan may havedifferent rates across high/low demand periods and areas to increase
fleet utilization and maintain a balanced supply-demand system. However, in most existing works, the heterogeneity of users is not considered, and plans consist of only a single attribute (i.e., travel time fee).
In this study, we formulate a mixed-integer linear programming modelwith the objective of profit maximization to optimize multi-attributeplans with time- and location-dependent rates, considering customer
preferences and travel behavior. A discrete choice model is incorporated to estimate customer response to the value of plan attributes. We
utilize real-world data from the Brooklyn taxi trip dataset to validate
results and provide managerial insights. Results demonstrate the effectiveness of this approach in improving system profitability, particularly
through time- and location-dependent rates. The model can find the optimal or near-optimal solutions for real-sized problems within a reasonable time limit. Moreover, we developed an approximate algorithm toefficiently produce high-quality results for large-scale instances withina shorter timeframe. Finally, we delivered valuable managerial insights
regarding customer segmentation, pricing plan complexity, and parameter sensitivity.

3 - Logistic Service Sharing between a Food delivery platform and an E-com platform

M Sivarama Anandha Krishnan, Rahul Marathe, Pradnya
KarbhariIn e-commerce, a robust logistic service provides a competitive edge,
necessitating significant investment. Many small businesses opt for
third-party logistics to cut costs. Our study explores enhancing revenue for such platforms by examining the benefits of sharing logistic
resources. Key questions addressed are: a) Is it advantageous for afood delivery platform (FDP) to collaborate on logistic systems withanother e-commerce platform, considering its current operations? b)What is the impact of logistic sharing between a food delivery platform and an e-commerce platform on restaurant behavior? We developed a non-cooperative game model involving the FDP, a restaurant, an e-commerce platform, and third-party logistics. Both restaurant and e-commerce demands were considered stochastic variables.
Two scenarios were evaluated: (1) Without sharing logistic resources
between the food delivery and e-commerce platforms, and (2) With
shared logistic resources. The aim was to determine the feasibility ofresource sharing. The study identified the optimal commission rates
from the restaurant and e-commerce platform to the FDP, the optimal
marketing index and the optimal service level offered to the restaurant
and e-commerce platform, respectively by the FDP, through solving a
Stackelberg game-theoretic model. By comparing outcomes from bothscenarios, we determine the viability of sharing logistic resources foreach player.

4 - Price optimization for car sharing

Rym M’Hallah, Christine Currie, Beatriz Brito Oliveira
Car sharing could support the transition toward net zero by reducing
private car usage. Its success depends on its financial sustainability
to service providers and attractiveness to end users. Dynamic pricing could incentivize users, balance supply and demand, and improvethe cost-effectiveness and attractiveness of car sharing. We describea fast method for optimizing the hourly rental price charged to a car
sharing customer where the price may depend on the number of carsalready on hire. The usage of the fleet can be described by a continuous time Markov chain model, which can be reduced to a multi-server
queueing model under relatively unrestrictive assumptions. The analytical tractability of the queueing model enables fast optimization to
maximize expected hourly revenue for either a single fare system or asystem in which the fare depends on the number of cars on hire, whileaccounting for stochasticity in customer arrival times and the durationsof hire. This allows for the development of dynamic pricing strategies
for car sharing and supports answering more strategic questions suchas the optimal fleet size. We present the optimal prices for a given
customer population and arrival rate and show how the expected revenue and car availability depend on the arrival rate into the system, thewillingness-to-pay distribution, and the size of the customer population. We present the results of experiments showing the optimal fleet
size for a given customer population.

MB-60

 MB-60
Monday, 10:30-12:00 - Room: S09 (building: 101)

Vehicle routing II
Stream: Transportation
Invited session
Chair: Richard Hartl
1 - A Novel CVRP Model with Simulated Annealing for Fuel
Delivery: A Case Study for Clients of Samat Transportation Company in Poland

Vitalii Naumov
The paper addresses the Capacitated Vehicle Routing Problem (CVRP)
in the context of fuel delivery to gas stations. The CVRP aims tominimize total travel distance for a fleet with limited capacity. Fueldelivery, however, introduces unique complexities within the CVRP
framework. We propose a novel approach that integrates the Simulated
Annealing (SA) algorithm with a customized CVRP model specifically designed for gas station networks. This model incorporates realworld constraints like vehicle capacity, fuel demands at each station,and road network distances. The paper outlines the design of the SAbased CVRP model for fuel delivery. We detail the objective function(minimizing distance) and the SA’s exploration mechanism for generating candidate solutions. To assess its effectiveness, the proposed
approach undergoes computational tests in Poland’s gas station network serviced by the Samat transportation company. We compare theperformance of our SA-based CVRP model with the conventional Integer Programming model for CVRP powered by Gourobi. The results
aim to demonstrate the efficacy of SA in finding efficient fuel delivery
routes, potentially leading to cost reductions and a lower environmental impact for transportation companies.

2 - Inventory Routing Problem: A New Integrated Clustering and Routing Algorithm

Okan Ozener, Ali Ekici
Inventory Routing Problem (IRP) arises from vendor-managed inventory business settings where the supplier is responsible for replenishingthe inventories of its customers over a planning horizon. In the IRP, thesupplier makes the routing and inventory decisions together to improve
the overall performance of the system. More specifically, the supplierdecides (i) when to replenish each customer, (ii) how much to deliver
to each customer, and (iii) how to route delivery vehicles between the
depot and the customers. In our setting, the supplier’s goal is to minimize total transportation cost over a planning horizon while avoiding
stock-outs at the customer locations. We assume that the supplier has afleet of homogeneous capacitated delivery vehicles and abundant availability of the product to be delivered to the customers. Each customer
has a constant demand/consumption rate and limited storage capacity
to keep inventory. In order to address this problem, we propose a novel
integrated clustering and routing algorithm. In the clustering phase, we
partition the customer set into clusters such that a single vehicle serves
each cluster. In the routing phase, we develop the delivery schedule foreach cluster. The novelty of the proposed approach is that it takes thethree main decisions (when to deliver, how much to deliver and how toroute) into account when partitioning the customer set into clusters.

3 - Key performance indicators for consistent vehicle routing problem

Guillermo Bianchi, Hernán Lespay
We proposed a mixed-integer linear programming (MILP) for the consistent vehicle routing problem with time windows (ConVRPTW),
which is solved with a Tabu Search metaheuristic. This study is motivated by the necessity of increasing the service level of companies in
the logistics area. The study considers a schedule of routes in multiple periods, where the set of clients and their demand fluctuates in theplanning horizon. The objective is to minimize the length of routes75MB-61EURO 2024 - Copenhagenfulfilling the requirements of service consistency. The solution is evaluated through Key Performance Indicators(KPIs) to analyze the balance between routes, design strategies to perform routing plans, andimprove future benchmarks in the literature.

4 - On combining conventional door-to-door and pneumatic waste collection systems

Maryam DehghanChenary, Richard Hartl, Stefan Irnich,Christian Tilk
The increasing global emphasis on sustainable waste management has
stimulated efforts to refine transportation infrastructure and travel regulations to improve the efficiency of garbage collection as a pivotal
element within sustainable urban municipal solid waste management
strategies. Therefore, improving waste collection systems can lead tovarious enhancements in quality of life. The most common waste collection form is the Door-To-Door Collection System, where household
waste is picked up using trucks or other vehicles. Some disadvantagesof this system are using trucks on narrow streets, hygienic effects, andmalodor related to waste storage. An alternative method is the use of
automated waste collection systems.
This study focuses on developing a system that integrates a stationary
pneumatic waste collection system with a door-to-door waste collection system. In this fashion, We provide a mathematical model for thiscombined waste management system with the goal of minimizing totaltransportation costs. We present a holistic solution approach based ona set-partitioning formulation utilizing vehicle routing problems andprice-collecting Steiner tree problems.

 MB-61
Monday, 10:30-12:00 - Room: S10 (building: 101)

Location of Alternative Fuel and Charging
Stations

Sarka Stadlerova, Peter Schütz, Robin Halseth Aakvik,
Kenneth André Inghelm Mørkved
The aquaculture industry contributes considerably to CO2 pollution in
Norway, whereas about 40% of the aquaculture sector emissions arecaused by aquaculture vessels. The aquaculture industry is expected
to grow in the coming years. However, emissions must be reduced by
55% by 2030 to meet the goals set in the Paris Agreement. Therefore,
it is critical to decarbonize the aquaculture operations. The transitionof the aquaculture vessels to zero-emission energy carriers such as hydrogen is a promising solution. However, the adoption of hydrogensolutions is hindered by non-existing hydrogen infrastructure.
To address this challenge, we formulate a location routing problem,
considering the location of hydrogen fuelling stations and the routingof aquaculture vessels. The aim is to decide the location of fuelling
stations, which routes to serve, and the number of necessary vessels
to satisfy demand. The objective is to locate hydrogen fuelling stations so that opening costs of fuelling stations, vessel costs and routing
costs are minimized. We solve the problem using a path flow model
formulation with a priori route generation.
Our case study is inspired by the real case from Norway considering alocation routing problem of well-boats in Lofoten and Vesterålen.

3 - Locating Electric Vehicle Charging Stations and Environmental Justice

Ozgur Araz, Ibrahim Capar, Tammy Beck
Recently, improving the infrastructure for Electric vehicles (EVs) has
been a major focus of organizations around the world. In this study, we
present a service network design framework to improve the infrastructure for EVs with quality-of-service constraints taken into consideration. We present a novel charging station location model with capacity
allocation, and we consider the community impact in locating thesestations. First, we present descriptive analysis of community impact ofcharging stations in the United Staes. Then, we highlight how charging
times at stations affect the quality of service in the whole service system by quantifying the improvements in flow coverage while reducing
waiting times, in addition to maximizing the community impact. The
value of faster chargers in increasing coverage and reducing the average waiting times is also highlighted in relation to a service provider’sbudget constraints.

Stream: Locational Analysis
Invited session
Chair: Ozgur Araz
1 - Strategic Locations of EV Charging Stations: Catering
to Various Charging Types and Diverse Driver Profiles

Jingyu Xiang, Paula Carroll, Annunziata Esposito Amideo
As a promising sustainable transportation solution, Electric Vehicles(EVs) require efficient strategies for their refueling to complete theirjourney. This study aims to address the charging station location
problem (CSLP), considering multiple charging options and varied
driver behaviours. We first integrate the Multi-Path Refueling Location Model (MPRLM) with various charging types, including slow,
fast, and battery-swapping charging stations. This model considers different charging rates of multiple charging stations, assuming a linear
recharging rate over time. To balance the investment cost of charging stations and the service level for EV users, our objective involves
minimizing the total cost for operators and the overall charging timefor EV users. Experimental tests on a 25-node network indicate theextended MPRLM outperforms the original model by better balancing
system costs, charging time, and station utilization. Then, the Integrated Behavioral Multipath Refueling Location Model (IB-MPRLM)
is introduced to incorporate varied charging profiles of EV users. We
categorize private EV drivers based on their sensitivity levels to range
anxiety, charging costs, and charging time. For each EV driver category, the satisfaction function and satisfaction threshold are established. In this IB-MPRLM, the charging station located at a certain
node can only refuel an EV when the user’s satisfaction is higher than
the threshold of the EV user.

2 - Locating Fuelling Stations for Zero-Emission Aquaculture Vessels

76 MB-62
Monday, 10:30-12:00 - Room: S12 (building: 101)

Container Stacking and Yard Planning I
Stream: OR in Port Operations
Invited session
Chair: Christopher Expósito Izquierdo
1 - Partial container premarshalling approaches using constraint programming

Celia Jiménez-Piqueras, Dario Pacino
The premarshalling problem aims to rearrange the containers in a port
yard bay, comprising a set of adjacent stacks, to prevent containers
from being placed above others that must be retrieved earlier. This
arrangement streamlines retrieval from the bay for transfers to inlandor maritime transport, as additional relocations are avoided at the timeof retrieval. Premarshalling is only performed during crane idle times,
and the original formulation of the problem assumes these times are
unlimited. However, constraints in practice may limit the time available, hindering full bay arrangement. In such cases, partial premarshalling can still enhance retrieval efficiency within time constraints,
but existing formulations for complete premarshalling lack adaptation for partial solutions, yielding suboptimal outcomes. Furthermore,while the goal of premarshalling is clear, to achieve a fully arranged
bay, various objectives for partial premarshalling can be pursued. This
study explores different criteria for partial premarshalling and investigates several constraint programming-based solution approaches.

EURO 2024 - Copenhagen
2 - A Target-oriented Robust Optimization Approach for Vehicle Positioning and Scheduling Problem at Terminal
Apron

Linfei Guan, Chenhao Zhou, Qinghe Sun, Ada Che
The container terminal comprises two modules: the yard and the terminal apron, where vehicles engaged in loading/discharging operations
are shuttled between the two modules using holding buffers to mitigate
congestion and delays. However, uncertainties such as container retrieval locations in the yard, vehicle speed, and potential waiting times
due to congestion or incidents, introduce unpredictability in containertransit time and may lead to delayed arrivals at the terminal apron. This
unpredictability presents challenges in estimating the makspan for handling vessels, particularly for loading operations. This study addresses
the integrated optimization of vehicle positioning and scheduling problem at the terminal apron, considering uncertain transit times. The research aims to enhance the tolerance of the terminal apron system todelays in the completion of loading operations in an uncertain environment, striving to develop an efficient and robust vehicle schedulingplan. Stochastic programming is utilized to model the ambiguous transit times, and a target-oriented robust optimization (RO) model is introduced to maximize the system’s tolerance to delays. Three approaches
are utilized to address the RO, and the performance RO is evaluatedacross four dimensions: mean, standard deviation, target completion
probability, and value at risk. Finally, the RO exhibited strong performance in numerical experiments constructed using real data in Tianjin
Port in China.

3 - A new modeling approach for the unrestricted block relocation problem

Fabian Wilschewski, Alf Kimms
We consider the block relocation problem (BRP), a combinatorial optimization problem that may arise in storage systems where items are organized in stacks. The objective is to retrieve all items in a predefinedorder with a minimal number of relocations. It can be distinguishedbetween a restricted and an unrestricted version of the BRP. While in
the restricted BRP (R-BRP) only relocations of items located above
the item to be retrieved next are permitted, in the unrestricted BRP(U-BRP) all possible relocations are allowed. Existing exact methods
concerning the BRP are frequently search-based methods which appear to be very effective. Nevertheless, recent literature concerning the
R-BRP has shown that model-based methods can be competitive andtherefore should also be taken into consideration. In this paper, we
propose a new model-based approach for the U-BRP. It eliminates thefact that the number of variables is increasing with the number of necessary relocations; a disadvantage most mathematical models for the
U-BRP have in common.

 MB-63
Monday, 10:30-12:00 - Room: S14 (building: 101)

Insurance Risk Management
Stream: OR in Banking, Finance and Insurance: New
Tools for Risk Management
Invited session
Chair: Susanna Levantesi
1 - Well-being horizons for silver and golden ages: An application of traditional and fuzzy Markov chains

Qi Guo, Irene Albarran, Pablo Jesús Alonso-González, Aurea
Grané
European societies are currently in a process of population ageing. Although this is the general trend, it would be desirable to know whether
this process is being produced with the same characteristics and intensity in all European countries. In this work information from thelast three waves of SHARE macro-survey is used for four countries

MB-63(Germany, Poland, Denmark and Spain) as the basis for a longitudinalwell-being and dependency indicator with the aim of studying whether
the characteristics of ageing are similar in these regions. First, longterm population distributions are obtained according to the value of theaforementioned indicator. Next, classical and fuzzy Markov chains areused to obtain steady-state distributions regarding age group, gender,
country and wave. Finally, a proper metric for probability distributions is used to cluster these profiles in several groups. Results lead
us to conclude that the ageing process is not homogeneous among thestudied populations.

2 - A beta binomial approach for the estimate of surrender
rates by GAMLSSDavide Biancalana, Fabio Baione
This study examines the estimate of surrender probabilities with explanatory variables using a Beta Binomial Generalized Linear Modelfor Location, Scale, and Shape (BBGAMLSS) and introduces an algorithm for the update of surrender probabilities by future observations. The Binomial Generalized linear Model is widely used in actuarial practice and literature to predict surrender probabilities per policy
count depending on the policy, policyholder, and economic variables.
We propose an alternative regressive model with a response variableassumed to be Beta Binomial. In contrast to a binomial, a discrete random variable called a Beta Binomial has a beta distributed probabilityof success at each of a certain number of trials rather than a fixed probability. The surrender is a binomial phenomenon where the probabilityof success is not fixed, because it depends on many variables that cannot be considered simultaneously in a single regressive model, without
introducing overparametrization. Lastly, the Beta Binomial randomvariable has a Bayesian interpretation, because the beta is a conjugate
prior to the Beta Binomial. Then, it is possible to define an algorithm
that allows updating the estimates of conditional surrender probabilities by the future observations of surrender rates avoiding the estimateof new parameters every year.

3 - Utility based evaluation of Reverse Mortgages

Gabriella Piscopo
The reverse mortgage (RM) contract allows elder homeowners to borrow money using their home as security for the loan maintaining theright to live in the house. The debt is repaid by the heirs when the
borrower moves out or dies. This contract can constitute a valid support for the spending needs that may arise during retirement. The paper deals with a decision problem of a homeowner who is approaching old age and has to evaluate contracting a Reverse Mortgage ornot. We built elders lifetime utility functions considering consumptions, bequest motivations and lifespan uncertainty and solve the maximization problem to find the optimal allocation of the wealth between
housing/nonhousing, consumptions and bequest with and without RM.
Through our analysis, we expect to find that, in presence of long-termcare expenses and house mainteinance costs, individual’s liquid wealthsignificantly increase with reverse mortgage. Moreover, homeownerwith a higher bequest motivation may have lower utility gains fromcontracting RM plan as well as individuals with a lower house value
respect to liquid assets.

4 - Sustainability risks affecting solvency ratios for insurance companies

Susanna Levantesi, Rita D’Ecclesia, Alessandro Dorazio,
Kevyn Stefanelli
This article presents an empirical investigation into the influence ofEnvironmental, Social, and Governance (ESG) scores on the solvency
ratio of insurance companies, employing a panel regression approach.
By analysing an extensive sample of worldwide insurance companies,
our study aims to provide a clear and detailed perspective on the dynamic relationships between ESG performance and the financial robustness of firms in the insurance sector. The results obtained throughpanel regression will offer crucial insights into understanding how ESG
variables may affect the solvency ratio, contributing to the growing
literature on financial stability and social responsibility within the insurance industry. This research endeavours to provide valuable information for industry stakeholders, regulators, and investors interested inunderstanding the impact of sustainable practices on the solvency levelof insurance companies.

77MB-64EURO 2024 - Copenhagen MB-64

Monday, 12:30-14:00

Monday, 10:30-12:00 - Room: S16 (building: 101)

Routing Unmanned Aerial Vehicles 1
Stream: VeRoLog - Vehicle Routing and Logistics
Invited session
Chair: Roberto Roberti
1 - On-Time Meal Delivery Assisted by Drone Resupply

Wenqian Liu, Lindong Liu, Xiangtong Qi
With the competition between online meal delivery services growing,on-time delivery has become a crucial target for meal delivery platforms to pursue, thus calling for new technologies and operationalmodels. Echoing such a need, we propose a drone resupply approachfor meal delivery, where drones transport meals from restaurants toriders en route, and then riders deliver these meals to customers. Thismodel involves the coordination of multiple trucks, drones, and depots,
as well as the joint optimization of rider routing and drone scheduling.
Our two-phase algorithm solves the problem by first identifying feasible rider routes along with the respective drone schedule for serving
order bundles, and second, selecting rider routes using a set coveringmodel. To address the issue of long service periods, we propose arolling horizon strategy to solve it dynamically. Extensive computational studies are conducted to evaluate the efficiency of the solution
method and the effectiveness of the drone-assisted delivery system.
The results demonstrate that adopting drone resupply in meal delivery
is more efficient than the traditional rider-only mode for maintaining
the target on-time service level in large service areas.

2 - Parcel Consolidation In Last Mile Delivery by Vehicle
and Drone

Semih Boz, Ahmed Ghoniem, Amro El-Adle
We study parcel consolidation within the context of last-mile delivery,
employing both a vehicle and a companion drone to efficiently serve
customers across multiple time periods. We present a mixed-integer
program and an optimization-based heuristic to address large-scale instances. The efficacy of our methodology is assessed through computational experiments on randomly generated instances, focusing on aresidential network in Amherst, Massachusetts.

3 - Robots at Your Service: Covering the Last-Mile in Omnichannel Retail by Robot

Tobias Huf, Manuel Ostermeier
The increasing number of home deliveries paired with various delivery modes and channels require retailers to establish omnichannel networks, integrating stores for direct customer deliveries. This enables
versatile shopping opportunities but also increases logistic complexity.
The innovative truck-and-robot concept poses an alternative to address
the resulting challenges. The concept relies on robots carried and released by trucks to serve customers in predefined time windows. We
extend this concept by integrating stores and corresponding customer
requests for direct supply from stores, resulting in a new concept thatoffers robots as a service to pick up and deliver goods to the customers.
The problem described is formalized as the Truck-and-Robot Pickupand-Delivery Problem (TnR-PDP) and solved using a tailored solution approach, the Adaptive Genetic Algorithm (AGA). The AGA isbased on a recombination-based search framework but tailored to the
problem-specifics (e.g., no or multiple visits per location) using specialized recombination operators and an adaptive search strategy forlocation and operator selection. Our numerical experiments show that
our algorithm works efficiently, outperforming existing benchmark approaches concerning runtime by up to 83%, while improving the solution quality on benchmark instances. Furthermore, an in-depth analysis regarding store integration is applied to provide managerial insights
and highlight the benefits of the proposed concept.

78

 MC-01
Monday, 12:30-14:00 - Room: Sportshallen (building: 101)

Ruben Ruiz
Stream: Keynotes
Keynote session
Chair: Dolores Romero Morales

1 - Optimal is the enemy of good: Solving very large scale
virtual machine placement problems at Amazon Elastic
Compute Cloud

Rubén Ruiz

A significant portion of the effort within the field of Operations Research is dedicated to the development of intricate ad-hoc algorithms
and models tailored for addressing diverse optimization problems. Frequently, the research community exhibits a preference for complexity in the proposed methodologies. Notably, an esteemed characteristic sought in these methods is the incorporation of problem-specificknowledge, enabling the attainment of results that get as close as possible to optimality.
In academic publishing, the pursuit of marginal gains in optimality
gaps is a common goal, even at the expense of introducing additional
complexity to models or algorithms. While there is consensus thatsuch endeavors contribute to advancements in algorithms and propel
the field of Operations Research forward, a frequently underestimateddimension is the practical applicability of these advancements in industrial settings.
Real industrial problems are often messy with imprecise (or numerous)
objectives, soft constraints, preferences and a myriad of situations thatdemand pragmatic approaches. Additionally, these problems undergo
rapid evolution with frequent model refinements, occurring often fortnightly. Here, the ongoing painful adaptation of intricate code bases
stemming from ad-hoc methods is not the favored choice. In contrast,
solvers offer greater flexibility, allowing faster implementation of new
constraints through their APIs than altering tailored algorithms. In industrial contexts, the preference lies in flexibility, maintainability, robustness, and reduced operational load, where a modest optimality gap
is deemed a minor trade-off.
Moreover, in the realm of large-scale real-world problems, mathematical solvers often prove impractical. It is commonplace to resort tosimplifying the problem for solvability. This practice raises a criticalquestion: Is it logical to insist on chasing optimality in solving a simplified problem with a reduced real-world fidelity? Furthermore, real
problems entail vast datasets, often including forecasted or approximated input data. Does it make sense to go great lengths to optimallysolve a problem when a portion of the input data involves approximations?
In this presentation, I advocate for relinquishing the pursuit of optimality and, instead, embracing heuristic solvers and simplified modeling approaches. Such strategies yield expedient, adaptable, maintainable, and easily implementable models. The discourse will draw uponvarious examples, spanning classical routing and scheduling problems, culminating in intricate real-world virtual machine placementbin packing problems encountered at Amazon Elastic Compute Cloud(EC2).

EURO 2024 - Copenhagen MC-02
Monday, 12:30-14:00 - Room: Glassalen (building: 101)

EEPA
Stream: EURO Excellence in Practice Award
Award Competition session
Chair: Ruth Kaufman
Chair: Frédéric Gardi
Chair: Tobias Achterberg
Chair: Mauro Dell’Amico
1 - Optimal Planning Tool for Agricultural Waste Management in Vietnam (OPT-AWAMA)

Trung Hieu Tran, Thi Thu Ba Nguyen, Thi Hoa Sen Le, Duc
Chính Phùng, Hoai Chung Pham, Phuong Le Nguyen, Thi
Anh Phuong Huynh
Vietnam’s agricultural sectors have rapidly developed to become major
contributions to the growth of economy (e.g., in 2019, Vietnam’s rice
export revenue achieved US$1.4 billion). Accompanying the development, however, agricultural waste management in Vietnam (i.e., approximately 76 million tons including 45.22 million tons of rice straw,
8.73 million tons of rice husk, 4.04 million tons of sugarcane bagasse,
6.33 million tons of maize by-products, 1 million tons of coffee shelland 10 million tons of vegetable by-products) has become a challenging issue for rural planners due to the lack of an efficient planning tooland impacts of climate change. As a result, it has increasingly caused
air and water pollution in many rural areas of the country. In thisproject, we thus collaborated with Vietnam institutions (including Hue
University of Agriculture and Forestry, Institute of Meteorology, Hydrology and Climate Change, and Transport and Development Strategy Institute) to develop a proof-of-concept planning model for ruralplanners to optimally locate facilities that produce bio-organic fertilizer from waste, and to determine the optimal set of routes for a fleet
of vehicles to collect and transport the waste from farms to facilities.
In the novel location-assignment-routing problem, we investigated unreliability in facility location and vehicle routing planning due to therisk of disruptions to location sites and roads (i.e., unexpected failureof location sites and roads due to flood impact). The model aims tominimise total expected cost of locating facilities and routing vehicles
under the risk of disruptions (due to flood impact) such that the amountof waste at farms is totally collected. A robust mixed-integer nonlinear programming model for this problem was formulated. The model
was further generalised to a multi-period planning problem given limited periodic budgets of locating facilities and deploying vehicles. The
model was linearized so that it can be solved using a general-purpose
solver. In addition, a meta-heuristic algorithm was developed to solve
the large-scale instances within a reasonable computation time. Finally, the model was applied for agricultural waste management in
Quang Tri province, Vietnam.

2 - Renault Group Packaging Return Logistics in Europe

Louis Bouvier, Thaddeus Leonard, Guillaume Dalle, Axel
ParmentierThis project emerged from a collaboration between the CERMICS
laboratory and Renault Group. Their challenge: optimizing reverse
empty-packaging logistics for car parts transportation from suppliers
to plants. Spanning an entire continent, this multi-attribute inventory
routing problem (IRP) involves 30 commodities, 16 depots, and 600
customers—far surpassing existing literature instances. Traditional
IRP algorithms do not scale, so we propose a large neighborhood
search (LNS). Our approach involves three key steps: (1) generalizing split delivery vehicle routing and IRP neighborhoods, (2) adapting a matheuristic for medium-scale IRP into a large neighborhood,and (3) introducing novel perturbations like customer and commodity reinsertion. Additionally, we establish a new lower bound basedon flow relaxation. To foster large-scale IRP research, we release anindustrial instance library and open-source our code. Rigorous numerical experiments validate the effectiveness of each LNS component.
The industrialization of our LNS, coupled with robust change management, has yielded substantial benefits. At Renault, we have achieved

MC-03

a 4−5% reduction in packaging logistics costs per vehicle produced, a
50% decrease in packaging shortages at suppliers, and a 10% reduction
in CO2 equivalent transport emissions for 2023. These gains translate
to millions of euros saved and kilotons of emissions reduced annually.

3 - OCP Optimizes Its Supply Chain For Africa and TheWorldEl Mehdi Er Raqabi, Issmail El Hallaoui, Nizar El Hachemi,
Francois Soumis, Ahmed Beljadid, Mohammed Ali
Bennouna, Nabil Si Hammou, Michel Fender, Anouar Jamali,
Hicham Guellaf, Ilyas Rakhis
Operational research specialists at the OCP Group, the Mohammed VI
Polytechnic University, and the Polytechnique Montreal operationalized a system optimizing OCP’s supply chain downstream activities.
The system simultaneously schedules production, inventory, and vessels while ensuring the highest demand fulfillment. Therefore, it has
become central to the OCP planning process. Planners use the optimizer’s solutions and insights to improve plans in different OCP sites.
Initially, the system was a bottleneck, curbing the use of other supply
chain management tools. OCP management now credits the system
operationalization with providing operational benefits, contributing toover a $240 million increase in annual turnover.

 MC-03
Monday, 12:30-14:00 - Room: 1005 (building: 202)

(Deep) Reinforcement Learning for
Combinatorial Optimization 1
Stream: Data Science Meets Optimization
Invited session
Chair: Yingqian Zhang
Chair: Kevin Tierney
1 - PPSTOW: An End-to-End Deep Reinforcement Learning
Model for Master Stowage Planning on Container Vessels

Jaike van Twiller, Djordje Grbic, Rune Jensen
Efficient supply chains are vital for both the worldwide economy andenvironmental sustainability. Container shipping plays a key role inthis, known for being an eco-friendly mode of transport. Liner shipping companies are actively working to improve operational efficiency
through stowage planning. Due to many combinatorial aspects, someof which are NP-hard, stowage planning is a challenging problem in
its representative form. Even though stowage planning can be decomposed into master and slot planning, the subproblems remain challenging. As a result, we are searching for scalable algorithms to solve thestowage planning problem.
In this work, we propose Proximal Policy optimization for master
STOWage planning (PPSTOW), a deep reinforcement learning approach to address master planning with focus on global problem objectives and constraints. The experiments show the effectiveness of
PPSTOW, as the framework efficiently finds near-optimal solutions for
simulated problem instances with realistic vessel sizes and practicalplanning horizons. In the future, we aim to refine the representativeness of our approach by integrating revenue management, as well as
local problem objectives and constraints.

2 - A Tale of Middle-Mile Logistics, Graph Neural Networks,
and Reinforcement Learning

Thibaut Cuvelier, Onno Eberhard, Bruno De Backer
Middle-mile logistics describes the problem of routing shipments
through a network of hubs while respecting deadlines upon arrival. We
consider that the hubs are linked by predefined lines, to which we haveto assign vehicles. A very challenging aspect of the problem comesfrom the finite capacity of the vehicles: allocating a shipment to a given
vehicle might block another one from using the same vehicle.

79MC-04EURO 2024 - Copenhagen

Typical exact solution methods, based on a multicommodity-flow formulation, scale poorly with the problem size and real-world instances
become quickly intractable. Instead, we turn to reinforcement learning (RL) by rephrasing the middle-mile problem as a multi-objective
Markov decision process, where the state is a graph: the lines (edges)
between the hubs and the parcels (nodes). At each round, we assign
one shipment to a vehicle or decide that it stays in the same hub. The
key ingredients of our proposed method are the extraction of smallfeature graphs from the state and the combination of graph neural networks (GNN) with model-free RL.

 MC-04
Monday, 12:30-14:00 - Room: 1001 (building: 202)

New Trends in Generative Adversarial
Networks and Deep Neural Networks
Stream: Recent Advancements in AI
Invited session
Chair: Duygu Üçüncü

We use the PPO (proximal policy optimization) algorithm, which
maintains both an actor and a critic, while being able to cope with avarying number of actions depending on the state. We compare linear
functions and GraphNet (a particular kind of GNN) to approximate thepolicy and value functions. GNNs can deliver up to 40% more shipments than a linear function and both approaches scale well with thenumber of shipments per truck.

3 - Addressing Real-World Side Constraints in Combinatorial Optimization with Deep Reinforcement Learning

Nayeli Gast Zepeda, Kevin Tierney, André Hottung

Deep Reinforcement Learning (DRL) methodologies have garnered
increasing attention in addressing combinatorial optimization challenges, particularly in domains such as routing and scheduling. While
recent approaches have demonstrated notable efficacy, especially inclassic problems like the Traveling Salesman Problem (TSP) and Capacitated Vehicle Routing Problem (CVRP), they often operate withinsimplified problem settings, lacking real-world side constraints. Consequently, DRL methods encounter difficulties in generating feasible
solutions for more complex scenarios. In this study, we address theselimitations by introducing additional real-world side constraints andexploring diverse mechanisms to accommodate them while steering
the search towards feasible solutions. Our experimentation extends toa variety of combinatorial optimization problems, including the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW)
and Skill-VRP, showcasing our approach’s effectiveness in handlingpractical constraints.

4 - Learning Efficient and Fair Policies for Collaborative
Human-Robot Order Picking

Igor Smit, Zaharah Bukhsh, Yingqian Zhang

In collaborative human-robot order picking systems, human pickers
and Autonomous Mobile Robots (AMRs) travel independently througha warehouse and meet at pick locations where pickers load items onto
the AMRs. In this paper, we consider an optimization problem in such
systems where we allocate pickers to AMRs in a stochastic environment. We propose a novel multi-objective deep reinforcement learning(DRL) approach to learn good allocation policies to maximize pick efficiency while also aiming to improve workload fairness amongst human pickers. In our approach, we model the warehouse states usinga graph, and define a network architecture that captures regional information and extracts information from efficiency and workload features effectively. We develop a discrete-event simulation model, which
we use to train and evaluate the proposed DRL approach. In the experiments, we demonstrate that our approach can find non-dominatedpolicy sets that outline good trade-offs between fairness and efficiency.
The trained policies outperform the benchmarks in terms of efficiency
and fairness, and moreover, they show good transferability properties when being tested with different scenarios in different sizes of thewarehouse.

80

1 - SDP Relaxations for Training ReLU Activation Neural
Networks

Karthik Prakhya, Tolga Birdal, Alp Yurtsever
Solving non-convex optimization problems is crucial for training machine learning models, including neural networks. However, nonconvexity often leads to less reliable and less robust neural networks
with unclear inner workings. While convex formulations have beenused for verifying neural network robustness, their application to training neural networks remains relatively unexplored. In this work, we
propose a semidefinite programming relaxation for training two-layer
ReLU networks in a lifted space, which can be solved in polynomial
time. Numerical experiments demonstrate that this SDP formulation
provides reasonably tight lower bounds on the training objective acrossvarious prediction and classification tasks.

2 - A new implementation with combining mixture of UNet and ResNet type architectures for image-to-imagetranslation

Erkut Arican, Khaled Al Hariri
Image-to-Image translation is an area of computer vision that has become popular recently, with various studies providing different implementations. By using Image-to-Image translation with a large dataset,
we can create different versions of the same image, and each version
will allow us to see how the image looks in a different type of scene.
This study aims to explore this field and provide a new implementationthat could provide new insight into how images can be transformed.
A famous image-to-image translation project inspires our implementation, which has a different generator architecture and normalization
method. The implementation utilizes a Conditional Generative Adversarial Network, which consists of a generator and a discriminator. We
have used a mixture of the U-Net architecture and the ResNet type architecture for the generator instead of only using a U-Net architecture.
For the discriminator, we have used the PatchGAN architecture, which
separates the image into different patches and classifies each patch of
the image as either real or fake. Generated images are evaluated manually, and the calculated Structural Similarity Index is planned to be
used in an automated evaluation system. We plan to continue improving our output and evaluate our results in other ways.

3 - Generative AI Governance by Blockchain Technology

Swati Sachan, Vinícius Dezem, Dale Fickett
Generative AI tools powered by Large Language Models (LLMs) havedemonstrated advanced capabilities in generating and articulating coherent textual content closer to the practitioners in domains such as
law and finance. Globally, firms have raised ethical concerns regarding LLM’s ability to mimic human reasoning, accountability for erroneous outcomes, and the security and privacy of confidential data sentby the prompt of Generative AI. This research aims to find a balance
between the responsible application of Generative AI and maintaining
human oversight over the generated content by utilizing the inherent
immutability and decentralization characteristics of blockchain technology. The proposed blockchain-based auditing system detects unauthorized alterations of data repositories containing ex-ante decisions
by an AI decision-support system and automated textual explanations
by Generative AI tools. The auditing algorithm compares the uniquesignature, known as Merkle roots of files stored off-chain (outside
blockchain), with their immutable blockchain counterpart. Automatedauditing by blockchain promotes the ethical use of AI technologies andEURO 2024 - Copenhagen
minimizes the risk of discrepancies in attributing accountability for adverse decisions. A case study on pre-litigation tort liability legal cases
is presented to demonstrate the practical application.

 MC-07
Monday, 12:30-14:00 - Room: 1019 (building: 202)

Cutting and Packing 3 - 3D loading
Stream: Cutting and Packing (ESICUP)
Invited session
Chair: Célia Paquay
1 - Solving a 3D-Bin-Packing-Problem with Deep Reinforcement Learning

Hendrik Jon Schmidt, Dennis Maecker
The 3D Bin-Packing-Problem (3D-BPP) is an NP-hard problem thatis particularly important in Operations Research whenever a load must
be placed on a carrier. This problem has been studied for a long timefrom a heuristic perspective in the area of container and pallet loading. Recently, Deep Reinforcement Learning (DRL) methods havebeen applied to solve 3D-BPPs. These methods show improved performance in decreasing the inference time for generating solutions while
their generated solutions can outperform those generated by heuristics.
However, the research has paid little attention to generating loading
patterns subject to stability constraints, which are relevant for making
the generated solutions applicable in real-world settings. This paper
develops a DRL algorithm based on Proximal Policy Optimisation to
explore whether DRL methods can be used to generate stable loading patterns for placing parcels on a Euro-pallet. Only the offline 3DBPP is considered. The DRL algorithm is combined with a physicalsimulation of the generated loading pattern to ensure the load’s stability. The algorithm is evaluated based on the recently introduced
BED-Benchmark dataset of realistic orders. The evaluation shows thatthe developed DRL algorithm, on its own, struggles to find suitable solutions for stably placing parcels on a pallet. One reason for this might
be the large action space for placing any parcel. This issue could be
addressed by combining the algorithm with heuristic solvers.

MC-08

Static stability is one of the most important packing requirements, preventing items from collapsing and preserving the safety of operatorsand cargo during loading/unloading operations. This constraint has often been oversimplified in existing online 3D heuristics, limiting theirpracticability in real-world settings. Given the diverse impacts of different static stability constraints on solution quality, we embedded fourconstraints, i.e., full-base, partial-base, and two polygon-based constraints, into online heuristics. The impact of constraints on the heuristics’ efficiency regarding bin volume utilization is analyzed on a real
dataset. Moreover, the static stabilities of packing layouts in all solutions obtained under four constraints are benchmarked using the staticmechanical equilibrium (SME). The SME approach offers a necessary
and sufficient condition for cargo stability, yet its application proves tobe very time-consuming and impractical in the online context, leading
us to use it as a benchmark. The results showed that a high percentage of items (i.e., over 87%) are statically stable under all constraints.
The findings also indicate a trade-off between increasing items’ stability and increasing the bins’ volume utilization. This study offeredvaluable insights for practitioners in selecting appropriate static stability based on their preferences in prioritizing item stability or efficientbins’ volume utilization.

4 - A GRASP for the three-dimensional loading vehicle
routing problem with split pickup and time windows under real-time disruptions

Emeline Leloup, José Fernando Oliveira, Célia Paquay,
Thierry Pironet
Logistics service providers (LSPs) have to respond to the growing
demand from their customers, who offer increasingly widespread ecommerce services. This work focuses on pickup operations, meaning
that, real-time changes, such as the arrival of new customers, can be accommodated as the vehicle is initially empty and progressively filledup.
The problem under study is a vehicle routing problem with split
pickup, time windows and three-dimensional loading constraints,
namely, geometric, vertical stability, orientation, and multi-load constraints.
Throughout the day, the LSP is likely to face real-time changes, called
disruptions, and needs to quickly adapt his ongoing routes to accommodate as many disruptions as possible while minimizing costs.
We will present a greedy randomized adaptative search procedure(GRASP) to deal with two types of disruptions: the arrival of a newcustomer and the addition of boxes to a planned customer request.

2 - A Digital Twin-driven approach for the mixed load palletizing problem.

António Ramos, Pedro Rocha, Fábio Moreira, Manuel Lopes MC-08

The problem of preparing mixed pallets is one of the main challenges
in automating tasks in distribution centers. Whether it’s the intrinsic
complexity of packaging problems or the need to incorporate physicalconstraints such as stability and interference between cargo and robots
in solving methods, traditional approaches to the problem tend to fallshort of real-world needs. In this work we propose an approach basedon Digital Twins. A Digital Twin is a dynamic virtual representation ofa physical object or system that uses real-world data, simulation models and/or machine learning. The framework of our Digital Twin isbased on three main components: classic optimization tools; physics
engines; and machine learning. A physics engine is a software systemthat simulates Newtonian physics in a simulated environment, including collision detection, rigid body dynamics and soft body dynamics.
In our Digital Twin framework, packing solutions are generated using classical optimization tools, the physical behavior of which is thensimulated in the physics engine. Both results feed the machine learning model so that it acquires the knowledge needed to build solutions
that can be used in the real world.

Monday, 12:30-14:00 - Room: 1020 (building: 202)

3 - Evaluating stability and efficiency: insights from incorporating static stability constraints in online 3D packing
heuristics

Sara Ali, António Ramos, José Fernando Oliveira

Applications of knowledge and innovationin financeStream: AI & Innovation in Sustainable FinanceInvited session
Chair: Januj Juneja
Chair: Apostolos Mouzakitis
1 - The role of timing in the firm’s decision to grow internally or by acquisition

Januj Juneja
We investigate patterns in the optimal timing of the firm’s strategic decision for growth internally or by acquisition. Using tools from spectral
analysis, and based upon the Fourier series, we devise an algorithm thatmonitors the timing of key firm policy decisions. We apply this algorithm to the firm’s decision to invest in three intangible assets related to
the firm’s organization capital for the purpose of generating financial
performance. We assess it using five measures and through the construction of three hypotheses tied to the time horizon for the return on

81MC-09EURO 2024 - Copenhagen

each investment, its effect on the capacity in the industry and economy,
and the degree to which each form of investment is dependent upon thecapital market for its mode of financing. Our results imply that there
is variation in the timing of each form of investment and interpret that
this variation owes to it taking longer for managers to realize gains
when investments are made through mergers and acquisitions relative
to internal growth. Managers experience an effect delay in this realization owing to delays in the procurement and application of intangible
investments obtained from mergers and acquisitions that require transfer of technical expertise whereas the smaller effect delay for internal
growth reflects the faster ability of knowledge-based firms to turn theknowledge already contained inhouse into financial performance outcomes. Suggestions for future research are provided.

2 - First- and third-party complementary apps: R&D investment, launching, and M&A strategies

 MC-09
Monday, 12:30-14:00 - Room: 10 (building: 116)

New Market Designs & Models for 100%
Renewable Power Systems
Stream: Energy Markets
Invited session
Chair: Silke Johanndeiter
1 - Price formation and intersectoral distributional effects
in a fully decarbonized European electricity market

Silke Johanndeiter, Niina Helistö, Juha Kiviluoma, Valentin
BertschPriel Levy, Tatyana Chernonog
With the massive growth of app usage and smartphone penetration, an
increasing number of third-party developers are offering their complementary apps aiming to target the massive audience of the leading coreapps, and thereby enhance user experience. At the same time, leading
developers launch their own complementary apps, which compete withthe third-party apps in terms of quality. The effect of the third-party developer on the first-party developer is twofold: On the one hand, theformer creates added value for the core app, which translates into additional profit for the first-party developer, generated by the core app.
On the other hand, the competition between first- and third-party substitutable apps threatens the first-party developer’s profit gained fromits own substitutable app. Similarly, the third-party developer rides on
the big market of the core app, while its own market share may beeroded due to the competition between the two substitutable apps. In
this study, we develop a game-theoretical model of a market consistingof three apps: a core app, and two partially substitutable apps, bothof which complement the core app. We investigate the app developers’ R&D strategies, and derive conditions for launching their complementary apps. In addition, the core-app developer’s M&A strategy isanalyzed.

3 - Managerial econophysics unveiled: a comprehensive
literature review on the amalgamation of business process management (BPM) and information entropy analysis

Apostolos Mouzakitis, Anastasios Liapakis
This paper explores the fusion of BPM and information entropy analysis. Its objective is threefold: to uncover potential synergies inenhancing organizational efficiency, present novel perspectives about
decision-making processes, and introduce a novel scientific sub-fieldtermed Managerial Econophysics. BPM, integrating IT and management, optimizes workflows, reduces costs, and boosts productivity.
The review traces BPM’s evolution from quality control to its modern
manifestations. It addresses challenges like randomness and complexity, proposing entropy analysis to tackle uncertainty. Entropy metrics
aid in performance optimization and resource allocation. The synthesis extends to econophysics, linking financial economics with entropy.
Notable figures like Popovic and Shannon are discussed, highlighting entropy’s role in financial market analysis. The broader scope of
econophysics, including social systems, is explored. This paper argues
for additional research into the application of econophysics in organizational settings, bridging the gap between economics, management,
and physics. It advocates for integrating econophysics and information entropy analysis into managerial science, creating innovative approaches to process management. Just as natural vectors combine toform geometric spaces, diverse scientific disciplines can merge to forge
new scientific frontiers. Managerial Econophysics emerges as an epistemological frontier in process management.

82

Traditionally, wholesale electricity prices were driven by fossil power
plants’ fuel costs. Optimising dispatch in five scenarios of fully decarbonised, sector-coupled European power systems, we show how future electricity prices continue to be driven by fuel prices. In contrast
to today’s market, their impact can also result from opportunity costsof cross-sectoral demand-side technologies. Particularly, we find thatelectrolysers become the main price-setting technology in Europe in
the presence of a significant domestic hydrogen demand and abundant
low-variable-cost electricity generation. Consequently, future electricity prices highly depend on these uncertain parameters. While flexible electricity consumption is able to adapt to varying price levels, investors in solar and wind power are similarly exposed to price risks as
inflexible electricity consumers. Therefore, they could mutually benefit from price-mitigating instruments, such as Contracts for Difference.
In the hydrogen sector, producers and consumers do not share sucha common interest as hydrogen consumers’ final energy consumption
costs are highly uncertain, but electrolysers robustly recover their investment costs across all scenarios and countries.
This work has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no.
864276.

2 - Economic benefits of residential heating and cooling
flexibility: consumer and system perspective

Eric Harrison, Topi Rasku, Juha Kiviluoma
Price-based residential demand response (DR) schemes should beplanned to enable win-win situations where both the flexible buildings
and energy system enjoy economic benefits. Our study modelled theelectrically heated Finnish residential building stock, assuming flexible space heating and cooling as well as water heating. We integrated
different penetrations of such buildings to a North European energy
system model. Two DR schemes were considered: a grid-interactive
scheme, in which flexible buildings participated in the minimization of
total system costs, and a self-optimizing scheme in which the buildings
minimized their own heating/cooling costs based on pre-determined
and fixed electricity prices. Annual operational costs of the combined
systems were solved as linear optimization problems to assess the costsavings potential from DR.
The results suggest that while flexible consumers often benefited directly from timing their consumption on more inexpensive hours,
wide-scale DR penetration can also impact non-flexible consumers byinfluencing electricity spot prices. In addition, basing wide-spread
DR on pre-determined and fixed pricing, such as day-ahead spot
prices, may carry risks for the energy system and electricity market
if many buildings shift their demand synchronously, causing new demand peaks.
H2020 TradeRES (grant agreement no. 864276)

3 - Assessing market effects of support instruments for renewables: Are they needed and how to design them?

Johannes Kochems, Evelyn Sperber, Christoph Schimeczek,
Kristina Nienhaus

EURO 2024 - Copenhagen
Motivation Future energy systems will be dominated by variable Renewable Energy Sources (vRES). Yet, it is unclear if all vRES investments will recover their costs on an energy only market (EoM) andwhich market designs could promote necessary investments. Thus. theGerman case study of the TradeRES project [1] aims to assess vRES
remuneration needs and policy options.
Methods We use AMIRIS [2, 3] to assess (among others) a scenario
with 85% vRES share (S0). We assess a pure EoM and five different
support instruments for vRES. These are: a fixed / variable market premium, two-way contracts for difference, capacity premia and a recent
proposal from [4] (Financial CfD). All premia are iteratively adjusted
so each vRES agent is refinanced. We evaluate cost recovery rates,
power market prices, curtailment and other measures of market performance.
Results The data for Scenario S0 shows that EoM income alone is insufficient, as PV and wind operators cannot fully recover their costs.
Market-based cost recovery rates vary depending on the support instrument due to the bidding and dispatch behaviour for vRES, resulting in different prices and market incomes. Our findings also indicate a significant sensitivity to the scenario considered, the degree ofmodel-endogenous flexibility sources as well as backup fuel (hydrogen) prices.
Sources [1] https://traderes.eu/ [2] doi: 10.21105/joss.05041 [3]https://dlr-ve.gitlab.io/esy/amiris/home/ [4] http://hdl.handle.net/10419

4 - The reform of Balancing Markets: Insights from Portugal and Spain

Hugo Algarvio, António Couto, Fernando Lopes, Ana
Estanqueiro
The legislation of the European Commission for regulating the European Internal Market of Electricity (EIME) establishes measures forthe electricity market harmonization. These measures are designed
to encourage the active participation of renewable generation in balancing markets (BMs). RESTrade (Multi-agent Trading of Renewable
Production) tool models and enables the simulation of existing secondary and tertiary markets but also simulates improvements to current market designs. These changes, developed within the scope ofH2020 TradeRES project (grant agreement no. 864276) are: i) 15minute trades of reserves and penalty prices, ii) participation of smaller(0.1 MW) aggregated or single players, and iii) separate procurement
and bidding of energy and capacity reserves. They are aligned with the
EIME legislation. Furthermore, the tool can also compute the imbalance settlement (IS) based on the Portuguese or Spanish mechanisms
such as a new approach for computing the dynamic procurement of
secondary capacity. This work uses RESTrade to simulate the BMs of
Portugal and Spain during 2019, where the weight penalties are around
18.5% and 21.3% of the wholesale market prices. The dynamic procurement increased the usage of its up and down capacities by 12% and
6% in Spain during the period 2019-2022, respectively. The simulationof a new IS mechanism during 2019 in Portugal and Spain, resulted ina reduced penalty of 12.2% and 4.4% of the wholesale prices paid,
respectively.

 MC-10
Monday, 12:30-14:00 - Room: 11 (building: 116)

Mobility and transportation in healthcare
Stream: OR in Health Services (ORAHS)
Invited session
Chair: Linke Potgieter
1 - Mobile clinic deployment in the Witzenberg region ofSouth Africa.

MC-10

In South Africa, the Department of Health has introduced mobile clinics to improve access to healthcare for rural communities. In this study,
we consider the Witzenberg region, where six mobile clinics have been
deployed. The aim was to determine routes and schedules to improve
the workload balance, fairness, and transportation cost, while ensuring
patients get satisfactory care. The problem is modelled in three phases
using both primary qualitative data and secondary quantitative data. In
phase 1, a multi-vehicle routing problem is formulated to construct feasible daily routes for the mobiles. Phase 2 distributes the daily routes
fairly between the mobile clinics to ensure fairness. Finally, in phase 3,
a vehicle routing formulation is used to determine a 4-week schedule
for each mobile clinic, by using the daily routes obtained during phase2 as input. Four different service time estimations are used as input,
resulting in four different schedules, each with their own advantages
and disadvantages, including cost-effectiveness, robustness, fairness,and continuity of care. AHP was then performed with main decision
makers to determine their preferred schedule. Final routes and schedules were determined based on model results, AHP results, and finalpractical input from the decision makers, resulting in an improvement
in workload balance, a 23% reduction in total distance travelled andwillingness by decision makers to implement the changes.

2 - Allocation-Routing Problem in a Multi-Hospital Home
Hospitalization System: A Case Study of a Healthcare
Provider in Portugal

Valentina Bonomi, Renata Mansini, Daniel Santos, Maria
Lopes, Ana Barbosa-Povoa
We study a Multi-Hospital Home Hospitalization Allocation-Routing
Problem (MH-HHARP) to provide a useful tool for a healthcare
provider in Portugal for evaluating the impact of the opening of a
new Home Hospitalization (HH) unit. Given the complexity of HH
systems, where specialized nurses and doctors allocate their time between inpatient and outpatient systems, it is critical to efficiently
manage their time and workload. For this purpose, we introduce a
Mixed-Integer Linear Programming formulation that aims at minimizing routing costs, alongside introducing two other functions: workloadbalancing among nurses and the minimization of employed doctors.
The model is validated using benchmark instances derived from theprovider’s historical data. Through single-objective resolutions, we
provide an economic analysis that assesses the cost implications andthe benefits of opening a new HH unit at the variation of objectivefunction. Furthermore, we explore bi-objective problems through anepsilon-constraints method, presenting significant Pareto frontiers tofacilitate the provider’s decision-making process regarding the tradeoffs between different objectives. A TOPSIS analysis is subsequently
applied to identify the optimal solution for the provider according tovarying objectives weights.

3 - The intra-hospital patient transportation problem withdiverse rolling equipment

Angel Ruiz, Vinicius Martins Ton, Jose Eduardo Pecora
Intrahospital patient transportation is a crucial activity to hospital operations that also impacts patients’ satisfaction. The problem consists inassigning each of the incoming transport requests to one of the available porters, in such a way that patients arrive to their destination
on time, or their lateness is minimized. Some patients (i.e., hospitalized patients) are transported on the bed they occupy, while others(i.e., external patients) require a wheelchair provided by the hospital.
Handling different types of transport equipment may require porters
to switch equipment between two transport requests. For instance, aporter may have to visit an equipment depot to pickup (drop off) a
wheelchair before (after) serving a request which requires it, increasing the total traveled distance, and reducing the availability of porters
to answer other requests. To the best of our knowledge, this work is
the first modeling and solving this realistic version of the intra-hospitalpatient transportation problem with various types of rolling equipment.
We propose and compare the performance of several approaches (including a mixed integer linear programming model, a constraint programming model, a constructive heuristic, and a local search heuristic)
with respect to metrics widely used by hospital managers over a set of
instances inspired by a real mid-size hospital. The local search heuristic produced excellent results and proved its suitability to be used inreal applications.

Hannah Callaghan, Linke Potgieter83MC-11EURO 2024 - Copenhagen

4 - Assess the Equity Impacts of different Transportation
Modes using Access to Health Facilities for Asthma Patients an Indicator

Mahnoush Minuyee, Merkebe Getachew Demissie, Anne
Hicks, Laleh Behjat, Barry O’Sullivan
Asthma exacerbations can occur due to various factors, including exposure to air pollutants generated by economic sectors, like the transportation sector. Here, we investigate the equity performance of different transportation modes using access to health facilities as key equity
outcome. Patients with asthma must get to hospitals soon. Asthma,
which affected 10% of the Canada’s population in 2021, is one of themost prevalent chronic respiratory diseases affecting children in Calgary in Canada. To achieve transportation fairness, our study focuseson the critical component of spatial accessibility to medical facilities
for the medical care of pediatric asthma. We intend to shed light on
how accessible it is for patients with asthma to get to the closest medical facilities using different transportation modes. We have developed acomprehensive equity analysis framework to measure access to healthfacilities. It combines coverage and reachability-based methods andutilizes both vertical and horizontal equity techniques. We calculated
accessibility metrics at different geographical levels to highlight howvarious transportation modes impact access to health facilities, suchas at the community district or city sector level. Our findings demonstrate that transportation planners can leverage insights generated fromboth vertical and horizontal equity analyses to enhance transportation
services in underserved communities, thereby improving access to various health facilities.

 MC-11
Monday, 12:30-14:00 - Room: 12 (building: 116)

Behavioral Decision Analysis II
Stream: Behavioural OR
Invited session
Chair: Gilberto Montibeller
1 - Time preference influences attribute weights and values
in multi-attribute decision-making

Pelin Gulum Tas, Yousef Maknoon, Jafar Rezaei
Eliciting attribute weights and values are two crucial steps in many
multi-attribute decision-making (MADM) methods. Due to their highreliance on judgmental inputs from decision-makers, these steps can be
subject to various behavioral factors, including time preference. Given
the frequent application of MADM methods in many strategic real-life
problems, examining time-related behavioral vulnerabilities and providing systematic approaches to overcome them become imperative.
This study investigates the time preference phenomenon in complex,
multi-attribute decision-making problems. A special experiment is designed following the Multi-Attribute Value Theory (MAVT) steps, anda health-related intertemporal decision problem is used. In a withinsubject design, participants’ preferences are used to elicit attributeweights and value functions considering different resolution times of ahealth-related outcome. Data were collected from six European countries via an online platform. The statistical analyses indicate the significant influence of time preference on elicited importance weights
and value functions. Subjects assigned different weights to the same
attributes and different values to the same outcomes depending on thetime of the realization of outcomes. These findings have significant
implications for both practitioners and researchers.

2 - Anchoring bias in Multi-Attribute Value Theory

Geqie Sun, Maarten Kroesen, Jafar Rezaei
Anchoring bias, a well-known cognitive bias, occurs when people
heavily rely on the initial information they receive, influencing subsequent judgments or decisions. This study examines anchoring bias

84

in multi-attribute value theory (MAVT), focusing on its impact during
value function and weight elicitation process. Hypotheses were formulated to investigate the presence of anchoring bias in MAVT and to
explore potential strategies for mitigating its effects. These strategies
were derived from the "consider-the-opposite" concept, a recognizedde-biasing approach for anchoring bias and were customized to fit the
MAVT process. To test the hypotheses, an experimental study was
conducted based on a real-life decision-making problem. We createda questionnaire in alignment with the MAVT methodology and collected data via an online platform. The findings indicate that anchoring bias has an impact on both the value function and weight elicitation
steps. Additionally, suggested mitigation strategies were observed tobe somewhat effective, offering decision-makers practical tools to partially address anchoring bias. In conclusion, this research contributes
to enhancing decision-makers’ awareness of anchoring bias as well as
offering actionable insights for decision-makers aiming for more accurate and unbiased decisions.

3 - Poor choices and post-decision surprises - the impactof evaluation uncertainties in multiattribute decisionmaking

Eeva Vilkkumaa, Taeyoung Kee, Juuso Liesiö, Pekka Malo
Decisions that involve multiple attributes are commonly supported
by decision analysis methods deploying a multi-attribute value/utility
function, which captures decision-makers’ preferences on multiple decision attributes and their risk attitude. Often, there are uncertainties
related to the evaluation of the decision alternatives’ attribute-specificperformances. In the context of single-attribute problems, these uncertainties cause the ex post realized performances of the selected alternatives to be systematically lower than the ex ante estimates, causing thedecision-maker to experience post-decision disappointment. We study
the impact of attribute-specific estimation uncertainties and nonlinear
utility functions on post-decision surprises and the quality of decision
making in a multiattribute setting. In particular, we present both analytical and simulation results to analyze the performance of several
selection approaches by examining whether these approaches lead tothe optimal choice that maximizes the expected multi-attribute utility and whether these choices correspond to the truly optimal choicesthat maximize the ex post true multi-attribute utility. Furthermore, we
demonstrate the impacts of these selection approaches in a realistic example on choosing a contractor for a construction project.

4 - Behavioral Decision Research: Descriptive and Prescriptive Perspectives

Detlof von Winterfeldt, Gilberto Montibeller
Behavioral Decision Research has provided a deep understanding of
how humans form judgments and make choices, since its emergencein the 1950s. Underlying this body of research is the contrast of actual
decision making with the paragons of rationality, which provide theidealized model on how humans should decide. Starting with the Ellsberg and Allais paradoxes and followed by Tversky & Kahneman’sseminal research, it has been clear that no real person is fully rational. Exploring deviations from rationality has been a major focus of
Behavioral Decision Research. This research has two quite distinct
branches: descriptive and prescriptive. Descriptive Behavioral Decision Research examines deviations from rationality and strives to develop theories or models to explain these deviations. Prescriptive Behavioral Decision Research also starts from observed deviations fromrationality, but rather than developing theories of models to explainthese deviations, it develops and tests tools or analytical methods tocorrect these deviations. The distinction between the descriptive andprescriptive perspectives in Behavioral Decision Research has often
been implicit and blurred in the Decision Sciences literature. In thispaper we aim to address this conceptual lacuna, proposing a taxonomy for these two perspectives in Behavioral Decision Research anddescribing their main achievements.

EURO 2024 - Copenhagen MC-12
Monday, 12:30-14:00 - Room: 13 (building: 116)

Agrifood supply chain decision problems
Stream: OR in Agriculture and Forestry
Invited session
Chair: Franco Basso
1 - Predicting price fluctuations of commodities by thejoint use of extreme value techniques and machinelearning methods: a case study of cherry price forecast

Myriam Gaete, Marcela C. Gonzalez-Araya
Predicting export product prices is important for export companies fornegotiating contracts with the associated participants of the supply
chain. In this study, extreme value techniques are used for normalizing data of price fluctuations, and then, machine learning methods
are applied for estimating future price fluctuations. This methodology is applied to a case study for estimating the export price of sweet
cherries. The results show low error in the price fluctuations’ forecast,with a MSE lower than 1. Besides the good performance, the proposed
methodology is easy to implement and can be executed in a low computational time by using personal computers.

MC-13

the associated supply and demand prices, and transportation costs. The
comprehensive numerical results reveal that reductions in transportation capacity and prolonged shipment times, as in the Panama Canal.

4 - A horizontal collaboration approach for grape transportation in a pisco cooperative

Franco BassoThis paper proposes a novel horizontal collaborative framework forgrape transportation within a cooperative that supplies grapes for pisco
production. Nowadays, each cooperative member collects and delivers grapes to previously assigned processing plants without coordinating with the other members. In order to evaluate the potential impact
of shared transport resources, we model collaboration among farmers
as a cooperative game with transferable cost, where a mixed integer
programming formulation that models transport activities is repeatedly
solved for computing the characteristic function. Although the grandcoalition is theoretically better than any other coalition structure, ourapproach limits the number of farmers per coalition to assess incremental collaboration’s benefits. By conducting a case study involving
sixty farmers, we show that a collaborative approach leads to a 13.4%
reduction in the cooperative’s transportation costs. However, we observe that the benefits of collaboration show diminishing returns. Particularly, we show that a coalition structure with a maximum numberof three players per coalition captures almost 94% of the maximum
potential savings.

2 - A Hybrid Solution Approach to Support Fresh Fruit Supply Chain Cold Storage and Transportation Decisions

Nicolás Reyes, Marcela C. Gonzalez-Araya, Wladimir E.
Soto-Silva, LluisM Pla
Planning the cold storage and transport requirements throughout the
fresh fruit supply chain is essential for decision makers, in order topreserve fruit quality and reduce fruit loss. This study presents a multiperiod mixed integer linear programming (MILP) model for supporting cold storage and transport decisions of the fresh fruit supply chain,
aiming to minimize total costs. For solving the proposed model, ahybrid solution method is developed, which combines a constructive
heuristic algorithm and an exact method to build solutions incrementally, that is, period by period. A real case study from a Chilean fruit
company is used for demonstrating the suitability of the hybrid solution method, which requires much lower computational time than theexact method for finding near optimal solutions (about 0.1% of thetime required by the exact method). The results show that the hybrid
method finds solutions with gaps in objective function values less than
2.0% compared to the objective function values obtained by the exact method. These results provide more alternatives for planning cold
storage and transportation requirements in the fresh fruit supply chain.

3 - Multicommodity Fresh Produce Trade Networks with
Quality Deterioration Under Congestion and Transportation Capacities

Deniz Besik, Anna NagurneyThis paper constructs a multicommodity spatial price equilibrium
framework designed to allow for the quantification of the impacts ofcongestion and transportation capacities on trade flows and productprices while capturing the degradation of fresh produce quality throughexplicit quality deterioration formulas. We present the multicommodity fresh produce trade network model under equilibrium conditions
both without and with minimum quality standards. Alternative variational inequality formulations of the governing equilibrium conditions
are derived, with one of them exhibiting particularly favorable features
for computational purposes. The numerical examples focus on the banana trade, since bananas are the most widely traded fresh produce
commodity globally, with leading exporters being Ecuador and Costa
Rica and major importers, United States and the European Union.
Through various scenarios, we simulate congestion and time delaysin the Panama Canal and report on the impacts of ongoing disruptions,on shipment times, equilibrium shipment volumes, the supply and demand prices, quality levels at the supply markets and at the demandmarkets, and transportation costs. Our baseline numerical example results closely align with real-life data on export volumes of bananas, MC-13
Monday, 12:30-14:00 - Room: 15 (building: 116)

Approaches for Integrating Quantitative
Modelling
Stream: Soft OR and Problem Structuring Methods
Invited session
Chair: Mike Yearworth
Chair: Isabella Lami
1 - Triggering a "Plural Subject" through the software "MuVAM" combining Strategic Choice Approach and Analytic Hierarchy Process

Elena Todella, Isabella Lami
This research intends to investigate, within a decision-making process, the affordances enabled by the use of a specific artifact, a newsoftware called Multi-Values Appraisal Methodology (MuVAM) created on a multi-methodological approach recently introduced in theliterature (Lami and Todella, 2023). MuVAM combines the Strategic
Choice Approach with the Analytic Hierarchy Process and it is basedon cloud-based technologies, which allow synchronized and connected
use, digitally and also in asynchronous mode. The study investigatesthe contribution of the software to group interaction and the development of a "Plural Subject" through the analysis of eight workshops,
held in four different European countries, in three languages (English,
French and Italian) and on four different case studies, all related tosustainable urban development. In particular, the applications differedwith respect to: i) the scale of intervention, ranging from the planningof an entire city area to that of a university campus; ii) the outcome,which in most cases concerned the definition of a master plan. Interestingly, the participants in the workshops were also quite diverse:
Bachelor’s, Master’s, PhD and Lifelong Learning Master’s degree students, with a varied experience in terms of educational level and background. Feedback from the workshops was collected through a reportor a Google survey, through a series of questions covering both groupdynamics and individual choices.

85

MC-14EURO 2024 - Copenhagen

2 - Hierarchical Process Modelling (HPM): integrating conceptual and quantitative modelling

Mike Yearworth
The development of a problem structuring sensibility in engineering
has led to a processual turn in engineering practice. By adopting astrong process epistemology - everything can be modelled as a process - it is possible to model purposeful activity conceptually, even if
it includes processes that will eventually become reified as objects inthe world. In addition to this delayed reification, Hierarchical Process
Modelling (HPM) has an inbuilt measurement schema for process performance that includes an explicit representation of uncertainty and thepossible calculation of overall performance using necessity and sufficiency conditions. Modelling proceeds from an initially agreed purpose amongst stakeholders through a how/why questioning dialectic ingroup model building. Since a processual interpretation of engineering practice has been made evident using this modelling approach, it
is possible to examine the implications for integrating hard/soft OR
practice. These are discussed, supported by case examples.

3 - Empirical and historical explorations of integrating
qualitative and quantitative analyses

Eleanor Reynolds

2 - Forecast Equilibrium in Electricity Markets

Vladimir Dvorkin
Renewable power producers participating in electricity markets build
forecasting models independently, relying on their own data, modeland feature preferences. In this paper, we argue that in renewabledominated markets, such an uncoordinated approach to forecasting results in substantial opportunity costs for stochastic producers and additional operating costs for the power system. As a solution, we introduce Regression Equilibrium—a welfare-optimal state of electricity
markets under uncertainty, where profit-seeking stochastic producers
do not benefit by unilaterally deviating from their equilibrium forecast models. While the regression equilibrium maximizes the private
welfare, i.e., the average profit of stochastic producers across the dayahead and real-time markets, it also aligns with the socially optimal,
least-cost dispatch solution for the system. We base the equilibrium
analysis on the theory of variational inequalities, providing results on
the existence and uniqueness of regression equilibrium in energy-onlymarkets. We also devise two methods for computing the regression
equilibrium: centralized optimization and a decentralized ADMMbased algorithm that preserves the privacy of regression datasets.

3 - Gas Trading with Tariff Versus Sanction as a Networked
Game

Frédy Pokou, Hélène Le Cadre, Marta Fochesato
This paper considers how we recognise the impact of qualitative analysis within interventions. First, empirical data is used to illustrate the integration of qualitative and quantitative within the context of an everyday workplace, considering patterns of integration and their perceived
effects. Second, the historical basis for valuing the integration of qualitative and quantitative is explored, considering the longer-term evolution and use of Soft OR and Problem Structuring Methods (PSMs).
Finally, I reflect upon the broader questions these observations raise
about the relevance of qualitative analysis and how we promote thisrelevance within and beyond our immediate community.

 MC-14
Monday, 12:30-14:00 - Room: 16 (building: 116)

Machine Learning for Electricity Market
Applications
Stream: Energy Markets
Invited session
Chair: Lesia Mitridati
Chair: Farzaneh Pourahmadi
1 - Less than meets the eye: simultaneous experiments asa source of algorithmic seeming collusion

Xavier Lambin
This article reassesses the assertions regarding algorithmic collusion
presented in Calvano et al. (AER 2020) and subsequent literature. We
first reveal an important mistake in the original papers, which refutes
the idea that the phenomenon of supracompetitive prices arises fromcollusive mechanisms where reward and punishment schemes support
high prices. Then, our theoretical and simulation-based analysis suggests that both the high prices and the apparent punishment schemes
result directly from simultaneous experimentation and the learning
inertia inherent in most reinforcement learning techniques. The cooccurrence of high prices and apparent price wars is not causal andboth stem from imperfect learning. We demonstrate that such seeming collusion can arise in memoryless environments, even with myopic agents, and much faster than previously thought. Our findings
alert on the risk of qualifying as collusion a phenomena that is not,and invite new, simple approaches to address the issue of algorithmic
supra-competitive pricing.

86

The economies of many geographic markets are dependent on fossil
fuels. Due to geopolitical tensions, some geographic markets may decide to partially or totally halt their gas trades. We therefore propose amodel which casts the optimal taxation problem as a networked gamein two settings: a) perfect competition, where a global market operator
is responsible for adjusting the export prices, leading to a partial equilibrium; b) a variational approach, where the export prices are determined endogenously as dual variables of the supply-demand balances.
Our model is built on an agent based representation of suppliers andgenerators interacting in a certain number of geographic markets. We
aim to assess the impact of tariffs or sanctions on the geographic markets’ imports of gas and on their utility. To that purpose, the optimal
taxation problem is framed as a Stackelberg game where a regulator
at the upper level is responsible for the sanction definition considering different criteria, while the geographic market at the lower level
reacts by adjusting their exports and trade prices. We derived closedform expressions for the export prices, and proved that the Stackelberg
equilibria can be explicitely mapped to the perfect competition equilibria. Finally, considering a degree of bounded rationality on the partof stakeholders, we rely on Prospect Theory to extend the optimal taxation games to in situations involving risks linked to the behavior ofother market stakeholders.

 MC-15
Monday, 12:30-14:00 - Room: 18 (building: 116)

Healthcare Analytics
Stream: OR in Health Services (ORAHS)
Invited session
Chair: Vedat Verter
1 - Leadership Engagement Acceleration & Partnership(LEAP) Digital Health Hub: Driving Sustainable Digital
Healthcare

Daniel Gartner, Ian Craddock, Andrew Dowsey, David Ellis,
Paul Harper, Hanna Isotalus, Maneesh Kumar, Richard
Luxton, Martin Pitt, Christos Vasilakis
The Digital Health Hub, funded by UK’s Engineering and PhysicalSciences Research Council, is a collaborative initiative in the SouthWest of England and Wales bringing together leading universities, supporting companies, National Health Service Trusts, and social care organizations to create a dynamic ecosystem for training, research, andEURO 2024 - Copenhagencollaboration. The hub has three pillars. Firstly, the Hub’s Skills andKnowledge Programme responds to the professional training needs ofindustry, health and social care providers, and academia, focusing on
two key themes: Transforming Health and Care Beyond the Hospital,
and Optimizing Disease Prediction, Diagnosis, and Intervention. The
aim is to develop the necessary skills and expertise to drive innovation and deliver effective digital healthcare solutions. Secondly, the
Hub’s Fellowship programme nurtures future leaders across academia,
industry, health and care sectors, and the wider community. With fivedifferent schemes, it offers a platform for aspiring leaders to influence
the future of digital health. Finally, the Hub’s Research programme
focuses on advancing pre-competitive research within the transformative areas of healthcare. By collaborating with organizations, the Hub
ensures that research efforts align with current health priorities andadd value to the healthcare landscape. Our presentation provides an
overview of the ’3.2 million three-year programme.

2 - On Reducing Medically Unnecessary Procedures
Through Analytics: The Design of Financial Incentives
for Maternity Care

Beste Kucukazici, Emily Zhu, Ting Wu
This work focuses on the design of financial incentives to reduce medically unnecessary C-sections, resulting in enhanced birth quality withalleviated economic burden for healthcare payers. To this end, wedevelop an innovative semi-supervised fuzzy clustering algorithm toclassify pregnant women into low- and high-risk groups by analyzing
approximately 18 million birth records from US. Our experiments on
real-life and synthetic data demonstrate the efficiency of our algorithm
for large datasets. Then, we validate the optimal delivery methods for
two risk groups through post-delivery outcomes for the mother andthe newborn by using inferential statistical analysis. Furthermore, wedevelop a metric to quantify the maternity risk index to be used instylized analytical models. Next, we develop an analytical framework
based on a principle-agent model to analyze the effect of different payment schemes from the quality of care and cost perspectives. We propose three payment systems, hybrid payment, risk-sharing model, andpenalty contracts to alleviate the shortcomings of fee-for-service andbundled payment schemes, thereby facilitating system optimal decisions. Finally, we present a simulation-based numerical study to empirically verify our analytical results.

3 - Data driven exploration and modelling of patient flows
through NHS Talking Therapies care pathways

Elizabeth Yardley, Alice Davis, Christos Vasilakis
It is estimated that one in six adults in England have a mental healthdisorder and would benefit from a course of psychological therapy. TheNHS Talking Therapies Programme treats over a million patients with
common mental health problems each year according to the principleof stepped care, where effective but less resource intensive treatments
are delivered to patients first and higher intensity interventions are thenprovided if required.
In order to meet NHS targets, the programme will need to increase
access rates, while meeting service standards for waiting times and recovery rates. Mental healthcare providers are therefore looking at allopportunities to increase capacity and productivity. The aim of thisproject is to develop innovative, advanced, analytical tools to help improve understanding and management of NHS Talking Therapies services’ demand and capacity.
This study analyses data from iaptus, the leading digital care record forpsychological therapy services, to investigate and model patient flows
through talking therapy care pathways using process mining and otherdata-driven methods, augmented by simulation modelling to evaluate
pathway performance and explore relationships between performanceissues. The major impacts of this project are expected to be improved
access to services, improved utilisation of resources, resulting in reduced waiting times, better recovery rates and reduced patient dropout.

4 - Measurement Noise and Judgment Bias in Hypertension Management

MC-16

Hypertension is a major public health issue and the most important
modifiable risk factor for cardiovascular disease. Effective hypertension management is hindered by (i) the noise in blood pressure (BP)measurements (technology factor) and (ii) the physicians’ judgment
bias (human factor). The latter refers to the physician’s error in inferring the patient’s true BP from the measurements. This study investigates the role of these two factors in hypertension management fromthe perspective of the clinical value of information.
We present an analytical framework with two main modules: a learning
module that models how clinical judgments about the patient’s underlying BP are made (with and without bias) and an optimization modulethat models how optimal treatment decisions are made under various
learning strategies.
Our results suggest that the value of information concerning the patients’ underlying BP depends on (i) the physician’s judgment bias anddecision flexibility, cardiovascular risk as well as the short-term andlong-term BP variability of the patient, and the measurement noiseof the device. In addition, among the two types of judgment bias
(i.e., under-estimation and over-estimation), the value of information
is much higher under the under-estimation bias, where the clinician
undervalues the significance of the observed BP in inferring the true
underlying BP.

 MC-16
Monday, 12:30-14:00 - Room: 19 (building: 116)

Experienced Routes for the TeachingStudents’ Problem
Stream: OR Education
Invited session
Chair: Alice Raffaele
1 - Data-driven decision making for secondary school students under the framework of Sustainable Development
Goals

Vanesa Guerrero
The "Data Science Gymkhana" is a yearly activity organized with theaim of promoting critical thinking and the development of the scientificmethod among secondary school students. It was born in 2020 as oneof the activities of the program STEM4Girls developed at Universidad
Carlos III de Madrid (Spain) to promote scientific and technologicalvocations among children and young people and, in particular, girls.
The Gymkhana seeks to make visible the importance of data science
as a driver of change and social transformation in achieving the Sustainable Development Goals (SDG). Each edition is focused on one
SDG and top researchers are invited to give a talk about her work. In
this edition an Operational Researcher has been invited in connection
to the SDG7: Affordable and clean energy. Students have to analyze
a data set related to the selected SDG, solve some riddles and present
their analysis in relation to the actions we can make based on data.
In this presentation, we describe the activity and discuss some of its
outcomes and impact.

2 - The experience of the CQIIA-MatNet center of the University of Bergamo

Francesca Maggioni
In this talk we will present recent experiences of the
CQIIA-MatNet center of the University of Bergamo(https://cqiia.unibg.it/it/ricerca/matnet). MatNet is a center which
is focused on teaching of mathematics and its applications and whichdevelops collaborations between universities and schools. We will
present the experience of the San Pellegrino Summer School on Mathematics and Artificial Intelligence and the seminars series devoted tohigh school teachers, organized in collaboration with Mathesis Bergamo under the umbrella of Piano Lauree Scientifiche (PLS).

Vedat Verter, Manaf Zargoush, Mehmet Gumus

87MC-17EURO 2024 - Copenhagen

3 - Teaching Predictive Analytics without Coding

Volker Kraft
Building and understanding powerful Machine Learning algorithms requires statistical thinking supported by accessible and powerful tools.
JMP Pro brings advanced predictive modeling into the classroom,
without any need for coding.
As teaching examples, automated Model Screening and state-of-the-art
Torch Deep Learning will be demonstrated from a student’s perspective. We will discuss course use and teaching best practices.

4 - Graduates’ Employability and the Digital Transition:
Tuning a Problem-Based Approach and Enhancing
OR/MS Courses

Joao Miranda, Ana Paula Teixeira
Nowadays, the digital transition and related developments on graduates’ employability are key for Higher Education Institutions (HEI),
balancing either their fast pace or the impacts multiplicity. For that, aProblem-Based Learning (PBL) approach is revisited and tuned withdigital support, in a manner to better promote the OR/MS learns within
a Management Science ’s graduation program. Specific problems arethus built to promote reasoning, to conjugate both the work context andprofessional environment, with quantitative approaches and OR/MS
methodologies. While addressing typical contents of Decision Aiding/Making, Linear Programming (LP), Integer Programming, and LP
special cases, important insights about optimality and uncertainty aretreated, and thereafter Forecasting methods are motivated and introduced. In addition, a set of simple problems and study cases is developed in a way to enhance graduates’ analytical and digital skills, andthe main outcomes are outlined. Finally, further developments in faceof the labor market challenges are also discussed.

5 - The ROAR project: How to improve Grade 10-12 interest
and motivation in Mathematics through a learning pathbased on Operations Research

Alice Raffaele, Gabriella Colajanni, Alessandro Gobbi,
Marinella Picchi, Eugenia Taranto
Operations Research (OR) is a discipline of applied mathematics usually taught at the university level. Nevertheless, many initiatives havebeen developed for younger students in the last few years. Startingfrom these, we designed Ricerca Operativa Applicazioni Reali (ROAR,
i.e.., Real Applications of Operations Research), a learning path forhigher secondary schools relying on active learning and constructionism. ROAR offers examples and problems closely connected withstudents’ everyday lives, balancing mathematical modeling and algorithms. ROAR aims to improve students’ interest, motivation, andskills related to STEM by integrating mathematics and computer science through OR. ROAR is composed of three units. The first one,addressed to Grade 10, is dedicated to introducing mathematical modeling, linear programming, and using GeoGebra and Solver. The second unit, for Grade 11, focuses on graph theory and network applications, strengthening modeling skills and the development of heuristic
algorithms. Finally, the third unit, for Grade 12, concerns the implementation of mathematical models with PuLP, an open-source Python
library. Every teaching unit ends with presenting a final project totackle in groups. In this talk, we provide an overview of the implementation of ROAR at the scientific high school IIS Antonietti in Iseo
(Brescia, Italy) from March 2021 to January 2023, and we present derived initiatives and available resources.

 MC-17
Monday, 12:30-14:00 - Room: 40 (building: 116)

Simulation models in healthcare
Stream: OR in Health Services (ORAHS)
Invited session
Chair: Sally Brailsford

88

1 - Evaluating the impact of staffing recommendations inmaternity units

Louis Niffoi, Oualid Jouini, Marija Jankovic, Pierre-François
Ceccaldi, Guillaume Lamé
Sufficient midwifery staff is crucial to the quality of maternity care.
However, the shortage of staff that France has been facing for several
decades creates situations of understaffing. In order to determine theoptimum number of midwives in a given department, several methods
have been presented in the literature, e.g., a 1998 decree that sets out
minimum staffing levels, recommendations from scientific societies, or
calculation tools like the British BirthRate Plus tool. Yet, we lack evidence on how these models affect quality of care. We observed the operation of a French public hospital maternity and built a discrete-event
simulation model using Simul8 software. The simulation model reproduces the structure, staffing schedules and patient characteristics of thematernity. Because it has been shown that one-to-one care (one midwife for one patient) has a positive impact on service indicators suchas readmission rates, stillbirth rates and average length of labour, weused the rate of one-to-one care in labor as our main output. We used
design of experiment methods to determine the most influent factors
in our model. Using this model, we analysed the impact of different
staffing recommendations on quality of care measured by the rate of
one-to-one care. In the future, the model will be enriched with timeand-motion analysis data to provide more accurate estimates of how
much time midwives can really devote to patient care in delivery units.

2 - Effect of timings and frequencies of HCV treatment
camps on epidemiological, health and cost outcomesin Indian Punjab

Soham Das, Varun Ramamohan
In this study, we developed an agent-based simulation model for hepatitis C virus (HCV) transmission in the context of Punjab, an Indianstate with a high prevalence of the disease. The model incorporated a
dynamic cohort, infection spread through all major modes and a previously validated discrete-time Markov chain for disease progression.
We experimented the impact of treatment policies varying in frequencies and timings of treatment camps for different uptake rates. For
modelling treatment, we used the cure rates and costs of sofosbuvir
plus velpatasvir, a type of directly-acting antivirals having high curerates. In low- and middle-income countries like India, where budget
and resources for healthcare are greatly constrained, it is of interest to
test whether annual camps are avoidable. Over a ten-year intervention
period, we tested the following policies: (1) annual treatment camp (2)a single treatment camp at the beginning of the intervention period (3)a single treatment camp at the end of the intervention period (4) twotreatment camps at the end of the fifth and tenth years of the intervention period (5) three treatment camps at the end of the third, sixth andtenth years of the intervention period. For all uptake rates, we foundthat while treating once at the end is the best to maximize life years,
quality-adjusted life years and net monetary benefits, yet the annual
treatment camp is the best to minimize costs, HCV spread and active
HCV prevalence.

3 - Using large routine health service datasets for modelling

Sally Brailsford, Tracey England
In the past lack of data was often cited as a major challenge in healthcare modelling, but in recent years large electronic datasets have become increasingly available for use in research. This talk discusses
the advantages and disadvantages of using such datasets, in particularroutine health service data primarily collected for other purposes, compared with collecting prospective data or using the clinical literature.
These ’pros and cons’ are illustrated by three case studies. The firstdates back to the 1990s and concerns screening for diabetic retinopathy; the other two are connected, much more recent, and concern carefor older people. All three projects involved large multi-disciplinary
teams of researchers. The first project involved the development of adiscrete-event simulation model, parameterised using data from the literature. The two related projects involved the development of system
dynamics simulation models, both using large routine health service
datasets to estimate the model parameters. While access to such data

EURO 2024 - Copenhagenmight seem to be a luxury, compared with the difficulties of obtaining
data for the diabetic retinopathy model, deriving the required information from the data was not always straightforward.

4 - An overview of the application of OR methods to alcohol drinking behaviours, and a geographical analysis ofalcohol consumption in WalesElin Williams, Daniel Gartner, Paul Harper, Geraint Palmer
Alcohol consumption is a recognised public health issue, and it is believed that the resultant harms are rising since the COVID-19 pandemic. Alcohol drinking affects healthcare including the demands ofalcohol-attributable diseases; mental health and housing services demands on social care; and demands on the criminal justice system resulting from violent and aggressive behaviours. This talk will present
an overview of the application of operational research (OR) methods tomodel alcohol drinking behaviours, specifically within healthcare, social care, and criminal justice settings. Some OR methods are utilised
frequently in alcohol consumption literature, namely agent-based modelling (ABM), Markov models, and network analysis. The recurrenceof ABM and network analysis highlight the social component of alcohol consumption. Other methods such as system dynamics are utilised
less frequently but could be useful models to include in the literature
to gain a whole-system perspective. Alcohol consumption data collected in Wales can be analysed geographically by health board, local
authority, and area deprivation, to gain insights into the diverse alcohol
drinking patterns reported, and how resultant harms vary geographically and by consumption behaviour. Based on the literature evidence,
alcohol consumption and alcohol-related harms data, and collaboration
with stakeholders, an initial proposal on how to model alcohol drinking
behaviours across the whole system is presented.

 MC-18
Monday, 12:30-14:00 - Room: 42 (building: 116)

Enhancement of circularity, inclusivity, andsmartness in cities I
Stream: Sustainable Cities
Invited session
Chair: Maurizia Pignatelli
1 - Supporting stakeholders’ and citizens’ participationand inclusion in planning maintenance processes: multidisciplinary testing practices in Piedmont and Liguria

Umberto Mecca, Allegra Alacevich, Michela Cardinali, Laura
Fornara, Andrea Longhi, Simone Martino, Manuela
Rebaudengo, Andrea Salice
Since the last three years, a multidisciplinary research group of CCR
and Politecnico di Torino has been scientifically supporting the activation of a new strategic digital tool aiming at the preservation ofCultural Heritage (CH) across Piedmont and Liguria and pursuing thetransition from existing cultural policies. The presented activities are
Institutions’ support initiatives promoted by Compagnia di San Paolo
through two editions of PRIMA, a call for proposals oriented towards
the cultural, economic and social sustainability of CH Conservation
Plans. Quite experimentally and in addition to the funding activities,
capacity building, thematic coaching and networking have been activated, indirectly educating to the importance of collecting, creating,managing and sharing knowledge, particularly on the issues of CH
programmed conservation. The sharing of interdisciplinary expertiseand local experiences represents an innovation element which, throughpeer-to-peer comparison and knowledge sharing, promote the activation of collaborative networks and open up to the use of new financial
tools for a more aware and sustainable CH management. In order tomeasure PRIMA social impact, it has built a monitoring system whose
first outcomes (growth of skills, interventions sustainability, involvement and participation of all stakeholders and community), allow to

MC-19

identify planned conservation as one of the most solid and concrete
keys to the achievement of UN SDGs in the field of CH.

2 - Parcel Lockers as a Solution to Urban Logistics Challenges: A Stochastic MIO Model Incorporating Customer Utility

Zisis Maleas, Georgia Ayfantopoulou, Josep Maria Salanova
Grau
In the modern urban environment, the rise of e-commerce has led tosignificant last-mile delivery challenges, notably increasing congestion
and air pollution. Estimates suggest that last-mile delivery accounts
for half of the total CO2 emissions and delivery costs of the overall
journey of a single parcel. A popular mitigation approach involves installing parcel lockers, promising yet demanding new models for optimal last-mile distribution network configuration. This study introduces
a novel stochastic Mixed Integer Optimization (MIO) model for the optimal placement of lockers across various demand scenarios, incorporating a customer behavior model based on utility functions and queuing characteristics for the selected location. We present an efficient
optimization strategy inspired by Benders Decomposition and explore
a scenario where last-mile operators utilize a government-establishedlocker network. Our findings, based on real courier company data tomodel demand and utility functions from regional surveys to design
the utility model of customer. Lastly, the study presents a Continuous Approximation (CA) model guiding optimal locker quantity andthe impact of the final locations and service level. The study discusses
method performance, practical application, and improvement opportunities, offering insights into sustainable urban logistics.

3 - Leveraging SROI for Enhanced Social Impact

Amirbahador Kouchakkapourchali, Adriano Bisello
Leveraging Social Return on Investment (SROI) for Enhanced Social
Impact offers a ground breaking approach to boosting the effectivenessof social initiatives through efficiency analysis, sustainable development, and decision support systems. This method quantifies the social,economic, and environmental value of projects, providing a monetary
perspective on their impact. By integrating SROI into decision-making
processes, organizations can pinpoint the most efficient and impactful
ways to allocate resources, aligning closely with sustainable development objectives.
The paper highlights how incorporating SROI metrics into decision
support systems aids in the systematic evaluation of social investments,
facilitating a data-driven approach to choosing projects that yield thehighest social returns. This not only enhances the strategic allocationof funds but also improves transparency and accountability, enablingorganizations to communicate their achievements more effectively.
In essence, using SROI as a tool for enhanced social impact fosters aculture of evidence-based decision-making. It ensures that investments
are directed towards initiatives with the greatest potential for positivesocial change, thereby contributing significantly to sustainable development efforts. This concise approach underscores the importance of
SROI in optimizing social impact and advancing the global agenda fora more sustainable and equitable future.

 MC-19
Monday, 12:30-14:00 - Room: 44 (building: 116)

Multi-energy systems
Stream: OR in Energy
Invited session
Chair: Russell McKenna
1 - Exploiting synergies between heat supply and demandthrough endogenous modelling of building envelope
retrofits

Julian Hermann, Corsin Taisch, Christoph Domenig, Russell
McKenna89MC-20EURO 2024 - Copenhagen

The built environment has a large share in energy consumption andCO2 emissions in Europe, mainly caused by fossil fuel-based heat
supply. In the energy transition context, a drastic decarbonisation of
the existing building stock is required. Additionally, there is a significant potential for heat demand and emission reduction via envelope retrofits, which can also reduce required heat supply temperatures.
Strong interactions occur between heat supply and demand, requiringa holistic assessment of investments on both sides. However, energy
system optimisation models usually treat heat demand profiles as static
inputs without considering envelope retrofits or treat retrofits in a simplified way using pre-defined sets. In both cases, the heat demand isexogenous and cannot be altered. Only a few studies endogenously
consider envelope retrofits and their impact on heat demand.
This work presents a mixed-integer optimisation model to overcome
the abovementioned limitations. Our model includes a simplified representation of the building’s thermal behaviour, allowing us to consider investments in envelope retrofits and supply technologies endogenously. Comparing our methodology with existing approaches, we canshow that considering envelope retrofits endogenously can yield more
optimal results. Thus, our methodology enables exploiting synergies
between investments in envelope retrofits and heat supply technologies.

2 - Integrating EV charging behaviour into a municipal energy system model to explore trade-offs between flexibility and battery degradation

Linda Brodnicke, Febin Kachirayil, Paolo Gabrielli, Giovanni
Sansavini, Russell McKenna
Electric vehicles (EV) play a crucial role in achieving decarbonization
targets and with increasing adoption, their charging patterns will haveincreasing impact on energy systems. In this presentation, we present atransferable methodology to assess the influence of EV charging strategies on the design and operation of district-level energy systems inurban, suburban, and rural municipalities in Northern Germany. We
use the energy systems optimization model RE3ASON to assess thecost-optimal trajectory until 2050 subject to environmental constraints.
We extend the detailed case studies by incorporating EV behaviour toevaluate the impact of passive charging, smart charging, and vehicleto-grid (V2G) strategies on the energy system. In a two-stage optimization, the travel times and distances are sampled from a comprehensive German data set, aggregated in a way that ensures that energy
and power limits of each EV are respected, and then implemented in
the main optimization stage. Battery degradation is linearly approximated, which is particularly relevant for V2G modelling and a novel
aspect of this study. Hence, our methodology can assess the trade-offs
between the additional flexibility that V2G provides and the resulting
costs in terms of battery degradation. Our findings provide quantitative
estimates for the additional benefits that various charging methods may
have in terms of reducing cost, facilitating photovoltaic integration andreducing battery storage requirements.

3 - A new modelling framework for offshore energy systems considering decarbonisation and decommissioning options for oil and gas infrastructure: a UK North
Sea case study

Anna Peecock, Josef Koell, Pietro Bianchi Marzoli, Jiangyi
Huang, Alfonso Martinez-Felipe, Russell McKenna
Exploiting synergies between the offshore wind and oil and gas (O&G)
sectors can support decarbonisation goals, whilst exploiting disusedhydrocarbon infrastructure for long-term economic value. Incorporating greater sectoral integration and diversity of energy carriers enables energy models to evaluate the impact of different policy levers
more realistically. We therefore seek to quantify the economic andenvironmental benefits achievable by exploiting future synergies between the O&G and offshore wind sectors. This paper analyses scenarios with competing decarbonisation and repurposing alternatives andthe resulting impacts on brownfield electrification and offshore hydrogen/ammonia production feasibility, as well as energy demands andemissions in the UK North Sea. Based on a mixed-integer linear programming (MILP) approach, the model determines optimal pathwaysfor each of 242 O&G platforms while minimising total system costs up

90

to 2060. Operational constraints ensure technical viability of proposed
technologies and through consideration of multiple energy carriers, amore realistic assessment of energy system integration is undertaken
compared to previous studies. Offshore heat and power demands arecalculated on a platform-specific level, and wind deployment scenarios are devised using a spatially-constrained bottom-up LCOE model.
Future work should focus on integrating this tool within large-scaleenergy models, to improve the representation of the offshore system.

4 - Optimizing hybrid renewable energy systems throughavailability-based design

Luciana Yamada, Flávia Barbosa, Luis Guimarães
Hybrid renewable energy systems (HRES) have emerged as an alternative to single-source systems. With the advantage of combining different energy profiles and exploiting the complementarity of sources,
HRES have demonstrated to be a good option for increasing the adoption of renewable energy (RE) technologies, ensuring a stable energy
supply, and making the system more cost-effective by sharing operations and maintenance activities, electrical infrastructure, and spaceleasing. Efficiently designing HRES is essential to ensure stable energy production while maintaining cost-effectiveness. When designing an HRES, the goal is to identify the optimal combination of RE
sources and determine their respective sizes and types of equipment.
Furthermore, the design process must consider the availability of thesystem and the operational plan with the most effective maintenancestrategies for each unique combination of energy sources. This study
aims to analyse the cost competitiveness of an HRES, assessing the
economic feasibility of: new HRES or transitioning existing installations into HRES with the addition of new RE sources (e.g. complement
an existent wind farm with photovoltaic panels). A cost model is integrated into the optimization approach to estimate lifelong farm costs
and maximize an economic indicator. The farm’s availability is modelled using a Markov chain. In addition, this study seeks to identify
cost-saving opportunities by integrating different energy sources.

 MC-20
Monday, 12:30-14:00 - Room: 45 (building: 116)

Ethics and OR, societal complexity andpublic service
Stream: OR and Ethics
Invited session
Chair: Dorien DeTombe
Chair: Cathal MacSwiney Brugha
1 - An ethical complex societal problem, slums in developing countries.

Dorien DeTombe
Many cities in the world have areas which can be called slums. Slums
are areas where poor people live in poor circumstances. The houses
are not comfortable or convenient. Often leaking roofs, no good sanitation; toilets and douches are missing, no proper cooking devices orelectricity. Slums create a lot of trouble, for the inhabitants themself
concerning safety and healthcare, but also for the whole city and for thecountry. Changing slums is a complex societal problem, which should
be handled according to the directions of the Compram methodology.
The Compram Methodology is a multidisciplinary approach for policy making on handling complex societal problems. These problems
exceed the boundaries of a discipline. Therefore, a multidisciplinary
approach is needed. In each complex societal problem there are elements of knowledge, power and emotion. All these elements should be
addressed in the problem handling approach. The Compram methodology emphasizes the exchange of knowledge and understanding bycommunication among and between the experts, actors and politicians
meanwhile keeping emotion in mind.

EURO 2024 - Copenhagen
2 - The ethics of wanted and unwanted effects of government policies to handle the worldwide covid19 pandemic

Cor van Dijkum
Using systems theory and the tools of systems dynamics the worldwide spread of the covid19 pandemic can be analyzed as an effect of
intrusions into ecosystems. The spreading of the covid19 virus started
in 2019 in China as a local intrusion into an ecosystem. Subsequently
the spreading of the virus in the word in 2019, 2020 and 2021 canbe explained from this perspective. Thereby taking into account the
globalizing of our world and the increasing influence of social media
in the communication about and handling of the pandemics. The variables that explained the expanse of the pandemic are summarized in a
global systems dynamic map. In this context the effect and efficacy of
the governance policies to handle the pandemic is analyzed. Thereby
taking into account economic inequality in and between countries andassociated social unwanted effects on the present and next generation.
Also the unexpected and disturbing outcomes of linear and nonlinear
feedback loops on the way people reacted on government policies andthe role social media played is studied. Thereby at last focusing on
the ethical questions concerning wanted and unwanted effects of thosepolicies. As a case study, with the aid of the system dynamics map asimulation model is developed to evaluate the policy of the government
in the Netherlands and develop for this case alternative scenario’s, witha better balance between wanted and unwanted effects on the population, thereby addressing ethical questions.

3 - Real and fake security, and the United Nations

Cathal MacSwiney Brugha
Real security protects and civilises, unites and reconciles, gives security. Fake security subjects and colonises, divides and rules, takes ’securities’. One example of fake security is where Israel seeks to secure
its future by taking and subjecting two properties for itself, the Gaza
Marine Gas Field, and the valuable trading route, from Asia to the Red
Sea to the Mediterranean. In doing so, they are endangering the security of 2.4 million Palestinians in the Gaza Strip, 113 million Egyptians
nearby, another 300 million people in the Middle East, and the securityof future generations of Israelis. Another example is where, to securefor itself the valuable agricultural land of Ukraine, Russia is destroying the security of 37 million Ukrainian people, and the food security of millions in Africa, that are short of food. The United Nations
Organisation could bring real security. A United Nations Protection
Force (UNPROFOR) could join the United Nations Relief and Works
Agency in the Near East (UNRWA), and bring security to the Gaza
Strip, and then to Ukraine. Further, the 2 trillion (tn) dollars wasted
on colonisers’ military budgets could then be spent by UNPROFOR
on further increasing global security, by reducing international crime:
maritime 0.2 tn, cybercrime 6 tn, and organised crime 10 tn. Then to
deal with global climate change. Only real climate security projects
and actions provide any prospect of protecting future generations fromglobal climate insecurity.

 MC-21
Monday, 12:30-14:00 - Room: 49 (building: 116)

Disaster Response: Search and Rescue,
Resource Allocation and Impactful
Prepositioning
Stream: OR in Humanitarian Operations (HOpe)
Invited session
Chair: Syed Tariq

MC-21

Emergency response refers to the systematic response to an unexpected, disruptive occurrence such as a natural disaster. The response
aims to mitigate the consequences of the occurrence by providing theaffected region with the necessary supplies. A critical factor for a
successful response is its timely execution, but the unpredictable nature of disasters often prevents quick reactionary measures. Preallocating the supplies before the disaster takes place allows for a faster
response, but requires more overall resources because the time andplace of the disaster are not yet known. This gives rise to a trade-off
between how quickly a response plan is executed and how precisely
it targets the affected areas. Aiming to capture the dynamics of thistrade-off, we develop a $K$-adjustable robust model, which allows amaximum of $K$ second-stage decisions, i.e., response plans. This
mitigates tractability issues and allows the decision-maker to seamlessly navigate the gap between the readiness of a proactive yet rigid
response and the accuracy of a reactive yet highly adjustable one. The
approaches we consider to solve the $K$-adaptable model are threefold: Approximately, via a partition-and-bound method, and optimally
via a branch-and-bound method as well as a static robust reformulationin combination with a column-and-constraint generation algorithm. In
a computational study, we compare and contrast the different solution
approaches and assess their potential.

2 - Relief Goods Prepositioning and Affectee Relocation
Model For Compound Flooding

Syed Tariq, Muhammad Naiman Jalil, Muhammad Adeel
ZaffarFloods affect more people every year than any other natural disaster.
Compound flooding is when multiple flooding events exacerbate theimpact and duration of a flood. This paper studies relief logistics forcompound flooding through the case of floods in Sindh province ofPakistan in 2010. The problem is treated as time-varying flows of relief
goods from relief warehouses through periodic relief caravans to relief
camps or storage close to them; and flows of affectees from shelter sites
closest to them to more accessible shelter sites. This study concludes
that relocation of affectees may be as cost effective as prepositioningof relief goods depending on optimal placement of relief warehouses.
Factors that reduce logistics cost and improve demand fulfillment include prepositioning relief goods near shelters, frequent relief deliveries, and smaller storage modules that can reflect relief demand between
caravan arrivals more closely.

3 - A novel approach for time-critical search planning inmaritime search and rescue

Yingying Gao, Qingqing Yang, Guopeng Song, Roel Leus,
Kewei Yang
In maritime search and rescue, an efficient search path is essential
to maximize the success rate of the operation, typically indicated by
the probability of containment and detection. This paper formulates
a time-critical search problem, where a group of searchers moves
through a discretized environment over time steps to locate the moving targets. Given the importance of finding drowning personnel
timely, the decay factor of the survival rate is considered and incorporated into the objective function. To that end, we propose a novel
approach specifically designed for this task, integrating Branch-andBound (B&B) with Reinforcement Learning (RL). To obtain more effective and non-myopic policies than handcrafted heuristics, we guidethe branching process in path planning via RL and design a novel set
representation and reward function for the bounding procedures associated with a policy, speeding up the convergence of unsupervised
exploration. Our experimental results demonstrate the effectiveness ofthe proposed method, particularly on large realistic instances, improving upon the current state-of-the-art. The method can be generalized
for different problem settings in operational decision support for maritime search and rescue.

4 - Approaches for Improving coordination in emergency
care delivery in emerging economies context

Venkataramanaiah Saddikuti
1 - Planning effective emergency responses: K-adjustablerobust optimisation for relief prepositioning

Fabricio Oliveira

Healthcare delivery in resource constrained environment is a big challenge for both healthcare professionals and policy makers. This ismainly due to higher levels of interdependencies among healthcare91MC-22EURO 2024 - Copenhagenproviders. Of late, healthcare professionals are adopting the management science approaches for improving quality, efficiency and timely
delivery of delivery of care at low cost. This mainly includes supply
chain management approaches which is built based on theory of coordination and collaboration with suitable incentives. In developing
countries where resources are scarce such coordination failures canhave potentially severe impact on patient health. However, there islimited understanding about how coordination takes place across andwithin the different healthcare service providers and how this influence emergency care delivery. This presentation focuses on coordination and collaboration challenges in trauma care delivery in India usinga patient survey. The findings suggest mechanisms to better integrate
the processes in the care delivery which include setting-up referral processes, ambulance services, using third party coordinators and process
improvement within the hospital following lean principles and suggestions for policy making are also highlighted.

 MC-22
Monday, 12:30-14:00 - Room: 81 (building: 116)

Advancements in energy system
optimization and analysis tools
Stream: Energy Management
Invited session
Chair: Feng Qiu
Chair: Yuqun Dong
1 - Unitcommitment.jl - An Open Source Julia Software
Package to Accelerate Power System ResearchFeng Qiu
Unit commitment (UC) is one of the fundamental optimization problems in power system research, on which many essential grid applications are built, such as operations, planning, and markets. Many powersystem research projects involve the development of a UC model or
its variant as a building block. Currently, most researchers prefer towrite their own UC codes and use public data with their own tweaks.
Although this approach allows authors more control over the code, thedisadvantages are significant: it wastes time on reinventing the wheel,
does not incorporate the state-of-the-art UC models/algorithms, andmakes it difficult to benchmark with other work due to differencesin implementation and datasets. In this work, we introduce an opensource Julia software package for UC-related research. The package
not only implements state-of-the-art models/algorithms for UC problems but also provides great extensibility for secondary development
and numerous test instances for benchmarking. This presentation will
introduce the UC package and illustrate how it can be used to accelerate power system research.

2 - Benchmarking Building Energy Consumption Performance by A Nonparametric Least Squares Method

William Chung
The Convex Nonparametric Least Squares (CNLS) method can be used
to develop a benchmarking model represented by a set of hyperplanes.
CNLS assumes that the regression function is either concave or convex.
However, there may be instances where the regression function exhibits concave and convex patterns, rendering this assumption invalid.
This paper addresses this drawback by proposing a new method called
Concave-Convex Nonparametric Least Squares (C2NLS), which incorporates concavity and convexity constraints in CNLS. It is provedthat C2NLS will have better goodness-of-fit performance than CNLS,
but the number of hyperplanes will also increase. Since C2NLS contains both concave and convex portions, it is not sufficient to rely
solely on the concavity assumption (or convexity assumption) during
the benchmarking process. To tackle this issue, it is suggested that bothconcave and convex portions be used separately and combined with theresulting benchmarking scores. An illustrative example is provided,92

and the energy performance of Hong Kong secondary schools is used
to demonstrate the goodness-of-fit of C2NLS.

3 - Certification of MPC-based zonal controller security
properties using accuracy-aware machine learning
proxiesManuel Ruiz
The fast growth of renewable energies increases the power congestion
risk. To address this issue, the French Transmission System Operator(RTE) has developed closedloop controllers to handle congestion. To
ensure their proper functioning, RTE wishes to estimate the probability
that the controllers ensure the equipment’s safety. The naive approachto estimate this probability relies on simulating a large amount of randomly drawn scenarios, and use all the outcomes to build a confidence
interval around the probability.
Although theory ensures convergence, the computational cost of powersystem simulations makes such a process intractable. The target of thepresent paper is to propose a faster process using machine-learningbased proxies. The amount of required simulations is greatly reduced
thanks to an accuracy-aware proxy built with Multivariate GaussianProcesses. Using a proxy instead of the simulator however adds uncertainty to the outcomes. An adaptation of the Central Limit Theorem isthus proposed to include the uncertainty of the outcomes predicted withthe proxy into the confidence interval. As a case study, we designed asimple simulator that is tested on a small IEEE network. Results showthat the proxy learns to accurately approximate the simulator’s answer,
allowing a significant time gain for the machine-learning based process.

4 - Transformation of heterogeneous energy and planningstorage capacity at regional strategic level: A financial
derivative perspective

Yuqun Dong
Energy storage plays a key role in harvesting energy among heterogeneou energy sources. To transforming heterogeneous energy andplanning storage capacity at regional strategic level, this paper simulates storage capacity settings for heterogeneous energy in a certain
region (Jiangsu Province in China) from the perspective of investment
portfolio. This paper then achieves the adjustments between generation side and storage side during operation under hedging ideology.
At last, this paper takes thermal energy on storage side as an example
to apply switching real option method in decision making for shiftingfrom thermal energy into renewable energy during disposal. The conclusion shows that under a financial derivative perspective, planning ofheterogeneous energy storage capacity is more efficient than existingregional plans and decision making of transformation contains investment triggers with higher option values. Thus, this paper can help thepower system plan energy storage and manage heterogeneous energy
more economically and completely at regional strategic level.

 MC-23
Monday, 12:30-14:00 - Room: 82 (building: 116)

Sustainable food supply chains
Stream: Circular Economy, Remanufacturing and Recycling
Invited session
Chair: Renzo Akkerman
1 - Sustainable design of agricultural waste supply chain
based on circular bioeconomy

Maryam Roudneshin, Amanda Sosa
Among the United Nations’ 17 Sustainable Development Goals, a critical aim is to provide "affordable, sustainable, and clean energy for all."
Additionally, the rising urgency to ensure energy security and address
global warming has highlighted the need to explore alternative energy

EURO 2024 - Copenhagensources beyond fossil fuels. Leveraging agricultural waste for bioenergy production has emerged as a promising solution, offering an opportunity to mitigate environmental impacts while creating renewableenergy. The success and attractiveness of this initiative to stakeholders
and investors significantly depend on designing efficient supply chain
networks, particularly in selecting optimal locations for biorefineries.
This research sets out to design a supply chain network that prioritizes
sustainability, encompassing economic, environmental, and social considerations. Initially, it employs a two-pronged approach, utilizing Geographic Information System (GIS) and MCDM, to pinpoint viablebiorefinery locations, taking into account both environmental and economic constraints.

2 - Technologies Compatibility in a Capacitated MultiDepot Routing distribution system for Food Sustainable
Supply Chain

Seyed Mojtaba Sajadi, Fatemeh Khezerlouye Aghdam, Mahdi
Bashiri
Food supply chains play a significant role in carbon emissions, andadopting emerging Industry 4.0 technologies can help mitigate this issue. The study investigates the compatibility of digital technologies atvarious echelons of the supply chain. Among the components of foodsupply chains, logistics are also particularly emissions-intensive andtherefore require careful attention. This study presents a mixed integer linear programming mathematical model for selecting compatible
technologies in food supply chains under a capacitated multi-depot vehicle routing distribution to minimise the supply chain’s carbon footprint and to align with global carbon neutrality targets. To address
the complexities of real-world scenarios, a matheuristic algorithm isdeveloped empowered by a Reinforcement Learning (RL) approach.
The findings indicate that employing suitable technologies with optimal compatibility levels and implementing an optimum routing willlead to achieving a net-zero supply chain and reducing overall costs.
The results underscore the importance of both technology selection
and compatibility in attaining sustainability objectives.

3 - Network Design of an Insect-based Supply Chain

Sara Martins, Maria João Martins dos Santos
Insect farming has recently gained notoriety as a more sustainable alternative in the food and feed industry. Insects need less land, water,
and feed than conventional livestock and can be fed on residual byproducts from the food transformation industry that would otherwise
be considered waste. By capitalizing on these factors, insect farming
demonstrates a substantially smaller environmental footprint compared
to conventional protein sources like cattle rearing and intensive agriculture. It is also important to design a sustainable supply chain, whendefining the facility locations and suppliers selected. Thus, this work
studies the network design problem of an insect-based supply chain,
which needs to account for the by-products selected as feed and their
distinct conversion rate into protein and processing costs.

4 - Scheduling organic waste collection operations in thecircular economy

Marieke Brederveld, Renzo Akkerman, Sander De Leeuw
Currently, most organic waste in the Netherlands is used to produce
biogas, but organic waste could also be used to produce higher value
bio-based materials. This however adds additional considerations to
the waste collection system, as it requires organic waste fractions to
be collected separately, and to be processed within strict time limits(e.g., before mould can start to grow). These shelf life and separation
requirements limit the possibilities to achieve economies of scale incollection. As a result, the environmental and cost benefits of waste
valorisation are compromised.
This research contributes to the existing literature by considering theshelf life for low-volume waste collection. In order to increase the efficiency of the collection of concentrated organic waste, we develop amodelling approach to schedule the collection of multiple waste types,
also considering the possibility of using different, possibly compartmentalised, vehicles. By allocating the vehicles, the aim is to keep
waste separated while reducing the travelled distance. The results show
trade-offs between for instance the value of biomass and the cost of

MC-24

transport. In addition, the scheduling model provides insight in theusefulness of different vehicles types and sizes in waste collection activities in a more circular economy.

 MC-24
Monday, 12:30-14:00 - Room: 83 (building: 116)

Clean Energy Supply Chains
Stream: Sustainable Supply Chains
Invited session
Chair: Karsten Kieckhäfer
1 - Sustainable Supply Chain Design for CO2 Electrolysis
under CO2-based Syngas Demand Uncertainty

Ozlem Mahmutogullari, Nevin Mutlu, Tarkan Tan, Floor
Alkemade
The industrial sector is one of the major contributors to greenhousegas emissions worldwide; it is therefore important to promote the useof alternative and emerging technologies that employ non-fossil-based
feedstocks and renewable energy sources to meet the net-zero greenhouse gas emissions target. The electrolysis of CO2 (CO2E) is a carbon capture and utilization technology with the potential to produce
chemicals and fuels by using water (H2O) and electricity. We consider
the generation of CO2-based syngas (COH2) by high-temperature CO2
electrolysis as an alternative route to steam methane reforming that employs natural gas. Syngas is used in the chemical industry as a chemical building block and in the transportation sector as an intermediate
for fuel production. Currently, fossil-based syngas is only consumed
at its production sites. In this paper, we propose syngas as a merchant product, but since its market transition is new, there is a lack ofcomprehensive data on its demand. This uncertainty in syngas market
demand can significantly affect the adoption and successful implementation of CO2E technology. To deal with the uncertainty, we propose arobust supply chain design for CO2-based syngas where strategic decisions regarding investments in the electrolyser locations and pipeline
infrastructure and operational decisions regarding the amounts of syngas produced and CO2 and syngas transported between echelons are
made over a multi-period planning horizon.

2 - Exploring Transportation Alternatives in Carbon Capture and Storage Supply Chains

Lisa Herlicka, Frank MeiselThis presentation addresses the development of a supply chain customized for Carbon Capture and Storage (CCS) processes. Carbon
Capture and Storage technology entails capturing CO2 emissions at
significant sources, transporting the captured greenhouse gas to designated storage sites, and permanently storing it within underground geological formations. While pipelines and maritime transport are being
investigated in various studies due to their ability to transport large volumes of CO2, vehicle transportation offers viable alternatives for the
initial phase of CCS deployment or to address unavoidable emissions
at a lower volume. We focus on the characteristics of emission sources,
transportation modes and conditions, and permanent storage locations
within the supply chain. Every element of the supply chain incurs
annual investment and operational costs. The size of the network is
determined by an annual emission capture target based on the existing
CO2 reduction targets. The problem is formulated as a mixed-integer
linear program that aims to minimize the net present value over a specified planning horizon. The model is solved by CPLEX. Preliminary
findings from a case study are presented, with analyses centered on
costs associated with network participation, utilization of shared transportation infrastructure contingent upon mode selection and transportconditions, as well as storage expenses.

93MC-25EURO 2024 - Copenhagen

3 - Balancing Costs and Supply Security Risk in Renewable Energy Systems: A Bi-Objective Optimization Approach and an African-European Case Study

Veis Karbassi, Jörn Meyer, Grit Walther
The global net-zero transition requires new international energy links
based on renewable energy carriers. Moreover, recent global energy
supply disruptions emphasize the need to integrate risk into future energy systems. Against this background, we aim to construct affordable,
secure energy supply chains for both importing and exporting countries
by incorporating risk in energy system design. Our linear bi-objective
quadratic optimization model analyzes total costs and security risk
trade-offs in renewable energy production. Using the e-constraint inPython and Gurobi, we apply our model to a case of renewable energy production in Africa and its supply to Europe. Our findings reveal how characteristics of exporting countries, e.g., socio-political,
environmental, and technological features and overall network structures, impact costs and security risk. Contrary to common assumptions of marginal risk improvements leading to significant additionalcosts, our model shows efficient risk mitigation is possible without asubstantial cost increase. Our research further confirms that Africa’s
renewable potential can both satisfy its domestic energy demand andcontribute to Europe’s energy imports, underscoring the continent’s
role in global energy transitions. This work provides a comprehensive
tool for decision-makers to evaluate cost efficiency vs. supply security
trade-offs in renewable energy system design, enhancing resilience andreliability during global energy transition.

4 - Risk-sharing collaboration models for the production of
Sustainable Aviation Fuels: an optimization model

Sophia Raaymann, Stefan Spinler
To decarbonize the aviation sector, the supply of sustainable aviation
fuels (SAF) needs to be substantially increased. However, the production of SAF is associated with substantial risks, like demand risk,
technological maturity risk, production cost risk or regulatory risk. To
share these risks in SAF production, we propose risk-sharing collaborations between SAF suppliers and buyers. This approach fills a gap
in existing research, which mostly focuses on technological aspects of
SAF like its scalability or regulatory incentives. We create an optimization model that stochastically models SAF market prices and production costs, includes contract parameters like reservation fees or contract
duration and models the heterogeneity in the SAF producer market
through parameters like production efficiency, feedstock prices, penalties or geography. Firstly, a centralized scenario in which the whole
supply chain’s profit is maximized is evaluated. Secondly, a decentralized scenario using a Stackelberg game is evaluated. The model
determines the collaboration model one risk-averse buyer and multiple
suppliers would agree on to maximize their profits. The results indicate
the best suitable risk-sharing collaboration model for both SAF buyers
and suppliers.

 MC-25
Monday, 12:30-14:00 - Room: 011 (building: 208)

Discrete, continuous or stochastic
optimization and control in networks,transportation and design III
Stream: Combinatorial Optimization
Invited session
Chair: Gerhard-Wilhelm Weber
Chair: Joanna Józefowska
Chair: Pedro Munari
1 - The one-commodity pickup and delivery location routing problem

94

Inmaculada Rodríguez Martín, Bencomo Domínguez-Martín,Hipólito Hernández-Pérez, Jorge Riera-Ledesma
The One-commodity Pickup and Delivery Location Routing Problem(1-PDLRP) is a new problem that combines characteristics of the Location and Routing Problem and the One-commodity Pickup and Delivery Traveling Salesman Problem. We are given a set of customers
that provide or demand given amounts of a product, and a set of potential facility locations that can be opened or not in order to give service
to the customers. Each facility has an opening cost and is the depot of avehicle. The problem consists in deciding which facilities to open, assigning customers to open facilities, and designing the routes that connect each facility with its customers. The objective is to minimize thesum of the cost of the routes and the facilities. This NP-hard problem
has not been previously studied. We propose for it two mathematicalformulations, compare them, and present a branch-and-cut algorithm
able to solve instances with up to 100 nodes.

2 - Solving the period travelling salesman problem

Sofia Henriques, Ana Paias
Considering a time horizon with different periods, the Period Travelling Salesman Problem (PTSP) is defined on a directed graph with adepot and n customers, in which each customer has a required numberof periods to be visited in. The objective of the PTSP is to assign eachclient to as many periods as it is required while determining the set ofroutes, one for each period, with minimum cost. Each route starts andends at the depot and visits the clients assigned to that specific period
only once.
The PTSP combines an assignment and a routing problem in a single problem, making it very difficult to solve to optimality, even for
instances with few clients. Most of the works in the literature study
heuristic methods for the PTSP, and the few exact methods in the literature only consider two periods in the time horizon, which is insufficient
to model many real-life routing problems involving periodicity. We
will present different flow-based compact formulations for the PTSP
and we will use the concept of projection to derive new ILP formulations with fewer variables but exponentially-sized sets of constraints.
Furthermore, we intend to propose new valid inequalities for the PTSP.
The conclusions from our theoretical and computational study will bepresented for instances with different characteristics.

3 - An exact algorithm for the multi-level multi-trip capacitated arc routing problem

Chenge Wei, Li Xue, Ada Che
This paper studies a real-life waste collection problem inspired by awaste collection system in China. In this system, manually operated
vehicles (MV) collect small, dense forms of waste on streetsides andunload at waste collection huts. Meanwhile, vehicles with compressors (CV) collect larger waste demands from the huts and stores along
the streets. The routing problems of MVs and CVs are regarded as
an open multi-trip capacitated arc routing problem and a capacitatedgeneral routing problem, respectively. The multi-level problem aims
to implement integrated optimisation of these two problems, which
identify a set of routes for MVs and CVs with minimum travel costs
that collect all waste. We first build a mixed integer linear programming (MILP) model to solve this problem. Moreover, we propose a
set covering model and a logic-based benders decomposition framework that embeds a branch and price (B&P) algorithm. Based on thefeature of the two-level problem, this framework decomposes the set
covering model into the master problem of MV routes and the subproblem of CV routes. To solve the sub-problem, the B&P algorithm
is then applied. We adopt three classic CARP branching schemes andextend the label-setting algorithm to generate CV routes with negative reduced costs. Preliminary experiments show that, compared withsolving the MILP model with commercial optimisation software, theproposed framework can effectively improve the gap within the sametime limitation.

4 - Smart refueling decisions using reinforcement learning
approach: a case study from trucking industry

Amin Asadi, Myrthe Kruit, Behzad Mosalla Nezhad,
Sebastian Piest

EURO 2024 - Copenhagen
Transportation services play a pivotal role in international trade, withtrucking companies handling approximately 77% of inland freight. Refueling costs constitute a substantial portion of operational expenses,
ranging between 20% and 30% in Europe. To address the complexities of refueling decisions in the trucking industry, we propose a datadriven sequential decision-making framework. Our approach captures
price fluctuations along trucking routes, incorporating factors such as
region, time, and possible price agreements with gas stations. Leveraging the Markov Decision Problem and Reinforcement Learning (RL)
methods, we develop a robust framework capable of minimizing refueling costs while accounting for fuel price uncertainties. In a realworld case study conducted with a logistics company in the Netherlands, our framework demonstrated significant savings in refuelingcosts. Through extensive experimentation, we validate the superiority of our approach compared to in-practice and benchmark policies, showcasing the practical utility and effectiveness of our proposed
framework.

 MC-26
Monday, 12:30-14:00 - Room: 012 (building: 208)

Combinatorial Optimization for Machine
Learning

MC-27

3 - Optimal Shapelets Tree for Time Series Interpretable
Classification

Stefano Gualandi, Lorenzo Bonasera
Time series shapelets are a state-of-the-art data mining technique forsupervised classification tasks in time series domains. Shapelets aretime series subsequences that retain the most discriminating power intime series. The main advantage of shapelet-based methods is theirgreat interpretability. We propose a novel Mixed-Integer Programming
model to optimize shapelets discovery based on optimal binary decision trees. Computational results for a large class of datasets show that
our approach achieves performance comparable with state-of-the-art
shapelets-based classification methods. To the best of our knowledge,
this is the first approach based on optimal decision tree induction fortime series classification.

 MC-27
Monday, 12:30-14:00 - Room: 047 (building: 208)

XAI in Business Processes
Stream: Mathematical Optimization for XAI
Invited session

Stream: Combinatorial Optimization
Invited session

Chair: Jari Peeperkorn

Chair: Matteo Salani
Chair: Stefano Gualandi

1 - Reducing Cognitive Load in Declarative Business Process Models

1 - Learning Directed Acyclic Graphs using Integer Linear
Programming

James Cussens
Directed acyclic graphs (DAGs) can be used to represent conditional
independence relations in joint probability distributions, and, with suitable assumptions, can additionally represent causal relations between
the variables in a distribution. The problem of learning the ’best’ DAG
from data is often cast as a discrete optimisation problem - which is
known to be NP-hard. Encoding this problem as an integer linear program (ILP) is not hard, but solving large instances requires a carefulcombination of cutting (adding linear constraints during solving) andpricing (adding new ILP variables during solving). In this talk I will
discuss ’lessons learned’ in tackling the DAG-learning problem with
ILP and discuss the more ambitious task of learning models (causal
and non-causal) which allow for latent variables.

2 - Simultaneous Training and Optimization of Few-Bit
Neural Networks through a Multi-Objective MILP

Simone Milanesi, Ambrogio Maria Bernardelli, Stefano
Gualandi, Hoong Chuin Lau, Neil Yorke-Smith
Training neural networks using combinatorial optimization solvers has
gained attention, especially in low-data scenarios. Utilizing advanced
solvers like mixed integer linear programming can precisely train networks without relying on intensive GPU-based methods. We focus
on few-bit discrete-valued neural networks, including Binarized Neural Networks (BNNs) and Integer Neural Networks (INNs). Theselightweight architectures are notable for their ability to operate on lowpower devices, for example, by being implemented using boolean operations. Our proposed method involves training a network for eachclass pair of the classification problem and using a majority voting
scheme for the output prediction. The optimization process adheresto the principles of robustness and sparsity by employing a multiobjective function. We compare this approach, named BeMi, to existing solver-based and gradient-based methods, particularly in few-shotlearning contexts with BNNs. We also assess the trade-offs between
INNs and BNNs. Empirical results on the MNIST dataset show that
BeMi achieves higher accuracy (up to 81.8%) with a significant reduction in active weights, outperforming previous methods.

Hugo A. López
Explainability in Artificial Intelligence (AI) is pivotal for enabling humans to comprehend both the outputs produced by algorithms and theunderlying reasoning processes. Extending it to the realm of Business
Process Management (BPM), it encompasses understanding the models generated by process discovery algorithms and deciphering the recommendations offered by process engines during process enactment.
Declarative Business Process Modelling (DBPM) emerges as a crucial
framework within BPM, focusing on elucidating cause-effect relations
between activities. This type of explanation is particularly valuable inknowledge-intensive processes with humans in the loop.
In this talk, I summarize our recent endeavors aimed at alleviating the
cognitive load associated with understanding declarative business process models. Our approach draws insights from process modeling,
event-based systems, human-computer interaction, and game design.
Specifically, we will explore techniques such as enhancing traceability, ensuring semantic transparency, leveraging explain-by-example
methodologies, and employing design-via-metaphors. These strategies
are designed to refine the visual representations of declarative process
notations, thus enhancing the accessibility and comprehensibility ofknowledge-intensive processes for novice users.

2 - Leveraging Manifold Learning for Constrained Counterfactual Explanations in Process Outcome Prediction

Jari Peeperkorn, Alexander Stevens
Harnessing the potential of machine and deep learning architectures
within Predictive Process Analytics (PPA) has yielded promising results, particularly in predicting future process outcomes. However, theopacity inherent in these algorithms poses a significant challenge forhuman decision-makers, limiting their understanding of the rationale
behind the predictive outputs. In response, counterfactual explanations, serving as human-understandable ’what if’ scenarios, offer intuitive insights into the decision-making process behind undesirable
predictions. Nevertheless, generating counterfactual explanations encounters specific challenges when dealing with the sequential natureof business process cases in PPA. To address this, our paper introduces
REVISED+, a novel data-driven approach aimed at generating more95

MC-28EURO 2024 - Copenhagen

feasible and plausible counterfactual explanations tailored to the complexities of sequential process data. The counterfactual generation algorithm is guided through high-density regions of the process data distribution, and Declare language templates are used to capture sequential patterns among the process activities. This enables REVISED+
to improve the feasibility and plausibility of the generated counterfactual explanations. Finally, we assess the validity of our counterfactualswith metrics that define the key properties of a good counterfactual.

3 - Exploring Explainable Predictive Process Monitoring

Andrei Buliga
This project focuses on advancing the field of Explainable Predictive
Process Monitoring (XPPM) by developing novel techniques for generating explanations in PPM, and developing novel evaluation metrics.
Furthermore, we aim to understand the interplay between different
types of explanations and how they can be leveraged to improve theexplainability process.

chine learning and data science
Invited session
Chair: Laura Albert
Chair: Gerhard-Wilhelm Weber
1 - Variable-size mixed-variable black-box optimization for
central air conditioning configuration

Shun Tanaka, Shiho Watanabe, Hiroshi Morita
We consider the variable-size mixed-variable black-box optimization,
which involves variable dimensions and optimizing a diverse and numerous sets of variable types through a black-box function. In thisstudy, we extend the mixed-variable black-box optimization problem
and use estimation methods to achieve more efficient optimal solution
search. This approach can be applied to find the equipment configuration of a central air conditioning system that provides an optimal
configuration that minimizes overall installation and operating costs.

The first aspect of this research concerns the generation of counterfactual explanations in PPM, which are explanations that suggest whatshould be different in the input instance to change the outcome ofa PPM model. Firstly, an evaluation protocol measuring the qualityof the generated counterfactual examples and the evaluated method’s
performance is proposed. In the second part, a methodology is proposed, that involves the use of genetic algorithms, incorporating temporal background knowledge at runtime. The methodology aims togenerate counterfactual explanations that guarantee the satisfaction of
the background knowledge, a critical aspect of counterfactual generation in XPPM. A second aspect of this research is developing novel
explainability method by using previously proposed Business Process
Management (BPM) techniques to adapt explanation generation mechanisms. A third aspect of this research project endeavours to combine
various explanation techniques to streamline the search process andimprove the overall quality of explanations, by leveraging multiple explanation types proposed in the literature.

2 - Detecting Drug Transfers via the Drop-off Method: A Supervised Model Approach using AIS Data

4 - Dynamic Condition Response Graphs as Explainable AI
in Business Processes

3 - Designing small-scale coordination clusters for academic office space usage

Britt van Leeuwen, Maike Nutzel
This paper introduces a novel approach for detecting sea-based drugtransfers through the utilization of Automatic Identification System(AIS) data. We propose a model for the detection of the ’drop-off’
method, a prevalent technique for contraband smuggling at sea. Unlike the prevailing focus on unsupervised methods in existing maritime
anomaly detection research, our paper introduces a supervised model
tailored to the ’drop-off’ method, particularly in the context of fishingvessels. We have developed a machine learning algorithm, employing a Long Short-Term Memory (LSTM) model capable of identifying’drop-offs’. This model holds the potential for integration into realtime surveillance systems. Furthermore, our model demonstrates generalizability, thereby enhancing maritime security efforts and providing
invaluable assistance in countering drug traffic on a global scale.

Thomas Troels Hildebrandt

Paul Bouman, Wilco van den Heuvel

We present the uses of Dynamic Condition Response (DCR) Graphs
as Explainable AI in Business Process Management. DCR Graphs isa declarative process modelling notation that can be used by domain
experts to model, simulate and support workflows and business processes in a flexible and maintainable way. The first version of the DCR
notation was published in 2010 in a PhD project of Mukkamala at IT
University of Copenhagen, as part of the Trustworthy pervasive healthcare services project lead by Hildebrandt. The notation was further
extended and accompanied by the first graphical editor as part of anindustrial PhD project by Slaats (2015), at the danish company Exformatics and IT University of Copenhagen. The development was significantly advanced during the EcoKnow.org research project (20172021) lead by Hildebrandt, leading to major additions to the language,
such as sub processes, decision modelling and data, and the development of tools for transformation of natural language textual regulations
and requirements to formal models process models, lead by López(2018), award winning algorithms for process mining (lead by Backand Slaats), and the creation of the company DCR Solutions (DCRSolutions.net), offering industrial strength design, simulation and execution tools. Recently, the tool chain has been extended in the PhD
project of Cosma (2024) with tools for mining hierarchical and timed
DCR Graphs and for transforming DCR Graphs to Petri Nets.

At universities, academic staff performs many different tasks, resulting in heterogeneous demand for focused work, meetings, teaching,
general collaboration and discussion with colleagues. These heterogeneous demands raise the question how appealing and effective working
conditions can be guaranteed when shared workplaces are a necessity,i.e. when there are fewer desks than FTE’s available for a department.
While large homogeneous office gardens with flex-work places maximize sharing, in practice they are disliked. We introduce the alternative
idea of assigning clusters, groups of 7 to 9 employees, to a fixed number of desks and offices. Within a cluster coordination then occurs at
a smaller scale, providing employees more agency in how to organize
their office space usage. An optimization model is developed that assigns employees to clusters based on a short survey, previous officepairings and supervisory relations. In the model, we aim to have a fair
distribution of "crowding" over the clusters for different times of theweek. We develop several alternative objectives and ways to measure
crowding and fairness in the model. The model was used during a pilot project with an academic department and some experiences fromthis project are discussed. We observe that for different clusters different coordination behaviour emerged. Also, junior employees more
often found the opportunity to work in a private office compared to thesituation with a fixed allocation.

4 - Computable general equilibrium study of migration in
Europe: the cases of Germany and Slovakia

 MC-28
Monday, 12:30-14:00 - Room: 065 (building: 208)

Advancements of OR-analytics in statistics,
machine learning and data science 3
Stream: Advancements of OR-analytics in statistics, ma-

96

Veronika Mitkova
The aim of our study is to investigate the effects of migration on wages
and incomes, and the impact of more liberal migration policies, which
increase labour quotas by 5 per cent. The study focuses on the impactof migration in 21 regions, with particular attention to Slovakia andGermany. The study was based on data from the Global Trade Analysis Project database, using experimental designs by Walmsley, Ahmedand Parsons, and adjusted to capture current global migration flows.
According to the findings of the study, the arrival of new, unqualified

EURO 2024 - Copenhagen
workers has a negative impact on the actual wages of unqualified residents, with the real wages falling by nearly 3 per cent, and the real
wages of skilled workers rising by 0.73 per cent. Similarly, due tothe arrival of new qualified immigrants, the real wages of qualified
residents have declined by 2.62 per cent, while the real wages of nonqualified immigrants have increased by 0.53 per cent. These results
can help policy makers to develop policies aimed at maximizing thebenefits of migration. Finally, the study provides a new insight into thewage effects of migration in different areas, particularly in Germany
and Slovakia.

 MC-29
Monday, 12:30-14:00 - Room: 157 (building: 208)

Vehicle routing I
Stream: Combinatorial Optimization
Invited session
Chair: Irene Loiseau
Chair: Rajan Batta
1 - Solution of a practical Vehicle Routing Problem for monitoring Water Distribution Networks

Dario Vezzali, Reza Atefi, Manuel Iori, Majid SalariIn this work, we introduce a generalization of the Vehicle Routing
Problem for a specific application in the monitoring of a Water Distribution Network (WDN). In this problem, multiple technicians must
visit a sequence of nodes in the WDN and perform a series of tests tocheck the quality of water. Some special nodes (i.e., wells) require
technicians to first collect a key from a key center. The key mustthen be returned to the same key center after the test has been performed, thus introducing precedence constraints and multiple visits in
the routes. To solve the problem, a Mixed Integer Linear Programming
model and an Iterated Local Search have been implemented. The efficiency of the proposed methods is demonstrated by means of extensive
computational tests on randomly created and real-world instances.

MC-30

multiple follower bilevel two-echelon multi-commodity routing problem to facilitate greener last-mile logistics. The leader represents aplatform, whose goal is to minimize both emissions and the number ofdelivery vehicles operating in an LEZ. The leader is also responsible
for allocating parcels to micro-hubs, as well as allocating parcels tolast-mile Delivery Service Providers (DSPs). On the other hand, thefollowers seek to minimize routing costs and maximize client satisfaction. There are two types of followers: first-mile followers who deliverparcels to micro-hubs, and last-mile followers who pick up the parcels
from the micro-hubs and deliver to their final destinations. Each firstmile follower solves a Vehicle Routing Problem (VRP) with time windows, while each last-mile follower solves a pickup and delivery, timedependent VRP with time windows. We also develop a cutting planealgorithm to solve our bilevel problem and evaluate the effect of ourapproach on several problem instances using real-world data.

4 - The adoption of self-driving robots in last mile healthcare logistics

Somayeh Allahyari, Gu Pang, Yufeng Zhang, Keru Duan
The adoption of self-driving robots across various delivery applications presents a transformative shift in urban logistics, offering enhanced efficiency, agility for fast and on-time deliveries, sustainability through zero emissions, and reduced human contact. The onsetof the COVID-19 pandemic expedited the execution of robot delivery
roll-out strategies, leading to market size growth and attracting newcompetitors. This study investigates the integration of autonomous delivery/collection robots into last-mile logistics for synchronised collection of medical samples in the healthcare sector, where vans areequipped with self-driving robots. A multi-echelon network topology is adopted by leveraging insights from the UK public health service’s (known as NHS) hub and spoke network strategy which prioritises faster and more reliable screening test results, including forcancer. The study addresses the new operational challenges associatedwith implementing this new self-driving robot technology, focusing
on routing and scheduling logistics problems. To tackle these challenges, a mixed-integer linear programming model and an Adaptive
Large Neighbourhood Search (ALNS) metaheuristic algorithm are developed. Experimental results demonstrate the effectiveness of boththe model and the algorithm in optimising last-mile service in thehealthcare sector, shedding light on the potential of self-driving robots
to revolutionise healthcare logistics.

2 - UAV Search and Routing Planning in a Disaster Area

Rajan Batta, Nastaran Oladzad, Esther Jose, Miguel Lejeune
The goal of this work is to identify the best Search and Routing Planning (SRP) for an Unmanned Aerial Vehicle (UAV) to maximize thetotal number of casualties detected in a disaster-affected area within alimited mission duration. First, a Mixed-Integer Non-linear Programming (MINLP) model is formulated. To linearize the model, continuous search time variables are discretized such that each can only takea finite number of possible values. Therefore, the resulting Mixed Integer Linear Programming (MILP) problem is an approximation of theoriginal problem. This approximation is shown to be highly accurate;
however, it can not solve problems with more than 10 search regionsin less than three hours. Second, an exact solution approach is introduced, which is capable of solving problems of size 14 or smaller
during the given computational time limit. Next, an efficient clustering
heuristic is suggested to solve larger instances of the original problem
with more than 200 search regions. Finally, a case study based on the2023 Turkey-Syria earthquakes is presented. The results reveal, despite thousands of casualties remaining missing for weeks in the reallife scenario, the proposed solution methods can help detect 60-77%
of casualties within a few hours in provinces under study.

3 - A bilevel two-echelon multi-commodity routing problem
to facilitate greener last-mile logistics

Ade Fajemisin, David Rey
An increasing number of cities are incentivising the consolidation ofdeliveries to micro-hubs outside Low-Emission Zones (LEZs), and theuse of lower footprint delivery vehicles such as cargo bikes and electricdelivery vehicles inside the LEZs. We therefore present a single leader, MC-30
Monday, 12:30-14:00 - Room: 064 (building: 208)

Continuous Solvers
Stream: Software for Optimization
Invited session
Chair: Maxime Dufour
1 - Progress towards a new interior point solver for HiGHS

Julian HallHiGHS is open-source software for large scale linear optimization, andhas established itself as offering the best benchmark performance due
to its innovative interior point (IPM) solver for LP, and powerful MIP
solver. In particular, the IPM solver has been hailed as a game-changer
for the field of open-source energy system modelling. However, forsome instances, the IPM solver is still prohibitively expensive. A new
IPM solver is now under development, and this talk will outline theprogress that has been made, as well as setting out other recent developments in HiGHS.

2 - Decomposition, Cutting Planes and Column Generation
with Interior Point Methods

Jacek Gondzio97MC-31EURO 2024 - Copenhagen

In this talk we shall explain the reasons why interior point methods(IPMs) deliver particularly attractive features when they are appliedin the context of decomposition methods, cutting plane schemes andcolumn generation techniques. These features include: (1) generating epsilon-subgradients in Benders and Dantzig-Wolfe decomposition, (2) finding (stable) well-centred solutions of restricted masterproblems, (3) delivering on-demand accuracy in column generation.
It goes without saying that these features cannot be delivered by thesimplex-based solvers. Some of the advantages will be illustrated whensolving very large discrete optimal transport problems.
References: J.Gondzio, Interior Point Methods in the Year 2024, Technical Report, February 28, 2024. Submitted for publication.

3 - Latest developments for mixed-integer nonlinear programming in Artelys Knitro

Maxime Dufour, Florian Fontan, Hugo Chareyre
1 Introduction
Artelys Knitro is a mathematical programming solver for nonlinear andmixed-integer nonlinear problems. As input, it accepts linear structures, quadratic structures and black-box functions, with if possible,
their first and second-order derivatives. Knitro relies on derivativebased algorithms to find locally optimal solutions. Knitro finds theglobal optimum for convex problems. For non-convex problems, Knitro converges to a first order stationary point (e.g. local optimum) for
continuous models and is a heuristic for mixed-integer problems.
2 Recent improvements for MINLP applications
In this talk, we will present the algorithms implemented in Artelys Knitro for mixed-integer nonlinear problems in Knitro 14.0, and detail the
recent developments for the nonlinear branch-and-bound algorithms.
Since Artelys Knitro 13.0, the nonlinear branch-and-bound has been
fully rewritten as parallel and deterministic. The algorithm has been
greatly improved by adapting the ideas developed for mixed-integer
linear programming. Those features include specific presolve operations and cuts for nonlinear applications, a heuristic portfolio to provide effort effective search improved better branching strategies anda restart procedure. It opens several perspectives for future developments and extensions for nonlinear models that we will present during
this talk. We will show the improvements on the classical datasets formixed-integer nonlinear problems.

 MC-31
Monday, 12:30-14:00 - Room: 046 (building: 208)

Causal Machine Learning
Stream: Analytics
Invited session
Chair: Wouter Verbeke
1 - A predict-then-optimize approach for uplift modelingwith continuous individual treatment effects

Simon De Vos, Wouter Verbeke
Uplift modeling facilitates decision optimization by predicting the
instance-dependent impact of treatments on specific outcomes. In asetting with binary treatments, cost-sensitive causal classification enables the classification of instances into treated or untreated groups,
aiming to maximize the expected causal profit which is the core objective. We extend upon the binary treatment case and allow for continuous individual treatment effects, represented by a dose-response
curve. From an application perspective, the motivation for integrating
continuous treatments stems from the potential convexity of individual
dose-response curves. Deciding which entities to assign what dosage isa function of individual dose-response curves, which reflect the change
in positive outcome probability per assigned dosage, and the cost andbenefit parameters of treatment and positive outcomes of the specificproblem setting at hand. Our predict-then-optimize approach involves

98

two steps. First, individual dose-response curves are estimated on observational data. Since historically treatments are not assigned at random but the allocation of doses depends on pre-treatment covariates,
observational data may be subject to selection bias. To address thisbias, we employ causal machine learning methods. Second, we formulate the individual dosage allocation as an optimization problem tomaximize the expected causal profit while adhering to constraints, suchas budget limitations or fairness criteria.

2 - Exploring Learning to Rank for Optimal Treatment Allocation

Toon Vanderschueren, Wouter Verbeke, Felipe Moraes, Hugo
Proença
Efficiently allocating treatments while accounting for operational constraints constitutes an important challenge across various domains. In
marketing, e.g., using promotions to target potential customers andboost conversions is limited by the available budget. While much research focuses on estimating causal effects, there is limited work on
learning how to optimally allocate treatments. Existing methods for
uplift modeling or causal inference that estimate treatment effects do
not consider how the estimated effects relate to decisions made basedon these estimates. Therefore, a potential downside of these methods
is that the resulting predictive model may not be aligned with the operational context, resulting in prediction errors being propagated to the
optimization problem and, subsequently, a suboptimal allocation policy. We explore an alternative approach based on learning to rank. In
this approach, the idea is to directly learn an allocation policy that prioritizing instances for treatment in terms of their treatment effect. We
explore different ranking metalearners and propose an efficient sampling procedure for the optimization of the ranking model to scale ourmethodology to large-scale data sets. We validate our methodology
empirically and show its effectiveness in practice through a series ofexperiments on both synthetic and real-world data.

3 - Optimizing Treatment Allocation in the Presence of Interference: a Causal Machine Learning Approach to the
Influence Maximization Problem

Daan Caljon, Jente Van Belle, Wouter Verbeke
In Influence Maximization (IM), the objective is to select the optimal
set of entities in a network to target with a treatment so as to maximize
the total effect. For instance, in marketing, the objective is to target
the optimal set of customers that maximizes the total response rate,
resulting from both direct treatment effects on targeted customers andindirect, spillover effects that follow from targeting these customers.
In selecting the optimal set of entities, current IM methods typicallyoverlook the features of the entities. Nevertheless, these features might
contain important information on the propagation of effects throughthe network. Moreover, current methods assume that all entities thatreceive treatment will be activated (e.g., buy the product), ignoringvarying susceptibilities to treatment depending on an entity’s characteristics.
Current IM approaches mainly rely on assumptions about how effects
propagate through the network, potentially leading to a suboptimal
selection of entities. To improve upon current methods, we propose
a causal machine learning approach which estimates both direct andspillover effects from data, instead of relying on assumptions. After training a causal estimator on observational data, in a second step,
treatment allocation is optimized by integrating the treatment effect estimates into current IM algorithms. This two-step approach is shown to
improve the expected influence spread compared to traditional methods.

4 - Estimating continuous treatment effects from observational data: An empirical evaluation of methods, scenarios, and challenges

Christopher Bockel-Rickermann, Tim Verdonck, Wouter
Verbeke
Our study investigates the use of causal machine learning for estimating the effects of continuous treatments from observational data. Thisis a challenging task, as in such data treatments are often not randomly
assigned, leading to "confounding", a systematic difference between

EURO 2024 - Copenhagen
units that were assigned different treatments. Additionally, many modern decision-making problems are high-dimensional and complex, hindering the application of traditional statistical causal inference techniques, and calling for specialized data-driven methodologies. We
review established causal machine learning methods for continuous
treatment effect estimation, as well as previously presented benchmarking datasets. The goal of our study is to derive sensitivities ofmodel performance to different data-generating processes and accompanying challenges, such as the strength of confounding, the amountof training data, and the complexity of relationships between variablesof interest. To further aid practitioners and researchers, we also test
several state-of-the-art methodologies from supervised learning in thecapability to estimate treatment effects, aiming to further aid the decision when to adopt targeted methodologies for continuous treatment
effect estimation.

MC-33

3 - High-Order Reduced-Gradient Methods for Composite
Variational Inequalities

Yurii Nesterov
In this talk, we present a unified approach for constructing efficientmethods for solving Variational Inequalities, presented in a composite
form (CVI). This class of problems is close to the maximal one, which
can be efficiently treated by numerical methods. At the same time, it ismore difficult than the class of Convex Optimization Problems. All efficient methods for VI use an additional "extra-gradient" step. We propose a new interpretation of this step as a cutting plane for the optimal
solution, reflecting the interaction of the monotone operator with theboundary of the feasible set. Contrary to the existing approaches, we
introduce a universal extragradient step, which does not depend on theparticular class of CVI. Consequently, our framework can be used fordeveloping optimal methods for CVI, which are based on high-orderoracles.

 MC-32
Monday, 12:30-14:00 - Room: 41 (building: 303A) MC-33

Advances in Complexity of Convex andNonconvex Problems

Monday, 12:30-14:00 - Room: 42 (building: 303A)

Stream: Advances in large scale nonlinear optimization
Invited session
Chair: Yurii Nesterov
Chair: Geovani Grapiglia
1 - Minimizing Quasi-Self-Concordant Functions by Gradient Regularization of Newton Method

Nikita Doikov
We study convex optimization problems with a Quasi-Self-Concordantsmooth components. Our problem class naturally interpolates between
classic Self-Concordant functions and functions with Lipschitz continuous Hessian. Previously, the best complexity bounds for this problem
class were associated with trust-region schemes and implementationsof a ball-minimization oracle. In this talk, we show that for minimizing
Quasi-Self-Concordant functions we can use instead the basic Newton
Method with Gradient Regularization. For unconstrained minimization, it only involves a simple matrix inversion operation (solving alinear system) at each step. We prove a fast global linear rate for thisalgorithm, matching the complexity bound of the trust-region scheme,
while our method remains especially simple to implement. Then, we
introduce the Dual Newton Method, and based on it, develop the corresponding Accelerated Newton Scheme for this problem class, whichfurther improves the complexity factor of the basic method. As a direct consequence of our results, we establish fast global linear ratesof simple variants of the Newton Method applied to several practicalproblems, including Logistic Regression, Soft Maximum, and Matrix
Scaling, without requiring additional assumptions on strong or uniform
convexity for the target objective.

2 - Universal Gradient Methods for Stochastic Convex Optimization

Anton RodomanovWe develop universal gradient methods for Stochastic Convex Optimization (SCO). Our algorithms automatically adapt not only to the oracle’s noise but also to the Hölder smoothness of the objective function
without a priori knowledge of the particular setting. The key ingredientis a novel strategy for adjusting step-size coefficients in the Stochastic
Gradient Method (SGD). Unlike AdaGrad, which accumulates gradient norms, our Universal Gradient Method accumulates appropriate
combinations of gradient- and iterate differences. The resulting algorithm has state-of-the-art worst-case convergence rate guarantees forthe entire Hölder class including, in particular, both nonsmooth functions and those with Lipschitz continuous gradient. We also present the
Universal Fast Gradient Method for SCO enjoying optimal efficiency
estimates.

Dynamics of the Firm II
Stream: Optimal Control Theory and Applications
Invited session
Chair: Peter Kort
1 - Equilibrium Carbon Permit Supply and CCS Investment
under Uncertainty

Nicola Comincioli, Peter Kort, Jacco Thijssen, Sergio Vergalli
This paper examines the optimal behavior of carbon-intensive companies operating under the European Union Emission Trading System(EU ETS), wherein they are obligated to purchase emission permits on
the secondary market if their emissions exceed their allowance. Specifically, we consider the scenario where these companies, typically required to procure permits at market prices, are endowed with the (real)
option to invest in Carbon Capture and Storage (CCS) technology to
mitigate their emissions and, thus, permit expenditures. The central
challenge is the determination of the optimal time for investment in
CCS within a stochastic framework characterized by uncertainty in EU
ETS permit prices. To enhance realism, we address the problem for aheterogeneous group of companies distinguished by their respective
costs of implementing CCS systems, reflecting varying degrees of environmental consciousness across industrial sectors. Furthermore, we
incorporate an increasing floor for permit prices to mirror policy efforts aimed at promoting green transition by elevating emission costs.
We solve this problem analytically and through numerical simulations
calibrated with real market data. In addition to offering insights into individual company behaviors, the findings can inform decision-makers
in refining environmental policies, particularly regarding the management of permits price floor and its potential to expedite the green transition.

2 - Dynamic Stability of Cooperative Investment under Uncertainty

Martijn Ketelaars
This article models the inherent cooperative and non-cooperative incentives of stakeholders in investment projects in a novel way by combining concepts from cooperative game theory and real options theory.
As stakeholders have outside options, in the sense that they may terminate negotiations with the current coalition and join another, we introduce and analyze a coalitional and dynamic stability concept. We showthat investment projects, in which cooperation between stakeholders
is necessary, are more prone to coalitional instability when there areinsufficient synergies between the stakeholders. We characterize theproportional investment scheme as the investment scheme that maximizes the total project value and that results in the earliest investment
timing. A failure to implement proportional investing leads to the formation of a smaller, less efficient, coalition. The vulnerability to fail is99

MC-34EURO 2024 - Copenhagenexacerbated in a market that is characterized by high profit growth andlow profit uncertainty, or vice versa. Finally, we explicitly consider
one-leader investment projects and characterize the prioritized investment scheme that maximizes the value of the leader. We show that thesame market conditions govern the stability of the prioritized investment scheme, which contributes to the robustness of our results.

3 - Investment decisions in Energy Storage Systems: a real
options approach

Marta Castellini, Chiara D’Alpaos, Fulvio Fontini, Michele
MorettoRenewable energy generation plays a crucial role in the energy transition. Nevertheless, as many Renewable Energy Sources (RES) are
intermittent, specifically photovoltaic (PV) generation, there are often
mismatches between energy production and consumption. These mismatches can be partially reduced by storage systems. In this paper weinvestigate the investment decision in a PV power plant coupled witha Battery Energy Storage System (BESS), namely an Energy Storage
System (ESS). In our setting, the BESS is connected to the national
grid and the PV plant: energy can be produced, purchased from thegrid, stored, self-consumed, and fed into the national grid. PV production and energy consumption loads evolve stochastically over time. In
addition, BESS are costly, and energy stored has an opportunity cost,which depends on the prices of energy purchased from the national
grid and energy fed into the grid, respectively. Nonethless, BESS cansignificantly contribute to increase ESS managerial flexibility and, inturn, ESS value. In detail, we investigate the optimal BESS capacitythat maximizes ESS expected net present value. We also analyze ESS
management costs to provide insights on ESS optimal management
strategy.

4 - Green Technology Investment: Announced vs. Unannounced Subsidy Retraction

Xingang Wen, Verena Hagspiel, Peter Kort
Policy uncertainty affects firms’ investment decisions and the corresponding societal welfare (total surplus). One common perception isthat, policy uncertainty has negative welfare effects, and therefore, theregulator should make policy changes "transparent" by announcing future policy changes. This paper investigates a firm’s green technology
investment decision in a dynamic setting, where it decides about theinvestment timing and size under the market uncertainty in the formof stochastic investment costs, and under the policy uncertainty in theform of unannounced subsidy retractions. In particular, we consider
two scenarios, depending on whether the regulator announces the subsidy retraction in advance. We show that, a larger policy uncertainty
makes the firm invest earlier and less. Besides, the subsidy retraction
announcement can motivate the firm to invest early to "take" the subsidy, which is a typical rent-seeking behavior. This has two negative
welfare implications: an inefficiently small investment size due to theearly investment, and a large subsidy cost due to the firm "taking" thesubsidy. Comparing the total surplus generated by firm’s investment
decision in the two scenarios, we conclude that the rent-seeking behavior in the announced scenario makes it possible that not-announcing theretraction threshold yields a larger total surplus. This result is robust
for several common subsidy types.

 MC-34
Monday, 12:30-14:00 - Room: 43 (building: 303A)

Optimization and learning for data scienceand imaging (Part III)Stream: Advances in large scale nonlinear optimization
Invited session
Chair: Simone Rebegoldi
Chair: Federica Porta
Chair: Elena Morotti
Chair: Alessandro Benfenati

100

1 - Real data EIT reconstruction using virtual X-rays anddeep learning

Siiri Rautio
In electrical impedance tomography (EIT), the aim is to recover the
unknown conductivity of a target by injecting currents and measuring
boundary voltages through electrodes. It is a nonlinear and severely illposed inverse problem. We introduce a new reconstruction algorithm
for EIT, which provides a connection between EIT and traditional Xray tomography, based on the idea of "virtual X-rays". We divide theexponentially ill-posed and nonlinear inverse problem of EIT into separate steps. We start by mathematically calculating so-called virtual
X-ray projection data from the measurement data. Then we perform
explicit algebraic operations and one-dimensional integration, ending
up with a blurry and nonlinearly transformed Radon sinogram. We
use neural networks to remove the higher-order scattering terms andperform deconvolution. Finally, we can compute a reconstruction of
the conductivity using the inverse Radon transform. We demonstrate
the method with experimental data. This is a joint work with Melody
Alsaker, Fernando Moura, Juan Pablo Agnelli, Rashmi Murthy, Matti
Lassas, Jennifer Mueller, and Samuli Siltanen.

2 - Space-Variant Total Variation boosted by learning techniques for subsampled imaging problems

Davide Evangelista
In solving inverse problems with subsampled forward operators, a crucial aspect is choosing an efficient regularizer. The isotropic Total Variation (TV) functional is often used uniformly, to get gradient-sparse
solutions, but it tends to remove small details and smooth edges. To
address this, we propose a space-variant weighted isotropic TV regularization, whose weights can be determined by a neural network. This
talk includes a rigorous theoretical analysis and presents experiments
confirming the potential of the proposed approach to enhance the quality of undersampled CT reconstructions.

3 - On stochastic first order optimization methods for deeplearning applications

Federica Porta, Giorgia Franchini, Valeria Ruggiero, Ilaria
Trombini, Luca Zanni
First order stochastic optimization methods are effective tools for theminimization of problems arising in deep learning applications. In thiswork we study a stochastic first order algorithm which rules the increase of the mini-batch size in a predefined fashion and automaticallyadjusts the learning rate by means of a monotone or non-monotoneline search procedure. The mini-batch size is incremented at a suitablea priori rate throughout the iterative process in order that the varianceof the stochastic gradients is progressively reduced. The a priori rate
is not subject to restrictive assumptions, allowing for the possibility ofa slow increase in the mini-batch size. On the other hand, the learning
rate can non-monotonically vary along the iterations, as long as it is appropriately bounded. Convergence results for the proposed method areprovided for both convex and non convex objective function. The low
per-iteration cost, the limited memory requirements and the robustness
against the hyperparameters setting make the suggested approach wellsuited for implementation within the deep learning framework, also for
GPGPU-equipped architectures. Numerical results on training deepneural networks for multi-class image classification show a promising
behaviour of the proposed scheme with respect to similar state of theart competitors.

4 - Accelerating convergent Plug-and-Play methods

Andrea Sebastiani, Tatiana Bubba, Luca Ratti
Plug-and-Play methods are obtained replacing with off-the-shelf denoisers, the proximal operator in many first order proximal optimization algorithms. Under suitable hypothesis, it is possible to derive thefunctional whose proximal operator corresponds to a particular classof denoisers, referred to as Gradient Step denoisers. This characterization result allows for the interpretation of Plug-and-Play schemes as theminimization of an underlying non-convex cost function, enabling thestudy of the convergence of such methods. In particular, this analysis
can be further extended to ensure the convergence of several accelerated methods. The non-convexity of the cost function strongly limits

EURO 2024 - Copenhagen
any improvemnt in the theoretical convergence rate. However, the numerical experiments empirically demonstrate the benefits of this acceleration, that allows to reduce the computational demand and workload
required to compute the reconstruction of an image.

 MC-35
Monday, 12:30-14:00 - Room: 44 (building: 303A)

Urban Logistics and sustainable
TRAnsportation: OPtimization under
uncertainTY and MAchine Learning
Stream: Stochastic, Robust and Distributionally Robust
Optimization
Invited session
Chair: Francesca Maggioni
1 - A Vehicle Routing Problem with Divisible Deliveries andPickups under Uncertainty

Francesca Vocaturo, Alessandro Gobbi, Daniele Manerba
Most e-commerce companies design combined delivery-pickup logistics systems, where the collection of unsatisfactory items is ensured
along with the traditional distribution of products to customers. In thiscontext, a successful option is to solve a Vehicle Routing Problem with
Divisible Deliveries and Pickups (VRPDDP) where each customer requiring both a pickup and a delivery service may be served, if beneficial, in two separate visits. In the version of the problem analyzed inour study, there are mandatory delivery and pickup demands that mustbe fulfilled by a fleet of homogeneous vehicles. In addition, it is needed
to ensure that a percentage of optional pickups is served. Optional
pickup demands are affected by uncertainty and random variables areused for their representation. In this sense, we refer to our problem
as stochastic VRPDDP and model it as a two-stage stochastic programwith recourse. More specifically, a set of routes is designed at the firststage. At the second stage, when uncertainty is revealed, it may be impossible to implement the solution as planned at the first stage. Then,
two different recourse actions are carried out. The computational experiments confirm the usefulness of using stochastic programming inrealistic setting.

2 - Variational Auto-Encoders and Generative Adversarial
Networks for scenario generation

MC-36

We study an Attended Home Delivery Problem with Recovery Options (AHDP-RO) as a supporting tool for companies that want to ensure timely services within the complex attended home delivery setting. Since missed deliveries can negatively impact customer satisfaction and delivery costs, the problem considers customers’ availability
through different profiles identifying the probability of them being at
home for each time slot of the delivery day. When a missed delivery
occurs, the problem implements different recovery policies based on
the customer’s preferences (e.g., leaving the package in a safe spot,scheduling a second delivery attempt on the same day, or directing the
package to a designated collection point). To optimize the schedulingof deliveries and minimize routing costs, we present a Mixed-Integer
Linear Programming formulation in which the cost of a missed delivery is proportional to a penalty (determined by the chosen recovery option) and to the probability of not finding the customer at home during
the selected time slot. Moreover, different solution approaches (basedon various exact or heuristic technologies) are proposed to solve realsize instances and, in turn, support a multi-attribute analysis for obtaining useful economic and managerial insights on the decisional process
we are facing.

4 - Sampling methods for multi-stage robust optimization
problems

Francesca Maggioni, Fabrizio Dabbene, Georg Pflug
In this talk, we consider multi-stage robust optimization problems ofthe minimax type. We assume that the total uncertainty set is the cartesian product of stagewise compact uncertainty sets and approximate
the given problem by a sampled subproblem. Instead of looking for
the worst case among the infinite and typically uncountable set of uncertain parameters, we consider only the worst case among a randomly
selected subset of parameters. By adopting such a strategy, two mainquestions arise: (1) Can we quantify the error committed by the random approximation, especially as a function of the sample size? (2)If the sample size tends to infinity, does the optimal value converge tothe “true” optimal value? Both questions will be answered. An explicit bound on the probability of violation is given and chain of lower
bounds on the original multi-stage robust optimization problem provided. Numerical results dealing with a multi-stage inventory management problem show that the proposed approach works well for problems with two or three time periods while for larger ones the numberof required samples is prohibitively large for computational tractability. Despite this, we believe that our results can be useful for problems
with such small number of time periods, and it sheds some light on thechallenge for problems with more time periods.

 MC-36

Enza Messina, Michele Carbonera, Michele Ciavotta

Monday, 12:30-14:00 - Room: 32 (building: 306)

In this talk, we address the problem of learning multivariate distribution from empirical data aimed at scenario generation. Capturing
the complex spatio-temporal relationship among multiple variables isa challenging task. Copula based models are usually applied in thiscontext for their ability to separate the multivariate structure from its
marginal distributions. However, when considering real case studies choosing the right family of copula models may be difficult. To
overcome these difficulties, in this work, we propose a data-driven(or model-free) approach by adopting generative methods. In particular, we explore the use of Variational Auto-encordes and Generative
adversarial Networks, for learning the multi-variate joint probability
distribution of link speeds on a road network, using real sensor data.
Experimental results, conducted on three distinct benchmark datasets,
highlight the potential of the proposed model in generating new scenario samples of multivariate variables hat preserve correlations among
variables, while producing samples that faithfully represent the empirical marginal distributions.

Game Theory, Solutions and Structures III

3 - On the Attended Home Delivery Problem with Recovery
Options: Economical analysis and solution approaches

Daniele Manerba, Valentina Bonomi, Renata Mansini,
Roberto Zanotti

Stream: Game Theory, Solutions and Structures
Invited session
Chair: Marco Dall’Aglio
Chair: Michele Aleandri
1 - Operations Research Games under Uncertainty andDistributional Ambiguity

Xuan Vinh Doan, Tri-Dung Nguyen
We introduce a new solution concept of (robust) least chance decisions for cooperative games under uncertainty and distributional ambiguity, which is motivated by the concept of least core solutions fordeterministic cooperative games. We develop a framework to findthose decisions and compute their (robust) least chance dissatisfaction for cooperative games under normally distributed uncertainty andmoment-based distributional ambiguity. We demonstrate how the proposed framework can be applied to operations research games such as
linear production games with detailed analytical results.

101

MC-38EURO 2024 - Copenhagen

2 - Sequencing situations with position-dependent effects
under cooperation

Alejandro Saavedra-Nieves, Manuel Alfredo
Mosquera-Rodríguez, Maria Gloria Fiestras-Janeiro
This paper innovatively addresses the effect of cooperation on sequencing situations with position-dependent effects. Specifically, we ensure
convexity of the associated sequencing games under the fulfilment ofcertain conditions on the neighbour switching gains. Besides, we propose two families of allocation rules based on sharing the neighbour
switching gains under two procedures, each one giving a path fromthe initial order to an optimal order. From a theoretical point of view,
an axiomatization of both families of allocations is provided, and theirstability is also ensured under those conditions related with convexity.
Finally, we analyse the specific sequencing problems arising from thetotal flow time minimization under the consideration of exponential
positional effects.

3 - Water banks as bargaining games

María Borrego-Marín, Amparo Mármol, Victoriana Rubiales
Caballero, Miguel A. Hinojosa
Water banks constitute a market mechanism that enables the voluntary reallocation of flows to respond to both temporary and structuralchanges in the availability of this resource. Good management of waterbanks makes it possible to reduce social conflicts associated with water
scarcity through temporary or permanent reallocation of the resource
based on the supply and demand of users. Negotiation games are amethodology to formalise and analyse the consequences of the interaction between potential water sellers and buyers. The possibility ofcooperation between the agents involved is analysed to obtain consensual solutions that improve the result that they can individually ensure.
In particular, Nash and Kalai-Smorodinsky bargaining solutions areproposed to identify feasible reallocations of water that satisfy certain
principles of rationality. In the absence of negative externalities, thewater transfers that are determined with these solutions allow improving the economic efficiency associated with the use of water.

4 - A Coopetition Index for Coalitions in Simple Monotone
Games.

Michele Aleandri, Marco Dall’Aglio
The measurement of power for players in cooperative games traditionally focuses on the effort that single players exert on every coalition ofother players. A more recent stream of works focuses on the evaluation of the power of groups (see Flores R., Molina E., Tejada J. (2019),
Evaluating groups with the generalized Shapley value 4OR, 17 (2),141-172) together with an estimation of their interactions, defined in
Grabisch M., Roubens M. (1999), An axiomatic approach to the concept of interaction among players in cooperative games. International
Journal of Game Theory 28: 547-565, as the average of finite orderdifferences for the players in the coalition.

1 - Optimality and uniqueness of the D_4 root system

Nando Leijenhorst, David de Laat, Willem de Muinck Keizer
The spherical code problem asks how to arrange N points on the unit
sphere in dimension n such that the distance between the closest pairof points is maximized. We prove that for 24 points in dimension 4,
the D_4 root system is the optimal configuration. We prove this byshowing that it is the unique solution for the kissing number problem in dimension 4, up to isometry. For this we use a semidefinite
programming relaxation of the the second step of the Lasserre hierarchy for spherical codes, for which we obtain an exact optimal solution
by rounding the numerical solution using the techniques of [Cohn, deLaat, Leijenhorst, 2024+].

2 - Convergence analysis of the sum-of-squares hierarchy
for polynomial optimization

Lucas Slot
The moment-SOS hierarchy provides a sequence of lower bounds onthe minimum of a polynomial f on a semialgebraic set S. These bounds
can be computed by solving semidefinite programs of increasing size,
corresponding to sum-of-squares representations of increasing degree.
As a consequence of powerful Positivstellensätze from real algebraic
geometry, the bounds are known to converge to the true minimum off under mild assumptions on the feasible region S. In this talk, we
discuss some recent progress on the asymptotic behaviour of the SOShierarchy for certain special choices of S, such as the unit hypercube.

3 - Spectral bounds for set avoiding graphs via polynomial
optimization

Tobias Metzlaff
The chromatic number of a set avoiding graph has a lower spectral
bound, which is often sharp. We assume that the vertices and the
avoided set are invariant under a crystallographic reflection group.
Then the spectral bound can be expressed as the minimum of a polynomial on a basic semi-algebraic set. We compute several bounds forinteresting graphs analytically and numerically.
Based on joint work with Evelyne Hubert, Philippe Moustrou and Cordian Riener.

 MC-40
Monday, 12:30-14:00 - Room: 96 (building: 306)

Advances in Stochastic Modelling andApplied Probability II

We examine the use of such indexes in the context of monotone simple games and we verify that the original interaction index captures thegroup’s relationship in terms of collaboration and competition for two
players, but fails to do so for larger groups.

Stream: Advances in Stochastic Modelling and Learning
Methods
Invited session

We therefore define a collaboration-competition index that is basedon the notion of essential criticality and coincides with Grabisch andRouben’s interaction index for two players. We analyze its properties
and we put the index at work with some applications.

Chair: Ioannis Dimitriou
Chair: Tuan Phung-Duc
1 - Fair and efficient sharing: dynamic pricing control inbatch service system with strategic customers

Ayane Nakamura, Tuan Phung-Duc

 MC-38
Monday, 12:30-14:00 - Room: 34 (building: 306)

Applications of polynomial optimization
Stream: Conic Optimization: Theory, Algorithms, and Applications
Invited session
Chair: David de Laat

102

Transportation systems, such as bus systems, are important areas inwhich to apply models for strategic customer behavior. We thenpropose a dynamic pricing mechanism, in which the fees for eachcustomer and the timing for the service commencement depend on
their experienced time cost, for batch service (shuttle bus) systems,
to achieve operational efficiency and passenger fairness under a strategic joining/balking scheme. In this dynamic pricing scheme, the monopolist decides the fee for each customer such that all customers on
the same shuttle have rigorously equivalent utilities (including waiting
costs), and, if the total time cost for waiting customers exceeds certain
criteria, the monopolist lets the shuttle depart even though the number

EURO 2024 - Copenhagenof customers is less than the full capacity. For this model, we show anequilibrium strategy within certain parameters, and propose an algorithmic procedure that rigorously calculates the transition probability
between states under long-term observation and construct a Markov
chain for the number of waiting customers. Based on the results, we
present a method for calculating performance measures such as themean of social welfare, monopolist revenue, and experienced utility.
Throughout some numerical experiments, we show that the dynamic
pricing control has the potential to improve both fairness among customers and performance efficiency.

MC-41

In this talk, we compare the externalities under the last-come firstserved with preemption (LCFS-PR) and first-come first-served (FCFS)
service distributions. Specifically, we establish a joint decomposition
for the externalities under LCFS-PR and FCFS in terms of a bivariatecompound Poisson process. This decomposition can be used to derive
several other results regarding the externalities: moments, asymptotic
approximations as the service requirement goes to infinity, asymptoticsof the tail distribution, and a functional central limit theorem. Thisis joint work with Royi Jacobovic (Tel-Aviv University) and Onno
Boxma (TU/e).

2 - Data-Driven Population Tracking in Large Service Systems

Fernando Bernstein
We develop a stylized theoretical framework for the problem of tracking the population in a service system with noisy input and output observations. The motivation for the project is the problem of tracking
the population of passengers in the TSA area at an airport in real timeusing noisy data from people counters. Our goal is to devise and analyze policies that use past people counter data to estimate the population in the system over a finite and discrete time horizon. We evaluate
the performance of policies in two distinct settings. In the busyness
tracking problem, the objective is to track whether the policy correctly
detects if the system census is larger or smaller than a threshold. In the
population tracking problem, the objective is to minimize the expectedmagnitude of the estimation error in each period. We show that our
problem is more challenging than dynamic learning problems studied
in the bandit literature. In the busyness tracking problem, we derivea general lower bound on the cumulative expected loss that grows linearly with the time horizon. In the population tracking problem, we
prove another general lower bound on cumulative expected loss that ison the order of the square of the time horizon. Given this complexity,
we develop and analyze policies that achieve the best possible performance in terms of the growth rate of cumulative expected loss. Furthermore, we investigate the benefit of conducting periodic inspectionsof the true census in the system.

3 - Effects of spectrum handoff delay on secondary users
in cognitive radio networks

Yuta Kamioka, Ken’ichi Kawanishi, Tuan Phung-Duc
The rapid development of ICT (Information and Communication Technology) in recent years has led to the problem of wireless bandwidth
depletion. One of the technologies to alleviate the problem is to makeuse of CRNs (Cognitive Radio Networks). This is a technology for efficient and dynamic use of frequencies. In CRNs, users are divided into
PUs (Primary Users) and SUs (Secondary Users). PUs are licensed
users to occupy the bandwidth. On the other hand, SUs are not licensed users and can use the bandwidth only if SUs know that PUs are
not present by sensing because PUs have preemptive priority over SUs.
Therefore, spectrum handoff in CRNs critically affects performance ofSUs.
This paper considers CRNs and analyzes performance measures ofSUs. In particular, we take into account of spectrum handoff delay in
CRNs and compare performance of SUs with and without the handoff
delay. We construct system models by continuous-time Markov chain
having transition rate matrix with block tridiagonal structure, which iscalled QBD (quasi-birth-death) process, to efficiently compute the stationary distribution. We show numerical examples of the performanceof SUs in terms of delay in system. We discuss the effects of spectrum handoff delay on the performance measures for the model withthe spectrum handoff delay and the conventional model without thehandoff delay.

4 - Externalities in the M/G/1 queue: LCFS-PR versus FCFS

Nikki Levering, Royi Jacobovic, Onno Boxma
Externalities are the costs that a user of a common resource imposes onothers. In a stable M/G/1 system, the externalities created by an arriving customer with known service requirement equal the total waitingtime that others will save if she would reduce her service requirement
to zero. Naturally, these externalities are stochastic, and the corresponding analysis relies heavily on the underlying service discipline.

 MC-41
Monday, 12:30-14:00 - Room: 97 (building: 306)

Stochastic and Deterministic Global
Optimization
Stream: Stochastic and Deterministic Global Optimization
Invited session
Chair: Thi My Le Le
1 - Advanced numerical methods for Lipschitz global optimization

Dmitri Kvasov, Yaroslav Sergeyev
Decision-making problems defined as global optimization problemsof an objective function subject to a set of constraints arise in various application fields. The objective function and constraints can beblack box and difficult to evaluate functions with unknown analytical representations. This means that there are black boxes associated with the functions which, given the values of the input parameters, return the values of the corresponding functions and the optimizer knows nothing about how these values are obtained. Therefore, one of the main goals in this context is to develop fast global
optimization algorithms that produce reasonably good solutions witha limited number of function evaluations. We will discuss various
deterministic approaches based on the Lipschitz continuity assumption to construct efficient and reliable numerical methods to solve
the mentioned problems. Among these, the divide-the-best scheme
for developing and studying numerical methods in a unitary way,
the index scheme for managing non-convex constraints and the local tuning on the function’s behaviour will be examined in particular (see https://doi.org/10.1007/978-3-030-54621-2_764-1). Some recently proposed high-precision techniques (including the infinite computing paradigm https://www.theinfinitycomputer.com) will also be
considered.
This work was partially supported by the Italian INdAM GNCS Project2023, number CUP_E53C22001930001.

2 - Inverse Optimization for Imputing Constraint Parameters in Mathematical Programs

Archis Ghate
In a typical (forward) optimization problem, a decision-maker uses
given values of model parameters to compute the values of decisionvariables. The goal in inverse optimization (IO) is instead to infer parameters that render given values of decision variables optimal. Most
papers on IO utilize duality to impute objective function parameters.
A corresponding literature for imputing constraint parameters is essentially non-existent, even for linear programs. The difficulty is thatthese IO problems include nonconvex bilinear constraints and/or objectives. We will discuss models and solution algorithms designed totackle these difficult problems. We will illustrate key ideas throughthe motivating problem of imputing transition probabilities in Markov
decision processes (MDPs). If time permits, we will also extend theseideas to inverse semi-definite programs (SDPs) and inverse Quadratic
Programs (QPs), where matrices on the left-hand-sides of constraints
are unknown.

103MC-42EURO 2024 - Copenhagen

3 - Global optimization in operations research problems

Anatolii Kosolap
Most optimization models of operations research problems are multimodal. Such problems are NP-hard. They include the optimization
problems with continuous and Boolean variables. Existing methods
and solvers do not allow us to effectively find optimal solutions to suchproblems. We propose to use the original method of exact quadratic
regularization to solve such problems. Quadratic regularization allows us to transform the multimodal problems into the problem of the
maximum of the norm of a vector on a convex set. For solving thetransformed problem, it is enough to have a local solver. We solved
many multimodal problems using this method. In particular, linear andquadratic knapsack problems, packing problems, scheduling problems,reliability of systems, and many others. We have performed significant comparative computational experiments. Our method allowed us
to obtain significantly better solutions for various operations research
problems.

4 - Solving Stochastic DC programs with DC constraints

Thi My Le Le, Hoai An Le Thi, Van Ngai Huynh
We consider a class of stochastic difference-of-convex-functions (DC)programs, which are optimization problems where the objective function is the expected value of a stochastic DC function based on a probability distribution, while the constraint functions are DC. There havebeen many methods developed for convex (nonconvex) stochastic optimization problems without constraints or with convex constraints;
however, methods designed for nonconvex and nonsmooth constrained
programs are rare. The DC algorithm (DCA) is acknowledged in theliterature on deterministic optimization as one of the few efficient algorithms for solving large-scale nonconvex and nonsmooth optimization
problems. Using penalty techniques, we transform nonconvex constrained stochastic DC programs into standard stochastic DC programs
and introduce novel stochastic DCAs to solve the resulting stochastic
DC programs. The convergence analysis of the proposed algorithms isthoroughly investigated, and numerical experiments are performed toevaluate their behaviors.

 MC-42straightforward application of Grover’s algorithm (referred to as GUM
by Creemers and Pérez in an earlier paper) dominates any quantum
counting algorithm when verifying whether a valid solution exists.
Next, we discuss the nested quantum search algorithm of Cerf et al.
(2000), and show that it is dominated by a classical nested search thatuses an approach such as GUM to find (partial) solutions to (nested)
problems. Last but not least, we also discuss amplitude amplification (aprocedure that generalizes Grover’s algorithm), and show (once more)that it may not be possible to outperform GUM.

3 - Analog Quantum Computing for Combinatorial Optimization Problems

M. Yassine Naghmouchi, Wesley Coelho
In this proposed talk, we delve into the promising realm of quantum
computing, specifically focusing on the utilization of neutral atombased Quantum Processing Units (QPUs) and analog quantum computing methodologies to tackle combinatorial problems. We will discuss
how neutral atom-based QPUs, with their precise control and manipulation capabilities, provide a robust platform for quantum computation.
Practical examples and case studies will be presented to demonstrate
the efficacy and potential of these quantum computing approaches. Additionally, we will discuss the current state of research, challenges, andfuture prospects in this rapidly evolving field.

 MC-43
Monday, 12:30-14:00 - Room: 99 (building: 306)

Simulation in economics I
Stream: Agent-based Models in Management, Economic
and Organisation Sciences
Invited session
Chair: Sascha Hägele
1 - Investigating the Impact of Anchoring Effects and Market Manipulation on Wealth Distribution in Agent-Based
Asset Markets

Monday, 12:30-14:00 - Room: 98 (building: 306)

Bin-Tzong Chie

Quantum Computing for Discrete andCombinatorial Optimization

In recent years, the proliferation of automated trading strategies has
underscored the significance of anchoring effects in financial markets.
Despite efforts to tailor these strategies to user preferences, anchoring effects persist, influencing decision-making among traders. Thisphenomenon, observed by Tversky and Kahneman, affects buyers andsellers differently, leading to market distortions. Additionally, the presence of market manipulators further complicates market dynamics, exacerbating wealth distribution imbalances. To better understand these
dynamics, we employ agent-based modeling, allowing for the exploration of interactions among different agents in the market. Throughsimulations, the study aims to shed light on the impact of anchoring
effects and market manipulation on wealth distribution, offering valuable insights for financial regulators. This study aims to address thesechallenges by constructing an agent-based asset market incorporating
anchored traders and market manipulators. Inspired by previous research, the study aims to understand how different scenarios and manipulative practices influence market efficiency and wealth distribution
using agent-based modeling.

Stream: Quantum Computing Optimization
Invited session
Chair: Stefan Creemers
1 - Discrete Optimization: A Quantum Revolution?

Stefan Creemers, Luis Fernando Pérez
We use Grover’s algorithm to build quantum algorithms that can beused to solve discrete optimization problems on a universal quantum
computer. We use our quantum algorithms to solve the binary knapsack problem, and highlight a number of challenges that are faced
when effectively using Grover’s algorithm to solve discrete optimization problems. We also present a number of procedures that can match
the performance of the best classical procedures for solving the binary knapsack problem and demonstrate that quantum computing may
cause a revolution in the field of discrete optimization.

2 - Discrete optimization: Limitations of existing quantum
algorithms

Luis Perez, Stefan Creemers
We investigate the limitations of existing quantum algorithms to solve
discrete optimization problems. First, we discuss the quantum counting algorithm of Brassard et al. (1998), and show that it has performance that is equivalent to that of a brute-force approach when approximating the number of valid solutions. In addition, we show that a

104

2 - A Promising Approach to the El Farol Bar Problem: Hysteresis in Decision Making

Burak Çetiner, Hakan Yasarcan
The El Farol Bar Problem (EFBP) is introduced by W.B.Arthur(1994)
to present Complexity Economics. Arthur claims that if all agents use
deductive reasoning, either all attend the bar or all stay home. Thus,
deductive reasoning fails. Therefore, all agents use inductive reasoning
with different strategies in forming their expectations about weekly attendance, which then be used in this binary decision-making process.
Arthur states that when inductive reasoning is used, the mean attendance always converges to the bar capacity. The best-known strategy inEURO 2024 - Copenhagenthe literature in terms of minimizing the variance in the attendance values is random attendance with the unstated assumption that the bar capacity is known. We suggest a new agent type called Yasarcan-Çetiner
agents, which use a hysteresis structure in decision-making. These
agents persist on keeping their current decisions as it is for a number of trials before switching their decisions; each individual agent has
specific persistence thresholds. Yasarcan-Çetiner agents perform better
than all existing agent types in the EFBP literature in terms of improving bar utilization and in minimizing the variance of weekly attendancevalues. It is not necessary for Yasarcan-Çetiner agents to know the bar
capacity a priori; learning the bar capacity is an emergent collective
behavior of the swarm, which is another contribution. We anticipate
that Yasarcan-Çetiner agents will be used in other type of economic
decision-making problems.

MC-44

 MC-44
Monday, 12:30-14:00 - Room: 20 (building: 324)

MCDM for project portfolio problems
Stream: Multiple Criteria Decision Analysis
Invited session
Chair: Maria Barbati
1 - Modeling Criteria and Project Interactions in Portfolio
Decision Analysis with the Choquet Integral

Matteo Brunelli, Salvatore Corrente
3 - Enhancing Household-Level operational decision making with Machine Learning

Aboubakr Achraf El Ghazi, Ulrich Frey, Evelyn Sperber
Agent-based modeling (ABM) presents an effective framework for analyzing complex and highly interconnected systems such as the electricity market. With the ongoing energy transformation, understanding
the individual decisions of actors such as households with PV-storage
systems (PVS), heat pumps (HP), and electric vehicles (EV) is crucial. However, ABMs for the electricity market on a national level withcomplex interconnected actors face scalability limitations with regard
to their individual decision making. Though, aggregating actors into
representative entities and applying a single decision strategy fails to
consider the diversity of individual decision-making processes. Moreover, this oversimplified approach neglects the influence of the varying
attributes of the individual actors. To solve this problem, we propose
leveraging machine learning (ML) techniques to address these requirements while controlling the scalability challenge. The main idea is toutilize ML methods to learn and predict the aggregation of individual
actor decisions. Central to our approach is a uniform forecasting of aggregated demand time series for all actor types, i.e., PVS, HP, and EV.
The underlying demand time series result from applying optimization
models specific to each actor type. The results show similarly goodpredictions for PVS, HP, and EV. This method enables a more nuanced
understanding the individual impact of decision-making processes ofhouseholds on a national level.

4 - Liability in Blockchain Consensus: An Analysis of the
Incentives Set by Proof of Stake Voting Mechanisms

Sascha Hägele
In the landscape of blockchain consensus mechanisms, Proof of Stake(PoS) has emerged as a prominent alternative to traditional Proof ofWork protocols, as evidenced by the consensus change of the second largest cryptocurrency, Ethereum. In PoS systems, validators aretasked with securing the network and validating transactions based on
the amount of cryptocurrency they ’stake’ as collateral. However, ensuring the integrity of PoS networks requires mechanisms to incentivise validators to behave honestly and to penalise malicious actors.
Drawing on insights from economic theory, in particular principalagent theory, this paper examines the role of liability in the PoS consensus, focusing in particular on the incentives created by voting mechanisms designed to enforce compliance. By developing a mathematicalmodel and using an agent-based simulation approach, I analyse the impact of these mechanisms on validator behaviour and network security.
My research highlights the importance of aligning incentives to deter undesirable actions while promoting network resilience and decentralisation. By comprehensively examining liability within blockchain
consensus, this paper contributes to a deeper understanding of the dynamics at play in PoS systems. By elucidating the interplay between
stakeholder incentives and protocol design, my findings provide valuable insights for the development and governance of blockchain networks in the pursuit of robust and secure decentralised ecosystems.

We introduce a comprehensive framework for addressing multicriteria
portfolio decision analysis challenges, particularly in situations where
between-projects independence or within-project independence may
not be assumed. The Choquet integral preference model, a widelyadopted non-additive integral in multicriteria decision analysis, is employed to consider potential interactions between projects and between
criteria. In this context, to streamline the preference model and maintain problem manageability, we opt for the 2-additive Choquet integral,which assigns values only to individual entities and pairs of entities.
An illustrative example demonstrates the application of our approachto a multicriteria portfolio decision analysis problem.

2 - Robust De Novo Programming Under Different Uncertainty Sets and its Application to Renewable Energy
SectorNoureddine KouaissahThis paper proposes robust type models of de novo programming (RDNP) using cardinality-constrained robustness via interval-based andnorm-based uncertainty sets. The R-DNP has not been researched,
and we aim to fill this literature gap. In particular, we develop therobust counterpart of the weighted DNP (W-DNP), Chebyshev DNP(C-DNP), and extended DNP (E-DNP) models to consider different
uncertainty sets and incorporate the decision makers’ preferences.
Methodologically, the proposed approach extends the conventional
DNP model to enable it to solve uncertain coefficients for each decisionvariable on the left-hand side of each objective function and on the total
budget, overcoming a limitation of the current multi-criteria solution
procedure of the DNP approach. The proposed methods provide decision makers more flexibility to express their level of conservatism andpreferences by setting aspiration levels. The proposed method’s usefulness over the standard DNP is demonstrated by providing an illustrative example. Moreover, we validate the proposed formulations forsolving real-world problems through a hypothetical application: optimizing the renewable portfolio for electricity generation in Morocco.
The results confirm the proposed methodologies and show that they
can assist decision makers in determining the optimal system design
for sustainable electricity generation under uncertain conditions.

3 - The Deck-of-Cards-Based Ordinal Regression

Maria Barbati, Salvatore Greco, Isabella Lami
We define a new multicriteria decision-aiding procedure to manage
decision-makers’ preferences. The preferences are elicited by conjugating the deck of cards method with the ordinal regression approach.
The deck-of-cards method allows the DM to express the ranking order of reference alternatives and the intensity of preferences between
reference alternatives. An ordinal regression procedure is then used todefine a multicriteria value function that represents the ranking of thereference alternatives as well as the preference intensity. We show how
this methodology can be usefully applied to different forms of a valuefunction, such as weighted sum, Choquet integral, and additive valuefunction.

105

MC-45EURO 2024 - Copenhagen MC-45
Monday, 12:30-14:00 - Room: 30 (building: 324)

Methods and Algorithms of Decision
SupportStream: Decision Support Systems
Invited session
Chair: Pavlos Delias
1 - Scalable algorithms for throughput time constrained
decision-intensive processes

Simon Voorberg
This work is on using Reinforcement Learning for Optimal Information Acquisition in Decision Processes. We use a model-based
Deep Reinforcement Learning (DRL) technique to optimize large scaledecision-intensive processes. We extend a basic model for decisionintensive processes with lead time constraints and parallel informationacquisition. In many situations multiple sources of information can berequested in parallel, such that we can put a maximum throughput time
constraint on the process while still minimizing for the total effort thatis invested in the process. These extensions cause the state space ofthe problem to explode and we aim at showing the value of DRL for
problems like this. For small size problems we make a comparison tothe optimal solution and for larger size problems we introduce someheuristics against which we compare the performance of the DRL solution. For the small size problems our solution performs within asmall range from the optimal solution. For the larger case we show
significant improvements compared to the heuristics.

2 - On a heuristic algorithm for optimal coordination of firefighting helicopters on large wildfires

Marta Rodríguez Barreiro, María José Ginzo Villamayor,
Fernando Pérez Porras, Silvia Lorenzo-Freire, María Luisa
Carpente Rodríguez
This work presents a complete model for the optimal planning of helicopter operations during the extinguishing of a large wildfire. In large
wildfires, many resources work together and must be coordinated inorder to extinguish the wildfire in the shortest possible time while minimizing the damage caused. It is the responsibility of the fire coordinator to plan the work of these resources. To assist in this task, a model
has been developed to find the optimal planning of helicopters in a firefighting situation. The model determines which aircraft should workon the fire and at what time. It also selects where the helicopters shouldload water and where they should rest. Using the level of efficiency associated with each area of the wildfire at each time, provided by thecoordinator, the model also indicates at which points on the wildfire
each helicopter will drop water. It also considers the fact that the aircraft work in groups, forming elliptical circuits that share water loadand drop areas. It also takes into account Spanish aviation regulation.
The model is presented in a time-extended graph, which allows theevolution of the wildfire to be reflected. The complexity of the model
makes it impossible to solve it in a short time with the tested commercial solvers. Therefore, a heuristic based on the Simulated Annealing
technique was implemented. The heuristic was tested with realistic
data instances, achieving good results in a short time.

3 - An Adaptive Large Neighborhood Search Algorithm for
Order Batching Problem

Ahmet Çınar, Betül Ahat, Mehmet Emre Altun, Bahar
Umman, Meltem SanisoğluOver the past decade, amidst the rise of e-commerce enterprises andthe transformative impact of the Covid-19 pandemic, the Order Batching Problem (OBP) has garnered significant attention. This heightened
focus is fueled by the substantial volume of order transactions and thesubsequent exponential expansion of warehouse dimensions, comprising multiple zones. These complexities have underscored the potential
for over-costing in picking operations, prompting the adoption of intelligent solutions. Despite this, scant attention has been devoted to106

addressing OBP at the scale of Trendyol Group, a prominent player inTurkey and an increasingly influential international e-commerce entity
offering a diverse array of product categories. This study particularly
focuses on an OBP problem that groups orders into worklists for warehouse pickers, with the objective of maximizing the average numberof items per zone. This approach seeks to minimize the duration ofitem picking for pickers, as a higher concentration of items per zone
enables greater efficiency in item collection. To address this problem,
we introduce a MIP model and implement a tailored Adaptive Large
Neighborhood Search (ALNS) algorithm. Through extensive computational experiments utilizing real-life instances, our findings consistently demonstrate that ALNS yields near-optimal solutions and substantially enhances operational metrics by approximately 30%.

4 - A Scheduling Algorithm for Drone-Sharing

Ulrike Ritzinger, Bin Hu, Hannes Koller, Peter Widhalm
Drones are becoming increasingly popular for various tasks in different domains, such as assessing infrastructure damage, conducting surveys, and responding to emergencies. Typically, users of drone services enter individual contracts with a service provider for each order
who provides the necessary technical equipment (drones, sensors, pilots, operating license). With the growing number of users, it has become inefficient to perform individual orders independently because
their synergies are not considered.
Therefore, a new business model called the drone-sharing economy isemerging. Sharing drones, equipment, pilots, and data among multiple users from a particular region can significantly increase efficiency.
This is made possible by holistic and cross-task mission planning witha high degree of automation where the challenge lies in optimizing theschedule of all orders to maximize their synergies while adapting todynamic conditions such as the weather and new or changing orders.
We propose a scheduling algorithm that employs variable neighborhood search to generate a rolling-horizon schedule for both drones
and pilots. Due to the various requirements of the orders such as
operational area, time windows, and others, multiple flight plans are
generated. The goal is to maximize the number of orders completed
by selecting the most fitting flight plan while considering constraints
like drone and pilot compatibilities, data quality, and changing weather
conditions.

 MC-46
Monday, 12:30-14:00 - Room: 40 (building: 324)

Practical Skills: Knowledge-Sharing
Tutorials -1Stream: Making an Impact
Invited session
Chair: Sander Van Aken
Chair: Jonas Witt
1 - Practical skills: Knowledge-sharing tutorials -1-

Making An Impact, Jonas Witt, Sander Van Aken
Expand your skills with these two 45’ practical sessions: Building And
Sharing Apps: An Introduction To Streamlit, and OR Goes Kubernetes: How To Run Long Running Optimization Jobs In The Cloud(i) Building And Sharing Apps: An Introduction To Streamlit (Sander
van Aken, Flixbus) Making impact with OR often requires an enduser or other stakeholders to interact with the models and algorithms
you build, or with its outcomes. With interactive applications, we canleverage the true power of human-machine collaboration, e.g. by letting users guide your optimization model with their knowledge, or enabling them to explore multiple solutions. Developing fully-fledged
front-end applications - or even discovering what is the right thing todevelop - can however take up a tremendous amount of time. For someanalyses or projects, it is not even worth the effort. The Python-based
framework Streamlit could be your new companion in these endeavours. In this tutorial, we demonstrate how we can use it to quickly
develop a first version and iterate on that.

EURO 2024 - Copenhagen(ii) OR Goes Kubernetes: How To Run Long Running Optimization
Jobs In The Cloud (Jonas Witt DHL) While response time often matters in OR applications, in many cases it does not (think network design, tactical capacity planning, . . . ). In these use cases, model usage
will be dispersed over time and not warrant the constant provisioningof a high-end compute resource at all times. In our OR applicationsat DHL Group, we experienced that this often led to some boilerplate
functionality that had to set up across use cases: Sequencing of requests, automatic deployment of workloads, status monitoring, result
retrieval, etc. In this tutorial we will share what we are currently building to replace boilerplate code with a scalable, modern tech-stack tosupport the deployment of tactical decision support tools.

MC-47(NbS). It intends to present an integrated approach based on multicriteria logics, with the goal of minimizing the ecosystem service supply and demand trade-off for Nbs allocation across urban areas. The
approach’s substance lies in the formalization of an optimization algorithm based on ES accounting and the AHP protocol, as well as
the financial, economic, environmental, social, and cultural constraints
that aid in the case-specific personalization of urban planning and environmental NbS design strategies. An description of the Italian casestudy, which was examined testing the proposed optimization algorithm, rounds up the presentation.

3 - Prioritizing Nature-Based Solutions for Sustainable Water Management: A water utility case study in São
Paulo, Brazil MC-47

Francisco Silva Pinto

Monday, 12:30-14:00 - Room: 50 (building: 324)

How to support complex decisions.
Negotiating the trade-off between Social,
Environmental and Economic values 1
Stream: Multiple Criteria Decision Analysis
Invited session
Chair: Alessandra Oppio
Chair: Marta DellOvo
Chair: Giulia Datola
1 - A Diachronic Index for Assessing Landscape Economic
Value

Sebastiano Barbieri, Marta Bottero, Caterina Caprioli, Giulia
DatolaLandscape, as an environmental asset, can be evaluated using various
techniques. One of the most common approaches is to use a system
of indicators, to take account of the different characteristics that makeup the landscape. Indicators alone cannot provide a comprehensive
overview of territories. In addition, territories are characterised bydynamics and characteristics that change over time, requiring a diachronic analysis. Within this context, the present work proposes a
Multi-Criteria Analysis (MCA) to provide a synthetic index capable ofdescribing the economic value of the landscape. For the elaborationof that index, many data referring to various aspects and different periods are considered. Specifically, the current state of the landscapeis compared with past recognized conditions, taking into account the
dynamics of agriculture, tourism, real estate market and forest dimensions over time. The aim is to provide systematic information useful
to support decision-makers and policymakers in the protection, conservation, and planning of the landscape at a regional scale. To test the
selected set of criteria and the construction of the index, the present
study applies the proposed method to the Piedmont region in northwest Italy, because of its diversity of landscapes, ranging from mountains and hills to plains.

2 - Optimising NbS Allocation to Negotiate the Trade-off
Between Ecosystem Service Supply and Demand in Urban Areas

Francesco Sica
The Ecosystem Services (ES) refer to a decision-making condition inwhich the existence of many and different aspects (regulatory, provisioning and cultural) imposes a reciprocal trade-off rules that gives
some greater legitimacy to others. Dealing with ES in a complex
decision-making system encourages evaluations to achieve a shortmedium-long-term equilibrium between ES supply and demand in thesocial, economic, and environmental context of reference. This is especially exacerbate in the urban settings, where types guidelines oftrade-offs alongside those of ecosystem services take action, e.g. incases to design and program urban land use via Nature-based Solution

As the global demand for sustainable water management solutions continues to escalate, the prioritization of Nature-Based Solutions (NBS)
has emerged as a critical strategy for addressing water challenges. This
study presents a comprehensive approach to prioritize NBS using Multiple Criteria Decision Analysis (MCDA) within the context of a water
utility case study in São Paulo, Brazil. São Paulo, a megacity grappling with water scarcity and quality issues, serves as an ideal setting
to explore the effectiveness of NBS in mitigating water-related challenges. Through an integrated MCDA framework, various criteria suchas environmental impact, total cost, integration with existing infrastructures, flexibility, simplicity and transparency, supply safety, among
others are evaluated to assess the suitability of NBS alternatives. The
study employs a participatory approach involving stakeholders fromdiverse sectors to ensure transparency and inclusivity in the decisionmaking process. By synthesizing qualitative and quantitative data, theMCDA facilitates the identification of optimal NBS interventions tailored to the unique socio-environmental context of São Paulo. The
findings of this study not only contribute to advancing knowledge on
NBS prioritization methodologies but also offer practical insights forwater utilities and policymakers seeking sustainable solutions to water
management challenges in urban settings.

4 - Enabling the Circular Economy in Agriculture: A
Methodological Proposal of Social Multi-Criteria Evaluation to Represent Multiple Values

Giulia Datola, Eleonora Catellani, Federica Ciccullo, Niso
Randellini, Alessandra Oppio

The ecological transition to enhance a sustainable and circular approach to resources is the current global challenge, as well as the complex decision-making process that the global community is called upon
to manage. In this context, the agricultural and food production sector
is trying to pass from a linear resource consumption to a circular management of resources, through the adoption of different circular technologies. Due to the high level of complexity, and multidimensional effects, this transition process is facing many challenges and issues. Oneof the main observed challenges concerns stakeholders’ involvement
and the identification of the different values engaged within this transition process. This issue is mainly due to the different levels at which
the decision problem of implementing the circular economy paradigm
in agriculture should be addressed. According to this challenge, thisresearch is interested in providing a methodological framework to apply the Social Multi-Criteria Analysis to address the implementationof circular technologies in agriculture within stakeholders’ perspectives, values, and probable coalition. The main objective is to provide
a decision support system able to manage and represent the complexity of the transition to the circular economy paradigm of agriculture.
The challenge of the research is using evaluation as a communication,
engagement and enabling tool to address innovation according to multidimensional values.

107MC-48EURO 2024 - Copenhagen MC-48
Monday, 12:30-14:00 - Room: 60 (building: 324)

DEA and stochastic models
Stream: Data Envelopment Analysis and its Application
Invited session
Chair: Andreas Georgiou
1 - A hybrid bi-level DEA model under uncertainty conditions

Eleni-Maria Vretta, Andreas Georgiou, Konstantinos Kaparis,
Kyriakos Bitsis, George Paltayian, Fenia Mavrodi, EmmanuelThanassoulis
The efficient allocation of limited resources along with profit optimization play a crucial role in large enterprises and organizations. Given
the continuous fluctuations in data and the lack of historical data, managers seek an optimal dynamic strategy to maximize the profits andat the same time keep organizational efficiency at satisfying levels.
In some business contexts a central decision-making unit might govern management decisions over multiple subordinate decision-making
units and in this study, we investigate efficiency in a stochastic framework using a combined bilevel - DEA approach. We define discrete
scenarios with some occurrence probabilities to assign values to uncertain parameters thus enabling decision readjustments upon the realization of each scenario, contributing to an enhanced overall strategy.
The proposed approach involves an upper-level problem that handles
organizational effectiveness (i.e. profit) and a lower-level problem that
handles subDMUs’ efficiency. Input and output bounds are also usedin the same sense as assurance regions along with lower bounds on
subDMUs’ efficiencies. The presentation concludes with applicationsin the banking or healthcare sector.
The research project was supported by the Hellenic Foundation for
Research and Innovation (H.F.R.I) under the "2nd Call for H.F.R.I. Research Projects to support Faculty Members & Researchers" (Project
Number: 3154).

2 - Stochastic Dynamic Cost-Efficiency: Developmentof a Chance-Constrained Data Envelopment Analysis(CCDEA) model with Window Analysis and OptimalControl Principles

Paulo Nocera Alves Junior, Ali Emrouznejad, Wilfredo
Yushimito, Carlos Monardes, Isotilia Costa MeloThis study presents the development of an innovative approach to
Stochastic Data Envelopment Analysis (SDEA), considering ChanceConstrained Programming (CCP), Window Analysis (WA), and Optimal Control Theory (OCT) principles in a framework with time andprobabilistic constraints. Besids, it involves the average values, covariance matrices and probabilities of each window over time. By incorporating dynamic elements from recent advancements, such as intermediate variables (inventory costs), this model examines the total cost
efficiency based on the relationship among variables, such as costs ofgoods sold (related to the demand), costs of purchased (or produced)
products (related to the production or ordered quantities), and inventory costs (related to the initial and final inventories of the period),along with their uncertainties. This enables a dynamic and stochastic
evaluation of the cost efficiency of inventory control systems. OCT
principles are applied to link variables and introduce dynamics into thesystem, whereas CCP and WA are utilized to construct temporal windows and integrate their stochastic properties. The result is an SDEA
model grounded in OCT principles to assess the total cost efficiencyof the control systems from Decision-Making Units (DMUs), as in theretail sector, over time and considering uncertainties. This approachconsiders real-world conditions, thereby improving analysis and contributing to the development of more efficient control systems.

3 - Stochastic multi-period and multi-stage efficiency measures

Shiang-Tai Liu

108

Many uncontrollable factors will affect company operations. In addition, the collected data is often snapshot observations, and if collected
earlier or later, the values may differ. A company’s inputs and outputs
over time are thus stochastic variables. Several approaches have been
proposed to deal with stochastic observations in efficiency measurement, including stochastic frontier analysis and chance-constrained
programming. Conventionally, each DMU’s input and output variables
are assumed to be independent. This assumption is unrealistic because
DEA requires isotonicity, which implies that the outputs correlate with
the inputs. Ignoring this correlation will produce misleading results.
In a network system, the merit of efficiency decomposition is that a
relationship between the system and division efficiencies is obtained,
based on which the division affecting the entire system’s performancethe most can be identified. When the observations are stochastic variables with multivariate normal distributions, they can be transformed
into a variable with a standard normal distribution. In addition, only
looking at a single time efficiency may lose the general picture of theoverall performance for a multi-period. This paper aims to develop amodel to measure the efficiency of DMUs with several divisions performing specific operations over a time period. This model is able tohandle multi-period stochastic data and decompose the system’s efficiency into division efficiencies.

4 - Planning methods using Data Envelopment Analysis
and Markov Systems

Andreas Georgiou, Emmanuel Thanassoulis, George Tsaples,
Konstantinos Kaparis
This paper addresses contexts in manpower or in the progression ofchronic diseases. It uses the notion of a cohort whose members advance through various states over time (e.g. employees or patients).
The aim is to steer the system towards a desired state, or set of states,
through interventions made by recruitment or treatments which areevaluated on their capacity to achieve this goal in fixed or free time
control settings. The presentation is based on a modelling framework,
which blends DEA with Markov Chains in radial and additive models.
The Markov process offers a set of equations that can be used to describe the movement of entities through time in a hierarchical system(e.g. illness states) and makes possible the investigation of interventions in order to guide the system towards a desired future structure.
A given set of possible policies (e.g., new treatments used in a healthsystem) are treated as DMUS at each stage and various models, including single or two-stage configurations with recruitment Decision
Making Units (DMUs), are presented in single or multiple targeting
environments. The paper concludes by deliberating on the merits andconstraints inherent in these models.
The research project was supported by the Hellenic Foundation for
Research and Innovation (H.F.R.I) under the "2nd Call for H.F.R.I. Research Projects to support Faculty Members & Researchers" (Project
Number: 3154).

 MC-49
Monday, 12:30-14:00 - Room: M1 (building: 101)

Lot-sizing with industrial applications I
Stream: Lot Sizing, Lot Scheduling and Production Planning
Invited session
Chair: Rui Borges Lopes
1 - A Mathematical Model for the Optimization of a Shoe Injection Machine

Eliana Costa e Silva, Adelaide Cerveira, Norberto Jorge
Gonçalves, Rui Borges Lopes, Conceição Nogueira
The present study introduces a novel mathematical model developed
to address a challenge posed at the 174th European Study Group with
Industry, by a prominent footwear company. The objective is to designa model that optimizes the utilization of shoe injection machines, machines used to produce a diverse range of shoe models and colors ofEURO 2024 - Copenhagen
shoes across up to 15 different sizes. The primary goal is to enhance
production efficiency throughout the manufacturing process, including
the preparation and finishing phases. To tackle this complex optimization problem, an integer programming model has been developed. Thismodel is designed to minimize setup times associated with changes incolor, model, and size, thereby streamlining the production process.
Moreover, considering the substantial investment involved, the model
also takes into consideration the limited availability of molds. In thiswork, some preliminary findings derived from both academic and realworld instances will be presented. Additionally, ongoing efforts andfuture enhancements aimed at refining the efficacy of the proposedmodel will be outlined. This work has been partially supported bynational funds through FCT - Fundação para a Ciência e Tecnologia,
under the project UIDB/04728/2020.

2 - Optimizing Shoe Injection Machine Utilization:
Heuristic ApproachA

Rui Borges Lopes, Adelaide Cerveira, Eliana Costa e Silva,
Norberto Jorge Gonçalves, Conceição Nogueira
In the 174th European Study Group with Industry, a footwear company
put forward a challenge that consisted of optimizing the utilization of
one of its shoe injection machines. This machine, capable of high cadence, has the versatility to produce various types of shoes in different colors and sizes. In order to increase its utilization, the company
wanted to reduce setup times associated with mold and color changes,
as it had a significant impact. To this end, it was to be determined the
production sequence that would lead to the highest throughput in thegiven planning horizon.
To tackle this problem, new constructive and improvement methods
were developed and incorporated into a metaheuristic, specificallythe Greedy Randomized Adaptive Search Procedure (GRASP). The
GRASP was tested using both an academic and a real-world instance.
For the academic instance, results were compared with an exact approach, while for the real-world instance a comparison was made withthe actual planning of the company. This presentation will discuss
the obtained results and will address some of the managerial insights
gained from this study.

3 - An optimization model for the sequencing problem withdependent and asymmetric setups: an application in
the chocolate milk industry.

Carlos Ernani Fries, Yasmin Izzo Choucair
Production planning problems have been extensively explored in theliterature due to their wide-ranging applications and the necessity toadapt them to evolving technologies. Optimization techniques for production sequencing have garnered significant attention among companies striving for sustained high levels of production efficiency to compete effectively in both domestic and international markets. This study
aims to propose and implement an optimization model that outlines
production sequencing plans tailored to the chocolate milk industry.
It takes into consideration the characteristics of the job shop environment, including asymmetric and sequence-dependent setup times.
These challenges pose computational hurdles due to their high combinatorial complexity, particularly when managing many products andmachines. In this study, three main features are emphasized due tothe quantity and diversity of SKUs they produce. Such diversity often
leads to frequent line stops for machine configuration, resulting in low
productivity indicators. The proposed model integrates mathematicalprogramming with heuristic approaches to address subproblems andminimize total setup time. Applying the model using real data resulted
in a noteworthy 24% reduction in the average total setup time observedin the company under study.

4 - Flexible production planning using BoM structure andtime-varying demand objectives

Yasmin RoshandelThis research addresses the production planning challenges inherent
in the bicycle manufacturing industry, focusing on the optimizationof bicycle production with demand deadlines. Our approach uses atime-dependent bill of material (BoM) structure, considering the availability of components to meet daily production capacities, guided by

MC-50

forecasted monthly demand objectives. The planning horizon spans
six months, requiring solutions that balance quality and computational
complexity. Leveraging techniques from integer linear programmingand heuristics, we aim to efficiently generate production plans for different types of bicycles composed of many different components. Our
methodology uses a multi-ranking system to select bicycles to produce
so that we balance demand, producibility, and planning complexity.
This strategy fits with the unpredictable nature of the bicycle market,
enabling quick responses to updated demand forecasts while maintaining planning robustness.

 MC-50
Monday, 12:30-14:00 - Room: M2 (building: 101)

Retail Inventory Management II
Stream: Retail Operations
Invited session
Chair: Heinrich Kuhn
1 - The Optimal Policy for a Cyclic Capacitated Lost Sales
Inventory

Ludwig Brieditis, Peter Berling
Determining optimal replenishment of brick-and-mortar retail stores isa challenging problem. Based on experience from a large north European grocery store, these retailers typically face a non-stationary demand with a recurrent periodic pattern where excess demand is lost.
Moreover, replenishment opportunities are limited by a weekly schedule with limited transport capacity allocated to each store.
The optimal replenishment decisions for a store in this setting is previously unknown. We show that a periodic modified base-stock policy
is optimal, i.e. ordering so that the resulting inventory is as close as
possible to the optimal base-stock level of the period by modeling the
problem as a Markov Decision Process. It is straightforward to use themodel to find the optimal base-stock levels. A numerical study shows
the resulting cost increases when using solution methods that disregard
some of the setting characteristics, e.g. non-stationary demand, limited
capacity, lost sales.
Efficiently solving this problem is important not only to minimize thecost at the stores but also to evaluate various planning decisions, e.g.
scheduling and routing, in the greater distribution network.

2 - Multi-Echelon Inventory Optimization: A Case Study
considering real-life constraints and uncertainties

Philip Mårtensson, Joakim Andersson, Johan Lidvall, Johan
MarklundThis paper presents an investigation into multi-echelon inventory optimization, emphasizing the integration of real-life constraints and uncertainties. By considering a case study for spare parts in the automotive industry, derived from practical inventory management scenarios, we address the complexities inherent in supply chain dynamics.
Through our analysis, we demonstrate the efficacy of our proposed approach in enhancing inventory control across multiple echelons while
underscoring the importance of considering real-world factors such aslead times, demand variability, and capacity constraints. Another aspect that is considered, in a distribution system with mixed ownershipof the retailers, is the implementation feasibility of transitioning froma traditional single-echelon inventory control system, requiring extensive stakeholder management and buy-in. The findings bridge the gap
between practitioners and researchers in this area, offering valuable insights into future research of applying multi-echelon theory and techniques in dynamic supply chain environments.

109MC-51EURO 2024 - Copenhagen3 - Inventory control of end-of-sales-life products with integrated online and offline sales channels

Ben Lowery, Anna-Lena Sachs, Idris Eckley
In a business with integrated online and offline storefronts, the management and control of stock for end-of-sales-life products facing uncertainty and decline in demand poses important operational challenges.
The retailer sells items through its stores and its warehouse. The stores
satisfy demand from its available stock, while the warehouse fulfils
online orders as well as a proportion of excess customer demand if thestores did not have enough stock on hand; with remaining unsatisfied
demand at the store becoming lost-sales.
We develop a two-echelon divergent, periodic-review inventory model,
with partial lost-sales at the store level, to determine order quantities
during the end-of-sales-life period. We solve small instances to optimality using dynamic programming techniques. Due to the curse ofdimensionality, we develop novel heuristics to make ordering decisions
that balance accuracy and interpretability for larger instances.

4 - The flexible shelf space refilling problem

Sandra Zajac, Moritz Hundhammer, Michael Sternbeck,
Heinrich Kuhn
Shelf space is a fundamental resource in retail and thus it is vital tomanage it efficiently. In traditional shelf space planning, the numberof facings is defined in advance and it is assumed that this number will
not be changed during the operational replenishment process in thestore. In operational practice, however, it is often beneficial to deviate
from this tactical shelf plan to reduce in-store replenishment costs. Increasing the facing number of a product decreases the required effort
for replenishing this product as more items fit in the showroom shelf
space. However, due to limited shelf space, it will be necessary to reduce the facing number of other products which in turn may lead to
additional in-store replenishment effort. Adapting the shelf layout further causes rearrangement costs, most notably facing elimination andshifting costs. This paper introduces the flexible shelf space refillingproblem in which total in-store replenishment and rearrangement costs
are minimized. We illustrate in detail the current process of adapting the number of facings during the refilling process and give some
empirical insights to demonstrate the practical relevance of the problem. To provide decision support, a first seminal mathematical modelis presented and its suitability to improve the operational shelf space
refilling process is demonstrated based on a first case study. Lastly, we
give an overview on possible future research directions in this new areaof research.

 MC-51
Monday, 12:30-14:00 - Room: M5 (building: 101)

Quantitative methods for systemic andclimate risk
Stream: Risk management in financeInvited session
Chair: Gabriele Torri
1 - On the measure of ambiguity on financial markets

Hachmi Ben Ameur, Zied Ftiti, Wael Louhichi, Jean-Luc
Prigent
The impact of ambiguity on financial markets continues to be a debated topic. Like most decisions in life, we face ambiguity, which is
the uncertainty about true probabilities. However, this aspect of uncertainty is often overlooked, despite its widespread presence in financial
markets and the significant impact it can have. A prime example ofextreme ambiguity is a financial crisis, where substantial uncertainty
arises and investor behavior is markedly influenced. Therefore, understanding the influence of ambiguity on financial markets is crucial.
This paper introduces two fundamental statistical tools for measuring

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ambiguity in financial data: the Wasserstein metric and the KullbackLeibler divergence. These tools assess how close or distant probability
distributions are from an expected probability distribution. These criteria are particularly useful for measuring ambiguity during financial
crises, especially in multi-dimensional cases. We conduct empiricalanalysis using major financial indices, such as the S&P 500, Nikkei
225, Eurostoxx 600, and DXY index, covering the period from January 2000 to December 2022. The findings have significant practicaland operational implications for portfolio allocation. It is crucial toconsider uncertainty regarding the true values of financial parameterswhen determining the optimal portfolio profile

2 - Pricing synthetic CDOs under infectious defaults withimmunization

Gabriele Torri, Gianluca Farina, Rosella GiacomettiThis work introduces a recursive algorithm for the portfolio loss distribution similar, in spirit, to the one commonly used for CID (conditionally independent) models. We model default events under a contagion mechanism which is the result of two independent components:
an infection attempt generated by defaulting entities and a failed defence from healthy ones. A recursive algorithm for the calculation of
the portfolio loss distribution is presented that shows a good level ofcomputational tractability even when an heterogeneous set of names
is considered. We apply it to the pricing and hedging of CDO instruments and compare its performance to the standard one factor Gaussianmodel.

3 - ESG enhanced tracking portfolio with quantile regression

Marco Bonomelli, Rosella Giacometti
The purpose of this paper is the formulation of an enhanced index replication model with second order stochastic dominance constraints. The
replicating portfolio is constructed minimizing an asymmetric deviation measure via quantile regression. According to the investor’s risk
aversion preference, the resulting linear optimization problem can besolved for different quantiles. In order to take into account the increasing attention to ESG issues, the strategies can be ehnanced, via
hard and soft constraints, focusing on the sustainability of the replicating portfolio. The performance of the optimal portfolios are tested
both in-sample and out-of-sample, and are compared to the solutionsof well-known models.

4 - Surface Measures of Tail Dependence and Climate
ChangeDavide Lauria
Systemic financial risk refers to the situation in which failures or disturbances in one part of the financial system can spread rapidly andextensively, resulting in significant disruptions to the broader economy. Measuring systemic financial risk is a complex task that typically involves various quantitative and qualitative indicators. Examples include the Systemic Risk Index (SRISK), Conditional Value-atRisk (CoVaR), and Financial Stress Index (FSI). Such measures areclosely related to tail dependence measures, which are introduced toquantify the extent of dependence between extreme events of a coupleof random variables. The scope of this research is to provide insight
into the possible connection between the risk factors behind climatechange and the strength of systemic risk among European companies.
To achieve this aim, we first introduce a systemic risk measure basedon a recently proposed coefficient of tail dependence which is derived
from surface integrals. We then compare the results with existing research. Subsequently, we dynamically relate such measures to the evolution of a set of climate change risk factors.

EURO 2024 - Copenhagen MC-52
Monday, 12:30-14:00 - Room: 8003 (building: 202)

Multi-objective Combinatorial Optimization
Stream: Combinatorial Optimization
Invited session
Chair: Trine Krogh Boomsma
1 - Sensitivity analysis of the cost coefficients in multiobjective integer linear optimization

Britta Efkes, Kim Allan Andersen, Trine Krogh Boomsma,
Nicolas Forget
We consider sensitivity analysis of the cost coefficients in multiobjective integer linear programming problems and define the sensitivity region as the set of simultaneous changes to the objective function
coefficients for which the efficient set and its structure remain the same.
In particular, we require that the component-wise relation between efficient solutions is preserved and that inefficient solutions remain inefficient. We present necessary and sufficient conditions on permissible
changes that satisfy the defined properties. In this talk we concentrateon changes to a single objective function coefficient and show that thesensitivity region is a convex set, i.e., an interval. In order to avoid
going through all pairs of feasible and efficient solutions, we introduce
further properties that identify inefficient solutions that remain inefficient whatever the change is. Based on this, a subset of the inefficientsolutions can be excluded from consideration. More specifically, we
prove that it suffices to inspect the inefficient solutions of a q-objective
problem that are efficient in one of two related (q + 1)-objective problems, in the case of only one change. Computational experiments withmulti-objective binary and integer knapsack problems demonstrate thegeneral applicability of our technique.

2 - Generator sets for Minkowski Sums - Theory and Insights

Mark Lyngesen, Sune Lauth Gadegaard, Lars Relund Nielsen
In this presentation we consider a class of multi-objective optimization
problems known as Minkowski sum problems or Pareto sum problems.
Pareto sum problems are multi-objective optimization problems with adecomposable structure where the (global) Pareto set (the nondominated points of the feasible objective space) can be described as thePareto set of the Minkowski sum of several (local) Pareto sets obtained
from subproblems. Much research has been done on efficiently filtering for the set of (global) Pareto sum given several known (local)
Pareto sets. In some cases, only subsets of the (local) Pareto sets are
required for generating the global Pareto sum. As such, if a subproblem solution does not contribute in generating the (global) Pareto sum,then the computational effort spent on finding the solution is effectively
wasted. For this reason, we investigate how small a subset of the Pareto
optimal solutions from subproblems is necessary to describe the Pareto
sum. Furthermore, we investigate when unnecessary Pareto optimal
solutions exists in the subproblems and how to identify them. For thispurpose we propose so-called generator upper bound sets, which canbe used when solving the subproblems to exclude some unnecessary
solutions. If and when these non-generating or unnecessary Pareto optimal solutions can be identified and excluded, the computational burden of identifying a generating subset of Pareto optimal solutions of asubset may be substantially reduced.

3 - On the extension of branch-and-bound methods
formixed-integer linear programs

Nicolas Forget, Sophie Parragh
In the recent years, a lot of attention was given to branch-and-boundtechniques for solving pure integer linear programs with an arbitrary
number of objectives. Significant progress and contributions were
made. On the other hand, the efficiency and practicality of branchand-bound methods for bi-objective mixed-integer linear was shown
through a variety of works in the past decade. Intuitively, the next
step is to address mixed-integer linear programs with three or more

MC-53

objective functions. Again, linear-relaxation based branch-and-boundframeworks appear to be the methods of choice because of the continuous nature of some variables. Unfortunately, due to the complex structure of the non-dominated set of such problems, straightforward extension of existing branch-and-bound frameworks to solve
multi-objective mixed-integer linear programs is not possible. In thistalk, we identify some of these obstacles(representation and navigation of the non-dominated set, update of the upper bound set, dominance test, etc.), and present various solutions we explored to address these issues. Moreover, we highlight implementation-related andperformance-related difficulties, and conclude with future works.

4 - Multi-objective Optimization of Retrial Machine Repair
Problem with Warm Standbys and Working Vacation

Kuo-Hsiung Wang
Abstract: This study investigates the retrial machine repair problem
with warm standbys and working vacation, in which the server works
with different repair rates rather than completely halting the repair during a vacation period. It is assumed that the server begins working
vacation when the system is empty. When a failed machine finds thatthe server is idle upon arrival, it is immediately repaired by the server.
If the failed machine finds that the server is busy upon arrival, it leaves
the repair facility to join the retrial orbit and attempts repair again.
The failure, repair, vacation, and retrial times are assumed to be exponentially distributed. A matrix analytic method was used to computesteady-state probabilities and several system performance measures. A
multi-objective model for the cost and expected waiting time was derived. The epsilon-constraint method and non-dominated sorting genetic algorithm were used to identify and compare the Pareto optimal
set and make comparisons. The Pareto front set was obtained and illustrated to improve the quality of decision making for system engineers.

 MC-53
Monday, 12:30-14:00 - Room: 8007 (building: 202)

Sustainable Energy
Stream: Sustainable and Resilient Systems
Invited session
Chair: Stella Nadine Steidl
1 - "Pig-Electricity Synergy" System in Sustainable Energy
Development

Huang Sun-weng., Jian-hua Xia, James Liou, Gwo-Hshiung
Tzeng
In the current scenario where energy crises are increasingly evident,
the development of renewable energy has become a crucial pathway
towards achieving sustainable development. The "Pig-Electricity Synergy" system emerges as an advanced energy solution, seamlessly integrating pig farming and solar energy storage technologies.The coreadvantage of this technology lies in its integrated application of solar
energy and storage technology, forming a diverse and reliable energy
management system.The uniqueness of the "Pig-Electricity Synergy"system lies in its successful realization of the integration of solar energy and pig farming, enhancing energy reliability through collaborative operations and significantly mitigating system risks. The resulting
diverse and flexible energy management system not only meets theescalating demands of energy but also contributes to enhancing the resilience of the overall energy framework. This study employs DEMATEL analysis and ISM modeling to delve into the pivotal role of the"Pig-Electricity Synergy" system in sustainable energy development.
Through a comprehensive analysis of the system’s technology, diverse
energy integration, and flexibility, the research aims to provide tangible
and viable solutions for sustainable energy development. The application of DEMATEL and ISM contributes to a deeper understanding of
the inherent relationships within the system, offering profound insights
for sustainable development decision-making.

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MC-54EURO 2024 - Copenhagen

2 - Assessing weather resilience of the Swiss electricity
system in 2035 via spatially-explicit modeling

Giacomo Rubino, Evelina Trutnevyte
Recent studies point to weather dependency of renewable electricity
sources as a limitation to the system’s resilience in case that highshares of renewable electricity are reached. The impact of weather
is, however, highly variable in space and time, making high-resolution
resilience assessment a necessity. This study aims to test Swiss electricity system in 2035 against 25 years of historical weather conditions
that affect hourly profiles of solar photovoltaic (PV), wind power, andhydropower, as well as electricity demand for heating. In particular,
four strategies of locating new solar PV, wind plants, and heat pumps
in Switzerland are investigated, based on (i) continuation of current
trends, (ii) technical potential, (iii) population, and (iv) minimum system cost approach. The analysis is conducted using a technology-richcost-optimization model EXPANSE that generates scenarios at a temporal resolution of three hours resolution and a spatial resolution of
Swiss 2136 municipalities. Six electricity system resilience indicators
are then calculated to compare the four strategies: equivalent availability factor, energy import dependency, diversification of energy supply, decentralization index, renewable electricity generation, and selfsufficient electricity supply.

3 - GIS-based optimization tool for green hydrogen integration in cities: A comparative case study of New Zealandand German cities

Stella Nadine Steidl, Jannik Haas, Rebecca Peer, Alaa
Alhamwi, Alejandro Zabala Figueroa, Wided Medjroubi
We introduce the GIS-based optimization tool FlexiGIS-H2, applied tourban areas in both New Zealand and Germany to identify the potential
for hydrogen to aid the energy transition within cities. Based merelyon open-source datasets, we connect existing urban energy infrastructure, electricity generation and hydrogen production potential and thespecific demand of a given city. Land use and building types are used
to calculate electricity demand, and available areas are analysed to estimate the potential electricity generation from solar and wind resources,
alongside the potential production of green hydrogen. Through costminimization, we optimize storage scenarios including batteries andgreen hydrogen, and solve for the most techno-economic solution tomeet electricity and hydrogen demand for urban applications.
In this work, we present the model FlexiGIS-H2 applied to the cities
Auckland, Christchurch, Karlsruhe, and Oldenburg, in New Zealandand Germany. Here, we delve into the application of FlexiGIS-H2,
our modelling outcomes for different cities, and ongoing developments
aimed at supporting decision-making for future energy system planning in urban areas. The resulting hydrogen portfolio and residual energy scenarios, and the optimisation of hydrogen as a flexibility option
in urban settings, can be used as inputs to national capacity expansion
tools, to provide local detail often lacking at the national level.

4 - Assessing the performance of optimized energy communities under uncertainties

Nathalie Frieß, Ulrich Pferschy, Joachim Schauer
Renewable Energy Communities (RECs) can provide a useful contribution to the decarbonization of the energy sector if they bring about
an actual change in physical electricity flows, i.e., if they lead to an
optimized use of locally produced energy. Main tools for reaching thisobjective are load shifting and intelligent battery management. Basedon forecasts of production and consumption profiles, the associatedoperational decisions can be optimized with the goal of improving theoverall performance of the community. The strong interdependence
between the community members’ individual resources prohibits theuse of simple heuristic decision rules for this purpose. As actual conditions may well deviate from the forecasts used for optimization, not
all optimized decisions will yield the intended outcome. Therefore, wedeveloped a Model Predictive Control inspired planning frameworkthat comprises an optimization model and a simulation model. Using
the most recent available short-term forecasts as input, the optimization
model is solved for a planning period of 96 time steps. The computed
optimization output for the upcoming time step then serves as input
for the simulation model, which models the actual real-world outcome

112of the planning decisions for the current time step. The introduced
framework can be used to generate realistic performance measures andto reach a better understanding of the benefits of optimization-based
decision-making in RECs.

 MC-54
Monday, 12:30-14:00 - Room: S01 (building: 101)

Ridehailing & Ridepooling
Stream: Public Transport Optimization
Invited session
Chair: Michael Stiglmayr
1 - A Dynamic Electric Dial-a-Ride Problem with Optional
Service Upgrades

Christian Ackermann, Julia Rieck
The demand for sustainable urban mobility has surged with the increasing focus on climate protection. Consequently, ride-sharing services
powered by electric vehicles (EVs) are gaining unprecedented attention. While EVs increase environmental benefits, they also present
challenges due to the scarcity of charging infrastructure. Moreover, standard dial-a-ride settings, with their broad time windows andlengthy ride times, fall short of the service quality needed to persuade
the broader public to switch from private cars.
Our study tackles a special Dial-a-Ride Problem (DARP), deploying
EVs to meet dynamically incoming customer requests within a framework of limited charging stations and non-linear charging functions.
To enhance appeal and utility, customers can opt for higher-priced
premium options, such as non-shared rides, reduced ride time, or reduced delay at drop-off. The service provider must immediately decide
whether to fulfill these premium requests or decline them and executethe customer request with standard service.
We conduct a comparative analysis of various charging strategies, evaluating their efficacy and relative impact on operational quality. Moreover, we tailor routing and scheduling strategies to accommodate themixed standard and premium request nature. In this way, environmental considerations of EV usage are combined with enhanced service
quality through premium options, resulting in a pragmatic approach toupgrading urban mobility.

2 - The Dynamic Electric Dial a Ride on a Fixed Circuit

Rotem Zohar, Ohad Eisenhandler, Kris Braekers, Mor Kaspi
Autonomous vehicles are set to transform urban mobility by embracing mobility as a service, presenting opportunities for cost reduction,
diminished vehicular ownership, and enhanced road safety. Despite
these advantages, shared autonomous mobility services face harsh restrictions, often confining them to fixed circuits and schedules. In theelectric dial-a-ride problem on a fixed circuit (eDARP-FC), a fleet ofelectric shuttles provide on demand services while performing multiple
laps on a designated circuit consisting of recharging depots and passenger stations. While such an operation has been investigated in theliterature in a static setting, the challenges of a dynamic context havenot been addressed so far. Therefore, we focus on the implications ofdynamically arriving requests on both modeling aspects and solution
techniques. This setting, requires making iterative decisions on request
acceptance, assignment to vehicles and battery management. The objective function consists of several weighted components, including
the number of accepted requests, users’ total journey time, and the total number of vehicle laps. Our approach explores several online policies, employs an event-based simulation framework for high-resolution
representation, and integrates a reinforcement learning model to solve
large-scale instances effectively. The goal is to derive explainable dynamic operational policies that provide better balance between operational costs and the quality of service.

EURO 2024 - Copenhagen
3 - Mixed-integer linear programming formulations for theline-based dial-a-ride problem

Marie Schmidt, Kendra Reiter, Daniela Gaul, Michael
Stiglmayr
On trajectories with low demand, scheduled busses are often not efficient. If operated frequently, they may be able to capture a significant fraction of overall demand, but often run with few passengers
on board or even empty. If frequencies are decreased, the service becomes inconvenient for passengers, and demand will further decline.
On-demand ridepooling has been proposed as an alternative. However, while high service levels can be achieved if enough capacity isprovided, such services are inefficient if demand is not properly pooled
or if a significant amount of deadheading occurs. For this reason, we
propose an intermediate solution where services are line-based but notschedule-based. In the line-based dial-a-ride problem (liDARP ) westudy the following optimization problem: Given a sequence of stops,
a number of vehicles, and a set of stop-to-stop transportation requests
with time windows for pickup and delivery, assign services to the vehicles and passengers to the services such that each service visits thestops in the indicated sequence, and passengers are transported fromorigin stop to destination stop by the service they are assigned to,whilem aximizing a weighted sum of transported requests, km driven,vehicles used, and car km saved. We compare three MILP formulationsof the liDARP, one that encodes assignment of vehicles and passengers
to services, one that relies on the 3-index formulation for the general
DARP, and one event-based formulation.

4 - The Multi-objective Dial-a-Ride Problem

Michael Stiglmayr
Ridepooling applications ask for fast computation of vehicle routes andpassenger allocations. This so-called dial-a-ride problem (DARP) has
to compromise between the efficiency of the ridepooling system (e.g.,routing costs) and customer satisfaction (excess ride time and the number of served requests), subject to time window constraints, vehicle
load constraints, and maximum ride time constraints. In this talk, we
present a multi-objective variant of DARP and investigate the trade-off
between the objectives of the service provider and those of the customers.
Moreover, we present a tight mixed-integer linear programming(MILP) formulation for the DARP by combining two state-of-the-art
models into a novel location-augmented-event-based formulation. The
formulation is tight in the sense that, if time windows shrink to a single point in time, the linear programming relaxation yields integer (andhence optimal) solutions. Strong valid inequalities and lower and upperbounding techniques are derived to further improve the formulation.
We then demonstrate the theoretical and computational superiority of
the new model: First, we prove that the new formulation has a tighter
linear programming relaxation than the two-index formulation. Second, extensive numerical experiments on benchmark instances showthat computational times are on average reduced by 49.7% comparedto the state-of-the-art event-based approach.

 MC-55
Monday, 12:30-14:00 - Room: S02 (building: 101)

Airline Applications I
Stream: Transportation
Invited session
Chair: Christian Truden
1 - On airline frequency competition for routes linking
emerging airports

Amit Bardhan
We analyse a scenario where two competing airlines, are considering
new routes to emerging airports. These airlines can design either direct

MC-55

routes or connect passengers via their respective hubs. The total market size for a route is influenced by two key factors: flight frequency
and network connectivity, while passengers prefer direct flights overthose with layovers. An airline’s revenue on a route hinges on both theoverall market size and its capacity share. Therefore, each airline faces
a strategic decision regarding the optimal frequency of flights to allocate on each new route. The cost of deploying capacity on a route encompasses several factors: direct aircraft operating costs, airport fees
(landing, gate usage), and passenger handling expenses. Airport fees
are typically higher at major hubs, which may also impose slot restrictions due to capacity constraints. Airlines’ objective is to maximize their profits. To analyse this frequency competition, we proposea game-theoretic model similar to developed in Wang et al. (2022).
By analysing the model’s equilibrium, we obtain insights, such as theconditions under which airlines might reallocate capacity from established, high-traffic routes to serve emerging airports. The validity ofthis model has been demonstrated using data from India’s Regional
Connectivity Scheme (RCS), which aims to promote air connectivity
to unserved and underserved airports.

2 - Assigning airport ground-handling operations: an rVNS
metaheuristic

Alexandre Dupaquis, Ronan Guivarch, Sandrine Mouysset,
Daniel Ruiz, Vincent Charvillat, Frédéric Bouilhaguet
Airport ground-handling companies want to improve their operations
all around the world want by automating current processes, and finding
extra optimizations otherwise unreachable for human dispatchers. We
aim to address the catering problem in the crossover between vehicle
routing problems and scheduling problem, also known as the simultaneous supply & delivery problem. The MTCVRSPTW-MB (MultiTrip Capacitated Vehicle Routing and Scheduling Problem with Time
Windows and Meal Breaks) is extended to a heterogeneous fleet oftrucks and drivers, with qualifications and different load patterns. We
introduce a greedy heuristic to solve a relaxed version of the problem,
as a baseline. Then, we implement a cooperative rVNS-based metaheuristic. We compare our algorithms on multiple real-life instances at
San Francisco International Airport. Our computational study showsthe effectiveness of each approach and brings out the strength of ourmetaheuristic. Our algorithms will help ground handlers specialized incatering operations, to speed up the decision-making process of assigning hundreds of tasks every day. In particular, we show the multiple
extra benefits of the metaheuristic approach, including minimizing fuelconsumption and the ability to re-assign tasks dynamically.

3 - Stochastic Dynamic Models for Reducing Air Traffic Delays at Heathrow

Rob Shone, Jamie Fairbrother
Imbalances between demand and capacity at the world’s busiest airports cause air traffic delays, which can have serious financial and environmental consequences. To alleviate the burden on runway capacity,
various types of interventions are possible. At the pre-tactical stage (upto a few hours before a day of operations), the schedule can be adjusted
by imposing ground delays on flights in order to avoid "hotspots" ofdelay occurring at peak times. At the tactical stage (during the dayof operations itself), sequencing of take-offs and landings can be optimised in order to minimise average time separations between runway
movements.
This talk will discuss the progress of an EPSRC-funded project(EP/X039803/1) aimed at integrating pre-tactical and tactical interventions in order to reduce air traffic delays at Heathrow Airport. We formulate a stochastic dynamic optimisation problem in which the "system state" at any given time includes hundreds of variables evolving via
continuous-time stochastic processes. Solution approaches via approximate dynamic programming are possible in theory, but very difficult toimplement in practice. Instead we consider an approach based on theemerging field of "simheuristics", which involves continuously simulating possible trajectories of future random events and using a ranking
and selection method to jointly optimise both runway sequencing andground holding delay decisions.

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MC-56EURO 2024 - Copenhagen

4 - Airport Slot Allocation for Maximal Destination Coverage

Christian Truden, Margaretha Gansterer
Due to the escalating congestion at airports, there has been a growinginterest in using optimization methods for airport slot allocation andflight scheduling. Level 2 and 3 airports nowadays have a substantial surplus of demand relative to airport capacity, which requires sophisticated slot allocation. This process is not only a computationallycomplex task but needs to satisfy the requirements of different players
following individual objectives. The resulting allocation of slots andsubsequent flight schedules significantly impacts the market power ofairlines. From the passengers’ perspective, the key consideration isconnectivity, emphasizing the importance of viable connections between destinations. We propose an innovative slot allocation modelthat prioritizes destination coverage while considering limitations onmarket power for participating airlines. Based on an extensive computational study, we elaborate on how varying degrees of market power
restrictions can positively or negatively influence connectivity.

 MC-56
Monday, 12:30-14:00 - Room: S04 (building: 101)

Traffic flow modeling
Stream: Transportation
Invited session
Chair: Yunzhu Lin
1 - Density estimation for a moving crowd without a physical boundary

Pratik Mullick
The collective motion of social agents arising from their interactions
has been the subject of intense scientific research. Understanding thecollective dynamics of human crowds is crucial for improving pedestrian traffic flow, ensuring crowd safety, effective urban planning, andpreventing crowd disasters. From the perspective of crowd management, it is crucial to understand the relationship between crowd densityand velocity, known as the fundamental diagram (FD). This relationship helps to study the capacity of spaces where traffic moves, such as
roads for vehicles and sidewalks for pedestrians. Constructing a realistic FD requires the utilization of an effective method for density estimation. While existing literature offers several methods, determining
the ’best’ method remains unresolved and may depend on the crowd
situation. Most research has focused on situations where the moving
crowd is constrained within a physical boundary, such as a corridor or
sidewalk. However, there is no well-defined method of density estimation for groups in an unbounded space. I wish to present our recently
developed voronoi-cell-based density estimation method that can estimate crowd density in a wide variety of situations, irrespective of thepresence of spatial constraints. Our proposed method of pedestrian
density estimation could facilitate the construction of FDs from thepoint of view of individual pedestrians, along their trajectories.

2 - Using Frequency Domain Analysis to Elucidate Travel
Time Reliability Along Congested Freeway Corridors

Qixiu Cheng
Travel time reliability (TTR) is an essential measure of service for traffic performance management, especially for congested freeway corridors. This paper proposes a systematic analytical framework to analyze the predictable and unpredictable variations in route TTR in corridor networks. More specifically, the predictable variation in route
TTR is estimated with a deterministic fluid-based polynomial arrival
queue (PAQ) model, while the unpredictable variation in route TTR isanalyzed through the residual induced by the PAQ estimation model.
Based on the output of the PAQ estimation model, a frequency-domain
approach is proposed to decompose the observed time-domain travel

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time into underlying unobserved factors in different temporal resolutions. With the discrete-time Fourier transform method and the Butterworth low-pass filtering technique, it is capable of analytically uncovering different temporal scales of predictable variations in TTR, including the trend of day-to-day variations, the dynamics of within-day
variations, as well as the stochasticity of within-period variations. Connecting with the unpredictable variations represented by the residual
due to the PAQ approximation model, we can comprehensively elucidate TTR for congested freeway corridors. Case studies are conducted
to show the effectiveness of the proposed frequency-domain approachin elucidating and measuring different contributing elements in TTR.

3 - Analytical Strategy for Optimizing Mixed Traffic Networks with Semi-Autonomous Vehicles

Hao Guan, Xiangdong Chen, Qiang Meng

Despite recent progress in semi-autonomous vehicle (semi-AV) development, achieving an optimal autonomous traffic system faces challenges due to the low penetration rate of semi-AVs and their need forhuman oversight. To improve traffic flow in such environments withhuman-driven vehicles (HVs) and semi-AVs, we advocate the use of
dedicated lanes and develop an innovative framework to optimize the
network performance by planning the mode choice of semi-AVs (either human-driven or auto-driven). Specifically, we introduce a threedimensional Macroscopic Fundamental Diagram (3D-MFD) to characterize the complex flow dynamics in mixed traffic. Our analysis
identifies that an optimal proportion of human-driven and auto-driven
vehicles can significantly maximize network flow. Inspired by this key
finding, we construct an optimization model to regulate semi-AVs’mode choice, aiming to optimize vehicle proportions within mixed
traffic networks. Although solving the model presents challenges due
to its nonlinearity and integer constraints, especially in large networks
with numerous intersections, the unique structure of the proposed 3DMFD enables us to derive an analytical solution. This solution significantly reduces the computational complexities of the optimization
model, allowing the resulting control strategy to be implemented inreal time and substantially improves network performance.

4 - Optimizing Urban Expressway Exit Ramp Lengths
through Microscopic Traffic Flow Simulation: A Case
Study Using PTV VISSIM

Yunzhu Lin, Yu Chang

This study presents a method using microscopic traffic flow simulation
to assess urban expressway exit ramp layouts, addressing the problemof ramps too close to surface road intersections, leading to insufficientqueuing space. Utilizing PTV VISSIM, a real-world case is analyzed
over 65 minutes, including a 5-minute warm-up phase. The core simulation spans one hour, segmented into six 10-minute intervals. The
research fine-tunes the Wiedemann 74 car-following model using comparative analysis of observed and simulated traffic volumes, adjusting
parameters to match site conditions. Validation criteria require a percentage difference of under 10% and a GEH statistic below 5. Building on the validated base traffic model, which uses a 90-meter ramp
length, the research extends the model to simulate scenarios with ramp
lengths of 150, 200, 250, and 300 meters. Through linear regression
analysis of the simulation data, relationships between traffic flow anddensity for these configurations are established separately. These findings are then mapped against the Highway Capacity Manual’s service
level density ranges to determine the optimal lengths for varying traffic
flow conditions. The findings indicate a significant flow increase when
extending the ramp from 150 to 200 meters, peaking at 250 meters.
However, it also shows that a 200-meter ramp connection length optimizes queuing lengths, suggesting a balance between increased flowrate and minimized vehicle congestion.

EURO 2024 - Copenhagen MC-57
Monday, 12:30-14:00 - Room: S06 (building: 101)

Dynamic portfolio selection: stochastic
optimization, filtering, and learningtechniques
Stream: Modern Decision Making in Finance and Insurance
Invited session
Chair: Sebastien Lleo
1 - Reinforcement Learning Methods in Risk-Sensitive Investment Management

Wolfgang Runggaldier, Sebastien Lleo
We investigate the benefits of relating reinforcement learning (RL)with risk-sensitive control. Our starting point is the duality between
free energy and relative entropy, see e.g. Dai Pra et al. (1996). It
establishes an equivalence between risk-sensitive control and standard
stochastic control problems with an entropy regularization term. Thisapproach has two major advantages: i) it does not require a preliminary change of measure à la Kuroda and Nagai (2002); ii) it is naturally consistent with the use of a regularization/penalization term in theliterature that connects reinforcement learning with stochastic control,e.g. Wang et al (2020). In this sense it also allows for a risk-sensitive
interpretation of the entropy regularization in RL.
We furthermore show how this connects to the existing literature on
risk-sensitive investment management (Kuroda and Nagai, 2002, Davisand Lleo, 2008, 2020, 2021), whereby cases with unknown parameters
or with partial observation showcase the advantages of reinforcement
learning.

2 - Robust Utility Maximization in Continuous Time: Filtering for Shaping the Uncertainty Sets

MC-58

state variable is the filter of the drift. The dynamic programming equation for this problem is studied and closed-form solutions for the valuefunction and the optimal trading strategy of an investor are derived.
They allow to quantify the monetary value of information delivered by
the expert opinions.
We also study diffusion approximations of the filter processes for highfrequency discrete-time experts. They allow to simplify the problem
and to derive more explicit solutions. Finally, we illustrate our theoretical findings by results of numerical experiments.
The talk is based on joint work with A. Gabih, H. Kondakji, J. Sassand D. Westphal.

4 - Portfolio optimization based on multiperiod continuous
stochastic dominance principles

Giorgio Consigli
We consider a multi-stage generalization of the interval-based stochastic dominance (ISD) principles introduced by Liu et al. (2021). The
ISD criterion was motivated specifically in a financial context to allow for contiguous integer SD orders on different portions of a portfolio return distribution against a benchmark distribution. A continuous spanning of SD conditions between first, second and third orderstochastic dominance principles was introduced relying on a reference
point. Here by extending the partial order to random data processes,
we apply ISD conditions to a multi-period portfolio selection problem
and verify the modeling and computational implications of such generalization. Several theoretical and methodological issues arise in thiscase that motivate this contribution. The problem is formulated in scenario form as a multistage stochastic recourse program and we study
two possible generalization of ISD principles in which we either enforce ISD constraints on each stage, independently from the scenario
tree process evolution, or we do so conditionally along the scenario
tree. We present a comprehensive set of computational results to show
that, depending on the benchmark investment policy and the adopted
ISD formulation, stochastic dominance conditions of first or second
order can be enforced dynamically over a range of possible values of
the reference point and their solution carries a specific rationale.

Jörn Sass
In financial markets simple portfolio strategies often outperform moresophisticated optimized ones. E.g., in a one-period setting the equal
weight or 1/N-strategy often provides more stable results than meanvariance-optimal strategies. This is due to the estimation error for themean and can be rigorously explained by showing that for increasing
uncertainty on the means the equal weight strategy becomes optimal,
which is due to its robustness. In earlier work, we extended this result
to continuous-time strategies in a multivariate Black-Scholes type market. To this end we derived optimal trading strategies for maximizing
expected utility of terminal wealth under CRRA utility when we have
Knightian uncertainty on the drift, meaning that the only informationis that the drift parameter lies in an uncertainty set. The investor takes
this into account by considering the worst possible drift within thisset. We showed that indeed a uniform strategy is asymptotically optimal when uncertainty increases. In this talk we focus on a financial
market with a stochastic drift process and possibly uncertainty. We
combine the worst-case approach with filtering techniques by defining an ellipsoidal uncertainty set based on the filters. We demonstratethat investors need to choose a robust strategy to profit from additional
information.

3 - Expert Opinions and Power Utility Maximization in aMarket with Partially Observable Gaussian Drift

Ralf Wunderlich
We consider optimal trading strategies in a financial market in whichstock returns depend on a hidden Gaussian mean reverting drift process. Investors obtain information on that drift by observing stock
returns. Moreover, expert opinions in the form of signals about thecurrent state of the drift arriving at fixed and known dates are includedin the analysis.
Drift estimates are based on Kalman filter techniques. They are used
to transform a power utility maximization problem under partial information into an optimization problem under full information where the MC-58
Monday, 12:30-14:00 - Room: S07 (building: 101)

Column Generation for Vehicle Routing
Stream: VeRoLog - Vehicle Routing and Logistics
Invited session
Chair: Jens Lysgaard
1 - A Column Generation Approach for the Routing of Electricity Technicians

Elise Bangerter, David Schindl, Meritxell Pacheco Paneque
The maintenance of an electricity distribution network involves numerous daily technical interventions. In this problem, we are given a set ofinterventions each with associated time windows, location, necessary
skills and duration, as well as a set of teams of technicians with associated set of skills. We need to find feasible routes on the interventions
for each team, considering the time windows and skills, and ensurethat each team returns to its departure depot before the end of the day.
The primary objective is to maximize the total duration of completedinterventions and as a secondary objective, we aim to minimize theoverall routing cost. This problem can be formulated as a capacitatedvehicle routing problem with time windows. Due to the large numberof teams and interventions, this results in a large-scale optimization
problem, and its operational nature limits the time available for exact
solving. Here, we propose a column generation approach where one
subproblem per vehicle has to be solved and each potential route ofa vehicle is considered as a new column in the master problem. To
generate these routes, we rely on dynamic programming. Real-world
instances from EDF (Electricité de France) of historical technicians’

115

MC-59EURO 2024 - Copenhagen

interventions will be used to evaluate the effectiveness of the proposed
methods.

2 - Mobile Production Vehicle Routing Problem

Min Wen, Stefan Ropke, Yu Wang
In this presentation, we will talk about the Mobile Production Vehicle
Routing Problem, a new variant of the Vehicle Routing Problem. The
objective of this problem is to determine a synchronized production
and delivery schedule to minimize travel and delay costs. Previous
work shows that CPLEX is unable to provide optimal solutions formost instances with more than 15 customers. This work aims to develop an exact algorithm to find optimal solutions for larger instances.
We develop a branch-and-price algorithm for solving the problem. To
accelerate the column generation procedure, an efficient dominancetest and three heuristic pricing algorithms are developed to quickly
find the columns with negative reduced costs for the pricing problem.
Computational results on benchmark instances will be presented.

3 - On the Separation of Rank-1 Cuts in the VRPSolver
Framework

Eduardo Queiroga, Teobaldo Bulhões, Anand Subramanian,
Eduardo Uchoa
As one of the most extensively studied problems in operations research, the Vehicle Routing Problem (VRP) attracts considerable academic and practical interest. The state-of-the-art exact approaches for
VRPs rely on the integration of cut and column generation within
Branch-Cut-and-Price (BCP) algorithms. A critical component ofmodern BCP algorithms is the separation of non-robust cuts, such as
rank-1 cuts with limited memory, which are considered strong but affect the structure of the pricing subproblem. In this research, we investigate algorithms for separating rank-1 cuts aimed at being both effective and efficient. The study involved implementing these algorithms
within the VRPSolver BCP framework and comparing its performanceagainst the existing local search-based separation method. Preliminary
results of this study for the classic Capacitated Vehicle Routing Problem (CVRP) will be presented.

4 - A Set Partitioning Model for MinMax Vehicle Routing

Jens Lysgaard, Sune Lauth Gadegaard
This paper proposes a new approach to modeling of vehicle routing
problems with a MinMax objective. At the core of the approach, a Set
Partitioning model is formulated and used for minimizing infeasibility of the considered vehicle routing problem for a given upper boundon the objective value. Generally, the optimal value of the MinMax
objective is obtained as the smallest upper bound for which the Set
Partitioning model obtains a total infeasibility of zero. Computational
experience shows very promising results.

 MC-59
Monday, 12:30-14:00 - Room: S08 (building: 101)

Pricing and learning 2
Stream: Pricing and Revenue Management
Invited session
Chair: Akshitha Suryapeta
1 - Unraveling Information Traps: Consumer Learning in
Assortment Planning with Online Reviews

Denis Saure
In this work, we study consumer learning in assortment planning, exploring the impact of online reviews on choice, convergence, and bias.
In a utility-maximizing customer scenario, we analyze feedback mechanisms used by retailers and consumers to estimate product qualities.
When consumers are myopic, estimating mean qualities without considering selection bias, product quality estimates converge with an

116

asymptotic bias. This extends findings in the literature for assortment
planning settings. Forward-looking consumers, anticipating and accounting for bias, prevent quality estimates from converging, hindering
consumer learning. However, when a subset remains myopic, forwardlooking consumers can disentangle bias, enabling convergence in quality estimates for both types. Illustrating implications for assortment
planning, we show how incorporating consumer feedback influences
revenue and consumer surplus. In online commerce, real-time consumer information allows personalized assortment decisions based onbrowsing history. Our findings illuminate the interplay between consumer learning, online reviews, and assortment planning, offering insights for navigating the e-commerce landscape.

2 - Using machine learning for constraint learning in multiproduct pricing optimization

Luis Aburto, Vicente Ahumada
Multiproduct pricing optimization is a challenging process. Calibrating demand models based on transactional data tends to produce bias
in cross-elasticity effects, mainly due to endogeneity issues, getting
nonsensical or extreme solutions in the optimization process. We explore and compare different supervised and unsupervised methods toextract constraints from the data to formulate a robust feasible space
for the pricing optimization procedure. These constraints or pricing
rules uncover latent business rules where relationships between product prices happen. Also, they can identify price solutions correlatedwith good product category performance. We apply our methodology
using the orange juice category dataset for different supermarket stores,
obtaining robust improvements from 15% to 40% in expected category
profit. Some of the methods used to extract pricing rules from data areassociation rules, SVM classifiers, robust optimization, and ellipsoidal
kernels.

3 - Integrating Predictive and Prescriptive Analytics for Assortment Optimization - a Machine Learning-based Approach

Niloufar Sadeghi, Siamak Khayyati, Cornelia Schoen
Product line design is one of the key problems that firms need tosolve. Therefore, they employ techniques to predict customer choice
behaviour and optimize the performance of the product assortment they
aim to offer.In most cases, the prediction problem and the optimization
problem are defined as separate problems and solved in a sequential
manner where first, the choice model is estimated and second, the assortment optimization problem is solved, using the choice model parameters as an input. Integrating estimation and optimization provides
an opportunity for the empirical model to be more accurate where it
matters - close to the optimal assortment. We develop a MILP formulation that is able to solve the two problems jointly. Using numericalexperiments, we analyse under what conditions and to which extent the
integrated approach is superior to the sequential approach.

4 - Estimating customer choice model and discrete dynamic pricing in the Online Bus Market

Akshitha Suryapeta, Suresh Jakhar
The Indian outstation tourism sector, valued at $66 billion, sees substantial bus travel (50% of outstation journeys). The online bus market,
currently at 5% ($1.2 billion), is projected to reach 9.5% by FY25. Oneof the driving factors for this growth is the shift in customer behavior.
Hence, our research focuses on studying customer behavior. The research objective is to evaluate the applicability of the choice modelin predicting bus booking patterns and further solve discrete dynamic
pricing problem. The primary hurdle is constructing an appropriate
choice model, which involves various options that a customer contemplates while making their decision and identifying relevant attributes
influencing their choices. To model choice behavior, the widely used
Multinomial Logit model is adopted. The second challenge lies in theincomplete available data, with access only to booking records andno insight into browsing behaviors. To address this, we utilize an
Expectation-Maximization (EM) method. In the first stage, we apply this estimation approach to simulated data, and then to the real bus
booking data. The accuracy of our estimation is verified through calculations of asymptotic standard errors and goodness-of-fit tests, comparing the observed and expected booking counts predicted by our model.

EURO 2024 - Copenhagen
The estimates are then used to solve the discrete dynamic pricing problem of the choice-based network RM model.

MC-61

3 - Solving the Stochastic Resource-Constrained Project
Scheduling Problem with Flexible Resource Profiles using Sample Average Approximation

Ann-Kathrin Mendl, Julia Rieck MC-60
Monday, 12:30-14:00 - Room: S09 (building: 101)

Project scheduling under uncertainty
Stream: Project Management and Scheduling
Invited session
Chair: Erik Demeulemeester
1 - A purely buffer-based approach to the proactive andreactive resource-constrained project scheduling problem

Yanfei Chen, Erik Demeulemeester, Jannik Matuschke
The proactive and reactive resource-constrained project schedulingproblem (PR-RCPSP), introduced by Davari and Demeulemeester(2019b), addresses uncertainties in real-world projects in a novel manner. A PR-RCPSP solution is a proactive and reactive policy (PRpolicy) that includes the baseline schedule and foresees potential transitions (reactions) to other schedules. In their subsequent work, Davariand Demeulemeester (2019a) categorize reactions into selection-based
and buffer-based. While both classes are crucial, buffer-based reactions hold greater significance. Recognizing the theoretical importanceand managerial value of the buffer-based reactions, we solve the PRRCPSP using a purely buffer-based approach. To achieve this goal,
we propose a method for generating a sufficient selection, along withproactive and reactive procedures, among other novel heuristics. We
construct schedule pools from which we obtain optimal PR-policies.
Experimental results show that our solutions outperform the best existing alternatives, particularly when reactions are less preferred, allwhile significantly reducing computational time. Remarkably, ourpurely buffer-based approach exhibits exceptional performance in instances featured by high resource factors or low resource strengths, situations where solving their deterministic counterparts presents greater
challenges. Our results offer valuable managerial insights for navigating projects under uncertainty.

2 - Solving an integrated project and personnel schedulingproblem using Dantzig-Wolfe decomposition

In today’s competitive environment with short product life cycles andhigh customer expectations, working in projects is a predominant formof work organization. As part of the planning process, individual activities of a project are scheduled according to an objective function(minimization of the project duration) and the available renewable resources (e.g., employees) are used efficiently to carry out the activities.
The resource-constrained project scheduling problem (RCPSP), also
known as the standard model for project scheduling problems, assumes
a constant activity duration and a constant resource requirement. Uncertainties that lie in the future are ignored. This entails the risk thatconsiderable disruptions may occur in the schedule and the generated
solutions are too restrictive for many practical applications. In orderto account for uncertainties, stochastic aspects and flexible resource
profiles are integrated into our problem.
Based on a chance-constrained model formulation, a sample averageapproximation model for flexible and stochastic project schedulingproblems is presented. Small and medium instances are solved in a
performance analysis. Furthermore, a suitable serial schedule generation scheme is developed, which is also applicable for larger instances.

4 - The resource constrained project scheduling problem
with stability constraints.

Maria Elena Bruni, Oncu Hazir
In this research, we study a variant of the project scheduling problem
with resource constraints (RCPSP) under uncertainty. In reality, infact, project parameters are subject to considerable uncertainty, which
generally leads to numerous schedule disruptions. Most optimization
approaches focus on the quality robustness and only evaluate the relevance of the solutions through the objective function value. However,it can be more important to optimize the solution robustness, concerning the structural similarities between the nominal solution and thedisrupted solutions. This translates into the need of developing robust schedules that can absorb disruptions as smoothly as possible and,once the uncertainty is revealed, find an alternative solution which is as
similar as possible to the nominal solution. Following this stream, inthis talk, we present a two-stage stochastic programming formulation
for the RCPSP where in the first stage, scheduling decisions are taken
considering the information available on the project parameters. In thesecond stage, the schedule is completely re-optimized leading to a new
set of starting times, induced precedence relations and resource flows.
We will incorporate solution robustness through stability constraints
added into the model.

Brede Sørøy, Henrik Andersson, Anders N. Gullhav
We are considering a scheduling problem which consists of bothscheduling activities in one or more projects, and the allocation of personnel and transportable equipment demanded to complete the activities. The activities are multi-modal and discretely preemptive, andthere are precedence constraints that need to be respected.
Due to the challenges of scalability and efficiency when scheduling using direct mixed-integer programming (MIP) formulation and a MIP
solver, we investigate the benefits of decomposing the integrated problem using a Dantzig-Wolfe reformulation. This method decomposes
the problem into a master problem and one or several subproblems tofind improving solutions.
The subproblem involves scheduling the activities of individual
projects, and its solution is fed to the master problem as a new column.
The master problem uses the project-schedule columns generated by
the subproblems to find the best project schedules to use so that personnel and equipment are allocated the most cost-efficiently. By decomposing the problem into project-specific subproblems that are quick tosolve, the efficiency of the overall solution method is significantly increased. Our approach demonstrates promising performance in termsof computational time and solution quality when compared to solving
the MIP formulation directly using a MIP solver.

 MC-61
Monday, 12:30-14:00 - Room: S10 (building: 101)

Hub Location
Stream: Locational Analysis
Invited session
Chair: Sibel A. Alumur
1 - A hybrid Lagrangian relaxation and Benders decomposition algorithm to solve stochastic hub location problems with profit maximization

Dung Tran, Nader Azizi, Thomas Archibald
This research addresses the profit maximization and pricing in a capacitated single assignment hub location problem. We assume thatthe demand between pairs of nodes in the network is price-sensitiveand it is subject to uncertainty. The problem is formulated as a twostage stochastic programming model, and a hybrid algorithm combining Lagrangian relaxation and Benders decomposition is developed to117MC-62EURO 2024 - Copenhagen

efficiently obtain optimal solutions. We also discuss some acceleration techniques to improve the performance of the proposed algorithm.
Some numerical results and managerial insights are presented.

derive insights into how a network that results from a merger should
be redesigned. We also study how uncertainty impacts the structure of
the redesigned networks.

2 - Hub location problem with a mixed green fleet

Okan Dukkanci, Achim Koberstein, James Campbell
This talk will introduce a novel hub location problem with a mixed fleetof diesel-based conventional vehicles and alternative fuel vehicles including electric vehicles and unmanned aerial vehicles. Compared todiesel-based vehicles, the driving range of alternative fuel vehicles interms of travel distance is more limited. In addition to the driving
range, the proposed problem also captures the capacity limitations ofthese vehicles. The objective function of the problem minimizes thetotal cost including the fixed cost of locating hubs and the transportation cost of different vehicle types. The latter cost component consistsof the recharging cost associated with energy (fuel) consumption depending on the traveled distance, the recharging fee costs depending on
the number of visits to recharging stations, and also the waiting costfor drivers and vehicles due to the recharging time of vehicles. We
develop a mixed-integer linear programming formulation with preprocessing rules. Different extensions of the problem including a multiple
allocation version and solution methods will be discussed. The talkwill present computational results over the CAB, TR, and the newly
introduced German datasets to analyze the impact of a mixed fleet ofrange and capacity-limited vehicles on optimal hub location and allocation decisions.

3 - A stochastic hub network design and protection problem

Armin Lüer-Villagra, Gonzalo Mendez-Vogel, VladimirMarianov, Pamela Alejandra Bustamante Faúndez
The design of resilient hub-and-spoke networks has been previously
explored in the literature, but in most cases, both the attack and defense strategies are deterministic and known by both the attacker andthe defender. We study the problem faced by a company that aims todesign a hub-and-spoke network despite the existence of one or morepotential threats to the network operation. The company is tasked witha complex decision-making process. It must strategically determine
the locations of its hubs, meticulously design the resulting hub-andspoke network, and judiciously select which infrastructure to protect,
all while considering potential threats to the network operation. The
company faces one or more attackers, whose actions could potentiallydegrade the network operation. Importantly, the company operates inan environment of uncertainty, as it does not possess knowledge ofthe actual attack strategy, but has prepared several attack scenarios.
The company’s primary objective is to minimize design and protection costs. In contrast, the attacker’s objective is to maximize them.
We expect that the resulting models will provide valuable insights on
how to design resilient hub-and-spoke networks, together with tools toquantify the impact of using heuristic protection strategies.

4 - Strategic Expansion of Freight Transportation Hub Networks under Uncertainty

Sibel A. Alumur, Hao Li, Gita Taherkhani, Mike Hewitt
We focus on freight transportation carriers that transport shipments thatare small relative to vehicle capacity and incur transportation costs thatexhibit economies of scale. To achieve profitability, such carriers routeshipments through a network of hubs. The locations of a carrier’s hubs
dictate the customer markets it can profitably serve. We consider acarrier that seeks to increase its profitability by growing its customer
base. To do so, it seeks to expand its network into new regions bymerging with carriers that already operate in those regions. We focus
on how, and by how much, the network that results from such a merger
should be redesigned to maximize profitability. To study this issue
we present deterministic and stochastic profit-maximizing hub location models that capture the interaction between economies of scale
and profitability. The models differ in whether and how they model therecognition of uncertainty in shipment sizes in the decision-making
process. We perform a case study based on operations from a multiregional less-than-truckload freight transportation carrier to derive insights into the profitability of different redesign strategies. We analyze
the networks prescribed by solutions to these optimization models to118 MC-62
Monday, 12:30-14:00 - Room: S12 (building: 101)

Port-Hinterland Transportation & Corridors
Stream: OR in Port Operations
Invited session
Chair: Zhaojie Xue
Chair: Jintao You
1 - Platform-based stable truck matching problem withtrailer-swapping mode

Wenxiang Peng, Zhaojie Xue, Xiangsheng Chen, Yubin Liao
This study investigates the platform-based stable truck-matching problem with trailer-swapping mode (STMP-TSM). The TSM is a novel
collaborative transportation approach in which trucks participate intrailer swapping, significantly decreasing the rate of empty trucks andreducing transportation costs. In the STMP-TSM, a platform delivers
a trailer-swapping scheme that satisfies all participating trucks. Correspondingly, an integer linear programming model is developed tomaximize the total truck utility of the STMP-TSM. A specific preference list based on a chain-data structure is meticulously constructedto obtain a stable matching scheme. The preference list enables moregeneralized truck matching. In addition, a series of acceleration strategies is proposed to expedite the generation of a preference list while
effectively reducing its length. An iterative preference-list-trim heuristic algorithm is designed, which strategically trims chains in the preference list to solve the STMP-TSM efficiently. As a benchmark, aninteger linear programming model is developed based on the preference list to solve the truck-matching problem using the TSM. Finally,a series of numerical experiments are conducted to evaluate the performance of the proposed algorithm, assess the practicality of the TSM,
and analyze the influences of the key parameters.

2 - An exact algorithm for the multi-trip container drayage
problem with truck platooning

Jintao You
Container Drayage Problem (CDP) refers to the optimization problemof routing and scheduling a set of container trucks around a container
terminal. Conventionally, a driver is stuck to one container truck andallowed to perform multiple trips (multi-trip) to the terminal within
their working time. The recent development of automation technologies enables semi-autonomous trucks to follow the leading humandriven truck as a platoon on the road; therefore, truck platooning cansave human labor and reduce the fuel cost of following trucks throughaero- dynamic drag reduction. In this paper, we study a multi-trip container drayage problem with truck platooning (MT-CDP-TP), wheremulti-trip, truck platooning, and fuel cost reduction are simultaneously considered in a CDP. Despite the operational benefits broughtby the MT-CDP- TP, the problem is challenging to solve due to its NPhardness when formulated as a multi-trip pickup and delivery problem with load-dependent cost. We propose a Branch-and-Price-andCut (BPC) algorithm, with a route-based set partitioning model andtight linear relaxations, to yield the exact solutions. Valid inequalities are generated based on a graph structure, where each node represents a feasible route, and each arc stands for the conflict between tworoutes. Moreover, we design a tailored pulse propagation algorithmwith novel pruning procedures based on the dual information from themaster problem and valid inequalities to solve the pricing problem eff

EURO 2024 - Copenhagen
3 - The Network Design and Refueling Station Location
Problem for Green Maritime Corridors and Emission
Trading

Xiaohuan Lyu, Frederik Schulte
The maritime shipping industry, responsible for 3% of global greenhouse gas emissions, is facing increasing pressure to decarbonization
and ultimately zero emission due to the escalating threat of climatechange. This urgent need has inspired the conceptualization of green
maritime corridors—a designated network of shipping routes, ports,
and associated infrastructure designed to advocate for shipping practices with low or zero emissions. Initial empirical studies have underscored the potential of green maritime corridors in advancing decarbonization. However, the shipping network and the requisite refueling stations to support alternative-fuel ships within the network
remain undeveloped. Additionally, establishing green maritime corridors requires collaborative efforts from multiple stakeholders, whereinshipping lines, port operators, and governments are pivotal contributors, jointly ensuring the safeguarding of their respective interests. We
first define the corridor network design and refueling station locationand propose an optimization model to maximize the overall benefits,with and without an emission trading system such as EU ETS. We
further design a cooperative game for profit sharing among involved
stakeholders such that they are incentivized to cooperate in establishing the corridors. In this way, we provide a first optimization approachfor green maritime corridor design, guiding policymakers and industry
players on the way to successful implementations.

4 - Port Supply Chain Network Design for Customs Operations

Luis Ascencio, Claudia Caballini, Rosa G. González-Ramírez,Maria D. Gracia, Julio Mar-Ortiz
Coordination of inland flows with the hinterland is a difficult task, andlack of it results in long truck queues at the gates and unsatisfactorily
conditions for truck drivers. In addition, the lack of space at ports andthe limited resources demand efficient container handling operations toprovide the required service levels to the ships and external trucks fortransferring cargo. One critical operation related to container (or cargoin general) handling has to do with customs checks and inspections.
The massive flow of cargo that is being handled at ports provides anopportunity for organized crime infiltration. For this reason, Customs
plays an important role in securing international trade operations andstopping the flow of illicit products. However, inspections are timeconsuming and costly, and container delays at an inspection facility
can cause supply chain disruptions. Hence, one critical task for Customs and Port Authorities is to define strategies and operational policies to perform this operation, in which logistics infrastructure plays acritical role. In this paper, we propose a supply chain network design
framework to locate different Customs facilities where the required inspections will be performed, with the aim of having more fluid operations in the dispatching of import containers. We assume that rather
than having each port terminal its own Customs facility, the authorities
have decided to locate external facilities to reduce container delays.

 MC-63
Monday, 12:30-14:00 - Room: S14 (building: 101)Natural Resource Management andCommodity Markets
Stream: OR in Banking, Finance and Insurance: New
Tools for Risk Management
Invited session
Chair: Isabel Figuerola-Ferretti
1 - EU ETS Market Expectations and Rational Bubbles

Christoph Wegener

MC-63

Concerns about a price bubble within the European Union Emissions
Trading System (EU ETS) emerged during the third trading period.
We argue that bubble tests based on costs for switching from cheap,
polluting to costly, clean energy sources is restricted to situations ofmarket certainty. This limitation is unrealistic, considering the ongoing CO2 reduction measures. Additionally, establishing fundamental
value through switching costs lacks a singular approach, leading to inconclusive findings. We propose a robust approach to infer bubbles
in the EU ETS. Empirical findings do not support the presence of abubble in the third or fourth trading period.

2 - Water as a commodity in hydropower generation

Isabel Figuerola-Ferretti, Eduardo Schwartz
The increased impact of extreme weather events and draughts has
prompted the rapid growth of the water market. This paper analyzes
the optimal operation of a reservoir that generates electricity and manages the water by trading water rights. We extend the framework introduced in a recent paper by Figuerola Ferretti, Schwartz, and Segarra(2023) to include the water price process in a model that accounts forwater inventory and the electricity price as two independent price processes. The model is implemented under the stochastic optimal control
approach and calibrated using monthly data for a reservoir in the estate of California. The water price dynamics includes the dependence
on the California Drought Severity Index. Results show that the underlying water and electricity price dynamics exhibit a high degree of
uncertainty and seasonality. They also demonstrate that, under the calibrated model parameters on average for the parameters used, around
25% of the revenue generated by the reservoir arises from the revenue
obtained from selling water rights. The water contribution to revenue
generation would increase under enhanced severity of climate change.

3 - Risk Management for Aquaculture Businesses in the
Presence of Multiple Risk Sources

Christian Oliver Ewald
We will provide an overview about current modeling and analysis relevant to the financial, biological and ESG related risks in the optimal
management of aquaculture businesses with partiuclar focus on the
Norwegian salmon farming industry. These rsults are based on thefollowing research papers:
Ewald, C.O. and Kamm, K. (2024) On the Impact of Biological Risk in Aquaculture Valuation and Decision Making.
https://doi.org/10.48550/arXiv.2402.08686
Ewald, C.O. and Kamm, K. (2024) On the impact of feeding cost risk
in aquaculturevaluation and decision making. Accepted and Published
Online Quantitative FinanceEwald, C.O. and Zou, Y. (2021) Analytic formulas for futures and options for a linearquadratic jump diffusion model with seasonal stochastic volatility and convenience yield: do fish jump? European Journalof Operational Research, 294(12)Ewald, C.O. and Ouyang, R. (2017) An analysis of the fish pool market
in the contextof seasonality and stochastic convenience yield. Marine
Resource Economics, 32(4), pp.431-449.
Ewald, C.O. , Ouyang, R. and Siu, T. K. (2017) On the market consistent valuation of fish farms: using the real option approach and salmon
futures. American Journal ofAgricultural Economics, 99(1), pp. 207224.as well as research that is currently in development.

4 - The Role of Investor Sentiment in Commodity Price
Behavior: Clarifying Evidence Based On Time-Varying
Causality Tests

Roderick McCrorie, Mario Lupoli
Time-varying causality tests robust to mild explosivity, bubbles andcrashes are used to provide a new, evidence-based perspective on commodity financialization, where money flows associated with index investors are conjectured to predict changes in commodity futures prices.
We show that Singleton’s (2014, Management Science) finding thatmoney flows helped predict changes in crude oil futures prices is almost entirely attributable to the atypical behaviour of oil prices aroundthe Global Financial Crisis, an aspect we show a standard Granger
causality test applied over the whole sample is unable to pick up. Our
approach helps settle what in the literature was an important but controversial finding. More generally, we corroborate some of the recent

119MC-64EURO 2024 - Copenhagen

results by Gilbert (2018) suggesting there is some causality between
index investment and non-ferrous metals and some agricultural commodities, albeit commodity-specific and in a milder form concentrated
in the aftermath of the GFC. The robustness of the test herein offerssome clarity to an academic literature that has been settling against theefficacy and wider applicability of Singleton’s results but has not been
persuasive enough to see the same views become pervasive among finance practitioners.

 MC-64
Monday, 12:30-14:00 - Room: S16 (building: 101)

Routing Unmanned Aerial Vehicles 2
Stream: VeRoLog - Vehicle Routing and Logistics
Invited session
Chair: Maria José Pinto
1 - Vehicle Routing Problem With Drones, Private Fleet andCommon Carrier

Shouvik Bhattacharyya, Shalabh Singh, Sonia Singh
This article aims to analyse two distinct facets of last-mile delivery,
namely drones and common carriers. Urban delivery is witnessing asurge in popularity of drones due to their ability to outpace traffic andexhibit minimal carbon emissions. Nevertheless, there are situations
where a logistic provider may need to subcontract certain clients to athird party, commonly referred to as a common carrier, due to manyreasons, including a faster turnaround time or a desire to reduce carbon emissions. The private fleet consists of multiple trucks and drones,
and they serve customers independently from the depot. The common
carrier informs the logistic carrier beforehand of the estimated delivery time and carbon emissions for all clients. The problem attempts
to investigate the relationship between the two objectives, ’makespan’
and ’carbon emissions’ of the whole network. A bi-objective Mixed
Integer Linear Programming (MILP) formulation is proposed. An iterative algorithm is designed for small to medium-sized problems thatcan sequentially generate pareto-optimal solutions. In addition, the results of a large-scale problem will be evaluated using a heuristic basedon neighbourhood local search. Performing a sensitivity analysis on
the parameters of the common carrier yielded some intriguing insights
for the managers.

2 - Multiple Traveling Salesperson Problem With Drones:
General Variable Neighborhood Search Approach

Selin Özpeynirci, Baybars İbroşka, Ozgur Ozpeynirci
A key factor to consider in the development of new technologies in a
number of fields is the use of unmanned aerial vehicles (UAV). This
rapidly developing technology is used in military, communication,
health, mapping, agriculture and transportation fields. The importanceof cargo transportation has grown due to the growth of e-commerce.
Currently, with advancing technology, and the effect of the pandemic,purchases are increasingly made over the internet. For cargo transporters, this situation leads to an increase in the number of destination
points, in distances traveled, and in the delivery frequency, and a decrease in the package sizes. As a result, the planning of transportation
has become increasingly complex. One solution is to make greateruse of UAVs in this sector, and to reduce reliance on trucks throughappropriate planning. This involves two aspects: the UAV delivering
to a point, while the cargo truck delivers to a separate point. In thisstudy, we consider a multiple traveling salesperson problem simultaneously using multiple trucks and UAVs for package delivery. We develop a general variable neighborhood search algorithm, and compare
the results with the existing studies in the literature. Computational
experiments show that our approach is able to find highly satisfactory
solutions in reasonable time, and outperforms the existing methods interms of best solution, average solution and solution time in majorityof the instances.

120

3 - Drone-Assisted Delivery Optimization: Balancing Time
and Cost With Multiple Truck Routes for Efficient Service

Shalabh Singh, Sonia Singh
Conventional last-mile delivery logistical systems remain overburdened despite large-scale optimization. This paper explores the usageof drones to complement road-based last-mile delivery systems to ensure more time-cost efficiency. The work also extends the truck shipment routes to the case of real-time multiple routes. A bi-objective
mixed integer linear program is introduced, accompanied by an exact
solution methodology utilizing multi-choice conic goal programming.
This approach efficiently solves smaller-sized problems. For larger instances, a three-step iterative heuristics is proposed. Exhaustive computational analysis and an illustrative case suggest that substantial savings in both time and cost can be achieved compared to traditionalframeworks.

4 - An Air Route Planning Under Radioactive Plume Scenarios With Temporal Evolution

Maria José Pinto, João Camilo da Silva, Claudio A. Federico,
Tássio Côrtes Cavalcante, Ana Cláudia Iwazaki, Monica
Maria De Marchi
This work aims to support the decision-making process in the scopeof Chemical, Biological, Radiological and Nuclear (CBRN) Defense,
looking for the minimum radiation accumulated dose along the routefor an aerial vehicle involved in rescue or transportation missions on
situations of exposure to ionizing radiation by overflight within radioactive or nuclear plumes. A previous methodology was proposed
to address the level of the radiation dose, the autonomy of the vehicle
and the obstacles, when the vehicle is flying at low altitude or in urban
areas where buildings or mountains are obstacles to the navigation.
In this work, this methodology is improved by also taking the temporal evolution of the radiation dose into account, since the radiationdose may be different at the same location, depending on the specificmoment it was measured due to the plume evolution. The proposed
methodology will be presented and applied to illustrative instances.

EURO 2024 - Copenhagen

Monday, 14:30-16:00
 MD-01
Monday, 14:30-16:00 - Room: Sportshallen (building: 101)

Pedro Amorim
Stream: Keynotes
Keynote session
Chair: Maria Antónia Carravilla
1 - Empirically driven operations research for improving retail operations

Pedro Amorim
Retail is often the last supply chain node a product/service ’visits’ before reaching the end customer. As a result, customers’ behavior towards different incentives is paramount to managing retail operations,which combine elements of customer relationship management, supply chain and inventory planning, product distribution and logistics,
pricing, and store management. Consequently, we must acknowledge
that it may be hard to adhere to the common assumptions of operations
research in this context – that all relevant variables and constraints canbe identified and accounted for to achieve the ideal outcome. In thistalk, we’ll give a framework and examples for conducting empiricallydriven operations research to improve retail operations. The "empirically driven" approach has two main components: find an empiricaljustification of model assumptions and parameters and perform an empirical assessment of model results and insights.

 MD-02
Monday, 14:30-16:00 - Room: Glassalen (building: 101)

EPOCG
Stream: EURO Prize for OR for the Common GoodAward Competition session
Chair: Cathal MacSwiney Brugha
1 - Rethinking Municipal Flood Mitigation: A Real-World
Application of Operations Research Methods for Improving Climate Resilience

Jan Boeckmann, Clemens Thielen
Adapting to the consequences of climate change is undeniably one ofthe paramount challenges of our time. Among these consequences,intense heavy rain events pose a significant threat by endangering human lives and causing severe damage to infrastructure through flash
floods. Recent events such as the heavy rainfall and flooding in Western Europe in 2021, Pakistan 2022, and Libya 2023, have once morecorroborated the enormous potential for damage and underscore theimportance of developing high-quality mitigation concepts in order toreduce the impact of these events on communities and their infrastructure. Such mitigation concepts, however, are typically elaborated
solely based on simulations resulting in a time-consuming trial-anderror approach that provides no quality guarantee for the obtained solutions.
In this talk, an approach that combines mixed-integer programming
and a combinatorial graph algorithm for the automatic computation ofhigh-quality flood mitigation concepts consisting of retention basins,
embankments, and ditches is presented. This, to the best of our knowledge, is the first automated decision support approach for this critical application that scales well enough to be applied to realistic scenarios. Apart from the developed optimization algorithms, the talkalso presents a comprehensive web-application that has been designed
to make the algorithms available to a broad variety of users beyond

MD-02

the OR community. The work has been conducted within the project"Incentive Systems for Municipal Flood Mitigation" in an interdisciplinary team consisting of two academic research groups, an engineering office, two municipalities, and a municipal water association. By
now, the resulting software has been used in practice by over 30 institutions from all over Germany reaching from local authorities overengineering offices to academic research groups. The empowerment ofusers with limited technical knowledge to design high-quality precautionary measures for pluvial flash floods sets the developed software
apart from state-of-the-art approaches and marks a significant innovation showcasing the OR community’s contribution to societally vital
topics.

2 - The potential of renewable electricity in isolated grids:
The case of Israel in 2050.

Ohad Eisenhandler, Gur Mittleman, Moshe Tshuva, Ronen
Eran, Lev Zhivin, Yossi Luzon
The global urgency to mitigate climate change and reduce greenhousegas emissions has intensified efforts to transition towards renewableenergy sources. Isolated electrical grids, characterized by their detachment from larger networked systems, present unique challenges
in this transition. This study, focusing on Israel’s grid as a model forthe year 2050, aims to address these challenges by employing a holistic approach that integrates advanced modeling techniques to assess
the potential of renewable energy integration effectively. Employing
a comprehensive methodology, this research incorporates a detailed
Performance Model (PM) and an Energy Management System (EMS)
model. The PM evaluates the hourly electrical output from diverse renewable sources, including solar (photovoltaics and agrivoltaics), onshore and offshore wind, sea waves, and organic waste, taking into
account Israel’s specific geographic and climatic conditions. The EMS
model then optimizes the alignment between this renewable energy
supply, the anticipated demand, and necessary storage solutions. Thisapproach includes extensive data collection on renewable energy potentials, area mapping for solar installations, and evaluations of windand sea wave energy potentials based on geographical and meteorological data. The innovative aspect of considering organic waste adds
an additional layer of resource diversification. The findings reveal asignificant potential for Israel to increase its renewable energy shareto between 80.1% and 99.5% by the year 2050, contingent upon theimplementation scenario. Achieving such a high level of renewable
integration hinges on developing substantial energy storage capacities. This research identifies a feasible combination of the Vehicle
to Grid (V2G) concept, stationary batteries, and pumped hydroelectric plants as viable solutions for meeting the projected total electricdemand of 183.3 TWh in 2050, with renewable resources providing
an installed capacity of 180.6 GW. A key outcome of the study isthe emphasis on agrivoltaics and the V2G concept as instrumental inenhancing the grid’s renewable capacity. These innovations not only
conserve land resources but also offer the requisite storage to manage the inherent variability of renewable energy sources. Moreover,the study underscores the importance of adopting a comprehensive energy management approach capable of accommodating the dynamic
nature of renewable energy production and consumption. This researchcontributes to the broader discourse on renewable energy potentials
in isolated grids, providing critical insights for policymakers, energy
planners, and researchers. It underscores the feasibility and necessityof employing advanced modeling techniques to evaluate and optimize
the integration of renewable energy sources, highlighting the need forsignificant storage capacities and innovative solutions like agrivoltaics
and V2G. The implications of this study for energy policy and planning are profound, especially for regions with isolated grids similar to
Israel. It encourages policymakers and energy planners to consider the
methodologies and findings presented to make informed decisions regarding renewable energy investments and infrastructure development.
Furthermore, the research highlights the pivotal role of innovation inenergy storage and management technologies in maximizing the potential of renewable energy sources. In conclusion, the integration ofrenewable energy into isolated grids, exemplified by the case study ofIsrael in 2050, is both a viable and essential strategy for moving towards a sustainable energy future. This study lays the groundwork for
future research to explore emerging technologies and storage solutions,ensuring that renewable energy can adequately meet growing demands
while addressing environmental concerns.

121MD-03EURO 2024 - Copenhagen3 - Formulation of Strategy Towards the ARCTIC in termsof Search and Rescue - Needs, Innovations, and AI/OR
based solutions

Monday, 14:30-16:00 - Room: 1005 (building: 202)

Ashraf Labib, Dylan Jones(Deep) Reinforcement Learning for
Combinatorial Optimization 2

This seminar presents a usage of operational research methodologies
during the ARCSAR (arcsar.eu) and AI-ARC EU-H2020 projects forenhancing safety and avoiding environmental damage in the Arctic andNorth Atlantic (ANA) maritime domain. The ARCSAR and AI-ARC(ai-arc.eu) projects arose due to concerns regarding the effects of increased maritime activity in the multi-national ANA region, causedby the melting ice due to climate change. The authors principal focus in the ARCSAR project has been working with search and rescue(SAR) and marine environmental response (MER) practitioners andother ANA stakeholders to classify and prioritise innovation needs.
The overall aim is to lower the likelihood and social, environmental
and economic consequences of large-scale incidents in ANA waters
such as (i) a cruise ship sinking, (ii) a significant oil spill or (iii) a nuclear leakage. The AI-ARC project focused on developing and demonstrating an AI-based virtual control room that can improve decisions
related to situational awareness, and assessment of risks against maritime incidents such as grounding, collision, oil spill, etc.

Stream: Data Science Meets Optimization
Invited session

The seminar will first describe the holistic operational research process
undertaken. This first involved a hierarchical classification of current
and future ANA innovation needs, based at the highest level aroundthe international maritime organisation (IMO)’s polar code. To achievethis, a series of workshops, questionnaires and interviews and literature
review were used. These drew responses from 131 ANA stakeholders,
including SAR and MER practitioners, environmentalists, NGO andcharity representatives, indigenous peoples group representatives, industrialists and academics. This resulted in a hierarchical network of
20 need and 75 sub-need categories. A quantitative methodology using a combined PICK chart and Multi-Criteria Decision Making wasthen used to develop a priority set of 17 sub-needs. The PICK chartclassified the needs according to importance and difficulty whilst anovel balanced knapsack goal programming model was developed tochoose a high importance set of sub-needs, whilst considering the balance between long-term (more challenging) and shorter term (moreimplementable) innovation needs and across the six polar need categories.
The process of disseminating the methodology to ANA stakeholders
and its usage to inform ARCSAR activities will be described. This
involved a series of stakeholder workshops, where SWOT analyses
around a set of the priority list innovations were undertaken. The priority needs were also used to inform significant activities, including a
LiveEX ship evacuation exercise in Svalbard Norway. With respect to
AI-ARC, the development of the risk index using AI based FL rulebased system and MCDM techniques to analyse data related to realtime AIS (ship conditions and position) combined with weather andhuman judgements will also be presented.
Finally, some highlights of the policy and practice impacts of the development of innovations to tackle the identified needs will be detailed.
These include: [U+F0B7] The development of a set of training materials and procedures to use by ANA communities in the case of a
nuclear incident [U+F0B7] An Arctic lessons learned arena for SAR
and MER practitioners to share good practice [U+F0B7] Developmentof an AI-based virtual control room for safer ANA maritime navigation [U+F0B7] Enhanced training exercises and protocols for handlingoil-on-ice in the case of an ANA oil spill [U+F0B7] Evidence given
to, and cited by, a UK parliamentary enquiry on future Arctic policy[U+F0B7] Enhanced SAR operations in the Antarctic maritime region
due to participation and knowledge transfer by Maritime New ZealandOverall conclusions will then be drawn, including transferability of thedeveloped methodology to other complex, multi-disciplinary situations
where innovations to enhance safety and ensure wellbeing are needed.

122 MD-03

Chair: Kevin Tierney
Chair: Yingqian Zhang
1 - PolyNet: Learning Diverse Solution Strategies for Neural Combinatorial Optimization

André Hottung, Kevin Tierney
Reinforcement learning-based methods for constructing solutions to
combinatorial optimization problems are rapidly approaching the performance of human-designed algorithms. To further narrow the gap,
learning-based approaches must efficiently explore the solution space
during the search process. Recent approaches artificially increase exploration by enforcing diverse solution generation through handcrafted
rules, however, these rules can impair solution quality and are difficult to design for more complex problems. In this paper, we introduce
PolyNet, an approach for improving exploration of the solution spaceby learning complementary solution strategies. In contrast to otherworks, PolyNet uses only a single-decoder and a training schema that
does not enforce diverse solution generation through handcrafted rules.
We evaluate PolyNet on four combinatorial optimization problems andobserve that the implicit diversity mechanism allows PolyNet to findbetter solutions than approaches the explicitly enforce diverse solution
generation.

2 - A regression model for selecting effective cuts in a
Branch-and-Cut algorithm

Marcello Sammarra, Giovanni Giallombardo, Giovanna
Miglionico
The performance of a Branch-and-Cut algorithm lies, among otherthings, in the ability of selecting a given number of available cuts to be
added to the current LP relaxation, in order get to tight bounds. Clearly,
adding many cuts the best bound improvement can be achieved, but,as a counterpart, also the time to solve the LP sensibly increases.
Moreover, it is possible that only a fraction of the added cuts reallycontributes to the bound improvement, the remaining being not tight.
This is to say the cut selection is a very challenging task. Modern
ILP solvers, like CPLEX, GUROBI, XPRESS, MOSEK adopt greedy
rules. Cuts are first ranked with a score function; then, cuts with thehighest scores are added to the LP relaxation. In general, a cut scoreis a combination of well-established measures of cut quality, e.g. violation, parallelism, etc. Adopting as cut score the bound improvement,in this talk we show how a regression model can be learned to predictthe cut scores for unseen ILP instances. Preliminary results and future
research directions are discussed.

3 - Learning to Branch in Combinatorial Optimization with
Graph Pointer Networks

Bo Jiang, Rui Wang, Gang Zhou
This paper proposes a novel graph pointer network (GPN) model tolearn branching rules for branch-and-bound (B&B) algorithms in combinatorial optimization. B&B is a widely used exact method that cansolve various NP-hard problems, such as set covering, facility location, and maximum independent set. However, B&B relies on manually designed heuristics to select the branching variables, which areoften suboptimal and inefficient. The GPN model aims to automatically learn these heuristics from data, by imitating the expert strong
branching rule. The GPN model consists of two components: a graphneural network that encodes the graph structure of the problem state,
and a pointer mechanism that outputs the branching probabilities basedon the global and historical features. The model is trained by minimizing the Kullback-Leibler divergence between the predicted and theexpert distributions. Experiments on three benchmark problems show

EURO 2024 - Copenhagenthat the GPN model outperforms both the classic and the state-of-theart machine learning-based B&B methods in terms of solving speed
and search tree size. The GPN model also demonstrates remarkablegeneralization abilities, adapting to unseen and larger instances. This
paper presents an innovative approach to enhancing the performanceof B&B algorithms using deep learning techniques.

MD-07

seeding legs of these instruments. The findings of this investigation
underscore the superior efficacy of the proposed ensemble approach
are compared with individual methodologies documented in extant literature. Although the YOLOv5 model manifests the highest individual
performance metrics, the ensemble learning paradigm transcends thesebenchmarks, culminating in the most promising outcomes relative tothe established performance criteria.

4 - Learning to solve combinatorial optimization problems
with a decision tree

Kevin Tierney
Deep reinforcement learning has made incredible strides in solving
combinatorial optimization problems (COPs) and nearly outperforms
the current state-of-the-art OR heuristics on several problems. However, a key drawback of deep neural network approaches is that they
are not interpretable, that is, it is essentially impossible to understandhow they actually solve optimization problems. To this end, I introduce
a fully interpretable mechanism for generating interpretable models forsolving COPs using a decision tree. The method harnesses a pairwise
ranking mechanism to construct solutions, thus allowing it to learn tosolve various instance sizes with a single model. To train the decision
tree, I introduce an end-to-end learning technique to generate trees thatare customized to specific datasets and show the effectiveness of thistechnique experimentally on several COPs.

 MD-04
Monday, 14:30-16:00 - Room: 1001 (building: 202)

Hybrid Appraches in Deep Learning andMachine Learning
Stream: Recent Advancements in AI
Invited session
Chair: Melisa Caliskan-Demir
1 - Auto-Pruned SOCP Clustering Problem

Duygu Üçüncü, Sureyya Ozogur-Akyuz
The selection of the best models in the ensemble has a pivotal rolein the overall performance of ensemble learning algorithms due to thefact that redundant solutions in the ensemble library will decrease theoverall prediction accuracy. Therefore, on account of eliminating suchcandidates, accuracy and diversity should be taken into consideration.
This study proposes a novel ensemble clustering method using asecond-order conic optimization model that continuously and convexly solves integer programming for decision-making in various disciplines. The proposed model optimizes the balance between accuracy and diversity, resulting in the selection of the best candidates forprediction. The remarkable contribution of the study is the automatic
sub-ensemble selection while optimizing for accuracy and diversity simultaneously. The model has been tested with real-world data and has
achieved competitive prediction performance.

2 - Ensemble of YOLO Networks for Welding Detection

Melisa Caliskan-Demir, Alev Taskin
The YOLO series of neural networks is widely recognized within thedomain of target detection due to its robust feature extraction capabilities, streamlined network architecture, and efficient detection speed,
thus rendering it a cornerstone in contemporary research and practicalapplications. Ensemble learning, a technique that combines multiple
learning algorithms, is employed in the literature to increase predictive accuracy. In this study, ensemble learning is judiciously employedwith state-of-the-art object detection algorithms, including YOLOv5,
YOLOv7, YOLOv8 and MASK R-CNN. The overarching objective isto refine welding detection outcomes by leveraging an original postwelding dataset comprising images captured from seed-throwing machinery utilized in agricultural contexts. The principal aim is to discern any potential faults within the welding processes applied to the MD-07
Monday, 14:30-16:00 - Room: 1019 (building: 202)

Cutting and Packing 4 - 3D irregularStream: Cutting and Packing (ESICUP)
Invited session
Chair: Jonas Tollenaere
1 - Optimized Packing Soft 3D Objects

Tetyana Romanova, Alexander Pankratov, Julia Bennell, Igor
Litvinchev, Oksana Melashenko
Packing 3D soft objects in an optimized container is considered. The
shapes of the objects can be varied in certain limits. However, thevolumes of the objects remain constant. Free translation/rotation for
objects is allowed. The soft objects must be arranged completely inside the container without overlapping. Corresponding nonlinear programming models are formulated for different shapes of objects using
the phi-function technique. A solution strategy based on the multistarttechnique is proposed. It involves three main stages: search for feasible starting points; find a local-optimal solution for each starting point
using a decomposition procedure; choose the best solution from thosefound at the previous stage. Computational experiments for packing
soft polyhedra in a cuboidal container are provided. Packing soft objects has various applications in biology and mechanics, material science and logistics.

2 - 3D Irregular Cutting and Packing for the Nuclear Waste
from Tokamak Fusion Reactor

Yifu Wei, Julia Bennell, Tatiana Romanova
The fusion reactor is widely regarded as the ideal future source ofgreen energy. Nevertheless,the fusion reactor inevitably generates radioactive waste. The waste must be stored in waste disposal facilities
for a significant length of time for legal and safety reasons. Subsequently, an optimised strategy of cutting and packing is critical to reduce overall costs which means a trade-off between cutting cost (thelength of cutting trajectory) and the packing cost (the number of containers utilised).
My PhD research aims to tackle a 3D irregular cutting and packing
problem: the primary shapes of the fusion reactor are treated independently, divided into distinct sub-problems. Each shape must be cut andsubsequently packed into fixed containers. Regarding the containers,
they are of a fixed, cylindrical or cubical design. The values of theircross-sectional area and height are predefined as parameters within themodel. For each container, we also consider other parameters including the maximum permissible weight, maximum volume and the upper
limits of radioactivity. The objective is to minimise the overall cost of
cutting and packing.
In this presentation, I will provide a comprehensive definition of the
problem, describe the way I will apply the phi-function techniquesalongside a construction heuristic, and provide some preliminary results to illustrate how the approach works.

3 - A quaternion formulation of the volume maximization
problemJonas Tollenaere, Tony Wauters123MD-08EURO 2024 - Copenhagen

In volume maximization problems, the objective is to extract the largest
three-dimensional item(s) with variable scaling from a larger container.
To achieve this, values for the continuous translation and rotation variables should be determined so that the items can be maximally scaled
up, without violating any containment or non-overlapping constraints.
Applications of these problems appear in various real-world contexts,
such as gem cutting and circuit manufacturing, where finding better solutions is typically highly valuable. While effective heuristic methods
have previously been developed to tackle these problems, the attention
to exact methods has been rather limited. One of the reasons for thisis most likely the complexity involved with mathematically optimizing
rotation in three-dimensional space. Angle-based rotation representations such as Euler angles lead to trigonometric expressions, which
are challenging for exact solution methods. Therefore, quaternions are
better suited for this purpose. Using quaternions, we can formulate
the orientation of an object through quadratic expressions. This allows us to mathematically formulate volume maximization problems
with convex containers as quadratically constrained programs, basedon quaternions, that can be solved exactly. In this presentation, we will
introduce volume maximization problems, give some insights into its
applications and heuristic methods, and formulate the aforementioned
quadratically constrained models.

4 - Optimization of automatic film dispensing of multi-layer
films - The method of establishing material selection
and scheduling

Gen-han Wu, Tzu-Yu Huang
In this study, we focus on the material selection and feeding schedule of four kinds of lamination films for the multi-layer automatic film
dispensing problem in the polarizer industry process. The material
selection is to select the appropriate size of each material film for lamination. The feeding schedule is to find the best sequence of automatic
film dispensing for the selected materials. The scheduler in the plant
has arranged the film dispensing manually, which has a great impact
on the loss of materials. We develop a particle swarm optimization algorithm and two improved hybrid differential evolutionary algorithms
to handle the material selection. The variable neighborhood search isdesigned to deal with the feeding schedule. The near-optimal solution
can be found by iterative search. A series of experiments are designed
to compare the search performance of these three swarm search algorithms. Three algorithms can achieve satisfactory results. In the future,these algorithms can be expected to develop the decision support system to the multi-layer film material selection and scheduling problem
in the polarizer industry.

 MD-08
Monday, 14:30-16:00 - Room: 1020 (building: 202)

EJOR: policy, facts and highlights
Stream: OR Journals
Invited session
Chair: Roman Slowinski
1 - The consistent electric-vehicle routing problem withbackhauls and charging management

Pamela C. Nolz, Nabil Absi, Dominique Feillet, Clovis
Seragiotto
We consider a consistent vehicle routing problem for the delivery ofparcels with electric vehicles. Stemming from a real-world problem,
we assume that vehicles can only be charged with electricity between
their delivery tours in the morning and their pickup tours in the afternoon. For this purpose, a charging station with a limited amount ofcharging slots is available at the depot. We aim at generating a set ofvehicle routes that are driver- and time-consistent and efficiently use
limited charging resources, while optimizing the sum of vehicle fixed
cost, vehicle/driver operating time, arrival time consistency and driver

124

consistency. We present a mathematical model to describe the problem in detail. For solving the real-world problem, a template-based
Adaptive Large Neighborhood Search is developed, complemented
with constraint programming for charging management and quadratic
programming for delivery and pickup trip scheduling. Computational
experiments for different settings and scenarios, based on data from anAustrian parcel delivery company, are presented and analysed.

2 - Deep Learning for Credit Scoring: Do or Don’t - A retrospective look

Bart Baesens, María Óskarsdóttir, Wilfried Lemahieu, Björn
Rafn Gunnarsson, Seppe vanden Broucke
New technologies such as deep learning should always be approachedwith scientific rigor. Using a unique and extensive collection of tabular
data sets, we wanted to explore the potential of deep learning for credit
scoring. We believe one of the key novelties of this paper is the robust
empirical comparison framework we set up, using multiple datasets,
evaluation metrics, confidence intervals, frequentist and Bayesian testing of significance. What we often encounter when reviewing papers,
is that researchers either don’t give baseline methods a fair chance or
cherry pick them when comparing against their own newly proposed
method(s). Furthermore, something rather basic but not unimportant
relates to the title and message of the paper. We opted for a simple,
easily Google-able title properly reflecting the research question which
not only draws attention but also allowed readers to very easily grasp
the key takeaways of our work. In this presentation, we elaborate moreon this as well as how this paper helped shape our future research.

3 - Policy and facts about the European Journal of Operational Research (EJOR)

Roman Slowinski, Emanuele Borgonovo, José Fernando
Oliveira, Steffen Rebennack, Ruud Teunter, Mike Yearworth
The session starts with two presentations done by authors of representative and highly cited papers published recently in EJOR. They
represent two categories: Innovative Application of OR, and Theory& Methodology. Some further research developments and practical
implications that followed these publications will be given by their authors. Then, the editors of EJOR will explain their editorial policy andwill give some current characteristics of the journal. They will also describe their approach to the evaluation and selection of articles and will
point out topics of OR that recently raised the highest interest. In thelast part of the session, the editors will answer some general questions
from the audience.

 MD-09
Monday, 14:30-16:00 - Room: 10 (building: 116)

Uncertainties in the Energy Transition
Stream: Energy Markets
Invited session
Chair: Christoph Weber
Chair: Marco Sebastian Breder1 - Distortions in energy related decision-making at households - An MCP approach incorporating system perspective

Marco Sebastian Breder
By using a variety of electrical appliances with smart control options,households might play a pivotal role in flexibility provision in the energy market. To exploit the flexibility potential in a most efficient manner from a system perspective, no distortionary impacts should affectoperational and investment decisions. To date, most electricity tariffs
do not reflect time-dependency in market prices. As a result, households’ investment and operating decisions are based on distorted costconsiderations. Scenarios become even more complex when EV ownerinstall a second meter for wallboxes. In this case, two different electricity tariffs may be concluded. We elaborate on the distortionary impacts

EURO 2024 - Copenhagenof tariffs on households’ investment and operational decisions under
the current regulatory framework. Here we focus on investments in
PV and battery systems under different tariff designs. Moreover, we
exploit the case when a second meter with an additional tariff is in use
for EV charging. We formulate the individual optimization problems
corresponding to different types of households facing different electricity tariffs. Thereby households are differentiated according to theircharacteristics, i.e. (potential) Prosumer and EV ownership. A stylized wholesale market will allow to elaborate feedback effects fromhousehold decisions. The indvidual problems are combined into an
overarching MCP problem.

2 - Cross-sectoral coordination in highly interconnected
multi-operator energy systems: A distributed framework with autonomous agents

Aiko Schinke-Nendza, Christoph WeberIn response to Europe’s climate objectives, this study investigates anovel distributed coordination framework for cross-sectoral integration in multi-operator energy systems, critical for achieving sustainability. Incorporating digitalization and decentralized resources, ourmethod overcomes the challenge of efficiently coordinating highly interconnected systems while preserving data confidentiality and promoting renewable integration. We address nodal pricing efficiencies
through a unique distributed algorithm that is based on autonomous
agents—corresponding to market participants and network operators.
This innovation ensures flexibility and efficiency, adhering to strict
European regulations. Our approach not only handles the complexity of multi-energy carriers and the intricacies of highly interconnected power systems but also enables scalable, privacy-aware coordination avoiding major drawbacks of existing market designs. Basedon a careful evaluation of decomposition techniques, we derive a distributed framework using the alternating direction method of multipliers (ADMM). Demonstrating the advances of this distributed algorithm, our research contributes to operational efficiency in multienergy systems, enabling an enhanced integration of decentralized resources in a cross-sectoral energy system to meet Europe’s ambitious
climate targets.

3 - The effect of the variation of PV’s effective capacity
during the day-hours on the optimal capacity mix in a
Cournot wholesale electricity market

Irena Milstein, Asher Tishler, C.k. Woo
This paper evaluates the effect of the variation of PV’s effective capacity across the day-hours and the seasons of the year on the optimal capacity mix, generation levels and prices of a wholesale electricity market in which power producers use natural gas (NG) and photovoltaic(PV) technologies. The actual levels of PV’s effective capacity during
the day hours resemble the probability distribution function of a normal
variable; it is very low in the morning, grows steadily until it reaches its
maximal level during the midday hours, and then continuously declines
during the afternoon hours. The shape of this distribution varies acrossthe seasons of the year. Applied to Israel’s stylized electricity market
in 2030, we find that representation of PV’s effective capacity by only
two levels ("high" during midday hours and "low" during morning andafternoon hours) yields very similar optimal capacity mix to a representation in which PV’s effective capacity changes during every hourof the day and over the four seasons of the year. We also show thatusing some NG during the day hours mitigates daytime price spikes
and enhances consumer welfare. Finally, we show how technology
improvements to the PV technology reduce the optimal PV capacityand electricity prices during the day hours.

4 - Evaluating the Economic Feasibility of SystematicPower Line Overloading in the German Electricity Transmission Grid Applying Chance-Constrained Programming

Hannes Hobbie, Lisa Lorenz, Miguel Anjos, Dominik Möst
Decarbonising the German energy system entails gradually substituting carbon-intensive power plants with renewable-based technologies.
The need to integrate sizeable renewable quantities into the German
electricity system affects the operation of future transmission grids.

MD-10

Impacts also include an increasing congestion management requirement and associated costs. Managing grid congestion involves adjusting the market-based dispatch of single generators subject to gridconstraints to find the least-cost system equilibrium. Grid constraints
comprise rated thermal power flow capacities limiting electricity transmission. However, line capacities, to some extent, are subject to uncertainty. Increasing the line capacity is frequently practised by TSOs
within certain boundary conditions, commonly called dynamic line
rating. This gives operators greater flexibility, and indicates the importance of determining efficient capacity values. In this research, we
propose to apply chance-constrained programming to investigate theeconomic feasibility of systematic power line overloading in the German transmission grid, utilising joint chance constraints to model probabilistic flow restrictions. Expected results will indicate its economic
value and effects on congestion management cost depending on theprobability level assumed. Outcomes also comprise line-specific information on realised single and simultaneous overload events acrossthe time and lines providing TSOs with decision support.

 MD-10
Monday, 14:30-16:00 - Room: 11 (building: 116)

Healthcare logistics and routing
Stream: OR in Health Services (ORAHS)
Invited session
Chair: Melanie Reuter-Oppermann
1 - Multi-modal routing and scheduling of drones, cargobikes and vans in the presence of weather uncertainties
for pathology collections in the Solent Area

Asrul Harun Ismail, Djamila Ouelhadj, Xiang Song, Andy
Oakey, Antonio Martinez Sykora, Tom Cherrett
This research explores the impact of weather uncertainties on the ability of drones to improve pathology collections over the business-asusual road-based logistics system in the Solent area of the UK. Weather
uncertainties impact on the manoeuvrability and speed of drones,
which can lead to delays and incidents. In this paper, we present a
novel framework that combines a machine learning drone flight duration prediction model with a meta-heuristic Bees optimisation algorithm to solve the drone-cargo bike-van multimodal medical logistics
problem in the presence of variable weather conditions.
Experiments have been conducted to evaluate how varying wind conditions affect drone schedule efficiency when used in combination
with other transportation modes (cargo bikes and vans), and quantify the logistics system’s overall economic and environmental impacts
as a result. The case studies used concern the routine collection ofpathology samples from doctors surgeries across the Southampton andPortsmouth area of the UK for delivery to pathology laboratories.
This study is part of the Solent Future Transport Zone project (FTZ)
funded by the UK’s Department for Transport and led by Solent Transport.

2 - A hybrid memetic-ANS optimization algorithm for thehome health care and home care routing and rescheduling problem

Qiao Pan
This paper addresses a realistic home health care and home care problem which has become increasingly complex in the face of demographic aging and post-COVID-19 disruptions. The HHC&HC sector
nowadays faces unique challenges in efficiently scheduling and routing
caregivers to meet the rising demand. Traditional approaches often fallshort in addressing the dynamic nature of care requests, especially inaccommodating new, same-day service requests without compromising scheduled visits. To tackle these issues, We define the problem
as an HHC&HC routing and rescheduling problem with rejection of
new customers, focusing on rescheduling for a single HHC&HC caregiver in response to new customer requests within a single period. A

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MD-11EURO 2024 - Copenhagen

mixed integer linear programming model is developed to cater to twogroups of customers: pre-scheduled existing customers and same-day
service new customers. The model emphasized maintaining minimal
disruptions to the original schedule for existing customers as a constraint, highlighting the balance between adhering to scheduled visits and accommodating new customers. A hybrid memetic-Adaptive
Neighborhood Search optimization algorithm is proposed to tackle themodel. This approach aims to minimize operational costs and opportunity costs while enhancing service quality and patient satisfaction.
Through computational experiments, our proposed algorithm demonstrates notable performance, offering significant improvements in both
efficiency and robustness.

3 - Allocation of Persons to Mental Health Homes byKnapsack-Based Routing

René Bekker, Rebekka Arntzen, Rob van der Mei
Persons with severe mental illness who cannot live independently canmake use of long-term residential facilities. The matching process between client and facility is complex, as both restrictions and client preferences play a crucial role. For this, we introduce the Knapsack-Based
Routing model, which combines and extends two lines of research:
Skill-Based Routing models and queues with resources. We design aneffective rolling-horizon heuristic by combining key ingredients fromdifferent elementary models. For small instances, we compare the performance with a Markov Decision Process formulation. For larger instances, we find that our heuristic outperforms the best-known policy for Skill-Based Routing with preferences for a benchmark instance(Chen et al, 2020). Finally, we apply our heuristic to a case study in
Amsterdam and show how the current placement policy can be improved.

 MD-11
Monday, 14:30-16:00 - Room: 12 (building: 116)

Behavioral Decision Analysis III
Stream: Behavioural OR
Invited session
Chair: Gilberto Montibeller
1 - The critical role of uncertainty communication in informed decision-making

Anna Cajochen, Nadja Contzen, Michael Siegrist, Judit
LienertOur society is currently facing various sustainability challenges suchas climate change, water pollution, and biodiversity loss. Decisionmaking problems aimed at solving these challenges are associated withconsiderable uncertainty. Uncertainty can arise from various sources
such as measurement errors, difficulties to establish cause-effect relationships, disagreements between experts, or future unpredictability.
Not only is the nature of uncertainty extensive, but other explanatory
factors for effective uncertainty communication such as individual differences between recipients and the context of the situation must also
be taken into account. Navigating the many factors essential to effective uncertainty communication makes this a challenging task. In
our work, we examine the critical role of uncertainty communicationfrom a decision analytical, economic, and psychological perspective.
We address the definition, interpretation, and diverse communication
formats for uncertainty, with a specific focus on enhancing understanding within multi-criteria decision analysis (MCDA) projects. It is crucial that stakeholders have an adequate understanding of uncertainty toidentify their preferences and appropriate options. Therefore, our contribution is valuable for finding options to solve pressing sustainability
problems and managing uncertainty in decision-making.

126

2 - Is lottery-based measurement of risk attitudes subject
to a central tendency bias?

Rudolf Vetschera, Karl Akbari, Markus Eigruber
Lottery based questionnaires that let subjects chose among several lotteries with identical expected values are a popular way to measure risk
attitudes. However, respondents have been shown to select a "middle" alternative too frequently, thus leading to an overestimation of
the share of risk neutral individuals in a population. In this paper, we
present the result of a comprehensive set of experiments testing the robustness of and different possible explanations for this phenomenon.
In particular, we test for robustness in terms of (small) differences in
expected values among lotteries, different levels of stakes involved in
the lotteries, or whether respondents are incentivized. We also test
for alternative explanations such as careless and overly fast responding, misunderstandings of the implications of answers or a general tendency to select an answer that is presented in the middle of a list. Our
results indicate that carelessness and higher stakes increase the bias,
while higher cognitive abilities and extreme levels of subjectively selfassessed risk attitudes decrease it. We find no significant effects ofsmall differences in expected values or incentives.

3 - Optimization Models for Cumulative Prospect Theory
under Incomplete Preference Information

Juuso Liesiö, Peng Xu
Prospect stochastic dominance conditions can be used to compare pairsof uncertain decision alternatives when the decision makers’ choice
behavior is characterized by cumulative prospect theory, but their preferences are not precisely specified. This paper extends the use ofprospect stochastic dominance conditions to decision settings in which
the use of pairwise comparisons is not possible due to the high or possibly infinite number of decision alternatives. In particular, we first establish equivalence results between these conditions and the existenceof solutions to a specific system of linear inequalities. We then utilize
these results to develop stochastic optimization models whose feasible
solutions are guaranteed to dominate a specified benchmark distribution. These models can be used to identify if there exists a decision
alternative within a set that is preferred to a given benchmark by all
decision makers with an S-shaped value function and a pair of inverse
S-shaped probability weighting functions. As such the models offer a
flexible tool to conduct behavioral analyses in decision settings suchas portfolio selection, procurement optimization or inventory management. We demonstrate the application of these models using real returndata on industry portfolios.

4 - Selecting and packing bins - a human perspective

Johannes Kuchler, Leon Binder, Michael Scholz
The exponential growth of e-commerce in the past years has led to anincrease in packages. Amazon, the biggest player in e-commerce, haspacked and shipped approximately 7.7 billion packages in 2021. To
handle that many packages, it is of utmost importance for e-commercecompanies to optimize the packing process. The packing process consists of two decisions typically made by humans: the selection of thebest-fitting bin for a given order and the packing of all articles of thatorder into the bin. There has been little research into how people makethese decisions. We conducted two experiments to study the driving
forces on the two decisions. In the first experiment, we varied theamount and heterogeneity of the available bins and the articles to bepacked to examine the influence of these factors on the duration andquality of the bin selection process. We found, among other interesting
insights, that the decision duration is, contrary to what is expected, notinfluenced by the heterogeneity of articles. In the second experiment,
we varied the number and heterogeneity of articles to be packed into apredefined bin to examine the influence on the packing duration. Key
findings of this experiment are that humans exclusively start by packing the largest item and that packing an additional article increases thepacking time by 4.3seconds on average. Our findings contribute to theunderstanding and optimizing the packing process.

EURO 2024 - Copenhagen MD-12
Monday, 14:30-16:00 - Room: 13 (building: 116)

OR in Agriculture
Stream: OR in Agriculture and Forestry
Invited session
Chair: Hugo Herrera
1 - Mission (Im)Possible: universities’ focus on the agrifood sector

Chiara Colamartino, Pierluigi Toma
The agri-food sector requires evolutions capable of addressing theever-present climate crises. Innovations in agri-food are increasingly
supported by research theories and methods. Universities may represent the local drivers of rural development, providing training andresearch to orient small and territorial realities to innovation. Collaboration between agri-food and university can help to improve practices
and safeguard the environment. First, this study explores the Strategic
Plans of Italian universities, examining their objectives via text analysis techniques and clustering words from strategic plans. Second, theresearch compares the distribution of keyword clusters with the growth
in revenues for agri-food companies, the actions conducted to implement the three Missions of universities, and the spread of agri-foodstartups and spin-offs on the territory. The Kernel Density Estimation(KDE) permits obtaining a non-parametric density estimator to explain
multi-modal variables by better following the pattern of the histogram.
These findings may emphasise universities’ external relations on the
territory to improve the innovation process and to boost partnerships
with nearby agri-food firms. Besides methodological instruments andinterpretations of intellectual capital for variable selection, the work
may introduce novelty to the literature on the collaboration of the agrifood sector to local development.

2 - Valuing harvesting technologies rental cost using asimulation approach

Sergio Maturana, Elbio Avanzini, Carlos Monardes, Pablo
AcevedoWhich harvesting technology to use is a crucial decision for farmers,
particularly when dealing with perishable products. Renting machinery is customary for many farmers, with costs fluctuating based on themachines’ features, yearly conditions, and the producer’s willingness
to pay. Negotiations on rental discounts often occur, especially when
renewing machine rentals.
Employing simulation and decision rules, we assess the problem ofchoosing between different technologies that a wine grape farmer
might use when harvesting. Besides the cost differences, the machines
behave differently in rainy conditions. The occurrence of rain and its
intensity is difficult to predict when the farmer must choose which machine to use. The farmer might also decide not to rent any equipment,so they won’t be able to harvest for some time.
The farmer navigates harvesting decisions through a multidimensional
utility function applied across numerous scenarios spanning several
years, with rainfall intensity as the critical uncertainty. We performed
numerical experiments using Monte Carlo simulation to evaluate theuse of different technologies. We analyzed the results to determine
how well the proposed approach can consider the operational complexity of harvesting using different types of machines and under uncertain
weather conditions. This can help make a better decision when choosing which machine to use in the initial year and the switching costs insubsequent years.

3 - Bioeconomy transition, using hybrid simulation to assess challenges and opportunities for livestock farmers
in Norway

Hugo Herrera
The bioeconomy is a crucial concept for addressing major environmental challenges. Transitioning towards a bioeconomy requires coordinated efforts from various stakeholders and has the potential to significantly transform value chains, particularly in the agriculture sector.

MD-13

However, the pathway to this transition is complex and dynamic. This
paper aims to shed light on this complexity by employing a hybrid
simulation approach that integrates system dynamics and agent-based
simulations. Our focus is to explore both the intended and unintended
changes that a bioeconomy transition might bring to dairy livestock
farmers in Norway. The contributions of this paper are twofold. First,
it examines the advantages and drawbacks of using hybrid simulations
to model farming systems. Second, it offers practical insights into theimpact of introducing and promoting new biotechnologies in the industry. In particular , the simulation results highlight significant challenges regarding a bio-based economy for dairy farmers resulting on
the potential exacerbation of the previous trend towards fewer, larger,
and more intensive farms. Moreover the simulation results also indicate a potential geographical concentration of farms in small areas of
Norway. The potential concentration of farms in very few hotspots fordairy farming is a consequence of the scales needed to justify investments in supportive infrastructure like bioreactors.

4 - Resilient Olive-Oil Supply Chain Network Design

Bilge Bilgen
This paper addresses the integrated planning problem encompassing
olive harvest and production within the olive oil industry. The aim is
to optimize a mathematical model that seamlessly integrates both olive
harvest and olive oil production processes. The primary goal is to maximize overall profit by determining the quantities of olives harvested
from various groves, olives procured from external farmers, olive oil
produced, and managing by-products to mitigate the adverse impactsof olive oil production. The problem is formulated as a mixed integer linear programming model (MILP). Profit maximization comprises
total sales revenue and total costs, covering harvesting, procurement,
fixed and variable processing expenses. System constraints include
harvest and production planning, capacity limitations, and processing
restrictions. The proposed MILP model encompasses several distinctive aspects of the problem, such as olive ripeness, olive oil quality,
farming methods (organic and conventional). Backup facility assignment and coverage distance resilience strategies are incorporated. The
effectiveness of the developed model is validated through a numericalexperiment based on a real-world case study, indicating that simultaneous consideration of harvesting and production processes significantly
enhances the profitability of the olive oil supply chain.

 MD-13
Monday, 14:30-16:00 - Room: 15 (building: 116)

Methodological Developments in Soft OR
and PSMs
Stream: Soft OR and Problem Structuring Methods
Invited session
Chair: Chris Smith
Chair: Ashley Carreras
1 - A Structured Decision Making approach to Improve
Population Health and Reduce Health Inequalities

Shraddha Ghatkar, Emma Comrie, Joao Duro, Ping Li,
Corinna Elsenbroich, Visakan Kadirkamanathan, Robin
Purshouse
Multi-Criteria Decision Making (MCDM) and Multi-Criteria Decision
Analysis (MCDA) are widely used with problem structuring methods
to support thinking and decision-making in real-world applications.
Within MCDM, Multi-Objective Optimization (MOO) is a commonlyused method and yet, review of the literature highlights lack of studies
employing a structured method to develop MOO problem formulationfrom a real-world application and to incorporate stakeholder involvement. Traditionally, research related to MOO focuses on developmentof solution algorithms, working with a pre-developed mathematicalproblem formulation. As part of an ongoing research consortium SIPHER (Systems Science in Public Health and Health Economics

127MD-14EURO 2024 - Copenhagen

Research), we are working with policy partners to develop a Decision Support Tool (DST) that can be used by budget holders in identifying where to target policy action while considering multiple andpotentially conflicting outcomes and performance metrics at the sametime. We have employed a mixed-method approach in the DST which
combines participatory systems mapping with subject-matter experts,
developing an evaluation model for the potential policy options and coworking with policy partners to elicit MOO problem formulations. In
this presentation, we give rationale for using this approach, our experience of using it along with policy partners, lessons learned and future
directions for research.

2 - The philosophical, theoretical and methodological development of a PSM through Action Research: From theSpecific to the Generic

Chris Smith
This paper considers the development of a Problem Structuring
Method (PSM) WASAN over an action research programme and what
we can learn about the applicability of PSMs to multiple contexts. The
programme focusses on WASAN’s developmental journey from a single use approach, only applicable to a limited context, to a PSM thathas generic applicability, that is can be used across multiple problem
contexts. The project tracks the changes made to WASAN through11 Learning Loop cycles across two new problem contexts with fivedifferent researchers using the approach to collect and analyse data.
The unit of analysis for the paper is the philosophical, theoretical andmethodological development of WASAN, to achieve this the paper
focuses on the learning from a second level analysis of the Action
Research Programme concerned with what we can learn about PSMs
more broadly. Here the focus is the different classes of elements within
WASAN which are required for it to be generically applicable, that is
applicable to a variety of problem contexts. Here the paper distinguishes between two classes of elements, those which required consistent replication regardless of context; termed methodological elements. Second, those which allow an approach to be applicable to the
local context thereby bridging the gap between the problem context
and methodology; we term these contextual elements.

3 - Indispensable and Identifiable activities and the link
between group goals and individual goals following
Causal Mapping Interventions.

Ashley Carreras, Welf Weiger
Within the Problem Structuring Methods literature, it is widely heldthat undertaking a Causal Mapping session on the ultimate goals of aset or prioritized actions contributes to a greater likelihood of achieving those goals. An alternative approach to understanding the natureof this link between group goals and individual actions undertaken in
the pursuit of these group goals is offered by drawing upon the psychological concepts of "Indispensable" and "Identifiable" activities. These
highlight the difference between self-serving and group-serving activities in a group environment. Participants of group assignments from an
MBA program were surveyed during and after the production and submission of a group assignment, with some participating in collective
agreement group activities before the commencement of the assignments.

4 - Listening to act: Using Rich Notes for problem structuring in small groups

Karthik Suresh
Practitioners using problem structuring methods (PSMs) help participants in groups make sense of a situation they consider to be problematic and find consensus positions that enable action to be taken
to improve the situation. Many approaches encourage participants toco-create models by picking up pens or markers and getting involved.
Less attention is paid to how participants or practitioners listen to different perspectives, integrate different points of view and document
findings. Listening is more than a single ability and can be conceptualised in different ways such as listening as information processing,listening as comprehension, and listening as retention, among others.
This paper introduces listening using Rich Notes, a novel approach tonote taking and diagramming during meetings that uses a non-linear,128

non-hierarchical, unconstrained and distributed layout that documents
what participants say, recognises interdependencies and generates options for action. Rich Notes has been developed within an action research programme consisting of over 300 real-world participant interactions.

 MD-14
Monday, 14:30-16:00 - Room: 16 (building: 116)

Modelling European market coupling
Stream: Energy Markets
Invited session
Chair: Marc Deissenroth-Uhrig
1 - Evaluating Offshore Electricity Market Design Considering Endogenous Infrastructure Investments: Zonal or
Nodal?

Kenneth Bruninx
Policy makers are formulating offshore energy in- frastructure plans,
including wind turbines, electrolyzers, and HVDC transmission lines.
An effective market design is crucial to guide cost-efficient investments
and dispatch decisions. In this talk, we jointly discuss the impact of offshore market design choices on the investment in offshore electrolyzers and HVDC transmission capacity. We present a bilevel model thatincorpo- rates investments in offshore energy infrastructure, day-aheadmarket dispatch, and potential redispatch actions near real-time to ensure transmission constraints are respected. Our findings demonstratethat full nodal pricing, i.e., nodal pricing both onshore and offshore,
outperforms the onshore zonal combined with offshore nodal pricing or
offshore zonal layouts. While combining onshore zonal with offshore
nodal pricing can be considered as a second-best option, it generally
diminishes the profitability of offshore wind farms. However, if investment costs of offshore electrolyzers are relatively low, they can serve
as catalysts to increase the revenues of the offshore wind farms. This
work contributes to the understanding of market designs for highly interconnected offshore power systems, offering in- sights into the impact of congestion pricing methodologies on investment decisions.

2 - Assessing the Impact of Cross-Border effects on DayAhead Market Electricity Prices - a Meta-study

Jilles De Blauwe
Cross-border effects play a significant role in shaping spot market
prices. Yet, a thorough meta-study synthesizing their influence andcomparing across different European markets remains a gap in relevant literature. Further, a comparison of the relevant literature on thebasis of the methodology followed in each study can prove useful. Thismeta-study will therefore investigate the relevant existing literature forpapers that have established a relation between a cross-border effectand the spot market prices. A number of key cross-border effects ofparticular interest are first identified, before compiling a comprehensive list of relevant papers that include those effects. Subsequently, thestudy will rigorously compare the selected papers on a methodologicalbasis. Here the study will classify the papers for modelling techniqueused, the time period on which influences are defined (short and longterm) and the market under study. These methodologies will then be
discussed. Finally, the conclusions of different papers looking at a particular effect are compared, to assess whether the difference in results
drawn in the papers might result from the modelling technique and themethodologies followed in that paper, or was rather due to some otherfactors. This meta-analysis will thus provide a valuable reference tool
for future studies that seek insight into methods to analyze or forecast
spot market prices while including cross-border effects.

EURO 2024 - Copenhagen
3 - Methodology and results of characterization of strategic bidding strategies

Henrik Mantke
As part of the research project "Bidding strategies and their influence
on electricity market prices in the EU single market (BETS)" a methodology for characterization of strategic bidding strategies was developed. One of the main targets of the project is the development ofa strategic bidding agent for agent-based electricity market models. A
special focus will be on the cross-border strategic bidding. An important step for the implementation of the agent is the definition of different strategies that the bidding agent should use, and that could be foundin today’s EU spot market. In this context, based on a profound literature research, a methodology for characterization strategic bidding wasdeveloped. A focus was set on aspects of the core bidding strategy, thefield of application, such as the markets or the used solution and pricemethodology. Also, different boundary conditions considered in theliterature, focus technologies or uncertainty about information for thebidders were considered in the characterization. With this methodology the relevance of different strategies could be analysed and relevant
cross-border strategies could be specified, even if they have been analyzed only for individual market areas.

4 - Local Network Coordination - A Market Design for Trading Congestions

Paul Seifert, Steffen Bakker, Stian Backe
Getting new grid access permits is challenging for Norwegian grid customers to a shortage of transmission system capacity. Grid reinforcements require long lead times and are not expected to ease the situation
soon. If capacity limits are reached, requests face rejections for grid
access. Grid managers have to ensure capacity limitations are met all
the time. Enough capacity is available for most of the time, but somecritical hours see spikes in which load profiles layer to unfortunate
spikes. The consequences are severe and hinder economic development. So far, there are no signals indicating the grid’s status on which
coordinated actions could be aligned. Upcoming congestion hours are
not forseeable, nor exist incentives to influence load profiles according
to grid status. Alternatively, we advocate for congested customers toteam up with entities with existing capacity rights and pool them in energy (capacity) communities. Steered by price signals from utility-costequilibria, members coordinate their load profiles and assign capacity
scarcity prices if a capacity violation occurs. We designed a two-stage
combination of load flow optimal dispatch models with an equilibrium
model to resolve the congestion. We demonstrate that this market design enables more customers to join the electricity grid through an efficient price-based trading mechanism, which adheres to grid restrictions, is incentive-compatible, and allows cost recovery.

MD-16

requirements by evaluating each ward separately and approximating
the process governing patients arriving from the other wards. We base
our approximation on interrupted Poisson processes, where the interruption times follow hyper-exponential distributions. By employing
this approach, we are able to evaluate the occupancy of all inpatient
nursing wards in a Danish hospital. Our numerical experiments indicate that this approach is insensitive to the length of stay distribution
type and accurately reflects the occupancy of many wards in the hospital.

2 - Dynamic Contracts For Queuing Service Systems

Ji Chen, Man Yu, Laurens Debo
We analyze a service provider’s optimal design of service contracts,
which specify service priority and pricing for consumers (e.g., patients) of different types. The analysis incorporates both static anddynamic contracts and considers consumer heterogeneity following either discrete or continuous distributions. It demonstrates the profit advantage of dynamic contracts over static contracts and characterizes
the conditions under which the advantage is most pronounced.

3 - Generalized or Specialized Hospitals: An Analytical
Queuing Approach to Explore Clinical Service Configurations

Mohammad Aminjarahi, Navid Izady, Dimitris
Paraskevopoulos
In multi-hospital networks (MHNs), a critical decision revolves aroundhow to allocate clinical services across different hospitals with limited
capacity. This decision typically falls between two extremes: a fully
generalized configuration where all services are offered in every hospital, and a fully specialized configuration where each hospital exclusively provides one service. However, research and practical evidence
indicate that neither fully specialized nor fully generalized configurations are optimal. In this paper, we present a mathematical model
along with an exact analytical solution to explore the optimal service
configuration within an MHN comprising two hospitals of equal fixedcapacity and two services. Our model addresses uncertainty by incorporating queueing theory and economies of scale, crucial factors
in service configuration problems. Through extensive numerical experiments, we demonstrate that, in most scenarios, a semi-specialized
configuration emerges as optimal, maintaining one hospital as generaland the other as specialized. Nonetheless, there are specific circumstances where alternative configurations, such as fully specialized or
fully generalized, may prove optimal.

4 - Admission Pricing In Public Emergency Departments:
Theory And Evidences From A Natural Experiment

Man Yu, Ji Chen, Tian Li

 MD-15
Monday, 14:30-16:00 - Room: 18 (building: 116)

Healthcare services
Stream: OR in Health Services (ORAHS)
Invited session

We study the pricing decisions of a medical service provider who operates both an emergency department and a non-emergency department
with different priority schemes and admission fees. Each patient independently makes her attendance decision according to the urgency
level of her medical condition, the admission fees, and expected waiting time at the two departments. We characterize patients’ equilibrium
attendances to the two departments. Effects of adjusting the admission
fees on patient attendances are examined both in an analytical modeland with empirical data from public hospitals in Hong Kong.

Chair: Man Yu
1 - A Markov chain for the evaluation of inpatient flow in
hospitals

Anders Reenberg Andersen
Many hospitals face the challenge of an increasing demand for admissions to their inpatient nursing wards. The possible solutions tothis problem include relocating patients, merging nursing wards, andoptimizing the balance of bed capacity between them. In any case,
hospital planners need a model that describes the influence of the admission rate, length of stay, allocated capacity, and relocation policies
on the occupancy of beds. This study proposes a model based on acontinuous-time Markov chain. We reduce the model’s computational

 MD-16
Monday, 14:30-16:00 - Room: 19 (building: 116)

OR Initiatives for Education, Sustainability,
and Developing Countries
Stream: OR Education
Invited session
Chair: Martina Gregori129MD-17EURO 2024 - Copenhagen

1 - Application of MARS Method to analyze students’choices of OR courses

Jinal Parikh, Gerhard-Wilhelm Weber
Given that operations research provides its learners with an exposure
to an array of problem-solving and decision-making analytical techniques that can be applied to all areas and domains for solving complex
decision-making problems, its importance in higher education cannot
be understated. The purpose of this paper is to first investigate the factors that influence students’ choices of OR courses at Universities ofHigher Education in India. Next, we apply MARS method to predict
students’ choices of these courses.

2 - Performance assessment of higher education institutions in the philippines: a dea approach

Milagros Baldemor
This study determined the performance assessment of the six (6) State
Colleges and Universities of Northwestern Philippines vis-à-vis theirperformance along the following indicators: Graduation Outcomes,
Research, Outreach and Inclusivity, and Teaching and Learning Outcomes for five (5) academic years 2016-2020. It utilized the Mamquist
DEA Approach, a non-parametric measure. Furthermore, it determined the peer groups and weights of the DMUs (Decision Making Units - the different Colleges and Universities), the virtual inputs/outputs or potential improvements of the colleges/universities tobe in the efficient frontier, the input and output slacks (input excesses
and output shortfalls) needed in the different indicators and the bestpractices to be considered by the inefficient and weak efficient DMUs.
The "best practice" in the frontier is the basis to calculate the adjustments necessary for the DMUs. Different indicators showed varied
performance levels in the different academic years but there are bestpractices from the "efficient" DMUs which could be adapted by the"weak efficient" and "inefficient" ones.

3 - Optimizing food donation delivery for nonprofit company Logica&Co

Martina Gregori, Anna Livia Croella
We present a case exercise teaching students how to apply mathematical programming to a real-life context. The case deals with food donation supply chain management. Using a project-based approach, thecase proposes a realistic scenario simulating the consultancy interaction with the (fictional) nonprofit company. The Logica&Co acts as a
two-sided platform connecting supply and demand. The objective isto define an effective strategy for collecting and delivering food donations from local businesses to soup kitchens over a semester-longtimeframe. The problem exhibits non-linear characteristics; however,
it is designed with adjustable difficulty levels. An interactive offline
tool, the SoS simulator, can be furnished to students for performanceassessment. the tool is publicly available for download and can be customized by the instructors. The case was proposed as a competitive
group challenge to students of the Bachelor’s or Master’s program inManagement Engineering at Sapienza University of Rome. Students
appreciated both the teaching methodology and the teamwork aspects;
they highlighted the utility of the SoS simulator tool.

 MD-17
Monday, 14:30-16:00 - Room: 40 (building: 116)

Medical decision making
Stream: OR in Health Services (ORAHS)
Invited session
Chair: Antonio Sergio da Silva
1 - Insufficient Addressing of Patient Needs with Multiple
Chronic Conditions in countries with large or growing
populations: Implications for Clinical Practice Guidelines

130

Ali Hajjar, Oguzhan Alagoz
Existing clinical practice guidelines tend to overlook the unique requirements of patients with multiple chronic conditions (MCC) by primarily focusing on the management of individual diseases. This inadequately personalized approach leads to adverse events and places afinancial strain on the healthcare system, particularly in countries withlarge or growing populations. To address this issue, our study utilizes astochastic modeling approach to optimize disease screening decisions
while considering financial and resource limitations from a broader societal perspective. Our findings indicate that women with pre-diabetes
should undergo less frequent screening compared to those without diabetes. Moreover, our numerical results demonstrate the robustnessof the optimal breast cancer screening policy in the face of relativechanges in mammography screening costs.

2 - Optimal Usage of Packed Red Blood Cells in Preterm
Neonates Requiring Frequent Small-Volume Transfusions

Malavika Krishnakumar, Merin Mathew, Anjali Ajith, Sruthi
Suresh, Perraju Bendapudi, Linda John, Dhanya A, Veena
Shenoy, Georg Gutjahr
Preterm neonates with low birth weight often require frequent smallvolume blood transfusions. Instead of addressing each transfusion
event individually, reserving blood bags in advance may prove to beeconomical and minimize donor exposure. However, it might introduce storage challenges and could lead to the wastage of blood. An
optimal strategy for reserving blood bags depends on neonatal clinicalcharacteristics and available blood reserves in the transfusion service.
To model this decision problem under uncertainty, we assume that the
known and unknown random variables are structured as a Bayesian
network and will use a partially observed Markov decision process.
At each day, the state of the process is described by the blood inventory and the neonatal clinical characteristics. Neonatal gestational age,weight, and medical conditions are observed variables. In contrast, thelength of stay and the total amount of required blood are unknown at
the time of decision-making.
The proposed model can be expressed as a dynamic Bayesian networkmodel in which the decision variables can be optimized by dynamic
programming. We explore the sizes of the problem instances that canbe solved in this exact way and also propose an efficient approximate
dynamic programming method for large instances. We illustrate themethods with one year of data from a neonatal ICU in South India
where financial and blood-availability constraints play a crucial role infinding optimal strategies.

3 - The "Netflix Model": A New Payment Model for Asymptomatic Disease Management

Huaiyang Zhong
In the U.S., over 3 million are estimated to have hepatitis C, withmany cases undiagnosed. Early detection of hepatitis C is crucial but
hindered by high screening and treatment costs. The paper discusses
how Netflix-style contracts could incentivize increased screenings andtreatment by offering fixed payments for unlimited drugs

4 - Selection of Glucose Monitoring Devices Using the CoCoSo Method: An Innovative Approach to Diabetes
Management

Antonio Sergio da Silva, Marcos Santos, Carlos Francisco
Simoes Gomes
This study aims to implement the Combined Compromise Solution(CoCoSo) method to assist decision-makers in selecting a device forContinuous Glucose Monitoring (CGM) that enables effective management of people with diabetes. The CoCoso method is a Multi-Criteria
Decision Analysis (MCDA) approach for selecting the best alternative
among the available ones, considering the relative importance of criteria. In the current study, seven different types of CGM devices are
examined, considering criteria such as accuracy, cost, number of compatible platforms, number of additional features, and setup time. The
Preference Selection Index (PSI) method was used to determine therelative importance of these criteria. Sensitivity tests were conductedEURO 2024 - Copenhagenby adjusting the value of the parameter λ between 0.25 and 0.75. The
results showed significant consistency, as evidenced by the Spearman
correlation coefficients, which remained above 0.96. This high levelof correlation indicates that, despite variations in the value of λ, therankings of alternatives remained stable, reinforcing the reliability andvalidity of the model used in the research. The study concluded that
the FreeStyle Libre 2 device is the highest-ranked alternative.

 MD-18
Monday, 14:30-16:00 - Room: 42 (building: 116)

Enhancement of circularity, inclusivity, andsmartness in cities II
Stream: Sustainable Cities
Invited session
Chair: Akito Murayama
1 - Understanding the Value of Retail Accessibility in Private Housing Markets: A Study from Turin, Italy

Marco Del Nibletto, Giulio Zotteri, Marta Bottero, Federico
Dell’Anna
The relationship between urban amenities and property values has been
widely studied, yet the specific contribution of retail accessibility tohousing prices in European urban areas remains underexplored. This
paper makes a significant contribution to this field by introducing a newclassification of retail categories within the Hedonic Pricing Method
and Geographically Weighted Regression (GWR) frameworks. It categorizes retail into five types: shopping, convenience, supermarkets,bars & restaurants, and traditional markets, to capture the influence ofthe retail landscape on housing values. A key innovation is the use ofa gravity-based accessibility index to quantify the proximity of various retail types to residential properties, using a decay parameter that
optimizes the explanatory power of retail accessibility. The findings
suggest that shopping, bars & restaurants have a positive effect, whileconvenience retail and traditional markets have a negative impact. The
study did not find a significant relationship between housing values
and accessibility to supermarkets. GWR was used for the first time inthis research domain, providing new insights into the spatial dynamics
at play. The study focuses on Turin, Italy, and provides valuable information for urban planners and policymakers regarding the differential
impacts of retail accessibility across urban areas. It highlights the importance of strategic support for the retail sector to enhance the valueof investments.

2 - Exploring Urban Regulation Effectiveness: Investigating Retailers’ Distribution Costs

Negin Jamili, Pieter van den Berg, René de Koster
Governments employ diverse strategies, including financial disincentives, restrictions, and subsidization, to shape urban environments andachieve specific objectives like reducing CO2 emissions. In the Netherlands, policies like time windows and vehicle restrictions are implemented to enhance urban transportation. Time windows reduce inconvenience caused by large trucks during peak hours, improving social sustainability, while vehicle restrictions alleviate traffic problems
and pollution, promoting a cleaner and safer urban environment. This
research research investigates the effectiveness of these regulations.
Specifically, we focus on retailers and their distribution and replenishment operations to examine the effects of regulations. So, the concept involves optimizing the operational aspects of the retailer, suchas distribution and replenishment, within the framework of specified
regulations. The output of this optimization process guides us throughgauging the city measures to figure out if they are in line with the goalsof regulations. We also explore whether city hubs may be an efficient
addition under a given regulatory framework. In the case of confirming the effectiveness of city hubs, subsidizing the city hubs is also an
option to address urban transport issues. The result of this study may
offer valuable managerial insights into the evaluation of the effectiveness of urban policies and regulations.

MD-19

3 - Multiple benefits spatial impact assessment for the
project Varcities

Sara Biancifiori, Sara Torabi, Adriano Bisello, Patrizia
Lombardi
The Varcities Horizon project aims to implement visionary naturebased solutions (VS), which contribute to the shaping of future green
cities and the well-being of citizens in seven European cities. The
present study aims to promote the replication of these solutions on a
local level, providing a decision support tool to guide local administrations with a common baseline. As part of the tool, a spatial impact
analysis will be presented, showcasing the positive impacts developed
from the project. The theory of multiple benefits considers the positive
impacts that can result from a NBS project without ranking or classification. The co-creation strategy for the project was structured into
four main stages: co-identification, co-design, co-implementation, andco-evaluation. During the co-design phase, the stakeholders involvedin each pilot selected a set of multiple benefits as goals for the development of the visionary solutions. During the co-monitoring phase,the solutions are being monitored through Key Performance Indicators (KPIs). To verify the multiple benefits achieved by the solutions,a validation workshop with stakeholders is proposed, followed by aspatial visualization of the KPIs connected to each Visionary Solution.
This will demonstrate the various benefits connected to the solutions.
The spatial impact assessment will assist administrations implementing VS, translating the theoretical concept of multiple benefits into a
more comprehensible one.

4 - Measuring Urban Smartness: A Comparative Study ofEuropean Cities Using Multi-Criteria Analysis

Sandra Milanović, Jelena J. Stanković, Ivana Marjanović
The prevailing and anticipated rates of urbanization are recognized as
substantial challenges to the society. Urban areas have been mapped
as primary drivers of economic development, however, the rapid expansion of cities in recent decades has raised concerns regarding theirsustainability, as evidenced by escalating infrastructural, ecological,
and social challenges. Bearing in mind the benefits of smart urban development, this paper aims to assess the level of achieved smartness
in European cities using a multicriteria analysis approach. By utilizing data from the latest Eurostat Urban Audit Perception Survey, theresearch assesses the perceived urban performance by inhabitants, encompassing various dimensions of smart and sustainable urban development. To analyse the diverse attributes of cities’ urban performance,
a multi-criteria model is developed. This model integrates entropy for
determining criteria weights and employs the PROMETHEE method
for ranking. Respondents’ subjective preferences are measured using a 4-point Likert scale, and responses are quantified for analysis
purposes. The resulting rankings are visually presented to identify
European urban areas where inhabitants perceive the highest overalllevels of smartness and sustainable urban development. The obtained
results may serve local self-governments, by offering a diagnostic toolfor evaluating the effectiveness of existing smart city initiatives andidentifying areas for improvement.

 MD-19
Monday, 14:30-16:00 - Room: 44 (building: 116)

Electric Vehicles within Electric Power
Systems
Stream: OR in Energy
Invited session
Chair: Pablo Diaz-Cachinero131MD-20EURO 2024 - Copenhagen

1 - Assessing the Capability of Electric Vehicles to Enhance Flexibility of the Chilean Power System

Gabriel Canales, Angela Flores
Chile is actively pursuing an ambitious agenda for the widespread electrification of the transportation sector, stablishing that by 2035, 100%of sales for all light and medium-duty passenger vehicles must be zeroemission. The large-scale integration of electric vehicles, while posing substantial challenges in managing the power grid, concurrently
presents opportunities to significantly enhance system flexibility. This
work analyzes the potential of electric vehicles to augment the flexibility of the Chilean power system. We introduce an operational modelfor electric vehicles, encompassing both personal vehicles and ridehailing services, while integrating intelligent charging and discharging
modes. The EV operational model is integrated into a unit commitment model, with the objective of assessing the impact of various electric vehicle operation modes on the power system operation. Applying
this model to a realistic representation of the Chilean power system
towards 2050, we evaluate the potential of electric vehicles in enhancing flexibility metrics. Furthermore, we investigate the influence of the
adoption of ride-hailing services on the flexibility provided by the vehicle fleet. This research contributes valuable insights within the context
of the Chilean power system, utilizing a realistic representation andconsidering the evolving landscape of electric transportation.

2 - An Iterative Multistage Model for Electric Vehicle Routing and Charging Management in Road Transportation
and Power Distribution Systems

Pablo Diaz-Cachinero, Javier Contreras, Jose Ignacio
Muñoz-Hernandez, Mahdi Pourakbari-Kasmaei, Matti
Lehtonen
Electric vehicles (EVs) emerge as a promising alternative to fossil fuelpowered vehicles, noted for their zero emissions. As more EVs taketo the roads, managing their interaction with road transportation andpower distribution systems becomes crucial to meet electrical demand.
This study presents a new multistage model that integrates EV delivery routing with energy charging management. The model considers
the constraints of EVs and parcel delivery in the road transportation
system, while the power distribution system operation is modeled via
a linearized AC power flow model including generators and conventional demand. This work considers an EV demand aggregator, whose
aim is to find the best strategy in terms of costs and benefits by adjusting EV routing via incentives for availability time and charging EVs atpoints with the lowest locational marginal prices (LMPs). The model
unfolds through three main iteratively interconnected stages. First, delivery allocation and detailed routing are tackled to minimize traveled
distances and operational costs. Then, an iterative linkage between
the road transportation and energy distribution systems is performed.
Finally, a bi-level model is proposed to optimize the charging strategy by the EV demand aggregator in the power distribution system,
minimizing costs based on LMPs. Numerical results validate the proposed model with a case study that includes 3,000 EVs within a 284intersection map and a 119-bus power distribution system.

 MD-20
Monday, 14:30-16:00 - Room: 45 (building: 116)

Military, Defense, and International Security
I
Stream: Military, Defense, and International Security
Invited session
Chair: Peter Bovet Emanuel
1 - Integrated
Risk-based
Bi-objective
Surveillance
Scheduling for Undersea Infrastructure Safety

Banafsheh Khosravi, Tsz Yu Suen, Ashraf Labib, Dylan Jones

132

This study presents a novel approach to schedule surveillance operations for critical undersea infrastructure. The problem addresses thevulnerability of undersea infrastructure to deliberate attacks, natural
disasters, accidents and anchor droppage incorporating their associated risks. The aim is to allocate and order the surveillance operations across different levels, including undersea, surface, and aerial
activities. There is a trade-off between minimising surveillance operations cost and maximising incident detection. Therefore, the problem is formulated as a novel bi-objective flexible job-shop scheduling model (BO-FJSS) considering operational constraints for multiple
types of surveillance assets, such as remotely operated underwater vehicles (ROVs), autonomous underwater vehicles (AUVs), unmanned
aerial vehicles (UAVs) and surface vessels. The study also emphasiseson the significance of various risk factors, achieved by utilising theAnalytic Network Process (ANP) to assess the risks at different depths
and incorporating them as weights in the BO-FJSS model. The computational experiments on a specific gas pipeline between the UK and
Norway, offer valuable insights into the diverse levels of risks associated with various events within different depth ranges. This information can assist in prioritising risk management strategies and efficiently
allocating resources.

2 - A quadtree-based heuristic and variable neighborhood
descent for the optimal location of heteregenous multistatic sonar network

Owein Thuillier, Nicolas Le Josse, Alexandru Olteanu, Marc
Sevaux, Hervé Tanguy
In the context of airborne Anti-Submarine Warfare (ASW), we focus
here on active sonar systems, i.e. systems based on the transmission
and reception of an acoustic wave reflected by the target (echo). Furthermore, our case study involves acoustic buoys, or sonobuoys, which
can be classified into three main categories: transmitter-only (Tx),receiver-only (Rx) and transmitter-receiver (TxRx). When transmitterand receiver are co-located - TxRx buoy - we refer to this as monostatism, while when transmitter and receiver are located in two distinct geographical locations, we refer to this as bistatism. A Multistatic
Sonar Network (MSN) is therefore defined as a combination of sonar
systems in monostatic and/or bistatic configuration. In this context,
given a limited number of heterogeneous sonobuoys and probabilistic
detection models, we seek to determine the optimal MSN, i.e. the onethat maximizes the area covered. To address this issue, we propose
a constructive greedy heuristic relying on a QuadTree-based approach(QT) enhanced by a local search through Variable Neighborhoud Descent (VND). This heuristic is based on a recursive principle - akin tobranch & bound - dividing the area of interest into sectors and accelerating the search for locally optimal positions through judicious computation of upper and lower bounds. Finally, we compare our methodwith the most efficient Mixed-Integer Linear Program (MILP) in theliterature as well as a Simulated Annealing (SA).

3 - Augmenting a Joint Force Dynamic Targeting Decisions

Peter Bovet EmanuelThis study is motivated by optimizing the utilization of military resources within a joint force involved in targeting activities during operations. The weapon-to-target problem (WTA) is type of assignment
problem as each target requires a matching with an appropriate weapon
and each weapon is a resource within a joint force’s targeting enterprise. Engaging high priority targets necessitates consideration including its characteristics, vulnerabilities, and level of protection. Furthermore, one’s own weapons must be effective against the specific target
to create a desired effect. High priority targets often require expensive weapons with high accuracy which makes these weapons scarce
resources. Emerging targets in the battlespace are associated with uncertainties, priorities, compatible weapon types and the corresponding
number weapons to a desired effect. The matching of weapon-to-target
is constrained by inherent capacities, the protection level of the targets,and subject to probability in the execution of each mission or target engagement. All prioritized targets need to be engaged and neutralized,
therefore potentially requiring a re-attack in a second stage. Therefore,
the study employed a multi-stage and multi-objective model to replicate a dynamic targeting environment and linearized the probability
aspect to enable integer linear programming (ILP) model. The study

EURO 2024 - Copenhagenpresents a new way of modelling WTA problems, augmenting a joint
force targeting decisions.

 MD-21
Monday, 14:30-16:00 - Room: 49 (building: 116)

Site selection and aid allocation inhumanitarian operations
Stream: OR in Humanitarian Operations (HOpe)
Invited session
Chair: Douglas Alem
1 - Central-Local Governments Cooperate to Pre-Position
Relief Supplies for Natural Disaster Response

Cheng-Lin Fong, Tsung-Sheng Chang
Prepositioning of relief supplies is crucial for the timely response todisasters. In a country, central government leads the management ofmass casualty incidents (MCIs), while local governments must be able
to handle non-MCIs that may occur in their responsible areas. To
preposition relief supplies, the central and local governments must determine the number, locations and sizes of facilities, and the amount ofdisaster relief supplies prepositioned in the facilities. Each local government individually optimizes its prepositioning decisions. However,once an MCI happens, the central government needs to coordinate local governments to offer sufficient suppliers to meet the demand fromthe affected areas. That is, the central government must take the local
governments’ prepositioning decisions into account to optimize its nationwide prepositioning decisions. Therefore, this research intends todesign incentive schemes that help the central government coordinate
the local governments to make optimal prepositioning decisions. To
this end, we mathematically model the central government’s prepositioning problem, and then propose a solution algorithm based on themodel to solve the problem. An empirical study is conducted to verify
the proposed mathematical model and solution approach.

2 - Multi-period facility location problem with resource allocation in humanitarian logistics

Yufeng Guo, Etsuko Nishimura
Large-scale natural disasters have caused significant casualties andenormous economic losses. Effectively responding to disasters has
become primary challenge for logistics authorities. After a disaster,
shortages or over-supply may occur in the resource supply chain. oneof the challenges that must be addressed is how to make up for shortages. To rationalise the use of limited resources, decision-makers
have to decide facilities’ operations and resources allocation basedon changes in disaster phases. With this issue in mind, we propose
a mathematical model addressing the facility location problem within
the context of post-disaster phases across multiple periods, integrating
equity, transshipment, and capacity limitations. To validate the mathematical formulation and assess the effect of individual and combined
concepts on the foundational model. We conducted a series of numerical experiments using python and commercial solver, Gurobi. The
results indicate that the equity primarily influences the maximum unsatisfaction rate on the demand side, while the transshipment predominantly impacts emergency logistics’ operations and resources allocation. The limitation of capacity mainly affects transfer ability between
emergency logistics centers. Furthermore, we gleaned additional managerial insights through sensitivity analysis of critical parameters, anddelineated practical application for integrating our model into disastermanagement system.

3 - Strategic Decision Support in Disaster Relief Logistics:
Stochastic Network Optimization for Efficient Resource
Location and Allocation

Katharina Eberhardt, Patricia Fuchß, Sonja Rosenberg, Frank
Schultmann

MD-22

In response to the complex challenges of disaster relief logistics, we
present a stochastic network modeling approach to develop insights
into strategic facility location, capacity planning, and resource allocation of relief supplies. The primary purpose of the proposed modeland numerical experiments is to present a cost-effective logistics network designed to efficiently handle multiple relief items across a spectrum of different crisis scenarios. By incorporating stochastic elements
into the analysis, we aim to capture the inherent unpredictability ofdemand fluctuations and supply chain disruptions. Moreover, our approach centers on optimizing facility sizes to harness economies ofscale and refining allocation decisions. To demonstrate the practicalapplicability of our model, we conduct a computational case study
utilizing instances from the German national food stockpiling system.
Additionally, we present a sensitivity analysis highlighting the impact
of network modifications, crisis intensity, and scale effects on facility location and assignment decisions. The results provide managerial
insights and economic considerations for decision-makers, enhancing
the resilience and adaptability of cost-effective disaster preparedness
and network design strategies.

4 - United Nations Humanitarian Response Depot’s Food
Support in South Asia via Multi-stage Stochastic Programming

Ruoyu Hu, Douglas Alem, Aakil Caunhye
As one of the largest humanitarian logistics supply chains, the task for
United Nations Humanitarian Response Depot (UNHRD) is how to allocate relief aid to save more people who suffered from disasters. Thistask is particularly challenging in areas like South Asia, where food aid
efforts are confronted with complex transportation conditions, significant economic disparities, and the frequent occurrence of disasters, notto mention that relief aid resources are often scarce. In this research,
we develop a novel Multi-stage Stochastic Programming (MSP) modelto help UNHRD support critical decisions regarding site selection andaid allocation. Differently from the main literature where these decisions are often made within a two-stage paradigm, our three-stage
perspective takes into account relief aid donation campaigns that are
triggered depending on the disaster impact and its effects. Our objective function maximizes coverage weighted by the population profile, leveraging suitability indicators from locations to enhance strategic decisions on aid distribution. The results overall highlight the roleof donated food aid in settings with severe constraints, demonstrating
the MSP model’s effectiveness in enhancing strategic response throughstaged information processing. This approach not only illustrates themodel’s adaptability in complex disaster scenarios but also its capacity
to mitigate uncertainties in aid demand coverage, even when increases
in coverage are marginal.

 MD-22
Monday, 14:30-16:00 - Room: 81 (building: 116)

Energy transition and operations
Stream: Energy Management
Invited session
Chair: Ece Cigdem Karakoyun
1 - Analysis and Mitigation of Unintended Carbon Emissions from Storage and Demand Response.

Olga Kuryatnikova, Clarisse Dupont, Yashar
Ghiassi-Farrokhfal, Derek Bunn
Storage and demand response could provide necessary flexibility toaccommodate the influx of intermittent renewable energy and facilitate the low carbon energy transition. However, the operation of theseflexible assets may inadvertently intensify carbon emissions, leading
to a conflict between profit maximization and environmental impact.
We develop a model to characterize the factors causing this effect. Essentially, both storage and demand response act as load-shifting agents(LSAs), and therefore, they indirectly influence emissions. The scopeof this influence depends upon the carbon intensity profile of the supply

133MD-23EURO 2024 - Copenhagen

function in the wholesale market and the round-trip efficiency of LSAs.
Some of the insights are surprising, e.g., the more efficient LSAs do notalways lead to lower carbon emissions. Next, we propose and analyze
several policy instruments aimed at reducing the unintended emissions
caused by LSAs. Finally, using data from the Dutch market, we simulate the impact of LSAs and the emission mitigation instruments. We
find that in 2019, simply adding LSAs to the market would reduce
the total carbon emission. Conversely, in 2022, some LSAs, such as
pumped hydro storage and lithium-ion batteries, would increase theemission if no CO2 mitigation instruments were applied.

2 - Optimal Hour-Ahead Commitment and Storage Decisions of Wind Power Producers

Ece Cigdem Karakoyun, Harun Avci, Tim Huh, Ayse Selin
Kocaman, Emre Nadar
Renewable energy generators often rely on their battery deployments
to meet their dispatch or purchase commitments in electricity markets. However, the literature optimizing the commitment decisions
with storage considerations is scarce. In this paper, we study the jointenergy commitment and storage problem for a wind farm paired witha battery. The power producer decides, in each hour of a finite planning horizon, how much energy to commit to dispatching or purchasing for the next hour as well as how much wind energy to generate andhow much energy to charge or discharge. The power producer pays apenalty cost if she does not fully meet her commitment. We model thisproblem as a Markov decision process with random electricity priceand wind speed. We prove the optimality of a state-dependent threshold policy under positive prices. This policy partitions the state space
into several disjoint domains, each associated with a different action
type, such that it is optimal to bring the storage and commitment levels
to a different threshold pair in each domain. We employ our structural results to develop a heuristic solution procedure in a more general problem where the price can also be negative. Numerical resultsfor data-calibrated instances indicate the high efficiency and scalabilityof our solution procedure: it yields optimal or near-optimal solutions
with a speedup of two orders of magnitude over the standard dynamic
programming algorithm.

3 - Conformal Prediction for Stochastic Decision-Making of
PV Power in Electricity Markets

Tarek Alskaif
The increasing integration of solar photovoltaic (PV) power poseschallenges for power system operation. Accurate forecasts of PV
power are both financially beneficial for electricity suppliers and necessary for grid operators to optimize operation and avoid grid imbalances. This talk will explore a framework utilizing conformal prediction (CP), an emerging probabilistic forecasting methodology, to assist
decision-making for PV power market participants on the day-aheadmarket (DAM). It will begin by demonstrating how machine learningmodels can be employed to construct the point predictions based on
weather forecasts. Subsequently, various variants of conformal prediction are introduced to quantify the uncertainty of these predictions.
The talk will then explore the application of several market bidding
strategies, including trust-the-forecast, worst-case, Newsvendor andexpected utility maximization, to facilitate decision-making for market participants on the DAM using CP methods. Through a case study
in the Netherlands, it will highlight how CP when combined with certain bidding strategies can lead to increased profit with minimal energy
imbalance, outperforming classical probabilistic forecasting methods,
such as linear quantile regression. The talk will conclude by highlighting the need to expand the framework to cover other short-term
markets, namely intra-day markets.

4 - Efficient representations for the scheduling of aggregate charging power of battery electric vehicles

Simon Tindemans, Nanda Kishor Panda
Battery Electric Vehicles (BEVs) must connect to a charging point torecharge their batteries. They are often plugged in for longer than theyneed to gain sufficient charge, so there is scope for smart schedulingof charging within the available window. Moreover, as the maximum
charging power increases, this flexibility potential increases, but so134

does its necessity: collectively, charging vehicles can easily overload
the capacity of the local electricity grid.
Aggregators that control the charging of BEVs use smart charging to
optimize the aggregate power demand, for example, to trade on electricity markets. However, even in a fully deterministic setting, direct scheduling of large numbers of BEV charging sessions (tens of
thousands) becomes a significant computational burden, because thenumber of variables increases linearly with the number of vehicles. It
would be better to directly represent the flexibility of the aggregate
power, but calculating the aggregate flexibility envelope is in general
an NP-hard problem.
I will present a flexibility representation (UL-flexibility) that can efficiently calculate the exact aggregate envelope, for the special casewhere all vehicles remain connected during a specified interval. This
representation involves only 2T parameters and can efficiently be used
to construct the polytope that constrains the aggregate power consumption of a fleet - independent on the number of BEVs.

 MD-23
Monday, 14:30-16:00 - Room: 82 (building: 116)

Scheduling for sustainability
Stream: Circular Economy, Remanufacturing and Recycling
Invited session
Chair: Andrea Mencaroni
1 - Scheduling Large-Scale Infrastructure Renovation
Projects Minimizing Emissions Using a Hybrid Genetic
Algorithm

Robbert Bosch, Patricia Rogetzer, Wouter van Heeswijk,
Martijn Mes
The safety and accessibility of infrastructure such as bridges and quay
walls in cities is important for the everyday life of its inhabitants.
However, due to wear and tear over the years, these infrastructure
pieces need to be maintained or renovated. These large-scale renovation projects generate CO2 emissions by the material production
chain, the logistics surrounding the supply of material and equipment
and removal of waste, and the increased traffic due to traffic diversions. The magnitude of these emissions can be difficult to predict,especially when multiple projects are executed in parallel due to timepressure. Parallel execution of projects may yield economies of scale
regarding logistics but can also cause excessive congestion. Optimizing the schedule of these projects by finding the right combinations ofprojects to perform simultaneously can reduce the total emissions andreduce project delays. To aid decision-making, we use multi-objective
optimization to minimize total emissions while simultaneously reducing project delays. We present a Hybrid Genetic Algorithm to produce
a partial Pareto frontier of schedules for large-scale urban infrastructure renovation projects. This research applies the described solution
method to the problem of renovation of bridges and quay walls in Amsterdam. The results illustrate the trade-off between sustainability andtempo of renovations.

2 - Towards energy-aware daily production optimization

Pieter Leyman, Stijn De Vuyst
Imagine a world in which production companies do not have to worry
about their energy supply. Not just because they have their own renewable energy sources (e.g., solar panels, wind turbines) and energy
storage (e.g., industrial batteries), but more importantly because their
entire production system has been designed and is managed to optimally use the available electrical and thermal energy. In essence, focus
has shifted from optimizing production goals and subsequently determining where and how to get energy from, to optimizing productionand energy goals together. As a result, production is more sustainable and cheaper in terms of energy costs, without sacrificing capacity.
Unfortunately, the energy crisis has proven that we are presently far
removed from such an ideal world.

EURO 2024 - Copenhagen
We will provide a holistic roadmap on energy-aware daily production
scheduling, in which the technological aspects of energy supply andstorage are taken into account, and are furthermore matched with energy demand from production. We will subsequently zoom in on a
specific problem, namely the order acceptance and scheduling (OAS)
problem, with the inclusion of electricity grid costs based on day-ahead
prices. Aside from a mathematical model, we will discuss a metaheuristic algorithm, and compare the performance of both on a small
dataset.

3 - Variability reduction in semiconductor manufacturing
using machine learning and explainable AI

Venugopal Ramadasu, Fabian Lindner, Gerald Reiner,
Germar Schneider, Gerhard Luhn
The impact of variability on a production system in terms of flow time
and work in progress is a well-known problem. Recent research works
investigating the application of machine learning (ML) models on production data to improve process performance have shown promising
results. Drawing inspiration from such works, in the first part of thestudy we do partial replicative research and apply existing ML models to identify the sources of variability such as machine parameters,
defects, product routing and so on that lead to increased flow time ina semiconductor manufacturing plant. Using the production data for
a work center in the wafer production process, we identify the criticalparameters in the work center that influence the flow time using ML
algorithms such as random forest, neural networks and gradient boosting. To enhance the interpretability of the results, we apply explainable
AI (XAI) approaches such as Shapley Additive Explanation (SHAP).
As a second step, the results will be presented to process experts toderive actions that can reduce the variability in the process and thus
enhance the efficiency in resource utilization. Through our work, we
expect, on the one hand, to reinforce the potential benefits of using
ML models to understand and improve production processes, and on
the other hand, to increase the trust of experts on the use of ML models and to leverage their knowledge to tackle the issues they face inmanufacturing.

4 - Reducing scope 2 emissions through carbon-aware
flow-shop scheduling

Andrea Mencaroni, Dieter Claeys, Stijn De Vuyst, Birger Raa,
Pieter Leyman
Detailed scheduling has traditionally been optimized for the reductionof makespan and manufacturing costs. However, growing awarenessof environmental concerns and increasingly stringent regulations arepushing industry towards reducing the carbon footprint of its operations. Scope 2 emissions, which are the indirect emissions related tothe production and consumption of grid energy, are in fact estimated tobe responsible for more than one-third of the global GHG emissions.
In this context, carbon-aware scheduling can serve as a powerful way
to reduce manufacturing’s carbon footprint by considering the timesensitive carbon content of grid energy and the availability of on-site
renewable energy.
This study introduces a carbon-aware flow-shop scheduling model designed to reduce scope 2 emissions. The model is formulated as amixed-integer linear problem, taking into account the forecasted gridenergy generation mix and available on-site renewable energy, alongwith the set of jobs to be scheduled and their corresponding energy
requirements. The objective is to find an optimal day-ahead schedulethat minimizes scope 2 emissions. The problem is addressed using
a dedicated memetic algorithm, combining evolutionary strategy andlocal search.
Results from a comprehensive case study confirm that by considering the dynamic carbon content of grid energy and on-site renewableenergy availability, significant reductions in carbon emissions can beachieved.

MD-24

 MD-24
Monday, 14:30-16:00 - Room: 83 (building: 116)

Sustainable Operations
Stream: Sustainable Supply Chains
Invited session
Chair: Jens Brunner
1 - Capitalizing on the Bill-of-Materials to efficiently respond to sustainability challenges

Renske Prins, Jan van Dalen, Rob Zuidwijk
Regulatory requirements and market demand necessitate that companies accurately report on sustainability, monitor risks, and undertake
actions to reduce emissions and improve ethical performance (SEC,
2022; CSR Germany, 2023; European Commission, 2023; Khan etal., 2021). This can be a daunting task in a complex global economy.
High-level strategies and highly detailed emission reduction initiatives
have been suggested (Bressanelli et al., 2022; Preut et al., 2021; Izmirli
et al., 2021; Andersen and Jaeger, 2021; Bogers, 2011; Puertas et al.,2008; Frias et al., 2015; Karell and Niinimaki, 2020; Akadiri and Olomolaiye, 2012). However, addressing this challenge requires scalable
solutions. This paper shows how the Bill-of-Materials (BoM), as theskeleton of a company’s operations and supply chain, can be an instrument for such scalability. By enhancing the BoM with the emissions
and activity data needed to guide these important initiatives, companies can improve accurate and continuous reporting. The enhanced
BoM graphs are used for emission optimization and product carbon
footprint calculation, and guide business decisions about the focus ofemission reduction efforts. Utilizing network analysis techniques, thestudy demonstrates the effectiveness of an enhanced BoM in scalingsustainable initiatives and complying with regulations, thereby bridging the gap between high-level sustainability strategies and detailed
emission reduction initiatives.

2 - Learning Priority Indices for Energy-Aware Schedulingof Jobs in Flow Shops with Batch Processing Machines

Daniel Schorn, Lars Moench
A scheduling problem for a two-stage flexible flow shop with parallelbatch processing machines (BPMs) is considered. Here, a batch is agroup of jobs that are processed at the same time on a single machine.
This problem is motivated by process conditions found in semiconductor wafer fabrication facilities (wafer fabs). Jobs having unequal
ready times are assumed. Moreover, critical queue time constraints,
i.e. maximum time lags, are taken into account. A blended objectivefunction combining the total weighted tardiness (TWT) and the total
electricity cost (TEC) under a time-of-use (TOU) tariff is considered.
A genetic programming (GP) procedure is designed to automaticallydiscover priority indices for a heuristic scheduling framework. Resultsof computational experiments based on randomly generated problem
instances are reported that demonstrate that the learned priority indiceslead to high-quality schedules in a short amount of computing time.

3 - Environmental and operational optimization of Central
Sterile Supply Departments in Danish and German hospitals

Lukas Messmann, Robin Schlembach, Jens Brunner,
Sebastian Schiffels, Axel TumaCentral Sterile Supply Departments (CSSD) play a critical role in theoperation of hospitals and for their overall environmental footprint.
Operationally, the batching of jobs and their scheduling and allocation
to washer-disinfectors and sterilizers is a complex decision-making
problem, with CSSDs being potential bottlenecks in surgery scheduling. Environmentally, on the one hand, CSSDs enable the reusing ofsurgical and other sterile equipment and thus help to reduce the environmental impacts of production and waste treatment associated withalternative disposable solutions. On the other hand, CSSDs themselves
consume substantial amounts of electric and thermal energy, chemicalcleaning agents, water, and disposable equipment (e.g., face masks,135

MD-25EURO 2024 - Copenhagen

gloves, fleeces). Critically, operational and (various) environmental
objectives are often strongly conflicting. In this work, we develop
a two-stage batch-scheduling flexible flow shop model of a CSSD,
which is parameterized with Life Cycle Assessment-based environmental data. The model and its parameters are validated by real-worldcases of CSSDs in German and Danish hospitals. We anticipate themodel to identify possible compromise solutions between conflicting
objectives (e.g., by adjusting utilization rates and time-temperature
profiles in autoclaves), thereby concurrently contributing to the operational efficiency and sustainable transformation of hospitals.

4 - Driving Sustainability through Symbiotic Supply
Chains: An Operations Research Modeling Framework

Susana Relvas, Mafalda Ivo de Carvalho, Vânia Veloso,
Bruna Mota, Ana Carvalho, Ana Barbosa-Povoa
In contemporary discourse, the pursuit of sustainability underscores
the profound impact of supply chains (SC) on environmental dynamics. Despite efforts to manage direct greenhouse gas emissions, the lesstangible indirect emissions require a comprehensive approach to emissions management. Industrial symbiosis (IS) in a context of Supply
Chain Management that results in Symbiotic Supply Chains (SymSC)
is a possible solution that yet remains underexplored. This work proposes a novel definition of Symbiotic Supply Chains (SymSC), building upon existing definitions and frameworks. It emphasizes the pivotal role of collaboration in the share of flows under the nexus ofmaterial-energy-water in a network involving multiple SCs. Given
their complexity, SymSCs require detailed planning and management
which can be facilitated with the integration of Operations Researchtechniques. We provide the basis for modeling guidelines to optimize
SymSC decisions, which build on the existing IS models available in
the literature. This study contributes to the understanding of symbiotic
relationships across SCs, focusing on key features characterizing optimization models in the context of IS. Recognizing that SymSCs holdpromise in advancing the sustainability agenda, a roadmap for future
research and decision-making tools to enhance sustainability performance in the context of SymSC is provided.

current period, future requests and their locations are unknown. The
objective is the minimization of the total expected technicians’ travel
and customers’ waiting costs. Several heuristic methods are proposed
to solve the multi-period problem. We perform an extensive numerical
study comparing the performance of the heuristic methods in different
problem settings and provide managerial insights.

2 - Enhancing on-time order fulfillment for autonomous order picking and delivery systems of online retailers

Zhengmin Zhang, Zhe Yuan
Many online retailers start to fulfill orders within one hour or evenminutes to guarantee a good customer experience. This study analyzes the on-time order fulfillment problem for an autonomous orderpicking and delivery system in online retailers. We draw inspirationfrom Meituan’s pioneering solution, "Autonomous micro-warehouseand delivery", in which micro-warehouse robots are used for orderpicking, and delivery robots are used for transporting orders to destinations. We investigate the dynamic priority policy to facilitate efficient task allocation for different order classes, which ensures prompt
service for high-priority orders and attentive handling of low-priority
orders with extended waiting times.
We develop a dynamic priority-based queueing network to model thesystem and propose an AMVA-based approximation method to solve
it. Numerical experiments validate the analytical model, providing
valuable insights into operational efficiency. This paper makes thefollowing contributions: (1) We are among the earliest to investigate
autonomous order picking and delivery systems using queueing networks. We propose a new approximation method to solve the proposed nested queueing network with dynamic priority. (2) We outline
procedures for determining the optimal numbers of micro-warehouserobots and delivery robots, considering the order fulfillment time requirements. (3) We find that the use of dynamic priority can improve
the on-time order fulfillment rate.

3 - Grey systems theory and a Markov chain model MCGM(1,1) applied to demand forecast for a 3PL provider

Francisco Trejo, Rafael Torres Escobar MD-25
Monday, 14:30-16:00 - Room: 011 (building: 208)

Discrete, continuous or stochastic
optimization and control in networks,transportation and design IV
Stream: Combinatorial Optimization
Invited session
Chair: Gerhard-Wilhelm Weber
Chair: Olabode Adewoye
Chair: Milagros Baldemor
1 - Allocation and routing of service technicians with different skill levels

Post-pandemic logistics showed that just-in-time was not always themost successful supply alternative. The effect in the supply chain wasa disruption, causing scarcity of material, delays in deliveries, and capacity constraints in transportation. This paper proposes to answer
whether the amount of data or high level of uncertainty exist. The ability of models created with large amounts of data may be not necessarily the answer. The proposal is a novel alternative forecast model when
the amount of data is not available (< 4 records) and integrates the past
demand as a Markov Chain and offers a model’s characterization todiscriminate a set of data that is suitable for the Grey Systems Theory
and a Markov Chain Grey Model MCGM (1,1). The model: 1) incorporates the past demand behavior, 2) establish the transition probability matrix, 3) defines enhanced forecast model through the GST GM(1,1), 4) develops the forecast, 5) measure its performance. The performance measurement was MAPE. The results exceed other models
and methodologies such as: exponential smoothing, moving averageand linear regression. The MAPE with the traditional methods was
6.58% vs. the MCGM (1,1) was 2.28% and provided a forecast range
comparable to ARIMA.

Xingyi Wang, Gudrun Kiesmuller, Rainer KolischTypically, companies for home appliances offer after-sales services,
especially repair services, to their customers. One of the challenging
planning problems that has to be solved in this context is the allocation and routing of service technicians that satisfy customers’ service
requests. In this paper, we consider heterogeneous technicians withtwo different skill levels. Junior technicians have less experience and,
therefore, may fail to repair the appliance successfully, and senior technicians will repair the appliance successfully. In case of a failed service repair by a junior technician, a second visit by a senior technician
is necessary. We formulate the problem as a sequential decision problem, where new customer requests arrive in each period, and decisions
on the allocation and routing of service technicians are made at the beginning of each period. While the customer requests are known for the136 MD-26
Monday, 14:30-16:00 - Room: 012 (building: 208)

Optimization problems on graphs
Stream: Combinatorial Optimization
Invited session
Chair: Silvano Martello
Chair: Paolo Toth

EURO 2024 - Copenhagen
1 - Carousel Greedy Algorithm for the Minimum Stretch
Spanning Tree Problem

Carmine Cerrone, Bruce Golden
The Minimum Stretch Spanning Tree Problem (MSSTP) is a variantof the classical minimum spanning tree problem. Consider a simple,
connected, undirected, and unweighted graph G=(V, E), where V is theset of vertices and E is the set of edges. The objective of MSSTP is to
identify a spanning tree T=(V, E’) within G that minimises the stretch,i.e., reduces the maximum distance in the spanning tree between adjacent nodes in the original graph [1]. This problem has practical applications in areas such as distribution systems, network design, transportation networks, sensor networks, phylogenetic tree reconstruction,and parallel machine architectures. This study introduces a promisingand efficient Carousel Greedy (CG) algorithm for tackling the MSSTP.
The CG algorithm is a generalised greedy algorithm designed to address the conventional limitations of greedy strategies [2]. We willshow that our CG algorithm outperforms the General Variable Neighbourhood Search (GVNS) in terms of solution quality, computationaltime, and consistency across benchmark instances.
[1] Kardam, Y. S., Srivastava, K., & Marti, R. (2023). General variableneighbourhood search for the minimum stretch spanning tree problem.
Optimization Letters, 17, 2005-2031.
[2] Cerrone, C., Cerulli, R., & Golden, B. (2017). Carousel Greedy: A
generalised greedy algorithm with applications in optimisation. Computers & Operations Research, 85, 97-112.

2 - From Incremental Transitive Closure to Strongly Polynomial Maximum Flow

James Orlin, Daniel Dadush, Aaron Sidford, Laszlo Vegh
The best strongly polynomial bound for maximum flows in a graphwith n nodes and m arcs is the O(nm) bound by Orlin (STOC 2013).
His algorithm runs in O(nm) time for graphs that are suitable sparse.
King, Rao, and Tarjan (J. Algorithms 1994) had already established
the O(nm) run time for all other networks.
Strengthening Orlin’s approach, we present a black-box frameworkthat makes any weakly polynomial maximum flow algorithms tostrongly polynomial. The algorithm relies on using Italiano’s data
structure for maintaining an incremental dynamic transitive closure,which runs in O(nm) time. Surprisingly, the remaining operations takemuch less time, except in the most dense networks. If one can improve
the run time for dynamic transitive closure, then one can speed up therunning time for the max flow problem as well. We also obtain improved running times for a range of special cases including instanceswith a bounded number of arcs with finite upper capacity and instanceswith bounded tree depth.

3 - PathWyse 1.0: a software library for the Resource Constrained Shortest Path

Matteo Salani, Saverio Basso
In this paper, we introduce PathWyse 1.0, a versatile dual licensed
library addressing the Resource Constrained Shortest Path Problem(RCSPP), a fundamental and hard combinatorial problem.
PathWyse is capable of modeling and solving a variety of standard RCSPPs implementing state-of-the-art algorithms. The library interfaces
with both C++ and Python, easing the task of modeling customized
problems with minimal programming required.
Algorithmically, the latest version presents parallel implementationsof both exact and heuristic algorithms. Particularly, heuristic algorithms are integrated into a competitive ensemble framework, boosting their effectiveness. Moreover, this release highlights an enhanced
bi-directional dynamic programming technique. The joining procedure, usually a time-intensive aspect of the algorithm, now utilizes binary search and Pareto frontier exploration, resulting in notable performance improvements.
We conducted computational experiments using instances from literature, encompassing scenarios featuring a singular resource constraint
across cyclic networks and large acyclic networks. We present findings
related to instances with multiple resource constraints within cyclic
networks.

MD-27

The flexible parameterization showcases how users can easily modeland efficiently resolve multi-constrained problems. To aid comprehension, we provide a tutorial on a column generation algorithm, utilizing
PathWyse as the solver for the pricing problem.

4 - An improved variant of the Iterated Inside Out algorithm
for solving the optimal transport DOTmark Instances

Federico Della Croce, Roberto Bargetto, Rosario
Scatamacchia
The optimal transport (OT) is a paradigmatic network problem andconsists in finding the minimum cost transportation plan that moves
quantities of a single item from a set of sources to a set of destinations, where, for each pair source/destination (i,j), a unit transportation
cost holds. The interest in OT has been renewed lately in artificial intelligence applications where a set of instances, called DOTmark, isnowadays the benchmark for OT. Recently, a new exact algorithm forsolving OT, called iterated Inside Out (IIO), has been proposed. The
strength of this method relies on the fact that potentially many pivoting
operations are performed for each computation of dual multipliers andreduced costs. Here, we propose a variant of IIO specifically tailored to
the DOTmark instances. This variant solves these instances exploiting
the peculiar structure of the transportation costs: these costs depend on
the couple of indexes (i,j) of the related variables and present strong
regularity in the way they increase or decrease according to a change
of index i or j. Given a basic solution, the described structure of costs
enables the algorithm to predict from scratch the positivity of a huge
set of reduced costs, thus avoiding their computation. Also, it enables
to detect the presence of non basic variables with negative reduced
costs within a reduced subset of variables. Computational results indicate that this approach is largely superior to the current state of the art
algorithms.

 MD-27
Monday, 14:30-16:00 - Room: 047 (building: 208)

Mathematical Optimization forCounterfactual Explanations
Stream: Mathematical Optimization for XAI
Invited session
Chair: Jasone Ramírez-Ayerbe
1 - Counterfactual Explanations for Linear Optimization
Problems

Jannis Kurtz, Ilker Birbil, Dick den Hertog
In recent years, there has been a rising demand for transparent andexplainable machine learning (ML) models. A large stream of works
focuses on algorithmic methods to derive so called counterfactual explanations (CE). Although significant progress has been made in generating CEs for ML models, this topic has received minimal attention
in the Operations Research (OR) community. However, algorithmic
decisions in OR are made by complex algorithms which cannot be
considered to be explainable or transparent. In this work we argue
that there exist many OR applications where counterfactual explanations are needed and useful. We translate the concept of CEs into theworld of linear optimization problems and define three different classesof CEs: strong, weak and relative counterfactual explanations. For all
three types we derive problem formulations and analyze the structureof it. We show that the weak and strong CE formulations have someundesirable properties while relative CEs can be derived by solving a
convex optimization problem. We test all concepts on a real-world dietproblem and we show that relative CEs can be calculated efficiently on
NETLIB instances.

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2 - Finding Regions of Counterfactual Explanations via Robust Optimization

Donato Maragno
Counterfactual explanations play an important role in detecting bias
and improving the explainability of data-driven classification models.
A counterfactual explanation (CE) is a minimal perturbed data point
for which the decision of the model changes. Most of the existing
methods can only provide one CE, which may not be achievable forthe user. In this work, we derive an iterative method to calculate robust CEs, i.e., CEs that remain valid even after the features are slightly
perturbed. To this end, our method provides a whole region of CEs
allowing the user to choose a suitable recourse to obtain a desired outcome. We use algorithmic ideas from robust optimization and prove
convergence results for the most common machine learning methods
including decision trees, tree ensembles, and neural networks. Our experiments show that our method can efficiently generate globally optimal robust CEs for a variety of common data sets and classification
models.

3 - Multistage counterfactual decisions

Emilio Carrizosa, Jasone Ramírez-Ayerbe, Dolores Romero
Morales
Counterfactual Analysis is a powerful tool for Explainable Artificial
Intelligence. In Supervised Classification, this means associating witheach record a so-called counterfactual decision, i.e., an instance close
to the record (closeness measured by an appropriate and contextdependent dissimilarity) and whose probability of being classified bya given classifier in the positive (desired) class is high. In other words,
the counterfactual analysis is a reference for a given individual: how
he/she should minimally change to improve the probability of beingclassified in the positive class. While the literature has focused on
the case of finding one counterfactual, we propose in the talk how toaddress the problem in which a sequence of k decisions, gradually increasing the probabilities of positive, and implying an acceptable costat each stage. A mathematical optimization model is considered andtheoretical properties are derived.

4 - One-for-many Counterfactual Explanations by Column
Generation

Jasone Ramírez-Ayerbe, Andrea Lodi
We consider the problem of generating a set of counterfactual explanations for a group of instances, with the one-for-many allocation rule,
where one explanation is allocated to a subgroup of the instances. For
the first time, we solve the problem of minimizing the number of explanations needed to explain all the instances, while considering sparsity
by limiting the number of features allowed to be changed collectively
in each explanation. A novel column generation framework is developed to efficiently search for the explanations. Our framework can beapplied to any black-box classifier, like neural networks. Comparedwith a simple adaptation of a mixed-integer programming formulation from the literature, the column generation framework dominatesin terms of scalability, computational performance and quality of thesolutions.

 MD-28
Monday, 14:30-16:00 - Room: 065 (building: 208)

Advancements of OR-analytics in statistics,
machine learning and data science 4
Stream: Advancements of OR-analytics in statistics, machine learning and data science
Invited session
Chair: Laura Albert
Chair: Gerhard-Wilhelm Weber138

1 - A hybrid resampling method for imbalanced classification problem

You-Jin Park
Classification has been widely used to categorize the existing instances(i.e., data points) and predict the new instances in various machinelearning applications such as fraud detection in financial sector, fault
and defect detection in manufacturing industry, and medical diagnosis, etc. However, most classification algorithms have been developed
under the assumption that the data distribution of among classes isbalanced even though unequal class distributions are quite common.
So, the class imbalance problem may affect the performances of many
machine learning tasks. Furthermore, the class overlap problem occurs when some instances are located in a certain common region in
the data space and it also has a significant impact on the performancein imbalanced classification problems. Thus, in this study, we propose a new hybrid resampling algorithm to improve the performanceof imbalanced classification problem by resolving both class imbalance and class overlap problems in imbalanced data simultaneously.
In particular, we propose a class impurity measure based on k-nearest
neighbor (k-NN) algorithm for adaptively oversampling and undersampling. To demonstrate the effectiveness of the proposed method,comprehensive experiments are executed on forty imbalanced datasetswith considering four classifiers and three classification performancemeasures. [This work has been supported by the General Research
Program funded by NSTC, Taiwan (Grant No. NSTC 110-2221-E027-106-MY3)]

2 - Context-aware processing rate guided production
scheduling: a forecasting embedded branch-and-priceheuristic method

Yige Sun, Sai-Ho Chung, Tsan-Ming Choi
Production scheduling seeks to identify effective job execution planson machines to enhance overall operational efficiency. Prior studies
in this area usually assume job processing rates (JPRs) to be constant.
However, based on our analysis of real-world production data, multiple factors such as machine use, operator skills, material supplies,
and the processing of prior jobs may influence the actual processingof a job in manufacturing operations. To catch the effect of production
variabilities and improve scheduling solutions, we introduce a measure
named context-aware processing rate (CAPR) in production scheduling. Based on this measure, a novel deep learning-empowered CAPRguided scheduling approach is developed. Specifically, the CAPR ispredicted for each processing context produced along with the pricing
process. The CAPR is used to find beneficial allocation and sequencing
of jobs to operators and machines with an efficient labelling algorithm.
Experiments based on real-world data from our studied printing company show that the proposed method enables a substantial decrease in
overall job completion time.

3 - The Sound of Emotions: An Artificial Intelligence Approach to Predicting Emotions from Musical Selections

Ron Hirschprung, Ori Leshman, Chen Hajaj
Music in the current digital era is consumed primarily on-demand, forexample by streaming from Spotify or YouTube, rather than by broadcasting, for example, by listening to a radio station. A strong correlation has been found between music and emotions. Hence, with ourability to select our own music and the availability of artificial intelligence (AI) tools, the importance of music emotion recognition (MER)
is significant. This research proposes a novel methodology for predicting emotions from a musical piece based on a two-layer AI. The dataset
and the process are exceptional in two ways: first, by their qualitative
character, which minimized the inherent subjectivity of self-reported
emotions, and also the high reliability of the collected data in comparison to other methods such as a questionnaire; and second, by theirquantitative character, given n=9,090 songs from about 4,447 people.
The model succeeded in predicting the primary emotion with accuracy=57% and one of the two leading emotions (the primary and thesecondary) with accuracy=75.1%, which is significantly higher thana random guess, and without accommodating demographic factors in
the model. We believe this knowledge may be a useful tool for therapists, coaches, teachers, etc. and may also raise public awareness of apotential privacy violation.

EURO 2024 - Copenhagen
4 - SynthEco: A digital system for analyzing multidimensional mechanisms of human behaviour in a
multi-layered and dynamic geospatial environment

Antonia Gieschen, Raja Sengupta, Duo Zhang, Catherine
Paquet, Fares Belkhiria, Laurette Dubé
Synthetic populations are datasets which are created to be statistically
representative of a chosen population using census data. They havebeen used in multiple contexts where researchers are interested in modelling effects on a population or individual level, including health related agent-based models and epidemiology. The SynthEco project
aims to provide a platform for researchers to create synthetic populations in the form of an open source Python package available on
GitHub through an iterative proportional fitting approach. The package allows for synthetic population creation from any census data andon different geographic levels through a simple plug-in system. Plugins exists for census data from the USA and from Canada, and researchers are invited to contribute their own plugins to create otherpopulations. The use of SynthEco will be demonstrated through several application cases in Montreal, Canada, such as modelling accessto healthy food and spatial inequality in financial wellbeing. These are
made possible through dataset linkage of the synthetic population with
geo-referenced discovery and population cohorts.

 MD-29
Monday, 14:30-16:00 - Room: 157 (building: 208)

Vehicle Routing II

MD-30

and valid inequalities. In the computational study, we compare theperformance of these formulations and different techniques.

3 - A mixed integer formulation and an NSGA-II for the Biobjective Cumulative Capacitated Vehicle Routing Problem with Priority Indexes

Karina Corona-Gutiérrez, Samuel Nucamendi-Guillén, Iris
Martínez-Salazar, Marcos Moreno-Vega
We propose a new mixed integer formulation and an efficient metaheuristic for the Bi-objective Cumulative Capacitated Vehicle Routing
Problem considering Priority Indexes (BCCVRP-Pr), a variant of theclassical Capacitated Vehicle Routing Problem in which customers areserved according to a certain level of preferences by a fixed fleet of heterogeneous vehicles. In this problem, two objectives are minimized:
the total latency and the total tardiness of the system. The proposed
mathematical formulation (a multilevel network approach) showed its
effectiveness by outperforming the previous models presented in theliterature, reporting efficient Pareto Fronts for instances up to 25 nodes.
Regarding the metaheuristic, we developed a Non-Dominated Sorting
Genetic Algorithm (NSGA-II) capable of dealing with larger instances(up to 100 nodes). To evaluate and compare the performance of themetaheuristic procedure against the results obtained by the proposed
exact model, we used four performance metrics: the quantity of nondominated points, the hypervolume, the coverage of two sets, and theelapsed computational time. According to the results, the algorithm
showed a compelling performance by providing high-quality Pareto
fronts at competitive computational times. Our research provides valuable insights into solving the Cumulative Capacitated Vehicle Routing
Problem with priority indexes, suitable for practical applications in industries that require prioritizing customer service.

Stream: Combinatorial Optimization
Invited session
Chair: Irene Loiseau
Chair: Juan José Salazar González

 MD-30

1 - Solving the Consistent Travelling Salesman Problem

Optimization Solvers

Juan José Salazar González, Daniel Díaz-Ríos
The consistent travelling salesman problem looks for a minimum-cost
set of Hamiltonian routes, one for every day of a given period. Whena customer requires service over several days, the service times on different days must differ by no more than a given threshold (for example, one hour). We analyze two variants of the problem, dependingon whether the vehicle is allowed to wait or not at a customer location before its service starts. There are three mathematical models in
the literature for the problem without waiting times, and this paper describes a new model appropriated to be solved with a branch-and-cut
algorithm. The new model is a multi-commodity flow formulation onwhich Benders’ Decomposition helps manage a large number of flow
variables. There were no mathematical models in the literature for thevariant with waiting times, and this paper adapts the four mathematicalmodels to it. We analyze the computational results of the formulations
on instances from the literature with up to 100 customers and threedays.

2 - Formulations and Exact Solution Methods for the Undirected Team Orienteering Arc Routing Problem

Wenjin Yan, Elena Fernandez, Ivana Ljubic
Given an undirected graph, we study the team orienteering arc routing
problem which asks to find a set of maximum profits routes startingand ending at the depot satisfying the vehicles’ travelling time constraints. This problem has many important applications in practice,such as waste collection, snow plowing, and security patrolling, etc.
In this article, we propose three new integer linear programming (ILP)
formulations with only binary variables for this problem. To solve thisproblem in large instances, we introduce a Logic-based Benders decomposition (LBBD) and incorporate it into a branch-and-cut framework. We design problem-specific techniques to further improve theperformance of the algorithm, such as Benders cuts strengthening approaches, repair heuristics, as well as symmetric breaking constraints

Monday, 14:30-16:00 - Room: 064 (building: 208)

Stream: Software for Optimization
Invited session
Chair: Frédéric Gardi
1 - Latest Development in the MibS Solver for Mixed Integer
Bilevel Optimization

Theodore Ralphs
In this talk, we’ll discuss the latest improvements in the solution
methodology of MibS, an open source solver for solving mixed integer bilevel optimization problems. Recent progress has improved theperformance of the solver substantially, as well as adding to its ability
to solve related classes of problems. We’ll provide some insights into
the source of these improvements.

2 - Flowty - a network optimisation solver

Simon Spoorendonk, Bjørn Petersen
Network optimization problems are recognised by their underlying
graph(s) and constraints. Examples are within planning & scheduling,vehicle routing, and multi-commodity flow problems. Flowty’s solver
exploits the graph structure and solves resource constrained shortest
path problems to generate variables using a column generation scheme.
Graphs are described as edge sets and a source and target vertices fora path. Paths can additionally be constrained with a set of resources.
Constraint modeling is done directly on the vertices and edges of thegraphs, the subproblem itself, as well as on traditional mixed integer
variables. This allows for very compact models, e.g., set partitioning
constraints on vertices and edge design variables in network design
problems, and the graph and resource constraints avoids the need forlinearisation of constraints (big-M).

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The literature has shown that column generation based algorithms aresuperior for many network optimization problems. The solver combines the familiarity of mixed integer programming models and graphstructures with algorithmic speed.
In this talk we will dive into modeling examples in Python, take a quick
look under the hood, and present recent performance benchmarks.

3 - Blackbox optimization with the MADS algorithm and the
NOMAD software

Sébastien Le Digabel
NOMAD is a free software package for blackbox optimization. It implements the mesh adaptive direct search (MADS), a derivative-free
optimization algorithm. NOMAD is designed to solve industrial problems, and therefore it includes several practical features such as constraints handling, the possibility of processing several objectives, noisy
evaluations, etc. Several parallel variants of the algorithm are alsoavailable. This talk introduces derivative-free and blackbox optimization, the MADS algorithm and the practical use of NOMAD. Realistic
test cases will also be discussed.

4 - Hexaly, a new kind of global optimization solver

Frédéric Gardi
Hexaly Optimizer is a new kind of global optimization solver. Its
modeling interface is nonlinear and set-oriented. It also supports usercoded functions, thus enabling black-box optimization and, more particularly, simulation optimization. In a sense, Hexaly APIs unify modeling concepts from mixed-linear programming, nonlinear programming, and constraint programming. Under the hood, Hexaly combines various exact and heuristic optimization methods: spatial branchand-bound, simplex methods, interior-point methods, augmented Lagrangian methods, automatic Dantzig-Wolfe reformulation, column
and row generation, propagation methods, local search, direct search,
population-based methods, and surrogate modeling techniques forblack-box optimization.
Regarding performance benchmarks, Hexaly distinguishes itself
against the leading solvers in the market, like Gurobi, IBM Cplex, andGoogle OR Tools, by delivering fast and scalable solutions to problems
in the spaces of Supply Chain and Workforce Management like Routing, Scheduling, Packing, Clustering, and Location. For example, on
notoriously hard problems like the Pickup and Delivery Problem withTime Windows or Flexible Job Shop Scheduling with Setup Times,
Hexaly delivers solutions with a gap to the best solutions known in theliterature smaller than 1% in a few minutes of running times on a basic
computer.

 MD-31
Monday, 14:30-16:00 - Room: 046 (building: 208)

Network Analytics
Stream: Analytics
Invited session

bankruptcy. Our research establishes the impact of incorporating network analysis of company relationships into bankruptcy prediction
models. It sets the stage for more sophisticated financial analysis techniques that synergize traditional financial metrics with cutting-edge
network analysis, and the advancement holds substantial promise forfinancial institutions and analysts, providing a more nuanced understanding of corporate bankruptcy risks.

2 - Breaking Barriers: Unveiling Gender Disparities in Corporate Career Paths Using Deep Learning

Cristian Bravo, Yuhao Zhou, Wenhao Chen, MaríaÓskarsdóttir, Collins Ntim, Matt Davison
This study delves into the interplay between gender, professional networking, career-path trajectory, and board director appointments in
Canadian publicly traded companies. To obtain our results, we combine multiple publicly available sources into a unique dataset. Thisdataset (consisting of over 700 Canadian firms covering more than
19,000 senior managers and board members in the 2000-2022 period),
charts detailed network information of both senior managers and board
members across five key dimensions: education, current and prior employment, and current and prior social engagement. By matching senior managers of both genders based on their career trajectories andbackgrounds, and applying Long Short-Term Memory (LSTM) deeplearning alongside network analysis, our research uncovers the distinct network influences impacting the board appointment prospects ofwomen versus men. The findings causally demonstrate a "glass ceiling", suggesting that women necessitate more substantial networking
credentials than men to secure equivalent corporate board positions,when controlled for career paths and backgrounds. This paper outlines
our data compilation process, matching and analytical methodology,
presents our empirical results and insights, and the broader implications these hold for policies aimed at encouraging both good corporate
governance and gender equality at senior level.

3 - Feature set aggregation using community detection in
networks

Christoph Lohrmann, María Óskarsdóttir
In supervised machine learning, especially with high dimensional data,
feature selection is an important approach that aims to ensure efficientcomputation, to achieve a high generalization ability of the learningalgorithm, and to support the interpretability of the results. However,the feature subsets selected during feature selection may vary depending on the partition of the training dataset used or the feature selection
method deployed. This instability can be a result of including redundant and irrelevant features in some of these subsets or not retaining all
relevant features in each of them. The goal of this study is to develop
a methodology to find a subset of the relevant features using network
science and community detection from among the individual feature
subsets. The individual feature subsets will be used to construct a network, where each node represents a feature, and edges between nodes
reflect whether two features were selected into the same subset. By
considering several runs of a feature selection method, features thatappear together often will have a stronger connection, represented bya higher weight on the edge between them. This network will then be
analyzed to discover communities, which ideally represent a set of features that is highly connected and, thus, is jointly highly important forthe classification task. Optimally, this also allows to visually and/or
numerically distinguish the sets of relevant, redundant, and irrelevant
features.

Chair: María Óskarsdóttir
Chair: Nidia Guadalupe Lopez Flores
Chair: Anna Sigríður Islind
1 - New drivers of bankruptcy based on Complex Networks

Jinxian Zhao
This study proposes a novel approach to devise new drivers forbankruptcy prediction using complex network analysis. These drivers
are company relational information-based drivers (CRIs) derived fromthe board of directors’ networks with different network configurations.
The effectiveness of these new drivers is demonstrated on a datasetof UK companies listed on the London Stock Exchange. Numerical results suggest a significant improvement in predicting corporate140 MD-32
Monday, 14:30-16:00 - Room: 41 (building: 303A)

Algorithms for machine learning and inverse
problems: adaptive strategies
Stream: Advances in large scale nonlinear optimization
Invited session
Chair: Silvia Villa

EURO 2024 - Copenhagen
Chair: Luca Calatroni
Chair: Cesare Molinari
1 - Inertial methods beyond minimizer uniqueness

Hippolyte Labarrière
When considering a convex composite minimization problem, it is wellknown that momentum allows first-order methods to be accelerated
both theoretically and numerically. In particular, we know that for asuitable choice of parameters inertial methods ensure fast convergence
rates under additional geometry assumptions such as strong convexity.
However, the improved convergence results demonstrated in the literature hold only if the function to minimize has a unique minimizer.
This extra assumption is limiting, since some common functions (suchas the LASSO function or $L1$ regularized functions in general) cansatisfy some growth condition without having a unique minimizer. The
question then arises: is this assumption necessary to prove fast convergence properties?
We propose an approach that aims to avoid this hypothesis while stillobtaining fast convergence rates. This strategy allows to extend several
known convergence results in the continuous setting (i.e. for dynamicalsystems associated to inertial schemes). We also provide fast convergence guarantees for the iterates of FISTA (introduced by Beck andTeboulle) and V-FISTA (proposed by Beck) in a relaxed setting, showing that this uniqueness assumption is not required for inertial methods
to be efficient.

2 - Adaptive restart of conservative dynamics for convex
optimization

Alessandro Scagliotti
In the last years, tools from Dynamical Systems have been fruitfully
applied to study existing accelerated optimization methods and to develop new ones. Typically, continuous-time models for accelerated
methods are mechanical systems with damping. In this talk, we present
an optimization method based on a conservative mechanical system,
where the objective function plays the role of the potential energy.
Due to the absence of damping, the convergence of this method completely relies on the adaptive restart strategy: a) the initial velocity is
set equal to 0; b) by the conservation of the mechanical energy, part ofthe initial potential energy is transformed into kinetic energy; c) whena proper restart condition is met, the velocity is reset to zero and the
kinetic energy at the restart time is instantly dissipated. We prove theconvergence result both for the continuous-time method and for thediscrete-time version. Finally, we discuss some possible extensions to
the nonsmooth case, with particular focus on l1-regularization.
[1] A. Scagliotti, P. Colli Franzone. A piecewise conservative
method for unconstrained convex optimization, COAP (2022). [2] A.
Scagliotti, P. Colli Franzone. A subgradient method with constant stepsize for l1-composite optimization, BUMI (2023).

3 - Monitoring the Convergence Speed of PDHG to Find
Better Primal and Dual Step Sizes

Olivier Fercoq
Primal-dual algorithms for the resolution of convex-concave saddle
point problems usually come with one or several step size parameters.
Within the range where convergence is guaranteed, choosing well thestep size can make the difference between a slow or a fast algorithm.
A usual way to adaptively set step sizes is to ensure that there is a fairbalance between primal and dual variable’s amount of change. In thiswork, we show how to find even better step sizes for the primal-dual
hybrid gradient. Getting inspiration from quadratic problems, we baseour method on a spectral radius estimation procedure and try to minimize this spectral radius, which is directly related to the rate of convergence. Building on power iterations, we could produce spectral radiusestimates that are always smaller than 1 and work also in the case of
conjugate principal eigenvalues. For strongly convex quadratics, weshow that our step size rule yields an algorithm as fast as inertial gradient descent. Moreover, since our spectral radius estimates only rely
on residual norms, our method can be readily adapted to more generalconvex-concave saddle point problems. In a second part, we extend
these results to a randomized version of PDHG called PURE-CD. We

MD-33

design a statistical test to compare observed convergence rates and decide whether a step size is better than another. Numerical experiments
on least squares, sparse SVM, TV-L1 denoising and TV-L2 denoisingproblems support our findings.

4 - Stochastic Primal Dual Hybrid Gradient Algorithm with
Adaptive Step-Sizes

Claire Delplancke, Antonin Chambolle, Matthias J. Ehrhardt,
Carola-Bibiane Schönlieb, Junqi TangIn this work we propose a new primal-dual algorithm with adaptive
step-sizes. The stochastic primal-dual hybrid gradient (SPDHG) algorithm with constant step-sizes (Chambolle et al. 2018, Ehrhardt et al.
2019) has become widely applied in large-scale convex optimization
across many scientific fields due to its scalability. While the productof the primal and dual step-sizes is subject to an upper-bound in order to ensure convergence, the selection of the ratio of the step-sizes
is critical in applications. Up- to-now there is no systematic and successful way of selecting the primal and dual step-sizes for SPDHG. In
this work, we propose a general class of adaptive SPDHG (A-SPDHG)
algorithms, and prove their convergence under weak assumptions. We
also propose concrete parameters- updating strategies which satisfy theassumptions of our theory and thereby lead to convergent algorithms.
Numerical examples on computed tomography demonstrate the effectiveness of the proposed schemes.

 MD-33
Monday, 14:30-16:00 - Room: 42 (building: 303A)

Optimal control in organizations
Stream: Optimal Control Theory and Applications
Invited session
Chair: Gernot Tragler
1 - Dynamic Pricing, Objective and Subjective Quality, and
the Price-Quality Relationship

Régis Chenavaz, Domenico De Giovanni, Pietro De Giovanni
Dynamic pricing and quality are important strategic variables for firms.
However, the effects of consumers’ objective and subjective quality
evaluations on dynamic pricing must be better understood. This article develops an optimal control model of a firm’s dynamic pricing andobjective quality investment decisions over time. Our research contributes to the literature on the price-quality relationship by providing a
dynamic pricing rule and identifying the conditions below which moresignificant objective and subjective quality may lead to an increase or
decrease in the price. Subjective quality impacts dynamic price but
not objective quality. In addition, a negative price-quality relationshipis possible with both objective and subjective quality. Consequently,
the distinction between objective and subjective qualities yields more
profitable marketing mix strategies.

2 - Competition between hospitals with age-structured patients and negative congestion effects: a differential
game approach

Dominika Machowska, Michael Kuhn, Andrzej Nowakowski,
Agnieszka Wiszniewska-Matyszkiel, Stefan Wrzaczek
We explore a noncooperative game framework involving two hospitals, where treatment quality suffers under congestion. Recognizingthat healthcare demand is significantly influenced by patient age, we
incorporate a continuous age distribution into our model. Each hospital aims to determine the optimal treatment(age-structured) intensitythat maximizes its objective: for a public hospital, this involves enhancing the cross life-expectancy as measure for the number and quality of treated patients (public hospital); for a private hospital, the goal
is to maximize profits based on public payments for treatment. The resulting problem leads to the introduction of differential games with the141MD-34EURO 2024 - Copenhagenclosed-loop information structure. The paper formulates conditions forverifying whether a given strategy profile constitutes a Nash equilibrium with the dual closed-loop information structure. The verification
theorem is then used to develop a numerical algorithm for determining
Nash equilibria in a finite number of steps. The numerical simulations
demonstrate how the Nash equilibrium can shift in response to varying
socio-economic factors.

3 - The optimal mix of incapacitations and social programs
for fighting cartel violence in Mexico

Gernot Tragler, Gustav Feichtinger, Dieter Grass, Jonathan
Caulkins, Rafael Prieto-Curiel, Stefan Wrzaczek, Gian Maria
Campedelli
Cartels in Latin America pose a significant threat to societal stability
and public safety, perpetuating extensive violence and socio-economic
disruption. Despite substantial investments in security measures bystates like Mexico and the U.S., violence continues to escalate, andthe power of cartels remains resilient. In this presentation we examine
the effectiveness of current strategies and propose a novel approach using optimal control theory to analyze cartel dynamics. By modellingthe allocation of resources between security initiatives and social programs, this study aims to identify an optimal strategy for reducing cartel violence and mitigating its impacts on society. Through this analytical lens, we offer insights into potential avenues for more effective
policymaking and resource allocation in the ongoing fight against organized crime.

 MD-34
Monday, 14:30-16:00 - Room: 43 (building: 303A)

Preconditioning for Large Scale Nonlinear
Optimization
Stream: Advances in large scale nonlinear optimization
Invited session
Chair: Panos Parpas
1 - Loraine - An Interior-Point Solver for Low-Rank
Semidefinite Programming

Soodeh Habibi, Michal Kocvara, Michael Stingl
The aim of this talk is to introduce Loraine, a new code for the solutionof large-and-sparse linear semidefinite programs (SDPs) with low-ranksolutions or solutions with few outlying eigenvalues, and/or problems
with low-rank data. We propose to use a preconditioned conjugate
gradient method within an interior-point SDP algorithm and an efficient preconditioner fully utilizing the low-rank information. The efficiency will be demonstrated by numerical experiments using the truss
topology optimization problems, Lasserre relaxations of the MAXCUT problems, and the sensor network localization problems. The
code is available in Matlab and Julia, and it can be used not only for
low-rank problems but also for any linear SDP.

2 - Simba: A Scalable Bilevel Preconditioned Gradient
Method for Fast Evasion of Flat Areas and Saddle Points

Nick Tsipinakis, Panos Parpas
First-order methods slow down when applied to high-dimensional nonconvex functions due to the presence of saddle points. If, additionally,
the saddles are surrounded by large plateaus, it is highly likely that thefirst-order methods will converge to a sub-optimal solution. A natural way to tackle the limitations of first-order methods is to employ
second order information from the Hessian. However, methods thatincorporate the Hessian do not scale to large models . To address theseissues, we propose Simba, a scalable preconditioned gradient method.
The method is very simple to implement. It maintains a single precondition matrix that it is constructed as the outer product of the moving
average of the gradients. To significantly reduce the computational142

cost of forming and inverting the preconditioner, we draw links withmultilevel optimization methods and construct randomized preconditioners. Our numerical experiments and testing against other state-ofthe-art methods verify the scalability of Simba as well as its efficacy
near saddles and flat areas. We also analyze Simba and show its linear
convergence rate for strongly convex functions.

3 - Parallel Neural Network Training via Nonlinearly Preconditioned Trust-Region Method

Samuel Cruz
Neural networks and the data sets used to train them continue to growin size. Traditional training methods like SGD and Adam require
extensive hyperparameter tuning, which is increasingly incompatible
with today’s demands as current applications require methods that ideally do not need hyperparameter tuning. With this in mind, we investigate additive domain decomposition methods for neural network
training. Due to their underlying additive decomposition, our methods
are parallelizable, and owing to a trust-region strategy, they largely obviate the need for hyperparameter tuning. Our test results suggest thatthe investigated methods are potential candidates for efficient neuralnetwork training.

4 - Stochastic Mirror Descent for Convex Optimization with
Consensus Constraints

Panos Parpas
The mirror descent algorithm is known to be effective in situations
where it is beneficial to adapt the mirror map to the underlying geometry of the optimization model. However, the effect of mirror maps on
the geometry of distributed optimization problems has not been previously addressed. In this paper we study an exact distributed mirror
descent algorithm in continuous-time under additive noise. We establish a linear convergence rate of the proposed dynamics for the settingof convex optimization. Our analysis draws motivation from the Augmented Lagrangian and its relation to gradient tracking. To further
explore the benefits of mirror maps in a distributed setting we present
a preconditioned variant of our algorithm with an additional mirror
map over the Lagrangian dual variables. This allows our method toadapt to both the geometry of the primal variables, as well as to thegeometry of the consensus constraint. We also propose a Gauss-Seidel
type discretization scheme for the proposed method and establish its
linear convergence rate. For certain classes of problems we identify
mirror maps that mitigate the effect of the graph’s spectral properties
on the convergence rate of the algorithm. Using numerical experiments
we demonstrate the efficiency of the methodology on convex models,
both with and without constraints. Our findings show that the proposed
method outperforms other methods, especially in scenarios where themodel’s geometry is not cap

 MD-35
Monday, 14:30-16:00 - Room: 44 (building: 303A)

Cross-dock Door Problems
Stream: Stochastic, Robust and Distributionally Robust
Optimization
Invited session
Chair: Laureano F. Escudero
1 - Cross-dock Door Assignment Problem, CDAP, decomposition methodologies

Aitziber Unzueta, Laureano F. Escudero, M. Araceli Garin
The Cross-dock Door Assignment problem (CDAP) consists of using a
distribution-docking terminal for helping on supply chain distribution.
Given a set of commodities to deliver from origin nodes to destination
ones, the products are unloaded using a set of inbound doors, handled
in the terminal, and reloaded using a set of outbound doors. The goal is
to minimize the transportation cost of the commodities to be handled at
the cross-dock. A class of strong lower bounds on the solution value ofa Linearized Integer Programming reformulation is introduced for thebinary quadratic optimization model to assign origin and destination

EURO 2024 - Copenhagennodes to strip and stack doors, respectively, in a cross-dock infrastructure. Strip- and stack-related decomposition submodels are developedby taking benefit of the Integer Linearization Property that appears in
the new model. A linear search heuristic is also provided for obtaining
feasible solutions by exploiting the special structure of the problem.
We present an extensive computational study on a testbed of 55 instances to show that the proposed joint scheme for lower bounding andfeasible solution providing is very efficient.

2 - Cross-dock Door Design Problem, CDDP, under uncertainty

M. Araceli Garin, Laureano F. Escudero, Aitziber Unzueta
The Cross-dock Door Design Problem (CDDP) consists of deciding
on the number and capacity of inbound and outbound doors for receiving product pallets from origin nodes and exiting them to destination
nodes. The uncertainty, realized in scenarios, lies in the occurrenceof these nodes, the number and cost of the pallets, and the capacity’s
disruption of the doors. The CDDP is represented using a stochastic
two-stage binary quadratic model (BQM). The first stage decisions are
related to the cross-dock infrastructure design, and the second stage
ones to the node-to-door’s assignments. This is the first time, as far as
we know, that a stochastic two-stage BQM has been presented for minimizing the construction cost of the infrastructure and its exploitation
expected cost in the scenarios. Given the difficulty of solving this combinatorial problem, a mathematically equivalent MILP formulation is
introduced. However, searching an optimal solution is still impracticalfor commercial solvers. Thus, a scenario cluster decomposition-based
matheuristic is introduced to obtain feasible solutions with small optimality gap and reasonable computational effort. A broad study tovalidate the proposal gives solutions with a much smaller gap than theones provided by a state-of-the-art general solver. In fact, the proposal
provides solutions with a 1 to 5% optimality gap, while the solver does
it with up to a 12% gap, if any, and requires a wall time two orders ofmagnitude higher.

3 - Cross-dock Door Design Problem, CDDP, Multi-horizon
Distributionally Robust Optimization

Laureano F. Escudero, M. Araceli Garin, Aitziber Unzueta
Distributionally robust optimization (DRO) is motivated as a counterpart of the usually unknown underlying probability distribution (PD)followed by the uncertainty in dynamic problems. An approach is presented for the highly combinatorial Cross-dock Door Design Problem(CDDP) solving to decide the number and nominal capacity of the strip
and stack doors. The strategic uncertainty is represented in a finite setof stagewise-dependent scenarios and the operational one is done ina finite set of stage-dependent scenarios. It is assumed the availability of a Nominal Distribution (ND) for the realization of the strategic
parameters in the immediate successors set of any node, and a ND of
the realization of the operational parameters in the stages through themulti-horizon scenario tree. Those ambiguity sets are obtained fromthe projections of appropriate perturbations of the cumulative distribution functions of the ND realizations in a set of modeler-driven PDs,
where the Wasserstein distance is satisfied for a given radius. A mixed
binary quadratic DRO-MH modeling paradigm is presented to consider the strategic and operational ambiguity sets in CDDP. The aim
is to minimize the overall highest expected DRO cost in the nodes,
among the ambiguity set members, subject to the constraint system foreach one.

MD-38

1 - Optimization and allocation for connection schedulingproblems

Laura Davila-Pena, Peter Borm, Ignacio García-Jurado, Jop
SchoutenThis work analyzes so-called connection scheduling problems (CSPs),a type of interactive Operations Research problem that combines elements from minimum cost spanning tree problems and sequencing
problems. Given a graph, our goal is twofold: firstly, to establish an
optimal connection order among players to minimize the overall costof connecting them to a source, and secondly, to develop a cost allocation strategy for this optimal order among the involved players. We
focus specifically on CSPs on trees, for which we propose a recursive
solving method integrated with an allocation approach.

2 - Game theoretical approach to determine feature importanceDani Samaniego, Laura Davila-Pena, Alejandro
Saavedra-Nieves
In the last years, the usage of Shapley value is being extended throughmachine learning algorithms as a method to determine the importanceof a feature on its contribution to a certain target value. This is doneby defining a cooperative game where features play the role of voters.
In this talk we will focus in the need of using a predictor to define thegame and its consecuences. We will point out other directions in orderto obtain the importance of the features in a more robust way. Also
we will comment real world use cases where the feature importances
plays a key role.

3 - Uncertain Preferences to deal with the Framing Effect in
the Graph Model for Conflict Resolution

Deoclecio Castro, Leandro RêgoIn this article, our goal is to present a method that enables us to perform
an analysis using the Graph Model for Conflict Resolution (GMCR)
in conflicts scenarios where the preferences of decision makers show
inconsistencies due to the Framing Effect. More specifically, we consider a GMCR with two decision makers who presented different preferences when, during the elicitation process, they were confronted withthe same problem framed in two distinct ways. This behavior is inconsistent with Decision Theory and may be explained by the boundaries
of human rationality. Our approach uses uncertain preferences to address the inconsistencies arising from the Framing Effect.

4 - Simple games with minimum

Sascha Kurz, Dani Samaniego
Every simple game is a monotone Boolean function. For the otherdirection we just have to exclude the two constant functions. Herewe consider simple games with minimum, i.e., simple games with aunique minimal winning vector. We present enumeration results aswell as formulas for their dimension.

 MD-38
Monday, 14:30-16:00 - Room: 34 (building: 306)

Applications of conic optimization

 MD-36
Monday, 14:30-16:00 - Room: 32 (building: 306)

Stream: Conic Optimization: Theory, Algorithms, and Applications
Invited session

Game Theory, Solutions and Structures IV

Chair: Julio C. Góez

Stream: Game Theory, Solutions and Structures
Invited session

1 - The exponential cone: machine learning and the projection algorithm

Chair: Sascha Kurz

Henrik A. Friberg143

MD-39EURO 2024 - Copenhagen

The exponential cone is an absolute beast with respect to its usages inmathematical modeling, and has proven to be a computational stable
and high-performant element of the MOSEK optimization core. In thistalk we will explore some of its surprising use cases, e.g. in machinelearning, before unveiling the successful story of the "projection onto
the exponential cone" algorithm. The Moreau decomposition theorem,
the KKT conditions, the convex analysis book by Rockafellar, unconstrained root finding and floating-point safeguards are all parts of thisstory.

2 - Lifting nonnegativity over general semialgebraic sets tononnegativity over simple sets.

Juan Vera
Semidefinite optimization hierarchies are the standard approach to
Polynomial Optimization and generalized moment problems. Several
authors have proposed hierarchies based on other conic alternatives
lately. These alternatives replace the sum of squares with different
classes of non-negative polynomials. The convergence of such hierarchies is rooted in Positivtellensatze, which guarantee particular typesof non-negative certificates.
We propose a universal approach to derive new Positivstellensatze forgeneral semialgebraic sets from ones developed for simpler sets, suchas a box or a simplex. We illustrate the approach by constructing Positivstellensatze over compact semialgebraic sets based on any given
cone of non-negative polynomials. Also, we show how to use our results to derive sparse Psatz and to extend them to unbounded semialgebraic sets.

 MD-39
Monday, 14:30-16:00 - Room: 35 (building: 306)

Stochastic Models in Manufacturing
Stream: Stochastic Modelling
Invited session
Chair: Tim Engels
1 - Solving General Assemble-to-Order systems via
Component-Based and Product-Based Decomposition
Methods

Mohsen Elhafsi, Jianxin Fang, Essia Hamouda
Assemble-to-order (ATO) strategies are widely used in various industries. Despite their popularity, ATO systems remain challenging, bothanalytically and computationally. We study a general ATO problem
modeled as an infinite horizon Markov decision process. In particular,
we consider a system with mixed-Erlang distributed component production/leadtimes, and Poisson demand for products. Demand is lost if
not immediately satisfied. As the optimal policy of such system is computationally intractable, we develop two heuristic policies based on decomposition methods: component-based and product-based. In orderto evaluate the performance of the heuristics, we develop a tight lower
bound using an Approximate Linear Programming approach that relies
on a judicial choice of basis-functions, for approximating the optimalvalue function. Our results show that the heuristics perform within
only few Average Percentage Deviation (ADP) from the lower boundand even a smaller ADP when compared to systems where the optimal
policy could be obtained. Moreover, we show that our componentbased decomposition heuristic only scales linearly with the number ofcomponents in the ATO system, and therefore is suitable for solving
large-scale ATO systems.

2 - Cost-Effective Quality Assurance: Optimizing Inspection Station Placement in Queueing Network Production
Systems

Boualem Rabta

144

This research addresses the optimization of inspection station placement in production systems modeled as queueing networks, where defects may arise at multiple stages. Our proposed optimization model
aims to minimize total costs and/or production lead time by determining the optimal number and placement of inspection stations within the
network. By strategically positioning inspection stations, we mitigate
the risk of defects propagating through the system, enhancing product quality and operational efficiency. We present numerical examples
and evaluate various scenarios to identify the most effective configurations. This research contributes to advancing operational efficiency
and quality management in complex production systems, offering practical insights for decision-makers in optimizing resource allocation andenhancing overall system performance.

3 - Achieving emergent symmetry in high tech supply
chainsMirjam Meijer, Dennis Schol, Willem van Jaarsveld, Maria
Vlasiou, Bert Zwart
For manufacturers of high-tech products, such as aircraft, lithographymachines, etc., it is of key importance that all required components areavailable at the time of assembly. This is a major challenge, as thenumber of components required for assembling such complex systems
is enormous and most of them are sourced at external suppliers. For a
supplier, the risk of over-investing in costly capacity is high, while thelost revenue of one missing component is much lower than the valueof the manufacturer’s end-product. Therefore, each supplier will trade
off the cost of investing in additional capacity against the risk of being unable to deliver. Ultimately, the overall delay in delivery of theend-product is determined by the supplier with the largest backlog. To
reduce the risk of component shortages, the manufacturer aims to motivate suppliers to invest in excess production capacity, for example bycharging a fee for each time unit the supplier delivers past a previously
agreed lead time. We model a manufacturing system consisting of amanufacturer and a large number of suppliers, each producing a single
component that is required in the manufacturer’s assembly process ofthe end-product. We consider the decentralized optimization problem
where each supplier determines their own optimal service rate. We analyze how the contract parameters can be set such that the suppliers
choose the service rates that are asymptotically optimal for the supply
chain as a whole.

4 - A Mean Value Approach to Continuous Polling Models
with Batch Arrivals

Tim Engels
Polling models are classes of queueing models in which a server attends to several queues. By cyclically visiting the queues, the server
serves awaiting customers. The analysis of these systems is involved
and provides computationally involved results. Therefore, the approximation of these models by their continuous counterparts can be quite
helpful. In this talk, we analyze continuous polling models with batch
arrivals and deploy a novel mean value analysis to (i) find the average
spread of customers and (ii) derive some key performance statistics ofpolling models; delivery times and batch sojourn times. The resulting approximations are shown to already be quite accurate for pollingsystems with small numbers of queues.

 MD-40
Monday, 14:30-16:00 - Room: 96 (building: 306)

Stochastic Modelling
Stream: Advances in Stochastic Modelling and Learning
Methods
Invited session
Chair: Flora Spieksma

EURO 2024 - Copenhagen
1 - An Application to Markov Decision Models in Healthcare Screening

Masayuki Horiguchi
In this talk, we consider the evaluation of periodic screening programme for woman breast cancer and formulate the model as a partially observable Markov decision process (POMDP). We convert a
POMDP with finite state, observation state and action spaces to an
equivalent completely observable MDP with continuous state and finite
action spaces. By this approach, we have an optimal policy from dynamic programming (DP) equation in an equivalent MDP, but we focus
on considering the evaluation in several scenarios of periodic screening for participants with silent condition of breast cancer and seeking
an answer which programme is better than others for themselves. The
aim of our research is, by using the data sets based on cancer registration and estimated parameters of survival rates and other ratios related
to screening and diagnoses in Japan, to evaluate the validity of generalrecommendation with respect to the consideration of human health inscenarios of breast cancer screening programme in POMDP.

MD-41

4 - On the Deviation matrix of an ergodic denumerable
Markov process in continuous time with applications

Flora Spieksma
The deviaton matrix is a measure of the cumulative deviation from thelimiting probabilities and plays an important role in e.g. Markov (decision) processes, perturbed Markov processes, queueing theory, andnetwork centralities. It is well-known that the deviation matrix in theirreducible case exists if and only if the second moment of the return
time from a fixed state to iself is finite. We provide a necessary andsufficient condition for the deviation matrix to exist in the form of asystem of two nested Lyapunov functions, the equivalent of which has
been known for a long time for discrete time Markov chains. However,it has not been realised earlier that finiteness of the deviation matrix
implies that the absolute cumulative deviation from the limiting probabilities is finite as well. There is a relatively simple (but not simply
proved) argument why this is true. We further present a formula for thedeviation matrix in terms of a rank one perturbation of the q-matrix.
We will illustrate our results with some applications

2 - Markovian Degradation Modeling of Rails

Steven Harrod, Albert Skovgaard Bisgaard
Rail defects pose a significant threat to railway safety and efficiency.
Probabilistic modeling of defect propagation has the potential of improving decision-making for circumvention of dangerous rail degradation.
We propose a continuous-time Markov chain with transition rates regressed on location-dependent covariates to model discretely observed
degradation trajectories discovered at the Norwegian rail network. We
propose two estimation approaches. The first approach obtains thefull data log-likelihood by Monte Carlo simulation of full data defecttrajectories, which informs the Expectation-Maximization algorithm.
The second approach maximizes the discrete data log-likelihood informed by analytical gradient information.
Both methodologies give rise to fast convergence of model estimateswith similar numerical values. Moreover, the estimated coefficientsfor the covariates are found to be statistically significant and in directional accordance with their physical interpretation. Model checking
suggests a robust framework that describes enough variance to satisfactorily account for the structural variability between the rail lines.
We apply the proposed model setting to produce probabilistic forecasts of a defect’s growth based on its location in a railway network.
The probabilistic setting allows for straightforward scenario generation
constituting useful input to decision-making models under uncertainty
for maintenance planning.

3 - Learning to Simulate: Generative Metamodeling via
Quantile Regression

Qingkai Zhang
Stochastic simulation models, while effective in capturing the dynamics of complex systems, are often too slow to run for real-time decisionmaking. Metamodeling techniques are widely used to learn the relationship between a summary statistic of the outputs (e.g., the mean orquantile) and the inputs of the simulator, so that it can be used in realtime. However, this methodology requires the knowledge of an appropriate summary statistic in advance, making it inflexible for many practical situations. In this paper, we propose a new metamodeling concept,
called generative metamodeling, which aims to construct a"fast simulator of the simulator". This technique can generate random outputs
substantially faster than the original simulation model, while retaining an approximately equal conditional distribution given the same inputs. Once constructed, a generative metamodel can instantaneously
generate a large amount of random outputs as soon as the inputs are
specified, thereby facilitating the immediate computation of any summary statistic for real-time decision-making. Furthermore, we propose
a new algorithm—quantile-regression-based generative metamodeling(QRGMM)—and study its convergence and rate of convergence. Extensive numerical experiments are conducted to investigate the empirical performance of QRGMM, compare it with other state-of-the-art
generative algorithms, and demonstrate its usefulness in practical realtime decision-making.

 MD-41
Monday, 14:30-16:00 - Room: 97 (building: 306)

Optimization on Geodesic Metric Spaces I:
Smooth caseStream: Optimization on Geodesic Metric Spaces:
Smooth and Nonsmooth
Invited session
Chair: Alexandru Kristaly
1 - Sharp spectral gap estimates for higher-order operatorson Cartan-Hadamard manifolds

Csaba Farkas
The goal of this talk is to provide sharp spectral gap estimates for problems involving higher-order operators (including both the clamped andbuckling plate problems) on Cartan-Hadamard manifolds. The proofs
are symmetrization-free – thus no sharp isoperimetric inequality isneeded – based on two general, yet elementary functional inequalities.
The spectral gap estimate for clamped plates solves a sharp asymptotic
problem from Cheng and Yang [Proc. Amer. Math. Soc., 2011] concerning the behavior of higher-order eigenvalues on hyperbolic spaces,
and answers a question raised in Kristály [Adv. Math., 2020] on thevalidity of such sharp estimates in high-dimensional Cartan-Hadamard
manifolds. As a byproduct of the general functional inequalities, various Rellich inequalities are established in the same geometric setting.

2 - Nonlocal critical growth elliptic problems with jumping
nonlinearities

Raffaella Servadei
n this talk we deal with some nonlocal critical growth elliptic problem driven by the fractional Laplacian, with particular emphasis on
equations in presence of jumping nonlinearities. Using variational andtopological methods, we prove the existence of a nontrivial solution forthe problem under consideration. These existence results can be seenas the nonlocal counterpart of the ones obtained in the context of the
Laplacian equations. In the nonlocal framework the arguments used in
the classical setting have to be refined. Indeed the presence of the fractional Laplacian operator gives rise to some additional difficulties, that
we are able to overcome proving new regularity results for weak solutions of nonlocal problems, which are of independent interest. This is a
joint paper with Giovanni Molica Bisci, Kanishka Perera and CaterinaSportelli (Journal des Mathématiques Pures et Appliquées, 2024).

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MD-42EURO 2024 - Copenhagen

3 - Solitons of the spacelike mean curvature flow in generalized Robertson-Walker spacetimes

Giovanni Molica Bisci
Our purpose in this talk is to present some results related to thestudy of solitons of the spacelike mean curvature fow in a generalized Robertson-Walker (GRW) spacetime. Under suitable constraints
on the warping function and on thecurvatures of the Riemannian fiber,
we apply suitable maximum principles in order to obtain nonexistence
and uniqueness results concerning these solitons. Applications to standard models of GRW spacetimes, namely, the Einstein-de Sitter spacetime, steady state type spacetimes, de Sitter and antide Sitter spaces,
are given. Furthermore, we establish new Calabi-Bernstein type results
related to entire spacelike mean curvature flow graphs constructed overthe Riemannian fiber of the ambient spacetime (Joint work with Márcio Batista, Henrique F. de Lima and Wallace F. Gomes New York J.
Math. 29 (2023) 554-579).

 MD-42
Monday, 14:30-16:00 - Room: 98 (building: 306)

Optimization in Quantum Information
Stream: Quantum Computing Optimization
Invited session
Chair: Gereon Koßmann
1 - Optimization of the Relative Entropy under linear constraints via semidefinite programming

Rene Schwonnek
Optimizing the quantum relative entropy under linear constraints is acentral problem in Quantum Key Distribution (QKD) and other fieldsof (Quantum) Shannon theory. In particular providing provable lower
and upper bounds is a highly relevant task. We provide a practicaland resource efficient method for this problem. At the core of ourwork stands a recently described, and pleasingly elegant, integral representation of the quantum relative entropy, which we employ in order to formulate the problem of reliably bounding it as an iteration ofsemi definite programs (SDP). In contrast to existing techniques, ourmethod comes with a provable convergence guarantee of quadratic order, whilst staying resource efficient with the matrix dimension of theunderlying SDPs. We furthermore can provide an estimate for the gap
to the optimum at each stage of the iteration. Combining this withsome clever heuristics for the iteration, we find that convergence canin practice be actually achieved much faster then theoretically guaranteed.

2 - From barren plateaus through fertile valleys: Conic extensions of parameterised quantum circuits

Timo Ziegler, Gereon Koßmann
Unconstrained optimization problems such as integer linear programs
can be relaxed to the task of finding the ground state of a physical system described by a quantum state. In the realm of quantum devices
having comparably low numbers in qubits prone to noise, quantum circuits consisting of a sequence of parameterised unitary gates controlled
by classical optimization methods are the prevalent technique of nearterm quantum algorithms. However, the omnipresent phenomenon of
barren plateaus - parameter regions with vanishing gradients - sets apersistent hurdle that drastically diminishes their success in practice.
In this work, we introduce an approach -based on non-unitary operations - that favours jumps out of a barren plateau into a fertile valley.
These operations are con- structed from conic extensions of parameterised unitary quantum circuits, relying on mid-circuit measurements
and a small ancilla system. We further reduce the prob- lem of finding
optimal jump directions to a low-dimensional generalised eigenvalue
problem. As a proof of concept we incorporate jumps within state-ofthe-art implementa- tions of the Quantum Approximate Optimisation
Algorithm (QAOA) - a prominent descendant of the class of variational

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quantum algorithms. We demonstrate the ex- tensions’ effectiveness on
QAOA through extensive simulations, showcasing robust- ness against
barren plateaus and highly improved sampling probabilities of optimal
solutions.

3 - Efficient Prediction of Quantum Ground State Properties via Classical Machine Learning with Geometric Locality

Viet Tran
An fundamental optimization problem in quantum physics is finding
the ground state of quantum many-body systems. Addressing this challenge, our presentation highlights numerical experiments that utilize aclassical machine learning (ML) algorithm leveraging a geometric locality inductive bias to efficiently predict ground state properties. Our
experiments on systems up to 45 qubits illustrate the model’s ability
to accurately predict ground state properties with only O(log(n)) instances from similar Hamiltonians, demonstrating a substantial efficiency improvement over previous state of the art methods with polynomial sample complexity. These findings validate theoretical improvements on the sample complexity bound for geometrically informed ML algorithms.
Additionally, if time allows, we will delve into preliminary findings
from ongoing research in the domain of shadow tomography facilitated by diffusion models, exploring their potential to further advanceour understanding of quantum systems.

4 - de-Finetti Methods for Channel Coding Theory

Gereon KoßmannMaximizing the channel fidelity is a major problem in quantum information theory. Many methods have been developed for inner points
and for some error models exact codes are available. In general, however, one would be interested in certificates, i.e. outer bounds. In thistalk, we will present a method for generating outer points and a hierarchy that also allows for inner points. The talk is based on the standard quantum de-Finetti hierarchy for problems with linear constraints(arxiv: 1506.08810). Starting there, we are able to prove the existenceof a sequence of inner points, which have similar error bounds as theouter bounds. This result yields that at least theoretically certificationof actual error correcting codes becomes possible.

 MD-43
Monday, 14:30-16:00 - Room: 99 (building: 306)

Simulation in economics II
Stream: Agent-based Models in Management, Economic
and Organisation Sciences
Invited session
Chair: Harit Joshi
1 - Modelling the choice between labour and leisure andshort-run profit maximization of the monopsonist bilevel programming approach

Mira Krpan, Zrinka Lukac, Vedran Kojić
This paper models the labour-leisure choice problem with CobbDouglas preferences and the short-run profit maximization problem ofa monopsonist with Cobb-Douglas technology as a bilevel programming problem. The solution of the problem is discussed and the results
are enriched by economic interpretations and numerical examples.

EURO 2024 - Copenhagen
2 - Attaining Approximate Stability in Large Two-Sided
Matching Markets

Chun Wang, Jie Gao, Shixuan Hou
A two-sided market is an economic platform that enables direct interactions between two distinct customer groups, providing each otherwith network benefits. Matching is the fundamental function that ensures the viability and service quality of such markets. While theGale-Shapley algorithm and its variants can be used to compute stablematchings for two-sided markets effectively, these methods typicallyrequire participants to rank all potential partners, posing a significant
cognitive burden to them and rendering it impractical for large-scale
markets involving hundreds or thousands of options on each side. This
paper presents an efficient framework for computing approximately
stable matchings in large-scale and dynamic market settings. Insteadof explicitly eliciting preferences, we employ probabilistic choice behavior models trained on participants’ historical interaction data withthe platform. These models serve as incremental preference elicitation
tools, which adapt themselves through continuous machine learningtechniques. Additionally, we devise procedures for robust matchingin the presence of probabilistic and evolving preference information
provided by the behavior models. We conduct a simulation study toexperimentally quantify the impact of enforcing approximate stability within a dynamic crowd-sourced delivery services setting, utilizing
preference survey data collected from crowd-sourced drivers.

3 - Platform coordination in ride-hailing through matchingand pricing

Harit Joshi, Saral Mukherjee
Ride-hailing has evolved from the decentralized model in which
taxis operate independently to the centralized model with on-demandmatching of customer travel requests with the available drivers. The
existence of a coordinator is a difference between the decentralized
model of hailing taxis and the centralized matching in ride-hailing platforms. More research needs to be conducted to quantify the differentiated value provided by the platform and how this value changes indifferent contexts. In this paper, we quantify the value of coordination
through matching in ride-hailing platforms by comparing differencesin performance metrics between centralized and decentralized models.
The performance metrics include supply-side metrics like driver utilization, demand-side metrics like customer search time, and platformside metrics like the number of unmatched customers. Using an agentbased model, we show that the value of coordination is different for thetwo sides of the platform. We explore how the value of coordination
changes with variations in (i) demand and supply characteristics likethe spatiotemporal distribution of demand, (ii) driver characteristics
like knowledge of high-demand zones, (iii) customer characteristics
like patience levels, and (iv) network characteristics like topography.
Our analysis offers insights to the platforms in understanding the roleof demand, supply, behavioral, and network characteristics in the value
generated through their operations.

 MD-44
Monday, 14:30-16:00 - Room: 20 (building: 324)

Robustness analysis in MCDA 1
Stream: Multiple Criteria Decision Analysis
Invited session
Chair: Eleftherios Siskos
1 - Integrated Assessment of a Robust Choquet Integral
Preference Model for Efficient Multicriteria Decision
SupportEleftherios Siskos, Antoine Desbordes, Peter Burgherr,
Russell McKenna

MD-44

Decision problems are often characterized by complex criteria dependencies, which can hamper the development of an efficient and theoretically sound multicriteria decision aid model. These criteria interactions have the form of either redundancy or synergistic effect andrequire arduous and demanding preference statements for their quantification. In this paper, criteria interactions in decision models are
addressed with the proposition of an MCDA framework, coupling thepreference elicitation protocol of the method of cards and the Choquet integral preference model, which is approached as an importanceindex. An interactive robustness control algorithm ensures the concurrent acquisition of a stable decision model and satisfactory evaluation
results. Robustness is assessed with a portfolio of robustness indicators, spanning from the variability of the preference parameters to the
reduction of the model’s feasible space and rank acceptability indices.
At the algorithm’s core, a heuristic module generates pairwise elicitation questions and selects those delivering the highest expected information gain. The whole framework is stress-tested with a small-scaledecision problem, where three versions of the heuristics are automatically applied, with the machine randomly answering the questions.
Subsequently, the same problem is approached with the involvementof a real decision-maker, to appraise the required cognitive effort andreceive valuable feedback.

2 - ScoreBin: Scoring Alternatives by Fusing a Crisp Outranking Relation and Indirect Decision Maker’s Judgments

Krzysztof Martyn, Magdalena Martyn, Milosz Kadzinski
We introduce a suite of multiple criteria methods that fuse two sourcesof information. On the one hand, we exploit a crisp outranking relation in the set of alternatives. For each option, we analyze the outranked and outranking alternatives to compute its strength, weakness,and comprehensive quality. We propose four variants that differ inweights assigned to outranking or being outranked by particular alternatives and how to quantify the difficulty or easiness of instantiatingsuch relations. On the other hand, we consider the Decision Makers’
holistic judgments, indicating subsets of alternatives deemed as comprehensively strong or weak. We apply the proposed methods to a casestudy concerning the performance of technological parks in Poland.
We also compare the results obtained with the novel approaches withthe state-of-the-art ELECTRE methods in an extensive experiment involving simulated decision problems.

3 - Attaining Robust Performance Targets in Data Envelopment Analysis with Application to Efficiency Evaluationof Airports

Dariusz Grynia, Milosz Kadzinski
We consider a ratio-based efficiency analysis in view of achieving performance targets by Decision Making Units. The targets refer to theefficiency scores or ranks and the truth of pairwise preference relations. These outcomes can be instantiated for at least one or all feasible
weights associated with inputs and outputs. We discuss Multiple Objective Optimization problems that minimize the required reductions ofconsumed resources or increases of produced results needed to reachthe pre-defined aims. The proposed models offer great flexibility to theDecision Maker, who can indicate which factors should be modified
and to which extent. The evolutionary optimization algorithm computes a set of solutions that exhibit the trade-offs between expectedmodifications of various factors. They can serve as the basis for interactively selecting the most preferred solution to be implemented inpractice. The use of the proposed framework is illustrated in the problem of analyzing the efficiency of Polish airports.

4 - Robust Additive Value-Based Efficiency Analysis with a
Hierarchical Structure of Inputs and Outputs

Anna Labijak-Kowalska, Milosz Kadzinski
We introduce a novel methodological framework based on additive
value-based efficiency analysis. It considers inputs and outputs organized in a hierarchical structure. Such an approach allows us to decompose the problem into manageable pieces and determine the analyzed
units’ strengths and weaknesses. We provide robust outcomes by analyzing all feasible weight vectors at different hierarchy levels. The

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MD-45EURO 2024 - Copenhagenanalysis concerns three complementary points of view: distances tothe efficient unit, ranks, and pairwise preference relations. For each ofthem, we determine the exact extreme results and the distribution ofprobabilistic results. We apply the proposed method to a case study
concerning the performance of healthcare systems in sixteen Polishvoivodeships (provinces). We discuss the results based on the entire
set of factors (the root of the hierarchy) and three subcategories. They
concern health improvement of inhabitants, efficient financial management, and consumer satisfaction. Finally, we show the practical conclusions that can be derived from the hierarchical decomposition of the
problem and robustness analysis.

 MD-45
Monday, 14:30-16:00 - Room: 30 (building: 324)

Emerging Trends in Decision Analysis
Stream: Decision Support Systems
Invited session
Chair: Pavlos Delias
1 - Information/Throughput Tradeoff Mitigation in Automated Sorting

Magnus Berg Ladefoged, Andreas Larsen
Conveyor belts are often used in conjunction with robotic arms forautomated manufacturing processes. For moving and packing, therobotic arms automatically pick the items on the conveyor and placethem in containers. This is however essentially a TSP, and efficiency
thus requires a sophisticated control scheme. Increasing the lookahead is a way to introduce more information to the system, however
this might lead to a decreased throughput as the system waits for theperfect item and sub-par items run off or are recycled into the system.
A key factor will be intelligently incorporating the look-ahead information in the constraints of the optimization problem, to limit the negative
impact on throughput of the system while maintaining the benefits of
the larger solution space. This paper proposes to extend the objective function to include the look-ahead and incorporating the associated time penalty in the problem constraints using a temporal-distancescoring metric. Furthermore, the decision cycle will be included in the
problem formulation as it directly relates to the problems’ temporalnature.

2 - Application of Predictive Analytics in Assessing Student Performance: A Case Study in a Computer Supported Collaborative Learning Environment

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3 - Modelling Dependencies Between Criteria with Fuzzy
Functional Dependence

Frantisek Zapletal
Evaluation of the importance of criteria using weights is usually an
integral part of solving multi-criteria decision-making (MCDM) problems. MCDM methods often require independent criteria, but this may
not be true for real problems. Significant dependence then leads toeither a reduction or a strengthening of importance. There are several approaches that can reflect the dependency between criteria, e.g.,
CRITIC method or the methods based on the Choquet integral. Existing approaches, however, suffer from drawbacks. They are basedon the correlation which does not reflect the direction of dependenceor expert evaluations. This contribution focuses on the application of
the fuzzy functional dependencies (FFD) to reflect the dependencies
between criteria in their weights. The model is tested using several
numerical examples.

4 - Advancing Business Process Evaluation: The Value of
Integrating Process Mining and Propensity Score Analysis

Pavlos Delias, Giannoula Florou, Nikolaos Mittas
Ensuring the efficacy of business process interventions is essential for
informed decision-making and sustained improvements. This study focuses on a pragmatic approach that integrates process mining and thestrengths of propensity score analysis to enhance precision and reliability in evaluating business process interventions. Drawing from ourprior work, we present a practical methodology tailored for organizations seeking an effective solution. We aim to construct a frameworkthat utilizes both event logs and observed characteristics to address
confounding factors, thereby enhancing the validity of conclusions
in observational studies. Our work is complemented by a prototype
tool designed to assist practitioners in selecting appropriate weighting
methods, evaluating group balance, and assessing covariate balance.
Through an applied case study on a simulated claims management process dataset, we illustrate how these techniques synergize to measureand enhance business process effectiveness and efficiency. This study
not only underscores the integration of process mining, business process management, and causal inference but also scrutinizes the inherent limitations and challenges. By providing a comprehensive view,
our goal is to offer organizations actionable insights, implementation
guidance, and an understanding of how this synergy can advance business process evaluation.

 MD-46

Yannis Psaromiligkos, Athanasios Spyridakos, Natasa
Themeli, Theodoros Anagnostopoulos, Christos Kytagias,
Dimitris Papakyriakopoulos

Monday, 14:30-16:00 - Room: 40 (building: 324)

In contemporary collaborative learning environments, characterized
by intricate activities and numerous stakeholder interactions, there’s
a pressing need to gather insights from diverse sources to attain acomprehensive understanding of learning dynamics and students’ behaviours. While Learning Management Systems serve as the cornerstone of these environments, their data analysis capabilities are often
limited to basic metrics such as visit frequency, completion rates, andscore statistics. Consequently, there’s a high demand for specialized
tools to support instructional designers. Decision support in the formof Learning Analytics, rooted in the domain of business intelligence,
has emerged as a forefront area of research in technology-enhanced
learning. In this paper, we present the application of predictive analytics within a multi-criteria dataset structure to categorize students
based on their performance in collaborative activities. Our approach
was deployed in a real-world scenario involving two cohorts of an undergraduate course conducted entirely online due to the pandemic. The
first cohort comprised 337 students organized into 81 groups, while thesecond cohort consisted of 341 students divided into 76 groups. We applied various machine learning algorithms to determine the algorithm
yielding optimal performance.

Stream: Making an Impact
Invited session

The Quest For The Right OR Professionals

Chair: Sander Van Aken
1 - The Quest for the Right OR Professionals - a Hiring Perspective (90’ panel discussion)

Making An Impact, Sander Van Aken, Michael Lindahl,
Susanne Heipcke, Henning Blunck
In this 90’ panel session, recruiters of OR professionals, data scientists
or analytics people will discuss and compare how they go about it, andaddress burning questions relevant to both those recruiting professionals, as well as those looking to secure a new industry position.
How do companies and teams come to define the profile to hire, crafting the job ad? What skillset and traits do you look for, and how do
you define that? How do you design a hiring process covering those
aspects, and what are the challenges and best practices for the recruiting team themselves? What advice would the hiring team give theircounterparts.

EURO 2024 - Copenhagen
Join us to learn about each other’s challenges, best-practices, and ask
your own questions during a Q&A.
For the latest details of speakers, please see the Making an Impact page on the main conference website
https://euro2024cph.dk/programme/making-an-impact-2024

MD-48

environmental, social and governance, enables construction companies
to assess comprehensively ESG performance. Future research opportunities include in the definition of a methodology to identify materialindicators for each company and the definition of benchmarks in thecontextualization of the framework.

3 - Territorial rebalancing in an axiological perspective. An
Reaction Capacity Index of Sicily’s inner areas MD-47
Monday, 14:30-16:00 - Room: 50 (building: 324)

How to support complex decisions.
Negotiating the trade-off between Social,
Environmental and Economic values 2
Stream: Multiple Criteria Decision Analysis
Invited session
Chair: Alessandra Oppio
Chair: Marta DellOvo
1 - Elaboration of a Multi-Dimensional Decision Support
System for Strategic Energy Planning: Application to
Production and Trade of Green Hydrogen

Maria Cristina Pinto, Maria Gaeta, Piero Boccardo, StefanoCorgnati
Even if energy-based, the transition towards decarbonization is a multidimensional process evolving over time and space and related to thebroader social, techno-economic, environmental, and geopolitical aspects. While facing the uptake of new technologies or strategies inthe energy system framework, it is important to address this complexity, to support policymakers in strategic planning. In this regard, thework aims to elaborate a structured science-based decision-making approach, through the exploitation of different instruments and tools, i.e.
multi-criteria decision methods, spatial analyses, modelling of energy
scenarios. A three-step procedure is developed, based on the evaluation of three concepts: (i) predisposition, through the application of aMulti-criteria Analysis; (ii) multi-dimensional suitability, exploiting a
Multi-criteria Spatial Decision Support System; (iii) competitiveness,by modelling energy scenarios. Elaborated to be scalable and replicable for different strategic energy planning processes, the approachis exploited to assess if and how (i) North African countries are predisposed to green hydrogen production, (ii) North African areas are
suitable for green hydrogen production, (iii) the trade to Europe canbe competitive in the long-term. This approach allows to investigate
the multi-dimensional patterns of the transition and its key players likehydrogen, through heterogeneous instruments able to express the heterogeneity of the process.

2 - An ESG Indicators Framework for Construction andReal Estate Companies

Daniela Santana Tovar, Sara Torabi, Patrizia Lombardi
The construction industry is widely recognized as one of the most environmentally sensitive sectors, having a significant impact when adopting sustainable development practices. Environmental, Social andGovernance (ESG) stand as the three primary pillars of a company’ssustainability performance, which contrast with the pillars defining
sustainable development. Notably, ESG play a critical role in evaluating company performance by revealing inherent risks. Despite thewidespread use of indicators, there exists a notable gap in those specifically oriented to assess company performance within the constructionindustry. However, research in how to achieve a standard framework
to evaluate and report ESG performance in companies are still very
limited. This paper aims to develop a framework of indicators relevant to the construction industry within a European context, serving
as a tool to measure ESG performance. The methodology consistsof the characterization of a set of indicators drawn from establishedprotocols for assessing and reporting ESG criteria such as GRI andGRESB. Through a selection process, 169 indicators categorized into

Maria Rosa Trovato, Ludovica Nasca
The marginalisation of the inner areas due to increased social, material, economic, infrastructural vulnerability is a phenomenon thatafflicts many countries today and is growing rapidly.In recent years,
specific policies, measures, and funding have been proposed aimed atcounteracting this phenomenon.To date, despite the increased awareness of the issue of marginal areas and the growing concern about the
progressive decline and abandonment of large areas of territory, current policies are slow to produce the expected effects.The heritage of
weak areas, if properly integrated, can become a resource, an opportunity for the whole territorial system. Decision-makers engaged in theprospect of the territorial rebalancing will have to be supported in identifying the residual values of these marginal areas, to recognise where
and how it can be significantly emphasised in the prospect of an integrated long-run redevelopment process. The research, according to a
multicriteria-based axiological approach, proposes the construction ofa Reaction Capacity Index (RCI) of the internal areas of Sicily. The
estimation and representation in QGIS of the RCI provides an overall framework based on which decision makers can identify specificpolicies and actions aimed at favouring a territorial rebalancing. The
methodological approach for estimating the RCI index that provides
an overall measure of territorial capital endowment is developed basedon the Multi-attribute Value Theory (MAVT).

4 - Spatial multidimensional evaluation of impacts generated by the Porta Nuova district in Milan (Italy): a purpose perspective.

Alessandra Oppio, Marta DellOvo, Danny Casprini
An urban regeneration project brings the creation of tangible and intangible effects that arise from the design choices adopted, as well asfrom the sustainability goals identified. The tangible effects of the intervention are traditionally intercepted by economic evaluation models
and allow design choices to maximize the developer’s economic return. In addition, intangible effects are generated, such as, the impact
on the quality of life, environmental impacts, and the social and cultural vibrancy of the area. Defining, and even more quantifying, theintangible effect of a project is complex, requiring effort in analyzing
the intentionality and additionality that lead to the creation of added
value for the area in which the project is located and for the community that benefits from it. The objective of the study is to identify andmeasure the impacts and the social additional value generated by theurban regeneration of the Porta Nuova district while considering theeffects it has triggered on neighboring districts in the city of Milan
and at larger scale. A multimethodological approach, based on Stakeholders’ Analysis, Multiple Criteria Spatial Decision Support Systems(MC-SDSS) and Community Impact Evaluation (CIE) has been developed to identify the purpose and the dimensions impacted by theurban project on the different stakeholders categories. The final result
highlights the spatial distribution and the intensity of impacts from apurpose perspective.

 MD-48
Monday, 14:30-16:00 - Room: 60 (building: 324)

DEA applications in Education and Health I
Stream: Data Envelopment Analysis and its Application
Invited session
Chair: Dimitrios-Georgios Sotiros

149MD-49EURO 2024 - Copenhagen

1 - Croatian Universities Under the Microscope:
Leads, Who Lags, and Why?

Who

Katerina Fotova Čiković, Damira Keček, Violeta Cvetkoska
In an era propelled by knowledge, our study pioneers a cutting-edge
evaluation of teaching and research efficiency in Croatian universities,
employing a sophisticated blend of AHP, DEA, and regression analysis. Our extensive literature review from the Scopus database, spanningscholarly discourse from inception to 2023, unveiled crucial focuses:
teaching and research efficiency, and their convergence. Distilling insights, we constructed a refined framework for inputs and outputs,
utilizing AHP, and engaging diverse respondents—Ministry of Education, university rectors, and higher education policy advisors—in astructured comparison. They assigned relative importance to input andoutput hierarchies for teaching and research efficiency. The highestranked factors, determined by AHP, shaped the DEA analysis, revealing the efficiency frontier and enabling targeted improvement strategies
for universities based on quantified efficiency scores. In parallel, ourstudy delves into the assessment of managerial ability within universities, a pivotal yet often overlooked aspect. Adopting methodologies
proposed by Demerjian and Lev (2021) and Banker and Park (2021),
we measure the efficacy of managerial decision-making in steering institutions toward relative teaching and research efficiency. By identifying determinants of managerial ability, our analysis contributes insights into the leadership dynamics shaping the overall efficiency of
Croatian universities.

2 - Evaluation of the efficiency of research outputs of Korean private universities: a two-stage network DEA approach

Mehdi Shamohammadi
The production and assessment of research outputs are pivotal aspectsof the university knowledge generation process, influencing spin-offs
and collaborations arising from industry-university research endeavors. While numerous studies have utilized research outputs to gauge
university performance, many have primarily concentrated on quantitative variables such as the sheer number of publications and patents.
This emphasis on quantitative indicators alone may lead to an overestimation and bias in assessing research performance in specific instances. Recognizing this limitation, our study aims to address this gap
by employing a two-stage network data envelopment analysis (DEA).
In addition to quantitative variables, our focus extends to incorporating quality attributes like publication and patent citations alongside
the number of research projects. Our dataset spans 57 research-active
South Korean private universities from 2010 to 2019. Empirical findings reveal that integrating quality attributes into an appropriate production model enhances the accuracy and reliability of efficiency measurement results. Furthermore, the use of the two-stage network DEA
allows for a nuanced observation of the disparity between the quantityand quality stages of university research outputs, presenting a valuableadvancement beyond traditional black-box DEA models.

3 - A generalized composition approach in Network Data
Envelopment Analysis for complex structures: An application to higher education institutions in Poland

Dimitrios-Georgios Sotiros, Julia Nasiadka, Gregory
Koronakos, Dimitris Despotis
Network Data Envelopment Analysis (NDEA) is an extension of Data
Envelopment Analysis (DEA) that takes into consideration the internal
structure of Decision Making Units (DMUs) in the efficiency assessment. In NDEA, every DMU is conceived as a network of several
sub-processes arranged into a series, parallel or mix of series and parallel (complex) structures. In the literature, most of the studies focus
on series structures while the drawbacks of the methods developed forthe efficiency assessment of parallel structures are neglected. In thispaper, we discuss the pitfalls of the most established NDEA method
for parallel structures, and we develop a new method to treat theseissues. Our method relies on Multi-Objective Programming (MOP)
techniques and can be generalized to any type of internal structure. We
compare the introduced method with other prominent methods in theliterature highlighting the differences and the advantages of the proposed method. Finally, we employ this method to evaluate the teaching
and research efficiency of Polish higher education institutions.

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 MD-49
Monday, 14:30-16:00 - Room: M1 (building: 101)

Lot-sizing with industrial applications II
Stream: Lot Sizing, Lot Scheduling and Production Planning
Invited session
Chair: Elisabeth Finhold
1 - Optimization of Blister Package Design in Pharmaceutical Industry

Seray Çakırgil, Eda Yücel
This study focuses on enhancing blister packaging operations at a
Turkish pharmaceutical company to improve efficiency, reduce costs,
ensure regulatory compliance, and contribute to reducing carbon emissions. Blister design significantly influences decisions regarding production, logistics, and investments. While existing research typicallyaddresses challenges after selecting packaging materials and designs,
we propose a novel mixed-integer mathematical model to address
packaging design, machinery investment, and production planning simultaneously. However, solving this complex problem is computationally challenging. Therefore, we introduce a solution method using a constructive approach and Adaptive Large Neighborhood Search(ALNS). Computational experiments on realistic datasets demonstratethat our proposed method effectively optimizes blister packaging andproduction planning for various product quantities and time periods.
Additionally, our work supports the industry’s sustainability goals bypromoting eco-friendly packaging practices.

2 - Integrated optimisation for aluminium rolling

Georgios Zois, Ioannis Avgerinos, Apostolos Besis, Yiannis
Mourtos, Athanasios Psarros, Stavros Vatikiotis
This work focuses on the aluminium production scheduling problem
of a leading European Copper and Aluminium industry. The problem forms an interesting variant of flexible flow-shop scheduling consisting of two successive stages, the preheating and the hot rolling ofaluminium slabs i.e.,: A set of slabs arrives over time and (in the preheating stage) is assigned to a set of parallel furnaces. Once preheatingis completed, each slab is unloaded and immediately transferred to a
hot rolling mill. Each furnace operates in a FIFO manner under slabs’
eligibilities and capacity constraints, while the hot rolling mill can handle a single slab every time instant, under predefined quality conditions
regarding the minimum and maximum number of slabs per production
cycle. Each slab is accompanied with preheating and rolling times andis subject to quality restrictions determined by the sequence of slabs inthe same production cycle. Slabs also require specific preheating operations and can only be preheated with slabs of compatible operationsin the same furnace. We propose an integrated optimisation framework, comprised by an exact method for each production cycle, which
combines a MILP and CP formulation for both stages, and a sequential heuristic approach for planning over multiple cycles. We provide
experimentation on real instances, showing the versatility of our approach in terms of supporting different planning approaches thus maximising quality of production and machines usage

3 - An Optimization Approach for Controlling Material Flowin a Production Network on a Tactical Level: A Case
Study

Elisabeth Finhold, Heiner Ackermann, Till Heller, Felix
Höhne
In this talk, we show how we tackle a production planning challengearising in the real-world application of rubber flooring production.
Here, the last of the four major production steps is hard to plan wellin advance, due to a large product variety, hard-to-formalize contextdependent rules, sometimes insufficient material supply and unforeseeable events such as staff shortages or machine failures. We developed

EURO 2024 - Copenhagena tactical planning approach in which we compute a rough quantitybased production schedule for the four major production steps by solving a linear optimization problem. This schedule adapts to the changing production environment and state of production seamlessly and has
two main functions: Firstly, it is used to monitor the flow of material
through production to proactively take action if a discrepancy is observed. Secondly, the model is setup in a way such that satisfactory
operational schedules for the respective production steps can be derived easily from a well-balanced rough schedule.
The approach was integrated into the company’s production planningsoftware. After a one-year test phase in which the model was further
customized to this specific production setting, the resulting schedule is
now used as the main decision-making tool and process control system
by several departments.

4 - A Generalized MILP Model based Co-Clustering Approach: An Application in Cellular Manufacturing

Rahul Prasad, Rajendran C
Efficient machine-part cell formation is crucial for modern manufacturing systems, ensuring high throughput, reduced setup times, and increased flexibility. This paper introduces a novel 2-phase MILP-based
Co-clustering model tailored for optimal cell formation in manufacturing systems. Addressing both binary and ratio (workload) input
datasets, the model introduces the noble similarity coefficient yielding
better results. The objective is to provide an efficient and versatile solution, with a particular emphasis on minimizing inter-cell workloads
and reducing idle time within the cells. Unlike probabilistic methods, our model guarantees a deterministic, globally optimal groupingof machines and parts using a mathematical model. The model is successfully applied to over 50 benchmark datasets of binary and workload drawn from Cellular Manufacturing Systems. The model demonstrates superior grouping efficacy and lower time complexity compared
to existing methods. Furthermore, a streamlined heuristic follows themathematical model, enabling efficient machine or part reallocation,
further improving the model’s efficiency.

 MD-50
Monday, 14:30-16:00 - Room: M2 (building: 101)

Warehouse Management
Stream: Retail Operations
Invited session
Chair: Rina Mary Mazza
1 - Simulation-based Optimization for Integrating Storage
Location Assignment and Manual Order Picking in
Warehousing

Rina Mary Mazza, Jean-François Cordeau, Pasquale Legato
We propose a simulation-based optimization (SO) framework that integrates a novel mathematical formulation for the storage location
assignment problem (SLAP) featuring vertical replenishment and asimulation-guided heuristic for manual order picking. Due to thehighly-combinatorial nature of the problem, to solve realistic instances, we design a customized iterated local search (ILS) metaheuristic. Specifically, it determines the "top m" promising moves at each
iteration of the ILS and then evaluates the corresponding solutions by
discrete-event simulation. This allows measuring the impact of location assignments on warehouse productivity, while also accounting formutual interferences and waiting phenomena arising during the operations carried out by multiple order pickers. The solution bearing thebest (estimated) throughput maximization-based objective function of
the order picking process is used to update the global optimum. Hence,
simulation guides the search and change of the current feasible SLAP
solution. Numerical results are presented for a real distribution center under a picker-to-products rack system following an S-shape policy with a skip-and-go rule to deal with lacking items. After a proper

MD-50

tuning of the ILS parameters and once the ILS has assigned the higherrotating items as close as possible to the loading gate, the SO framework allows to achieve significant improvements on warehouse productivity.

2 - Storage assignment policies for retail warehouses processing store-specific shipment buildups

Julian Baals, Nils Boysen, Simon Emde, Felix Weidinger
Shop-floor space in urban areas is scarce and expensive. To avoid
a time-consuming zigzag movement when replenishing densely arranged shelves, store-specific shipment buildups (SSSBs) mirror thestore layout in the packing pattern of the carriers (e.g., roll cages) on
which merchandise arrives in brick-and-mortar sales outlets. If a carrier is pre-sorted according to the layout of its designated store, thenthe sales personnel can follow a clear route through the store while replenishing the shelves. The time savings in the stores come at the priceof additional effort in the retail warehouses having to assemble SSSBs,
however. If picker-to-parts order picking is applied to assemble SSSBs,
then it is the order pickers who must zigzag through the warehouse. In
this paper, we develop and test the performance of novel storage location assignment policies that consider order frequencies and store
layouts when deciding on the storage positions of stock keeping units(SKUs) on the shelves of a picker-to-parts warehouse. We evaluate
these novel policies both with the theoretical optimum if perfect information were available and with traditional SLA policies merely basedon order frequencies. These computational tests show that our novel
policies can greatly reduce the picking effort during the assembly of
SSSBs in picker-to-parts warehouses.

3 - Swarm or System-directed? Analysis of Human-Robot
Matching Policies in Collaborative Order Pick Systems

Mahdi Ghorashi Khalilabadi, Debjit Roy, René de Koster
Autonomous Mobile Robots (AMRs) collaborate with humans inorder-picking and can increase order-picking efficiency by reducing
walking. AMRs can operate under two policies: Swarm or SystemDirected. This study models the Swarm policy using a closed queuing
model to determine system throughput capacity. The queuing model
incorporates AMR-picker matching via a synchronization station. We
derive closed-form expressions for network steady states and use theseto calculate order throughput. The impact of various factors on overall system performance is analyzed such as order profiles, matchingpolicies (closest or random), AMR and picker speeds, warehouse size,
item assignment methods, and AMR-to-picker ratios. Furthermore, we
compare the order throughput of the swarm with the system-directed
policy to ascertain superior performance under different circumstances.
Results indicate that the swarm policy generally outperforms, particularly at higher AMR-to-picker speed ratios. However, its effectiveness diminishes with larger order sizes and lower AMR-to-picker ratios. Overall, the study provides insights into optimizing e-commerce
warehouse operations through effective decision-making in swarm collaboration, aiming to enhance productivity and efficiency in order fulfillment.

4 - Direct Dispatch and Order Fulfillment Models in Retail
Supply Chains

Narayan Rangaraj, Geetha Bhargavi Katikala, Tilakraj Singh
We explore the potential of implementing a dual supply chain system
to replace a conventional one driven by a typical large Consumer Packaged Goods (CPG) firm. The dual supply chain would deliver finishedproducts both through distribution centers and through direct dispatch
to retailers. Demand information and the cost structure from the company’s production facilities and existing supply chain were analyzed tobuild and validate a model for the dual supply chain within planning
constraints.
The objective is to reduce operating costs by using the opportunity
for direct dispatch, considering network restrictions. Direct dispatch
possibilities were identified, based on customer segmentation, acrosstime and across locations. Aggregation is possible both temporally(combining orders over multiple days, subject to inventory and service
level constraints) and spatially (combining orders at nearby locations,subject to vehicle movement constraints). Heuristic approaches andexact optimization models were applied. The detailed example considers 243 customers (retailers) of a plant of the CPG firm. Of these,
24 feasible direct dispatch customers were identified, yielding a Direct151

MD-51EURO 2024 - Copenhagen

Dispatch Opportunity Rate of 14.41% for the plant resulting in cost reduction. The model is extendable to multi factory situations, and leads
to interesting modeling issues in fixed cost allocation and considerationof different aspects of service in retail supply chains.

 MD-51
Monday, 14:30-16:00 - Room: M5 (building: 101)

Portfolio risk management
Stream: Risk management in financeInvited session
Chair: David Neděla
1 - Decision-making of Liquor Retailer based on Modified
Portfolio Theory

Xiaotong Sang
A complete retail supply chain consists of suppliers, retailers, and customers. Retailers generally bear greater risks, such as how to choose
suppliers and how many suppliers to choose. Portfolio theory seeks
to optimize portfolio structure and maximize returns. This project isbased on the perspective of the retailer and applies the Portfolio Theory to the decision-making of the retailer. The aim of this project is
to select an order structure and financing method for the retailer thatmatch the expected revenue based on mean-variance theory. For thispurpose, this project collected data on a liquor retailer, basically classifying suppliers into two categories, which include five products, andfinding out how the retailer should build orders to achieve the maximum expected revenue. In addition, considering the situation that theretailer has inventories and has difficulty financing, this project uses
the classical newsvendor model to optimize the management of theinventory of the retailer and gives financing suggestions based on the
results. Conclusively, find out the supplier portfolio under the modified
portfolio theory and give private suggestions.

non-dominated sorting of selected asset statistics. In contrast to previous research, we designed an innovative application on statistics obtained from approximated return series using nonparametric regression
and principal component analysis (PCA). Additionally, we study its
impact on mean-variance and recently proposed complex mean-trend
risk large-scale portfolio selection strategies. In particular, this process
examines the efficient frontier of portfolios on the basis of differing return and risk perspectives. In the empirical part applied to the US stock
market data, we present an ex-post and ex-ante analysis with results for
40 portfolio strategies. The results obtained strongly indicate that formost risk-averse investors, the mean-trend risk strategies with the preselection outperform the same strategies without the preselection, as
well as the mean-variance strategies.

4 - Dynamic Investment Model for Pension Funds: Maximizing Mean-Risk Performance with SD constraints

Audrius Kabasinskas, Milos Kopa, Kristina Sutiene,
Sebastiano VitaliThis study introduces a dynamic investment model for pension fundmanagers, aiming to optimize mean-risk performance over a 21-year
horizon. The model reacts to past asset returns, with annual rebalancing, and seeks to outperform benchmarks using second-order stochastic dominance. We explore the possibility of identifying a universally
preferred dynamic strategy for all risk-averse investors and examine its
characteristics compared to benchmarks. The model maximizes mean0.5*CVaR with stochastic dominance constraints, utilizing historicalextraction for future returns. Benchmarks follow a 1/N portfolio strategy, evolving asset allocations over time according to pension fund regulations. Results for the benchmark show an average terminal wealthof 2.7 EUR after 21 years, with a 5% probability of falling below 1
EUR. The optimal strategy, without ESG restrictions, outperforms thebenchmark, exhibiting higher cash and bond investments, resulting inan expected terminal wealth of 6 EUR. When considering only ESG attractive assets, the optimal strategy is less profitable (expected terminal
wealth of 3.6 EUR), yet it outperforms the benchmark in second-orderstochastic dominance. The optimal strategy demonstrates sensitivity toasset bounds and ESG considerations, emphasizing the importance ofstrategic asset selection over risk aversion parameters. The project hasreceived funding from the Research Council of Lithuania (LMTLT),
agreement No S-MIP-21-32.

2 - Based on the applicability of the KMV model in Chinese
commercial banks

Shikai Gao
The project selected ten listed commercial banks as the research object, and based on the financial data of the sample banks in 2022, theKMV model was used to measure the credit risk of the banks. The aimof this project will be to test the applicability of the KMV model in themeasurement of the credit risk of commercial banks in China. First, thevolatility of stock prices, the value of bank assets, and their volatility
are calculated according to the Black-Scholes option pricing model.
Then the KMV model is applied to calculate the default distance ofeach commercial bank. Finally, the expected default frequency is obtained. The expected default frequency obtained is compared with thestandard range of Standard & Poor’s and Moody’s ratings. The final
results show that the expected default frequency of the banks calculated using the KMV model generally matches the credit ratings of thebanks by the credit rating agencies. The higher the expected default
frequency, the lower the credit rating. Lastly, relevant suggestions are
made for commercial banks to improve credit quality and strengthen
the level of supervision.

3 - Mean-trend risk portfolio selection with non-dominatedsorting asset preselection

David Neděla, Sergio Ortobelli Lozza, Tomáš Tichý
By monitoring the financial markets, we can discover a huge numberof potential assets to be invested in. It is very complicated and timeconsuming to analyze all assets and then use all of them in optimization according to the preferred portfolio strategy. For this reason, it ispotentially useful for portfolio managers to focus on a specific sample of assets identified by the filtering process. This paper proposes
an efficient approach for asset preselection based on multidimensional152

 MD-52
Monday, 14:30-16:00 - Room: 8003 (building: 202)

Scheduling and Routing Problems
Stream: Combinatorial Optimization
Invited session
Chair: Léa Ricard
Chair: Fabian Torres
1 - A Two-Echelon Vehicle Routing Problem with Mobile
Satellites and Multiple Commodities

Aria Dahimi, Virginie Lurkin, Mehrdad Mohammadi, Tomvan Woensel
The surge in last-mile delivery demands, driven by the growth of ecommerce, poses intricate challenges in meeting tight schedules, navigating congested urban areas, and addressing environmental concerns.
In this study we introduce an approach to address a part of last-mile delivery challenges by integrating a mobile two-echelon strategy. We extend traditional two-echelon systems to consider multiple depots, multiple commodities, and flexible meeting points (mobile satellites). To
solve this complex problem, we propose a mixed-integer linear programming (MILP) model and a math-heuristic algorithm, SALNS,
combining large neighborhood search (LNS), LP scheduling, and anovel schedule approximation method. Comprehensive experiments
demonstrate the algorithm’s efficiency, achieving identical or superior results to exact benchmarks while running, on average, nine times

EURO 2024 - Copenhagenfaster. Notably, the algorithm robustly generates feasible solutions for
medium and large instances in practical times. Furthermore, sensitivity analyses reveal the critical roles of fuel, wage, and time-window
parameters in shaping delivery strategies, providing valuable insights
for decision-makers.

2 - The Vehicle Routing Problem with Drones and Outsourcing

Xuan Ren, Ola Jabali, Roberto Roberti, Gongqian Liang
Recent scientific literature has shown that combining drones withtrucks is effective for last-mile delivery. With the growing volumesof next-day delivery, not all customers may be served within a given
day, especially when considering a limitation of the drivers’ working
time. To this end, we investigate the vehicle routing problem withdrones and outsourcing (VRPDO), where customers are served either
by a given truck-drone fleet (each truck equipped with a single drone)
or by outsourcing, with each customer incurring an individual fixed
cost. The objective is to minimize the transportation costs of trucks
and drones as well as the outsourcing costs. We propose a variableneighborhood search (VNS) algorithm for this problem, where we represent a VRPDO solution as customer sequences. Each sequence isevaluated by solving a problem that we refer to as the fixed sequence
assignment problem (FRAP). Given a customer sequence, the FRAP
assigns each customer to the truck, or the drone, or be outsourced,
and determines truck and drone routes, in order to minimize total costs
while satisfying a maximum-duration limit. We treat the FRAP as aresource-constrained shortest path problem and introduce an exact labeling algorithm to solve it. We further develop a heuristic labelingalgorithm (HLA) for the problem and reinforce the VNS by presenting
filtering strategies based on the HLA. Computational results show theeffectiveness of our proposed approach in terms of solution quality.

3 - Data-driven optimization of bus schedules under Uncertainties

Léa Ricard, Andrea Lodi, Guy Desaulniers, Louis-Martin
Rousseau
The vehicle scheduling problem (VSP) is one of the sub-problems of
public transport planning. It aims to minimize operational costs while
assigning exactly one bus per timetabled trip and respecting the capacity of each depot. Public transport planning is subject to various endogenous and exogenous causes of uncertainty, notably affecting travel
time and energy consumption. Despite the uncertainties involved, the
VSP and its variants are usually solved deterministically to address
trackability issues. However, considering deterministic travel time inthe VSP can compromise schedule adherence, whereas considering deterministic energy consumption in the electric VSP (E-VSP) may leadto solutions with sub-optimal true costs (including recourse costs andthe cost of ownership of battery electric buses).
This presentation proposes a methodological framework aimed at integrating uncertainties, specifically travel time and energy consumption uncertainties, into the VSP. Three distinct stochastic, data-driven
mathematical models and branch-and-price algorithms are introduced
to address two variations of the problem: the multi-depot VSP (MDVSP) and the E-VSP. The objective is to find bus schedules that offer agood trade-off between operational costs and service reliability, as well
as between operational costs and battery degradation in electric buses.

 MD-53
Monday, 14:30-16:00 - Room: 8007 (building: 202)

Disaster & Emergency Management
Stream: Sustainable and Resilient Systems
Invited session
Chair: Simona Cohen Kadosh

MD-53

1 - Emergency Evacuation Routing from the Passenger
Ship

Chen LiIn the past decades, real-life security problems occurring on board passenger ships in the open sea have become a great challenge. Evacuating a large number of evacuees in a limited time period is a difficult
task, especially in a ship with a very complex structure inside. In thispaper, we focus on the development of a fast algorithm for the emergency evacuation from a moving or sinking ship to safe points (suchas lifeboats on the board). The main objective is to minimize the total
network clearance time. We propose a cell-transmission-based mixed
integer programming model that accounts for evacuation dynamics,
congestions and balanced outflows from exits. A Lagrangian relaxation is used to decompose the problem into two subproblems. For the
first subproblem, we develop a time-expanded network and propose
an alternative cycle-canceling algorithm to solve it. To demonstrate
the efficiency of the proposed solution technique, we provide computational results on a realistic example in the numerical part.

2 - Partial Scenario Set Cover and its Application to Emergency Planning

Shai Dimant, Sven Krumke
In the classical Set Cover problem, one is given a finite ground set Q, acollection S of subsets of Q to choose from, each of which is associatedwith a nonnegative cost and the goal is to choose a minimum-cost subcollection of S whose union covers the entire ground set Q. The Set
Cover problem assumes that every element of the ground set is always
present. In practice, however, this may not always be the case. In thecontext of locating emergency facilities, the ground set corresponds to(not precisely known) emergencies. The regions in which emergencies
can occur are modeled as the elements. The subset of regions in which
an emergency occurs is uncertain and modeled as a scenario from theuncertainty set. For each facility, there is a set of regions that can bereached within a guaranteed response time. Following a robust optimization approach, we consider a second collection U of subsets of Q.
Each set in U represents a possible scenario of the emergencies. The
goal now is to choose a minimum-cost sub-collection of S such that atleast a certain number of scenarios from U are covered. This leads to
the Partial Scenario Set Cover problem (PSSC) which generalizes thePartial Set Cover problem, which is itself a generalization of the classical Set Cover problem. We study the complexity of PSSC, provide
approximation algorithms based on LP-rounding and generalizationsof the classical greedy algorithm for Set Cover.

3 - Optimal Transportation Infrastructure Protection Investment Timing for Climate Change

Elise Miller-Hooks, Ashkan Zare Hadesh
Protecting coastal roadways and other transportation infrastructurefrom the impacts of climate change is crucial to the long-term performance of transportation networks and the welfare of the communities they support. Climate change is responsible for sea-level rise,increased storm intensity and frequency, and larger storm surge events.
Actions, like building seawalls and elevating streets or rail, are typical engineering methods for mitigating the impacts of these changes
and related events. These actions often involve major capital investments and implementation schedules. Thus, it is critical to determine
a best protective investment strategy and strategy implementation timing. This talk will describe OR-based methods for creating an implementable tool to support the taking of timely capital investment decisions toward transportation system protection against uncertain coastal
flooding events and other risks of SLR and changing climate conditions.

4 - Locating emergency treatment sites (ETS) with decision
support system (DSS) for preparedness and responseto earthquake event for local municipalities

Simona Cohen Kadosh, Sagit Kedem-Yemini, Zilla
Sinuany-Stern, Yuval Bitan
Although research on facility location problems is extensive, temporary medical facilities and their application have not received much attention in the domain of preparedness or the immediate response stage153MD-54EURO 2024 - Copenhagen

after an earthquake where a huge number of casualties will need medical treatment. In this study, we take an applicable step in developinga decision support system (DSS) for local municipalities to assist in
positioning emergency treatment sites (ETS) close to destruction sites
for minor injuries (over 80% of total casualties), immediately after an
event, and thus reducing hospitals’ loads. The DSS is based on characterization of the location network and hybrid simulation that can beapplied both as a standalone emergency tool or as a planning tool for
preparedness. Using a simulation/optimization model that determines
the locations of ETSs, the DSS tool can compare multiple parametersand give recommendations within seconds. The proposed DSS tool
includes three main components: database, decision engine and userinterface, based on GIS technology (geographic information system).
Preliminary feasibility study was conducted and recommends the best
location for ETSs to simulating various destruction site locations withpromising results. The development process is done in collaboration
with a specific local municipality which will also serve as a beta site
for the final DSS product. Keywords: Decision support system (DSS),
Humanitarian application, Facility location

 MD-54
Monday, 14:30-16:00 - Room: S01 (building: 101)

Europe’s Rail MOTIONAL: Algorithms forrailway planning
Stream: Public Transport Optimization
Invited session
Chair: Giorgio Sartor
Chair: Dennis Huisman
1 - Enhancing recoverable robust train routing in railway
stations

Philipp Widmann, Norman Weik
Busy railway stations with multiple intersecting lines often form critical points in railway timetabling, where delays from one line can propagate through the network. Increases in railway traffic as well as plansfor integrated clock-face timetables thus require careful planning of
platform assignments and routes through the station to limit the effectof delays and disruptions on the overall service quality.
We present a new method for the long-term train routing problem inthe station context. The goal is to find a microscopically feasible,
robust train routing that is compatible with a predetermined network
timetable. The method consists of a combined MILP model: A multicommodity flow formulation ensures the feasibility of the train routing
by prohibiting conflicts between chosen routes. To ensure robustness
to small initial delays, we directly include a scenario-based delay propagation model and minimize the delay propagation in the worst case,thus extending previous work on recoverable robustness. We discussmodelling choices and computational performance, present results ona case study and give an outlook on possible applications for long-term
infrastructure planning.

2 - Optimized Short-Term Insertion of Additional Trains
Into Existing Timetables

this problem has received relatively limited attention in the literature.
This presentation aims to describe and formalize the problem, to position it within the literature and to introduce a first solution approach.
Computational experiments and their numerical results will be discussed.

3 - User experience and timetable optimization

Sara Gestrelius
Many optimization models and algorithms have been proposed for the
railway timetabling problem over the past decades. However, in practice, timetables are still constructed by planners with little to no optimization support. One step towards closing the gap between the stateof art and state of practice is to research user experience aspects related
to optimization-based timetable planning tools, and thereby better understand when and how the planners could gainfully use the algorithms
in their everyday work.
In MOTIONAL WP6, RISE investigates user experience aspects thatcome into play when a timetable planner working for an infrastructure
manager wants to use optimization/automation to solve conflicts for a
train, or a set of trains, in the long-term planning process. "Participatory Design" is used to design a timetable planning support tool with
optimization. The purpose of participatory design is to give a variety
of stakeholders who will use the tool the opportunity to provide input on function, interaction, and visualizations to create a high-quality
tool that supports their daily work. At the same time, optimization
researchers will ensure that the design suggestions are likely to be implementable.
In this presentation we will share our current status of work, including
knowledge gained from interviews, a hierarchical task analysis and afirst sketch of the functionalities developed in the "Participatory Design" process.

4 - Incremental timetabling for handling Temporary Capacity Restrictions

Giorgio Sartor, Carlo Mannino, Bjørnar Luteberget, Oddvar
Kloster
In railways, Temporary Capacity Restrictions (TCRs), such as track
closures and speed reductions, can make it very difficult to operate
trains according to the timetable. Depending on the severity of theTCR, the timetable might require relatively simple repairing actions(e.g., retiming, resequencing) or major modifications (e.g., cancellations, short-turning, rerouting). In general, the objective for the newtimetable is to be as similar as possible to the original, but the precise
definition is based on opinions and experience from the route planners,
making it difficult to formalize. In contrast to most of the current literature that tries to approximate this objective, we propose the following
iterative process: 1) route planners use an interactive GUI to sketch anew (potentially infeasible) reference timetable, and 2) mathematicalprogramming is used to find a feasible timetable that is closest to thereference one. Step 1 and 2 can be repeated until the route planners are
satisfied. We call this method Incremental Timetabling, to highlightthe fact that route planners can iteratively steer the timetable towards
the desired one, while our model simply takes care of its feasibility.
We present an application of this procedure to parts of the Norwegianand Italian Railways.

Karim Terfasse, Nabil Absi, Stéphane Dauzere-peres, Remy
Chevrier
With safer, cheaper and sustainable transportation of goods attracting
major interest in recent years, rail freight is becoming increasingly attractive compared to road freight transport. For railway timetabling,
this requires a better utilization of the infrastructure’s capacity. However, the unpredictable nature of this growing rail freight demand poses
numerous challenges to infrastructure managers, one of them being
the short-term scheduling of additional freight trains within existingtimetables. Indeed, residual capacity management is a good lever to accommodate freight transport requests, while ensuring safety and minimal disruption of passenger trains. Despite its significant relevance,154 MD-55
Monday, 14:30-16:00 - Room: S02 (building: 101)

Airline Applications II
Stream: Transportation
Invited session
Chair: Silvia Padron

EURO 2024 - Copenhagen
1 - Optimizing Air Cargo Terminal Operations: A Model for
Alleviating Unloading Bay Bottlenecks through Truck
Scheduling

Arnab Chakraborty, M Vimala Rani
The efficiency of air cargo terminals is crucial for the success of the
supply chain. Freight forwarders deliver cargo to cargo terminal operators (CTOs) who manage handling, customs, and loading onto airliners. Delays in truck unloading can cause bottlenecks, impacting
aircraft departures. Research focuses on manpower, truck scheduling, cargo routing, and aircraft loading. Existing studies address
truck scheduling challenges, adapting insights from container terminals. This study proposes a multi-server queuing system to optimize
truck arrivals and unloading bay usage, aiming to minimize costs.
Methodology involves modeling truck arrivals and service times, withresults expected to reduce costs and alleviate congestion at unloading
bays. Limitations include potential applicability only to specific airports.

2 - A two-phase stochastic approach for sizing andscheduling of battery swapping and charging of electricvehiclesSimon van Oosterom, Mihaela Mitici
The development of Electric Vehicles (EVs) has exceeded all expectations. At the current pace, opportunities will open up for electric
freight transport and aviation. For these industries with tight schedules, however, long battery recharge times will be problematic. The
ability to swap depleted batteries may provide an alternative. Swaps
can be done quickly, and the EV can be deployed while the used battery recharges at a depot.
This paper studies scheduling operations of battery swapping andcharging for a EV fleet that needs to perform a tight schedule. We
propose a stochastic recourse optimization model to size the infrastructure: the charging power and the number of spare batteries. Thisaims to minimize the infrastructure and charging costs, given a stochastic amount of traffic to be fulfilled. The charging costs are determinedin the second stage of the model, which optimizes the charging operations of the vehicles, given a day’s traffic schedule. We expand on
current studies by leveraging the knowledge of the traffic schedule, andby allowing for preemptive charging.
We show our method for an airline operating small electric aircraft,where swaps are done at hub airports. We use the domestic networkof Norway’s Widerøe Airlines of 43 airports and 56000 annual flights.
We show that 4400 kW in power supply and 23 spare batteries are required for electrification. Furthermore, we show that this grants highscheduling flexibility with a smaller infrastructure.

3 - Cross-organisational Coordination in Apron operations

Silvia Padron, Maurizio Tomasella, Elisa Guardo, Daniel
Guimarans, Cemalettin Ozturk
Turnaround apron operations involve a diverse array of interconnected
services required for aircraft handling. Short-term planning of these
services demands comprehensive coordination between the participating organisations to effectively manage the multiple interactions
arising. This includes coordination efforts between aviation service
providers (SPs) which are subcontracted by airlines for turnaroundservices and a coordinator such as the airport operator. Traditional
coordination approaches largely focus on the priorities of a single
stakeholder, typically airlines and airports. This paper introduces fourcross-organisational coordination strategies for apron operation planning with varying levels of SP participation in decision-making. A
simulation-optimisation approach based on Constraint Programmingis used to model and solve the apron planning within the proposed
coordination settings. Experiments suggest that equitable central coordination enhances robustness and resource efficiency of plans, whilemore pragmatic strategies can address specific challenges of SPs suchas resource scarcity, still yielding positive outcomes in terms of schedule stability.

MD-56

4 - Optimising Airport Ground Handling Operations Considering Multi-Skilled Personnel

Maurice Krauth, Henning Preis, Noah Jalowski, Nikola
Bešinović
Ground handling operations at airports include a wide range of services such as baggage handling, aircraft maintenance, and passenger
services. The complex associations and dynamic nature of these operations demand not just efficiency but also adaptability and precision inresource management.
This research introduces a novel approach for scheduling groundhandling operations, by integrating Resource Constrained Project
Scheduling Problem (RCPSP) and Hopcroft-Karp algorithm to matchground handling operations and multi-skilled personnel. First, the taskscheduling is addressed, which involves the adapted RCPSP model tothe ground handling operations, considering various constraints including time windows, personnel availability, and task dependencies. Second, the allocation of multi-qualified personnel is tackled, which introduces a modified Hopcroft-Karp algorithm to match the scheduled
tasks with the applicable personnel based on their skills and availability. The overall objective is the minimisation of delays, i.e. deviations
from the planned activities.
We demonstrate the performance of this approach under different conditions such as delays or resource shortage for an exemplary data setof an airport. We compare different scenarios and present the benefitsof this approach.

 MD-56
Monday, 14:30-16:00 - Room: S04 (building: 101)

Optimization in transportation infrastructure
design and management
Stream: Transportation
Invited session
Chair: Marie Schmidt
1 - Using Constraint Programming to Plan Annual Maintenance of Railway Tracks

Maria Andreina Francisco Rodriguez, Justin Pearson, Jerker
Sundström
Railway infrastructure maintenance is essential for the continuous operation of train transportation; it is costly and can only be performedin limited time windows. Maintenance companies are contracted to fix
segments of railway tracks that fall below a certain quality threshold.
We will be concerned with tamping, which is the process of smoothingout track irregularities.
Due to the short time windows, maintenance companies must choose
which segments will be tamped each day. The tamping machines havefast travel times but are slow to tamp with significant starting and stopping times. Therefore, tamping as many contiguous segments as possible is more efficient. Before the end of a shift, the machine must returnto the depot.
We use constraint programming to create multi-daily maintenanceplans (and re-plans) that maximise the total length of tamped segments. Our test data is based on real-life planning requirements withinstances of continuous tracks of approximately 120 KM divided into
more than 800 segments of varying sizes from 30 to 400 meters. Our
results are promising, and we can quickly find near-optimal solutions
using Google OR-Tools. Our model has several non-obvious improvements, such as symmetry breaking and over-approximations to the upper bound, significantly improving solving time.

2 - Network Design for Evacuation Preparedness under
Cost and Travel Congestion Considerations

Halit Uster, Nadere Mansouri155MD-57EURO 2024 - Copenhagen

We present an evacuation network design problem under cost andtravel congestion considerations while incorporating worst-case evacuation time for the evacuees. Employing a time-expanded network,
we model for effective and controlled evacuation by determining active shelter locations and evacuee routes that can be implemented in
the preparedness stage in response to foreseen extreme events, such as
hurricanes and flooding. To solve our model, we devise an efficient
Benders Decomposition (BD) framework with convergence enhancements for solving large-scale instances while also taking advantage of
specific characteristics of the problem. We design and implement anexperimental study to test our BD technique and to conduct sensitivity
analysis for various parameters and model validation using data fromCentral Texas.

3 - Capacitated Multi-Enabler Transportation Network Design for Autonomous Trucks

minimum-HIX, as well as mean-HIX, portfolio can be seen as the mostdiversified one since it has the lowest degree of co-movement. We
make use of a reformulation method for determining the minimumHIX, and mean-HIX, portfolios, as their closed-form expressions are
not readily available in a general setting. This also allows us to
study the mean-HIX efficient frontier. We prove the existence of theminimum-HIX, and mean-HIX, portfolios in the general setting, andprovide their closed-form expressions in the two-stock case. We also
study how to determine the minimum-HIX, and mean-HIX, portfolios
when short-selling is allowed. This requires us to generalize the definition of HIX in order to ensure that the (theoretical) comonotonic
portfolio always exceeds the (actual) portfolio in convex order.
[1] Dhaene, J., Linders, D., Schoutens, W. & Vyncke, D. (2012), ’The
herd behavior index: A new measure for the implied degree of comovement in stock markets’, Insurance: Mathematics and Economics
50(3), 357-370.

Ebrahim Mohammadi, Marie Schmidt, Rob Zuidwijk
Autonomous trucking is expected to increase transportation productivity, especially for long-haul transportation. Providing infrastructureand enablers with enough capacity to support this type of transportation is essential for the successful deployment of autonomous trucks.
In this paper, we investigate the capacitated transportation network design problem for autonomous trucks. We assume that the autonomous
network requires several types of enablers with limited capacity to support autonomous truck transportation. A new mixed-integer linear programming model is proposed to locate various capacitated infrastructures and enablers required for long-haul autonomous transportation
over multiple periods. This model strategically integrates the location
decisions of multiple enablers over the planning horizon. Enablers,such as refueling stations, emergency maintenance stations, and transfer hubs, are characterized by unique operational features and capacity
considerations. The model is implemented numerically on benchmark
cases, such as the Sioux Falls network, and sensitivity analysis for key
parameters is provided.

 MD-57
Monday, 14:30-16:00 - Room: S06 (building: 101)

Methodology in asset allocation andbanking
Stream: Modern Decision Making in Finance and Insurance
Invited session

3 - You Shall Not Pass - Minimizing false-positive ensemble
classification through threshold optimization

Richard Oberdieck, Ruben Menke, Christian Møller Karsten
As a bank, we handle millions of transactions a day. Almost all of these(>99.99%) are completely fine and legitimate transactions. However,every once in a while a sanctioned entity is either trying to send or receive money with a payment we process. We want, and are legally required, to stop such payments. Due to the high volume of transactions
a day, algorithms play a crucial role in flagging potentially suspicious
payments. If any of these algorithms judge there to be a match to a
sanctioned entry, the payment is investigated manually.
This poses a challenge though: the number of true positives is very low,
so even the best algorithms will mostly flag false positives (>95%).
This means almost all of the payments an analyst goes through are fine,they just have an unlucky similarity to a sanctioned entity. To reduce
this issue, the algorithmic thresholds have to be as tight as possible,
while still finding all the true positives.
In our presentation, we will show how this problem can be posed asa mixed-integer programming problem and the results it yields. We
also discuss how to extend the formulation to allow for a risk-based
approach, where a certain number of misses of true positives are allowed, in order to further reduce the number of false positives. This
results in a fascinating pareto front, which is used as a basis of discussion around price of catching all true positives.

 MD-58
Monday, 14:30-16:00 - Room: S07 (building: 101)

Chair: Rita Pimentel

Real-Life Applications in Routing

1 - Accelerated Static Hedging Method for Pricing American Options

Stream: VeRoLog - Vehicle Routing and Logistics
Invited session

Lung-fu Chang
This article provides an accelerated static hedge portfolio (SHP)
method for evaluating American options based on stochastic volatility
and double jump processes. Our proposed model is a generalizationof the static hedge portfolio approach of Derman, Ergener, and Kani
to evaluate American options based on the Richardson extrapolation.
Numerical results show that the numerical efficiency of our accelerated static hedge portfolio approach is comparable to the least-squares
Monte Carlo simulation method. Numerical results demonstrate thatour proposed method is efficient and accuracy in evaluating American
options with stochastic volatility and double jump processes.

2 - Portfolio selection based on the Herd Behavior Index

Churui Li, Daniel Linders, Wing Fung Chong
In this paper, we propose to determine optimal portfolios using theHerd Behavior Index (HIX, Dhaene et al. (2012)). The HIX is adiversification measure that provides information about the extent towhich stock prices move together in the same direction. The optimal

156

Chair: Martin Klingebiel
1 - Optimization of Road Transport With Crossdocks: A
Case Study at Inditex With Matheuristics

Julio Lopez, Julio González-Díaz, Telmo Costa CarballoWith a complex network of warehouses and depots, optimizing groundtransportation is a goal for sustainability and efficiency. Our problemaligns with CVRPTW, a Vehicle Routing Problem with Limited Capacity and Time Windows for pickup and delivery. However, since mostwarehouses must deliver to most depots, freight exchanges at predetermined nodes in the network play a leading role in an optimal solution
to the problem.
To tackle this challenge, our initial approach involved modeling the
problem using direct mixed integer and linear programming techniques. We carefully tailored the model to meet our additional requirements, with emphasis on accurately representing the exchange of cargo
at designated points.

EURO 2024 - Copenhagen
However, as expected, this problem with our logistics network andcrossover points is not solvable in our time requirements due to its inherent computational complexity. To address this challenge and speed
up the solving process, we explored some math-heuristic approaches,
acknowledging potential compromises in optimality.

2 - Travel and Modify: Travelling Salesmen Problem in Environments Altered by the Salesman

David Woller, Miroslav Kulich
The presented work is inspired by a real-world application in autonomous open pit mining. A drill rig is tasked with digging a large
number of blast holes, organized in a tight semi-regular pattern. Digging each hole generates a pile of excess material, that represents a new
obstacle for the drill rig. The goal is to determine the piles placementand drill all holes, while minimizing total distance traveled, avoiding
all already placed obstacles and getting stuck between them. We propose the concept of self-deleting graphs, where visiting a vertex causes
deletion of a predefined set of edges. This formalism can capture thedynamic addition of obstacles in the motivating application, which isdependent on the previous drill rig path. On these graphs, we formulate variants of the classical Hamiltonian Cycle Problem and Traveling Salesman Problem and design novel solvers for these problems,
both exact and heuristic. In the next step, we address the problem ofpile placement in a close vicinity of each hole, given a fixed tour. We
formulate the problem as Path-Conforming Circle Placement Problem,
propose a heuristic solver and derive solution bounds. Finally, we combine both problems together and fully address the original application
as the Traveling Salesman Problem with Circle Placement, which wesolve by combining the already developed approaches. The resulting
solver is evaluated on several newly created datasets mimicking thereal-world applications.

3 - Route Optimization of Freight Transportation Under
Multiple Real Operational and Legal Constraints

Alfredo G. Hernandez-Diaz, Ana Pérez Rubio, Cristina Tobar,
Francisco Javier Diego Martín
The objective of the work is to help a large international transport operator in the optimized planning of its fleet, which constantly performs
full and break-bulk routes, as well as groupages adapted to any requirement of its customers. We are faced with a periodic vehicle routing
problem with capacities and time windows (PCVRPTW) with some
additional requirements and constraints such as: multiple-week planning, time windows for available days and hours, maximum daily andweekly driving and working times, daily, weekly and bi-weekly breaks
for drivers, maximum distance between an unloading and the next assigned load, the first and last load of each week must be the least possible distance from the drivers’ homes, the customer wants all vehicles
to travel at least 3500 km weekly and reached this value wants to balance the distances, among others. One of the main challenges lies in
the logistics operator’s desire to reschedule all pending tasks every 15
minutes, since, on the one hand, they are constantly receiving new orders and, on the other hand, unforeseen incidents arise that alter theoriginal schedule. Therefore, the algorithm must be fast enough toprovide quality solutions in approximately 4 to 5 minutes. This paper
will show the mathematical programming model, the implementation
performed with Hexaly, and the solutions obtained in real instances,obtaining the first feasible solutions in a few seconds and quality solutions in a few minutes.

4 - A Branch-and-Price-Algorithm for Tactical Maintenance
Planning for Rotor Blades on Onshore Wind Turbines

Martin Klingebiel
We address the maintenance planning for rotor blades on onshore windturbines at a tactical level, motivated by the case of a German onshore wind turbine manufacturer. The goal is to select maintenanceteams from external service providers and assign maintenance tasks
for a maintenance season while minimizing the total costs. The operative scheduling of the maintenance tasks and routing of the maintenance teams are anticipated to determine the capacity needs. We
consider team-specific work time regulations, locations, qualifications,cost rates, means of transport, task-specific time windows, and weather

MD-59

conditions. However, planning such a maintenance season is very challenging. For this reason, we formulate a mixed-integer linear problem
for routing and scheduling the heterogeneous teams to perform maintenance tasks at different locations with minimum total costs. We propose a branch-and-price algorithm approach based on a Dantzig-Wolfe
decomposition to solve it. The pricing subproblems are solved using
a labeling algorithm. The approach is implemented using the SCIP
framework. We present the underlying problem decomposition, implementation, and first numerical results using a state-of-the-art commercial solver as a benchmark.

 MD-59
Monday, 14:30-16:00 - Room: S08 (building: 101)

Customer behaviour
Stream: Pricing and Revenue Management
Invited session
Chair: Luciano Costa
1 - Fast Algorithms for Capacitated Continuous Pricing
with Discrete Choice Demand Models

Tom Haering, Fabian Torres, Michel BierlaireThis research introduces the Breakpoint Exact Algorithm with Capacities (BEAC) and the Breakpoint Heuristic Algorithm (BHA), both ofwhich offer substantial advancements in solving the choice-based pricing problem (CPP) with and without capacity constraints. The BEAC,
enhancing the Breakpoint Exact Algorithm (BEA) with a capacity
management strategy, outperforms the state-of-the-art mixed-integer
linear programming (MILP) approach by 20 times in computational
speed. The BHA, employing a coordinate descent method, excels inhigh-dimensional scenarios, showing remarkable efficiency in both capacitated and uncapacitated cases. Notably, it outpaces the MILP bya factor of 100 to 5000 for the capacitated case, and the state-of-theart Branch and Benders Decomposition approach by several orders ofmagnitude for the uncapacitated case, while maintaining an average
optimality gap of less than 0.2%. The dynamic line search extensionof the BHA succeeds in identifying the global optimum in all tested instances, albeit with a significant speed reduction. For future research,other extensions of the BHA to escape local optima should be considered.

2 - A tactical time slot management problem under mixed
logit demand

Dorsa Abdolhamidi, Virginie Lurkin
The growth of e-commerce has led to increased requests for homedeliveries, including attended home deliveries for subscription-based
platforms, which require frequent customer presence at home. To accommodate customer availability, many online retailers offer different
delivery time slots. Providing these time slots is convenient for customers but results in higher uncertainty for the retailer. This paper
introduces a novel model tailored for subscription-based e-retailers inwhich customers’ decisions regarding delivery slots and their corresponding impact on routing costs are explicitly considered. The slots’assortment and discounts on prices are considered control variables
in our model. Our research employs a mixed-integer linear programming framework integrated with a mixed logit model, capturing customer preferences’ stochastic and diverse nature in delivery time slots.
We use an adaptive large neighborhood search to be able to solve thisproblem for large instances of this problem. Numerical experiments
are conducted to show the effectiveness of these approaches. Results
indicate the importance of accounting for the uncertain heterogeneous
behavior of customers when deciding about the assortment and pricediscount rates of time slots.

3 - Modern column generation for the estimation of nonparametric discrete-choice models

Luciano Costa, Gerardo Berbeglia, Claudio Contardo,
Jean-François Cordeau157

MD-60EURO 2024 - Copenhagen

Discrete choice models (DCMs) provide probabilities for individuals
choosing a certain alternative when faced with a set of limited options.
DCMs can be parametric or non-parametric. Parametric models areeasier to estimate but require assumptions about individuals’ preferences, while non-parametric models rely solely on training data without any assumptions. Ranked-list methods are popular non-parametricmodels and capture individuals’ behavior by associating them withpreference lists of options sorted in decreasing order of preference. Individuals are assumed to always choose the option best placed in theirpreference list when confronted with an alternative. Despite the generality and simplicity of ranked-list methods, a major drawback associated with them is the exponential increase in the number of potential
lists. Column generation (CG) can be employed to address this issue,
with the CG subproblem being modeled as a generalized linear ordering problem (GLOP). In this work, we propose a dynamic programming algorithm to solve GLOPs. The proposed method is generic andcapable of handling different settings without requiring drastic changes
in its implementation. When incorporated into maximum likelihood
and minimum L1 estimators, our algorithm efficiently generates preference lists. The algorithm performs well when facing instances withseveral observations, which is crucial as non-parametric choice models
heavily rely on data volume for accurate estimations.

4 - Data-driven advertising policy on e-commerce platform
with budget constraint

Yunzhi Cao, Houmin Yan, Yedong Wang
Advertising is one of the crucial tools for retailers on the e-commerce
platform for promotion, such as Amazon. This study focuses on the
joint bidding and pricing policy for retailers subject to budget limitation under multiple periods. The bidding is for the impression on thesponsored advertising list. The click-through rate and conversion ratefunctions with respect to bid and price are learned and estimated basedon historical data. We formulate a stochastic model and construct a dynamic programming formulation. We prove the existence of the uniqueoptimal solution and characterize the structure of an optimized bidding
and pricing policy. Through theoretical analysis and a case study, weinvestigate the relationships among the optimal policy, value function,and key factors, and demonstrate the effectiveness of the optimal policy
and the proposed dynamic model.

 MD-60
Monday, 14:30-16:00 - Room: S09 (building: 101)

RCPSP and extensions
Stream: Project Management and Scheduling
Invited session

2 - Bi-objective Multi-skill Resource-Constrained Project
Scheduling with Resource Efficiency

Laura Anton-Sanchez, Javier Alcaraz, Francisco
Saldanha-da-Gama
A variant of the resource-constrained project scheduling problem(RCPSP) is the multi-skill RCPSP (MS-RCPSP) where each resource
has one or several skills and each activity may require several resources
with certain skills. The standard goal of the MS-RCPSP is to determine
a start time and the allocation of resources for each activity to complete
the project as soon as possible, i.e., to minimize the makespan. In thiswork, we consider a more realistic scenario where all the resources
do not master all their skills with the same efficiency (this is often thecase, for example, when human resources are involved). To model thissituation, we consider that a more efficient resource can contribute withmore than one resource-unit to the execution of an activity. In addition,
we consider the cost for resource usage and address this new versionof the problem from a multi-objective perspective, minimizing boththe makespan and the total cost. We present an optimization model forthis new problem and develop exact methods to find the set of Pareto
solutions.

3 - Exploring New Insights into the Multi-Mode ResourceConstrained Project Scheduling Problem

Sofía Rodríguez Ballesteros, Javier Alcaraz, Laura
Anton-Sanchez
This study introduces a new variant of the Multi-Mode ResourceConstrained Project Scheduling Problem (MRCPSP), integrating two
common real-world aspects: time-dependent resource costs and timevarying resource capacities. Time-dependent resource costs imply thatcosts vary based on resource utilization time, while time-varying resource capacities involve resource availability across different planning horizon periods. Employing a multi-objective framework, thestudy aims to minimize both the makespan and the total resource usage
costs. A mathematical formulation is presented, and attempts are madeto identify the Pareto front (PF) using an exact method. However, due
to the problem complexity, a metaheuristic approach is developed toapproximate the PF. We propose a new NSGA-II based metaheuristic, designing a novel solution encoding and the corresponding genetic
operators to accommodate the new problem elements. Furthermore,
we propose a fitness function to handle infeasible individuals within
the multi-objective context. Computational experiments are conductedusing instances derived from existing literature, demonstrating the algorithm’s strong performance.

4 - Workload-Based Extensions of Mixed-Integer Programming Models for Resource-Constrained Project
Scheduling

Chair: Norbert Trautmann

Norbert Trautmann, Jonas Saupe, Mario Gnägi

1 - Improved Results on RCPSP Using Failure-Directed
Search in Constraint Programming

Several mixed-integer programming models have been proposed forscheduling project activities under predefined resource capacities andprecedence constraints. We introduce a model expansion that considers
aggregated capacity limitations. A computational evaluation for threestate-of-the-art models from the literature shows that the extension isadvantageous for each of the tested models.

Zdenek Hanzalek, Vilem Heinz, Petr Vilím
The Failure-Directed Search (FDS) was introduced as a state-of-theart algorithm for proving optimality and lower bounds for scheduling problems such as Job Shop Scheduling Problem (JSSP) andResource-Constrained Project Scheduling Problems (RCPSP). We reimplemented the FDS algorithm in a new CP solver called OptalCP
to understand its notable performance better and use these insights toimprove its performance further. Despite its genericity and simplicity, FDS proved optimality or solved optimally numerous scheduling
instances that were open for decades. In this paper, we investigate
the properties of FDS. Experimental results show the improved FDS
is 1.7x faster on JSSP and 2.1x faster on RCPSP benchmarks and is
3.5x times faster on JSSP and 2.1x times faster on RCPSP than thestate-of-the-art FDS algorithm in IBM CP Optimizer 22.1. The new
FDS improved existing lower bounds of 66 out of 78 and 43 out of 78
randomly picked JSSP and RCPSP instances, respectively.

 MD-61
Monday, 14:30-16:00 - Room: S10 (building: 101)

Location under uncertainty
Stream: Locational Analysis
Invited session
Chair: Juan A. Mesa

158

EURO 2024 - Copenhagen
1 - Minimizing maximum regret via robust optimization ina two-stage facility location problem under demand uncertainty and facility cost uncertainty

Ronald McGarvey, Andreas Thorsen
Consider a situation in which an initial decision is made to prepare a set
J of locations for potential future use supporting a set of customers under uncertain demand and uncertain future facility construction costs.
Once the demand is revealed, the decision maker then makes a recourse
decision selecting the set K of actual facility locations to be opened,
where K is a subset of J. We present robust MILP formulations of thisproblem, assuming an objective function that minimizes the maximum
regret. A budgeted uncertainty set is assumed, in which the realized
demand is assumed to be equal to a most-likely value plus a potential
positive deviation minus a potential negative deviation. Use of a regretbased objective function has a significant impact on the solution time
when directly solving with a commercial MILP solver. In part, this isdue to the fact that we assume demand could potentially be less thanthe expected value at any customer, which is relevant for a regret-based
objective (but not for a total cost-based objective). We then develop a
computationally-efficient solution algorithm, based on a column-andconstraint generation (CCG) approach, and examine numerical test instances to identify the relationship between problem conditions andsolution structure.

2 - The Stochastic Capacitated Facility Location Problem
with Backlogs: Exact and Heuristic Algorithms

José Emmanuel Gómez-Rocha, José-Fernando
Camacho-Vallejo, Eva Selene Hernández Gress, Cipriano
SantosThe Capacitated Facility Location Problem (CFLP) is a well-known
combinatorial optimization problem extensively studied in the field of
location sciences. It has numerous applications in industrial engineering, humanitarian logistics, telecommunication networks, and otherdomains. Incorporating uncertainties in demands, stochastic programming emerges as a suitable approach to address this problem. However, the main drawback is that due to stochasticity, not all customer
demand may be satisfied. To address this issue, we introduce the concept of backlogs into the CFLP. As a result, the CFLP with backlogs
and normally distributed demands is proposed, where the cost of losing a customer is considered as a penalty cost in the objective function.
To solve the problem we propose an exact and a heuristic method. The
exact method is a Benders decomposition based on Branch-and-Cut.
The heuristic method follows a Fixing-First scheme based on pricing
strategies that efficiently solves the problem within a reasonable computational time. The effectiveness of the proposed algorithms is evaluated by comparing it against a deterministic equivalent solution givenby the general-purpose solver Gurobi. Computational experiments are
conducted on a set of challenging instances using a sample average approximation scheme. To validate the applicability of the problem under study, a real case study involving Mobile Health Clinics (MHCs)
located in Mexico was analyzed.

3 - Locating multiple facilities on a network to maximize thesum of expected utility

Ken-ichi Tanaka
We consider the problem of locating multiple facilities on a network.
It is assumed that all facilities are owned by a single decision maker,
and each facility has an attractiveness. The users’ choice behavior of
facilities is described by multinomial logit model where the utility ofeach user is given by an increasing function of the attractiveness ofeach facility and a decreasing function of the distance to the facility.
We consider the problem of finding facility locations that maximize
the sum of the expected utilities for all users. This problem is a naturalextension of the p-Median problem by assuming probabilistic choice
behavior. The proposed model is formulated as a nonlinear integer programming problem. We present two solution approaches. One is basedon a formulation in which the objective function is approximated by aset of linear functions, and the other is a heuristic method based on vertex substitution. Using example road networks, we investigate how the
distance decay coefficient affects the locational patterns of facilities. In
addition, we present a generalized problem involving optimization of
the facility size under the budget constraints.

MD-62

 MD-62
Monday, 14:30-16:00 - Room: S12 (building: 101)

Seaside Planning II
Stream: OR in Port Operations
Invited session
Chair: Frank Meisel
Chair: Eduardo Lalla-Ruiz
1 - Just-in-Time sailing for container ships

Rommert Dekker
Just-in-Time sailing is a recently introduced concept by the IMO toavoid ships waiting before terminals. In this talk we investigate towhich extent this concept saves money as well as CO2 emissions. First
we consider the case without arrival slots and investigate how muchslowing down saves. Secondly we consider the case that ships also
have information about the ships behind them. Finally we consider thecase where terminals define arrival slots at a tactical level with penalties for late arriving. We consider several strategies for ships and investigate the case with two shipping lines with different arrival reliability.

2 - Model and a Memetic Algorithm for Berth Allocation in
Multipurpose Inland Waterway Terminals

Jiyin Liu, Adnan Pasha, Rajat Rastogi
Inland water transport terminals, especially those in developing countries, are commonly used for handling multiple cargo types. Berthallocation in such multipurpose terminals has special features different
from that in specialized seaport terminals. While existing berth allocation research primarily focused on specialized terminals, this study investigates the discrete berth allocation problem in multipurpose inland
water terminals considering the diverse cargo types and unique operational constraints involved. The problem is first formulated as a mixed
integer linear programming model with the objective of minimizing
the waiting and completion times of barges. A memetic algorithm isdeveloped to obtain near-optimal solutions efficiently. Real-world datafrom an Indian inland water terminal are used to conduct computational experiments which demonstrate the model’s effectiveness andthe algorithm’s efficiency. The developed method has the potential to
be incorporated into the operations planning system of inland waterterminals in developing nations to help improve their productivity.

3 - On the berth allocation problem under disruptive events
for a multipurpose terminal

Rosa G. González-Ramírez, Karol Suchan, Eduardo
Lalla-Ruiz
Maritime ports are strategic nodes in the global transport chain andplay an important role in the efficiency of cargo transport as transfer
nodes. One of the most important planning decisions at the seaside
interface is the berth allocation problem (BAP), which consists of determining the berths’ assignment to the incoming vessels in a port terminal such that spatial and temporal constraints are respected while
minimizing vessel handling times. In this paper, we address the berthallocation problem considering the operations of a multipurpose terminal in Chile. Several terminals operate in the port and there exists a
regulation that determines priorities for the incoming vessels and preemption of vessels is allowed. The port has recently suffered fromweather surges or swells which resulted in port closures that affect theoperations and seaside plans, in addition to more typical disruptions
such as delays in the arrival times of vessels and black sailings. We propose a rolling horizon methodology and an extension to the generalized
set partitioning-based formulation proposed by Christensen and Holst(2008). We generated some instances to test the proposed approachbased on historical data provided by the port and derive some recommendations. References Christensen, C. G., & Holst, C. T. (2008).
Berth allocation in container terminals. Master’s thesis, Technical University of Denmark.

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MD-63EURO 2024 - Copenhagen

4 - A novel formulation and benchmark suite for the container stowage planning problem

Agnieszka Sivertsen, Line Reinhardt, Rune Jensen
Maritime transportation, crucial for global trade, moved 165 million
twenty-foot equivalent units in 2022, with a stable annual growth of
3.5% over the past two decades [1]. Despite being the most ecofriendly mode of cargo transport [2], container stowage optimization
can achieve further efficiencies and CO2 reductions. Despite its economic and environmental importance, the Container Stowage Planning
Problem (CSPP) has seen limited research attention, with only 200 related publications since the 1970s [3]. The main challenges come fromthe industry’s insularity and the scarcity of publicly available data,
leading to oversimplified and insufficient models. In partnership with
Sealytix, we have created the Representative Container Stowage Planning Problem (RCSPP) to make the CSPP more accessible. Sealytix’s
industry knowledge has helped capture essential aspects without unnecessary complexity. The RCSPP is partnered with a comprehensive
benchmark suite using real-world data, evaluated with a Large Neighborhood Search algorithm.
[1] UNCTAD, 2022. Review of maritime transport 2022, United Nations Publications, New York, NY. [2] IMO, I.M.O., 2021. Fourth imo
greenhouse study 2020, International Maritime Organization, London,
England. [3] van Twiller, J., Sivertsen, A., Pacino, D., Jensen, R.M.,
2023. Literature survey on the container stowage planning problem.
European Journal of Operational Research MD-63
Monday, 14:30-16:00 - Room: S14 (building: 101)

Optimization Model for Novel Risks in
Finance and Climate
Stream: OR in Banking, Finance and Insurance: New
Tools for Risk Management
Invited session
Chair: Nikolas Topaloglou

without a normality assumption and explained the equity home puzzle through the ambiguity channel. First, we develop a parsimonious
model without risk or ambiguity aversion parameters and show it tobe consistent with second stochastic dominance. We generalize themodel for continuous ambiguity aversion. These models account forperceived ambiguity (beliefs) and ambiguity aversion (tastes) as potential puzzle explanations.
Taking the models to the data, we obtain optimal allocations matching those of international investors in 21 developed and 19 emerging
markets. This is shown both for worst-case ambiguity aversion under perceived ambiguity well within the market estimat ambiguity sets
or for mild ambiguity aversions given the market-estimated ambiguity
sets. The global average ambiguity aversion is about 0.6, statisticallydifferent from the ambiguity-neutral 0.5. Our estimates of ambiguity
aversion from the observed asset allocations for a large sample of countries closely match the scant experimental evidence from US, Dutch,
and Chinese population samples. Our findings are robust to two fundamentally different methods for measuring ambiguity.
The worst-case model applied to a homogeneous ambiguity set predicts lower ambiguity with less diversified portfolios. We verify thison a large dataset of US household portfolios, and by running

3 - Recent Results in Contextual Portfolio Optimization

Roy Kwon, Andrew Butler’Smart Predict and Optimize’ is an emerging paradigm where the parameters and optimal solutions for an optimization problem are jointly
determined. We present some recent results using this paradigm formean-variance optimization. We show that the integrated (joint) approach can result in parameter estimations and optimal solutions that
perform better out of sample than those obtained from the ’predict or
estimate first, then optimize’ approach. Computational challenges will
be discussed which motivates the development of decomposition methods based on the alternating direction method of multipliers (ADMM)
for the optimization layer in the neural network that represents the integrated approach.

 MD-64
Monday, 14:30-16:00 - Room: S16 (building: 101)

1 - Climate transition risk to sovereign debt sustainability

Veronica Mammetti, Giacomo Morelli, Stavros A. Zenios
We incorporate integrated assessment models (IAM) of climate change
in stochastic debt sustainability analysis (DSA) and study the effects oftransition risk on sovereign debt dynamics. Following recent literatureon the effects of climate transition on default probabilities, we introduce a climate risk premium into standard DSA. We calibrate this climate transition-debt channel for several eurozone sovereigns. A calibration spanning the Paris Agreement documents a significant increasein transition risk following this agreement. We then use ensembles of
IAMs to obtain forward-looking projections of the climate premium
under the orderly and disorderly scenarios from the Network for theGreening of the Financial System. Using the combined IAM-DSA
model, we identify adverse debt effects from the increasing transition
risk across countries and all IAMs. The adverse effects materialize earlier under the orderly transition, but under disorderly transition, they
are significantly more impactful. We find, with high confidence level,that modest extra fiscal effort can maintain debt sustainability underorderly transition, albeit this is not the case for disorderly transition.
Our analysis also identifies significant quantitative differences acrossIAMs, although the general conclusions remain consistent across models. We identify significantly different effects among countries depending on their level of debt and climate exposure.

2 - An explanation of under-diversification puzzles throughambiguity tastes and beliefs

Stavros A. Zenios, Somayyeh Lotfi
We developed portfolio selection models with heterogeneous ellipsoidal ambiguity sets of correlated returns and a performance ratio

160

Vehicle Routing Under Uncertainty 1
Stream: VeRoLog - Vehicle Routing and Logistics
Invited session
Chair: Alice Raffaele
1 - Vehicle Routing With Stochastic Demand, Service andWaiting Times - The Case of Food Bank Collection Problems

Sonja Rohmer, Meike Reusken, Gilbert Laporte, Frans
Cruijssen
Food banks play an important role in fighting food waste as well as
alleviating hunger, yet often struggle to effectively collect all food donations due to their highly uncertain operating environment. Addressing this issue, this research introduces the capacitated vehicle routing
problem with travel time restrictions and stochastic demand, service
and waiting times, which can be generalized to other routing applications. The problem aims to determine a minimum number of vehicles,
while cost-effectively planning routes for these vehicles so that eachroute violates the vehicle capacity and the travel time limit only witha very small probability. The resulting problem is highly complex andthus solved by means of a matheuristic, which decomposes the problem into its natural decision components. As such, it first determines
the number of districts into which the service area should be partitioned, before allocating each customer to exactly one district and thenplans a route for each district. Extensive numerical experiments, involving both randomly generated and real-life instances, demonstrate

EURO 2024 - Copenhagenthe matheuristic’s effectiveness in solving instances with up to 100 customers. In addition, we present valuable managerial insights by applying our matheuristic to real-life instances from Dutch and Canadianfood banks.

2 - Deep Controlled Learning for the Delayed Time Window
Assignment and Vehicle Routing Problem

Layla Martin, Sifanur Celik, Albert Schrotenboer, Tom van
WoenselWhen delivering chilled/frozen, expensive, or odd-sized goods, customers must be present to receive the shipment. In current operations,this often results in failed delivery attempts or considerable inconvenience for customers who need to be present long, often the entire
day. To alleviate this inconvenience, logistics service providers (LSPs)
moved towards communicating time windows, either directly after thecustomer shipment is received or batched the day before. Longer waiting for a time window poses an inconvenience for customers. We suggest a scheme in which the LSP decides (i) whether to assign a time
window to a newly arriving customer, and if the LSP assigns a time
window, also if some previously postponed customers shall also receive a time window, (ii) the actual time window, and (iii) the routes
satisfying the time windows. To solve the resulting semi-Markov decision process, we adapt three solution methodologies: Deep Controlled
Learning, the Rollout Algorithm, and the Multi-Scenario Approach.
We show that Deep Controlled Learning manages to balance solution
quality and runtime. We further find that customers located very closeor very far from the depot receive time windows quickly, while othercustomers have to wait longer. On the other hand, customer arrival
time does not influence the waiting time substantially.

3 - Robust Policies for a Multi-Stage Fleet Sizing Problem
Under Demand Uncertainty

Alice Raffaele, Demetrio Laganà, Roberto Musmanno,
Roberto Roberti
In a stochastic dynamic setting over a planning period of days, we consider a set of customers, each placing exactly one order on a known
day. Each order is associated with an uncertain demand and can beeither served or outsourced by paying a penalty. Customers have day
windows that, if some flexibility is allowed, can be extended by anticipating or delaying the service by paying a penalty. At the end of eachday, we decide which pending orders to serve on the following day
by exploiting a heterogeneous fleet of vehicles. The goal is to minimize the total costs due to fleet sizing and customer inconvenience for
anticipations, delays, and outsourcing. We formulate the problem asa Markov decision process and solve it with Approximate Dynamic
Programming by integrating robust-optimization techniques in Direct
Lookahead Approximation policies. Our study is motivated by an ondemand waste collection service. Nevertheless, the problem has applications in many other contexts, such as reverse logistics and technician
routing [1, 2].
References [1] S. Mishra and S. P. Singh, Designing dynamic reverse
logistics network for post-sale., title, Annals of Operations Research,
pages 1-30, 2022. [2] M. Nowak and P. Szufel, Technician routing andscheduling for the sharing economy, European Journal of Operational
Research, 2023.

MD-65

Ulrike Leopold-Wildburger
By studying the history of scientists respectively scientific disciplinedevelopments a series of background issues can be explained. Jakob
Krarup is THE person who was able to connect his ideas with otherdisciplines as well as being the key to future development directions.
Operations research as a quite young discipline, nevertheless full ofcontradictions, makes it mandatory to be overviewed by excellent connoisseur like Jakob had been. He was able to describe the evolution
of OR in terms of developing applied methods of decision making insocio-technical systems, by using new methods explaining contemporary demand of army, society, industry, economics as well as politics.
As a well known scientist, as an unique teacher and a really good friend
the university of Graz will always remember him. The Austrian OR
society ÖGOR is proud to list him as a honorary member since 2020.

2 - Some thoughts of sincere gratitude to Professor Jakob
Krarup - our mentor and friend

Gerhard-Wilhelm WeberThis talk adds some professional and, not least, personal elements tothe appreciation of Professor Jakob Krarup. They cover the period
since 2000, address some of his important scientific achievements,
some joint conferences and conference organizations, especially within
EURO and EUROPT, and in particular his warm-hearted and loyal,caring and fatherly personality that manifested itself in the minds andhearts of many people and forever in EURO, IFORS and all Operational Research.

3 - Jakob Krarup. Very personal remarks

Stanislaw Walukiewicz
The story of the DAPS society and participation of other foreigners at
the DAPS activities.

4 - Cooperation with Jacob Krarup, his impact for OR development in Baltics

Leonidas Sakalauskas
A talk about cooperation with Jacob and his impact to developing
LitORS OR initiatives, the XXI ESI in 2003, Neringa, Lithuania, XXVEURO Conference, 2012, July, Vilnius, Lithuania and others.

5 - A few souvenir photos of our friend Prof. Jakob Krarup,
a remarkable personality and great supporter of theEURO member societies

Lidija Zadnik Stirn
Our friend Prof. Jakob Krarup was a remarkable person whose impact
on our community, commitment, vision, leadership, wisdom, integrity,
inclusiveness, efforts to foster collaboration, willingness to listen to every voice, kindness, empathy and humor will remain with us forever.
He was the heart and driving force of the EURO family, developing
friendships between many members that often extended to families.
His words were: "Without a doubt, friendship is what will remain mostprecious to me when everything else is forgotten“. Not only his ideas
for the development of EURO and the challenges that need to be overcome if OR is to be successful in the future, but also his Christmas andbirthday cards, numerous photos of his family and friends, his garden,
nature, even sport and music, stories of past (especially EURO) events
will remain as memories of Jakob.

 MD-65
Monday, 14:30-16:00 - Room: R021 (building: 358)

Memorial Session for Jakob Krarup
Stream: Memorial Session for Jakob Krarup
Invited session
Chair: David Pisinger
1 - Jakob Krarup as permanent guest professor at Karl
Franzens University Graz in Austria

161

ME-01EURO 2024 - Copenhagen

Monday, 16:30-17:30
 ME-01
Monday, 16:30-17:30 - Room: Sportshallen (building: 101)

Berit Brouer
Stream: Plenaries
Plenary session
Chair: Dario Pacino
1 - Charting the Course: How to power decision making
in the Maersk transportation network with decision science

Berit Brouer
This presentation will highlight how decision science can power decision making on the journey from designing a global ocean transportation network serving 100000+ customers across 130 countries to theintricate orchestration of schedules for 700+ vessels to transport ourcustomers goods and ensure availability of equipment to enhance ourcustomers supply chains all the way. At every phase decision sciencecan play a pivotal role in increasing consistency and quality of decision
making. We will also delve into the orchestration of problem solving
between the different phases.
The presentation will emphasize how the implementation of models
and algorithms is really just ten cents a penny - successful implementation of decision science necessitates harmonizing with organizational
dynamics, processes, and technological frameworks. We’ll explore theevolution of decision science products at Maersk emphasizing the criticality of obsessing about customer and business outcomes through performance and process metrics needed to sustain the vitality and healthof decision science products.

162

EURO 2024 - Copenhagen

Tuesday, 8:30-10:00
 TA-01
Tuesday, 8:30-10:00 - Room: Sportshallen (building: 101)

Pascale Zarate
Stream: Keynotes
Keynote session
Chair: Audrius Kabasinskas

TA-04

framework to generate hard instances for obtaining robust VRP solutions. Given a pretrained deep model, we first develop an attack
method that comprises an autoregressive sampling network (ASN)
and a hardness measurement network (HMN). The two networks aretrained alternately by reinforcement learning, aiming to generate hard
instances for the given deep model and gauge the attack effect (i.e.,
hardness) of the instances, respectively. Then, we propose a simple
yet effective training algorithm to robustify the deep model, which is
progressively replaced by the continually trained HMN. Experimental
results show that the attack method significantly degrades the performance of various deep models and conventional heuristics. Moreover,
the training algorithm showcases the ability to enhance the robustnessof the deep model, demonstrating its promising zero-shot generalizability.

1 - Decision support systems: History and trends

Pascale Zarate
Decision Support Systems (DSS) were born in the 70’s. Since thisdecade, we can find in the literature several tracks of evolution forthese systems. We will draw the evolutions of DSS: describing the architecture, the usability, the functionalities, the programming technics
as well as the way to use these systems. We will show how Artificial Intelligence and Machine Learning influenced the development of
DSSs. Different projects will be presented related to DSSs.

 TA-03
Tuesday, 8:30-10:00 - Room: 1005 (building: 202)

(Deep) Reinforcement Learning for
Combinatorial Optimization 3
Stream: Data Science Meets Optimization
Invited session
Chair: Yingqian Zhang
Chair: Kevin Tierney
1 - Optimizing a Multi-echelon, Multi-Product, Lost-Sales
Inventory Management System through Deep Reinforcement Learning

Fatemeh Fakhredin, Joern Meissner
Our study is going to address a lost-sales inventory management problem spanning multiple echelons and products, aiming to maximize network profit while determining optimal quantities from upstream suppliers. We utilize deep reinforcement learning (DRL) to optimize inventory management in these complex supply networks.
We begin by constructing a complex inventory management model accommodating lost sales and intricate cost structures. Employing DRL,
we develop a framework to tackle this problem and evaluate its efficacy.
Our investigation extends to various scenarios, encompassing changes
in lead time, fixed and sourcing costs, holding expenses, lost sales
penalties, and demand variations. Therefore, the overarching researchquestion we will attempt to address is: Does deep reinforcement learning produce superior policies for the multi-echelon, multi-product,
lost-sales inventory management problem, in comparison with standard reinforcement learning?

2 - Learning to Generate Hard Instances: Towards Robust
Solutions for Vehicle Routing Problems

Xia Jiang, Yaoxin Wu, Yingqian Zhang
Deep models have shown promising results in solving vehicle routing
problems (VRPs). However, existing models are often trained on instances from specific distributions and their worst-case performance islargely underexplored, thereby hindering the understanding and improvement of their robustness. In this paper, we present a generic

3 - AM4FJSSP: An Attention Model formulation for theFlexible Job Shop Scheduling Problem

Lin Xie
The flexible job shop scheduling problem (FJSSP) is a complex combinatorial optimization CO problem (COP) in manufacturing, involving
the allocation of operations across multiple machines while considering varying processing requirements and machine capabilities. In
contrast to normal job shop scheduling operations in the FJSSP may
be processed on multiple machines with different processing times, introducing additional complexity. Current deep reinforcement learning
implementations for the FJSSP make use of graph neural networks,
which are limited to local message passing and therefore underperform compared to the attention model (AM) on various COPs. Especially for the FJSSP with complex inter and intra node relationships,
the self-attention mechanism of the AM could enhance feature representations. However, the AM is defined for problems with a single
entity type, like customer nodes in the TSP, whereas machines andoperations of the FJSSP pose different node types in a complex heterogeneous graph. Therefore, this work proposes an extension of theAM, integrating self- and cross-attention blocks to allow for message
passing between all nodes and thus to capture inter and intra node relationships. Further, we present a factorized action-space formulation
for the FJSSP, which - other than the composite action-space used incurrent implementations - does not grow quadratically. Consequently,
a better sampling efficiency and scalability of our approach is to be
expected.

4 - Job Shop Scheduling Benchmark: Environments andInstances for Learning and Non-learning Methods

Yingqian Zhang, Robbert Reijnen, Yaoxin Wu, Zaharah
BukhshWe introduce an open-source GitHub repository containing comprehensive benchmarks for a wide range of machine scheduling problems,
including Job Shop Scheduling (JSP), Flow Shop Scheduling (FSP),
Flexible Job Shop Scheduling (FJSP), FJSP with Assembly constraints(FAJSP), FJSP with Sequence-Dependent Setup Times (FJSPSDST),
and the online FJSP (with online job arrivals). Our primary goal is toprovide a centralized hub for researchers, practitioners, and enthusiasts
interested in tackling machine scheduling challenges.

 TA-04
Tuesday, 8:30-10:00 - Room: 1001 (building: 202)

Topics in Mixed Integer Programming andNonconvex Optimization
Stream: MINLP
Invited session
Chair: Jan Kronqvist

163

TA-06EURO 2024 - Copenhagen

1 - Distance geometry without the graph

Leo Liberti TA-06
Tuesday, 8:30-10:00 - Room: 1013 (building: 202)

The Distance Geometry Problem (DGP) is as follows: draw a weighted
graph in a K-dimensional Euclidean space (K given) so that vertices arepoints and edges are segments with the same length as their weights.
In many applications the input is not quite a graph, but simply a listof distance values without their adjacencies. Thus, the assignment of
distance values to graph edges must be carried out concurrently to therealization in the K-dimensional space. This is known as the unassigned DGP (uDGP). There is a natural MINLP formulation for theuDGP, which is very hard to solve even for tiny instances. We present
alternative formulations as well as relaxations, restrictions, and otherapproximating reformulations.

Advancements of OR-analytics in statistics,
machine learning and data science 12

2 - Optimization over trained graph neural networks:
mixed-integer formulations, symmetry breaking, andoptimal molecular design

1 - Spatial analysis techniques for diagnosis situational of
low birth weight at term in the state of São Paulo

Shiqiang Zhang
Optimization over trained machine learning models has applications
including verification, minimizing neural acquisition functions, and integrating a trained surrogate into a larger decision-making problem.
We formulate and solve optimization problems constrained by trainedgraph neural networks (GNNs). For the classical GNN architecturesconsidered in this talk, optimizing over a GNN with a fixed graph isequivalent to optimizing over a dense neural network. Thus, we study
the case where the input graph is not fixed, implying that each edge isa decision variable, and develop two mixed-integer optimization formulations. To circumvent the symmetry issue caused by graph isomorphism, we propose two types of symmetry-breaking constraints,
and provide a proof that adding these constraints will not remove all
symmetric solutions. As a byproduct, a graph indexing algorithm isconstructed to yield an indexing satisfying these constraints. To test
our symmetry-breaking strategies and optimization formulations, weconsider an application in optimal molecular design.

3 - Optimization-based algorithm for robustness enhancement of neural networks

Shudian Zhao, Jan Kronqvist
This talks introduce an optimisation-based method for generating robust neural networks against adversarial attacks for image classifications. Instead of using large training data and heaving computing expense in classic training process, the proposed method only requiresa small size of adversarial dataset in each outer iteration to improve
the robustness a model trained with default settings. This algorithm
includes two levels of updating. In the inner iteration, we add cuts toimprove the linear approximation for the nonlinear constraints regarding the adversary behaviour and level of loss function. Then, to balance
the performance of the model on both clean dataset and the adversarial
dataset, we select the Pareto set by line search and update the modelwith a weighted objective function. In the outer iteration, we generate
new adversarial data and repeat the same process until stopping criteria
are satisfied. This talk concludes with numerical restyle with CNN and
ResNet models.

4 - Solving large-scale ACOPF problems

Matias Villagra, Daniel Bienstock
The ACOPF problem is a notoriously difficult nonconvex optimization
problem arising in the operation of power grids. To date, all numerically effective relaxations – that is to say, both accurate and practicable,at scale – require second-order constraints. However, solvers have considerable difficulty tackling such constraints. In this talk, we will describe an outer approximation linear cut scheme that yields relaxations
which are both tight and can be solved quickly and robustly. Additionally, we will describe a warm-start scheme that further improves
on performance. Finally, we will present a theoretical justification for,both, the success of the SOCP relaxations and their outer approximations. Joint work with Matías Villagra (PhD student, Columbia University).

164

Stream: Advancements of OR-analytics in statistics, machine learning and data science
Invited session
Chair: Antonio Mauttone
Chair: Gerhard-Wilhelm Weber

Diego Eduardo Quagliato Scarelli Cava, Fernando Augusto
Silva Marins, Elen Yanina Aguirre Rodríguez, Aneirson Silva,
Elias Carlos Aguirre Rodríguez
Low birth weight (LBW) is defined when newborns weigh less than
2,500 grams,and is considered at term when born at greater than orequal to 37 weeks of gestation. The incidence of LBW is considereda significant public health issue, closely associated with an increased
risk of mortality and future morbidity. Furthermore, it is important tohighlight that between 15% and 20% of children born worldwide have
LBW. Therefore, to overcome this situation, researchers started to develop and use the Geographic Information System to identify areas at
risk of health problems and create solutions for these places. Based on
this, this study aimed to utilize spatial analysis to verify the distributionof LBW at term and identify high-risk areas within the 645 municipalities in the state of São Paulo from 2012 to 2021. Specifically, thisstudy used the global Moran index to measure the degree of spatial association in the study area and the local Moran index to identify risk
areas with high prevalence. The findings indicate potential presenceof significant clusters of municipalities revealed by the global moran
index, which was substantiated by the local moran index, highlighting
three small clusters in the northern part of the state. This approach
can assist health professionals and governmental bodies to formulate
precise strategies and initiatives to mitigate LBW in these localities.

2 - Classification for natural language processing

Ioannis Tsiligkaridis
Natural Language Processing (NLP) introduces computational representations of language with the word vectors as a potent approach. This
work focuses on Sentiment Analysis for classifying the emotional intent of text. A variety of different types and layers of Neural Networks(NN) are used. A group of Deep Learning (DL) algorithms are used forclassification starting from a Simple Dense Neural Network with Embedding layer (DNE) and continuing with the Convolutional Neural
Network (CNN), the Long-Short Term Memory (LSTM), the LSTM
with Dropdown (LSTMD), and the Ensemble model with LSTM and
CNN(LSTM_CNN). Hyperparameters tuning for the NNs (i.e. learning rate, etc.) are also used. The use of hyper parameters will be able
to boost the accuracy levels even higher. The grid search method isused for discovering the LSTM hyperparameters for better accuracy.
All approaches are tested for accuracy with Large Movie Review
Dataset (IMDB). In DNE, misclassification of false positive and false
negative is noticed. CNN, with one-dimensional sequence of words,
improves DNE performance at classifying film reviews. The filter of
the CNN layers obtained better results at learning short sequences. The
LSTM surpasses LDTMD and LSTM_CNN.

3 - Spatio-temporal analysis of variable-density clusters in
hot spot policing

Ben Moews
The effective prevention of crime requires the availability of empirical data to optimise public resource utilisation and public safety outcomes. In this context, spatio-temporal incident reports can be used
for newly developed variable-density cluster analysis approaches, forwhich empirical data collected over more than two decades in the Cityof Chicago is used. This provides insights into the evolution of crime

EURO 2024 - Copenhagentype ratios in the twenty-first century so far, with particularly notableeffects from the recent COVID-19 pandemic due to shifts in space occupancy, with primary functions of city areas playing an importantrole. An analysis of spatial autocorrelations at different distances, asa methodology transfer from cosmology, demonstrates variations inincident uniformity between clusters and outlier areas, and highlights
the need to question the currently wide-spread practice of cluster epicentre policing. One of the points the discussion focusses on is therole of criminology-oriented optimisation as an aspect of community
operational research. This is particularly relevant when it comes to acknowledging risks due to known and unknown data biases, for example
due to extraneous and temporally constrained events as well as impacts
from spatially varying police-community relations.

4 - Investigating fairness in decisions involving ordinalclassification - A COVID-19 case study

Edward Abel, Sajid Siraj
Ensuring fairness is important when evaluating decision support systems aiming for impartial decision outcomes and trust within thosebeing affected. During the COVID-19 pandemic, numerous nations
implemented tiered restrictions as a localized strategy to balance economic activities against restrictions of movement independently in different geographical areas. However, such approaches sparked debate
on the fairness of these restriction decisions. We examine fairness concerning the UK government’s allocation system for tiered restrictions
which can be modelled as an ordinal classification problem. For this,we collected and integrated data from multiple official sources to investigate potential inconsistencies, such as comparing the North and theSouth of England. We explore if there is inconsistency, by first training
an ordinal classification model using only data from one geographicalarea, then testing the model with data pertaining to another geographical area. This helps us identify and measure classification errors interms of underestimates and/or overestimates of predicted values compared to the actual values. Such analysis can be useful for exploring
ordinal classification problems, both for post-hoc analysis of past decisions, and for providing transparency as part of future decision dissemination. Our approach could be applied to other ordinal classification
problems, to explore fairness within other domains.

TA-09

2 - Exact and Heuristic Solution Approaches for Busy Time
Minimization in Temporal Bin Packing

John Martinovic, Nico Strasdat
Given a set of jobs (or items), each of which is characterized by its resource demand and its lifespan, and a sufficiently large number of identical servers (or bins), the busy time minimization problem (BTMP)
requires to find a feasible schedule (i.e., a jobs-to-servers assignment)
having minimum overall power-on time. Although being linked to thefield of temporal bin packing, BTMP represents an independent branchof research. Typically, such considerations (and generalizations of it)
are very important in data center workload management to keep operational costs low. Hence, finding efficient and powerful solution
techniques for BTMP is a relevant topic in cutting and packing, bothfrom a theoretical and practical point of view. In this talk, we give an
overview of heuristic and exact approaches for the problem under consideration and analyze their theoretical properties and computational
behavior. As a first main contribution, we suggest a new best-costbased heuristic showing convincing results in a wide variety of numerical test. In terms of exact approaches, we propose some improvements
for the ILP models from the literature and establish a new combinatorial flow-based formulation. Based on extensive numerical tests withdifferently-characterized benchmark sets, the flow model is shown (i)to improve the state-of-the-art approach for general instances, and (ii)to be competitive with a matching formulation tailored for a specialcase.

3 - The cutting stock problem with limited open stacks

Kelly Cristina Poldi, Gabriel Gazzinelli Guimaraes
A Cutting Stock Problem (CSP) consists of determining the best way
in which a set of larger units (objects) should be cut in order to fulfill the demand for a set of smaller pieces (items) while minimizing acertain objective that can be, for instance, the number of objects or material waste. In industrial contexts, automatic cutting machines have alimited number of unloading stations and, thus, it is essential, in mathematical modeling, for accurate representation to consider these constraints added to the CSP. Such problem is known as the Cutting Stockwith Limited Open Stacks Problem (CS-LOSP). In this research, we
present an integer linear programming formulation for the CS-LOSP,
and some computational experiments carried out on randomly generated instances.

 TA-07
Tuesday, 8:30-10:00 - Room: 1019 (building: 202)

Cutting and Packing 5 - related topics

 TA-09
Tuesday, 8:30-10:00 - Room: 10 (building: 116)

Stream: Cutting and Packing (ESICUP)
Invited session

Decentralized multi-energy markets

Chair: Rosephine Georgina Rakotonirainy

Stream: Energy Markets
Invited session

1 - Reducing shipping cost of packages by optimizing thebin setLeon Binder, Johannes Kuchler, Michael Scholz
With the increasing number of parcels shipped worldwide, new ways
to reduce the total shipping costs and CO2 emissions are actively
searched. Past research primarily focused on finding bins that minimize the wasted volume for packing orders. Retailers, however, must
decide about the bin sizes they will use for packing before any order
will be packed. Having many bin sizes on stock will allow a retailer
to more often select a bin with only some small wasted volume. Manybin sizes, on the other hand, increase costs for buying and storing bins
as well as for selecting the best bin. To the best of our knowledge, thepreliminary decision of what bin sizes a retailer should have on stock
to minimize the total shipping cost is a yet unexplored optimization
problem. We focus on the NP-hard problem of finding bin sizes (i.e.,bin set) that minimize the costs for packing and transporting a given setof orders. More specifically, we i) define the problem of finding costminimal bin sets, ii) provide an adapted metaheuristic to identify goodsolutions to the defined problem and iii) discuss possible extensions of
the provided solution.

Chair: Russell McKenna
1 - Climate policy design in liberalized electricity markets
with incomplete risk trading

Emil Dimanchev, Steven Gabriel, Filippo Pecci, Stein-Erik
Fleten, Magnus KorpåsClimate policy makers aim to incentivize risk-averse firms to deploy
large amounts of capital for clean electricity investments. This task ismade harder by the limited availability of long-term contracts in electricity markets, which is known as the missing market problem. We
study how this market failure affects optimal policy choices in powersystems. To analyze optimal policy, we propose a bi-level programming approach that endogenizes policy decisions. Our model combines an "upper level" policy optimization problem and a "lower-level"
generation expansion problem. The lower-level problem features riskaverse investors making decisions under demand and fuel price uncertainty as well as optimal power system dispatch. While bi-level
programming imposes a computational burden, our formulation partly
alleviates this problem. We are able to solve our model for a powersystems featuring both variable renewables and energy storage with165

TA-10EURO 2024 - Copenhagentime-linking constraints. We use this model to study the optimal design of several common climate policies including renewable portfolio
standards, investment tax credits, and carbon pricing. Preliminary results show that optimal climate policy instruments are more stringent
in power systems subject to the missing market problem than in systems with complete markets. We also find that missing long-term markets can make renewable portfolio standards more cost-effective thancarbon pricing in some cases.

2 - Delivering Reliable Reserve Commitments from Intermittent Electricity Resources

Anil Kaya, Yashar Ghiassi-Farrokhfal, Steffen Rebennack,Mohammad Reza Hesamzadeh, Kai Pan, Derek Bunn
The expansion of renewable energy generation increases the need forshort-term reserve facilities to compensate for their short-term variations. This makes reserve markets increasingly profitable and attractive for renewable energy producers (REPs), who are facing diminishing subsidies and returns. We study this problem by formulating
the operation of a profit-maximizing REP, with and without storage,
providing reserve services as a multi-stage stochastic integer program,separately with the support of discrete (D-ID) or continuous (C-ID)
markets. We combine the Benders decomposition and stochastic dual
dynamic programming algorithm (SDDP) to solve the problem efficiently. Our analysis of real data from the German market provides
interesting insights into REPs’ participation in short-term reserve markets.

3 - Market effects of Power-to-Gas technologies and theirrole under future gas supply shocks using a coupledelectricity-gas-hydrogen market modelYi Wan, Tom Kober, Russell McKenna, Martin Densing
Power-to-Gas (P2G), by converting surplus electricity to hydrogen using electrolysers, will be a crucial component in the future low-carbon
energy system. This study investigates the market impacts of largescale P2G deployment, with a specific focus on electrolysers and hydrogen storage, under a stringent climate scenario. We develop a gametheoretic equilibrium model of coupled day-ahead electricity, naturalgas, and hydrogen markets for the Central Western Europe region.
One novelty of this model is that we consider multiple hydrogen supply pathways, including national imports, elastic hourly hydrogen demand, and external costs of hydrogen blending in the representationof the (potential) wholesale hydrogen market. Besides lowering hydrogen market prices and increasing electricity market prices, electrolysers may also increase electricity price volatility due to overlapping
peak-load periods for electricity and hydrogen. The analysis of hydrogen storage highlights its role in mitigating general market volatility,
albeit with only modest impacts on electricity markets, which indicates
its limited potential to provide flexibility to the electricity market. Additionally, hydrogen storage may increase natural gas and hydrogen
prices through its interaction with Steam Methane Reforming. This
study contributes to the existing literature by providing insights for theeffects of P2G on future energy markets in terms of intricate market
dynamics and interactions induced by P2G.

4 - Large prosumers under capacity constraints: Market
power effects of vertical integration and of storage

Martin Densing, Yi Wan, Russell McKenna
With the advent of batteries and P2X technologies, energy producers
can also participate as consumers on energy markets, which increases
the flexibility of such prosumers. We consider a single time-step market equilibrium where a large player is an integrated producer and consumer under capacity constraints. The capacity constraints are relevant
for emerging consumer-side technologies, such as limited electrolyzer
capacity. We use conjectural variations to model different levels ofmarket power, and we solve the model analytically. Under market
power, we compare the equilibrium solution with the one for a separated producer and consumer. We calculate corresponding production
and consumption equilibrium amounts, prices, and social welfare. The
main finding is that the social welfare change of vertical integrationis ambiguous and depends on the parameters, for example the limited
capacity of the consumption technology. Using the same analytical166

method, we consider also a symmetric two time-step duopoly of capaciated storage and production under market power. Also in this case,we show how the equilibrium solution in terms of prices and storage
amounts depends on parameters, for example the intensity of market
power measured by the conjenctural variation parameter. The findings
may be used in energy-policy support to identify different cases forde-bundling energy technology assets.

 TA-10
Tuesday, 8:30-10:00 - Room: 11 (building: 116)

Radiotherapy and chemotherapy planning
Stream: OR in Health Services (ORAHS)
Invited session
Chair: Anders Forsgren
1 - Outpatient Chemotherapy Planning at Hacettepe Oncology Hospital: A Mixed-Integer Linear Programming
Model

Gunsu Dagistanli, Meral Azizoglu
With the rising need for chemotherapy treatment exceeding the limited capacities of oncology clinics and hospitals efficient planning becomes crucial. For that, first stage is allocating patients to treatmentdays within the constraints of their treatment plans and hospital schedules. Second stage is preparing the daily appointment schedules ofpatients to maximize resource utilization and patient satisfaction considering nurse, chair, and other constraints. Based on the observationsat Hacettepe Oncology Hospital in Ankara, we present a compact deterministic Mixed-Integer Linear Programming (MILP) model to address the first stage, known as the Outpatient Chemotherapy Planning(OCP) problem. The model accommodates multi objectives to comply
the hospital policies and decision maker’s goals. We find that for any
selected objective, the solutions retuned by our model outweigh thesolutions currently implemented.

2 - A stochastic programming approach for chemotherapy
planning

Melih Celik, Gunsu Dagistanli, Serhat Gül
Cancer is one of the most prevalent causes of death worldwide, second
only to heart disease. Chemotherapy is the most widely used approach
to treat cancer patients. There are many challenges for planning of
chemotherapy appointments in oncology clinics, including the limited
capacity of clinics and the uncertainty of the duration of treatment. For
this end, an effective planning of chemotherapy schedules is vitally
important.
In this study, we consider the problem of chemotherapy planning,where patients are assigned to days in the planning horizon according to their treatment frequency. Due to the uncertainty of durations,
we model the problem as a two-stage stochastic program, with an objective that minimizes a combination of earliness and tardiness of appointment days, as well as idle time and overtime of chemotherapy
chairs. As realistically sized instances of the problem are impossible
to be solved by commercial solvers, we propose a scenario reductionbased heuristic approach that relies on k-medoids clustering to find therepresentative scenarios. We test the performance of our approach ona dataset from a chemotherapy clinic and drive managerial insights.

3 - Fuzzy optimization in beam Intensity Modulated Radiotherapy Treatment

Nicole Cristina Cassimiro de Oliveira, Aurelio Oliveira
Cancer is one of the main public health problems worldwide. Most
cancer patients are treated with radiation. This treatment uses a sourceof ionizing radiation that destroys the tumor cells, in which it is possible to vary the intensity of the radiation fluence and the radiation
beam can be shaped in such a way to radiate only around the tumor.
Computed tomography scans are performed in order to define the target volumes and organs at risk, and then define the patient’s treatment

EURO 2024 - Copenhagenplan, in which sufficient amounts of dose are prescribed for the tumor to cure, while minimizing the inevitable dose to the other regions.
The prescription is normally made by the oncologist, which may be
associated with uncertainties. Therefore, the dosage values are considered as fuzzy numbers. In this context, the dose distribution problem
in radiotherapy planning translates into a fuzzy optimization problem
in which mathematical programming techniques are used for the solution. Fuzzy constraints are mathematically translated by the surpriseapproach. The proposed methodology was applied in several cases ofcancer using the TROTS database. The results obtained are comparedby means of Dose-Volume Histograms and contour graphs and dose
surface. In conclusion, the approach using fuzzy numbers produced
viable treatment plans, inferring that the model is an important tool indecision-making in the planning of radiotherapy treatment.

TA-12

portfolio generation techniques. The results of the experiment demonstrate that path dependence can occur in policy generation. We reportthinking patterns of subjects which relate to biases and heuristics.

2 - Pedagogy and prescriptive analytics

Jeffrey Keisler
Operations research involves mathematical modeling and analytic
techniques. Some people learn modeling and then analytics, somelearn the other way around, and some learn them simultaneously. Most
modern textbooks seem to teach them together and center the modeling. Are there problems with that? I will explain why I think so. Can
we do better? I will discuss how I think we can.

3 - Behavioral Decision Analysis
4 - Motion-adaptive optimization for proton therapy withpencil beam scanning

Ivar Bengtsson, Anders Forsgren, Albin Fredriksson
Pencil beam scanning (PBS) is an advanced technique for treating
cancer with proton radiation. It can deliver radiation with great precision, allowing the tumor to be targeted with less harm to healthy
tissue. However, the precision comes with a large sensitivity to uncertainty of the patient’s position and anatomy. Whilst robust optimization has proven successful in managing positional uncertainty in
PBS treatments, treatment of motion-subjected tumors due to variational anatomy, such as those in the lungs, may benefit from a motionadaptive treatment planning approach.
In this work, we build on previous attempts to explicitly plan with respect to the respiratory patterns of lung patients, as well as motionadaptive optimization proposed for photon treatments, to propose amotion-adaptive control strategy for PBS. During the delivery of thetreatment, the patient’s breathing motion is tracked and predicted into
the future. The predicted motion is then used to re-optimize the treatment over a receding horizon.
We show that the motion-adaptive strategy is superior to robust optimization in terms of both consistently treating the target with sufficient radiation and sparing nearby healthy tissue. Although much work
remains to demonstrate the feasibility of implementing the proposed
strategy in real-time, our results demonstrate the benefits of successfully implementing a motion-adaptive strategy, further realizing the
precision advantage of proton therapy with PBS.

 TA-11
Tuesday, 8:30-10:00 - Room: 12 (building: 116)

Behavioral Decision Analysis IV
Stream: Behavioural OR
Invited session
Chair: Gilberto Montibeller
Chair: Emanuele Borgonovo
1 - Generating Policy Alternatives for Decision Making: A
Process Model, Behavioural Issues, and an Experiment

Gilberto Montibeller, Florian Federspiel, Matthias Seifert
This presentation lays out a foundation and taxonomy for Behavioral
Decision Analysis, which can unify this work across various domains.
Traditional research in the domain of Decision Analysis has focusedon the design and application of logically consistent tools to support
decision-makers during the process of structuring problem complexity, modeling uncertainty, generating predictions, eliciting preferences,
and, ultimately, making better decisions. Two commonly held assumptions are that the decision maker’s cognitive belief system is fully accessible and that this system can be understood and formalized bytrained analysts. However, in past years, an active line of researchhas emerged studying instances in which such assumptions may nothold. We aim to unite this community under the common theme of
Behavioral Decision Analysis. The taxonomy we suggest categorizes
research based on task focus (prediction or decision) and behavioral
level (individual or group). Two theoretical lenses that lie at the interface between (1) normative and descriptive research, and (2) normativeand prescriptive research are introduced. We then proceed to highlight
representative works across the two lenses focused on individual andgroup-level decision-making.

4 - Local knowledge and operational performance: Evidence from a digitized setting

Marijn Verschelde, Changyu Men, Maud Van den Broeke,
Bart Roets
In digital control rooms, controllers’ experience significantly impacts
their performance. They accumulate expertise through repetition,termed local knowledge, given the stable and repetitive nature of theirtasks. Alternatively, they broaden their skills by working across various workstations, termed neighbor knowledge. Our study investigates
the role and impact of these types of knowledge on operational performance, and examines the portability of neighbor knowledge within
digital control room settings. For this purpose, leveraging a purposefully built comprehensive dataset from a major European rail system
operator, we analyze how workstation-based and train-based experiences influence controllers’ operational performance, measured bytrain delay. Our findings reveal that accumulated work at the sameworkstation improves operational performance, underscoring the importance of local knowledge. Further, when focusing on operational
performance regarding passenger trains, we observe higher performance for controllers with more experience in passenger trains, butalso lower performance for controllers with more experience in freighttrains, indicating potential drawbacks of neighbor knowledge on operational performance.

Raimo P. Hämäläinen, Tuomas Lahtinen, Kai Virtanen
The generation of alternative policies is essential in complex decisiontasks with multiple interests and stakeholders. A diverse set of policies
is typically desirable to cover the range of options and objectives. Decision modelling literature has often assumed that clearly defined decision alternatives are readily available. This is not a realistic assumption
in practice. We present a structured process model for the generationof policy alternatives in settings that include non-quantifiable elements
and where portfolio optimisation approaches are not applicable. Behavioural issues and path dependence as well as heuristics and biases
which can occur during the process are discussed. The behavioural experiment compares policy alternatives obtained by using two different TA-12
Tuesday, 8:30-10:00 - Room: 13 (building: 116)

OR in Livestock farming
Stream: OR in Agriculture and Forestry
Invited session
Chair: Pol Llagostera Blasco

167

TA-13EURO 2024 - Copenhagen

1 - Conventional or organic cattle farming? Trade-offs andoptimal gross margin analysis

Mario Guajardo, Rasmus Bang, Leif-Jarle Asheim, Ola
Flaten, Bjørn Gunnar Hansen, Jon Kristian SommersethInterest for organic products and concerns regarding sustainable practices raise an important question for farmers: should they run theirfarms conventionally or organically? We address this question bymeans of a mathematical programming model tailored to Norwegian
cattle farms. The model aims to optimize gross margin, subject togovernment support stipulations outlined in the Norwegian agriculturalagreement and a number of other features. A computational study allows us to assess the economic performance of different farm settings
and the trade-offs between crop yield, livestock capacity, organic premiums, and government payments. We derive some insights into theconditions when organic outperforms conventional systems, and viceversa. These hold significant relevance not only to farmers, but also tofarm advisors and policy makers.

In today’s highly industrialized and specialized pig production landscape, efficient decision-making is essential to navigate the complexities and maximize benefits. This study investigated the delivery of fattening pigs from farms to slaughterhouses, considering diverse growth
patterns. An optimal marketing strategy is developed to maximise
profits by adopting an all-in, all-out management approach. The research utilized the K-means algorithm to categorize actual growth dataof fattening pigs into three clusters, each analyzed using the Gompertz
animal growth model to assess growth rate, acceleration, and relative
growth rate. Subsequently, a simulation of a fattening farm was conducted using a mixed integer linear programming (MILP) model toevaluate various parameters and develop optimal marketing strategies.
These strategies, oriented for pig delivery schedules, cost analysis,
and profitability assessment, were designed without needing individual
control and weight measurement of fattening pigs. Sensitivity analyses
were performed on key production factors affecting profitability, suchas pig transaction price, feed price, and pig mortality during fattening,leading to the development of marketing strategies for each scenario.

2 - Managing Hog Farm Operations under Uncertainty: Inventory and Selling Strategies

Panos Kouvelis TA-13

Problem Definition: We study the finishing stage (weeks 23-26 in the
pig’s life) planning problem of a pork producer who gets to see howmarket-ready hogs he has available at the beginning of each week forsale and the current market prices. Then, he must decide how manyhogs to sell through to a meatpacker and on the open market. The
producer has a contract to deliver a fixed quantity to the meatpacker
each week according to a predetermined price formula that depends oncommodity indices. He pays a penalty if he fails to deliver. The numbers of hogs that become market-ready every week, all costs, and all
prices are stochastic. Methodology: We use a dynamic programming
approach (optimal policy, a one period look ahead approximation). Results: We show there are two types of thresholds in optimally making decision switches (holding to contract fulfilling, holding to spot
selling), each one specific to the under-weight and the regular-weighthogs. The thresholds are sensitive to the number of available hogs
and prices and not straightforward to derive. An approximate dynamic
program preserves the optimal policy structure and offers a practicalsolution approach.

Tuesday, 8:30-10:00 - Room: 15 (building: 116)

3 - Comparative Study of Genetic and Differential Evolution Algorithms for Pareto Front Approximation in Dairy
Feed Optimization

Gastón Notte, Pablo Chilibroste, Martin Pedemonte, Hector
CancelaEvolutionary algorithms have emerged as potent solutions for addressing multi-objective optimization challenges across various practicalscenarios. This study introduces a multi-objective dairy feed resource
allocation optimization model and conducts a performance analysis
of four methods to approximate the Pareto front; two genetic algorithms (GA: NSGA-II, SPEA-2) and two differential evolution algorithms (GDE-3, a variant of Pareto-based DE). Experiments utilizing
real-world data were performed to evaluate the algorithms based on criteria such as execution times, Pareto front solutions, and performanceindicator values, at the same time looking for the best parametric combinations for each algorithm. The results showed significant differences among the algorithms, both in their proficiency to approximate
the Pareto front and in their execution times. SPEA-2 demonstrated
superior results in terms of convergence, diversity, and cardinality, butsuffered prolonged execution times. Future research could address thisissue, combining SPEA-2 with other algorithms for efficient Pareto
front approximations. Regarding the parametric combinations, significant differences were observed. For both GA, the importance of using
a low value in the mutation probability is highlighted. For the GDE-3,
a low value in the population size (N) and a high value in the crossoverprobability (CR) stand out. Finally, for the Pareto-based DE, higher
values for N and CR gave better results.

4 - Maximizing profits by performing optimal marketing decisions in pig fattening farms

Pol Llagostera Blasco, Yun Bao, LluisM Pla

168

OR Innovations in Policy Making - B
Stream: Soft OR and Problem Structuring Methods
Invited session
Chair: Ine Steenmans
Chair: Irene Pluchinotta
1 - Reflections on the Opportunities to Innovate Policy andusing Problem Structuring Methods to Transform UK
Public Sector Organisations

Martin Parr, Sophie Jackson, Adam Townsend
In the UK our public sector services are described as being delivered
efficiently. Each project will have been funded based on needs, but at a
macroeconomic level there is great opportunity to make improvements.
So, why don’t we make these changes? In part because it is difficult
and the benefits won’t be seen by the people who deliver the change.
There is also no enduring structure and mandate within government toperform systemic change, although there is a structure for audit - highlighting more focus on risk than on the opportunity. Transformationalchange is possible and innovation is achievable. There are many opportunities to design policy to realise significant benefits, but we lackpolitical will and appropriate control systems. A simple assessment
can be obtained by comparing publicly available reports from key UK
agencies such as The Metropolitan Police. It can easily be shown that
there is a wide gap between the mission for each organisation and what
they deliver. In considering systems level improvements, a project will
be examined using methodologies such as Soft System Methodology(SSM) and the Viable System Model (VSM). This project will demonstrate that there is a need to develop policy using systems approaches.
It will highlight the broader opportunities that may be possible, andthat transformational change can be delivered for the UK government.

2 - Bronze, silver or gold? What’s the added value of investing more in the analysis of medical technologies?

Alec Morton, Jamaica Briones, Pete Baker
Health Technology Assessment (HTA) is an applied discipline which
involves providing support to decision makers about the costeffectiveness and budget impact of medical technologies, with a view
to informing decisions about reimbursement in a given country’s healthsystem. However, HTA is itself resource-intensive, and requires analytic skills which may be scarce, particularly in low and middle-income
countries. In response, many countries have between explored with"adaptive" HTAs (aHTAs). The idea in an aHTA is for analysts ina target country to adapt the analysis and findings from HTAs fromother countries, rather than undertaking HTA de novo. aHTA is thus
less-resource intensive and quicker than full HTA, but at the expense of

EURO 2024 - Copenhagenlower contextual relevance. Drawing on systematic reviews and metaanalyses for a number of medical technologies, we run a series of simulations to quantify the financial and health loss of using aHTA for atypical country for a range of cost-effectiveness thresholds, and to explore the factors which drive high losses. This work is a staging post
to building an optimisation model which would support a country to
optimise its HTA budget across full and adaptive HTAs.

3 - Causal Loop Diagrams to address Nexus BiodiversityClimate- Society governance challenges: insights fromthe gold mining in Ghana case study.

Raffaele Giordano, Stefania Santoro, Alessandro Pagano,
Mohammed Armani
Designing policies for managing the Biodiversity Climate Society(BCS) Nexus claims for a better understanding of the complex anddynamic interactions among its components and an effective engagement of the stakeholders. To this aim, a Participatory System Dynamic
Modeling (PSDM) framework was adopted in BIOTRAILS project(https://biotrailsproject.eu/). The PSDM framework is composed byseveral steps. This contribution focuses on the system mapping phase,
whose scope is to map the interactions among the different elements of
the BCS nexus, accounting for the stakeholders’ understandings anddiversity of problem framings. Causal Loop Diagram was used to formalize and analyse the stakeholders’ knowledge. Methodological approaches based on Ambiguity Analysis and structural analysis werecombined to identify the BCS nexus challenges and detect leverage
points. Those elements will be, in a later stage of the project implementation, used to inform the stakeholders’ involvement in Nexus policy
design. The developed methodology was experimentally implemented
to the gold mining in Ghana, to analyse their impacts on biodiversity
losses and the dynamic evolution of the BCS nexus. A PSDM exercise
was held in Kumasi to map the BCS Nexus system. The preliminary
analysis of the obtained CLD show how stakeholders perceived gold
mining impacts on the local environment and socio-economic context,
and allowed us to start the process for the Nexus policy design.

4 - Challenges for systemic public policy evaluationIne Steenmans
In recent years there has been a renewed interest in ’systems-based’,’systemic’, and ’analytics-driven’ evaluation of public policy. This interest has been supported with contributions from systems, policy, andevaluation sciences communities (see Reynolds et al. 2016; OECD
2019; Blomkamp 2022; GO-Science 2022). Often the disseminationand learning from these contributions remain largely focused withinthese disciplines. I argue the resulting disconnect between related work
challenges OR researchers and practitioners in identifying where they
can meaningfully contribute to address current needs.
This paper aims to support OR contributions to future innovations inpublic policy evaluation by presenting a review of the current state ofknowledge and practice in three parts. Three questions are used to
explore OR’s relationship to policy evaluation:
1. What makes an evaluation ’systemic’? 2. What challenges are currently faced in systemic policy evaluation? 3. How is and can the fieldof OR contribute to resolving these challenges?

 TA-14
Tuesday, 8:30-10:00 - Room: 16 (building: 116)

Enhanced statistical methods for energy
challenges
Stream: Energy Markets
Invited session
Chair: Reinhard Madlener

TA-14

1 - Algorithm-Enhanced Fuel Poverty Prediction Using
Household Characteristics, Socio-Economic Factors,
and Clustering Analysis

Reinhard Madlener, Rahil Dejkam
This paper contributes to the literature on predicting fuel poverty risk
by applying clustering analysis to identify fuel-poor households. The
study develops a novel approach to clustering-based XGBoost modelby integrating data from a representative household survey in the UK(N=11’974). The unobserved heterogeneity in fuel poverty households
survey hides certain relationships between the contributory features insurvey and fuel poverty. This study explores the application of the
k-prototypes clustering method to group households into heterogenous
clusters. The study segments the entire dataset into three clusters. FourXGBoost models were developed and applied to the entire dataset andeach cluster to predict fuel poverty in households. The list of input
features includes housing characteristics, socio economic features, andenergy-cost variables. Additionally, for analyzing all features the shapley additive explanations (SHAP) method has applied, showing thedifferent degrees of effects of each feature on fuel poverty in the entire dataset. The k-prototypes clustering-based XGBoost model is apromising approach to identifying the households at the risk of fuelpoverty. Policymakers can gain new insights by identiying key socioeconomic factors to alleviate fuel poverty.

2 - Do Oil Market Shocks Affect Financial Distress? Evidence from Firm-level Global Data

Mohammad Mahdi Mousavi
This paper investigates the impact of three oil price shocks, i.e. oil
supply shock, oil aggregate and specific demand shocks, on firm-level
distress using a dataset including 8,130 firms across 48 countries from2002 to 2022. The study also analyzes the role of energy diversification in the relationship between oil-specific demand shock and firm
distress. The paper finds that aggregated demand and specific demand
shocks increase firm distress risk while supply shocks reduce such risk.
Furthermore, the findings suggest that energy diversification mitigates
the impact of specific demand shocks on firm distress. The paper also
implements several robustness checks, and the results remain the same.
Potential policy implications are also discussed.

3 - Enhanced electricity prices forecasting from spillovers
among commodities

Giovanni Bonaccorsi
The deregulation of electricity markets has emphasized the importanceof accurate daily electricity price forecasting (EPF) due to electricity’s non-storability and the necessity for day-ahead bidding to aligndemand with production. Accurate EPF is crucial for utilities and consumers, enabling effective bidding strategies, adjusting consumption tobenefit from lower prices, and allowing producers to manage generation assets more efficiently. The challenge in EPF arises from the complex dynamics of electricity prices, influenced by factors like weather
conditions, business activity, and consumer habits, which introduce
volatility, seasonality, and unexpected prices surges. Despite the success of deep learning methods in the literature on EPF, recent global
events such as the COVID-19 pandemic and the Russo-Ukrainian war
have shown the need for more adaptable forecasting models. This paper proposes an adaptive Long short-term memory (LSTM) model that
responds to external shocks by monitoring a disruption signal basedon the spillover of shocks across different consumer categories. Thisapproach aims to enhance the accuracy of EPF by accounting for both
direct and indirect effects of external shocks on electricity prices, using a dynamic threshold to update the forecasting strategy based on
observed systemic changes. We demonstrate the better forecast performance of our approach and provide an application with a model ofenergy storage arbitrage.

4 - Applying Data-Driven Robustness for Weather year uncertainty to generate Robust Energy Systems

Sebastian Kebrich
Limiting climate change demands an extensive expansion of variable
renewable energy technologies. Capacity expansion models estimate
the necessary installations of these technologies. However, a major
challenge of energy systems based on these technologies are interannual weather fluctuations across weather years leading to uncertainties

169TA-15EURO 2024 - Copenhagenin capacity expansion as well as operation to match supply and demand. The presented study investigates the 38 government districtsof Germany using 40 years of weather data with hourly resolution to
calculate energy systems able to supply energy in all cases. Basedon computational experiments with weather data from multiple years,
we identify critical dark lull-patterns in weather years. We showcase
how such patterns can be incorporated into arbitrary reference years
by manipulating time-series data. The developed methodology takes
uncertainties in the capacity factor based on the weather data into account and weighs them relative to the demand. Large discrepancies
between capacity factors and demand indicate time periods with potential gaps between supply and demand. The resulting energy systems based on this manipulated weather year are then taken as input
for cross-validation to proof feasibility of the resulting systems. The
results show that already the inclusion of only a few, well selected time
periods leads to robust energy system models with minor optimality
gap and can ultimately to a substantial decline in calculation time.

an iterated greedy heuristic to solve large instances. We implement
different construction and destruction procedures, restart and acceptance strategies, and also a local search method to improve solutions.
Based on these components, we build an algorithm framework capableof generating different versions of iterated greedy heuristics. We evaluate both the exact and heuristic approaches to real-world instances oflocation and allocation of examination equipment (e.g. mammography
units). We use a parameter configurator to automatically search forthe best iterated greedy heuristic to solve these instances, producing
high-performing algorithms. The heuristic produces near-optimal solutions while solving larger instances that cannot be solved exactly ina reasonable time. Besides that, the proposed approaches can support
decisions in healthcare, allowing better use of available resources andimproving people’s access to healthcare.

3 - Mathematical Models for New Hemodialysis Center Location Determination and Patient Scheduling

Gercek Budak, Gökçe Sabriye Horuk, Berfin Osedya Menlik,
Aleyna Ağırman

 TA-15
Tuesday, 8:30-10:00 - Room: 18 (building: 116)

Location planning in healthcare
Stream: OR in Health Services (ORAHS)
Invited session
Chair: María Merino
1 - A Hierarchical Compromise Model and Matheuristic Algorithm for Stochastic Location-Allocation in Healthcare

María Merino, Imanol Gago-Carro, Unai Aldasoro, Dae-Jin
Lee
In critical situations, the precision of emergency medical service(EMS) responses is of highest importance, as it can significantly impact patient outcomes. The effectiveness of EMS operations relies
heavily on the strategic positioning and allocation of ambulances. This
study addresses the ambulance location-allocation challenge in theBasque Country (Spain), where a fleet of 90 ambulances, including
both Basic and Advanced Life Support resources, serves a population
exceeding 2 million residents.
To tackle this issue, we leverage historical data and use a Box-Cox
Cole and Green distribution to forecast response times. We propose
a two-stage stochastic mixed 0-1 linear programming model aimed at
optimizing the primary objective of maximizing expected coverage.
Additionally, the model accounts for various secondary objectives, including minimizing average response time and incorporating risk aversion measures such as Conditional-Value-at-Risk, within a hierarchicalcompromise framework.
Due to the computational complexities inherent in this model, we introduce a novel matheuristic algorithm that combines primal decomposition and machine learning techniques. Computational experiments
on medium and large-scale instances exhibits promising performancein efficiently addressing the intricate EMS optimization problem. This
research contributes to enhancing the effectiveness and responsivenessof EMSs, leading to improved patient care during critical emergencies.

2 - Improving the location and allocation of healthcare resources

Marcelo de Souza, Tailini Schultz, Lucas Pietro Biasi Rayzer
The appropriate location of healthcare resources and a proper allocation of patients are essential to ensure access to healthcare for as many
people as possible. Examples of healthcare resources include examination and treatment equipment, and healthcare facilities, such as emergency centers. We propose an integer programming formulation for thelocation-allocation of healthcare resources based on the p-median location problem with additional constraints. We aim to minimize the average distance between patients and resources. Besides that, we present

170

Location selection and assignment problems, which are of great importance in healthcare systems, are strategic decision problems. In
location selection, topics such as the capacity of the sectors, the market area where the sector is located, the performance of the systemand efficiency affect the decision maker’s choice. This study initializes to determine the location of the service center where the most
preferred hemodialysis treatment is going to be offered in the treatment of Chronic Renal Failure (CRF), which is increasing rapidly,
and on the assignment of patients to hemodialysis centers. Decision
problems have been identified by different decision makers regarding
CRF, which has morbidity and mortality values. Among the decision
problems listed from the perspective of different decision makers, themathematical model proposal for the new hemodialysis location determination determined as the subject of the study. In the problem,
it is aimed to optimize service access to hemodialysis patients withthe hemodialysis center planned to be opened by determining the area
where hemodialysis treatment is most needed with the land allocation
model. The aim of the study was to provide hemodialysis services at
a minimum distance with different variations to hemodialysis patientsin Ankara, Turkey which was selected as the pilot region. Four different objective functions are designed as the decision maker may havedifferent preferences.

4 - Multiobjective robust optimization approaches for personalized medicine supply chain network design underfailure rate uncertainty

Meng Wan, Songsong Liu, Richard Allmendinger
Nowadays, the development and practice of personalized medicine become strategically important. It focuses on individual patient needs
and conditions, and due to the shelf life of personalized medicine, it
has high requirements on temperature in production and transportation.
Designing a stable personalized medicine supply chain network to efficiently complete the manufacturing and delivery of drugs is crucial
to reduce supply chain costs and improve overall efficiency to promote
positive industry development. A multi-period, multiobjective production and distribution network of personalized medical supply chain isestablished in this paper, considering distribution, manufacturing, logistics, time and logic constraints, to minimize cost and time and maximize demand coverage. With the influence of manufacturing failure
rate as an uncertainty factor, the robust optimization method is used
to deal with the uncertainties. In this paper, two clustering-based decomposition methods are proposed as the solution approaches, which
are shown to solve models more efficiently than the proposed MILP
model. Monte Carlo simulation is used to validate the performancethe robust optimization approaches. Finally, the proposed model is extended to consider the fairness on waiting time among patients. The
research outcomes could help to improve the operational efficiency ofthe personalized medicine supply chain and promote the sustainable
development of the personalized medicine industry.

EURO 2024 - Copenhagen TA-16
Tuesday, 8:30-10:00 - Room: 19 (building: 116)

Sports scheduling and optimization
Stream: OR in Sports
Invited session
Chair: Dries Goossens
1 - Optimisation for the Fantasy Football League

John Wilson, Mehmet Begen, Vineet Yagnik
Fantasy Premier League is a "play along" fantasy game that follows
the real-world events of Premier League games. It allows Premier
League fans to compete against each other in their knowledge of players and teams. Players can set their own teams of players in the Premier League, who then earn points through their real-world performance in the Premier League. There is a substantial body of researchon American Football Fantasy Leagues. However, possibly during to
the low scoring nature of soccer as compared to American football, theFantasy Premier League has not drawn as much attention. We show
how to incorporate publicly available information into frequentist andBayesian statistical models that will be used in optimization model toselect teams for each week.

2 - Strategies for surviving single elimination tournament
knockout pools

John Trono
In a typical knockout (KO) pool, each entrant must select a winningteam every week of the regular season. If they fail to do so, they nolonger have a chance of winning that pool since that loss (or tie), by
their chosen team, has removed them from the list of entrants who arestill alive in that pool. Also, each entrant is allowed to select a teamjust once during that season. If this style of pool is extended to a single elimination tournament (SET), like Wimbledon, the World Cup,NCAA’s March Madness, etc., then different players/teams must be
taken in each round. Strategies for outlasting every other entrant insuch a KO pool will be considered in this talk, and results from recent
March Madness tournaments will also be examined. These strategies
are quite different from those that may typically be utilized during a
regular season KO pool as now one hopes to survive and advance pasteach round (where significantly fewer choices are available after eachround, which is not the case in a weekly KO pool). Therefore, outlasting all other entrants (who have not already been knocked out) is muchharder to achieve. (Many of these SET KO pools require 2 or morechoices per round - at least until the semi-finals have been reached.)

3 - Scheduling Bolivia’s First Division football season by
integer programming

Gonzalo Zamorano, Guillermo Durán, Mario Guajardo, Denis
Saure
In this talk we describe our work in scheduling the main professional
football league of Bolivia. Following existing work in sports scheduling, we develop an integer programming approach for the season
schedule in its two tournaments, the first one organized by groups andthe second one, a double round robin scheme. In addition to the traditional problem of scheduling games to rounds, we also schedule thegames to specific dates and time slots within each round. Also, besides
usual considerations such as league format, game broadcasting, andinternational tournament calendars, an important aspect of this league
is the geographical location of the teams, which are spread around regions with extreme differences in altitude. Our work has been implemented in practice to schedule the 2024 season of the league. This is
the fourth soccer league in South America (Chile, Argentina, Ecuador,
Bolivia) programmed by the group of authors of this work.

4 - Beyond leagues: a single incomplete round-robin tournament for multi- league sports timetabling

Dries Goossens, Miao Li, David Van Bulck

TA-18

Most sports associations regularly face the problem of determining
and scheduling games for a huge number of non-professional (youth)
teams. During planning, it is key to respect venue capacities and minimize travel distance. A classic approach is to split up teams overleagues, and then having each league play a round robin tournament.
In a round robin tournament, each team competes against every otherteam in the tournament an equal number of times. In this talk, we
propose an alternative approach, organizing a single yet incomplete
round robin tournament. In this format, each team plays the same number of games, but teams are not required to face the same opponents.
We exploit this flexibility to reduce the total travel distance and venue
capacity conflicts. Besides integer programming models, we provide
theoretical results and an iterative two-phase decomposition heuristic.
This heuristic first determines the home-away status of teams basedon the venue capacity of clubs, and next selects suitable opponents
while minimizing travel distances. Extensive experiments using reallife benchmark instances from the literature confirm the advantage ofan incomplete round robin tournament compared to the classic multileague approach and validate the effectiveness of the proposed heuristic.

 TA-18
Tuesday, 8:30-10:00 - Room: 42 (building: 116)

Optimization of sustainable urban mobility
Stream: Sustainable Cities
Invited session
Chair: Luciano Costa
1 - Greedy heuristic for solving real size instances of thebicycle network improvement problem

Emmanuel Neron, Felix Repusseau, Tifenn Rault, Matteo
Doyen
We are interested in the bicycle network improvement problem. Let a
set of paths obtained from GPS traces represent the cyclist’s path ona graph. This graph consists of nodes, i.e. intersections, and arcs, i.e.
roads with or without bicycle facilities. Thus, for each arc, we get thedanger as a function of its length, the bicycle facilities, and the maximum car speed. A user’s cost is given by the cost of his best path,
while the cost of an arc is given by a linear combination of its distanceand its danger, depending on his own preference. The user’s preference
between danger and distance is also given. We focus on where cycling
facilities must be placed to minimize the sum of users’ costs. Eachtime a cycling facility is decided, it costs money (depending on thelength of the arc to be improved), and the total budget is limited. This
problem is NP-hard. We present a greedy heuristic. At each step of thealgorithm, we evaluate for each arc the impact on each user of upgrading that arc. We choose the arc that maximizes this impact on the sumof the users’ costs. Using two Dijkstra algorithms (one forward, one
backward) per user allows us to compute this impact efficiently. Moreover, this algorithm can be easily parallelized (the phase of computing
the two Dijkstra algorithms per user) using multithreading. Some real
instances (40 000 nodes and more that 20 km of plant proposal) can betreated in a few seconds on some classical desktop computer.

2 - OPTIDENS : A Mathematical Programming Approach forthe 15-minute city model

Serigne Gueye, Cyrille Genre-Grandpierre, Jean-Christophe
Mounier
The 15-minute city model proposes a redesign of the urban system toprovide access to daily needs or destinations (work, food, health, education, culture and leisure) within a 15-minute walk, cycle or public
transport journey. One of the main objectives is to reduce car dependency and it’s side effects. The 15-minute may be implemented bythinking a city as a possibility of choice leading to a Social Interaction
Potential (SIP). That is the possibility of easily reaching numerous and171TA-19EURO 2024 - Copenhagen

diverse people; a potential of jobs or any other amenities. The problem
is that the high level of these potentials, which depend on density andtransport speed, has been achieved for decades mainly thanks to the
speed of the car. Cities are currently built on temporal proximity (bycar) rather than physical proximity, which generates car dependency.
In order to move towards a more sustainable mobility, it is imperative
to return to more physical proximity, to a functioning based more on
short distances, which is the idea of the 15-minute city. The question,
then, is how to return to an urban functioning based on short distances
without abandoning the essence of cities, in particular a high level of
SIP. We have developed an optimization model, called OPTIDENS,
which allows to find (if possible) the suitable location (or relocation)of any set activities in a city, in such way to respect a given SIP at
the lowest travel speed. Some numerical results on data from real-lifecities will be given.

3 - Optimizing the Determination of Criteria for Cyclists’Route Selection

Rafael Praxedes, Stefano Ardizzoni, Luca Consolini, Mattia
Laurini, Marco Locatelli, Irene Saccani, Anand Subramanian
The promotion of sustainable transportation modes, such as cycling,
has emerged as a crucial aspect of urban planning and design. The
design of an efficient bicycle network is a complex optimization problem that requires analysis of different factors including infrastructure
layout, connectivity, safety, and accessibility. However, before building the network, it is fundamental to understand the behavior of its
users. The fastest routes are not always the preferred options for cyclists, who consider different criteria when choosing the best routes
from their origin to their destination. Therefore, the aim of this study
is to analyze the cyclists’ choice behavior in the city of Parma, Italy.
This analysis consists of defining a set of user profiles, where eachprofile corresponds to a combination of criteria used by the cyclist tochoose a route, such as shortest time, safety, and connectivity. We
proposed a bilevel formulation and developed a hybrid method to address this problem, which combines the solution of multiple shortest
path problems with a heuristic method to determine the profiles. Thisapproach takes advantage of solving the problem in a closed form bycomputing beforehand as many shortest path solutions as the numberof profiles and then solving a simple quadratic programming problem.
We validated our method using both randomly generated and real datacollected from bicycle sharing companies in the city of Parma.

4 - Optimal design of an electric carsharing system under
uncertain trip duration and energy consumption

Céline Gicquel, Mouna Kchaou-BoujelbenElectric carsharing systems allow individuals to borrow electric cars
on a short-term basis for a rental rate charged by the time or the distance driven. These systems are seen as a promising way to both lower
the CO2 emissions linked to urban mobility and reduce urban congestion through a decrease in the number of privately owned cars parked incities. We focus on the optimal long-term design of a station-based carsharing system in which shared cars should be picked-up and dropped
off by customers at stations. More precisely, we seek to determine thenumber of chargers to build in each station and the size of the vehicle
fleet to deploy in the network. One major difficulty here is that to bepractically relevant, the modelling of this strategic problem should be
able to estimate the future revenue that one may get by operating the
proposed system on a daily basis. Our model should thus consider operational decisions such as trip request acceptance, car dispatching tostations, intra-day car charging and relocation. Furthermore, in practice, uncertainties on the trip duration and energy consumption may
prevent cars from being available when and where needed with a sufficient battery state of charge. To handle this difficulty, we first propose
a new scenario-based two-stage stochastic programming approach. We
then seek to assess the value of a stochastic solution by discussing numerical results carried out on randomly generated instances.

172 TA-19
Tuesday, 8:30-10:00 - Room: 44 (building: 116)

OR in Gas Networks
Stream: OR in Energy
Invited session
Chair: Daniel De Wolf
1 - Integrated Planning of Electricity and Gas Networks for
the Energy Transition Using Stabilized Benders’s Decomposition

Christian Perau
Integrating increasing capacities of renewable energy sources necessitates sector coupling. The coupling between the electricity and gas
sectors is critical since they can be coupled bi-directionally via electrolyzers and gas turbines. Furthermore, the natural gas system must
either be defossilized or the energy carrier must be switched to prevent
greenhouse gas emissions. Hydrogen is expected to partially replace
the currently used natural gas. Different infrastructure elements can be
repurposed to transport hydrogen, which could reduce infrastructure
costs significantly. Currently, the different systems are planned independently. However, planning the system using an integrated model
might lead to significant cost reductions due to exploiting synergies
between the systems. The integrated optimization could also enhance
the system’s reliability and efficiency. We propose utilizing stabilized
Bender’s decomposition to optimize integrated energy systems, including the electricity, natural gas, and hydrogen sectors. This approach
improves performance by effectively decomposing the problem into
smaller, more manageable subproblems. Integrating bundle methods
improves the convergence of Bender’s decomposition. We demonstrate the performance of stabilized Bender’s decomposition within acase study of an IEEE system. The complex integrated energy systemmodel can be solved close to optimality by adopting methodologicaladvancements such as stabilized Bender’s decomposition.

2 - On a Tractable Single-Level Reformulation of a Multilevel Model of the European Entry-Exit Gas Market with
Market Power

Julia Grübel, Veronika Grimm, Martin Schmidt, Alexandra
Schwartz, Ann-Kathrin Wiertz, Gregor Zöttl
We propose a framework that allows to quantitatively analyze the interplay of the different agents involved in gas trade and transport inthe context of the European entry-exit system. While previous contributions focus on the case of perfectly competitive buyers and sellers
of gas, our novel framework considers the mathematically more challenging case of a strategic and monopolistic gas seller. We present a
multilevel framework that is suitable to capture the sequential nature of
the decisions taken. We then derive sufficient conditions that allow forreformulating the challenging four-level model as a computationallytractable single-level reformulation. We prove the correctness of thisreformulation and use it for solving several test instances to illustrate
the applicability of our approach.

3 - The reorganization of the natural gas transport network
in Belgium following the cessation of gas deliveries
from the Netherlands.

Daniel De Wolf
The Belgian gas transport network is divided into two separate networks: the low calorific gas network from the Netherlands and thehigh calorific network with gas arriving from other producers. The
Netherlands has announced the gradual cessation of the delivery of
Dutch gas to Belgium. It is necessary to reorganize the transport ofgas. Compared to the existing model for the Belgian network, two important changes must be modeled: on the one hand, the disappearanceof the historic supplier will imply significant changes in gas flows in
the single network merger of the two old ones. This involves possibly
resizing of gas pipelines, reversing the direction of use of compression
stations or creating new ones.

EURO 2024 - Copenhagen
The other important change in the model is the objective function.
Historically, a single company purchased the gas and transported it.
Its main objective was therefore to minimize its gas purchasing costs.
This no longer corresponds to the current situation where the commercial function is separated from the transport function. We take into
account the new situation: namely the minimization of gas transport
costs.
We present real data of the new Belgian network and the new formulation to provide a case study for all future developments in the field ofgas network operations, an activity particularly subject to variations inrecent years in Europe.

TA-21

 TA-21
Tuesday, 8:30-10:00 - Room: 49 (building: 116)

Humanitarian aid provision and disposal
Stream: OR in Humanitarian Operations (HOpe)
Invited session
Chair: Dilek Gunnec
1 - Optimizing Disaster Relief Logistics in Chile:
Location-Inventory-Routing Framework

A

Lorena Pradenas, Dagoberto Cifuentes, Víctor Parada TA-20
Tuesday, 8:30-10:00 - Room: 45 (building: 116)

Military, Defense, and International Security
II
Stream: Military, Defense, and International Security
Invited session
Chair: Michael Bendersky
1 - Student Timetabling Optimisation for the Australian Defence Force

Chile’s susceptibility to natural catastrophes, encompassing earthquakes, tsunamis, and volcanic activities, is exacerbated by climatechange, resulting in heightened occurrences and increased severity of
wildfires, floods, landslides, and droughts. Within this context, optimizing logistics planning and routing is critical to ensure timely aid
delivery to affected communities. Our study introduces a novel routing model tailored to address unique geographical and disaster-related
challenges faced in Chile. Unlike existing approaches, our model integrates comprehensive inventory control and distribution hub placement, optimizing relief supply routes in natural disasters. We propose
a mixed integer linear programming (MILP) model to formulate a supply chain network strategically allocating distribution centers, regulating their inventories, and planning for ground and aerial transportationof relief goods. The efficacy of our proposed model is assessed throughexperimentation using real-world data sourced from various locationsin Chile that have experienced natural disasters.

Katie Mortimer, Terry Caelli
Personnel in the Australian Defence Force must complete a numberof training courses throughout their career. Currently, timetabling is
performed manually across hundreds of courses and associated sessions, resulting in inefficient or incorrect timetables. To address this,we have created an optimisation algorithm to improve timetabling efficiency and administrative workloads. For this problem, there are anumber of hard constraints, including: a maximum number of students
per session, students cannot attend overlapping courses, and a numberof fixed training pathways or course sequences. Soft constraints suchas prioritisation of students is also important. In this domain efficiency
is defined in terms of the total time delays in students being able tocomplete their training.
The algorithm uses a combination of Dynamic Programming (DP) and
Genetic Algorithms (GA) to create optimal timetables where DP is
initially used to find the optimal timetable for each student over course
pathways and session options - which decrease as each timetable isgenerated over the student priority queue. The GA is then used to,where possible, improve upon these initial solutions by considering
more efficient permutations and combinations of specific course andsessions over all the student DP solutions. The results showed a 45%
increase in efficiency, reducing the average student delay by approximately half compared to the manual solution, and in a fraction of thetime.

2 - Artillery Firing Shift with Two Registration Targets

Michael Bendersky

2 - Capacitated Mobile Facility Location Problem with Mobile Demand: Efficient Relief Aid Provision to En Route
Refugees

Dilek Gunnec, Amirreza Pashapour, Sibel Salman, Eda Yücel
Forced displacement entails relief aid distribution efforts among enroute refugees to alleviate their migration hardships. This study aims
to assist humanitarian organizations in cost-efficiently optimizing logistics of mobile facilities to deliver relief aid to transiting refugees ina multi-period setting. The problem, referred to as the Capacitated
Mobile Facility Location Problem with Mobile Demands (CMFLPMD), involves refugee groups following specific paths and receiving
aid at least once every fixed number of consecutive periods. The overall costs associated with capacitated mobile facilities are minimized.
We formulate a mixed integer linear programming (MILP) model andpropose two solution methods: an accelerated Benders decomposition
approach and a matheuristic algorithm relying on an enhanced fixand-optimize agenda. We evaluate our methodologies using realistic
instances based on the 2018 Honduras migration crisis. Numerical results reveal that the accelerated Benders decomposition excels MILP
with a 46% run time improvement on average while acquiring solutionsat least as good as the MILP. Our matheuristic acquires high-quality solutions with a 2.4% average gap compared to best-incumbents rapidly.
Sensitivity analysis highlights the managerial advantages of implementing CMFLP-MD solutions.

3 - The Stochastic Location-Routing Problem with TruckDrone Tandem for Humanitarian Aid Delivery

Hannan Tureci Isik, Melih Celik, Ece Sanci
Firing shift is the shifting of artillery fire from one target (registration) to another by the application of corrections determined from theadjustment of the initial firing data for the first target to the second (application). In this paper, a shift method based on simultaneous firing at
two different registration targets rather than a single one is introduced.
A method is presented for using the observed data to explicitly solve
for four major environmental parameters that affect ballistic trajectories and subsequently update them for the application mission. The
distribution of the remaining errors is determined and compared withthat of other correcting algorithms. Numerical examples show the potential of the proposed method.

Timely response is crucial in humanitarian logistics to reduce suffering and loss. Network disruptions after a disaster, or due to unreliable networks, can cause prolonged travel times or isolate locations.
These uncertainties necessitate a systematic approach to anticipate
challenges in humanitarian logistics and ensure timely aid delivery.
We propose a novel network design problem that strategically locates
depots to enable efficient post-disaster deliveries. We incorporate thepost-disaster uncertainties through stochastic travel times on the road
network and formulate the resulting stochastic location-routing problem as a two-stage stochastic program. We explore the integration ofdrones with ground vehicles to overcome the network inaccessibility
and long travel times on the road network to improve response times.

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TA-22EURO 2024 - Copenhagen

We focus on independent truck-drone deliveries from depots to transport lightweight aid bundles, such as medical supply kits, weather protection items, or communication tools. We develop a tailored heuristic
based on variable neighbourhood search and compare its performancewith CPLEX in small instances. We implement the heuristic in a casestudy of the Van Earthquake 2011, Türkiye. Through this implementation, we evaluate the value of the stochastic modelling approach andthe tandem delivery model and perform sensitivity analysis. Our results show that the use of drones can significantly enhance delivery
times by more than 50% in our case study.

4 - Disposal mechanism design for relief supply recycling
in post-disaster humanitarian logistics

Anqi Zhu, Yu Fan, Xihui Wang
Relief supplies are crucial in response to a disaster. However, current
studies focus on the raise, delivery and allocation of relief supplies during the process of disaster relief, and it remains uncertain that how todeal with the recyclable relief supplies after responding to a disaster.
Motivated by the field investigation and interview, in this paper, we
aim to design a disposal mechanism to help the local authority decide
the optimal value level, which determines whether the relief supplies
should be stored for future use or disposed through an auction. Furthermore, we consider whether the local authority can benefit morefrom processing (e.g., classification and cleaning) the relief items for
auction. A case study based on the practical background in China has
been conducted to prove the feasibility of our mechanism. The resultshows that through our disposal mechanism, the local authority canachieve a balance between reducing the high inventory cost and avoiding potential waste of useful relief items, which gains more benefit andfits for the Sustainable Development Goals.

 TA-22
Tuesday, 8:30-10:00 - Room: 81 (building: 116)

Optimization of energy storage systems
Stream: Energy Management
Invited session
Chair: Emre Nadar
1 - Pumped hydro energy storage: structural results andsolution algorithms

Emre Nadar, Harun Avci, Ece Cigdem Karakoyun, Ayse Selin
Kocaman, Parinaz Toufani
We study the energy generation and storage problem for a very general configuration of a pumped hydro energy storage (PHES) facility
having two connected reservoirs fed by natural inflow. The operator
decides in real-time how much water to pump or release in the PHES
facility, thereby determining the amount of electricity to buy from orsell to the market. We model this problem as a Markov decision process under uncertainty in streamflow rate and electricity price. We
prove the optimality of a state-dependent threshold policy under positive electricity prices: The state space can be partitioned into several
disjoint domains, each associated with a different action type, such that
it is optimal to bring the total amount of water in the PHES facility andthe amount of water in the upper reservoir to a different pair of statedependent target levels in each domain. Leveraging our structural results, we develop a policy-approximation algorithm as a heuristic solution method when the electricity price can be negative. This algorithm
yields near-optimal solutions in our data-calibrated instances one order of magnitude faster than the standard dynamic programming algorithm. Modifying this algorithm by imposing partial-state-dependent
target levels leads to up to 29 times faster solution times and reduces
total cash flows by only 1.32% on average from optimal.

2 - Energy Storage System Design and Management for
Renewable Supply and Demand

Gülin Yurter, Ayse Selin Kocaman, Emre Nadar
Renewable energy sources have gained prominence in reducing the dependency on fossil fuels and minimizing their negative environmental
impacts. Considering renewables’ uncertain and variable nature, it has
become necessary to design and manage hybrid energy systems. We
study the optimal design and management problem for hybrid energy
systems involving a pumped hydro energy storage facility and a wind
farm. We first develop two-stage stochastic programming models tofind the optimal configuration and sizing decisions. We then utilize aMarkov decision process model to find the optimal energy generationand storage decisions. Using real-life data and considering economic
benefits, we investigate how hybrid systems can mitigate the negative
effects of uncertainty in supply and demand.

3 - Operational benefit of transforming cascade hydropower stations into pumped hydro energy storage
systems

Parinaz Toufani, Emre Nadar, Ayse Selin Kocaman
This study evaluates the potential benefit of retrofitting existing conventional cascade hydropower stations (CCHSs) with reversible turbines so as to operate them as pumped hydro energy storage (PHES)
systems. We examine the energy generation and storage problem for
a CCHS with two connected reservoirs that can be transformed into
a PHES system in a market setting where the electricity price can be
negative. We formulate this problem as a stochastic dynamic program(SDP) under uncertainty in the streamflow rate and electricity price.
We analytically derive an upper bound on the profit improvement thatcan be obtained from the PHES transformation. We conduct numerical experiments with data-calibrated time series models and observethat the PHES system provides a greater benefit under more limited
streamflow conditions or more frequently observed negative prices.

4 - Optimal operation of a residential smart grid with shortterm and long-term storage

Eléa Prat, Pierre Pinson, Richard Lusby, Riwal Plougonven,
Jordi Badosa, Philippe Drobinski
To enhance buildings’ energy independence and carbon neutrality,
technologies like energy storage and local renewables are increasingly
deployed. Among these, long-term geothermal storage for residentialbuildings is emerging and poses new challenges in multi-timescale optimization. We investigate the operation of a novel residential smart
grid, based on a real project. It integrates electricity and heat systems
via a heat pump and includes short-term electricity storage, long-term
thermal storage, photovoltaics, and solar thermal assets. While theoperation is optimized daily, seasonal considerations are crucial, especially to ensure that the thermal storage fills during summer. The
question then is how to account for long-term effects in the daily optimization. Our study relates to two key domains of operations research:
inventory management and resource allocation. Utilizing a year-longreal dataset, we explore different methods to guide the energy storage levels by day’s end. These methods range from historical databased thresholds to rolling-horizon strategies, assessing the impact of
optimization window length. We also evaluate the relaxation of endof-horizon constraints using penalties. We analyze the strengths andweaknesses of each method, acknowledging the challenge of unreliable long-term forecasts. These insights facilitate decision-making forresidential energy systems and provide valuable implications for capacitated warehouse problems in other contexts.

 TA-23
Tuesday, 8:30-10:00 - Room: 82 (building: 116)

Policy and legislation for a circular economy
Stream: Circular Economy, Remanufacturing and Recy-

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EURO 2024 - Copenhagencling
Invited session
Chair: Songsong Liu
1 - Incentivising-sharing-enabled optimal placement strategy on a reusable transport item rental platform

TA-24

production. For each case study, we point out if the legal CE innovation is an emerging research field that needs to be investigated or if the
OR community has picked up the problem.

4 - A review of the synergistic role of optimisation, gametheory, and policy interventions in making the fashionindustry circular

Min Guo, Hing Kai Chan, Xiang T. R. Kong

Marjan Olfati, Afshin Mansouri

A rental platform offering customised reusable transport items (RTIs)
grapples with placement challenges impacting RTI availability and usability. Notably, RTIs often stay idle in customer warehouses, adding
no value. Thus, incentivising the business customers’ sharing and optimising the platform placement decision are worth further research,
but have not been well discussed in previous literature. Therefore, thisstudy introduces an optimisation decision framework aimed at encouraging B2B sharing and refining RTI placement and customer incentives, innovatively using empirical and optimisation models. A quantitative model evaluates business customers’ willingness to share (WTS)
under varied incentive schemes and RTI needs. Incorporating WTS,
we develop a bi-objective optimisation model with a large-scale heuristic algorithm to determine optimal RTI purchasing and sharing incentives pricing. Our real-case analysis illustrates that an RTI-sharing
strategy reduces operational costs by 18.5% and boosts customer welfare, achieving a win-win scenario. Additionally, our findings suggest
the platform’s strategies on sharing incentives and resource procurement should adapt based on customer WTS preference, RTI reusability, and holding durations. This research could extend to similar rental
networks or sharing platforms, underlining the significance of strategic
resource placement and sharing incentives.

Dealing with post-consumption clothing products is key to make thefashion industry circular. This will require effective reverse logistics
to support the Re-x processes of the circular economy. The design ofeffective reverse logistics networks is a multifaceted task that need tocater for the different stakeholders and multiple decision criteria. This
paper provides a systematic overview of the literature in the context ofthe fashion industry in three key areas: (i) reverse logistics, (ii) circular
economy, and (iii) policy interventions. The focus of the first is optimisation and operations research models as well as game theory models.
The second area looks into the application of the circular economy
and sustainability. In the third area, the main policy interventions include extended producer responsibility (EPR), taxation, eco-labelling,
and waste management. A comprehensive search was conducted on
Web of Science. The managerial insights are proposed as follows:
Designing operations research models assists decision-makers to optimise transportation, profit, waste, and location-allocation of facilities.
Game theory models drive stakeholders to compete strategically, finding equilibrium in decision-making for resource allocation, costs, andpricing. Using game theory models can also exhort players (retailers,
recyclers, charities, and customers) to collaborate, earning profit andfostering a sustainable supply chain, minimising waste and maximising resource reusing.

2 - From a linear supply chain to a circular economy: The
impact of sustainable sourcing labeling regulations

Lena Silbermayr, Danja Sonntag, Martin Waitz
Across the clothing industry accounting for 60% of the world’s textile
waste, less than 1% of the used products undergo closed-loop recycling enabling the use for equal-value applications. This highlights the
problem of a highly linear supply chain in the textile industry, where
textiles are produced, used and mainly disposed of.
This paper contributes to the current debate on transforming the textile
industry into a circular economy. Taking into account the challenges
of a highly seasonal industry with high demand uncertainties, we combine inventory management practices to reduce waste with a systematic
analysis of the economic and environmental impacts of different sustainable sourcing labeling regulations. In particular, we focus on threepotential policies set by governmental regulators and their impact on
the manufacturer’s sourcing decision regarding traditional and chemically recycled feedstock. In the analysis, we incorporate the effectof the manufacturer’s purchasing decisions and governmental regulations about sustainable sourcing labeling on consumer demand driven
by consumer environmental awareness. We derive the manufacturer’s
optimal sourcing and production quantities and analyze their impact
on economic and environmental key performance indicators.

3 - Do upcoming EU circular economy legislations require
new OR applications?

Sonja Rosenberg, Frank Schultmann
Fostering and strengthening the circular economy (CE) with all of its
dimensions is one objective of the European Union to reach its goalof becoming climate-neutral by the year 2050. Therefore, the EU has
started with revision, extension, and the new development of circular economy regulation and guidelines. We provide a comprehensive
discussion that shows how operation research methods can underline
the goals of the upcoming regulations and serve as decision support
for actions of circular economy shareholders on the European market. Therefore, we develop case studies based on different European
regulations. The EU legislations under consideration include the EU
battery regulation, the directive on common rules promoting the repairof goods and the Eco-design requirements for sustainable products.
We investigate how legal CE innovations affect different supply chain
problems in case studies. Legal CE innovations include, for instance,
digital product passports or the obligatory usage of recycled content in TA-24
Tuesday, 8:30-10:00 - Room: 83 (building: 116)

Sustainability in Vehicle Routing
Stream: Sustainable Supply Chains
Invited session
Chair: Khakim Habibi
1 - Sustainable Group Traveling Purchaser Problem: Unlocking Value and Environmental Benefits with Bundle
Discounts

Zahra Sadat Hassanpourjesri
The Traveling Purchaser Problem (TPP) has been a central focus in
supply chain management, aiming to optimize the procurement process while minimizing total purchasing and transportation costs. We
consider a new variant of TPP, one that incorporates shared vehicles
among the purchasers and harnesses the power of bundle discount policy from suppliers. In our proposed structure, buyers can not only
use the capacity of each other’s vehicles but also integrate their demands, and by group purchasing receive their needed items throughbundle discounts at the lowest price. In a typical bundle discount policy, a buyer might be reluctant to buy the whole bundle’s items individually. Consequently, this study delves into the amalgamation ofdemands of different purchasers to harness the advantages of bundle
discounts. Meanwhile, each bundle contains some environmentallyfriendly goods that determine its sustainability score. The innovationof this paper provides an opportunity for purchasers to obtain significant cost savings by engaging in group purchasing bundles with thehighest sustainable scores. This allows them to procure eco-friendly
products while simultaneously benefiting from discounted prices. We
propose a tailored mathematical model for this new version of TPP,
which we then solve using an approximated heuristic algorithm. The
findings underscore the substantial role played by group purchasing
strategies in cost mitigation and the enhancement of sustainable procurement.

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TA-25EURO 2024 - Copenhagen

2 - Vehicle Routing Problem with Backhauls: An Application with Electric Vehicles

Haluk Yapicioglu
In Vehicle Routing Problem with Backhauls (VRPB) is a special typeof vehicle routing problem in an effort to reduce the overall cost of
shipment from/to a depot. In VRPB, customers are divided into two
categories as linehaul customers and backhaul customers. A vehicle
that starts from depot first delivers goods to linehaul customers, thenbring goods from backhaul customers to the depot on the way back. In
this study, we tackle the VRPB problem with electric vehicles. Electricvehicles require special attention in route planning as they also require
stops for charging. To this end, we developed a two-stage optimization procedure which constructs the routes for delivery and backhauls
and determine the location of EV charging stations by taking into account both constructed routes and charging needs of the vehicles. The
method is presented on an example dataset received from a manufacturing company located in Eskisehir, Türkiye.

3 - Sustainable Routing and Perishable Inventory Optimization under Demand Uncertainty for a 2- stage Divergent Supply Chain: A MILP-Based Hybrid Modelling
ApproachDevansh Rai, Chandrasekharan RajendranIn the dynamic landscape of perishable product management, efficientinventory optimization is crucial for minimizing waste and maximizing profitability. This research focuses on enhancing inventory optimization for perishable products in a 2-stage divergent supply chain
involving a distributor and multiple retailers. The study proposes anovel approach that integrates sustainable inventory routing and inventory ordering policies to address challenges in inventory management. The model approach is a two-stage stochastic optimization problem with a multi-period perspective. The framework considers pricedependent demand, perishable product characteristics, back-ordering
at the distributor, lost-sales at the retailers, demand uncertainty, agebased markdown pricing, and distributor-led inventory rationing. The
research introduces a comprehensive policy and routing framework
combining innovative inventory ordering policies and routing strategies. The proposed hybrid policy will be benchmarked against established inventory models, including (s,Q), (s,S), Haijema (s,S,q,Q), andother continuous review policies, all integrated with inventory routing. The new inventory policy can facilitate informed ordering decisions, preventing overstocking and understocking, thereby addressing
real-world uncertainties and practical constraints in perishable productsupply chains. Additionally, a heuristic method can be developed toprovide efficient solutions within a limited timeframe.

4 - Bi-objective sustainable crowdshipping with multitypes of occasional drivers

Khakim Habibi, Daniel Sanchez Pineda, Audrey Cerqueus,
Alexandre Dolgui
Being competitive in last-mile delivery is a key factor for retailers,considering both cost and customer satisfaction. Crowdshipping addresses this by using ordinary people making deliveries on their habitual routes, being presented as a sustainable option. Yet, industrial
initiatives have shown that it does not imply to be sustainable as compensation can attract more people, executing deliveries out of their normal routes. Our work introduces a crowdshipping problem optimizing both cost and CO2 emissions in a deterministic approach, referred
to as the Bi-Objective Vehicle Routing Problem with Multi types ofOccasional Drivers and Time Windows (BO-VRPMODTW). It generalizes the participation of Occasional Drivers (ODs) using different
types of vehicles (fueled and non-fueled). The compensation policy
defined assigns higher compensation to the non-fueled type. An epsilon constraint approach and a Column Generation-based epsilon approach were developed by considering short instances of clustered andrandom clients. The first experiments exhibit a competitive CPU time
obtained by the latter approach. Also, it solves small and medium instances with an optimality gap smaller than 5%. Managerial insights
on the impact of OD usage cost and CO2 are provided.

176

 TA-25
Tuesday, 8:30-10:00 - Room: 011 (building: 208)

Applications of combinatorial optimization I
Stream: Combinatorial Optimization
Invited session
Chair: Dominique de Werra
Chair: Reinhardt Euler
1 - Dispatching medical samples: a case study

Jean-Charles Billaut, Hélène Blasco, Marina Vinot
Amyotrophic Lateral Sclerosis (ALS) is a rare fatal motor neuron
disease that causes degeneration of the upper and lower motor neurons. No diagnosis and prognosis biomarkers are identified to help
patients’ management and research in this field is highly active. Different ALS centers are in charge of patients’ care in France with visits
every 3 months for clinical and biological explorations. Many research
projects are based on biological fluids samples exploration and involve
different centers hospitals as ALS is considered as a rare disease. Consequently, the need to centralize samples in the specialized laboratories in charge of specific biological analyses is crucial. The questionis how to organize the flow to optimize the use of these precious samples. Several biological fluids (blood, cerebrospinal fluid, etc.) arecollected from each patient in each cohort in several tubes of varying
quantities. Each product must reach the other centers, which express
needs for each product. Three constraints complicate this (sort of graphflow) problem: (1) dividing a sample into several portions is a tediousaliquoting operation, (2) a shipment requires a freeze/thaw operationand (3) a patient sample cannot arrive in two different tubes at the samesite. We minimize the maximum number of aliquoting operations andlimit the number of freezing operations. We model this problem, showthat it is NP-complete, propose an MILP model, a lower bound and aheuristic algorithm.

2 - A new class of premature, partial latin squares

Reinhardt Euler
A partial (i.e., partially filled) latin square PLS is called premature, if
it is not completable to a full latin square (of the same order) but completable to such a latin square after deletion of any of its symbols. The
complete knowledge of this kind of PLS would give an answer to thecompletability problem for latin squares in the following sense: a PLS
can be completed if and only if it does not contain a premature PLS
as a subsquare. One may also think of a hypergraph whose maximal
independent sets are given by the full latin squares of a given order
and whose hyperedges are to be described explicitly. We survey someknown examples for a premature PLS and present a new class obtained
from a combination of two well-known types.

3 - On the occurrence of optimally colored $K_t,t$ incanonical 1-factorizations of complete graphs

Sebastian Urrutia, Dominique de Werra
Recoloring methods applied to optimally edge colored completegraphs of even size are fundamental components of local search procedures aiming to obtain 1-factorizations with specific properties. Then,
there is an interest in finding in a 1-factorization, colored subgraphsthat allow a local recoloring to obtain a new 1-factorization. The occurrence of bichromatic cycles, lanterns and optimally colored complete subgraphs in certain classes of 1-factorization has been previously studied. In this note we investigate the existence of optimallycolored complete bipartite subgraphs in canonical 1-factorizations. We
conclude that, whenever $n-1$ is a prime number, no optimally coloredcomplete bipartite subgraphs with more than 4 nodes occur in canonical 1-factorizatoins of complete graphs with $n$ nodes. For othervalues of $n$ we show that optimally colored complete bipartite subgraphs with more than 4 nodes do occur and we show how to constructsome of them. For some small bipartite subgraphs the construction
given is proven to be unique.

EURO 2024 - Copenhagen
4 - On Minimum Euclidean Steiner Laman Graphs

Naoki Katoh, Hitomi Hayashi, Yuya Higashikawa, Takashi
Horiyama, Risa Ishikawa, Yuki Kawakami, Yuki Kobayashi,
Ayano Nishii, Junichi Teruyama, Yinfeng Xu, Azusa
Yamamoto
An Euclidean Laman graph is a geometric graph on n vertices in theplane with exactly 2n − 3 edges such that, for all k > 1, every k-vertex
subgraph has at most 2k − 3 edges. In this study, we introduce a variantof the well-known Euclidean Steiner tree problem, say the Euclidean
Steiner Laman graph problem (ESL for short). Given a point set P in
the plane, the goal is to find an Euclidean Laman graph of minimum
total length such that any point in P is contained as one of its vertices.
We also consider the problem of finding a plane (i.e., non-crossing) solution under the same setting, say the Euclidean Steiner plane Lamangraph problem (ESPL for short).
First, we prove an optimal solution for ESL has no crossing, i.e., an
optimal solution for ESPL is also optimal for ESL. Then, we design apolynomial time approximation algorithm that outputs a plane Lamangraph and guarantees an approximation ratio at most asymptotically
1.5+ with an arbitrarily positive .

 TA-26
Tuesday, 8:30-10:00 - Room: 012 (building: 208)

Applications to Logistics andTransportation
Stream: Combinatorial Optimization
Invited session
Chair: Francisco Saldanha-da-Gama
Chair: Sina Rastani
1 - Robotic Assembly Line Balancing Problem Considering
Uncertain Demand by Q-learning method

Yuchen Li
Coordinated decision making in manufacturing sector is crucial forbusiness development. In this paper, a multi-objective robotic assembly line balancing problem is considered, which has the following features:1) it involves a two-stage decision making process where the firststage is to design the robotic assembly line in terms of the task androbot assignment, and the second stage is to determine how muchto produce for each product model; 2) the demand for each productis uncertain; 3) two objectives are considered including the numberof workstations and carbon emissions. A reinforcement learning (Qlearning) method is devised to solve such problem. Computational
studies are performed to validate the performance of the proposed algorithm.

2 - Robust optimization models for single-leg and network
airline revenue management

Mustafa Pinar, Irem Bahtiyar, Ali Eren Demir, Itir Karaesmen
Effective capacity allocation methods play crucial roles in Revenue
Management. Yet, current methods for determining optimal capacity
controls under uncertainties, such as stochastic optimization, often assume a known distribution for unknown parameters. This assumption
may degrade the model’s performance when faced with unexpected
data patterns. This paper explores a novel approach through robust optimization formulations for addressing stochastic resource allocation
problems. We introduce heuristics based on these robust formulations
to derive actionable results. Through extensive simulations focused onseat allocation problems within the revenue management domain, ourproposed formulations demonstrate significantly improved worst-caseperformances. Notably, even under favorable scenarios, our solutions
remain comparable to existing methods in the revenue management
literature.

TA-27

3 - Solution approaches for multi-source capacitated facility location problems with fairness objectives

Gianfranco Guastaroba, Carlo Filippi, Juan José Salazar
González
Modern societies are developing an ever-increasing concern toward issues related to equity, often synonymously called fairness. In location
problems, scholars soon realized the importance of incorporating equity in siting decision models. Often a decision-maker has to locate
a set of facilities, which provide a service (or good) and are reachedby customers at their own costs. In such situations, improving systemefficiency requires minimizing the average cost paid by customers toreach a facility, while improving fairness requires minimizing the variability in the cost distribution. The latter is especially relevant in the
public sector, where providing customers with a fair access is a crucial
concern. We measure accessibility fairness by using the conditional
beta-mean, which can be intuitively defined as the average distancetraveled by beta% of the customers that travel the longest distances toreach the assigned facility. We show that incorporating such measure
into a classic multi-source CFLP leads to a non-linear MIP. Borrowing
some ideas from bilevel programming, we propose two linear reformulations of the latter MIP, and develop two solution methods that work
along the general lines of a cutting-plane algorithm. Computational
experiments show the effectiveness of the solution methods proposed.
Further, a trade-off analysis between efficiency and fairness show thatthe proposed approaches may be used as managerial tools to balancethese two objectives.

4 - A risk-averse latency location-routing problem withstochastic travel times

Francisco Saldanha-da-Gama, Alan Osorio-Mora, Paolo Toth
In this paper, a latency location routing problem with stochastic travel
times is investigated. The problem is modeled as a two-stage stochastic program, by setting the location decisions at the first stage, andthe routing decisions at the second one. A risk-averse decision-makeris assumed. An efficient and effective multi-start variable neighborhood search algorithm is proposed for tackling the problem. A sampling method is also presented for dealing with uncertainty captured
by a continuous probability distribution. Several insights are provided
based on the results of a series of computational tests performed toasses the methodological contributions provided by this work.

 TA-27
Tuesday, 8:30-10:00 - Room: 047 (building: 208)

Counterfactual Analysis Across Diverse
Domains
Stream: Mathematical Optimization for XAI
Invited session
Chair: Jasone Ramírez-Ayerbe
1 - Counterfactual Analysis for Explicability and Fault prevention of Complex Energy Systems

Martina Fischetti, Emilio Carrizosa, Juan Miguel Morales
Energy systems are often very complex systems, where different components interact in a not-trivial manner. In this context, identifying
faults in the system and especially identifying their reason can be acomplex engineering problem. Take for example an offshore wind
farm. As offshore wind turbines can be very expensive to reach, faults
are very expensive to handle. Therefore it is very important for energy
companies to reliably identify real alarms. For this reason, many companies are nowadays using ML to identify failures. We would like tolook a step further: we want to understand how the status of the turbine
should be changed not to have a failure. We will use Counterfactual
Analysis, once a classifier has been trained, to identify how records
should be changed in their features to being classified in the "good"177

TA-28EURO 2024 - Copenhagenclass. Finding counterfactual explanations amounts to solving a mathematical optimization model, where we want to minimize the numberof changes (or the total cost associated to changes) to an instance tomaximize its probability of being classified as a good instance, subject
to different constraints. We will present some preliminary results on
the topic

2 - Contributions of surrogate models and counterfactual
explanations to marketplace blackbox audits

Benoit Rottembourg, Jeanne Mouton, Jordan Thieyre
In a global context where competition authorities are investigating
and sanctioning Amazon marketplace for practices of self-preferencing
at the expense of their business users and consumers, we observe a
trend of imposing remedies on dominant players in digital markets.
Therefore, competition authorities and regulators need tools to audit
the compliance of these dominant players in the e-commerce sector
over the obligations and remedies they are imposing on dynamic, andpersonalized algorithms. Most of these algorithms embed MachineLearning components, introducing opacity and potentially biases in thedecision-making process.
The aim of our presentation is to explore the benefits of using blackbox auditing techniques and counterfactual explanations to provide insights into the behavior of these online algorithms. We anchor our
research in the literature of product preeminence from vertically integrated players, of choice ranking, and of the specific literature related
to Amazon search ranking, automatic pricing and Buy Box’s algorithms ([1];[2]). Through a longitudinal study of the ranking of several
thousand products on Amazon, we will illustrate the potential of "surrogate" models and the decision-support elements they might provide.
[1] Chen et al "An Empirical Analysis of Algorithmic Pricing on Amazon Marketplace" 2016 [2] Gómez-Losada et al "Automatic Eligibilityof Sellers in an Online Marketplace: A Case Study of Amazon Algorithm" 2022

3 - Counterfactual Analysis and Target Setting in Benchmarking

Dolores Romero Morales, Peter Bogetoft, Jasone
Ramírez-Ayerbe
Data Envelopment Analysis (DEA) allows us to capture the complex
relationship between multiple inputs and outputs in firms and organizations. Unfortunately, managers may find it hard to understand a DEA
model and this may lead to mistrust in the analyses and to difficulties
in deriving actionable information from the model. In this paper, we
propose to use the ideas of target setting in DEA and of counterfactual analysis in Machine Learning to overcome these problems. We
define DEA counterfactuals or targets as alternative combinations of
inputs and outputs that are close to the original inputs and outputs ofthe firm and lead to desired improvements in its performance. We formulate the problem of finding counterfactuals as a bilevel optimization
model. For a rich class of cost functions, reflecting the effort an inefficient firm will need to spend to change to its counterfactual, finding
counterfactual explanations boils down to solving Mixed Integer Convex Quadratic Problems with linear constraints. We illustrate our approach using both a small numerical example and a real-world dataset
on banking branches.

 TA-28
Tuesday, 8:30-10:00 - Room: 065 (building: 208)

Fairness and responsible AI
Stream: Advancements of OR-analytics in statistics, machine learning and data science
Invited session
Chair: Tatiana Gridchina
Chair: Mikhael Nediak
Chair: Yuri Levin
Chair: Shimanto Rahman

178

1 - Fair mixed effects support vector machineJoão Vitor Pamplona, Jan Pablo Burgard
When using machine learning for automated prediction, it is importantto account for fairness in the prediction. Fairness in machine learning aims to ensure that biases in the data and model inaccuracies do
not lead to discriminatory decisions. E.g., predictions from fair machine learning models should not discriminate against sensitive variables such as sexual orientation and ethnicity.
A fundamental assumption in machine learning is the independenceof observations. However, this assumption often doesn’t hold true fordata describing social phenomena, where data points are often clustered based. Hence, if the machine learning models do not accountfor the cluster correlations, the results may be biased. Especially highis the bias in cases where the cluster assignment is correlated to thevariable of interest.
We present a fair mixed effects support vector machine algorithm that
can handle both problems simultaneously. With a reproducible simulation study we demonstrate the impact of clustered data on the qualityof fair machine learning predictions.

2 - A generalized voting game for categorical network
choices

Yueh Lin, Stefano Nasini, Martine Labbé
This paper presents a game theoretical framework for data classification, based on the interplay of pairwise influences in multivariate
choices. This consists of a voting game wherein individuals, connected through a weighted network, select features from a finite list.
A voting rule captures the positive or negative influence of an individual’s neighbours, categorized as attractive (friend-like relationships)
or repulsive (enemy-like relationships). Payoffs are assigned basedon the total number of matching choices from an individual’s neighbours. We show that our approach constitutes a natural generalizationof the K-nearest neighbours’ method, establishing the proposed gameas a theoretical framework for data classification. Computationally, weconstruct a mixed-integer linear programming formulation to approach
the Nash equilibria of the game, facilitating their applicability to realworld data. Our results provide conditions for the existence of Nashequilibria and for the NP-completeness of its characterization. On theempirical side, we use the proposed approach to impute missing dataand highlight its competitive advantage over the K-nearest neighbour’s
approach.

3 - Enhancing B2B customer retention: a streamlined
profit-driven approach using fairness-inspired preprocessing techniques

Shimanto Rahman, Bram Janssens, Matthias Bogaert
In the realm of Customer Relationship Management, customer retention is pivotal for sustained growth. The positive correlation between
customer satisfaction, retention, and profitability guides traditional
Customer Churn Prediction models to rank high-value customers lower
on average than their low-value counterparts. Ranking based on churnpropensity without considering the profitability of retaining specificcustomers may lead to suboptimal decision-making. This is especiallypronounced in Business-to-Business (B2B) industries. Various profitdriven metrics and algorithms address this, but their integration and extension demands considerable effort, hindering widespread adoption.
This paper proposes a streamlined approach, incorporating preprocessing techniques inspired by fairness literature, including Massaging, Reweighting, and Resampling. These techniques rectify theunderrepresentation of high-value customers within the targeted group,
improving churn campaign profitability. Preliminary results on two
B2B datasets show these techniques outperforming baselines.
The research contributes by proposing techniques to amplify the oftenunderrepresented high-value customer fraction within the targetedgroup. Framing the challenge as one involving the sensitivity of highvalue customers provides a simpler interface for improving churn campaign profit on par with existing profit-driven techniques.

EURO 2024 - Copenhagen
4 - Analysing business intelligence adoption through natural language processing: a study of online customer
testimonials

Masoud Fakhimi, Ashkan Lotfipoor, Alex Hagen-Zanker
Enterprises increasingly recognize the potential of business intelligence (BI) systems to enable data-informed decision-making. This
study offers a novel, large-scale analysis of online customer testimonials to illuminate successful BI adoption patterns. Our research leverages natural language processing (NLP) techniques for systematic andthorough analysis of these rich, yet unstructured, data sources. Employing a qualitative approach, we used Python to scrape approximately 1000 testimonials from ’Customer Stories’ sections of BI system websites (e.g., Power BI, Tableau, Knime). A large language
model (LLM), Llama 2, was used to systematically extract and analyse
key variables: motivations, implementation considerations, challenges,
security concerns, monetization, collaboration, dynamic capabilities,
and behaviour change. Our findings reveal common motivations for BI
adoption, typical implementation pathways, and recurring challenges
that organizations encounter. Additionally, the study uncovered how
successful BI implementations monetize their data-driven insights andfoster collaboration across teams. This customer-centric perspective
will provide actionable guidance for businesses considering BI investments, enabling them to anticipate potential roadblocks and maximize
the return on their BI initiatives.

TA-29

our wood waste intelligently could help to curb this phenomenon andhave a significant ecological impact. This wood waste can be in theform of beams or pallets and could be considered as wood slats. They
could be combined, assembled, and glued to create Cross-LaminatedTimber (CLT) panels for the construction industry.
We aim to develop optimization techniques to recycle raw wood waste
by providing the layout schemes to create CLT panels. The objective
can be either to maximize the CLT panel production or to minimize thewood surplus when a given set of panels should be built.
We conducted a literature review to identify relatively similar problems in the operations research field. The skiving stock problem andthe dual bin packing problem are the two closest problems to our. The
second’s name is quite misleading since our problem is technically nota dual version of the cutting stock/bin packing problem, but a problem
on its own as shown in the literature.
In this work, we propose a clear description of our problem and different mathematical formulations with cuts for the variants. The results ofvarious numerical experiments with field data from the wood industry
are presented. As it is an NP-hard problem, we identify the limit size ofthe instances for which the problem can still be solved in a reasonableamount of time.

3 - Planning for a Transition to a Low Emissions profile fora large Commercial Vehicle Fleet

Brian Dangerfield TA-29
Tuesday, 8:30-10:00 - Room: 157 (building: 208)

Applications of combinatorial optimisation
in industry and services I
Stream: Combinatorial Optimization
Invited session
Chair: José Fernando Oliveira
Chair: Adriana Cherri
1 - Multi-hospitals collaborative operating room scheduling with downstream capacity constraints

Yang Wang, Daiqiang Yin, Juanru Wang, Abraham Punnen,
Zhipeng Lyu
We investigate a new multi-hospitals collaborative operating room
scheduling problem with consideration of the downstream recovery
beds to make integrated decisions from tactical and operational levels.
For solving this challenging problem, we propose an effective multioperator driven iterated tabu search (MOITS) algorithm to achieve agood search balance between intensification and diversification. The
greedy initial solution construction procedure employs a priority scoring rule to sequence patients to obtain a high-quality initial schedule.
The multi-operator driven tabu search procedure employs four move
operators to manipulate surgeries in different OR time blocks, twomove operators to change recovery hospitals of the scheduled surgeries, and one move operator to change specialties of the OR time
blocks. New evaluation functions are designed for the recovery beds
related move operators to allow capacity violations during the search.
The elite set guided adaptive perturbation procedure uses historical information from a pool of best found solutions to adjust the OR time
blocks assigned to each specialty. Experimental results indicate thatour proposed MOITS algorithm is capable of finding much better solutions with an order-of-magnitude time reduction than Gurobi acrossproblems of different sizes.

2 - Mathematical formulations for wood recycling optimization

Pauline Bessemans, Célia Paquay
The rising demand for materials such as wood undeniably contributes
to the depletion of natural resources and global warming. Recycling

This presentation describes the formulation of a linear programming
model and its use as a decision support tool to assess the phasing out ofdiesel vehicles and their replacement by low emission vans (LCV) andtrucks (HGV). The company concerned offered one of the largest fleetsof commercial rental vehicles in the UK and was keen to maintainits credentials with respect to decarbonisation. The main considerations for management were the planned lifetime of the various existing
diesel vehicles, the likely capital budget for new vehicles, a companyset reduction in CO2 emissions over the planning horizon, a reductionin the ratio of diesel to electric vehicles and all this in the context ofa percentage annual growth rate for the total fleet size. The objectivefunction was to minimise emissions over 5 years and one of the main
outcomes was whether the company’s planned profile for reduction in
CO2 emissions was feasible given the capital spending budget for new
vehicles. The difference in purchasing costs of diesel and electric vehicles is quite considerable and, so to both adhere to the purchasing
budget and achieve the planned emissions reductions, it would not be
possible to adopt a cliff edge change in the vehicle purchasing profile.
The continued acquisition of some diesel vehicles would be necessary.
The model (developed using the Xpress platform) is described in detail along with the reaction of the company management to the results
obtained.

4 - Exact and non-exact approaches for the multi-stage
stochastic two-dimensional cutting stock problem withusable leftovers

Adriana Cherri, Douglas Nogueira do Nascimento, José
Fernando Oliveira, Beatriz Brito Oliveira
The Two-Dimensional Cutting Stock Problem with Usable Leftovers(2D-CSPUL) consists of cutting large plates to produce smaller ordered items, minimizing waste and allowing the generation and subsequent use of leftovers. This work explores a multi-stage stochastic
approach to the 2D-CSPUL, incorporating demand uncertainty overmultiple periods and enabling more adaptive and resilient production
strategies. By adopting a scenario-tree approach, the study represents
uncertainties throughout the planning horizon, offering a closer reflection of real-world scenarios. Additionally, anticipatory production of
items ahead of immediate demand is allowed. Costs associated withthe storage of leftovers and the inventory of items are also considered.
The objective is to minimize material waste and storage costs of items
and leftovers across all stages. The main contribution of this paper isa new mathematical model proposed to represent this problem. Dueto its complexity, heuristic procedures based on the relax-and-fix strategy were proposed to achieve practical solutions efficiently. Computational results prove the effecitiveness of thre approach. This researchhas been funded by the Fundação de Amparo a Pesquisa do Estado de179

TA-30EURO 2024 - CopenhagenSão Paulo - FAPESP, Brazil (Process number 2022/05803-3 ) and Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq,
Brazil (Process number 317460/2021-8).

4 - Finding optimal solutions to previously unsolved combinatorial optimization problems by using more than
100,000 cores

Yuji Shinano TA-30
Tuesday, 8:30-10:00 - Room: 064 (building: 208)

Parallel Solvers
Stream: Software for Optimization
Invited session
Chair: Yuji Shinano
1 - Parallel General-Purpose Dynamic
Solvers for Combinatorial Optimization

Programming

Ryo Kuroiwa, Yuji Shinano, J. Christopher Beck
Domain-independent dynamic programming (DIDP) is a recently proposed model-based paradigm for combinatorial optimization based on
dynamic programming (DP). In DIDP, similar to mathematical programming, a user formulates a combinatorial optimization problem asa declarative DP model and then solves the formulated model by calling a general-purpose solver. The currently developed DIDP solvers
are based on heuristic search algorithms studied in the artificial intelligence community. In this work, we develop parallel DIDP solvers
using parallel heuristic search algorithms. First, we empirically confirm that the multi-thread versions of our parallel DIDP solvers outperform commercial multi-thread mixed-integer programming and constraint programming solvers in multiple combinatorial optimization
problems. Then, we develop distributed parallel DIDP solvers using
the Message Passing Interface. Our experimental evaluation demonstrates the high scalability of the parallel DIDP solvers in a massively
parallel distributed environment.

2 - A parallel Benders’ decomposition algorithm for solving the integrated long-haul and local vehicle routing
problem

The Ubiquity Generator framework (UG) was originally designed toparallelize powerful state-of-the-art branch-and-bound based solvers(we call these "base solvers"). Two of the most intensively developed
parallel solvers are FiberSCIP (for a shared memory computing environment) and ParaSCIP (for a distributed computing environment).
Both of which use SCIP as the base solver. Since debugging on distributed environments is inefficient, FiberSCIP was developed, which
has the same parallelization algorithms as ParaSCIP (since UG abstracts the parallelization library) but can run on a single PC. Dueto the major debugging effort of UG and SCIP via FiberSCIP, ParaSCIP could solve more than 20 open instances from MIPLIB by using
up to 80,000 cores and none of these results has been proven wrong
so far. Since UG provides a systematic way to parallelize a state-ofthe-art sequential or multi-threaded solver to run on a large-scale distributed memory environment, with version 1.0, UG is generalized toa software framework for high-level task parallelization. That is, withversion 1.0, UG will not only parallelize the tree search of branchand-bound-based solvers but allow the parallelization of other kindsof solvers. In this talk, we present the latest status for exactly solving
previously unsolved instances of combinatorial optimization problems.

 TA-31
Tuesday, 8:30-10:00 - Room: 046 (building: 208)

Analytics and the link with stochastic
dynamics I
Stream: Analytics
Invited session
Chair: Gerhard-Wilhelm Weber
Chair: Nina Kajiji

Stephen Maher, Junko Hosoda, Yuji Shinano
The integrated long-haul and local vehicle routing problem with anadaptive transportation network is a very challenging optimisation
problem. The adaptive nature of the transportation network meansthat the resulting optimisation problem is extremely large and difficult to solve using general purpose solvers. As such, only very small
instances of this problem can be solved without the use of decomposition techniques. We propose a Benders’ decomposition approach that
identifies transportation network design in the master problem and thensolves the integrated long-haul and local vehicle routing problem in the
subproblem. The Benders’ decomposition approach is used to solve a
relaxation within a parallelisation framework that concurrently solves
an iterative algorithm for finding high quality primal solutions. The
results will show that applying Benders’ decomposition increases thescale of problems that can solved and improves the upper and lower
bounds that can be achieved.

3 - Improving Lower Bounds for Large Scale QAPs

Koichi Fujii, Sunyoung Kim, Masakazu Kojima, Hans
Mittelmann, Yuji Shinano
We report the progress of our project of solving large-scale Quadratic
Assignment Problems (QAPs). We present a method that efficiently
computes global lower bounds for QAPs by employing the Newtonbracketing method along with Lagrangian doubly nonnegative (DNN)
relaxation. The integration of auxiliary information derived from the
Newton-bracketing method, combined with the checkpoint mechanismof the parallelization framework Ubiquity Generator (UG), enables thedetermination of strong global lower bounds for large QAPs. Thismethod has led to the improvement of lower bounds for several unsolved problems listed in QAPLIB, the standard benchmark for QAPs.

180

1 - Forecasting regional GDP using cointegration withbusiness cycle indicators

Nariyasu Yamasawa
In Japan’s local governments, the shortage of personnel has made itdifficult to compile statistics. While regional Gross Domestic Product(GDP) is released promptly in countries like the United States and theUnited Kingdom, in Japan, there is a lag of about two years before annual figures are published. Several prefectures used to release preliminary figures for regional GDP on a quarterly basis, but their number
has gradually decreased, and currently, only three prefectures do so.
Many local governments are creating business cycle indices similar to
the OECD’s Composite Leading Indicator (CLI) to survey the current
state of the economy. However, business cycle indices do not reveal
the level of economic activity, nor do they easily allow for comparisons between different prefectures. in this study, a method to create
regional GDP from business cycle indices was explored. It is assumed
that there is a long-term stable relationship (cointegration) between
the economic business cycle, which represent the cycle of the economy, and regional GDP, which represents the overall movement of theregion. By constructing an Autoregressive Distributed Lag (ARDL)
model and initially applying it to the country as a whole, a long-term
relationship was confirmed. By applying this relationship to the Gross
Regional Product (GRP) of each prefecture, a method to estimate regional GDP was devised.

2 - Modelling wind behaviour: using statistical distributions to represent wind speed and direction

Helena Alvelos, Francisco Marques, Ana Raquel Xambre,
Agostinho Agra, Filipe Alvelos

EURO 2024 - Copenhagen
Wildfires cause significant losses in both material value and human
lives. Thus, preventing their occurrence or minimizing their impact
when they do happen is paramount. Decision models have thus become increasingly popular in assisting the operational management offirefighting units. The management of wildfires can be divided into:(i) deployment, (ii) dispatch and (iii) positioning. Deployment decisions are made before the fire is detected, dispatch decisions are taken
after the ignition is detected, and positioning decisions describe where
the dispatched resources should suppress the fire. The study presented
here is part of a project that combines two stages: the prepositioning (deployment) of resources, and the resource movement during the
suppression (positioning). Naturally, the problem involves underlying
uncertainty, two of its main sources being the ignition location and thewind properties. The study aims to obtain statistical information about
the behaviour of wind speed and wind direction by modelling historical
data of a case study region in Portugal. Several statistical distributions
were tested using goodness of fit tests, in order to select the most appropriate probability distributions. The results were then used in the
ambiguity set of a mixed integer programming distributionally robust
optimization model.

 TA-32
Tuesday, 8:30-10:00 - Room: 41 (building: 303A)

Nonsmooth optimization and applications,
Part I
Stream: Advances in large scale nonlinear optimization
Invited session
Chair: Valentina De Simone
Chair: Gerardo Toraldo
1 - An interior proximal gradient method for nonconvex optimization

Alberto De Marchi, Andreas Themelis
We consider structured minimization problems subject to smooth inequality constraints and present a flexible algorithm that combines interior point (IP) and proximal gradient schemes. While traditional IP
methods cannot cope with nonsmooth objective functions and proximal algorithms cannot handle complicated constraints, their combined
usage is shown to successfully compensate the respective shortcomings. We provide a theoretical characterization of the algorithm andits asymptotic properties, deriving convergence results for fully nonconvex problems, thus bridging the gap with previous works that successfully addressed the convex case. Our interior proximal gradientalgorithm benefits from warm starting, generates strictly feasible iterates with decreasing objective value, and returns after finitely many
iterations a primal-dual pair approximately satisfying suitable optimality conditions. As a byproduct of our analysis of proximal gradient
iterations we demonstrate that a slight refinement of traditional backtracking techniques waives the need for upper bounding the stepsizesequence, as required in existing results for the nonconvex setting.

TA-33

The proposed method is then adapted to solve imaging problem inradio-astronomy. To reconstruct such images, a high number of observations in the Fourier space are combined. Such high number creates a computational bottleneck. We demonstrate that IML FISTA canprovide considerable acceleration by using coarse approximation constructed in the observation space.

3 - A Scaled Gradient Projection method for the realizationof the Balancing Principle in TGV-based image restoration

Germana Landi, Marco Viola, Fabiana Zama
In the last few years, Total Generalized Variation (TGV) regularization
has proved to be a valuable tool to remove blur and noise from an image while avoiding the staircase effect and preserving the sharp edges.
The TGV model depends on two regularization parameters whose values must be appropriately selected to obtain good-quality restored images. In this work, we use the Balancing Principle (BP) to formulate
the TGV-based image restoration problem as a constrained minimization problem whose objective is an implicit function of the two regularization parameters depending on the image to be restored. The values of the regularization parameters, and the corresponding restored
image, satisfying the optimality condition of the formulated problem
guarantee that the data fidelity and regularization terms are balanced.
A Scaled Gradient projection method is proposed specifically tailoredto the BP-based optimization problem. The numerical results show thatthe proposed approach can effectively restore input images corrupted
by several kinds of noise.

4 - A Variational Model for graph p-Laplacian eigenfunctions under p-orthogonality constraints

Giuseppe Antonio Recupero, Serena Morigi, Alessandro
Lanza
The p-Laplacian is a non-linear generalization of the Laplace operator.
In the graph context, its eigenfunctions are used for data clustering,
spectral graph theory, dimensionality reduction and others problems,
as non-linearity better captures the underlying geometry of the data.
We formulate the p-Laplacian nonlinear eigenproblem as an optimization problem under p-orthogonality constraints. The problem of computing multiple eigenvectors of the graph p-Laplacian is then solved
by iteratively minimizing the constrained generalized Rayleigh quotient. We analyze two different optimization algorithms to solve thevariational problem. The first is a manifold gradient descent whichsolves a smooth unconstrained minimization over the p-orthogonality
subspace. The second is an Alternate Direction Method of Multipliers
which leverages the scaling invariance of Rayleigh quotient to solve
a constrained minimization under a p-orthogonality constraint. We
demonstrate the effectiveness and accuracy of the proposed algorithms
and compare them in terms of efficiency.

 TA-33
Tuesday, 8:30-10:00 - Room: 42 (building: 303A)

Heterogeneity in optimal control problems

2 - Multilevel proximal methods for image restoration

Guillaume Lauga, Elisa Riccietti, Nelly Pustelnik
Solving high-dimensional optimisation problems is a difficult task, andnumerous methods have been proposed to compensate for the high costin computation time. The approach explored in this work exploits the
structure of these optimisation problems, in order to reduce the computational cost of their solution.
Specifically, we focus on the multi-resolution structure at the heart ofmulti-level optimisation methods. These approaches take advantageof the definition of coarse approximations of the objective function to
make its minimisation more efficient. In this talk, we present a proximal multilevel algorithm IML FISTA - Inexact Multilevel FISTA - suitable for the solution of optimisation problems where the non-smoothcomponent of the objective function has no explicit formulation for theproximal operator.

Stream: Optimal Control Theory and Applications
Invited session
Chair: Silvia Faggian
1 - Design of an ecological corridor surrounding restricted
urban areas: An optimal control approach

Lino J. Alvarez-Vazquez, Néstor García-Chan, Aurea
Martinez, Miguel E. Vazquez-Mendez
The use of optimization techniques for the optimal design of roads
and railways has increased in recent years. The environmental impact of a layout is usually given in terms of the land use where it runs
(avoiding some ecologically protected areas), without considering air
pollution (in these or other sensitive areas) due to vehicular traffic on

181TA-34EURO 2024 - Copenhagenthe road. This work addresses this issue and proposes an automatic
method for obtaining a specific corridor (optimal in terms of air pollution), where the economically optimized road must be designed ina later stage. Combining a 1D traffic simulation model with a 2D air
pollution model and using classical techniques for optimal control ofpartial differential equations, the problem is formulated and solved in
the framework of Mixed Integer Nonlinear Programming. The usefulness of this approach is shown in a real case study posed in a region that
suffers from serious episodes of environmental pollution, the Guadalajara Metropolitan Area (Mexico).

2 - Self enforcing agreements, delayed information andtaxes in a differential game modelling extraction of common renewable resources

Agnieszka Wiszniewska-Matyszkiel, Rajani Singh TA-34

We analyse a differential Fish Wars game with n players and the infinite time horizon. We calculate the social optimum, a Nash equilibrium
and partial cooperation equilibria. The Nash equilibrium always leads
to depletion of fish, while the social optimum results in sustainability
for a whole interval of parameters. Partial cooperation is in between,with the chance for sustainability growing with the number of cooperators, but only a coalition of size 2 is stable, which makes sustainability
unlikely also in this regime. This is "the tragedy of the commons" in
its most drastic form due to logarithmic payoff. We solve "the tragedy"
by two frameworks to: either by an environmental agreement or bya tax-subsidy system. In the first framework, we analyse agreements
in which each signatory reduces fishing to the social optimum level
until they detect that another signatory has defected. We also assume
it takes time to detect a defection. We are interested in the maximal
detection time for which the agreement still remains self-enforcing.
Counter-intuitively, it turns out that the more players, the higher thismaximal detection time, so the more stable the agreement. Concerning
the second framework, besides solving our specific "tragedy", we also
propose a general algorithm for finding financial incentives enforcing
the optimal profile in a large class of differential games and a large
class of financial incentives or proving that such an incentive does notexist.

Tuesday, 8:30-10:00 - Room: 43 (building: 303A)

3 - Climate policy in a heterogenous world

Giorgio Fabbri, Raouf Boucekkine, Fausto Gozzi, Cristiano
Ricci, Ted Loch-Temzelides
We introduce a dynamic game framework that does not rely on linearity as- sumptions, while at the same time retaining tractability. As
an illustration, we employ this framework to derive new insights on
climate-related policy. We build a game-theoretic version of the dynamic integrated assessment model in Golosov et al. (2014). Our
framework allows us to characterize emissions, consumption, and welfare across different equilibrium outcomes by computing the Nash andStackelberg equilibria of a dynamic game capturing aspects of climate
negotiations between the "global north" and "global south." We use
these to draw various comparisons between such outcomes and theefficient frontier. We find that both the global north and the global
south over-consume/pollute in the non-cooperative equilibria and theglobal south underinvests in abatement. Stackelberg leadership by the
global north in climate negotiations reduces global emissions and increases welfare over the Nash outcome, although the emissions remain
far from the optimum. Asymmetric vulnerability to climate change
leads to a reversal in emissions, where the less vulnerable country
emits more. We employ robust control to study how deep uncertainty
affects outcomes.

4 - Symmetric equilibria in spatially distributed extraction
games with non linear growthSilvia Faggian, Giorgio Fabbri, Giuseppe Freni
This study examines the dynamics of the exploitation of a natural
resource distributed among and flowing between several nodes connected via a weighted, directed network. The network represents thelocations and interactions of the resource nodes. A regulator decides todesignate some of the nodes as natural reserves where no exploitationis allowed. The remaining nodes are assigned (one-to-one) to players,
who exploit the resource at the node. This study extends that in Fabbri

182

et al. (2024) to nonlinear dynamics of the capital stock evolution at
the nodes, taking into account evolution equations of logistic or Solow
type, establishing the existence of a Markovian equilibrium which islinear in the capital stock and confirming that, consistently with thecase of a linear evolution, the equilibrium exploitation and resource
stocks depend on the productivity of the resource sites, the structure of
the connections between them, and the number and preferences of theagents. The best locations to host nature reserves are identified per themodel’s parameters and correspond to the most central (in the senseof eigenvector centrality) nodes of a suitably redefined network thatconsiders the nodes’ productivity.

New Algorithms for Nonlinear Optimization
Stream: Advances in large scale nonlinear optimization
Invited session
Chair: Benedetta Morini
1 - Enhancing the convergence speed of line search methods: Applications in Neural Network training

José Ángel Martín-Baos, Ricardo Garcia-Rodenas, Luis
Rodriguez-Benitez, Maria Luz Lopez
The training of machine learning models, such as neural networks, relies on optimisation techniques that necessitate large volumes of data.
The algorithms that have demonstrated satisfactory performance on
this task frequently use linear searches on subsets of the data. This ensures that, despite the potential low quality of the search direction, theoverall computational cost remains low, making this strategy globally
efficient. In these methods, strategies employing a constant learning
rate have proven to be particularly effective. This paper introduces anovel scheme designed to significantly expedite the convergence process of line search-based methods. Our approach incorporates additional high-quality linear searches derived from the convergence process of the methods, and by making use of an Armijo rule, it dynamically adjusts the step size through successive reductions or expansions
based on the evaluated quality of the descent direction. This strategic
adjustment enables more substantial progress in the search direction,
potentially reducing the number of iterations needed to reach an optimal solution. We have applied our proposed solution to accelerate
the performance of widely-used algorithms such as Gradient Descent(GD), Momentum GD, and Adaptive Moment Estimation (Adam). To
illustrate the practical implications and effectiveness of our approach,we present a comprehensive case study focusing on the training of
Deep Neural Networks and Kernel Logistic Regression.

2 - Sphere covering and approximating tensor norms

Zhening Li
The matrix spectral norm and nuclear norm appear in enormous applications. The generalization of these norms to higher-order tensors
is becoming increasingly important, but unfortunately they are NPhard to compute or even approximate. Although the two norms aredual to each other, the best-known approximation bound achieved bypolynomial-time algorithms for the tensor nuclear norm is worse thanthat for the tensor spectral norm. We bridge this gap by proposing
deterministic algorithms with the best bound for both tensor norms.
The main idea is to construct a selection of unit vectors that can approximately represent the unit sphere, in other words, a collection of
spherical caps to cover the sphere. For this purpose, we explicitly construct several collections of spherical caps for sphere covering withadjustable parameters for different levels of approximations and cardinalities. These readily available constructions are of independentinterest, as they provide a powerful tool for various decision-making
problems on spheres and related problems. Our results are generalized to the `p-sphere covering and approximating spectral and nuclear
p-norms.

EURO 2024 - Copenhagen3 - First-order Trust-region Methods with Adaptive Sampling

Sara Shashaani
We are interested in optimizing noisy black-box problems that are nonconvex in expectation. Such settings occur in a multitude of engineering and machine learning applications. For the noisy black-boxes thatprovide direct derivative observations, we devise new variants of trustregion methods that use adaptive sampling schemes and adjusted acceptance criteria to improve the iteration and sample complexity ofthese algorithms. The resulting methods will have clear implementableguidelines for speedy and robust finite-time performance.

 TA-35
Tuesday, 8:30-10:00 - Room: 44 (building: 303A)

Optimization under uncertainty: theory andsolution algorithms
Stream: Stochastic, Robust and Distributionally Robust
Optimization
Invited session
Chair: Martin Glanzer
Chair: Francesca Maggioni

TA-36

4 - Guaranteed bounds for optimal stopping problems using kernel-based non-asymptotic uniform confidence
bands

Martin Glanzer, Sebastian Maier, Georg Pflug

In this talk, a new approach is presented for obtaining guaranteed upperand lower bounds on the true optimal value of stopping problems by
nonparametric regression. Unlike existing simulation-and-regression
approaches, such as those based on least squares Monte Carlo and information relaxation, whose estimates depend on the sampled paths of
the underlying stochastic process and are therefore stochastic in nature,
our guaranteed bounds allow replacing the sampling error with a prespecified confidence level. The key to our approach is the use of nonasymptotic uniform confidence bands based on a kernel ridge regression (KRR) estimator to over- and underestimate the conditional expected value from continuation. Since our high- and low-biased KRR
estimators are guaranteed to over- and underestimate, respectively, thetrue but unknown conditional expectation at every step of the backward recursive procedure with a pre-specified confidence level, we obtain probabilistically guaranteed bounds on the problem’s true optimalvalue. We derive closed-form formulas for both the fixed design andthe random design case, which makes our approach readily applicablein both simulation and data-driven situations. We illustrate the applicability of the proposed bounding procedure by valuing a Bermudanstyle put option.

1 - Scaled Cuts for Multistage Stochastic Mixed-Integer
Programs

Ward Romeijnders
We consider Benders’ decomposition algorithms with affine optimality cuts for multistage stochastic mixed-integer programs. Exploiting
extended spaces, we derive a hierarchy of convex polyhedral lower
bounds for the expected cost to-go functions in the problem. Theselower bounds, however, are not tight for mixed-integer state variables
in general. That is why, we also introduce so-called scaled cuts. The
advantage of these scaled cuts is that they allow for parametric nonlinear feasibility cuts in later time stages, but that the optimality cutsfor the expected cost to-go functions remain linear. We establish convergence by proving that the optimality cuts in the first stage recover
the convex envelope of the first-stage expected cost to-go function.

2 - Uncertain standard quadratic optimization under distributional assumptions: a chance-constrained epigraphic approach

Daniel de Vicente, Immanuel Bomze
The standard quadratic optimization problem (StQP) consists of minimizing a quadratic form over the standard simplex. If the quadratic
form is neither convex nor concave, the StQP is NP-hard. This problem has many relevant real-world applications ranging from portfolio
optimization to machine learning. To accommodate uncertainty in thedata matrix, we present a model with known distribution of the data
matrix where both the StQP after realization, and the here-and-now
problem are indefinite. Similarly to some robust formulations of uncertain StQPs, it turns out that a chance-constrained epigraphic StQP
is in fact equivalent to a deterministic (possibly non-convex) StQP. We
test the performance of a chance-constrained epigraphic StQP to theuncertain StQP by simulation.

3 - Gradient-Based Stochastic Optimization via Finite Differences: When To Randomize?

Michael Fu, Jiaqiao Hu
We consider optimization of noisy black-box functions via gradientsearch, where the gradient is estimated using finite differences. Applying a recently developed convergence rate analysis leads to a finitetime error bound for a class of problems with convex differentiable
structures. The results provide insight as to when using randomized
gradient approaches such as simultaneous perturbation stochastic approximation might be advantageous, based on problem dimension andnoise levels.

 TA-36
Tuesday, 8:30-10:00 - Room: 32 (building: 306)

Game Theory, Solutions and Structures V
Stream: Game Theory, Solutions and Structures
Invited session
Chair: Paola Labrecciosa

1 - Bicriteria Nash Flows over TimeTim Oosterwijk, Daniel Schmand, Marc Schröder

Traffic congestion imposes a huge economic loss. As such, there has
been a huge effort to understand congestion using theoretical models.
The dynamic model that gained most attention is the deterministic fluid
queuing model. A common drawback of most models is that users
only aim for minimizing their arrival time. However, users are notalways that single-minded. We extend the state-of-the-art game theoretic traffic models with a multi-criteria objective function. We assume
that users try to minimize costs subject to arriving at the sink before agiven deadline. Here, costs could be thought of as an intrinsic preference regarding different route choices and queuing dynamics only playa role for the arrival time. We determine the existence and structureof Nash flows over time and fully characterize the price of anarchy forthis model, which measures the ratio of the quality of the Nash flow
and the optimal flow. We evaluate the quality both with respect to thethroughput for a given deadline and the makespan for a given amountof flow. We prove the following results. (1) In series-parallel graphs,
both prices of anarchy are unbounded. (2) In parallel path graphs thethroughput-PoA is at most 2, or at most e/(e-1) if all transit times are 0.
Both bounds are tight. (3) In parallel path graphs the makespan-PoA is
at most e/(e-1), independent of transit times, and this is tight. All our
upper bounds are also valid for dynamic equilibria in the deterministic
fluid queuing model.

183

TA-37EURO 2024 - Copenhagen

2 - Oligopoly Exploitation of Common-Pool Nonrenewable
Resources: Price vs Quantity Competition

1 - A survey on polyhedral convex set optimization

Andreas Löhne

Luca Colombo, Paola Labrecciosa
It is a well-known result in static oligopoly theory that the Bertrand
equilibrium (simultaneous price competition) is more efficient than the
Cournot equilibrium (simultaneous quantity competition): consumers
surplus and social welfare are higher in the former than in the latter.
In this paper, we reconsider the validity of this efficiency result in acontinuous-time duopoly game where production requires exploitationof a common-pool resource. The paper closest to ours is Colombo andLabrecciosa (JET 2015, CL henceforth). As in CL, we assume thatthe extracted resource is used in the production of horizontally differentiated products a la Singh and Vives (RAND 1984). Unlike CL, we
assume that the resource is nonrenewable, that the marginal extraction cost is decreasing in the resource stock, and that the goods can beeither substitutes or complements. A key assumption in our nonrenewable resource model is that there exists economic rather than physicalexhaustion. The equilibrium concept we use is Markov Perfect Equilibrium. We are interested in equilibrium harvesting rates, consumers
surplus, profits, and social welfare in the two different market structures. In contrast with static oligopoly literature, we show analyticallythat price competition does not necessarily lead to larger output, lower
profits, larger consumers surplus and social welfare.

3 - A coopetitive oligopoly with quantity goals

Maria de los Angeles Caraballo, Asunción Zapata, Luisa
Monroy, Amparo Mármol
This paper considers an extended setting of a Cournot oligopoly inwhich firms face a cost when deviating from a fixed quantity that isexogenously given. To analyze these situations, we assume that firms
show a coopetitive behavior. The literature usually describes coopetition as the act of cooperation between competing firms by forminga strategic alliance designed to achieve mutual benefits and competitive advantages. Based on this concept, we consider that a firm hasa coopetitive behavior when it cares both about its own utility (competition) and also, at least, about the utility of one of the others (cooperation). Moreover, depending on the attitude that a coopetitive firm
exhibits with respect to the utilities of the others, different types of firm
can be distinguished. Within this framework, the aim of this paper is
to analyze the effects of the degree of coopetition on the quantity offered by the firms at equilibrium. Interestingly, we show that in somesituations, the firms with a higher degree of coopetition drive the lesscoopetitive firms out of the market. Furthermore, we obtain the path ofequilibrium quantities for a set of values for the degree of coopetition.

4 - Learning and Forgetting in Oligopoly

Paola Labrecciosa, Luca Colombo
In this paper, we are interested in assessing how changing the market structure interacts with learning spillovers and organizational forgetting to affect incentives to innovate via learning. In particular, we
are interested in studying the relationship between market competition and learning-by-doing, a type of innovation, aiming to contributeto the classical debate over the relationship between market structure and innovation. We provide conditions under which a symmetric
Closed-Loop Nash Equilibrium exists. Then we conduct a comparative steady state analysis with respect to the parameters of the model.
The main conclusion of our analysis is that conditions on the diffusion
rate of knowledge and the rate of organizational forgetting exist underwhich an increase in market competition is not necessarily welfareenhancing.

Set optimization is a generalization of multiobjective optimization
where a value of the objective mapping is not a single vector but a
set of vectors. Polyhedral convex set optimization is the simplest classof set optimization problems comparable to linear programming in thefield of scalar optimization. We survey some recent developments including solution concepts, solution methods and applications.

2 - Dimension reduction in multiobjective optimization

Ina Lammel, Volker Maag
Many real-world optimization problems depend on several criteria.
The computational effort of approximating the nondominated set ofthese multiobjective optimization problems increases quickly with thenumber of objectives. For a large number of objectives, computing agood approximation of the nondominated set may exceed the available
computation budget.
To compute approximations of high-dimensional nondominated sets,
we incorporate dimension reduction techniques into the approximation process. We analyze the problem to identify strongly-correlated
directions of the nondominated set and reduce the objective space dimension accordingly. After computing an approximation in the lowerdimensional space, we project the approximation back to the originalfull-dimensional space. We relate the approximation of the nondominated set constructed by this algorithm to the approximation of the
full-dimensional set and develop criteria under which the approximation in lower-dimensional space still yields a good approximation when
projected back to the full-dimensional space.
Finally, we demonstrate the usefulness of the method on examples
from different fields of application.

3 - Weight Space Decomposition for Multiobjective Linear
Programming in the Context of Equitable Optimization

Firdevs Ulus, Ozlem Karsu
We consider equitable linear optimization problems (ELOP), which are
multiobjective optimization problems, with each objective representing the benefit that one entity receives. In such problems, the conceptof dominance is replaced by equitable dominance. The aim is to findthe set of equitably nondominated points, which can be done by solving ordered weighted averaging (OWA) scalarization. Each solution
corresponds to a different set of weights that make the solution optimal,
hence a different degree of inequity aversion. We discuss a novel useof the parametric simplex algorithm to address ELOP. The algorithm
not only provides the set of equitably nondominated solutions but also
yields the corresponding weight space decomposition. We also propose an alternative method based on geometric duality to compute theweight space decomposition given the set of nondominated solutions.

4 - DMulti-MADS, a Multiobjective Algorithm for Constrained Derivative-Free Optimization

Ludovic Salomon TA-37
Tuesday, 8:30-10:00 - Room: 33 (building: 306)

Objective Space-Based Approaches inMultiobjective Optimization
Stream: Multiobjective Optimization
Invited session
Chair: Firdevs Ulus

184

The last decade has seen the development of new convergence-based
derivative-free algorithms. Among them, DMulti-MADS is a direct
search algorithm, which extends the single-objective algorithm MADS
to multiobjective optimization. In this talk, we present recent improvements to this method, from the consideration of general inequality constraints and mixed-integer variables to the development of new searchheuristics. We illustrate the performance of these new features in numerical experiments and engineering applications.

EURO 2024 - Copenhagen TA-38
Tuesday, 8:30-10:00 - Room: 34 (building: 306)

Special Classes of Convex Conic
Optimization problems
Stream: Conic Optimization: Theory, Algorithms, and Applications
Invited session
Chair: Tatiana Tchemisova
Chair: Olga Kostyukova
1 - Duality gap and examples in infinite-dimensional conic
linear programming problems

Constantin Zalinescu
It is well known that the use of Rockafellar’s perturbation method isvery efficient for getting duality results in convex analysis. The use ofthis method in linear programming is less spread. It is our aim to show
that, using Rockafellar’s perturbation method, one can obtain duality
results in infinite-dimensional conic linear programming under very
general conditions. Moreover, we analyze a sample of Kretschmer’s
gap example used in N.E. Gretsky, J.M. Ostroy and W.R. Zame’s paper
Subdifferentiability and the duality gap, Positivity 6 (2002), 261-274.

2 - Conic optimization problems over semidefinite plus
nonnegative cone

Tatiana Tchemisova, Olga Kostyukova
The paper is dedicated to optimization of linear functions over the coneof doubly non-negative matrices and its dual, the sum of semidefinite
plus nonnegative cones. Several equivalent conditions of strict complementarity and nongeneracy are given. The findings are illustrated
with examples.

3 - Refined TSSOS

Daria Shaydurova, Volker Kaibel, Sebastian Sager
The moment-sum of squares hierarchy by Lasserre has become an established technique for solving polynomial optimization problems. It
provides a monotonically increasing series of tight bounds, but has
well-known scalability limitations. For structured optimization problems, the term-sparsity SOS (TSSOS) approach scales much better
due to block-diagonal matrices, obtained by completing the connectedcomponents of adjacency graphs. This block structure can be exploitedby semidefinite programming solvers, for which the overall runtime
then depends heavily on the size of the largest block. However, already the first step of the TSSOS hierarchy may result in large diagonal blocks. We suggest a new approach that refines TSSOS iterations using combinatorial optimization and results in block-diagonal
matrices with reduced maximum block sizes. Numerical results on abenchmark library show the large potential for computational speedup
for unconstrained and constrained polynomial optimization problems,
while obtaining almost identical bounds in comparison to establishedmethods.

 TA-39
Tuesday, 8:30-10:00 - Room: 35 (building: 306)

Stochastic Models in Service Operations I
Stream: Stochastic Modelling
Invited session

TA-39

1 - Quality-Speed Trade-Offs in Dynamic Service Systems:
How to Anticipate Future Demand Changes

Raik Stolletz, Jannik Vogel, Tim Weber
Quality-speed trade-offs occur in many service systems in which employees have discretion over their service times. An increase in service
time often results in an increase in the quality of a service but also increases congestion. We investigate this trade-off in a dynamic settingwith time-dependent and stochastic demand. To the best of our knowledge, this work is the first analysis of the quality-speed trade-off withexogenous changes in the demand. It shows how service providers
can anticipate future expected demand changes with optimized service
rates.
We investigate a piecewise-stationary approach whereby periods are
not interrelated. Based in this, a closed-form stationary smoothingheuristic accounts for future demand changes. A performance evaluation based on a stationary backlog-carryover (SBC) approach considers
the interrelations of performance measures between periods directly,
and hence, allows to determine service rates simultaneously over all
periods of the planning horizon.
We show that forecasted demand changes are anticipated by service
rates several periods in advance. Substantial benefits are achieved byincorporating forecasted demand changes in the decision making oftime-dependent service systems affected by the quality-speed trade-off.

2 - Modelling and Optimization for Service System with
Ticket Queues

Chesoong Kim
Classical queuing models assume that each incoming user is accepted
into the system if there is at least one free space in the queue and isserved in a certain order. However, frequently in real systems, users
may be impatient and leave the system after some waiting time if theirservice has not begun. Another kind of practically important and interesting queues is so-called ticket queues. In such queues, each arrivinguser receives a numbered ticket and observes the number of the user
being served, which is broadcast on a display panel. In a ticket queue,
no physical queue is formed. Thus, based on information about the difference between his own number and the displayed number, the user
decides whether to balk or wait for service. In contrast to the usual
queues with balking users, the balking user in ticket queues leaves thesystem physically, but his number remains in the queue. We consider aticket queueing system with a finite buffer of capacity. Arriving users
obtain a ticket for service but may balk the system with the probability
depending on the queue of tickets ahead of customers. Tickets issued
to users remain in the ticket buffer whether they participate or not. The
server is unaware about the presence of users in the system and spends
some time for their service even the respective user already left thesystem. In this paper, good reviews of relevant research are presented
and the stationary distribution of the considered system is analyzed.

3 - Improving Congestion in an Efficient & Robust mannerusing time slotting at Airport Security

Joris Slootweg, Rob van der Mei
We show that the efficiency of the security process at airports can besignificantly improved by adoption of a system where passengers areable to book a time slot. This time slot allows passengers to get priority access at the given time for security. We show that a simple loadbalancing heuristic already guarantees that participants have minimal
waiting time while at the same time reducing the average waiting timeof all passengers. Our proposed method is able to reduce the waitingtime even further. Furthermore, our approach specifically takes into
consideration the uncertainty in the arrival pattern and security capacity at the moment the time slots need to be allocated. Therefor, it canguarantee minimal waiting time in all scenarios for participating passengers. Furthermore, this approach leads to significantly lower wait
time for non-participants than the baseline heuristic or having no virtual queuing at all in almost all scenarios. Finally our numerical experiments show that our approach is able to reduce the sensitivity to
the uncertainty in the arrival behavior of passengers and the security
capacity.

Chair: Benjamin Legros

185TA-40EURO 2024 - Copenhagen

4 - Why rational queueing of delay risk-sensitive users undermines the benefits of added resources

3 - Strategic reneging in service systems with working
breakdowns

Benjamin Legros, Johan van Leeuwaarden, Jan C. Fransoo

Odysseas Kanavetas, Dimitrios Logothetis, Athanasia Manou

While policymakers regularly add capacity to reduce congestion in
public services like roads and hospitals, empirical studies show that
adding resources does not always reduce congestion and delays. To
clarify this phenomenon, we propose an explanatory model of rational
delay-risk sensitive users who are less inclined to access services whendelays are more variable. Using a rational queueing approach, we showthat there are largely two opposite effects of increased resource capacity. The public interest benefits through improved throughput and social welfare, while individual users can experience degraded service
in terms of expected delays. We offer as an explanation that adding
resource capacity reduces delay variability, so that delay-risk sensitive
users will more readily join. In addition, we show that resource capacity not only influences the volume of demand but also its nature:
increased service capacity attracts users who are less sensitive to expected delays and more sensitive to delay variability, which implies
that large-capacity systems predominantly attract users who prefer reliability over efficiency.

In this study, we analyze the reneging behavior of customers in a
Markovian M/M/1 queue with an alternating service process, representing a normal operation mode and a working repair mode with reduced service speed. The customers are strategic and, as they continuously observe the system’s state, they decide whether to stay or
to renege. For this model, we derive equilibrium customer reneging
strategies and the corresponding performance measures of the system,
considering both the baseline model with discrete units of customers
and its fluid counterpart. To capture the effect of reneging, we compare
their performance with similar systems, operating under a no-reneging
policy. Both theoretical findings, as well as numerical experiments,
reveal that allowing reneging significantly affects system performance.
Finally, to assess the quality of the fluid approximation we conduct a
numerical comparison between the fluid and the baseline version of themodel.

 TA-41
 TA-40

Tuesday, 8:30-10:00 - Room: 97 (building: 306)

Tuesday, 8:30-10:00 - Room: 96 (building: 306)

Optimization on Geodesic Metric Spaces II:
Nonsmooth case

Queueing Models with Strategic Customers
Stream: Advances in Stochastic Modelling and Learning
Methods
Invited session
Chair: Odysseas Kanavetas
1 - Reneging behavior of customers in a queueing system
subject to breakdowns and repairs

Dimitrios Logothetis, Odysseas Kanavetas
We consider a Markovian queueing system with a single server which
is subject to breakdowns. When a breakdown occurs, the server continues its operation but at a reduced service speed. The repair process initiates only when the system is empty and during that time, customers continue to accumulate without receiving service. Customers
are strategic, and while observing the state of the system, they may
choose to renege at any point. To study the impact of reneging behavior, we also consider a similar model where reneging is prohibited, andcustomers only decide whether to join the system or to balk. In thesescenarios, we derive the equilibrium strategy of the customers and wecompute the steady state distribution under equilibrium. In particular, we find that in equilibrium, the customers follow a triple thresholdstrategy which we characterize. To analyze the effect of the customer
reneging behavior, we conduct a theoretical comparison of these systems and perform several numerical experiments. We identify cases
where reneging is beneficial in terms of social welfare, as well as cases
where prohibiting reneging ensures greater welfare.

2 - Learning and pricing in queues with unobserved balking

Shreehari Bodas, Michel Mandjes, Liron Ravner
Impatience of customers plays a crucial role in modelling service systems. In many situations, any arriving customer is informed about thecurrent congestion level. In this way they can obtain a sense of theirprospective waiting time, based on which they can decide whether or
not to join. If this waiting time exceeds their patience threshold, theybalk; otherwise they join. In this talk we discuss queues with impatient customers, by in particular considering a relevant related decision problem: how to set a fixed admission price so as to maximize
revenues? We discuss approaches based on applying existing techniques such as Stochastic Gradient Descent via Infinitesimal Perturbation Analysis and Stochastic Approximation methods such as theKiefer-Wolfowitz algorithm.

186

Stream: Optimization on Geodesic Metric Spaces:
Smooth and Nonsmooth
Invited session
Chair: Alexandru Kristaly
1 - Convergence and stability of optimal transport on irreversible metric spaces

Wei Zhao
The talk is devoted to the optimal transport on irreversible metricspaces. While the compact setting is mostly similar to the reversible
case developed by J. Lott, K.-T. Sturm and C. Villani, the noncompact
case provides various surprising phenomena. Since the reversibility ofnoncompact irreversible spaces might be infinite, it is motivated to introduce a suitable nondecreasing function that bounds the reversibilityof larger and larger balls. By this approach, we are able to prove satisfactory convergence/stability results in a suitable - reversibility depending - Gromov-Hausdorff topology. A wide class of irreversible
spaces is provided by Finsler manifolds, which serve to construct various model examples by pointing out genuine differences between thereversible and irreversible settings. It is a joint work with Alexandru
Kristaly.

2 - Hardy inequalities via Riccati pairs

Sándor Kajántó
We present a simple approach for proving Hardy inequalities on Riemannian manifolds, with sectional curvature bounded from above. By
using simple convexity arguments and divergence/comparison theorems, we show that the validity of these inequalities reduces to the solvability of corresponding Riccati-type differential inequalities. Since
the proofs omit symmetrizations, the method allows for broad applicability: inequalities formulated on model space forms naturally extend
to manifolds with smaller sectional curvature.

3 - Sharp functional inequalities on metric measures
spaces via optimal transport theory

Alexandru Kristaly
In this talk we present some sharp functional (Sobolev, log-Sobolev)
inequalities on metric measure spaces that are curved in the sense of
Lott-Sturm-Villani, by using optimal transport theory. As an application, we provide a sharp weighted hypercontractivity estimate for the
Hopf-Lax semigroup related to the Hamilton-Jacobi equation.

EURO 2024 - Copenhagen TA-42
Tuesday, 8:30-10:00 - Room: 98 (building: 306)

Quantum Computing for Continuous
Optimization
Stream: Quantum Computing Optimization
Invited session
Chair: Sander Gribling
1 - Solving quadratic binary optimization problems using
quantum SDP methods: Non-asymptotic run-time analysis

Fabian Henze, Viet Tran, Birte Ostermann, Richard Kueng,
Timo de Wolff, David Gross
In principle, quantum computers can solve semi-definite programs using resources that scale extremely well in the matrix dimension, thoughvery unfavorably in the precision. In 2019, Brandao, Franca, andKueng proposed to use quantum SDP solvers in order to speed up
the Goeman-Williams-type relaxations of combinatorial optimization
problems. One motivation for their ansatz was that the final rounding
step that maps SDP solutions to binary variables should be comparatively insensitive to limits in the precision. They did indeed succeed
to identify an algorithm that achieves an asymptotic quadratic speedup
over state-of-the-art classical methods. Here, we present an analysisof the non-asymptotic resource requirements of this ansatz. The work
consists of two parts. First, we optimize the original algorithm with aview on performance for finite problem instances. In particular, we formulate a version with adaptive step-sizes, an improved detection criterion for infeasible instances, and a more efficient rounding procedure.
In a second step, we benchmark both the classical and the quantum
version of the algorithm against synthetic and real-world data. Unfortunately, we find that even the optimized quantum algorithm will
not beat more standard classical approaches for input sizes that can berealistically solved at all.

2 - Quantum speedups for linear programming via interior
point methods

Sander Gribling
We describe a quantum algorithm based on an interior point method forsolving a linear program with n inequality constraints on d variables.
The algorithm explicitly returns a feasible solution that is eps-close to
optimal, and runs in time O(sqrtn poly(d,log(n),log(1/eps)) which is
sublinear for tall linear programs (i.e., n » d). Our algorithm speeds
up the Newton step in the state-of-the-art interior point method of Lee
and Sidford [FOCS ’14]. This requires us to efficiently approximate
the Hessian and gradient of the barrier function, and these are our main
contributions.
To approximate the Hessian, we describe a quantum algorithm forthe spectral approximation of AT A for a tall n-by-d matrix A. The
algorithm uses leverage score sampling in combination with Groversearch, and returns a delta-approximation by making O(sqrtnd/delta)row queries to A. This generalizes an earlier quantum speedup forgraph sparsification by Apers and de Wolf [FOCS ’20]. To approximate the gradient, we use a recent quantum algorithm for multivariate mean estimation by Cornelissen, Hamoudi and Jerbi [STOC ’22].
While a naive implementation introduces a dependence on the condition number of the Hessian, we avoid this by pre-conditioning ourrandom variable using our quantum algorithm for spectral approximation.

 TA-43
Tuesday, 8:30-10:00 - Room: 99 (building: 306)

Simulation in innovation
Stream: Agent-based Models in Management, Economic

TA-43

and Organisation Sciences
Invited session
Chair: Theresa Elbracht
1 - Decision-making Modeling and Simulation of Digital
Platform Innovation Team based on Member Interaction

Yanqiu Song, Junlin Chen, Tanxiaosi Luo
Our research proceeds in three ways. Firstly, we identify patterns ofmember interaction and key influencing factors via brings together developments in the literature over the past several years. Second, key
influencing factors are included in the team decision model to build
interactive team decision rules containing the individual member decision rules and the overall team decision rules. Finally, the simulation
model of team decision is built on the Anylogic platform to verify the
dynamic evolution of innovative team interactive decision in the digitalplatform system. Our principal contributions can be highlighted as follows: (1) Literature research has found that the factors that affect theinteractive decision-making of innovation teams include the individual cognitive level of members, risk preferences, and member satisfaction. (2) If there are significant differences in risk preferences among
team members, they may find it difficult to reach consensus quickly
during decision-making and exhibit conflicting behaviors in repeateddecision-making, resulting in low innovation value in the decisionmaking outcomes. (3)A team with a high level of cognitive ability ismore likely to form higher innovative value in decision-making. (4)In
repeated interactive team decisions, satisfaction can affect members’
willingness to communicate and change their choices, thereby affecting the team’s decision-making outcomes.

2 - Innovation diffusion in the presence of opposing information sources: An agent-based simulation approach

Frederik Tolkmitt, Christian Stummer
The market introduction of radically new products and their respective
innovation diffusion can considerably be affected by consumers’ increased uncertainty due to diverging information received from opposing sources. Such information can originate, for example, from strong
general attitudes being prevalent in certain consumer groups (e.g., regarding the pros and cons of integrating AI capabilities in products) or
it may even be spread by a competitor employing a negative-word-ofmouth campaign (e.g., when questioning the eco friendliness of electric vehicles). In such a setting, consumers receive ambiguous signals,
which should increase their uncertainty and potentially delay productadoption. However, commonly used belief updating models (such as
Bayesian learning), in a counterfactual manner, assume a decrease of
uncertainty whenever new information is received even if this information substantially deviates from previous information. In our research,
we demonstrate this effect by means of a computational simulation experiment based on a straightforward agent-based model of innovation
diffusion and we propose a novel approach that more realistically captures consumer uncertainty with respect to insufficient or ambiguous
information.

3 - The heterogeneity effect in modeling innovation diffusion: Findings from an agent-based simulation experiment

Theresa Elbracht, Christian Stummer
Analytical models of innovation diffusion need to reduce real-world
complexity in order to be numerically tractable. The seminal Bassmodel, for example, assumes (i) that consumers are fully connected toeach other and thus are correctly informed about the share of adoptersin the population at each point in time and (ii) that consumers are
identical in their innovativeness and their propensity to conform withothers. Both assumptions are counterfactual with respect to real consumers, who can only oversee the behavior of a limited set of peers
and have individual traits (e.g., some are innovators in adopting new
products and do not care much about the share of adopters among their
immediate peers, while others are laggards). In our research, we investigate whether accounting for consumer heterogeneity makes a difference with respect to a better fitting of computed sales to real-worldsales (with data taken from Bass’s work).
As a means of introducing consumer heterogeneity in an innovation
diffusion model, we exploit the strengths of agent-based modeling,187

TA-44EURO 2024 - Copenhagen

which allows us to connect consumers through a realistic social network and assign them individual attitudes (drawn from a normal distribution around the population’s mean values provided by Bass). Our
talk details the setup of four simulation experiments in which we varyconsumer heterogeneity in the two above-mentioned dimensions, anddiscuss our findings.

and the richness of knowledge provided by the DM. The analysis captured the trade-off between the considered aspects, indicating that more
flexible approaches with richer representation provide more expressive

3 - Modeling of Stochastic UTAdis and its application in a
DSS addressing agrofoood marketing decisions

Nikos Tsotsolas TA-44
Tuesday, 8:30-10:00 - Room: 20 (building: 324)

Robustness analysis in MCDA 2
Stream: Multiple Criteria Decision Analysis
Invited session
Chair: Nikos Tsotsolas
1 - Resolving Ambiguous Preference Information in Group
Decision-Making: A Novel Optimization Model forConsensus-Reaching

Jutta Geldermann, He Huang, Erik Pohl, Cathy Macharis,
Sebastian Schär
Within many multi-attribute decision-making (MADM) methods,
inter-criterial preferences of the decision-makers (DMs) are expressed
via criteria weights. Due to its convenience and simplicity, the revised
method of Simos is widely applied to elicit criteria weights interactively with the DMs. Especially in group decision-making situations
it provides procedural ease and offers a means to engage the dialogue
with and in between DMs. In Simos’ method, each DM orders a setof criteria cards according to their relative importance to derive criteria
weights. However, this can lead to ambiguous preference mappings
and thus ambiguous criteria weight vectors. Therefore, the robust extension of Simos’ method determines all possible weight vectors froman ordered set of criteria cards via linear programming, where the setof feasible solutions forms a hyper-polyhedron. Although the concerns
about robustness are expressed in the scientific literature on Simos’
method, there is currently no methodological approach to process thisinformation for consensus-reaching in decision problems with multiple
DMs. We present an optimization model that determines a consensual
alternative by utilizing the ambiguity of Simos’ method. The model isbased on the preference ranking organization method for enrichment
evaluation (PROMETHEE) and demonstrated at hand of a case study
for the assessment of seawater desalination plant concepts in Jordan.

2 - A Comprehensive Framework for Multi-Criteria Sorting
Model Selection Based on Experimental Analysis ofModel Expressiveness and Robustness of Recommendation

Michał Wójcik, Milosz Kadzinski, Moha Ghaderi
We present research results on the expressiveness of models and therobustness of recommendations for various approaches to solving thevalue-driven threshold-based multiple criteria sorting problem. The
basic model we consider is UTADIS, which assumes the evaluationof alternatives based on monotonic, preferentially independent criteria. We also present several extensions to the model. Some of themrelax monotonicity constraints, and others introduce interactions between criteria.The considerations focus on the preference disaggregation paradigm, reproducing the Decision Maker’s preferences, expressed as assignment examples. We present the results of the performed experimental analysis for six different approaches, focusing
on the aspect of expressiveness, which is the ability to reproduce indirectly expressed preferences, and robustness, which determines thestability of the recommended assignments, tested based on all models
consistent with the provided recommendations, using Robust Ordinal
Regression and Stochastic Multicriteria Acceptability Analysis. The
analysis results were presented and discussed based on seven quality
measures, divided into the considered problem dimensions, such as thenumber of classes, criteria, characteristic points in marginal functions,188

A DSS has been developed to support B2B marketing strategy regarding agrofood products based on previous buying decisions. The DSS
collects data directly from a blockchain B2B platform, which supports
the whole procurement process from farm to fork. These marketing decisions refer to the determination of values of products’ characteristics,
such as price, quality, certifications, logistics, for a better market placement of a specific product. Following the aggregation-disaggregation
approach the preference models of different buyers are elicited basedon their buying behavior of competitive products. The present research
discusses the modeling and application in the DSS of a specific formof UTAdis method, which encompasses the notions of Stochastic UTA
in order to deal with several evaluations of products characteristics invarious B2B negotiations and transactions that lead to different buying
decisions. The modified method is called Stochastic UTAdis and its
application allow the users of the DSS to evaluate different marketingmix scenarios resulting to the allocation of a product into three predefined buying decision categories.

4 - Strategic Categorization of Swiss Energy Policies: A
Robust Hybrid MCDA Approach for Socio-Political Assessment

He Huang, Eleftherios Siskos, Peter Burgherr
In pursuit of climate neutrality and increased energy security, Switzerland’s transition to renewable energy needs to be steered by key energy
policies. However, socio-political barriers, such as social acceptance,
rather than technical constraints, mostly hinder the successful implementation of these policies. Such socio-political aspects need to bebetter understood, examined and accounted for during energy policy
analysis and consultation. To address this need, we propose a hybrid
multi-criteria decision analysis (MCDA) framework that integrates the
fuzzy best-worst method (BWM) with PROMETHEE II and incorporates a comprehensive assessment of energy policy risks and benefits
by an expert. This approach not only elicits the socio-political dimensions of policy performance, but also employs a strategic sorting technique for targeted action plans. The methodological framework is coupled with a robustness analysis that validates the reliability of the results under different future scenarios, ensuring the resilience of policy
recommendations under uncertainty. The findings prove the efficacy of
the framework to address uncertainties and include the social element
in robust policy assessment and strategic planning.

 TA-45
Tuesday, 8:30-10:00 - Room: 30 (building: 324)

Multiple-Criteria Decision Support
Stream: Decision Support Systems
Invited session
Chair: Pavlos Delias
1 - Development of strategic map (BSC) with fuzzy logic
and multi-criteria optimization

Armando Meza, Juan Perez, Héctor López-Ospina, Luis
QuezadaThe most relevant task for the implementation of the Balanced Scorecard is the mapping of strategies since it provides a structure to demonstrate how strategies link the intangible properties of an organization
with the value creation process, in turn; it shows how the objectivesof the different perspectives are related to achieving the vision. This
research contributes to models and cutting-edge methodologies in theEURO 2024 - Copenhagencontext of the systematic development of strategic maps of organizations through the creation of a framework to help in decision-making.
Our objective is the design of a methodology that supports the construction of strategic maps in organizations through the implementation of multi-criteria decision making and optimization methods thatallow for establishing and reducing causal relationships between thestrategic objectives of the 4 perspectives of the BSC (Learning and
Growth, Internal Processes, Customers and Financial)and the selection of the most representative for the fulfillment of the strategy. The
MCDM used to meet the objective was DEMATEL determining thecausal relationships and the effects of the variables of the strategic
map with the information obtained from a group of experts. The linear programming optimization model that was applied is based on the
reduction of arcs within the strategic map, eliminating the objectives
that obtained the lowest rating in the expert consultation and the maximization of the selected financial objectives.

2 - Integrating uncertainties in a multi-criteria decision
analysis with the ENTSCHEIDUNGSNAVI

Mendy Tönsfeuerborn, Sven Peters, Rüdiger von Nitzsch
The ENTSCHEIDUNGSNAVI is an open-source decision support system based on multi-attribute utility theory, which has implemented
various methods for dealing with uncertainties. To model decisions
with uncertainties, decision-makers can use two categories: Forecast and Parameter Uncertainty. Forecast uncertainty is modeled with(combined) influence factors using discrete, user-defined probability
distributions or predefined ’worst-median-best’ distributions. Parameter Uncertainty allows imprecision for utilities, objective weights,
and probability distributions. To analyze these uncertainties, the
ENTSCHEIDUNGSNAVI offers several methods and tools, like a robustness check, based on (Monte Carlo) simulations and a sensitivity analysis. The objective weight analysis provides insights into
the effects of different objective weight combinations. Indicator impacts, tornado diagrams, and risk profiles visualize the impact of uncertainties in a decision under risk. The latter enables a check forstochastic and simulation dominance. This article presents a complete
range of methods for dealing with uncertainties in the ENTSCHEIDUNGSNAVI using a hypothetical case study.

3 - SAPEVO-H’ a Hybrid and Hierarchical Decision Support
System for Multi-Criteria Analysis in Complex Environments

Miguel Ângelo Lellis Moreira, Teresa Pereira, Marcos Santos,
Carlos Francisco Simoes Gomes
In the realm of decision-making within highly complex environments,
evolving numerous variables, circumstances, scenarios, and multiple
decision-makers with different perspectives and knowledge concerning a problematic situation, the study proposes extensive modelling
for the SAPEVO multi-criteria family of methods. The new modelling,
SAPEVO-H’, exposes a novel hybrid and hierarchical approach, promoting multi-criteria analysis facing the evaluation of a hierarchicalstructure of variables assessed by distinct decision-makers, segmented
according to their specific areas of responsibility. The model embodies an axiomatic enhancement, enabling a more versatile analysis that
accommodates quantitative and qualitative data. A notable feature of
SAPEVO-H’ is its capacity for group evaluation, organizing stakeholders into levels and assigning variables based on their expertise andprior knowledge without attributing disproportionate influence to any
single decision-maker. To support practical application, we present
an online computational platform. This technological framework fosters integration among diverse decision-making groups through anasynchronous evaluation environment, enhancing transparency in thedecision-making process. It reveals individual and collective preferences alongside the traceability of utility construction, elucidating the
intricate relationships among variables, established preferences, andthe connections between stakeholder perceptions.

4 - Choice and the Machine: overcoming the challenge
in the selection of multiple criteria decision analysis
method through the use of large language model.

Aritad Choicharoon, Richard Hodgett

TA-47

Multiple Criteria Decision Analysis (MCDA) methods are widely usedin various disciplines as a logical solution to a complex decisionmaking problem. The majority of the research to date has been focusedon the development of new MCDA methods for different decisionmaking challenges. However, given this large number of potential solutions, there is an accessibility challenge in identifying an appropriate
method, especially for people who are unfamiliar with them. There
has been work which proposed a framework to help select a MCDA
method, but the challenge with this is that it requires an understandingof MCDA terminology.
We propose a decision support system to solve such a challenge, utilising artificial intelligence in the form of a large language model (LLM)
as an interface between the decision-maker and a framework for theselection of MCDA methods from a pool of 56 potential options. The
decision-maker would briefly describe their decision-making problem
in a natural language format which would then undergo a LLM reasoning process to extract the necessary information and align themwith the choice framework model. It will identify and interact withdecision-makers to provide and justify a method that is most appropriate to their problem.

 TA-46
Tuesday, 8:30-10:00 - Room: 40 (building: 324)

Generative AI For OR People: Disruptor,
Enabler Or Distraction?
Stream: Making an Impact
Invited session
Chair: Michael Lindahl
1 - Generative AI For OR People: Disruptor, Enabler Or Distraction?

Making An Impact, Michael Lindahl
Large Language Models (LLMs) have taken the world by storm since
the release of ChatGPT. These models can work with large amounts
of unstructured data, such as text and images. Their output is easy
to understand, but they cannot reason and apply logic. On the other
hand, Mathematical Optimization Models require structured and complete data but provide optimal output. However, it can often be a challenge to collect the necessary data in an organization and ensure thatusers can interact with the final application and understand the output. Can these very different models complement each other when webuild OR applications? This 90’ session will be led by Michael Lindahl. Michael has been building OR-powered decision applications
across different industries for more than 10 years. Since the releaseof ChatGPT, he has been exploring the synergies between simple optimization and LLMs by building the application www.findgaven.ai on
top of their API’s. Michael will give an introduction to large language
models, explaining their capabilities, the pitfalls, and how they can fit
into the OR toolbox. This will lead to discussion, with the audience
invited to share not only their questions but also their own experiences,
aspirations, and concerns.

 TA-47
Tuesday, 8:30-10:00 - Room: 50 (building: 324)

How to support complex decisions.
Negotiating the trade-off between Social,
Environmental and Economic values 3
Stream: Multiple Criteria Decision Analysis
Invited session
Chair: Alessandra Oppio
Chair: Marta DellOvo

189

TA-48EURO 2024 - Copenhagen

1 - Defending the Weighted Geometric Mean Aggregation

Pavel Janda, Daniele Chiffi
The weighted geometric mean (WGT) is a commonly discussed procedure for aggregating judgements in multicriteria decision analysis(MDA) and social epistemology, but it has drawbacks. We combine
MDA and social epistemology results to defend the ratio judgements
aggregation with the WGT. We argue that aggregation with the WGTovercomes many drawbacks if one aggregates ratio judgements insteadof absolute judgements. We use an axiomatic approach to argue thatone should aggregate ratio judgements (we especially favour odds) instead of absolute judgements for the following reasons. First, the mathematical structure underlying ratio judgements (and their aggregation
with the WGT) allows one to satisfy many desirable axioms that - forabsolute judgements - lead to undesirable results. For example, weavoid dictatorial decisions, can weigh experts’ judgements according
to their expertise, and satisfy unanimity. Secondly, aggregation of ratio
judgements is better suited to control and mitigate possible manipulations such as rank reversal than aggregation of absolute judgements,
which can improve legal and ethical aspects of decisions. We present
an intuitive sufficient condition that, if met, avoids any rank reversal
by adding or deleting alternatives. We also present a sufficient condition for preventing rank reversal for marginalisation, roughly speaking,when initially separated alternatives are transformed into a single alternative using disjunction.

2 - A Fuzzy Logic Application to Manage Costs in LargeScale Projects

Rubina Canesi, Chiara D’Alpaos
In large-scale projects, such as transport infrastructure developments,
the cost planning phase stands out as one of the most crucial for the
project success. Cost and time overruns, during the construction phase,are often the cause of project failure. A meticulous, conscious, andaccurate ex-ante analysis of cost and time assessment can greatly contribute to the efficient and effective completion of a project. Various
approaches have been developed to estimate and mitigate cost overruns. Such mitigation is subject to careful analysis of project risks, encompassing construction, environmental, social acceptance, and market risks. In this contribution, we propose the implementation of therisk assessment tool suggested by the National Anti-Corruption Authority (ANAC) using fuzzy logic to enhance its effectiveness. Thisapplication can provide public authorities with a tool to assess expectedrisk during the design and implementation phases of the project. By associating probability matrices of occurrence and expected impact withfuzzy prioritizations, this tool can facilitate risk assessment and management during the design and construction phases as well as consensus creation process.

creates targeted action plans for specific improvements. This datadriven approach fosters accurate and reliable evaluations, promoting
continuous improvement in Colombian engineering education. Ultimately, DEA and Bootstrap offer a robust and objective method to optimize educational efficiency, identify best practices, and define achievable learning objectives.

2 - Analysis of citation impact of ORMS journals by DEA
modelsJosef Jablonsky
Performance and citation impact of scientific journals are measured bytraditional metrics such as impact factor, article influence score, journal citation indicator, and others. While the impact factor is based on
the total number of citations and does not reflect the quality of journals cited, the article influence score considers the past importanceof the citing journals. This paper aims the analyze the possibility ofmeasuring the performance of journals by data envelopment analysis(DEA) models. We applied traditional radial and SBM DEA models
with weight restrictions where the outputs of the models are the citation counts from Q1 to Q4 categories and other journals according to
the impact factor. This basic model is extended by considering the impact factor of the journals from the previous year as one of the inputsof the model. The results of the study are illustrated in the set of 80
journals from the Web of Science category Operational Research andManagement Science (ORMS). The dataset for the study was obtained
from the Journal Citation Reports in the period from 2017 until 2022.
The relative efficiency scores and the ranking of the journals obtainedby the models are presented and compared with traditional metrics.

3 - Performance Trends in Educational Equity in the OECD:
An International Assessment using Malmquist Indices

Giovanna D’Inverno, Cristina Polo, Gabriela Sicilia, Rosa
Simancas
In this research we assess the performance trends of the OECD education systems in terms of equity. To do that, we apply a novel methodology that combines a robust conditional Benefit of the Doubt (BoD)
model with Malmquist indexes. This approach allows us to compare
the performance of education systems over time based on a comprehensive indicator, without losing its multidimensional nature and respecting the different priorities and peculiarities of each educational
system, making the comparison between countries fairer. This assessment enables us to identify which educational systems have improved
their performance in equity over time (best practices) and which countries have a greater room for improvement. In addition, we assess theimpact of different institutional factors and educational policies (e.g.,early tracking, school choice systems, ability grouping and retention
grade practices, scholarship and voucher programs, among others) on
equity performance, in order to identify effective interventions to promote more equitable education systems.

 TA-48
Tuesday, 8:30-10:00 - Room: 60 (building: 324)

DEA applications in Education and Health II TA-49

Stream: Data Envelopment Analysis and its Application
Invited session

Stochastic lot-sizing problems

Chair: Giovanna D’Inverno
1 - Enhancing Engineering Education: A Novel DEABootstrap Approach for Efficiency and Improvement

Enrique Delahoz-Domínguez, Rohemi Zuluaga-Ortiz, Tomás
Fontalvo-Herrera
This study proposes a novel method for assessing engineering programs. It combines Data Envelopment Analysis (DEA) and Bootstrap
techniques to evaluate the efficiency of educational institutions. By
employing DEA models (CRS and VRS), the approach pinpoints areas
where foundational skills for professional development are lacking, revealing opportunities to enhance educational quality. Furthermore, it

190

Tuesday, 8:30-10:00 - Room: M1 (building: 101)

Stream: Lot Sizing, Lot Scheduling and Production Planning
Invited session
Chair: Martin Grunow
1 - A robust model for the lot-sizing problem with uncertain
demands

Agostinho Agra
We consider the lot-sizing problem with set-ups where the demands
are uncertain. The demand in each time period is assumed to belong
to an interval. We propose a novel approach to evaluate the inventory

EURO 2024 - Copenhagencosts where between two consecutive production periods, the adversary chooses to set the demand either to its higher value or to its lower
value in order to maximize the inventory (holding or backlog) costs.
A mixed-integer model is devised and a column-and-row generation
algorithm is proposed. Computational tests based on random generated instances are conducted to evaluate the model, the decomposition
algorithm, and compare the structure of the solutions from the robustmodel with those from the deterministic model.

2 - Stochastic programming for responsive production operations under uncertain disruptions

formulate this problem as a multi-stage stochastic program and propose a scenario tree approximation. We solve the resulting problem ina rolling horizon procedure. Our preliminary numerical results demonstrate the value of our proposed formulation for production and inventory planning of perishables and the value of substitution.

 TA-50

Arsham Atashi Khoei, Vaggelis Giannikas

Tuesday, 8:30-10:00 - Room: M2 (building: 101)

Manufacturing systems aim to be streamlined and produce innovativeand high-quality products just in time to meet consumer needs. Just-intime manufacturing processes have suffered significantly from disruptions in the supply chain, demand uncertainty, shop floor break downs,
product quality failures, etc. To be responsive to all possible disruption scenarios in the future, first we need to recognize and assess theconsequences of any changes we will have to make under these disruptions. Then, by use of an optimization method, we need to minimise the impact of these unexpected disturbances. For this purpose,
we observe all these disruptions under several possible scenarios anddevelop a stochastic programming formulation. The objective function of this formulation minimises the total expected costs of a production operation that consists of production costs, inventory holding
costs, inspection costs, and the penalty costs for the uncompleted orders. The uncertainties in this formulation stem from machine breakdowns, product quality loss, and demand fluctuations. Since the developed stochastic quadratic integer formulation is not efficient to solve
large instances with a large set of scenarios, we study a heuristic solution approach based on scenario reduction logic. The generated optimization tool will support people in production industries to observe
the consequences of different scenarios with alternative solutions andto make the best decision in terms of imposed costs.

Retail Analytics

3 - A hybrid approach for sales and operations planningunder demand and supply uncertainty

Thorsten Greil, Martin Grunow
Sales and operations planning (S&OP) problems are rarely addressedwith stochastic methodologies since available real-world data often
limits applicability. We address this issue in a case study with a global
electronics manufacturer and develop a hybrid approach to an S&OP
problem under demand and supply uncertainty for multiple products.
Decisions cover the full range of S&OP, i.e. raw materials, production, transport to distribution centers, inventories, and demand fulfillment. To tackle demand uncertainty, we use scenario-based two-stage
stochastic programming. Scenarios are created with readily available
data such as point forecasts and correlated forecast errors. To overcome data limitations, we employ shrinkage covariance estimation.
Furthermore, we address supply lead time uncertainty with parametric cost function approximation for a central raw material’s stock level.
We thus capture the extent to which raw material safety stock should bealtered beyond the level implied by our demand-based stochastic program. We chose this approach instead of extending our scenarios sincecompanies tend to have no regular lead time review process, hardly
store related data in a structured way, and do not forecast lead time.
Another reason is the higher computational burden if we extend scenarios by supply uncertainty. While conducting out-of-sample rolling
horizon evaluation, we optimize the parametrization online and compare our hybrid approach to its deterministic counterparts.

4 - Multi-stage Stochastic Programming for Lot Sizing with
Perishables under Demand Substitution

Bahar Cennet Okumusoglu, Martin Grunow
For perishable products, it is common industry practice to resort tosubstitution in case of stock-outs. In this work, we investigate thestochastic capacitated lot-sizing problem for perishable products underdynamic demand substitution. We consider a single-echelon lot-sizing
setting with multiple products that require the same limited production
capacity. We assume that products have fixed shelf-lives and constantproduction lead times. To represent real-life conditions, we allow forcorrelated product demands that evolve dynamically over time. We

TA-50

Stream: Retail Operations
Invited session
Chair: Arnd Huchzermeier
1 - Long-term Forecasting of Seasonal Goods using Debiased Expert Judgment: Beyond the Pandemic

Arnd Huchzermeier
The problem of forecasting demand for seasonal products became evenmore challenging for retailers amid the Covid-19 pandemic. They are
now faced with the difficult task of forecasting demand in the "newnormal". On the one hand, the pandemic’s future trajectory is hard to
predict. On the other hand, an increase in supply constraints forces
retailers to confirm orders several seasons in advance. Given this increased uncertainty in future demand, we develop a forecasting modelthat combines de-biased expert judgment to utilize their knowledge ofshifts in consumer behavior, the effects of inflation and other global
factors on product demand, and statistical methods well suited for extrapolating repeated patterns from the past. Using data from a premium bicycle manufacturer, we show that accurate demand forecasts
for the next three years can be obtained by integrating experts’ estimates of the category growth rate with a seasonal decomposition of
the pre-pandemic demand curve. Our method yields a forecast error(MAPE) of 18.05%, on average, on data from early 2022. Moreover,
our integrated human-algorithm forecasts perform better than statistical time-series forecasts, indicating that experts play a decisive rolein predicting demand for seasonal products during and after the pandemic.

2 - The Shift from Immediate Discount Coupons to Reward
Points Coupons: Does It Promote Short- and Long-Term
Buying?

Yordan Sölter, Arnd Huchzermeier, Christian Schlereth
Loyalty programs are widely popular amongst firms and consumers.
These programs usually offer reward points for purchases that can beredeemed at the firm to pay for future transactions. Firms may use reward points as an incentive in promotions instead of offering a pricediscount. Offering reward points as incentive in a promotion may address challenges associated with price discounts, such as the direct
margin impact and failing to boost repeat purchases. In using reward
points from their loyalty program as an incentive in promotions (reward points coupons) instead of immediate discounts (price discountcoupons), firms seek to optimize the gross profit from the promotion by
reducing costs and strengthening repeat purchase behavior. We compared both coupons in a field experiment with a retailer, finding: (i)Both boost short-term sales significantly. (ii) Compared to immediate discount coupons, future-credit coupons cut redemption revenues
by 50%, despite higher average transaction spends, due to fewer redemptions. Despite cost benefits, they yield less profit than immediate
discounts. (iii) Redemption of future-credit coupons fails to increase
total spend or repeat purchases post-promotion in comparison to theredemption of an immediate discount coupon. Retailers’ hopes forloyalty points over immediate discounts in promotions don’t pan out.

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TA-51EURO 2024 - Copenhagen

3 - Bespoke Direct Marketing Optimization

Philipp Pithan, Arnd Huchzermeier
Firm growth and profitability are typically achieved through new customer acquisition and loyal customer retention, often relying on nondiscriminatory mass marketing. Given the rise of customer databases
and firms’ natural desire to build lasting customer relationships, retailers increasingly employ direct marketing measures. Yet the associated marketing campaigns tend to be wasteful and myopic, leaving a vast potential untapped. Bespoke Direct Marketing Optimization(BDMO) comprises (i) campaign selection, (ii) campaign sequencing,and (iii) the targeting of (sub)segments for (re)activation while considering multiple objectives. We propose an optimization model thatsolves these decisions simultaneously while adhering to multiple business constraints, incorporating actual customer buying behavior, andaccounting for the multi-period customer migration, which, despite its
major implications, is often ignored by other DMO models. We also
describe a heuristic that can be applied to larger problem instances. We
dramatically reduce the complexity and runtime of the optimization
problems while still obtaining nearly optimal results. Using a realworld planning problem, we perform a numerical study for testing andbenchmarking. There is a limit to the performance effects of increasing the budget for direct marketing models; any improvement beyond
that limit is possible only with models and/or measures that are morebespoke.

4 - The Analytical Edge in Circular Economy: Predictive
Analytics for Reusable Packaging Success

Alexander Niclas Popovic, Arnd Huchzermeier
Driven by environmental pollution concerns and legislative pressure,an increasing number of Industry 5.0 initiatives and impact startups
have recently emerged, focusing on establishing circular economy
business models. One emerging industry is combating plastic waste
through innovative reusable packaging systems.
In this study, we examine the case of the German startup Vytal, which
offers a deposit-free reusable packaging system for take-out food, utilizing serialized containers. We develop a forecasting framework to
estimate the distribution of net demand, consisting of container issues
and returns. Accurate net demand forecasts are critical for optimizing
asset utilization and inventory management of reusables. We propose
a forecasting framework based on a pool of distributed lag models for
estimating container issues and returns. Each model exploits the relationship between container issues and returns to make accurate predictions, and we combine them into an "optimal" model using an adaptive
Markov Chain Monte Carlo algorithm. Furthermore, we examine several extensions of our basic model and analyze the benefits of including additional covariates, as well as leveraging cross-location forecasts
into our model.
We use a unique dataset from Vytal to demonstrate that our proposed
forecasting framework outperforms existing models and algorithms
from both the literature and our case company in terms of forecast
accuracy and computational inference efficiency.

widely explored and continues to be an active area of research. Twomain directions could be revealed. Investor-centric studies seek to directly link ESG to CFP using benchmarks and adopting a portfolio
accordingly, while corporate-focused research explores different elements of performance like innovation and operational efficiency tounderstand the impact of ESG initiatives on CFP. These studies commonly employ correlation analysis, mediating factors, or regression
analysis to disclose those patterns. Notably, some studies employ Data
Envelopment Analysis (DEA) to assess corporate efficiency by minimizing their resources and maximizing ESG. We contribute to this field
by using a benefit-of-the-doubt (BoD) model based on the DEA, which
is used to calculate a composite indicator of ESG for manufacturingcorporates. Then, in the second stage of this model the panel regression
is used to identify elements of CFP and their significance for the ESG
outcome. The results of corporate ESG efficiency is discussed in a viewof corporate operating country, region and development level. The research has received funding from the Research Council of Lithuania(LMTLT), agreement No. S-PD-22-23.

2 - Exploring aspects of risk and sustainability in portfolio composition and Assets and Liabilities Management(ALM) for a Brazilian fintech

Fernando Luiz Pereira de Oliveira, Carolina Vieira, André
Robine, Gustavo Souza
This work presents a case study focusing on the importance of Assets
and Liabilities Management (ALM) in a complex economic scenario,
of a Brazilian fintech company with a significant cash flow of 27 billion
BRL per year. Applying methods from modern portfolio theory, multivariate analysis and data mining, we identify the classifying variables
that affect credit risk and liquidity for this company and demonstrate
how through a data-oriented risk profile one can optimize profitability
with strategies that nonetheless comply with United Nations’ Sustainable Development Goals (SDGs). Specifically, we approach the adequacy of the company’s ALM strategy to SDG 8 (Decent Work andEconomic Growth), which is of particular importance in the Brazilian
context and Latin America in general.

3 - Reputation risk mitigation in investment strategies

Alexandra Moura, Carlos Oliveira
We consider an investment model in which a firm decides to invest
in the market, taking into account its future revenue and the possible
occurrence of adverse events that may impact its reputation. The firm
can buy an insurance contract at the investment time to mitigate reputation risk. The firm decides when to enter the market and the insurance
strategy that maximizes its value. We consider three types of insurance
contracts and different premium principles. We provide analytical conditions for the optimum and study several numerical examples. Results
show that the firm’s optimal strategy depends on the risk size, the firm’s
risk aversion, and the insurance premium.

 TA-52
Tuesday, 8:30-10:00 - Room: 8003 (building: 202)

 TA-51
Tuesday, 8:30-10:00 - Room: M5 (building: 101)

Corporate finance risk management
Stream: Risk management in financeInvited session
Chair: Aleš Kresta
1 - Two-stage DEA model to evaluate corporate efficiency
in sustainability

Kristina Sutiene, Clara Vaz, Raminta Vaitiekuniene
The relationship between environmental, social, and corporate governance (ESG) and corporate financial performance (CFP) has been

192

Mixed Integer Optimization I
Stream: Combinatorial Optimization
Invited session
Chair: Laura Palagi
Chair: Fabio Furini
1 - Benders decomposition for robust and congested partial set covering location

Alice Calamita, Ivana Ljubic, Laura Palagi
We present the congested facility location problem with partial coverage under the assumption of uncertain customer demand. First, we formulate the deterministic problem and its robust counterpart as mixedinteger quadratic problems. Second, since the size of the robust counterpart grows with the number of customers, which could be significant

EURO 2024 - Copenhagenin real-world contexts, we investigate the use of Benders decomposition to effectively reduce the number of variables by projecting out of
the master problem all the variables dependent on the number of customers. We discuss single-tree and multi-tree Benders approaches andintroduce a perturbation technique to deal with the degeneracy of the
Benders subproblem efficiently. Our tailored Benders approaches outperform the state-of-the-art solver Gurobi on adapted instances fromthe literature.

2 - On mines, flows and investments: a new algorithm forthe precedence constrained continuous knapsack problem.

Valerio Dose, Fabio Furini, Marco Locatelli
Imagine a student who must obtain a certain amount of credits to graduate and needs to choose which courses to take. Naturally, they would
like some of them more than others, but it may be the case that to attend
a course they are required to complete also some of the less preferred
ones. Thus, what is the best plan to employ?
This is an example of a precedence constrained knapsack problem incombinatorial optimization. In this talk we will introduce some continuous relaxations of this problem which have applications in the mining
industry, and can be suitably used to model other investment planningproblems.
We will present a new algorithm to compute a solution, which is devised studying the behavior of the classical primal simplex algorithm
when applied to the LP formulation. This reveals one of the very
few examples of a simplex algorithm with an underlying combinatorial
structure.

3 - Critical infrastructures location via submodular maximization

Fabio Furini, Stefano Coniglio, Ivana Ljubic
We study a family of discrete optimization problems asking for the
maximization of the expected value of a concave, strictly increasing,and differentiable function composed with a set-union operator. The
expected value is computed concerning a set of coefficients taking values from a discrete set of scenarios. The function models the utility
function of the decision maker, while the set-union operator models acovering relationship between two ground sets, a set of items, and a setof metaitems. This problem and it can be modeled as a mixed integer
nonlinear program involving binary decision variables associated with
the items and metaitems. Its goal is to find a subset of metaitems thatmaximizes the total utility corresponding to the items it covers. It hasapplications to, among others, maximal covering, location of criticalinfrastructures and influence maximization problems.

 TA-53
Tuesday, 8:30-10:00 - Room: 8007 (building: 202)

Charging Infrastructure toward Sustainable
Transport
Stream: Sustainable and Resilient Systems
Invited session
Chair: Jesper Bang Mikkelsen
1 - Optimal Price Menu Design of Electric Vehicle Charging
Stations

Alix Dupont, Yezekael Hayel, Jean-Baptiste Bréal, Panagiotis
Andrianesis
The growing adoption of Electric Vehicles (EVs) creates new challenges for EV Charging Station Operators (CSOs) due to the increasing
charging demand, but also opportunities to leverage EV user flexibility.
We consider an EV CSO offering several charging power rates at different prices, and we explore the design of a price menu. In our setting,

TA-53

EV users view charging more as an opportunity than as an indispensable need. They are inflexible in their parking duration, which depends
on their on-site activities, but they are flexible in terms of their energy
demand, and choose the option that maximizes their welfare, i.e., theirutility minus their cost of charging. We formulate the optimal pricemenu design problem of a profit maximizing CSO as a mixed integer
linear programming problem, and we compare against the outcome ofsocial welfare maximization. We further account for the provision ofdemand response by the CSO, i.e., lowering its power consumption
for a certain period given a certain price for remuneration, by adjusting the price menu (in real time), to incent EV users to choose lower
power rates. Our numerical demonstrations provide useful insights on
the construction of the optimal price menu.

2 - An optimization-simulation model for locating anelectric vehicle charging station using clean energy
sources..

Zaira Ramirez, Aida Huerta-Barrientos, Alma Elia
Vera-Morales
Nowadays, the countries of the European Union contemplate an energy
mix that includes a high percentage of renewable energies, favorable
for the charging of electric vehicles and for the reduction of polluting gases. Unfortunately, the energy used to charge an electric vehicle
in Mexico comes from a highly polluting energy mix that primarily
uses fossil fuels, which generates 18% of CO2 emissions. Multiple
authors suggest that electric vehicle charging should be done using renewable energy sources. In Mexico, there are no sustainable alternative
energy sources that allow for the charging of electric vehicles, compromising the sustainability of this technology and its development.
This research focuses on the optimal location, through an optimizationsimulation model, of a charging station that transforms energy obtained
from clean sources, which will allow meeting the demand for charging
electric vehicles, leveraging pet waste in the context of parks in Mexico City. First, the Factor Method was used to calculate the optimal
location of charging for electric vehicles. Second, the optimal location
was implemented based on an agent-based simulation approach using
AnylogicTM software to analyze the dynamics of electric vehicles andpets. Third, simulation scenarios were built considering an increase on
demand. The results obtained support decision-making regarding theimplementation of the charger in parks, demonstrating the utilizationof organic waste generated by pets.

3 - Charging Infrastructure Optimization for Independent
Electric Vehicle Routing: A Case Study in Norway

Jesper Bang Mikkelsen, Remy Spliet
Conventional vehicles are characterized by long and reliable driving
ranges and are furthermore supported by a well-established refuelinginfrastructure. Therefore, vehicle refueling has traditionally been ignored in vehicle routing problems in the literature. However, with the
adoption of electric vehicles (EVs) in distribution fleets, a new extension to the vehicle routing literature has emerged. Today, EVs are limited by short driving ranges and, especially for heavy-duty EVs, theinfrastructure of charging stations is still sparse. Hence, much effort
has been made in the literature to develop complex routing models
that integrate routing and refueling decisions for EVs. However, witha sufficient infrastructure of charging stations, vehicle refueling may
yet again be ignored, such that solutions for traditional vehicle routing
problems are feasible for EVs.
In this study, we introduce the Charger Location Problem (CLP), whichaims to determine charging infrastructure requirements for supporting
charger independent EV routing. We present four variations of the
CLP based on how frequently recharging is allowed and the numberof trips considered. Through a case study in Norway, we demonstrate
the significant impact of strategically locating charging stations along
highways. Our findings underscore the importance of proactive infrastructure planning in facilitating the transition to independent EV
routing, offering insights for policymakers and fleet operators.

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TA-54EURO 2024 - Copenhagen TA-54
Tuesday, 8:30-10:00 - Room: S01 (building: 101)

Resilience in Public Transport Planning
Stream: Public Transport Optimization
Invited session
Chair: Yongqiu Zhu
Chair: Xiaojie Luan
1 - Robustness and Resilience Evaluation Method for HighSpeed Railway Timetable

Peiran Han, Lingyun Meng, Xiaojie Luan, Nikola Bešinović,Jianrui Miao, Yihui Wang, Zhengwen Liao
The railway system frequently encounters abnormal events such as disturbances and disruptions, highlighting the importance of evaluating
the resistance capability of train timetables. However, there is limited
research focusing on their boundaries and dynamics. This study contributes to two main aspects: Firstly, to evaluate the functionality ofrobustness, we construct a delay propagation network to measure several indicators (heterogeneity, slack time, and delay). Based on thismethodology, we can clarify the generation and operating mechanismof robustness. Secondly, to quantify the resilience of timetables withdifferent headways, we employ a heuristic approach to obtain saturated
timetables, which serve as input for a rescheduling simulation engine.
We establish several resilience metrics and utilize Data Envelopment
Analysis (DEA) to evaluate the comparative performance of various
timetables across different disruption scenarios. Finally, we apply thisevaluation framework to the Beijing-Shanghai HSR line. The proposed
method provides railway operators with a systematic approach to assess and enhance train timetables, thereby guiding decision-making
processes and contributing to the development of more robust and resilient railway operations.

2 - Delay cascading dominates large-scale disruptions intransport

Debabrata Panja
The dynamical intertwining of many heterogeneous operational elements, agents and locations are oft-cited generic factors to make myriad socio-technical systems such as supply chains, (inter) national tradeand human mobility, prone to large-scale disruptions. These disruptions are emergent phenomena, and their underlying causes are _endogenous, stemming from the systems’ operational planning_. We
demonstrate this by analysing spreading of delays on the Dutch train
network: the emergence of large-scale disruptions rests on the dynamic interdependencies among multiple ‘layers’ of operational elements (services, rolling stock and crew). The interdependencies provide pathways for delay cascading, which gets activated when, constrained by local unavailability of on-time resources, already-delayed
ones are used to operate new services. Cascading locally amplifies delays, which in turn get transported over the network to give rise to new
constraints elsewhere.
Systemically building in (temporal) buffers can arrest delay propagation in socio-technical systems. However, the general tendency in Operations Research, reinforced by competitive pressures, is to myopically in reduced buffers. Ironically, such optimisations can push a system to the proverbial "cliff edge", increasing its fragility to large-scaledisruptions. We therefore draw attention to the proposal that any welfare function that a system operator seeks to optimise should contain ameasure of resilience.

3 - Optimization model and algorithm of train timetablestructure for train timetable resilience enhancement

Ruyue Zhao, Lingyun Meng, Xiaojie Luan, Zhengwen Liao
For fully meeting the transportation demand, railway network has
gradually become density and busy, making a close coupling relationship between trains. However, this leads to the severe delay propagation between trains, when disturbance occurs. And train timetable
resilience gives timetable the ability to resist disturbances. The key

194

to improve the resilience is to reduce the propagation of train delay,
and the structure of train timetable plays a decisive role in this regard.
And heterogeneity of train timetable is a key factor. When the heterogeneity is high, the timetable struc-ture is complicated, and oncethe disturbance occurs, it will constitute a complex delay propagation
chain between trains. So, it is necessary to improve the resilience by
optimization of timetable structure. In this paper, we study the optimization of timetable structure towards the im-provement of resilience.
And we take the maximum train timetable resilience as the objectivefunction, construct an optimization model of train timetable structureand design a two-stage solution algorithm. Besides, the results of casestudies show that the train timetable structure optimization model andalgorithm we proposed is effective. And we further verify by simulation that timetable structure optimization method has a significant
effect on improving the resilience.

4 - A multiobjective model for adaptable train unit scheduling in normal and emergent conditions

Zhiyuan Lin, David Watling
Train unit scheduling optimisation(TUSO) involves allocating train
unit vehicles to fulfil timetabled trips, with the option to couple ordecouple units to allow flexible rolling stock configurations for a train.
TUSO typically balances two conflicting objectives by Pareto multiobjective(MO) optimisation:meeting passenger demand and minimisingrolling stock use. However, certain emergencies, eg adverse weather
or signal failure, require a significantly reduced timetable, making it
challenging to meet full passenger demand with fewer services. In thiscase, a different rule is used that as long as adjusted demand is met,a solution is acceptable regardless of other objectives. This is beyond
the capability of Pareto methods, as setting a large weight on demandis oversimplification. Pareto methods also face challenges in decision
making involving uncertainty/fuzziness. Experts may find real-time
selection of frontier solutions impractical. We propose a novel MO
TUSO model for both normal and emergency cases on a synthetic
TUSO instance. By tuning its parameters, the model can switch between the two, or intermediate states. It provides a probabilistic metricfor gauging solution desirability across a continuum, as seen by different experts/rules via tuned parameters, instead of binary choices in
efficient and inefficient options.This is valuable for uncertainty or realtime scenarios where the ’best’ solution relies on complex factors, eg
hierarchically prioritising demand.

 TA-55
Tuesday, 8:30-10:00 - Room: S02 (building: 101)

ML & OR Applications in Transport
Modelling
Stream: Transportation
Invited session
Chair: Nikola Bešinović
Chair: Maurice Krauth
1 - Ruin-and-recreate heuristics for the fixed charge transportation problem

Gavin Le Roux, Stephan Visagie
Recently, the literature has become too focused on variations of the
fixed charge transportation problem (FCTP), such as solid, step, capacitated or multi-stage, despite a significant number of gaps remaining in the research for pure FCTPs. The research presented is focusedon developing ruin-and-recreate (R&R) heuristics specifically for pure
FCTPs, which have not been implemented before. R&R heuristics are
new methods applied to combinatorial optimisation problems (specifically vehicle routing problems) in which a solution is destructed (possibly reaching an infeasible solution) and rebuilt in an intelligent manner
to maintain feasibility. This is done repeatedly until a desired solution
is reached.

EURO 2024 - Copenhagen
Four ruin methods utilising the tree structure of FCTP basic feasible
solutions, seven recreate methods divided into single-cell and multicell heuristics, and various metrics to determine entering and leaving
variables in each iteration of a ruin or recreate method, are developed.
Different variations of the algorithms are executed on a set of existingand newly generated benchmarks for algorithmic comparisons. Different characteristics of a problem, such as the problem size, fixed charge
to variable cost ratio, average units supplied/demanded and arc density,
are reported on to determine strong performing algorithms in given situations. Lastly, the convergence of the algorithms as well as the tradeoff between solution quality and computational time are explored.

TA-56

freight trains are considered and included in the schedule as requests
arrive. In the first stage, we use a dynamic infrastructure cost structure
to schedule successive trains. In the second stage, we apply the initial solution from the first stage for a subsequent global optimizationof the requested freight train services. The applied two-stage optimisation approach minimises the deviation from requested train paths
and allows determination of critical infrastructure elements for each
included subset of trains. This model improves the quality of freighttrain scheduling and offers a better inclusion of yards leading up to theterminals into the train scheduling.

2 - Two Level Time Minimizing Transportation Problem

Sonia Singh, Ankit Khandelwal
For a given time minimizing transportation problem comprising of msources and n destinations, the set of m sources is to be optimally partitioned into two mutually disjoint subsets L1 and L2 where, L1 contains
m1 sources called Level-I sources and L2 contains the remaining (m− m1) sources termed as Level-II sources. First, the Level-I decision
maker sends the shipment from Level-I sources to partially meet thedemand of destinations. Later, Level-II decision maker sends the material from the Level-II sources to meet the left over demand of thedestinations. A finite number of mixed 0-1 programming problems are
solved to judiciously generate a subset of partitions of the set of msources. The aim of this study is to find an optimal partition of the setof m sources such that the sum of times of transportation in the Level-I
and Level-II shipments is the minimum. The proposed algorithm tofind the global minimizer has been successfully run on a variety of
randomly generated test problems differing in input data.

3 - Enhancing Emergency Response Efficiency via Deep
Reinforcement Learning: A Novel Model for Dynamic
Dispatching

Felix Rauschert, Matthias Ribesmeier, Pascal Kerschke
Emergency Medical Services (EMS) are essential for providing timely
pre-hospital care. Efficient EMS dispatching, a critical yet complex
decision-making process, involves not just quick responses to emergency calls but also optimal resource allocation amidst unpredictabledemands. While traditional dispatch strategies typically prioritize
sending the nearest vehicle, research has shown this approach does notyield the best outcomes. We introduce a comprehensive solution that
integrates factors like scalability, uncertainties, dynamic environments,
and adaptive strategies, thereby enhancing the EMS system’s ability torespond more effectively and efficiently.
We propose a novel approach using Deep Reinforcement Learning(DRL) to refine EMS response efficiency. By integrating key metrics such as emergency severity and response time, we offer a strategic decision-making framework that significantly improves operational
outcomes. Utilizing publicly available data from San Francisco, wehighlight our DRL model’s effectiveness in surpassing traditional dispatching methods. Our approach uniquely leverages a larger stateaction space for DRL and incorporates crucial performance indicators,
making EMS deployment more nuanced and efficient than in previous
studies. This results in faster, more effective incident responses than
traditional strategies, showcasing the potential of advanced machinelearning in emergency dispatch optimization.

4 - Two-stage approach for rail freight terminal schedulingwith mainline passenger traffic interaction

Daniel Haalboom, Nikola Bešinović
In the European rail network, infrastructural limitations of terminals
pose critical bottlenecks for the scheduling of rail freight services. In
addition, passenger traffic on mainlines leading up to the terminals
puts additional constraints on efficient rail freight scheduling possibilities. Thus, integrating terminal activities and mainline operations ina rail freight scheduling approach could significantly improve infrastructure usage. However, interactions between freight and passenger
traffic in freight terminal scheduling have received little attention inscientific literature so far. We propose a two-stage approach to schedule rail freight services in a freight network with interacting mainline
passenger services. The focus is on short-term scheduling, and thus

 TA-56
Tuesday, 8:30-10:00 - Room: S04 (building: 101)

Methods and models for sustainabletransport solutions
Stream: Transportation
Invited session
Chair: Aliki Pouliasi
1 - E-Truck Fleet and Infrastructure Co-design via Joint Optimization and Agent-based Simulation

Juan Pablo Bertucci
In the immediate advent of zero-emission zones fleet operators aretransitioning to electric fleets. To maintain their current operations,a clear understanding of the charging infrastructure needed and its
relationship to existing power grid limitations and fleet schedules isneeded. In this context, we optimize the charging infrastructure andthe routing and charging schedules for a logistic distribution network
in a joint fashion, and validate our results via agent-based simulations.
Specifically, we first cast the joint infrastructure design and operationalscheduling problem as a mixed-integer linear program. Second, we devise an agent-based model that serves as a virtual experimental replica
to test different design and operation solutions. Finally, we run experiments where we compare rule-based with optimized design and operation strategies in key operational metrics and power requirements
in a case-study of The Netherlands. The results indicate that having
local vehicle charging rules helps decrease the impact of uncertainties
in consumption, thus enhancing logistics reliability. However, central
coordination leads to more cost-efficient solutions and less stringent
power requirements.

2 - Estimating greenhouse gas emissions of electric delivery trucks

Tugce Yuksel, Mert Ozcelik, Sinan Yildirim
Previous literature show that emission benefits of electric passenger
vehicles can vary a lot when regional differences such as grid mix,
ambient temperature, patterns of vehicle miles travelled and driving
profiles are considered. In this study, we propose a simulation model
which can be used to extend the analysis to estimate GHG emissions ofelectric delivery trucks considering all these differences. We first develop an energy consumption model for delivery trucks based on realworld usage data. Our model can estimate energy consumption per unit
distance using average speed, ambient temperature, trip duration andacceleration characteristics. We then create a simulation framework
which can be used to estimate the energy consumption from a fleet fora certain timeframe and calculate the emissions due to charging. Our
framework can incorporate regional differences and it can create charging profiles (time and duration of charging) under different strategies
such as immediate charging after the final trip, convenience charging
or delayed charging. The results are combined with emissions factors
from regional electricity grid to estimate use-phase emissions due tocharging. As a case study, we use our simulation framework to quantify the effect of ambient temperature on CO2 emissions of electricdelivery trucks in the United States and present the regional implications.

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TA-57EURO 2024 - Copenhagen

3 - A Stochastic Shared Car Relocation Problem to Optimize Long Term Strategic Inventory Decisions

Mehmet Serdar Erdogan
In one-way flexible car-sharing systems, customers can drop-off therented car at any location inside the operation area of the company andprice of the rental is proportional to distance or duration of the rental.
Rules of this system result in a supply and demand imbalance since
supply of each zone changes dynamically. High-demand zones tend tohave lower supply, and low-demand zones tend to have higher supply.
Therefore, car-sharing companies relocate the cars between zones tomatch the supply and demand. This relocation process is costly andrequires optimization for a profitable business. In this study, we formulate a two-stage stochastic programming model to optimize the relocation decisions in a dynamic one-way flexible car-sharing system.
The model decides inventory levels of each zone at the beginning ofeach time period, number of cars to be relocated between each pairof zones, and unsatisfied requests at each zone to minimize total costof relocation and lost sales.This model is very effective for small andaverage size instances. To solve the large instances, we developed an
approximate clustering method where zones are clustered into multiple clusters. This approximate clustering method can solve instances
up to 40 zones and 36 time periods. To enhance the implementation,
we have also proposed an extended model where the staff movement
was taken into account. Results show that inventory levels at each zone
tend to reach a steady-state.

4 - A bicycle rebalancing model to minimize cost of operations and unmet demand in a docked network

Aliki Pouliasi, Amalia Ι. Nikolopoulou, Konstantinos
Gkiotsalitis, Konstantinos Kepaptsoglou
Bicycle sharing systems have the potential to significantly alleviatetraffic congestion and minimize demand for parking spaces in urban
centers. One critical factor influencing the success of a bicycle sharing system is the effectiveness of rebalancing operations. These operations involve restoring the number of bicycles at each station to its
expected demand through pickup and delivery activities performed bytrucks. The Static Docked Bicycle Rebalancing Problem (SBRP) focuses on determining a cost-effective sequence of visited stations for atruck, along with the corresponding quantity of bicycles to be pickedup or delivered at each station. Deviating from past studies, we propose a new formulation for the SBRP problem which minimizes thecost of rebalancing operations, while factoring in the cost of unmet
passenger demand. The developed mathematical model is a mixedinteger nonlinear program, which is reformulated as a mixed-integer
linear program. Then, our study introduces an exact branch-and-cut
algorithm for addressing the SBRP and presents the results of computational tests. Lazy constraints and valid inequalities are introduced
to improve performance and reduce computational time. The experimental findings demonstrate that we can solve to global optimality instances with up to 20 stations. A construction heuristic (nearest neighbor) is also introduced to provide a warm start to the branch-and-cut
search, resulting in a more effective search of the solution space.

 TA-57
Tuesday, 8:30-10:00 - Room: S06 (building: 101)

Robust decisions in finance andinvestments
Stream: Modern Decision Making in Finance and Insurance
Invited session
Chair: Bertrand Tavin
1 - Real options under ambiguity with rare events

Benoit Chevalier-Roignant, Andrianos Tsekrekos, Athanasios
Yannacopoulos

196

We consider a real-options problem in which the underlying projectvalue follows a geometric or exponential Levy process, capturing rare
events besides continuous fluctuations. Such rare events lead to ambiguity because of inconclusive empirical data or market incompleteness. We use ambiguity theory—leveraging the notion of variational
preferences and $g$-expectations—to pin down for the general case a
pricing kernel under which to value real options and derive the firm’s
optimal exercise strategy under this kernel. We also provide sufficientconditions for the optimality of a threshold policy in the general case.
For the case with multi-priors preferences, we obtain explicit expressions for the optimal investment threshold, expected investment time,and value function and prove comparative statics to assess analyticallythe effect of small jumps on these. Closed-form expressions are notavailable for multipliers preferences, but we provide approximate solutions for the cases with negligible and deep ambiguity. Rare events,
which are priced under ambiguity aversion, generally lead to a higher
investment threshold, delayed investment and higher option value.

2 - On the separation of estimation and control in risksensitive investment problems under incomplete observation

Sebastien Lleo, Wolfgang Runggaldier
A typical approach to tackle stochastic control problems with partial
observation is to separate the control and estimation tasks. However,
it is well known that this separation generally fails to deliver an actual optimal solution for risk-sensitive control problems. This paper
investigates the separability of a general class of risk-sensitive investment management problems when a finite-dimensional filter exists. We
show that the corresponding separated problem, where instead of theunobserved quantities, one considers their conditional filter distribution given the observations, is strictly equivalent to the original control
problem. We widen the applicability of the so-called Modified Zakai
Equation (MZE) for the study of the separated problem and prove that
the MZE simplifies to a PDE in our approach. Furthermore, we derive
criteria for separability. We do not solve the separated control problem
but note that the existence of a finite-dimensional filter leads to a finite
state space for the separated problem. Hence, the difficulty is equivalent to solving a complete observation risk-sensitive problem. Our
results have implications for existing risk-sensitive investment management models with partial observations in that they establish theirseparability. Their implications for future research on new applications
is mainly to provide conditions to ensure separability.

3 - Decision criteria under ambiguity for robust portfolio
choice given a sample of assets’ returns

Eric AndréThis paper applies axiomatic models of decision under ambiguity tothe optimal portfolio choice problem with two objectives: firstly to
account for the probabilistic model uncertainty, secondly to take into
account the information contained in an observed sample of assets’ returns. Especially, a criterion is proposed which is a special case of the
Variational Preferences. Restated in the Bayesian statistics notations, it
is a regularized optimal problem which explores possible probabilistic
models weighted by their likelihood given the observations. Examples and numerical simulations are provided to illustrate its properties,
notably its robustness to misspecified models by comparing its performance to the benchmark portfolios of the literature. More generally,
this paper aims to show that axiomatic models which have been developed to accommodate attitudes toward ambiguity provide a behavioral
justification for aversion to model uncertainty in optimization problems, and lead to practical criteria which take into account the sample
information.

4 - Management of adverse events: risk mitigation or exiting?

Carlos Oliveira, Rita Pimentel
In this presentation, we consider a firm that may face sudden decreases
in its revenue. Its revenue is modeled by a geometric Brownian motion,and the cumulative effect of negative shocks is modeled by a compound
Poisson process. The firm has two options: either to exit the market or
to adopt risk mitigation measures to reduce the impact of the revenue

EURO 2024 - Copenhagendecrease. The firm’s option value is modeled as an optimal stopping
problem, which we analyze in this presentation. Furthermore, we examine the impact of protective strategies on the firm’s option value.

 TA-58
Tuesday, 8:30-10:00 - Room: S07 (building: 101)

Warehouse Operations
Stream: VeRoLog - Vehicle Routing and Logistics
Invited session
Chair: Fabio Sgarbossa
1 - Scheduling of Retrieval Operations in an Automatic
Warehouse: Models and Algorithms (Preliminary Exploration)

Runjie Li, Xiangtong Qi
The automatic warehouse discussed in this article is based on a puzzlebased storage (PBS) system with block movement, which allows multiple items in a line to move simultaneously, and simultaneous movement, which allows multiple movements to occur at the same time.
PBS is one of the most space-efficient types of storage systems, andthe way of block and simultaneous movement makes it more timeefficient. However, retrieving multiple target items from this kind of
automatic warehouse has not yet been rigorously studied. In this paper,
we present a time-expanded based on mixed integral linear programming (MILP) formulation that aims to minimize the summation timeof retrieving multiple target loads. Another objective of minimizing
the makespan can also be minimized to serve the objective of summation retrieving time. In addition, constructive algorithms are developed
for fast obtaining retrieving plan for multiple target items based on
the shortest unimpeded retrieval for some special cases. Experiments
show that the proposed formulation can solve small to medium size
instances, especially when the storage density is not extremely high.
We also conducted experiments to explore the efficiency of the proposed automatic warehouse and demonstrate the utility rate of empty
locations.

2 - Integer Linear Programming Model for Sequencing andStorage Assignment Operations in a Multi-Aisle AS/RS
With Non-Dedicated Cranes

Edgar E. Córdoba-Sarmiento, Edwin Garavito, Laura Y.
Escobar-Rodríguez
In recent years, automatization has taken on a relevant role in the lowskilled operations of different productive sectors. One of the mostimpacted sectors is logistics, which significantly influences producthandling, warehousing, and shipping processes. In this context, Automated Storage and Retrieval Systems, AS/RS, become an ideal solution for logistics warehousing systems. Such implementation allows
inventory management based on storage and retrieval orders optimizing the use of available physical space and material handling processes
and improving system performance in terms of storage and retrieval
times, material flow, energy consumption, operating costs, and otherassociated variable measurements. Multiple physical and control characteristics make up the global design of an AS/RS, involving the typeof system technology, the number of aisles, the rack dimensions, storage and sequencing policies, among others. In this work, we studied
sequencing and storage assignment operations in a multi-aisle AS/RS
with class-based storage, block sequencing strategy, and non-dedicatedcranes. We proposed an Integer Linear Programming model to findthe best operation decisions, considering the dynamic behavior of the
AS/RS and evaluating some control decisions related to these operations. Our findings showed that the fill grade factor, the size of theclasses, and the size of the sequencing block affect the performance ofthe system in terms of travel time metrics.

TA-59

3 - Let Customers Scatter the Inventory: A Multi-Objective
Storage Location Assignment in Warehouses

Mostafa Bahadornia, Katrien Ramaekers, Kris Braekers,
Trijntje Cornelissens
The rapid growth of online retailing necessitates flexible warehousemanagement strategies to adapt to this evolving landscape. One of the
key challenges in this area is to reduce the order-picking travel distance. This travel distance is highly affected by the Storage Location
Assignment (SLA) decision, which determines how products are allocated to locations in the warehouse. This study aims to develop a mixed
SLA strategy which tries to adopt different SLA strategies to some degree that is tailored to the customer order pattern. To do so, four criteria are defined to assess the SLA state: (a) Scatteredness: increases theaccessibility of each Stock Keeping Unit (SKU) by spreading its units
through the storage locations. (b) Integrity: avoids collecting a single order-line from multiple locations by keeping sufficient number of
each SKU in its storage location(s). (c) Association: stores correlated
SKUs close to each other. (d) Depot adjacency: stores high-demand
SKUs near the depot(s). To address the dynamic nature of businessneeds, a data-driven approach is introduced to weigh each criterion.
Then, A multi-objective mathematical model, incorporating contextual constraints and these weighted measures, is proposed to optimize
the SLA. As the ultimate goal is to reduce the order-picking travel distance, the proposed model and hypothesis will be validated for thisgoal under various business environments.

4 - Library Logistics Operations: An Analytical Study on
Material Handling Processes

Fabio Sgarbossa, Niloofar Jefroy, Daniela Granados, Debjit
Roy
The library users’ experience depends on the library’s performance on
their operations. Material handling processes are crucial to improvethose library’s operations and service levels, assuring books and items
availability. Even if a flow of research has flourished into implementing new solutions, e.g. Autonomous Mobile Robots (AMRs), in response to operational challenges that limit library performance, scientific literature still lacks modeling such solutions. Hence, this study
aims to investigate such solutions and their impact on improving theoverall performance of library logistics operations. With the Trondheim Public Library as a case study, several scenarios are analyzed. We
considered two layouts: centralized and decentralized with different
modes to transport the materials: manual and AMRs. We modeled thescenarios through Queueing Theory. For manual movements, we used
Open Queueing networks (OQN), while scenarios used with AMRs are
Semi-open Queuing networks (SOQN). SOQN has shown promising
applicability in similar contexts to support the decision-making processes for planning and controlling the material flow. The proposed
Queueing networks are validated through simulation. Based on thisand varying critical parameters such as flow volume, intensity, variability, library layout, and operational modes, we obtain performancemeasures to characterize the analyzed scenarios for library logistics
operations as well as the advantages and disadvantages.

 TA-59
Tuesday, 8:30-10:00 - Room: S08 (building: 101)

Learning and pricing
Stream: Pricing and Revenue Management
Invited session
Chair: Michi Nishihara
1 - Good stuff: Matching supply and demand by introducing an opaque product: Pricing, learning, fun.

Laura Sprenkels

197TA-60EURO 2024 - Copenhagen

In this project we help a retailer increase his revenue and decrease his
waste by introducing a new selling strategy. Instead of offering his
original, fixed priced assortment, he will additionally sell an ’opaque
product’ with a dynamic price as he sees fit. As he does not now how
his clientele will respond to this new product and its price, we help
him to find the right balance between earning (by optimizing his revenue) and learning (by gathering enough information to be able to estimate the effect of the new product). An interesting problem with manymathematical challenges.

2 - AI meets Sustainability: Using Digital Twins to Leverage Reinforcement Learning - Based Dynamic Pricing
in Circular Markets under Competition

Rainer Schlosser
Nowadays, customers as well as retailers look for increased sustainability. Recommerce markets - which offer the opportunity to tradein and resell used products - are constantly growing and help to use
resources more efficiently. To manage the additional prices for the
trade-in and the resell of used product versions challenges retailers as
substitution and cannibalization effects have to be taken into account.
An unknown customer behavior as well as competition with other merchants regarding both sales and buying back resources further increases
the problem’s complexity. Reinforcement learning (RL) algorithms offer the potential to deal with such tasks. However, before being appliedin practice, self-learning algorithms need to be tested synthetically to
examine whether and which work in different market scenarios. We
evaluate and compare different state-of-the-art RL algorithms withina recommerce market simulation framework. We find that RL agents
outperform rule-based benchmark strategies in duopoly and oligopoly
scenarios. Further, we investigate the competition between RL agents
via self-play and study how performance results are affected if moreor less information is observable. Using an ablation study, we test the
influence of various model parameters and infer managerial insights.
Finally, to be able to apply self-learning agents in practice, we show
how to calibrate synthetic test environments (digital twins) from data
to be used for effective pre-training.

3 - The effects of a financial covenant on optimal capital
structure and firm value

Michi Nishihara, Takashi Shibata, Benoit Chevalier-Roignant
This paper develops a capital structure model with a financial covenantthat sets an upper limit on a firm’s debt-earnings ratio. Shareholders
will reduce debt or default when the ratio exceeds the upper limit. In
the model, firm value, debt repayment policy, and capital structure arederived explicitly. For low levels of the limit, shareholders prefer toreduce debt every time the ratio exceeds the limit. Then, the covenant
removes cost of debt, while it decreases equity value by restricting
shareholders. By this trade-off, the covenant can improve firm value.
The covenant can also improve firm value by suppressing the leverage ratchet effect and removing the restriction on future debt issuance.
With the covenant, the firm can begin with high leverage to take advantage of no cost of debt. The covenant tends to improve firm value forhigher bankruptcy cost and volatility, and the additional debt channel
can greatly improve firm value for higher growth and tax rates. These
results are consistent with empirical evidence and support the optimal
contracting hypothesis regarding debt covenants.

 TA-60
Tuesday, 8:30-10:00 - Room: S09 (building: 101)

Project Management
Stream: Project Management and Scheduling
Invited session
Chair: Luis Quezada
1 - Best Practices of Digital Transformation for Managing
Large Industrial Maintenance Projects

198

Dimitrios Emiris
Industrial maintenance projects, known as shutdown projects, arehighly impacted by the recent growth of digital transformation (DT)efforts. Technological and systemic advancements, sensors integration, data analytics tools, information systems, etc., are some of thefactors constantly affecting and changing the operation of most industrial companies. This work drills down to efficient approaches in DT
aspects of major maintenance projects and condenses the on-field experience from a dozen such projects over the past decade. Specifically,
it highlights best practices on technological, systemic and managerial
actions, such as: (i) the gradual development of knowledge databases
to improve project planning efficiency by up to 38%; (ii) digital collaboration techniques to elicit requirements and reduce scope creep byalmost 16%; (iii) data analysis from on-board sensors to diagnose theoperational condition and proactively identify maintenance needs; (iv)the use of digital maps of industrial plants for optimization of works
sequences to respect safety constraints; and (v) the development ofdigital tools for near-real time collection of progress data from remotesites and diverse stakeholders to quickly update plans, forecast reliably,
identify issues and cultivate employee engagement. The presentationof the temporal evolution and development of implemented solutions
for each best practice highlights the combinatorial benefits of DT andestablishes guidelines for the future.

2 - Exploring and Prioritizing Critical Success Factors(CSFs) in Project Management Utilizing MADM approach and PMBOK guide

Ashkan Keykavoussi, Ahmad Ebrahimi
This article explores Critical Success Factors (CSFs) in project management, focusing on key elements for project success. By analyzing common CSFs such as clear and realistic project objectives andstrategies, top management support and commitment, effective projectplanning and scheduling, stakeholder engagement, effective communication, risk management, optimal resource allocation, a competentand motivated team with clear roles and responsibilities, adaptability
to change, and leadership, alongside more specific factors, the researchaims to deepen the understanding of the factors that drive project success. Through a comprehensive literature review and utilization ofMulti-Attribute Decision Making (MADM) methods, the study provides valuable insights to enhance project outcomes and achieve organizational goals. By ranking and prioritizing these critical factors
based on input from experienced project managers and practitioners in
the field, using expert judgment and the Project Management Body ofKnowledge (PMBOK) guide, the research offers practical recommendations to enhance project performance and ensure successful projectdelivery. This effort aims to bridge the gap between project management theory and practice, ultimately leading to more effective project
results.

3 - Decision model for prioritizing public investment
projects in a health service

Luis Quezada, Diego Pozas, Astrid Oddershede, Pedro
Palominos
This paper introduces a methodology for prioritizing investment initiatives within a Regional Health Service. The absence of a structured
methodology directly impacts the timely delivery of solutions in public health to citizens. Currently, investment priorities lack clear definition and justification, affecting the focus on crucial citizen issues that
require prioritization in both the planning and execution of the investment portfolio.
To face this challenge, the Analytic Hierarchy Process (AHP) is employed. The prioritization relies on the judgments of experts and stakeholders engaged in the Health Service. Recognizing the constraints oflimited financial resources, a method for scheduling projects over timeis also incorporated.
The result of this model is a robust approach that enables the effective prioritization of public investment projects outlined in the portfolio. This, in turn, addresses the needs of citizens in public health withprojects that materialize within more limited timelines and are basedin criteria justifying the allocation of resources.

EURO 2024 - Copenhagen
4 - Batching and scheduling with OptalCP

Diego Olivier Fernandez Pons
In production optimization for manufacturing, directly scheduling the
production lines to fulfil the make-to-order, make-to-stock and inventory needs is usually too computationally demanding. Commonly,
the problem is divided into a batching phase that creates production
batches and pre-assigns them to orders, and a scheduling phase thatassigns the batches to machines while minimizing completion time.
This decomposition is usually difficult to adjust, as a too tight batchingphase may result in a solution that is far from optimality or even makethe scheduling sub-problem infeasible, when a too loose one will resultin a scheduling problem that is too hard.
Scheduling engines have gained popularity for the scheduling phase
due to their ability to quickly return solutions of high quality while
supporting a large variety of scheduling constraints, when the batchingphase is usually done with mixed-integer linear programming.
OptalCP is a state-of-the-art scheduling engine in the lineage of IBM
ILOG CP Optimizer, that first started providing a MIP-like experiencefor scheduling engines (lower bounds, model & run). We have extended it to also solve batching and scheduling problems without decomposition.

 TA-61
Tuesday, 8:30-10:00 - Room: S10 (building: 101)

Covering Location Problems
Stream: Locational Analysis
Invited session
Chair: Esra Karasakal
1 - Valid inequalities for the capacitated facility location
problem with preference constraints

Sophia Wrede, Christina Büsing, Markus Leitner
In the classical capacitated facility location problem (CFLP), a set of
facilities needs to be chosen in order to cover the demand of customers.
Customers are assigned to any open facility such that the capacity of
the facility is not exceeded and the total cost consisting of opening andassignment costs is minimised. However, in many real-world applications customers are not willing to travel to any open facility assigned tothem but want to select an open facility according to their preferences.
Such deviations can turn feasible solutions for the CFLP infeasible.
The capacitated facility location problem with preference constraints(CFLP-PC) takes this behavior into account by assigning customers
to their most preferred open facility. In this talk, we focus on two
types of cover-based inequalities for the CFLP-PC. The first type corresponds to the classical cover inequalities, which we strengthen bytaking advantage of a specific structure arising from the combinationof capacities and preference constraints. In the second inequality type,we make use of information on the set of open facilities that arises
from covers. We discuss the complexity of the corresponding separation problems and evaluate the performance of the inequalities for two
preference types in a computational study.

2 - Multi-product maximal covering second-level facility location problem

Luisa I. Martínez-Merino, Marta Baldomero-Naranjo,
Antonio Manuel Rodriguez-Chia
In this work, we consider a hierarchical facility framework consisting in three levels. The first-level is composed by a set of facilities
which manufacture certain products, the second-level facilities act as
warehouses, and finally, the clients demanding some of these products
compose the third-level. The products must be supplied to the clientsby using the second-level facilities. It should be noted that, in order tosatisfy the customers’ demand, there must be a double coverage: theTA-62

client must be covered by a second-level facility, and this, in turn, by
a first-level facility. This kind of problem can be identified in several
applications as healthcare systems, telecommunication networks or ecommerce. The aim of the proposed model, denoted as SL-MCFLP,
is to determine the location and product configuration of second-level
facilities in such a way that the overall client satisfaction with respect
their coverage is maximized. We propose a mixed integer linear programming formulation, and we introduce several valid inequalities toimprove its performance time. In some cases, the number of valid inequalities is exponential and, consequently, separation algorithms aredeveloped. In addition, several variants of a matheuristic approach are
introduced.

3 - Downgrading edges in the maximal covering location
problem

Marta Baldomero-Naranjo, Jörg Kalcsics, Antonio ManuelRodriguez-Chia
In this presentation, we tackle the downgrading maximal covering location problem within a network. In this problem, two decisions are
made: determining the facility locations and increasing edge lengths toreduce coverage. Two actors with conflicting objectives are involved:a) The location planner aims to maximize the covered demand while
anticipating that an attacker will attempt to reduce coverage by increasing the length of the edges. b) The attacker seeks to maximize thedemand initially covered by the facilities but left uncovered after thedowngrade. To achieve this, they can increase the length of certain
edges within a specified budget.
We introduce a bilevel mixed-integer program to formulate the problem, a preprocessing phase, and a matheuristic algorithm to address
it. Additionally, computational results are presented to illustrate thepotential and limitations of the proposed algorithm.

4 - A robust maximal covering location model considering
partial coverage

Esra Karasakal, Burak Köksal, Orhan Karasakal
Maximal Coverage Location Problem (MCLP) attempts to find a predetermined number of facilities to maximize the number of demand
points that can be covered. In MCLP, while all demand points withina critical distance of a facility are completely covered, demand points
outside this region are not covered at all. In Partial MCLP (MCLPP), another critical distance is introduced, which allows coverage between two critical distances, monotonically decreasing with respect
to demand points’ distance from facilities. In this study, we explore
MCLP-P under coverage uncertainty. We utilize a robust optimization
framework and introduce an approach to hedge against uncertainty. We
present the model and the solution approaches and compare the performance of the proposed solution approaches on randomly generated
datasets.

 TA-62
Tuesday, 8:30-10:00 - Room: S12 (building: 101)

Seaside Planning III
Stream: OR in Port Operations
Invited session
Chair: Filipe Rodrigues
Chair: Frank Meisel
1 - An integrated Berth Allocation and Quay Crane Assingment Problem with Missed Transshipments Recovery

Baptiste Coutton, Dario Pacino, Alberto Delgado, Ignacio
Blanco199

TA-63EURO 2024 - Copenhagen

Global freight transportation relies heavily on maritime shipping using
containers. To avoid congestion and disruptions in supply chains, port
terminals need to plan their operations carefully to make sure ships areloaded and unloaded on time. At a terminal, it is common that somecontainers need to be transshipped between vessels. A missed connection requires finding alternative routes and can have a significant
negative impact on delivery times and customer satisfaction. However,forcing a planned transshipment to happen under unfavorable circumstances can be very expensive if it affects other vessels calling the port,
while allowing it to be missed and using alternative routes could bedone at low cost. Therefore, terminal operators are frequently facing
trade-offs that are challenging to arbitrate manually due to the scale ofoperations. In this context, the use of OR-based decision-support tools
can be of great help. We present a mathematical model of a problem
where the objective is at the same time to 1) find a berthing and quay
crane allocation plan for the vessels calling a terminal over a planninghorizon and 2) assign an alternative route to each container that missesa transshipment, at a minimum total cost. We call it the Berth Allocation and Quay Crane Assignment Problem with Missed Transshipment
Recovery. Besides a MIP formulation, we present resolution methods
for a set of instances derived from industrial cases and provide insights
about the solutions

2 - A novel robust exact decomposition algorithm for berthand quay crane allocation and scheduling problem considering uncertainty in worker productivity

In this study, we address the integrated production planning, berthallocation, and quay crane scheduling problem. The problem is formulated as a Mixed-Integer Linear Programming (MILP) model withthe objective of maximizing total revenue while considering demurrage costs. The computational results are derived from both real-life
instances and generated instances associated with the OCP Safi complex; an industrial fertilizer site operated by the OCP Group. The results show that we achieve near-optimal solutions in less than one hourusing the CPLEX MIP solver.

 TA-63
Tuesday, 8:30-10:00 - Room: S14 (building: 101)

Models for Financial Data and Risk
Management
Stream: OR in Banking, Finance and Insurance: New
Tools for Risk Management
Invited session

Kaoutar Chargui, Tarik Zouadi, Tarik Chargui

Chair: Alessandro Mazzoccoli

This study proposes a novel robust version of berth and quay crane allocation and scheduling problem integrated with a quay crane worker
assignment problem. To align with on-field data, we consider in thisversion that the processing rates of the quay cranes workers is not deterministic, but subject to uncertainties. The uncertainty of this parameter is addressed using robust optimization, by considering an uncertainty set of scenarios. A novel two-stage mixed integer linear programming model is proposed to minimize the worst-case tardiness ofvessels and find the worst-case scenario from the uncertainty set. The
model is first tested using a commercial solver, but it could not be
solved, even for small instances. Thus, as we noticed that the problem has a decomposable structure, we developed an exact decomposition algorithm that splits the model into a master problem and a setof subproblems to alleviate the complexity. We also strengthened thealgorithm by introducing novel (re)formulation enhancement, valid inequalities and lower bounds. To assess the performance of the proposed algorithm, we conducted a series of computational experiments
on a dataset of instances designed from a real-case database of a container terminal. Also, we performed a sensitivity analysis to evaluate
the value of considering a robust version of the model compared to a
non-robust version.

1 - Detecting patterns in financial data through timefrequency domain clustering

3 - Distributionally robust optimization for the berth allocation problem under uncertainty

Filipe Rodrigues, Agostinho Agra
There are several optimization problems occurring in port terminals,
and the berth allocation problem (BAP) is amongst the most important
ones. The BAP is highly affected by uncertainty due to many factors
like weather conditions and mechanical failures. Therefore, it is crucial to take uncertainty into account when approaching BAPs. Here,
we study the BAP under a distributionally robust optimization (DRO)
approach. We assume that each vessel has an associated deadline to finish its operations, but delays can occur. Therefore, we aim to minimize
the worst-case of the expected sum of delays of vessels with respect toa set of possible probability distributions of the handling times. The
proposed model is solved by an exact algorithm enhanced with several
improvement strategies that drastically reduce the associated running
time. The DRO model depends on a risk-parameter that makes it possible to obtain solutions with different degrees of protection against
the risk. Extensive computational results comparing DRO, stochastic
programming, and robust optimization are also reported.

4 - A MILP formulation for Integrated Production Planning,
Berth Allocation, and Quay Crane Scheduling Problem
in tidal ports with multiple quays

200

Nizar El Hachemi, Ibtissam Guissou, Michel Fender, Tarik
Aouam, Agnes Gorge

Francesca FortunaThis talk introduces a strategy for detecting representative patterns ina time series of financial data. The proposal can be framed in Functional Data Analysis, where the time series is considered as a sample
observed on a discrete time grid of an underlying continuous function. We are interested in studying the dynamics of this time series
from the perspectives of temporal evolution and frequency content. To
this end, we adopt a wavelet transformation of the raw time series torecover the underlying function. Wavelets provide a multi-resolution
representation where the signal is represented as the sum of a coarse
signal approximation and a set of multiscale detail coefficients providing information about the temporal data at different frequency levels.
This allows highlighting trends and cycles in the time domain, also assessing variance at different frequency levels. Our contribution is thedevelopment of a clustering method for Functional Data, in which thetime series, represented as a matrix of wavelet coefficients, is navigatedto discover interesting patterns. We propose splitting the time domain
into non-overlapping frames and clustering the data by a functional
k-means. In this approach, each centroid is a representative patternof time frames. The distance used to compare data is an optimallyweighted Euclidean distance giving weights to frequency components.
In this sense, we propose a new clustering objective function and analgorithm that allows optimizing it.

2 - Wavelet-Entropy Risk-Predictability Measure for financial time series

Alessandro Mazzoccoli, Loretta Mastroeni
Data has emerged as one of humanity’s most critical resources. Specifically, the endeavor to forecast future events using data has garneredwidespread attention. However, heightened volatility, infrequent occurrences, and rare events hinder data predictability, consequently elevating risk levels. Consequently, the inability to accurately predict future events exacerbates uncertainty and variability within a given scenario, signaling a subsequent rise in risk. In this paper, we examine
data predictability by introducing a novel metric based on entropy andthe wavelet transform. Notably, we demonstrate that data exhibit lesspredictability than anticipated due to the aforementioned fluctuations
and low-frequency events. Moreover, we employ our methodology
on real-world data, particularly focusing on commodity time series.
Consequently, with this new metric, we ascertain a notable degree of
unpredictability in the price time series under scrutiny, attributable toheightened volatility and the impact of low-frequency events.

EURO 2024 - Copenhagen
3 - Waveform Dictionary Matching Pursuit for Denoising
Step Function Signals in Finance

Pierluigi Vellucci
In signal analysis, noise often obscures crucial patterns in processes ofinterest, particularly in economic contexts where factors like liquidity
trades and agent errors contribute to data distortion. Addressing thischallenge involves managing noise through mathematical approaches.
One common scenario arises when denoising signals characterized bystep functions with numerous discontinuities, prevalent in economic
and financial time series. The violation of stationarity assumptions
complicates noise removal, often mischaracterizing noisy points as signal discontinuities, leading to erroneous forecasting and inference.
This paper proposes a flexible denoising approach leveraging a sparse
representation framework, particularly focusing on approximating step
functions using rectangular functions. The method adopts the WeakOrthogonal Greedy Algorithm (WOGA) or matching pursuit (MP) to
iteratively remove noise from step function-derived signals. By selecting elementary functions from a highly redundant set, the algorithm
constructs an approximate orthogonal basis expansion of the signal,
effectively capturing its discontinuities.
The paper establishes theoretical foundations for the proposed method,
demonstrating its efficacy in capturing discontinuities while simplifying calculations. Specifically, theorems elucidate optimal conditions
for waveform dictionary selection, facilitating efficient denoising iterations.

4 - Corporate Sustainability Committment and marker risk

Rita D’Ecclesia, Kevyn Stefanelli, Susanna Levantesi
The impact of Environmental Social and Governance (ESG) factors
on the performance of financial stocks are still controversial in the literature. We aim to identify groups of companies according to their
ESG temporal dynamics to evaluate whether their fluctuations impact
the market risk factors. We assess a potential relationship between financial and non-financial risks within the Fama and French framework(Fama and French, 2015) according to the ESG score and its components. We discriminate companies that increase their ESG complianceusing a hierarchical time-series clustering approach and computing thesimilarity between two-time series by Euclidean distance and Dynamic
Time Warping (DTW). The latter is useful when the time series havedifferen shifts and speeds. We compare four hierarchical methods withdifferent linkage criteria (average, complete, Ward.D, Ward.D2). The
optimal number of clusters is selected based on 4 cluster validity indices (Silhouette, Calinski-Harabasz, Clustering Order Preservation,
and Dunn). The data utilized in this paper refer to the S&P500 andcome from the Renitiv database. The CVIs indicate two clusters as thebest choice. However, while the Euclidian distance with the averagemethod is the best combination for the ESG score and its components
E and S, the DTW distance with the ward.D method is the best for the
G score. Overall, we find different effects on the market risk, which
depend on the ESG component TA-64
Tuesday, 8:30-10:00 - Room: S16 (building: 101)

Vehicle Routing Under Uncertainty 2
Stream: VeRoLog - Vehicle Routing and Logistics
Invited session
Chair: Daniel Santos
1 - A Pick-Up and Delivery Vehicle Routing Problem With
Stochastic Demands and Unordered Customers

Epaminondas Kyriakidis, Theodosis Dimitrakos, Andreas
Papasalouros

TA-64

A vehicle starts its route from a depot and visits N unordered customers. New goods are delivered to the customers and expired goods
are collected from them. The demands of the customers are stochastic
with known distributions. The actual demands are revealed when thevehicle arrives at each customer’s site. During its route, the vehicle
may return to the depot in order to unload expired goods and to restockwith new goods. The cost structure includes travel costs between customers and travel costs between each customer and the depot. Afterthe first visit at each customer’s site two decisions must be made. The
first decision is to choose the next customer that the vehicle will visit.
The second decision is to choose how the vehicle will go to the next
customer. The problem is to find (i) the minimum total expected costfor servicing all customers and (ii) the optimal decisions that must be
made after the first visit at each customer’s site. A suitable stochastic
dynamic programming algorithm is developed for the solution of theabove problem.

2 - Solution Strategies for the Stochastic Dynamic Contagious Disease Testing Program

Emilio Jose Alarcon Ortega, David Wolfinger, Margaretha
Gansterer, Karl Doerner
In this talk, we present a stochastic and dynamic variant of the Control
Disease Testing Program (CDTP) that originated in the late COVID19 global pandemic. The CDTS was proven to be a key strategy tocontain and control the pandemic, where suspected cases required toremain isolated and be tested. These results must be available quickly
for the CDTS to be effective. We address the stochastic and dynamic
version of the problem remark: repetition of the first sentence, maybe
this part can be deleted, where only some suspected cases are known inadvance, and new suspected cases could appear randomly throughout
the course of the day or planning horizon. When a new suspected casearrives, it must be decided whether to include the new test request in
the current plan or reject it. The specimens of the accepted requests
must be collected on the same day; either by assigning the case to atime slot in a test-center or by visiting the patient with a mobile testteam. On the other hand, rejected requests must be included in the planfor the next day. The aim of this problem is to decide how many mobile test-teams to use, how many test-centers to open and where, whichsuspected cases to visit with a mobile test-team and which to assign toa test-center, and design the vehicle routes for the mobile test-teams.
To solve this problem, we propose a solution method based on valuefunction approximation and compare the effectiveness of the algorithmwith respect to benchmark solution approa

3 - PyVRP and ICD: Results From the EURO Meets NeurIPS
2022 Vehicle Routing Competition

Leon Lan, Niels Wouda, Wouter Kool, Jasper van Doorn,
Arpan Rijal, Sandjai Bhulai
In this talk, we highlight our work that secured first and third place inthe EURO Meets NeurIPS 2022 Vehicle Routing Competition on thestatic and dynamic track, respectively. First, we introduce PyVRP, an
open-source and state-of-the-art VRP solver in Python that builds on
Thibaut Vidal’s hybrid genetic search algorithm. Through PyVRP, wehope to provide researchers and practitioners the means to build upona state-of-the-art VRP solver easily and quickly without diving deep
into the algorithmic details. Second, we present iterative conditional
dispatch (ICD): a simple yet effective algorithm for dynamic vehicle
routing problems with stochastic requests. ICD iteratively solves sample scenarios to classify requests to be dispatched, postponed, or undecided. The set of undecided requests shrinks in each iteration until
a final dispatching decision is made in the last iteration. A significant
strength of ICD is that it is conceptually simple and easy to implement. This simplicity does not harm performance: we show that ICD
can nearly match the winning machine learning-based strategy of theEURO Meets NeurIPS 2022 Vehicle Routing Competition. PyVRP
and ICD are forthcoming in the INFORMS Journal on Computing andTransportation Science, respectively.

4 - Synchronization in Vehicle Routing:
Simulation Approach

Optimization-

Daniel Santos, Raquel Bernardino, Filippo Visintin201

TB-01EURO 2024 - Copenhagen

In this presentation, we address the vehicle routing problem with time
windows and synchronized visits with uncertain service and travel
times. Specifically, a subset of the clients require simultaneous service
by two vehicles, which is initiated only after the arrival of both vehicles
and, consequently, enforces a waiting period for the vehicle that arrived
earlier. To tackle this problem, we propose an optimization-simulation
framework. Through an iterative process, a stochastic optimization
model generates potential solutions which are then assessed througha simulation model. If the solution is found to lead to a bad average
and/or worst-case performance, new scenarios suggested by the simulation are added to the stochastic optimization model. This iterative
process continues until all performance criteria are satisfied. We validate our approach through experiments on benchmark instances fromthe literature.

Tuesday, 10:30-12:00
 TB-01
Tuesday, 10:30-12:00 - Room: Sportshallen (building: 101)

Emma Frejinger
Stream: Keynotes
Keynote session
Chair: Virginie Lurkin
1 - A Contextual Stochastic Optimization Perspective on
Demand Prediction for Decision Making

Emma Frejinger
Decision makers are often faced with problems that are subject to uncertainty. Consider the problem of planning transport services for an
upcoming season, determining optimal locations of new infrastructure,or establishing production plans and pricing strategies for a product.
In this context, demand uncertainty is challenging to deal with, notably because it is decision-dependent. In this talk, we discuss data
and modeling challenges associated with understanding and predicting
demand. Focusing on the competitive facility location problem, we
describe a methodology to deal with decision-dependent demand uncertainty without making strong distributional assumptions. We also
provide a high-level overview of contextual stochastic optimization.
Studied in the literature under a variety of names, contextual optimization refers to data-driven approaches to prescribe decisions by exploiting relevant side information. We position demand modeling for decision making in this context and outline future research directions on
integrated learning and optimization.

 TB-03
Tuesday, 10:30-12:00 - Room: 1005 (building: 202)

Machine Learning in Applied Optimization
Stream: Data Science Meets Optimization
Invited session
Chair: Michael Römer
1 - Learning optimal courier assignments in on-demanddelivery platforms

Gökhan Ceyhan, Pol Arias
On-demand delivery platforms are characterised by uncertain demandwith spatiotemporal patterns and short delivery time targets. Platform
operators manage a courier fleet aiming to deliver the orders as quickly
as possible and in a cost-efficient way. A core component of the platform is the courier assignment algorithm assigning incoming orders tocouriers in real-time and determining a sequence of pickups and deliveries for each courier. Column generation is commonly used to solvethese types of problems in practice: First, a set of feasible columns isgenerated via sequencing algorithms. Then, a set-partitioning problem
is solved with the generated set of columns. As the size of the problem
increases with the number of columns included in the problem, it isessential to include only the columns that are likely to be optimal in
the problem. Our study aims to learn the optimal columns via machinelearning utilising the characteristics of the problem instance and thefeasible decision space. We then improve the efficiency and the solution quality of the algorithm by favouring the columns that are more
likely to be part of the optimal solution. We conduct computational
studies on a set of historical instances utilising the optimality predictions generated by the trained machine learning model and evaluate interms of solution quality and run times.

202

EURO 2024 - Copenhagen
2 - A decision support framework for automated reviewer
assignment using nlp and optimization

Meltem Aksoy, Seda Yanık, Mehmet Fatih Amasyali
Addressing the inefficiency of traditional manual reviewer assignmentsin the peer-review process, this study introduces a decision support
system that automates the assignment task. Utilizing information
retrieval, natural language processing (NLP) and optimization techniques, it tackles the reviewer assignment problem through a structured, three-stage approach. Initially, it gathers diverse informationfrom various sources to build a comprehensive database of proposals
and reviewers. Then, it applies word embedding techniques to convert multilingual proposal and reviewer texts into vector representations, and it uses the cosine similarity metric to determine content similarity between each proposal-reviewer pair. Concurrently, it assesses
reviewer competency by analyzing their past evaluation performanceand areas of expertise through predefined knowledge rules. In the final stage, a multi-objective integer linear programming model assigns
reviewers to proposals, optimizing proposal-reviewer similarity and reviewer competency while avoiding conflicts of interest. Furthermore,
it explores a max-min approach to improve outcomes for the leastadvantaged proposals. This model is enhanced by two additional models to ensure balanced reviewer workloads. The system’s efficiency istested with a real-world dataset from the project proposal evaluation
process of a regional development agency. The results show that the
proposed system significantly outperforms traditional methods.

3 - Harnessing the Power Trained Reinforcement Learning
Agents in Job Shop Scheduling Problems

Constantin Waubert de Puiseau, Hasan Tercan, Tobias Meisen
The Job Shop Scheduling Problem (JSSP) has been extensively studiedin operations research for decades, resulting in the development of various solution methods. Recently, deep reinforcement learning (DRL)
has emerged as a promising approach to automatically learn generalized construction heuristics from simulations. Construction heuristics
iteratively generate solution sequences in which operations are integrated into a schedule. The neural networks of DRL-agents predictthe probabilities per operation, that integrating it next in sequence willlead to the shortest schedule. Often multiple solutions are sampled
stochastically from the predictions of trained agents. However, due tothe symmetry of the JSSP, many of these sequences result in the same
makespan or even the same schedule. This motivates the use of moresophisticated search strategies that cover a wider range of solutions andutilize trained agents effectively. This study compares theoretical andpractical aspects of integrating learned priors into depth-wise searchstrategies, such as stochastic sampling and Monte-Carlo tree search,aiming to find the shortest makespan in limited computational time.
While predictions for sampling are most efficiently parallelized, othermethods effectively prune the search tree to require fewer predictions
in total. Our results with state-of-the-art DRL agents indicate that variations of stochastic sampling perform best, considering realistic timeand hardware constraints.

4 - Decision-focused learning with machine learning proxies for energy storage system optimization in energy
markets

Ruben Smets, Mathieu Tanneau, Jean-François Toubeau,
Kenneth Bruninx, Pascal Van Hentenryck, Erik Delarue
In various sectors like energy, finance, and supply chain management,
decision-makers optimize their decisions using forecast-informed optimization. In particular, energy market participants base their decisions on price forecasts. In this context, Decision-Focused Learning(DFL) has emerged, where the downstream decision problem is explicitly considered in the training procedure of the forecaster. This
forecasting model is typically a neural network, which is trained usinga gradient descent-based procedure. A pivotal challenge in DFL training is ensuring that the optimal decisions are differentiable with respect
to the input parameters, being the price forecasts. A promising solution
involves implicit differentiation of KKT conditions, albeit this significantly slows down training since it requires solving many optimization
problems with every forward pass during training. We propose an innovative workaround using a Machine Learning (ML) proxy to replace

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the optimization problem in the forward pass. This approach is applied to Energy Storage Systems (ESS) participating in day-ahead andreal-time balancing markets. Applying duality theory to the ESS optimization program allows us to simplify the ML proxy model, limiting
its output to the dual variable of the energy balance constraint. We
observe similar out-of-sample profit performance as traditional DFL
methods, while reducing training time by 70% to 95%, illustrating theimproved scalability of the proposed method.

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Tuesday, 10:30-12:00 - Room: 1001 (building: 202)

Topics in Mixed Integer Nonlinear
Programming 1
Stream: MINLP
Invited session
Chair: Jan Kronqvist
Chair: Luca Mencarelli
1 - A combined Lagrange-Benders approach to Delay Constrained Routing

Antonio Frangioni, Laura Galli, Enrico Sorbera
Modern computer networks are required, on one hand, to support highbandwidth applications, on the other hand, to have stringent Quality ofService (QoS) guarantees. This is a relevant practical issue, since many
applications over IP networks require QoS in terms of real-time guarantees, that is, controlled end-to-end delay. This implies that Internet
Service Providers are required to negotiate delay bound within theirService Level Agreements, which in turn requires appropriate trafficengineering support. From a mathematical point of view, this problem,
known as Delay Constrained Routing (DCR), can be formulated as aMixed-Integer Nonlinear Program, where one needs to simultaneously
compute paths and reserve resources along the paths of the network,
since the maximum delay of a flow depends (in a non-linear way) on
both. Even in the single-flow case, DCR is significantly more difficult than classical shortest path routing problems. Yet, DCR presents
an interesting mixture of combinatorial and continuous structures andnaturally lends itself to decomposition methods. We propose a combined Lagrange-Benders approach that provides both upper and lower
bounds of very good quality in extremely short computing times.

2 - A dual decomposition approach for convex problems
with linear complementarity constraints

Luca Mencarelli, Giandomenico Mastroeni
In this talk, we tackle general convex mathematical programs witha complementarity constraint. We introduce a novel decomposition method for this latter problem build on a sequence of convex
parametrised sub-problems. Via Wolfe duality theory, we derive optimality conditions to improve the optimal value of the parametrised subproblems and valid linear inequality cuts. Moreover, we present preliminary encouraging experimental results for quadratic convex problems with complementarity constraints and convex binary programs.

3 - On the Piecewise Convex Approximations of TwoVariables Functions

Claudia D’Ambrosio, Antonio Frangioni, Claudio Gentile
In the literature, different approaches have been proposed to approximate two-variable nonlinear functions. In particular, classic piecewise
linear approximation, based on triangulation of the function domain
is one of the most widely used in practice. We start from a different
approach, originally proposed in Rovatti et al. (2014), that allows one
to obtain piecewise linear approximation by dividing into rectangles
the nonlinear function domain. Rovatti et al. studied formulations formodeling such an approximation, using a number of binary variables
that is equal to j + k, where j (respectively, k) is the number of intervals in which the first (resp. second) variable is divided. Note thatthe classic formulations of piecewise linear approximations based on203

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triangulations need 2 x j x k binary variables instead. In this work,
we consider the generalization of the work by Rovatti et al. (2014) tothe piecewise convex approximation case. In particular, we explore thestrengthening of mathematical formulations to model such approximations via perspective reformulations. Finally, we show their interest
thanks to some preliminary computational results.

4 - Efficient and effective optimization methods for sparse
generalized inverses

Jon Lee, Marcia Fampa, Gabriel Oliveira da Ponte, Luze Xu
The Moore-Penrose (M-P) pseudo-inverse has a prominent place inmatrix theory and applications. It is well-known that the M-P pseudoinverse is characterized by the four M-P properties. But not all of theseproperties are needed for the use of it in applications like least-squares
fitting. In particular, when a matrix is not full rank, as is common inmodern applications, there are much sparser (and even block structured, for explainabilty) generalized inverses than the M-P pseudoinverse that solve the least-squares problem for arbitrary response vectors. Besides sparsity and structured sparsity, we are interested in lowrank and low-norm solutions, for further explainability and numericalstability. So, we attack the problem of generating such generalized
inverses using optimization methods. Our techniques include: linear
programming (LP), second-order cone programming (SOCP), localsearch based approximation methods, the alternating direction methodof multipliers (ADMM), and accelerations of these ideas via new structural results on generalized inverses.

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The emergence of social media has transcended the constraints of timeand space in communication, facilitating instant interaction among individuals. However, the influx of massive information has led to thewidespread dissemination of fake news and deep fakes across onlinesocial networks. Identifying the source of fake news promptly andaccurately has become a focal point of research. To mitigate the escalation of such situations at an early stage, only information from alimited number of observers can be utilized. Hence, this work first
optimizes the deployment of observers in large-scale social networks,
achieving maximized coverage and sufficient information collection.
Moreover, each observer can provide only limited information, including the time of receiving information and their respective location.
In this work, multi-source spatiotemporal data from pairs and a series of observers are integrated by the evidential reasoning algorithm,
with weight coefficients determined by the structural characteristics ofobservers. Through simulation experiments conducted on real-world
networks and information propagation paths extracted from Twitter,the proposed method accurately traces the propagation source, demonstrated by superior accuracy and error distance compared to typicalalgorithms. Such advancements can be further applied in other contexts, such as the management and intervention of infectious diseases
within epidemic networks.

3 - Multi-armed bandits games

Kemal Gursoy
A sequential optimization model, the multi-armed bandit problem, isconcerned with optimal allocation of resources between competing activities, in order to generate the most likely benefits.
In this work, following the objective of a multi-armed bandit problem,
we consider a game theoretic model to approach to an ensemble ofmulti-armed bandits.

Tuesday, 10:30-12:00 - Room: 1013 (building: 202)

Advancements of OR-Analytics in Statistics,
Machine Learning and Data Science 13

4 - Diagnostic advancements: machine learning in identifying nontuberculous mycobacteria (NTM) in non-cystic
fibrosis bronchiectasis (NCFBr) population

Chen Hajaj

Stream: Advancements of OR-analytics in statistics, machine learning and data science
Invited session
Chair: Gordon Dash
Chair: Nina Kajiji
Chair: Gerhard-Wilhelm Weber
1 - Characterisation of robustness measures for complex
networks under attacks

Reetendra Singh, Shivshanker Singh Patel
In the globe, human and non-human systems are highly connected ascomplex networks. Complexity is understood as the property that isdefined by the nature of interconnectedness, links and size of the network. As example, business and society are made of supply chain andsocietal community network. These networks are susceptible to attacks; we have considered two scenarios: (A) purposeful attack and (B)random attack. This study aims to investigate the robustness of complex networks during the attacks and how the complexity of networks
changes under the attacks. And suggests algorithms and analytics toidentify vulnerable complex networks. The application is shown on
two different data sets for complex networks. The first is for the supply chain with materials flow and contractual relationships networks.
The second is for community networks. Results and analysis showthat some complex networks are less vulnerable to attacks, and someare more vulnerable. Further, the results show the characterisation of
the robustness measure for different complex networks during attacks
and the changes in the network property under the attacks.

2 - Evidential reasoning-based inference of early-stage information propagation source in large-scale social networks

Yuwang Chen, Tao Wen

204

In the realm of pulmonary health, diagnosing Nontuberculous mycobacteria (NTM) infections presents a formidable challenge, particularly in patients with non-cystic fibrosis bronchiectasis (NCFBr).
These infections are notoriously elusive, with their clinical presentations and radiological findings mirroring a plethora of other pulmonary
conditions, thereby complicating accurate diagnosis. Our study embarked on an ambitious journey to harness the potential of machinelearning in predicting NTM infections within this patient demographic.
Through a comprehensive retrospective analysis of 771 NCFBr patients, our investigation illuminated the presence of NTM in 12.2% ofcases, as confirmed by bronchoscopy cultures. Our findings advocatea paradigm shift towards incorporating machine learning as an integral component of the diagnostic toolkit for NTM infections in NCFBr
patients Delving into the intricacies of machine learning, we meticulously evaluated seven distinct models, with the Naive Bayes and Random Forest models emerging as frontrunners, boasting AUCs of 0.81
and 0.71, respectively. This comparative analysis not only underscoredthe viability of machine learning in the clinical prediction landscape
but also highlighted the critical role of radiological and clinical parameters—most notably, patient age and the presence of GERD—in
refining predictive accuracy.

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Tuesday, 10:30-12:00 - Room: 1019 (building: 202)

Behavioural Studies in Health Care
Stream: Behavioural OR
Invited session
Chair: Monica Oliveira

EURO 2024 - Copenhagen
1 - Collaboration and decision-making patterns between
allied health- and primary care professionals using
game theoretical models

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new value aspects for evaluating SUMD. The adopted multimethodology was effective in depicting interrelationships between value aspects
and promoting stakeholder agreement.

Tijmen Geurts, Femke Bekius, Dorien Oostra, Marieke Perry
Allied health professionals possess expertise to improve primary carefor frail older adults but structural interprofessional collaboration remains difficult. We aim to characterize person and group-level behavioral factors influencing collaboration and decision-making between
care professionals. We conducted 24 semi-structured interviews withallied health- and other primary care professionals and asked about collaborative experiences between disciplines. We deductively coded theinterviews with elements (actors, actions and outcomes) of three gametheoretical models (Principal-Agent (PA) game, Volunteer’s Dilemma(VD) and Battle of the Sexes (BS)) to characterize collaboration patterns. We found empirical situations of behavioral patterns representing characteristics of each model. Based on these patterns, we clustered situations on two axes, resulting in four categories. PA games
differentiate between the principal using power or the agent’s information to decide, and direct versus indirect collaboration between professionals. VD-games show waiting or volunteering behavior, central to patient care or professionals themselves. BS-games differ between collaborative- and conflict-inducing behavior, and resulting collective or individual actions. Our findings may help raise awarenessof (un)desirable dynamics in primary care collaboration and decisionmaking. Furthermore, it provides starting points to change behavioral
patterns and improve collaboration.

2 - Developing an Evaluation System for Health-Smart
Homes for the Elderly Based on Dynamic Z-Number
Fuzzy Delphi Method

Jian-hua Xia, Jhy Ping Jhang, Huang Sun-weng.,
Gwo-Hshiung Tzeng
Traditional smart homes emphasize the convenience brought by information technology, yet the elderly face certain challenges in accepting
and mastering technological capabilities. Thus, understanding what
constitutes an elderly-friendly health-smart home according to their
expectations becomes a focal point in housing policies for ultra-agedsocieties. This study aims to construct an evaluation framework forhealth-smart homes based on exploring the perspectives of the elderly.
Firstly, a semi-open format will be utilized to collect indicators of concern for the elderly. Secondly, dynamic Z-number will be employed
to quantify the inconsistency and uncertainty in the decision-making
process of the elderly. Finally, fuzzy Delphi method will be utilized togather decision consensus. The research outcome will establish standards for evaluating elderly-friendly health-smart homes, contributing
to the overall development of housing policies in ultra-aged societies.

3 - Promoting circular economy concerns in medical devices’ procurement through structured collaborative
processes

Francisco Viterbo, Ana Carvalho, Ana Vieira, Mónica
Oliveira
Portuguese health spending has been rising, with the global health sector contributing 4.6% to worldwide greenhouse gas emissions. SingleUse Medical Devices (SUMD) are increasingly used in healthcare andtheir procurement is mostly price-based, with literature recognising theneed to implement sustainable procurement practices but studies in thearea being scarce. This study aims at identifying, with stakeholder engagement, which aspects related to the evaluation of SUMD should
be considered to leverage a more circular healthcare system. A novel
multimethodology, integrating Problem Structuring Methods within aDelphi to promote structured collaboration and address aspect interconnectedness, is proposed and tested in a case study with twenty Portuguese stakeholders. In Round 0, participants ideated new aspects toconsider in evaluating medical devices to promote circular economy
concerns. These aspects were organised into a cognitive map madeavailable to participants in rounds 1 and 2, where participants statedtheir agreement level with each one and revised their views in light of
the group’s answers; and provided feedback about the Delphi. Twelve
experts completed all rounds and results were validated through individual interviews with a core strategic group. Results suggest many

 TB-09
Tuesday, 10:30-12:00 - Room: 10 (building: 116)

Game Theoretic Market Equilibrium
Modelling
Stream: Energy Markets
Invited session
Chair: Mel Devine
1 - Valuing the Electricity Produced Locally in Renewable Energy Communities through Noncooperative Resources Scheduling GamesLouise Sadoine, Zacharie De Greve, Thomas Brihaye
Renewable energy communities play a crucial role in the energy transition. Members own distributed energy resources and are stakeholdersof their own energy supply. We propose two market designs for the
optimal day-ahead scheduling of internal energy exchanges. The firstone implements a collaborative demand-side management scheme inside a community where members objectives are coupled through grid
tariffs, the second allows the valuation of excess generation in the community and on the retail market. Two grid tariff structures are tested,
one academic and one which reflects the Belgian regulations in termsof grid tariffs. Individuals’ bills are obtained through 4 cost allocation methods. Both designs are formulated as optimization problems
and as noncooperative games. In the latter case, the existence and efficiency of the corresponding (generalized) Nash Equilibria are studied
and algorithms for finding these are proposed. The models are tested
on a use-case of 55 members and compared with a benchmark situation where members act individually. We compute the global and individual costs, inefficiencies of decentralized models compared to social
optima, as well as technical indices. First, we show that there always
exists an equilibrium that is a social optimum. Secondly, we show, analytically when possible, and empirically if not, that the community andthe individuals’ bills obtained with the centralized and decentralized
approaches are equivalent.

2 - The interactions between demand- and supply-side investment decisions in an oligopolistic electricity market.

Mel Devine, Valentin Bertsch
To meet carbon reduction targets, there will need to be investment inwind energy, solar PV, and battery storage in electricity markets acrossthe world. In this work, we consider what the optimal investment mix
for these technologies will be from the perspective of both generating
firms and consumers. We present a stochastic Mixed Complementarity
Problem where several generating firms maximise their profits whilevarious consumer groups minimise their costs. All players modelled
make hourly operational decisions in addition to long-term investment
decisions. The generating firms may exert market power. The uncertainty of wind and solar PV are the sources of the model’s stochasticity. We apply the model to a case study of the Irish electricity system in 2030. We consider the optimal investment mix when market
power is both present and absent from the market. We observe thatthe presence of market power increases electricity prices which leads
to increased generating firms’ profits and consumer costs. It also leads
to increased investment in renewable technologies and battery storage,
which leads to reduced carbon emissions. Furthermore, we consider
the effect a Feed-in-Premium (FiP) has on renewable investment andobserve a counter-intuitive result whereby the absence of a FiP leads
to less investment in renewables from generation companies but, consequently, increased investment in renewables from consumers.

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TB-10EURO 2024 - Copenhagen

3 - Risk Measures in Stochastic Complementarity Models
of Energy Markets

Dáire Byrne, Mel DevineWith the transition towards renewable energy sources, there is a growing need to capture uncertainty within energy market models, withfirms facing stochasticity in terms of costs, demands, and generation
capacity. With this heightened variability comes additional exposure
to risk, leading firms to exhibit risk-averse decision-making. In orderto account for this, risk measures can be appended to models. A number of such measures, particularly conditional value-at-risk, stochastic dominance constraints, and concave utility functions, have gained
varying degrees of traction in energy market modelling literature. This
study aims to investigate the consequences of these risk measures on
the behaviour of price-making players acting competitively and seeking to maximise profits. It comprises a discussion of these risk measures and their relative merits. It further includes an analysis of the
impact of incorporating risk measures into stochastic equilibrium models, both from an analytic and numerical perspective. Moreover, it discusses the circumstances under which these complementarity problems
can be converted to an equivalent problems in convex optimisation, andthe use of Arrow-Debreu securities to conceptually complete the market. This work aims to elucidate and illustrate the behaviour of theserisk measures and their appropriate application to energy markets.

4 - A Multi-Commodity Partial Equilibrium Model of Imperfect Competition in Future Global Hydrogen Markets

Lukas Barner
Recent techno-economic studies have investigated procurement costsof hydrogen and related derivatives on various international traderoutes. However, strategic behavior of exporters has rarely been considered in this context, despite similar behavior frequently observedin the fossil fuel world and market characteristics indicating somepotential. This work introduces a novel techno-economic model ofoligopolistic trade tailored around the value chain of potential future
global hydrogen markets. It is formulated as a mixed complementarity problem with a convex reformulation, allowing to solve both competitive and oligopolistic equilibria much beyond the capabilities of
traditional complementarity models. Illustrative results show, that assuming perfectly competitive price formation in early phases of future
global hydrogen markets may lead to overly optimistic price projections, especially within Europe, Japan and South Korea. In extreme
cases, hydrogen prices may increase to ∼200 EUR /MWh by 2030,
compared against a competitive baseline of ∼150 EUR /MWh. In contrast, prices may decrease by ∼25 EUR /MWh in exporting regions,such as Northern Africa or South-Eastern America.

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Tuesday, 10:30-12:00 - Room: 11 (building: 116)

Surgery Scheduling and Operating Room
Planning
Stream: OR in Health Services (ORAHS)
Invited session
Chair: Theresia van Essen

observation that in the literature, most studies tend to focus solely on
specific cases of the surgery scheduling problem or operate under deterministic assumptions contrary to the problem’s nature. Note that theclassical problem is twofold: first, one needs to decide which procedures are done on which OR, and second, in which order do the procedures need to be scheduled. We propose a mathematical formula thatmerges applied probability and mixed-integer programming to minimize overtime and idle time. We demonstrate how our solution approach can be adapted to specific cases and integrated with additional
constraints. We show superior results compared to the ones that simply assumed deterministic service times, and we also contrast them toscheduling rules. This underpins the importance of taking variation
into account. Furthermore, our experiments show that even with a limited degree of information about the uncertainty, the performance ofthe surgery schedule can be considerably improved.

2 - Advancing elective surgery scheduling considering operating theatre and non-operating theatre resources
with time and demand uncertainties

Mona Koushan, Lincoln Wood
The anticipated surge in demand for elective surgical services highlights the urgency of optimising hospital resources, especially in publicly funded systems with limited budgets. Effective coordination ofmaterials and resources under operating theatre (OT) capacity management is essential to meet the rising volume of elective surgeries. This
study introduces a novel multi-stage care facility model, emphasising
the significance of both OT and non-OT processes, aiming to balance
financial constraints with community satisfaction. It explores enhancing OT scheduling using the theory of variation and uncertainty buffering, incorporating a time buffer into the surgical process. Furthermore,
the study addresses uncertainties related to emergency arrivals and processing time, employing robust optimisation to ensure adequate capacity at each stage of the surgery process, thereby enhancing the model’s
relevance and realism. Solving this model, the study proposes metaheuristic algorithms like Hybrid Nondominated Sorting Genetic Algorithm and hybrid Multi-Objective Firefly Optimisation algorithms
with Hybrid Non-dominated Sorting Genetic Algorithm to efficiently
address real-world scale scheduling problems. These meta-heuristics
facilitate rapid adjustments in schedules in response to evolving circumstances, making them valuable tools for healthcare practice. This
study enhances surgical service delivery and resource management in
hospitals by advancing understanding of OT scheduling.

3 - Machine Learning of Chance Constraints for Scheduling Surgeries

Thomas Adams
We describe several methods for approximating chance constraints on
the duration of surgeries assigned to a surgical session. We use historical data to create an empirical distribution of the session durationand employ SVMs and Probit regression to learn the probability thata session runs overtime, given the surgeries scheduled in it. Thesemodels are chosen as linear constraints can easily be formed from theresults. Through numerical experiments, we demonstrate the accuracyof these models to act as binary classifiers, and their effectiveness as
approximations to chance constraints. We compare them to a distributionally robust approach to approximating the chance constraints, andshow that the they result in similar, in many cases exactly the same,schedules but require much less time to solve the optimisation problem.

4 - Dealing with uncertainty in surgery duration

Theresia van Essen
1 - Intra-day optimization of the surgical suite: allocating
and sequencing surgical cases

Taha Huzeyfe Aktas, Alex Kuiper
Limited resources in healthcare give rise to efficient use of resources.
As operating rooms (ORs) and associated resources are dominant factors in the hospital’s operational cost, our aim is to improve surgery
scheduling. Considering Leeftink, G. & Hans, E.W. (2018) as ourstarting point - a large benchmark of surgery scheduling problems our objective is to improve the scheduling by taking uncertainty about
surgery durations into our approach. The motivation stems from the206

Operating rooms are one of the most expensive resources of a hospital, which makes it important to use them as efficiently as possible.
This involves maximizing utilization, but also minimizing the probability of overtime, which are two conflicting objectives. The problemof scheduling surgeries is usually modelled as an integer linear program (ILP), where maximizing utilization is considered as objective,
while restricting the probability of overtime. The constraint used to restrict the probability of overtime includes the lognormally distributed
surgery duration, which typically leads to a nonlinear constraint where
the total surgery duration in one operating room is a stochastic variable. In the literature, various ways to deal with this nonlinearity areEURO 2024 - Copenhagenconsidered: 1) only use the expected surgery duration and limit the total surgery duration, 2) model the uncertain surgery duration using anormal distribution and linearize the resulting constraint using a piecewise linear function, 3) model the uncertain surgery duration using a
discrete probability distribution resulting in a linear constraint, and 4)
model the uncertain surgery duration using the lognormal distribution
and use column generation and simulation to solve the resulting model.
We implemented and compared all mentioned methods on both solution quality and computation time. To determine the solution quality,
we tested the resulting schedules using simulation.

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Tuesday, 10:30-12:00 - Room: 12 (building: 116)

Behavioral OR general papers
Stream: Behavioural OR
Invited session
Chair: L. Alberto Franco
Chair: Raimo P. Hämäläinen
1 - The role of theory in behavioral operations research

Michael Leyer, Ayşegül Engin, Marta Lopes, Anita Onay,
Lisa Scholten
Behavioral OR (BOR) is positioned between hard and soft OR andprovides analyses of human behavior in decision making so that it canbe modeled with OR tools. Although behavioral analyses have always
been integral to OR, scientific rigor and especially theory-based analyses are required to strengthen the research field. From our perspective,
BOR bridges distinguishable world views and describes how behavioral dimensions influence OR problems and solutions. The goal ofthis research is to clarify the areas where BOR research can provide insights to increase scientific rigor. We provide a theoretical foundationthat improves the positioning of BOR using the concept of "wickedness". It structures typical OR problems according to their degree of
wickedness that increases with the complexity of problems and the
difficulty regarding stakeholders and institutions. Decision making invery wicked settings is grounded in belief functions that can differ between individuals due to insufficient knowledge or deviating goals of
individuals involved. By identifying relevant assumptions from theories, this research aims to clarify beliefs so that they can be calculated
in models. This analysis serves as a foundation to derive relevant typesof theories that can be used from other fields (mainly from related fields
e.g. psychology). Moreover, novel specific OR theories are intended
to be developed based on the adopted ones. Our work contributes tothe understanding of BOR as a discipline.

2 - Facilitated modelling revisited: A conceptual development

TB-12

3 - Incorporating Human Factors and Human Behavior into
Research on Workload Control

Anita Onay
To enhance production planning through a proactive lead time management approach, the Workload Control (WLC) concept encompasses arobust framework with multiple hierarchical planning layers. Despite
the considerable body of research on Workload Control (WLC) andhierarchical production planning, there exists a noticeable gap in incorporating behavioral operational research (BOR) into these domains.
This present study explores the relationship between instructions fromthe upper planning layer and the human response behavior of a lower
hierarchical level within a comprehensive WLC system. The article
particularly emphasizes that the arousal level, a human factor, influences the response behavior of shop floor operators. Through laboratory experiments with a real effort task and mediation analysis, thisresearch reveals that tense arousal significantly impacts operators’ response behavior in terms of productivity within a WLC environment
with hierarchical planning layers. This outcome contradicts the assumption of the standard hierarchical model of rational behavior at thebase level. Furthermore, the study contributed to the theory underpinning the WLC concept and its implementation issues with regard to ahuman-operated shop floor.

4 - Keeping your enemies closer? The effect of the fear of
replacement by AI on AI-augmented decision-making

S. Iman Moosavi, Jan C. Fransoo, Prisca Brosi
In addition to various behavioral biases and contextual factors affecting AI-augmented decision-making, emotions play an important role insuch tasks. Across different emotions, the fear of being replaced by AI
systems is one of the most reported ones among those who are working
with AI. Our study explores how the fear of being replaced by an AIalgorithm for operations decision-making affects individuals’ relianceon AI suggestions and subsequent decision-making performance. Using an online experiment, we manipulate this emotion by varying the
likelihood of the AI to replace participants in a demand-forecastingtask. Participants are provided with additional information comparedto the AI, mirroring real-life scenario of private information, and informed that they may lose their job and the opportunity for bonuses in
additional tasks if they fail to improve on the algorithm’s suggestion.
Our pre-test’s results suggest a marginally significant negative effectof the fear on the adoption of AI recommendation through decisionmaking process. However, there is no significant effect observed on
decision-making performance.

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Tuesday, 10:30-12:00 - Room: 13 (building: 116)

OR in Forestry II
Stream: OR in Agriculture and Forestry
Invited session

L. Alberto Franco, Etienne Rouwette

Chair: Lidija Zadnik Stirn

Facilitated modelling (FM) is an intervention process by which operational research models are jointly developed with a group tasked withaddressing a situation that requires attention and action. The process
requires operational researchers to act both as analysts and facilitators,
and is designed to help the group make progress with regards to thesituation of interest. The aim of this paper is to propose a conceptual
framework that can act as a guide for the study of FM processes andoutputs, and the design and use of FM interventions. The framework iscomprised of three elements: groups as motivated information processors, models as boundary objects, and facilitation as intervention. Eachof these elements is discussed with working propositions formulated
for core FM processes and outputs. Implications of the framework forthe study and practice of FM are discussed, and directions for future
research proposed.

1 - Enhancing Wildfire Management: A Comprehensive
Approach Integrating System Dynamics and Remote
Sensing Technologies

Müjgan Bilge Eriş, Fatih Duygun Demirel, Eylül Damla
Gönül Sezer
Wildfires pose significant threats to human life, ecological balance,
and the economy. The unpredictable and highly dynamic nature of
wildfires emphasizes the need for analyzing wildfire management using non-linear methods. This study explores the causal relationships
among the physical nature of fires, resource management, and thecosts associated with prevention and suppression under various wildfire management policies via system dynamics approach. Our study
extends previous works by incorporating remote sensing technologies

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TB-13EURO 2024 - Copenhagenin the prevention stages of wildfires. The utilization of such technologies play a significant role in preventing the escalation of potential fires
and ensuring the effective use of operational resources. Additionally,
calculating the probabilities of errors through Failure Mode and EffectAnalysis for potential errors in wildfire management further enrich thesystem dynamics model. The insights from the model will be provided
for policy makers.

2 - Horizontal collaboration in forestry: game theory models and algorithms for trading demands

Gautier STauffer, Mourad Baiou, Gianpaolo Oriolo
In this paper, we introduce a new cooperative game theory model that
we call production-distribution game to address a major open problem
for operations research in forestry, raised by R"onnqvist et al. in 2015,
namely, that of modelling and proposing efficient sharing principles forpractical collaboration in transportation in this sector. The originalityof our model lies in the fact that the value/strength of a player does not
only depend on the individual cost or benefit of the objects she owns
but also depends on her market shares (customers demand). We show
however that the production-distribution game is an interesting special
case of a market game introduced by Shapley and Shubik in 1969. As
such it exhibits the nice property of having a non-empty core. We thenprove that we can compute both the nucleolus and the Shapley value
efficiently, in a nontrivial and interesting special case. We in particularprovide two different algorithms to compute the nucleolus: a simple
separation algorithm and a fast primal-dual algorithm. Our results canbe used to tackle more general versions of the problem and we believethat our contribution paves the way towards solving the challenging
open problem herein.

3 - Resilience of the long-term balance of ecosystem services in the landscape through integration of DEA, fuzzy
AHP and DP: a case study in Slovenia

Lidija Zadnik Stirn, Špela Pezdevšek Malovrh, Vasja Leban
Landscape planning is not only about material production, but also
about the provision of other services. The conservation and enhancement of landscapes to ensure ecosystem services (ES) for the benefit
of society is an important issue in the field of OR. The aim of thisstudy was to gain experience on how a decision support system basedon MCDA can be used in practice for long-term landscape management. We present a planning tool that integrates DEA, Fuzzy AHP and
DP. As a first step, it was crucial to know the current state of the landscapes. To this end, we used DEA to assess the operational efficiencyof landscapes depending on a specific region (landscape unit), considering land use, land cover and ecosystem condition. Knowing that theprovision of EFP is site-specific and dynamic due to changing natural
conditions and influences as well as various other socio-economic andpolitical factors, decision-makers define alternative management programs/scenarios for each unit under consideration. In the second step,
the management criteria were selected and evaluated by the stakeholders. The Fuzzy AHP was chosen as the preferred weighting method forcomparing the programs for each unit. Once all the alternatives, criteria and weightings are defined for each alternative per unit, the next
step is to create a DP network for each unit to forecast the evolutionof the unit to the target state, which depends on the ES supply/demandratio in the unit.

Leroy White
The intersection of artificial intelligence and problem structuring
presents new challenges and opportunities. The paper delves into leveraging AI problem structuring via data gathering, pruning, and strategy
dialoguing, emphasizing the importance of moving from treating AI asa search tool to fostering meaningful dialogue. Experiments with generative AI based on large language models in strategy creation reveal
valuable insights for ideation and evaluation.

2 - AI-enhanced spaces for problem structuring: better
understanding, stakeholder engagement, and decision
support?

Katharina Burger
This talk will delve into the opportunities and challenges of AI for
problem-structuring that takes place in immersive virtual environments(IVEs). With the help of AI-powered technology, virtual reality (VR),is rapidly transforming how spaces for participatory problem structuring can be designed and experienced. For instance, AI is credited withmaking IVEs more realistic, interactive and adaptable, with 3D models
that may aid in knowledge sharing and development. By allowing us
to create immersive and interactive digital environments, participants
are enabled to explore and refine their ideas in new and exciting ways.
However, the use of virtual environments can alter the dynamics ofinterpersonal communication and collaboration. Moreover, while AI
may enhance the usability of IVEs, questions remain about how interaction in IVEs generates specific impacts on knowledge production insuch settings. In this talk, we will consider how problem structuring
may evolve with the integration of AI-generated resources in and forinteraction in IVEs while also considering some of the challenges involved relating to understanding, stakeholder engagement and decision
support.

3 - Exploring Participatory Approaches: Integrating AI with
Collective Intelligence in the Archivio Atena project

Sabrina Sacco, Piero Zizzania, Giuliano Poli, Maria Cerreta
In recent years, AI has witnessed a shift towards the inclusion of participatory approaches. This transition highlights the need of integrating AI with various methodologies, e.g. citizen science, crowdsourcing or digital democracy platforms. However, adopting a participatory
approach means engaging communities and activating their CollectiveIntelligence (CI) across all stages of the AI system. Grounded in Activity Theory, this research aims to explore the potential of AI in gaining
CI, facilitating the elicitation of collective values within communities
and nurturing awareness and trust in AI tools. The research focuses on
the Archivio Atena project, a culture-led initiative in Atena Lucana, in
the South of Italy. Using image data collected during the project activities, the study encourages the community to participate in a collective
supervised learning process for the AI, by tagging them with valuebased labels. This iterative process enables participants to recognize
collective value patterns, supporting data clustering and furnishing anevaluation tool for impact metrics within the future projects. The integration of AI with participatory methodologies offers a promising
approach to address subjective and collective issues, beyond quantitative and objective realms. By leveraging AI in the context of PSMs,
this research outlines both the limitations and potentials of AI in facilitating the promotion of CI and defining useful criteria for impact
evaluation processes.

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Tuesday, 10:30-12:00 - Room: 15 (building: 116)

Impact of AI on Soft OR - A TB-15
Tuesday, 10:30-12:00 - Room: 18 (building: 116)

Stream: Soft OR and Problem Structuring Methods
Invited session

Appointment planning

Chair: Leroy White

Stream: OR in Health Services (ORAHS)
Invited session

1 - AI and problem structuring

Chair: Alex Kuiper

208

EURO 2024 - Copenhagen
1 - A Column Generation-Based Exact Algorithm for Multiple Appointment Patient Scheduling

Mohammad Reihaneh, Sina Ansari, Farbod Farhadi
Scheduling patient appointments at a hemodialysis center introduces aunique scenario. In contrast to other healthcare appointment scheduling challenges, patients undergo a series of dialysis treatment appointments rather than a single one. In this study, we formulate the multipleappointment system as a set-partitioning problem and solve it usinga Branch and Price (BP) algorithm. The pricing subproblem proves
to be challenging for the dynamic programming algorithm. Hence,
we further decompose the pricing sub-problem and propose a column
generation-based algorithm for solving the subproblem.

2 - Mathematical
Programming
Telemedicine Appointments

for

SchedulingCharlotte Marshall, Daniel Gartner, Geraint Palmer, Paul
Harper, Alka Ahuja, Gemma Johns
With telemedicine coming to the forefront during the COVID-19 pandemic, flexibility in terms of modes of care delivery has emerged.
We are working with a partner within NHS Wales, TEC Cymru, whoworked to rapidly implement video consultations within Wales during the pandemic and continue to promote the use of telemedicine
in Wales. In this paper, we consider the scheduling of patients’ appointments via three different modes of delivery: traditional face-toface, video conferencing platforms, and telephone. The solution of
the model not only has an impact on satisfying patient and clinician
preferences, but also could potentially reduce travel for patients andstaff. We model the problem as a multi-mode resource constrained
project scheduling problem, with the aim of maximising patient andclinician preferences for delivery method. Two model formulations
are presented to solve this problem. The first assigns appointments totimeslots while the second formulation assigns an ordering of appointments. We compare these two models, evaluating which is more usefulin different scenarios.

3 - Rationing by Waiting Time for Outpatient Clinical Appointment Workflows via Novel Patient Access Workflows

Esma Gel, Derya Kilinc, Mustafa Sir, Kalyan Pasupathy
Increasing demand for healthcare has necessitated the need to ration
care resources by prioritization. While such prioritization is effectively
implemented in many settings, their use in appointment workflows
have been impossible since appointments are provided on a first-comefirst-serve basis. We present a novel approach to enable rationing by
waiting time in outpatient settings offering clinical appointments. Under the Time Windows Access Protocol (TWAP), scheduling agents areprovided a distinct contiguous portion of the booking horizon to search
for an available appointment upon an appointment request at from apriority class. Patients with requests from high priority classes (i.e.,
urgent, complex, high match with provider services) are uniformly incentivized to book and attend appointments with low wait offers, whilesufficient dilution of requests from lower priority classes are inducedwith higher (but still, medically safe) levels of wait. We present a
computationally efficient methodology to determine the optimal time
windows for given delay-dependent appointment realization probabilities that reflect patients’ delay tolerance (i.e., sensitivity to wait). We
demonstrate the potential use and effectiveness of TWAP for new patient appointment requests under different rationing schemes (i.e., thedesired patient mix) using simulations as well as real-life transactional
data from a leading academic medical center.

4 - Convexity in Transient Queues with Appointmentdriven Arrivals and Implications on Optimal Appointment SchedulingAlex Kuiper, Christos Zacharias
The inter-day (which day?) and intra-day (when?) appointment
scheduling problems are often studied in isolation, whereas in reality, they are intertwined. If the waiting list becomes too large (small),one would want to extend (reduce) the appointment book, but such decisions directly impact the optimality of the schedule. Using a continuous intra-day scheduling paradigm, these problems are connected in a

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dynamic environment. Extending convexity properties of the appointment scheduling problem renders the corresponding dynamic programamenable to building upon recent advances in literature.
Since the optimal solution is not tractable, two intra-day scheduling paradigms are introduced: a theoretical approach (based on reoptimization) and a heuristic, which is based on schedules that exhibit
so-called sequential refinability. Together, they bound the optimal policy for the dynamic inter/intra-scheduling problem. At the same time,
their small difference underscores the potential of using this heuristic
in healthcare practice.

 TB-16
Tuesday, 10:30-12:00 - Room: 19 (building: 116)

Ranking in sports
Stream: OR in Sports
Invited session
Chair: Balázs R. Sziklai
1 - Alternative UEFA country rankings based on pairwise
comparisons

Dora Greta Petroczy, Laszlo Csato
UEFA country coefficients quantify the performance of UEFA associations’ football clubs in international club competitions. They arebased on the results achieved over the previous five years, with points
awarded for match results and bonus points allocated for reaching different tournament stages. The points earned by all clubs from eachcountry are divided by the number of participating clubs to calculate
the coefficient. The ranking of UEFA associations by country coefficients is used to determine the number of clubs from the associationthat can participate in UEFA club coefficients, as well as the qualifying stage where they enter. Furthermore, two UEFA Champions
League slots are provided for the two best associations in the previous
year from the 2024/25 season. Therefore, it is crucial to obtain a fairranking of European football leagues. For this purpose, we propose
new methods based on pairwise comparisons that—in contrast to thecurrent UEFA country coefficients—account for the strengths of opponents, and calculate alternative rankings based on matches playedin the UEFA Champions League, UEFA Europa League, and UEFA
Europa Conference League over the least 10 years. They will be compared to the official UEFA ranking, and the number of upsets (matches
where the favourite team loses) will also be determined.

2 - FIVB ranking: a misstep in the right direction

Leszek Szczecinski, Salma Tenni, Daniel Gomes de Pinho
Zanco
In 2019, Fédération Internationale de Volleyball (FIVB) introduced anew "power" ranking, which relies on the probabilistic model relating
the teams’ strengths to the obtained outcomes. While there are manyrankings which already use such models, the FIVB ranking is the first
official one which uses the explicit probabilistic model in multilevel
games (that is, games with more than three results).
In this work, we explain/assess the model and the on-line ranking usedby the FIVB, placing it in a general statistical framework. In particular, we describe the ordinal model used in the ranking, show why itcannot be used directly for on-line ranking, and explain the approximations adopted by the FIVB. We introduce analytical perspective
which explain why the approximations are suboptimal and propose analternative formulation which significantly simplifies the ranking. Our
analysis is backed up by the numerical optimization, where we use theofficial FIVB games to fit and to optimize the models we consider.
We conclude that, while the use of the explicit probabilistic model for
multilevel ordinal data is an interesting and refreshing change in theworld of rankings, and should be a preferred ranking strategy, the resulting algorithm is too complex and suboptimal. We thus formulate a209

TB-18EURO 2024 - Copenhagen

number of recommendations, which vary in the amount of requiredchanges and the performance/complexity trade-offs of the resulting
rankings.

3 - Comparison of Football Leagues Using the Sum of
Ranking Differences

Balázs R. Sziklai
The aim of the study is to demonstrate a new non-parametric statisticalprocedure, the Sum of Ranking Differences (SRD), through an example taken from sports. The method was introduced by Héberger (2010)
and is becoming increasingly popular in various fields of applied sciences. With this procedure, solutions or methods can be comparedthrough a reference. In our study, we analyze the similarities and differences in the style of play of four major European football leagues(La Liga, Bundesliga, Premier League, and Serie A). First, we characterize the leagues based on the teams’ statistical attributes featured
in the games. We assess the importance of game elements such as
goals scored and conceded, ball possession, successful passes, etc., byexamining how closely teams are ranked by each element in comparison to their final standings on the scoreboard. Essentially, this is an
SRD calculation where the teams’ final scores in the league serve as
the reference. Aggregating the results for the leagues, we obtain a datatable that can be subjected to yet another SRD analysis. By setting any
league as a reference, we can measure how much the playing style ofa particular league differs from the others.

 TB-18
Tuesday, 10:30-12:00 - Room: 42 (building: 116)

Optimization of sustainable urban mobiltiy II
Stream: Sustainable Cities
Invited session

attributes that include financial viability, alignment with institutionalframework, effects on climate change and environment, effects on society and the wider economy. A set of weights defines the values andpriorities of the local stakeholders. As this problem is non-linear andthe objective function is discontinuous, a metaheuristic is needed tofind a good solution. It appears that the Evolutionary Algorithm produces good solutions at reasonable computational times. The proposed
framework can identify an optimal solution configuration for problems
with discontinuous objective functions and predefined preferences. In
practical terms, this research not only advances the field of urban mobility but also offers a scalable model for other cities facing similar
challenges.

3 - A Multi-Objective Optimization Approach for Electric
Bus Fleet Conversion on the Istanbul Metrobus Line

Zahide Özden Ceylan Güngör, Sule Itir Satoglu
Transportation causes roughly 25% of global greenhouse gas emissions due to its reliance on fossil fuels (European Commission, 2011.
In Turkey, road transport alone emits a staggering 93% of the sector(DEK, 2017). To combat this, electrifying public buses offers a promising solution for sustainable cities. Electric buses boast zero tailpipe
emissions, significantly improving air quality (Hensher et al., 2022).
They outperform diesel buses, which emit 1.35 kg of CO2 per mile,
highlighting their potential to reduce emissions.
This study explores the optimal electric bus fleet conversion planningproblem (EBFT problem) aligned with the Paris Agreement’s emission
reduction goals. We propose a linear programming model for EBFT,
considering direct/external costs alongside salvage value. The model
optimizes for both economic (minimizing total fleet conversion cost)and environmental (minimizing total emissions) objectives. We incorporate four scenarios representing different electrification levels.
A case study using Istanbul’s Metrobus data demonstrates a scenariodependent optimal conversion plan. For Istanbul, gradual conversion
achieves a 21% emissions reduction by 2030. This study employs
a comprehensive optimization approach, considering multiple objectives, timeframes, and vehicle ages.

Chair: Virginia Pellerey
1 - Guiding Local Authorities in Parcel Locker Adoption: A
Case Study in Norway

Cosku Can Orhan, Stein W. Wallace
Urban population growth and the expansion of e-commerce have
driven up the volume of deliveries, the need for faster shipping services, and a greater occurrence of delivery failures in cities. Consequently, there has been a noticeable increase in the number of trips
made within urban areas to meet these demands, which intensifies theburden of freight transport. Driven by these considerations, we collaborate with local authorities in Bergen, Norway to analyse the impactsof freight transport on city liveability. In this work, we analyse theadoption of parcel lockers to provide authorities with insights on how
the city could benefit from them as a sustainable delivery solution. By
adopting clustering and vehicle routing models of freight carriers at a
strategic level, we offer guidance to authorities on essential factors for
improving city liveability through the utilization of parcel lockers. In
this regard, we use data on the road network and population from local authorities, as well as the distribution data from a major carrier in
Norway.

2 - Optimization of electric mobility investments in Kathmandu

Eric Vong, David Ruehmkorf, Dario Fiorica, George
Panagakos, Michael Bruhn Barfod
The EU-funded SOLUTIONSplus project aims at promoting emobility in 9 cities around the world, including Kathmandu, Nepal.
This paper develops a systematic approach for selecting the fleet ofelectric vehicles (among 8 different types including buses, shuttlevans, 3-wheelers, trucks, and waste collectors) that maximizes societal
benefit within a specific budget. It introduces a framework combining Multi-Criteria Decision Making (MCDM) and optimization techniques. The MCDM aspect employs over twenty key performance indicators to assess the impact of various alternatives against specific210 TB-19
Tuesday, 10:30-12:00 - Room: 44 (building: 116)

The role of storage in energy problems
Stream: OR in Energy
Invited session
Chair: Ann van Ackere
1 - Big hydro, two islands, one goal: Exploring New
Zealand’s path to Net-Zero with Green Hydrogen integration

Rafaella Canessa, Jannik Haas, Rebecca Peer, Manuel Wetzel,
Hans Christian Gils
Green hydrogen can play a crucial role in the energy transition but thisrole remains largely unquantified. This work presents the adaptationof REMix, an energy capacity expansion optimisation tool, for New
Zealand (REMix-NZ). With this, optimal pathways for achieving netzero goals across energy sectors are evaluated. Unlike previous applications, REMix-NZ considers New Zealand’s unique characteristics,
including: • High share of hydropower: The model leverages existing
variable hydropower resources as the backbone while integrating additional renewables like wind and solar PV. • Medium-sized islanded
system: This allows for detailed analysis of optimal energy import andexport strategies, particularly for green hydrogen and its derivatives.
We focus on the role and impact of green hydrogen, investigating its
potential to decarbonise challenging sectors. This results in a large optimisation problem involving thousands of investments and millions ofoperation decision variables. The results illustrate the trade-offs andsynergies between domestic energy needs and potential hydrogen exports, providing valuable insights for policymakers and stakeholders.

EURO 2024 - Copenhagen
2 - Multi-Objective Energy-Aware Scheduling: A Memetic
Two-Phase Evolutionary Approach incorporating RealTime Energy Market, on-site Generation, and Storage
Systems

Sascha C Burmeister, Daniela Guericke, Guido Schryen
Dynamic energy tariffs and on-site energy generation offer manufacturers new opportunities to optimize their energy consumption. In
addition to conventional goals such as minimizing makespan, manufacturers can focus on minimizing energy costs or emissions. However, scheduling processes in the face of uncertain future energy prices
and emissions is a major challenge. Energy storage systems (ESS)
can compensate for differences between predicted and actual energy
costs and emissions. To make an investment decision for ESS, decision makers need to assess their impact on energy costs and emissions,as well as their return on investment. In the literature, the Green Flexible Job Shop Scheduling Problem (FJSP) is concerned with resourceand environmental aspects in addition to economic objectives. However, existing approaches neglect the combination of a multi-criteria
objective with an uncertain dynamic energy mix and the use of ESS.
We propose a two-phase approach based on a memetic NSGA-III andmathematical programming with the goal of minimizing a schedule’s
makespan, energy costs, and emissions, incorporating dynamic energy
prices and emissions, on-site generation, and ESS. We evaluate theapproach using FJSP benchmark instances from literature as part ofa rolling horizon approach with real energy market data. We investigate the impact of ESS by presenting estimated Pareto fronts, showing
potential savings in energy cost and carbon emissions.

3 - Security of supply in the transition to economically viable large-scale electricity storage

Ann van Ackere, Erik Larsen
While electricity storage is pivotal in the move away from fossil fuelbased generation to renewables, its roll-out might have undesirable
side-effects. We develop a system dynamics simulation model to investigate the impact of introducing large-scale, economically viable
storage, on security of supply during this transition period. As efficientstorage options partly mitigate the intermittent nature of renewables
such as wind and photovoltaics, their capacity factor will go up, resulting in an increased derated capacity margin, and thus a reduced needfor future investments. However, the speed of the technological evolution that will enable large-scale viable storage, and the implementation rate, are hard to predict. Incorrect anticipation of future capacity
factors could lead to a complicated transition period. If investors areover-optimistic regarding the speed of this evolution, they are likely
to overestimate the efficiency of existing renewable generation and be
reluctant to invest, leading to capacity adequacy problems and a riskof shortages, if not blackouts. On the contrary, underestimating the
speed of this evolution would lead to costly over-capacity. The modelallows us to investigate different scenarios based on investors’ anticipations, initial share of renewables, etc. We observe capacity shortages
in some scenarios, while others indeed generate significant excess capacity. Furthermore, we argue how the regulator might mitigate someof these issues.

4 - Stochastic multiobjective optimization for hybrid distributed generation with battery storage systems

Paulo Rotella Junior, Aglaucibelly Barbosa, Luiz Celio Souza
Rocha, Arthur Leandro Guerra Pires, Karel Janda
Hybrid energy projects are attracting global interest due to the essential
search for sustainable development, the alternative to oil price volatility, and the economic and environmental advantages of renewable energy sources. To contribute to filling the gap in the existing literature,
which lacks an analysis considering the randomness of variables ina multiobjective optimization model, this study proposes a stochastic
model for assessing the economic feasibility of a hybrid energy system comprising wind energy, photovoltaic generation, and battery energy storage. The model incorporates risks and uncertainties inherent
in the critical variables associated with such investments. The multiobjective optimization employs the Design of Experiments with Response Surface Methodology and utilizes the Desirability function to

TB-21

optimize the process. As a result, to maximize the average and minimize the standard deviation of the Net Present Value, an optimal configuration was obtained, represented by the combination of a systemwith 92% wind energy, 230 kWh/month of demand, use of a lithiumion battery in a total scenario. Given the specific parameters and assumptions applied in this study, it is concluded that there is economic
feasibility of wind photovoltaic projects with energy storage in batteries in regions with highly favorable wind regimes.

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Tuesday, 10:30-12:00 - Room: 49 (building: 116)

Demand Forecasting in Humanitarian
Operations
Stream: OR in Humanitarian Operations (HOpe)
Invited session
Chair: Emilia Grass
1 - The Value of Demand Prediction for Improved Food Security

Alborz Hassanzadeh, Valérie Bélanger, Marie-Ève Rancourt,
Feyza Sahinyazan
Hunger and famine pose great risks to global health and are the secondin the UN’s seventeen sustainable development goals to be achievedby 2030; however, they prove to be quite challenging to eradicate or
alleviate. To mitigate their devastating impact, each year, aid agenciesdeliver tons of food commodities to populations in need. However,the delivery of food commodities is often expensive, and because of
the complex intertwining factors shaping food security, it is very difficult to definitively predict future outcomes and demand for food aid.
Without a timely identification of vulnerable populations, food aid often fails to arrive in the right place at the right time. We develop astochastic optimization framework to assess the value of information:
our analytical results quantify the advantages of incorporating food insecurity predictions in decision-making. Such predictions facilitate informed prepositioning decisions. Since acquiring relevant data might
require heavy investments, we also analyze the delicate balance between allocating the limited available budget to the prepositioning offood commodities and investing in the acquisition of accurate data.

2 - A Machine Learning Approach to deal with Ambiguity in
the Humanitarian Decision Making

Emilia Grass, Janosch Ortmann, Burcu Balcik, Walter Rei
One of the major challenges for humanitarian organizations when planning relief efforts is dealing with the inherent ambiguity and uncertainty in disaster situations. The available information that comes fromdifferent sources in post-disaster settings may involve inconsistencies,
which can severely hamper effective humanitarian decision making.
In this talk, we present a new methodological framework based ongraph clustering and stochastic optimization to support humanitariandecision makers in analyzing the implications of divergent estimatesfrom multiple data sources on final decisions and efficiently integrating these estimates into decision making. We illustrate the proposed
approach on a case study that focuses on locating shelters to serve
internally displaced people in a conflict setting, specifically, the Syrian civil war. We use the needs assessment information published bydifferent reliable sources to estimate the shelter needs in Idleb, Syria.
The analysis of data has revealed a high degree of ambiguity due to inconsistent estimates. We apply the proposed methodology to integrate
the ambiguous and divergent estimates into the decision making for
determining shelter locations. The results highlight that our methodology leads to higher satisfaction of demand for shelters than other approaches. Moreover, we show that our solution integrates information
coming from both sources more efficiently thereby hedging against the
ambiguity more effectively.

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TB-22EURO 2024 - Copenhagen

3 - An efficient 2-competitive online algorithm for kit update at MSF Logistique

Mickael Gaury, Boris Detienne, Gautier STaufferThis paper explores strategic optimization in updating essential medical kits crucial for humanitarian emergencies. Recognizing the perishable nature of medical components, the study emphasizes the needfor regular updates involving complex recovery, substitution and disposal processes with the associated costs. The goal is to minimize costs
over an unpredictable time horizon. The introduction of the kit-update
problem considers both deterministic and adversarial scenarios. Key
performance indicators (KPIs), including updating time and destruction costs, are integrated into a comprehensive economic measure, emphasizing a strategic and cost-effective approach. The paper proposes
an innovative online algorithm utilizing available information at eachtime period, demonstrating its 2-competitivity. Comparative analyses
include a stochastic multi-stage approach and four other algorithms
representing former and current MSF policies, a greedy improvementof the MSF policy, and the perfect information approach. Analytics
results on various instances show that the online algorithm is competitive in terms of cost with the stochastic formulation, with differences
primarily in computation time. This research contributes to a nuanced
understanding of the kit-update problem, providing a practical and efficient online algorithmic solution within the realm of humanitarian
logistics.

4 - Forecast-driven collaborative repositioning optimization. An application to Madagascar

Birce Adsanver, Burcu Balcik, Valérie Bélanger, Marie-Ève
Rancourt
Disasters are increasingly affecting human lives with greater frequency
and severity over the years. Depending on the forecasting capabilities,
it is possible to anticipate and mitigate the impacts of certain events.
Anticipatory actions, also known as forecast-based early actions, playa crucial role in this regard by allowing humanitarian actors to takeproactive measures before the disasters strike. This study focuses on arepositioning problem in anticipation of storms within a collaborative
countrywide network. The network involves multiple humanitarianagencies that strategically preposition relief supplies across different
regions. When a storm is anticipated to hit the country, the agencies
begin receiving forecast updates providing predictions on storm conditions. They estimate disaster impacts by utilizing the available forecasts and relocate the prepositioned supplies to the potentially affected
regions for a faster response. We address this problem by proposing ananalytical approach that provides relocation recommendations basedon the forecasts. We collaborate with the Emergency Supply Prepositioning Group to design and integrate such a decision support tool
into their "STOCKHOLM" platform, which offers prepositioned stock
mapping and analyses to the stockholders. We test our approach andanalyze the benefits of collaboration with a case study on Madagascar.
The analyses are conducted by processing data related to the logistics
network and storm forecasts.

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Tuesday, 10:30-12:00 - Room: 81 (building: 116)

Optimization for electric vehicles
Stream: Energy Management
Invited session
Chair: Miguel Anjos
1 - A TOU pricing approach for EV charging via DRL

Abdullah Kayacan, Mehmet Yasin Ulukus, Hatice Tekiner
Mogulkoc, Engin Cicek, Murat Bilsel, Bahadir Tunaboylu
Economic and ecological advantages make electric vehicles (EVs)more desirable than internal combustion engine vehicles. The charging need of EVs can have a critical impact on electricity distribution212

at certain penetration levels. Irregular intraday electricity demand may
cause overloads, thus increasing costs and decreasing quality. Appropriate charging pricing policies can be effective in reducing the imbalance in electricity demand. In this study, we examined the effects offixed and time-of-use (TOU) pricing policies when users have different
charging options. We developed a comprehensive simulation model toexamine users’ behavior in different charging option distributions. A
Deep Reinforcement Learning (DRL) model is proposed to determine
the pricing policy that offers the best price levels in terms of invariability of the intraday aggregate EV load profile. Our experiments indifferent charging option availability levels show that the TOU pricing
schemes obtained from the proposed DRL model offer prices leading
to more balanced load curves compared to fixed-price charging pricing. We also show that peak loads are more likely to occur when EV
drivers have similar charging options. In these cases, generated TOU
price tables reduce the standard deviation of the load profile more noticeably. This study is supported by TUBITAK under grant number
221M111.

2 - Management of electric vehicles to provide energy flexibility under uncertainty

Antoni Bosch Pons, Fernando García, Ferran Pinsach Batet,
Lucia IgualadaIn recent years, the adoption of Electric Vehicles (EVs) has increased,
driven by government initiatives to require them to reduce greenhousegas emissions. This increased adoption, while environmentally beneficial, has also led to an increase in electricity consumption. This
paper analyses the advantages of bidirectional vehicle-to-grid (V2G)
chargers for Charging Station Operators (CSOs): Electric Vehicles energy flexibility could be a valuable resource during grid congestion
episodes. We analyze this flexibility by proposing a two-stage stochastic programming model under uncertainty, regarding the presence of
EVs at charging stations. We utilize a mixed Long Short-Term Memory (LSTM) neural network to generate scenarios for each charger andthen blend and reduce them into the most representative ones. We
propose a large-scale optimization approach for solving the two-stage
stochastic model. The use of a large-scale optimization approach helps
to reduce the time for executing the optimal solution, which is beneficial to ensure management of grid variability. The results demonstratethat electric vehicles can provide demand flexibility, resulting in potential cost reductions and contributing to the prevention of power gridcongestion during periods of low generation or high demand. Also, thedeveloped solutions help to integrate renewable resources into the gridand reduce greenhouse gas emissions.

3 - Degradation-Conscious Charge Management: Comparison of Different Techniques to Include Battery Degradation in Electric Vehicle Charging Optimization

Ferran Pinsach Batet, Tomas Montes, Lucia Igualada, Josh
EichmanWith the rise of battery electric vehicles and advancements in smart
charging, there is potential to enhance flexibility and decrease charging expenses. However, degradation is often cited as a major concern for vehicle owners and must be considered. This paper focus on
unidirectional smart charging, often overlooked despite its prevalence.
To do so, the authors proposed three kinds of Mixed Integer Linear
Problems (MILPs): 1) implementing good usage practices, 2) twosteps direct evaluation of a semi-empirical battery degradation model,
and 3) introducing economic weighting for charge rate and State ofCharge degradation into the objective function. The objective function
includes energy price arbitrage against retail tariffs and to increase realism, MILP models introduced minimum charging value and a maximum charging curve. Each of these models have different strengths
and weaknesses which are explored in detail. The authors performed
384 scenarios, considering different charging time slots, initial State ofCharge, State of Health, maximum delivering power, and energy pricesignal, all applied across 6 models. On average the adoption of smart
strategies implied a total cost (energy bill plus battery degradation associated cost) reduction between 13.3% and 14.4%, compared withimmediate charging. When comparing against a simple smart model
without consideration for degradation, total cost improvements ranged
between 0.9% and 1.4%.

EURO 2024 - Copenhagen
4 - Optimal Electric Vehicle Charging with Dynamic Pricing,
Customer Preferences and Power Peak Reduction

Miguel Anjos, Luce Brotcorne, Gaël Guillot
We consider a provider of electric vehicle charging that operates a network of charging stations and uses time-varying pricing to maximize
profit and reduce the impact on the electric grid. We propose a bilevel
model with a single leader and multiple disjoint followers. The customers (followers) makes decisions independently from each the other.
The provider (leader) sets the prices for each station at each time slot,
and ensures there is enough energy to charge. The charging choice ofeach customer is represented by a combination of a preference list of(station, time) pairs and a reserve price. The proposed model takes
thus into accounts for the heterogeneity of customers with respect toprice sensitivity and charging preferences. We define a single level reformulation based on the reformulation for the rank pricing problem.
Computational results highlight the efficiency of the new reformulationand the impact of the model on the grid peak.

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Tuesday, 10:30-12:00 - Room: 82 (building: 116)

Supply chain design in the circular economy
Stream: Circular Economy, Remanufacturing and Recycling
Invited session
Chair: Andrea Tuni
1 - Designing a closed-loop supply chain for wooden cable
drums

Germán Paredes-Belmar, Franco Basso, Mariapaz Widow,
Graciela González, Joaquín Ossandón
Closed-Loop Supply Chains (CSLCs) deal with the entire supply chain
product cycle in both forward and reverse ways. This includes logistic
operations such as handling, storing, and transporting recyclable products, waste, or packaging. This work focuses on a CLSC for Returnable Transport Items (RTIs) for packaging, inspired by a real-worldcase of a Chilean electrical conductor that uses wooden cable drums
for product distribution. Currently, there is no plan for recovering or
reusing this RTI. This study addresses this issue by considering bothforward logistics and reverse logistics by reusing, recovering, or repairing the cable drums. A mixed-integer linear programming (MILP)
model is designed to minimize the total costs of the CLSC while meeting demand and capacity constraints. The results show the convenienceof adopting a CLSC approach, where it is possible to reduce costs by
30%-40% compared to using current forward logistics.

2 - Data-driven robust optimization of closed-loop supply
chain design for household plastic waste

JunSeop Lee, Byung Do Chung
As household plastic products have become essential parts of our lives,
increasing the plastic recycling rate is imperative. However, the global
average recycling rate for plastic is remarkably low at 9%, and the low
profitability of uncertain waste management is one of the major reasons. In this study, we developed a data-driven robust optimization
model of closed-loop supply chain design to address the uncertain volume of plastic waste. Subsequently, we proposed a theorem capable ofreformulating the worst-case of the uncertain public sectors model as atractable model. Based on this proposed theorem, we have constructeda data driven robust bilevel optimization program to express decentralized decision-making between a manufacturer and public sectors.
Conducting sensitivity analysis using real annual operational data, weconfirmed that if public sectors adopt a conservative solution regarding
uncertain plastic waste volume, it can lead to a decrease in manufacturer profit and a reduction in the plastic recycling rate. Furthermore,
we found that if the manufacturer is concerned about fluctuations in

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the purchasing price for plastic waste resulting from the conservative
solution of public sectors, the manufacturer may attempt to purchaseplastic waste from various regions.

3 - Large stochastic facility location problems for insulation waste treatment: Reduction-based solution techniques

Ruurd Buijs, Rob van der Mei, Elenna Dugundji, Sandjai
Bhulai
The transition to the circular economy goes hand in hand with major redesigning challenges in manufacturing and logistics. Reverse
supply chains form an important area of study within this context.
The study of reverse supply chains touches upon many different disciplines, and from an Operations Research perspective provides interesting optimization problems. Our study concerns the reverse supply
chain of insulation panels in the construction sector that contain rigid
polyurethane foam (PUR). We examine a stochastic facility location
model for locating treatment plants. Context specific constraints incorporating a cap on emissions and the economically rational behaviourof waste sorting companies are embedded into the model. Uncertainty
affects the model, as the exact location and quantities of different typesof insulation panels are not known exactly. Furthermore, some of themodel parameters depend on future policy and advances in technology. For large buildings, we assume that the panels can be collected
directly from the demolition site. As each of these buildings thus has
an individual demand for collecting the material, both the model andthe number of uncertain parameters to become very large. We investigate the performance of different methods of reducing the problem sizefor our model. We address methods to reduce the number of scenarios,and methods to reduce the number of demand points individually, andin synthesis with one another.

4 - Circular Supply Chain Design for Refurbishing Modular
Products

Andrea Tuni, Nikoletta L Trivyza, Massimo Rapisarda
Circular supply chains are key enablers to implement the circular
economy (CE), however they are yet to be established in industries
with significant environmental impacts. Refurbishing is ranked highamong circular strategies as it extends the lifespan of products andpreserves product functionality across multiple lifecyles. Nevertheless, it requires a challenging coordination of forward and reverse supply chains. This work investigates the optimal circular supply chain
re-design for the refurbishment of modular products under three scenarios: (i) cost minimisation, (ii) CO2e emissions minimisation, (iii)
cost and CO2e emissions minimisation in an eco-efficient configuration. A mixed-integer linear programming model was developed andapplied to a case study of a three-tiers global mining equipment supply chain, contextually determining the location of the refurbishment
facilities. Findings highlight that supplier selection has a significant
impact on the overall supply chain re-design, while locating the facilities in customers’ proximity yields environmental benefits. This
work contributes to the CE literature by designing optimal circularsupply chains, which integrate a high-R imperative, such as refurbishing, while simultaneously considering product modularity, multimodal transportation, economic and environmental objectives. The
model could further support practitioners aiming to re-design circularsupply chains based on the refurbishment of modular products.

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Tuesday, 10:30-12:00 - Room: 83 (building: 116)

Sustainable Logistics
Stream: Sustainable Supply Chains
Invited session
Chair: Chuanwen Dong

213

TB-25EURO 2024 - Copenhagen

1 - The impact of carbon tax on last-mile retail supply chain

Chuanwen Dong
This study proposes and analyzes a last-mile retail supply chain model.
Consumers follow an Economic Order Quantity model to decide theirquantity and frequency of shopping from the closest retail store. The
retailer determines its store density to minimize the total logistics costs
per unit of goods sold. In anticipation of the decisions from consumers
and the retailer, the government imposes a carbon tax to reduce emissions. In contrast to the literature, we find that the impact of the carbon
tax on the last-mile supply chain is not monotone. Depending on theparameters, a carbon tax may lead to a sparser or denser retail network. Surprisingly, there may even be a win-win scenario where acarbon tax incentivizes the retailer, who is primarily focused on cost
minimization, to adopt a supply chain design that simultaneously minimizes costs and emissions (the win-win). Through numerical analysis
using data from the United States and Germany, we find that denser
last-mile retail networks are currently necessary to reduce emissions
in both countries. The win-win scenario could potentially be achieved
through, e.g., innovations in electrification.

2 - Optimizing City Logistics: Assessing the Efficiency ofIntegrated Water- and Land-based Transportation Systems

Cigdem Karademir, Breno Alves Beirigo, Bilge Atasoy
This study explores the efficiency of integrated water- and land-basedtransportation (IWLT) systems in addressing the needs of growing urban populations. This has become more challenging under the regulations limiting the sizes and the reach of freight vehicles in cities to improve mobility and reduce carbon footprint. In the proposed systems,
vessels can provide flexible and efficient means of transporting goods
to supply light electric freight vehicles in the cities. The cost of an
IWLT system depends on the design of transshipment facilities, where
goods are transferred from the water to the road network. Besides allocating resources in these facilities, this study also examines flexible
IWLT systems using public spaces as on-demand meeting points likeparking areas and public transportation stops. Such systems introduce
more complexity for the logistics services due to the interdependence
between interacting vehicles requiring synchronization. We develop a
decomposition model to formulate the optimization problems at both
networks as well as at transshipment points under the real time resource capacities. Experiments on the benchmark instances show thaton average, flexible IWLT services provides a savings of 5% in termsof total travelled distances, 10% less on the roads with fewer vehicles
and 30% more over waters, compared to the dedicated systems. Moreover, we assess these systems based on a case study we designed for adelivery service in Amsterdam, the Netherlands.

3 - Estimating the load consolidation potential for ecommerce deliveries in Austria - a scenario-based approach

Alexandra Anderluh, Clemens Raffler, Julia Simhandl
The importance of online shopping has risen sharply in recent years.
The Covid-19 pandemic brought up new customer segments, new industries and local shops were opened on a large scale by connecting
to online retail. This leads to a significant increase in the volumeof freight traffic. Various innovative approaches have already beentested, but most initiatives for cooperation between logistics service
providers have not succeeded so far. Appeals to individual responsibility for bundling orders on the customer side also appear to be ineffective. Therefore, restructuring the commissioning process for delivery
services could be the way to go. The idea of an independent online
platform for the organization of logistics services could provide a onestop-shop solution for these problems: instead of the current decentralized placing of delivery orders through individual dealers, deliveries could be centrally coordinated in the interests of the society andthe environment. Hence, spatial, temporal as well as modal optimizations should achievable. To evaluate the potential of such a solution
the amount of load consolidation needs to be determined. A scenariobased approach is currently developed to derive appropriate estimatesfor Austria. First estimations show a reduction of greenhouse gas emissions caused be delivery vehicles of up to 20% for Austria in total but
more detailed estimations are required to cover more realistic settings.

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4 - Optimizing Downstream Logistics: Shipment Consolidation Strategies for Sustainable Supply Chains

Mahesh Gudipati, Ram Kumar, Ramesh Krishnan, Satyam
Sahay
Global greenhouse gas (GHG) emissions in 2023 have crossed morethan 58GT, and transportation is one of the key contributors to theseemissions. Supply chain play an important role in efficiently moving
goods from manufacturers to end customers. However, the traditional
approach to supply chain management often results in excessive carbon dioxide (CO2) emissions due to inefficient logistics processes. The
supply chain landscape of automobile manufacturing organizations ischaracterized by complex networks involving multiple stakeholders,transportation modes, disparate inventory management systems, andcomplex supplier and dealer network. This complexity often leads tosuboptimal transportation routes, underutilized capacity and excessive
empty backhauls, all contributing to high CO2 emissions. Addressing Scope 3 CO2 emissions has become critical for organizations toalign with sustainability goals. This paper attempts to address someof these challenges by focusing on shipment consolidation strategies
to reduce the cost and GHG emissions in downstream logistics. By
consolidating shipments, companies can streamline their distribution
processes, reduce the number of vehicles on the road, and optimize
delivery routes, leading to tangible reductions in CO2 emissions. The
presentation will discuss a case study from the automotive industry,illustrating the significant impact of time-based consolidation on emissions offering practical insights for academic and industry leaders.

 TB-25
Tuesday, 10:30-12:00 - Room: 011 (building: 208)

Applications of combinatorial optimization II
Stream: Combinatorial Optimization
Invited session
Chair: Antonio Alonso-Ayuso
1 - Integrated Schedule Planning for Regional Airlines Using Column Generation

Alberto Santini, Vikrant Vaze
Medium-term airline schedule planning is traditionally tackled ina sequence of steps, including route selection, frequency planning,timetable design, fleet assignment, aircraft routing, crew scheduling,
and tail assignment. Optimising each step separately is globally suboptimal, but is often the only viable strategy because of the computational difficulty of solving the joint problem. This paper focuses on
simultaneously optimising medium-term strategic decisions related tofrequency planning, timetable design, and fleet assignment, as well as
dealing with limited changes to the route selection process. Two conditions make this joint optimisation possible. First, we focus on thespecial case of regional airlines that work as feeder for larger mainline
carriers. Second, we exploit a tight exponential-size formulation; wesolve its continuous relaxation via column generation and obtain integer solutions via a restricted master heuristics. In this way, we obtaingaps consistently under 0.2%. An important characteristic of our approach is that it incorporates explicit passenger preferences via discrete
choice modelling. Preliminary results on the network of Mesa Airlines and Republic Airways show a significant potential for increased
profits and load factors. The main reason is a better synchronisationof feeder flights allowing for more connections and, thus, capturing alarger share of passengers whose itinerary involve two flights with alayover at the hub.

2 - Satellite Observation Scheduling Problem: an application on Mars Express mission

Benedetta Ferrari, Maxence Delorme, Manuel Iori, Marco
Lippi, Roberto Orosei

EURO 2024 - Copenhagen
In recent years, space exploration has received increasing attention,
thus underlining the need for efficient management of space missions.
Within this field, Satellite Observation Scheduling Problem (SOSP)
concerns the scheduling of observations performed by satellites orbiting around Earth or other celestial bodies. The SOSP has been largely
addressed in the last 30 years, dealing with both Earth-centered andouter space missions, with a lot of variants developed to cope with diverse applications and constraints. Our research focuses on the studyof SOSP for the radar MARSIS onboard Mars Express mission, whichobserves the subsurface of Mars to map the presence of water. Specifically, the Mars Observation Scheduling Problem (MOSP) aims to optimally schedule MARSIS observations to reach a maximum quality
coverage of the South Pole of Mars. The MOSP is of high difficulty
and has been only manually solved until now. To solve the MOSP we
use an approach combining machine learning and optimization. We
first employ a neural network to predict the quality of future observations, starting from a massive historical dataset. We then look forthe solution that maximizes the predicted quality by invoking different
techniques, including an Integer Linear Program and a set of constructive heuristics and matheuristic algorithms. The resulting algorithm istested on several real-world instances and scenarios derived from theMars Express mission, showing good performance.

3 - Robust Scheduling of Medical Appointments for Outpatients

Cristina Requejo, Marco Roma, Paola Cappanera, Maddalena
Nonato, Marco Gavanelli
Chronic patients affected by Non-Communicable Diseases live athome, but often book appointments at a hospital for regular visits, tests
or treatments according to their care pathway, which can integrate themedical protocols for several morbidities. It is good practice to schedule multiple services for the same patient on the same day, for the sakeof a holistic approach to patient care. The services have a known duration and are partitioned by medical specialty; the operators are also
partitioned and can provide all and only the services of their specialty.
Services can be provided in any order, each operator can see only one
patient at a time, and each patient can receive only one service at atime, without preemption. Since resources are limited, both in termsof operators and shifts, the goal of the problem is to schedule as many
services as possible. However, the duration of a service is only anapproximation and unexpected events, such as equipment failures orextra care for fragile patients, can delay its end time. Given a schedule, a delay propagates forward to all the subsequent services of theoperator providing the service, as well as to the subsequent activitiesof the patient involved. Our main contributions are the formalization ofa scheduling problem with limited operator shifts, and the methodological approach developed to deal with the uncertainty in the duration ofservices.

4 - 2D Variable Size Cutting Stock Problem: a real application in the honeycomb cardboard industry

Antonio Alonso-Ayuso, Paula Terán Viadero, F. Javier
Martin-CampoThis contribution presents a linear mixed-integer optimization framework designed to tackle the two-dimensional variable-sized cutting
stock problem (2D-VSCSP), particularly incorporating guillotine cuts.
In the 2D-VSCSP, the objective extends beyond merely identifying cutting patterns; it involves determining optimal dimensions (width andlength) for the panels to be manufactured, aiming to minimize material
usage. The Cutting Stock Problems with Variable-Sized Stock were recently introduced in various sectors, notably the textile and cardboard
industries. This paper introduces a model relevant to the cardboard
industry, accommodating cutting patterns with multiple item types, afeature not previously explored. This enhances the model’s flexibility
and widens its range of applications. The impetus for this researchstems from the honeycomb cardboard sector and is developed in collaboration with a company based in Spain. Furthermore, the model’seffectiveness is validated using real-world data characterized by significant variability, demonstrating substantial reductions in material utilization compared to the company’s current operational practices.

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 TB-26
Tuesday, 10:30-12:00 - Room: 012 (building: 208)

Advanced Topics in Combinatorial
Optimization
Stream: Combinatorial Optimization
Invited session
Chair: Silvano Martello
Chair: Paolo Toth
1 - Dynamic operator management in meta-heuristics using machine learning

Maryam Karimi Mamaghan, Mehrdad Mohammadi, Wout
Dullaert, Daniele Vigo
This study addresses the impact of search operators on the performanceof meta-heuristics. The efficient selection and management of operators from a diverse set are essential for optimization. Two key aspects
are explored: portfolio selection, determining which subset of operators to consider at each search stage, and operator selection, deciding
how to choose operators within that subset for each iteration.
A novel framework is developed to dynamically handle the portfolioof operators, incorporating the tabu concept and Q-learning. Unlike
traditional static methods, this study focuses on creating an adaptive
online portfolio. Acknowledging that not all operators are useful, themechanism adds effective operators and excludes ineffective ones atdifferent stages, optimizing overall performance. Inefficient operators
are temporarily removed based on a tabu list size and may be reintroduced later. Additionally, new operators may be selectively included
for specific search stages. Following portfolio selection, Q-learning
dynamically chooses the most efficient operator for each iteration.
To evaluate the proposed framework, the permutation flowshop
scheduling problem is tackled. Results demonstrate improvements in
optimality gaps and convergence rates compared to offline portfolio
selection and existing algorithms. The study highlights the efficacy ofthe dynamic approach in enhancing meta-heuristic performance andadaptability in complex optimization scenarios.

2 - New insights in hub location models

Mercedes Landete, Juan Manuel Muñoz-Ocaña, Juanjo Peiró,
Antonio Manuel Rodriguez-Chia, Francisco
Saldanha-da-Gama
In this work we present different advances in exact techniques for solving hub location problems. Firstly, we carry out the analysis of theclassical model with path variables for multiple-allocation and we propose a new family of clique constraint. Some properties of the new
inequlities and corresponding lifting are discussed, and a separation
heuristic algorithm is proposed. A set of computational experiments
are reported to evaluate the usefulness of the proposals when embedded in a commercial solver.The new family of inequalities is notably
effective in r-allocation problems. Secondly, we propose a model forthe single-allocation hub location problem with upgraded connections.
In this case, the goal is to invest the budget both in reducing the costof the network and in improving some of its connections. For this newmodel we propose an exact resolution algorithm based on the ordered
median problem and on an existing approximation for large class ofbinary quadratic programs. In the computational analysis we compare
the results for complete and incomplete hub networks. Finally, an analysis of the added-value of upgrading is conducted.

3 - Models for the Traveling Salesman Problem with JobTime

Pablo Gutierrez, Carlos Contreras-Bolton
The traveling salesman problem with Job-Time (TSPJ) is a variant of
the classic traveling salesman problem. In the TSPJ, the traveler visits
a set of nodes, ensuring one visit to each of them while he initiates ajob. The time of each job depends on the node where it is performed.
Once started, the traveler moves to the next node, and the job continues autonomously. The aim is to minimize the maximum completion215

TB-27EURO 2024 - Copenhagentime, also known as the makespan. However, since the problem is NPhard, the existing mixed integer linear programming (MILP) models
cannot be efficiently solved for medium and large instances. Therefore, we propose a new MILP model for the TSPJ, which is improvedby the computation of valid lower and upper bounds. Moreover, we
propose strengthened exponential-size formulations that explicitly incorporate subtour elimination constraints and additional valid inequalities. Based on the proposed model, we also develop an exact branchand-cut (B&C) algorithm that is able to solve instances of the TSPJ.
The new model and B&C algorithm are tested on benchmark symmetric instances from the literature, four sets of instances ranging in size
from 17 to 1200 vertices. The computational results show that our
B&C outperforms the state-of-the-art MILPs in the two smaller sets of
instances. Meanwhile, for medium and large sets, our exact method
achieves promising results for both instance sets.

4 - Optimizing Team Formation in Offshore Vendor Centers

Alexandros Baltas, Panagiotis Repoussis, Theodoros Lappas
In recent decades, the adoption of outsourcing to offshore vendors
across various domains, such as software engineering and IT support,
has grown significantly. Managing these vendors effectively, particularly in scenarios involving multiple cost centers, presents a rangeof challenges including issues of trust between vendors and clients,
effective knowledge gathering and retention, cultural differences, theimpact of client organizational structures on team dynamics, and complex revenue considerations across cost centers. This paper extends
the well known Team Formation Problem (TFP) that is focused on theformation of efficient teams based on skills and experience, by introducing a tailored variant for managing offshore vendors in multi-cost
centre environments. We introduce a new mathematical model that accounts for multiple projects and clients, the varying expertise levelsof workers and flexible definitions of team structures. The proposedmodel aims to optimize resource utilization and maximize throughput.
Additionally, our research includes an experimental study employing
synthetic datasets with varying sizes and characteristics to validate theapplicability of our model in real-world scenarios.

variants of the established methods Causal Inference and (online) Reinforcement Learning and we compare insights obtained from the synthetic dataset with historical datasets to assess consistency when applied to real-world data. This research aims to contribute to a morecomprehensive understanding of current methodologies.

2 - Approaches for multi-perspective trace clustering explainability

Yannis Bertrand, Jochen De Weerdt, Estefanía Serral
IoT devices are often present around business processes (BPs), collecting data about parameters that have an impact on the process. E.g., thepressure in a tank can be a relevant aspect of the execution of a BP.
Trace clustering (TC) is used to group similar process instances, usually based on their sequential activity patterns. TC is often used to
produce models describing different process variants discovered fromseparate clusters. However, TC can also be applied for exploratory
data analysis (EDA).
This is particularly interesting in the context of multi-perspective BPs,
which are surrounded with IoT devices. Multi-perspective TC (MPTC)
can be used to group together cases with similar activity sequences andsensor data. However, explaining these clusters to process users thenbecomes more challenging.
To shed some light in the results of MPTC, we propose to use various complementary explainability approaches: 1) Partial dependency
plots: they give a visual representation of the contribution of each perspective to the TC, and help users explore their clusters. 2) Permutation
feature importance: give a more quantitative measure of the impact ofeach perspective, on the global TC. 3) Prototypes and criticisms: Show
concrete instances and actively try to challenge TC results, showing therobustness of the clusters.
Each of these approaches focuses on a particular aspect of the MPTC,
and supports a better understanding of the sources of variation of theBP.

3 - Introducing SuTraN: A Transformer-Based EncoderDecoder Model for Enhanced Suffix Prediction in Business Process Monitoring

Brecht Wuyts

 TB-27
Tuesday, 10:30-12:00 - Room: 047 (building: 208)

Advances in Process Mining
Stream: Mathematical Optimization for XAI
Invited session
Chair: Jari Peeperkorn
1 - Balancing Realism and Simplicity: A Synthetic Dataset
Approach for Comprehensive Model Benchmarking in
Prescriptive Process Monitoring

Jakob De Moor
Prescriptive Process Monitoring (PresPM) is an emerging field dedicated to devising methods for suggesting interventions in business processes to optimize them. Machine learning models are applied using
event logs to attain desired outcomes for each case of a process. Current research in PresPM reveals two significant gaps. The first involves
the underutilization of synthetic data in existing literature. Synthetic
datasets offer easy customization and the ability to generate groundtruth counterfactuals for a given case, providing precise model evaluation and explainability of an intervention recommendation. However,these datasets are often overlooked and can be unrealistic. The second
gap revolves around the often exclusive focus on a narrow range of machine learning approaches and intervention types, restricting a holistic understanding of PresPM methodologies. To bridge these gaps,
we conduct a thorough comparison of machine learning methods using a synthetic dataset mimicking a local bank’s loan application process. This dataset strikes a balance between real-life complexity andsimplicity, allowing meaningful comparisons. Our approach involves

216

Predictive Process Monitoring (PPM) enhances the field of Process
Mining (PM) by applying predictive analytics to forecast the future
course of ongoing business processes. The prediction of suffixes,
which involves predicting the sequence of forthcoming events, including their activity labels, timestamps, and remaining runtime, is particularly intricate. Present methodologies predominantly encounter two issues: firstly, they are predominantly trained to anticipate the next event,employing iterative feedback mechanisms for generating suffixes upon
deployment—a strategy that not only introduces inaccuracies but also
limits the exploitation of informative event attributes; secondly, theprevalent reliance on Long Short-Term Memory (LSTM) networks impedes their performance due to their inherent challenges with longrange dependencies and information loss over lengthy sequences. In
response, this study unveils the Suffix Transformer Network (SuTraN),
pioneering the use of an encoder-decoder Transformer framework forcomprehensive suffix prediction in PPM. Tailored for this purpose, SuTraN innovatively forecasts complete event suffixes through a singularforward pass, eliminating the need for iterative loops and leveraging
the full spectrum of event data without the constraints found in LSTMdependent methods. Our evaluations, conducted on several real-world
event logs, confirm that SuTraN delivers improved performance in suffix prediction relative to established methods.

 TB-28
Tuesday, 10:30-12:00 - Room: 065 (building: 208)

Advancements of OR-analytics in statistics,
machine learning and data science 5
Stream: Advancements of OR-analytics in statistics, ma-

EURO 2024 - Copenhagenchine learning and data science
Invited session
Chair: Hector Cancela
Chair: Gerhard-Wilhelm Weber
1 - How to improve accessories sales forecasting of a
medium-sized Swiss enterprise? A comparison between statistical methods and machine learning algorithms

Agneta Ramosaj, Nicolas Ramosaj, Marino Widmer
Forecasts accuracy is definitively a crucial topic for industrial companies. Indeed, its impacts are huge especially for finance and production departments. It can occur high costs for the company if theforecasts are not accurate, due to stock-outs or excesses of inventory,
for example. Therefore, the purpose of this study is to optimize accessories forecasting for a medium-sized Swiss enterprise. To do that,different forecasting techniques are tested and a comparison is madebetween statistical methods and machine learning algorithms. The results have been adjusted thanks to the key account managers (KAM)
expertise. In this talk, a comparison between exponential smoothing,seasonal autoregressive integrated moving average (SARIMA), SARIMAX (SARIMA with exogenous regressors) and Machine Learningalgorithms such as k-nearest neighbors (k-NN), LASSO regression,
linear regression and even random forest is presented. To compare
these different methods, two measures of statistical dispersion arecomputed: mean absolute error (MAE) and root mean squared error(RMSE). These results have been standardized for a better comparison.
It results that for our dataset SARIMAX (with the KAM’s expertise as
exogenous variable) gives better results that all the machine learningalgorithms tested.

2 - How transport mode choices are influenced and what
they mean for sustainable mobility: a data-driven studyof the Asprela University Campus

Sayeh Fooladi Mahani, Beatriz Brito Oliveira, Lia Patrício,Vera Miguéis, Maria Antónia Carravilla, José Fernando
Oliveira
This study aims to investigate sustainable urban transportation in the
Asprela Campus at Porto. This area was chosen because three universities and a hospital are located there, and also attracts a varietyof travelers such as students, workers, and visitors. This allows thestudy to explore the different preferences and perceptions that shapeurban transportation. The study analyzes the choices of urban transportation, such as public transit, micro-mobility, ride-hailing, and carsharing, made by individuals aged 18 years or older who commute to
the area for various purposes, such as work, study, hospital visits, andothers. To gather the data, the survey was developed in three parts,
the initial part asks how the importance of the factors affecting transportation mode choices, then asks about the specific modes used toaccess the area, and finally asks socio-demographic information. The
survey’s novelty is asking about factors affecting urban travel choicesin the first part and examining actual behaviors in the second part, it
gives a comprehensive view of urban transportation choices, closing
the gap between preferences and real-world actions. The study’s results can help mobility companies and policymakers improve multimodal transportation and sustainable mobility. It also finds key factors
for choosing urban transportation modes, which can assist in developing enhanced demand models for urban mobility design and strategic
decisions.

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We employ a reinforcement learning approach to dynamically address
the stochastic travel times to observe changing states and make decisions accordingly. Our reinforcement learning approach incorporates
a Dynamic Graph Temporal Attention model that dynamically extracts
information from the stochastic environment. We have tested the performance of our proposed approach on the simulation. Our approach
can quickly provide high-quality solutions for all datasets using limited computing resources. The efficiency study on different models
demonstrates that new components, such as the selection mechanism,dynamic attention component, and temporal pointer can improve the
DGTA model. These findings highlight the importance of incorporating stochastic elements in the model to achieve better performance.
Furthermore, the trained DGTA model exhibits generalization capability to different instances with varying customer locations and uncertainty levels of travel times without requiring additional training time.
Our work contributes to advancing the field of DTSP-TDS, with potential applications in various industries, such as healthcare logistics.

4 - Evaluating diversion and treatment policies for opioid
use disorder

Laura Albert
The United States (US) opioid crisis has led to over 840,000 fatalities since the 1990s. It has strained hospitals, treatment facilities, andlaw enforcement agencies due to the enormous resources and procedures needed to respond to the crisis. As a result, many individuals
who use opioids never receive or finish the treatment they need andinstead have many interactions with hospitals or the criminal justicesystem. This paper introduces a discrete event simulation model that
evaluates three opioid use disorder treatment policies: arrest diversion,
re-entry case management, and overdose diversion. Publicly available
data from 2011 to 2019 in Dane County, Wisconsin (in the US), isused to forecast opioid-related outcomes through 2032. Through analyzing a variety of policy-mix implementations, this study offers aversatile framework for evaluating policies at various implementationlevels. The results demonstrate that treatment policies that create newpathways and programming by utilizing treatment services and successfully divert at least 20% of eligible individuals can lead to more
opioid-resilient communities and result in substantial savings.

 TB-29
Tuesday, 10:30-12:00 - Room: 157 (building: 208)

Applications of combinatorial optimisation
in industry and services II
Stream: Combinatorial Optimization
Invited session
Chair: José Fernando Oliveira
Chair: Adriana Cherri
1 - Fruit-based juice production process: a lot-sizing andscheduling problem

Deisemara Ferreira, Juan Sebastian Piñeros

Dawei Chen, Christina Imdahl, David Lai, Tom van Woensel

This study deals with the planning and programming production problem of a fruit-based beverage company. This is a two-stage lot sizing
and scheduling problem (tank and machine stages) with some specificfeatures, such as a production buffer between the stages and a periodic
clean-in-place (CIP) in each stage. Companies spend around 20% of
the day cleaning line equipment. These features make this problem different of other beverage problems and includes additional difficulties
to solve it.

We introduce a novel approach using deep reinforcement learning to tackle the Dynamic Traveling Salesman Problem with TimeDependent and Stochastic travel times (DTSP-TDS). The main goal isto dynamically plan the route with the shortest tour duration and visit
all customers while considering the stochastic nature of travel times.

This problem with these characteristics is difficult to solve for large
real instances and has great applicability. Based on a mathematicalmodel analytical methods are proposed, in order to support decision
making in a more efficient way in the planning and lot sizing seeking
to minimize the costs associated with production.

3 - The dynamic travelling salesman problem with timedependent and stochastic travel times: a deep reinforcement learning approach217

TB-30EURO 2024 - Copenhagen

Computational tests were performed with instances found in the literature, based on real data, and with generated instances. The proposed methods were tested and the results were compared with thebest method found in the literature for the problem under study.
Acknowledgments: This work was supported by the São Paulo Research Foundation (FAPESP) under Grants numbers 2023/08977-5,
2022/05803-3; the Brazilian National Council for Scientific and Technological Development (CNPq) under Grant number 315874/2021-0.

2 - Scheduling electricity distribution field maintenance bya prize-collection VRP approach

Roberto Tavares, Arineia Assis, Mario Batalha, Roberto
Martins, Herick Moralles
Daily field maintenance activities are common in a wide range of service providers and industrial applications. Electricity distribution systems are a classical example, commonly characterized by a set of pending services that must be accomplished by a set of maintenance teams.
There are three main daily decisions usually involved in the schedulingof such services: (i) what services will be executed on a specified date;(ii) what is the sequence of each service list of each maintenance team(usually based on routing constraints); (iii) which team will execute
each set of services. In this research, we address a real-world problem
faced by an electricity distribution company. In this case, there are a setof activities available to the maintenance teams. For each activity is assigned a "prize" (strongly related to the activity’s nature and deadline).
The time required to start an activity may be influenced by the previous
task (e.g., there is no need to perform two security checks on activities
executed at the same place). This problem is faced as a derivation of
the prize-collection vehicle routing problem (PCVRP). This researchpresents a MILP formulation and proposes a meta-heuristic approachto generate daily maintenance schedules. Both solution strategies areapplied to problem datasets based on historical data of large cities attended by the electricity distribution company. Our results show theapplicability of the meta-heuristic to solve this real-world problem.

3 - Control of pattern complexity in two-dimensional rectangular cutting problems

Maria Antónia Carravilla, Matheus Campinho, Elsa Silva,
José Fernando Oliveira
Cutting and packing (C&P) problems are hard combinatorial optimisation problems that arise in a variety of manufacturing and processindustries. These problems arise whenever a larger object needs to be
divided into smaller parts in order to minimise waste, and their optimal
solution contributes to the reduction of raw material costs. However,many companies ignore optimisation approaches to their C&P problems in favour of production efficiency, prefering simple ways of cutting raw materials (cutting patterns), which require less human labour
and less sophisticated and cheaper machinery, to complex, but in termsof raw material consumption optimised, cutting patterns. Therefore, it
becomes critical to explicitly consider the impact of pattern complexity
when solving C&P problems. In this talk, we will present a number ofcharacteristics that influence pattern complexity, such as the number ofdifferent piece types, the total number of cuts, different types of strip
structures, etc. Based on the floating-cuts mathematical model for twodimensional rectangular cutting problems, these characteristics will bemodelled, requiring new variables, additional constraints and resulting
in new components of the objective function. Within a multi-objective
optimisation framework, the conflicting objectives of waste and pattern complexity minimisation are addressed. Computational results arepresented that show and quantify this trade-off. [Projeto CIBELE doi:
10.54499/2022.02767.PTDC]

4 - Solving Nesting Problems with Guillotine Cuts: A Novel
Branch-and-Cut Algorithm

José Fernando Oliveira, Luiz Henrique Cherri, Adriana
Cherri, Everton Fernandes da Silva
Cutting and packing problems have been studied for decades and theresults of this research have a huge potential to improve many industrial production processes, both from an economic and environmental
point of view. A widely studied problem is the two-dimensional irregular strip packing problem, aka nesting problem. This problem consists

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of placing a set of two-dimensional irregular polygonal pieces on aboard with a fixed height and a length long enough to be consideredinfinite. The classical objective is to minimise the length of the board
used to cut all the pieces. The two-dimensional irregular packing problem has been tackled by both heuristic and exact methods, allowing or
not the rotation of the pieces. Despite the diversity of papers on thisproblem, there is a lack of research on the generation of cutting patterns that satisfy the constraints of guillotine cutting for irregular items,
which is fundamental in the glass cutting industry, and only heuristic
methods were proposed to solve two-dimensional irregular cutting andpacking problems with guillotine cuts. We will present an innovative
branch-and-cut method to represent and exactly solve all variants ofirregular cutting and packing problems with guillotine cuts that deal
with a single board (aka as bin, plate or strip), namely the irregular
strip packing problem, the irregular placement problem, the irregularknapsack problem, and the irregular identical item problem.

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Tuesday, 10:30-12:00 - Room: 064 (building: 208)

Modeling Languages
Stream: Software for Optimization
Invited session
Chair: Robert Fourer
Chair: Christian Valente
1 - Building, testing, analyzing, and debugging FICO
Xpress Mosel projects

Susanne Heipcke, Yves Colombani
The Xpress Mosel language is used for implementing large optimization and analytics projects. Many such projects are deployed as Xpress
Insight apps, either on individual software installations or in a cloudbased environment. To develop, build, deploy, and maintain such alarge code base a team of OR specialists typically works in collaboration with other experts (UI specialists, problem domain experts). The
availability of a suitable toolset for building, analyzing and testing thecode in a largely automated way is a must nowadays. The Mosel distribution comes with a set of open-source tools supporting these development tasks. In this talk we review the roles and usage of the different
tools to support the operation of enterprise-grade OR applications. We
start with the build process (including the generation of online documentation), we then move on to the topics of testing and code analysis(profiling and coverage), and finally debugging. We consider in particular the case of Insight apps.

2 - GAMS Engine SaaS: A Cloud-Based Solution for LargeScale Optimization Problems

Frederik Proske
GAMS Engine SaaS is a cloud-based service that allows users to run
GAMS jobs on a scalable and flexible infrastructure, currently provided by Amazon Web Services (AWS). It was launched in early 2022
and has since attracted a variety of customers who benefit from its
features, such as horizontal auto-scaling, instance sizing, zero maintenance, and simplified license handling. GAMS Engine SaaS is especially suitable for workloads that require large amounts of compute
power and can be adapted to many different scenarios. In this presentation, we show a case study of a large international consultant agency
that uses GAMS Engine SaaS to run Monte-Carlo simulations of alarge energy system model in response to varying climate change scenarios. We describe how they leverage the GAMS Engine API to submit and monitor their jobs, how they select the appropriate instancetype for each job, and how they can use custom non-GAMS code
on Engine SaaS. We also discuss the challenges and benefits of using GAMS Engine SaaS for this type of application, and provide someinsights into the future development of the service.

EURO 2024 - Copenhagen
3 - AMPL: Advances in Python Integration, Cloud Deployment, and Open-Source Tools

FIlipe Brandão
Python and its vast ecosystem are great for data pre-processing, solution analysis, and visualization, but Python’s design as a generalpurpose programming language makes it less than ideal for expressing
the complex optimization problems typical of prescriptive analytics.
AMPL is a declarative language that is designed for describing optimization problems and that integrates naturally with Python.
This presentation shows how the combination of AMPL modeling withPython environments and tools has made optimization software morenatural to use, faster to run, and easier to integrate with enterprise systems. We demonstrate how AMPL and Python work together in a rangeof contexts:- Installing AMPL and solvers as Python packages anywhere - Fast
data transfer from/to Python data structures such as Pandas and Polars
dataframes - Deploying models to the cloud quickly and easily
We also present our new open-source library for C/C++ and Python
that makes it easy to connect modeling systems to AMPL-enabled
solvers. In addition to our solver drivers, which have been open-source
for many years, we are now offering open-source access to all of the
deployment tools that we offer. Currently only AMPL takes full advantage of the functionalities that we provide, but the ability to solve
problems using the full features of AMPL-enabled solvers is open toother systems as well.

4 - Advances in Automated Conversion of Optimization
Problems

Christian Valente, Robert Fourer
We take it for granted that an optimization package accepts both minimization and maximization problems, recognizes them as equivalent,and converts all minimizations to maximizations (or vice-versa) before
solving. This is only the very simplest example of the many conversions carried out routinely by large-scale optimization software. The
range of expressions recognized by modeling languages and solvers
has been steadily extended in ways that make optimization models
easier to describe, validate, and maintain — but that make conversion
possibilities ever more numerous and complex.
Continuing this trend, automatic conversions have been a central feature in the design of a new solver interface framework for the AMPL
modeling language. This presentation describes a range of challenges
that have been faced in detecting formulations that solvers can handle,
and in implementing conversions to forms that solvers require. Examples combining a variety of discrete and nonlinear expressions leadto some general recommendations for design and implementation of
automated conversions.

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demonstrated on wage data from the Czech Republic. For the construction of mixtures, it is possible to use densities of classical distributions for modelling wage data such as normal, logarithmic normal,
Johnson’s and other distributions. When creating the final mixture, wesolve two problems. First, we need to determine the number of components in the mixture. Secondly, we need to estimate the parametersof individual densities in the mixture. Since we have a large amountof data broken down by different criteria (age, gender, region, education, etc.), we can naturally divide the data into a large number ofcategories. Each category is then represented by one component in
the resulting mixture. Detailed data breakdown allows us to calculate
weights of individual components in the resulting mixture. In addition
to the classic procedure known from theory, the authors in the article
also offer a non-standard approach, which allows not only to model
the course of past data, but also the possibility of estimating the future
development of the wage distribution. In the conclusion, attention is
also paid to the stability of the models.

2 - GIMI: Generative iterative multiple imputation for improving health trajectory estimation in the ICU

Anahita Khojandi, Matt Baucum, Senne Van Steelandt, Rama
Vasudevan, Robert Davis, Nadeem Shafi
As the field of precision medicine grows, accurately forecasting patients’ health trajectories is essential for developing data-driven treatment models. Yet researchers must still contend with high levels of
missingness due to issues such as missed bedside readings, data entry error, or in the case of outpatient monitoring, inconsistent patient
follow-ups. When forecasting patient health trajectories, even small
imputation errors can compound over the given time horizon, complicating the task of missing data imputation and jeopardizing model
accuracy. To address this, we develop a novel on-training imputation
algorithm - Generative Iterative Multiple Imputation (GIMI) - which
estimates missing health data by directly maximizing the accuracy ofpatients’ predicted health trajectories. Rather than separating the imputation and forecasting steps, GIMI leverages state-of-the-art generative neural networks to simultaneously forecast patients’ missing andobserved health trajectories, and is trained in an end-to-end fashion to
maximize forecast accuracy. Using a large clinical dataset of intensive
care unit (ICU) patients, we show that GIMI outperforms commonlyused benchmarks in forecasting patient health trajectories in the presence of missing data. Notably, we show that the imputation errorsof several existing imputation techniques compound over the healthtrajectory time horizon, whereas GIMI’s prediction accuracy remains
stable.

3 - Analysis of characteristics of human score and improvement of feedback of evaluation results of automated essay scoring system

Megumi Yamamoto

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Tuesday, 10:30-12:00 - Room: 046 (building: 208)

Analytics and the link with stochastic
dynamics II
Stream: Analytics
Invited session
Chair: Gerhard-Wilhelm Weber
Chair: Jinal Parikh
1 - Probability models based on a mixture of densities

Lubos Marek
When we build the model of probability distributions, we can workwith one distribution or use a mixture of distributions. In general practice, one density distribution is usually used. We will show anotherapproach, using a mixture of distributions. The whole process will be

We propose and construct an automated essay scoring system based on
rubric to reduce the workload on teachers. This system calculates thirteen items, including written content, document style, writing skills,
and output the overall evaluation results. We collected and analyzed
human score data to improve accuracy of overall evaluation. Specifically, we asked 6 faculty members to score 25 essays manually using our automated scoring system rubric to identify the characteristics
of items that affect the overall evaluation. The results revealed that
low-rated essays had similar evaluation results among faculty members. Correlations were also obtained between the automated and human scores for document style, skill, and contents among the rubric
items. However, we could not obtain the common evaluation items
among the teachers for high-rated essays and established the absenceof an effective automatic calculation method for the overall evaluation.
Therefore, we classify low-rated essays and others using the accumulated low-rated essay data and present a comprehensive evaluation of
low-rated essays by automated scoring. On the other hand, for others
including high-rated essays we suggest that a feedback system highlighting essential evaluation contents rather than the overall evaluation
would be beneficial.

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4 - Analysis of the implementation of digitisation in humanresource management depending on the size of the organisation or economic sector

Terezie Krestová
Digital transformation has become a worldwide phenomenon and has
a significant impact on the functioning of organisations in many areas.
In our research, we focus on the application of digitisation in the fieldof human resource (HR) management. There are many possibilities forutilising digitisation in HR, e.g., employee recruitment, more efficientplanning of interviews, productivity measurement, psychodiagnostics
tests, adaptation of new employees, and other areas. A questionnaire
has been developed to determine i) the extent to which digitisation is
implemented in organisations in the Czech Republic, ii) which specificelements of digitisation are used, iii) what consequences organisations
observe in this context, and iv) whether barriers to the introduction
and implementation of digitisation are observed in relation to the ageof employees. Statistical methods are used for analysis in which we
focus on whether the implementation of digitisation varies dependingon the size of the organisation or the economic sector. Attention is
also paid to whether it can be argued that the benefits of digitisation
increase disproportionately when implemented in multiple areas. We
employ the GUHA method to discover the knowledge from the dataand generate statistical hypotheses. Hypotheses are tested by means of
the chi-square test. Additionally, selected multidimensional statisticalmethods, such as factor or cluster analysis, are also applied to evaluate
the obtained results.

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Tuesday, 10:30-12:00 - Room: 41 (building: 303A)

the arising optimization problem. This is a penalization on the portfolio turnover, thus it limits the number of transactions by preserving the pattern of active positions. The model leads to a nonsmooth
constrained optimization problem, where the inequality constraints are
aimed to guarantee at least a minimum level of expected wealth at eachdate. We develop iterative algorithms based on first order methods. We
validate the approach showing results of tests performed on real data.

3 - Parallel computing in optimization methods used in estimating risk-neutral densities through option prices

Antonio Santos, Ana Monteiro
Option pricing is an active area of research in financial economics.
The risk-neutral density is a critical element in pricing derivative assets, and it can be estimated using nonparametric kernel methods. Recent research considered large-scale optimization problems, which significantly improved the robustness of estimating risk-neutral densities
through observed option prices. In nonparametric estimation methods, kernel bandwidth estimates are crucial elements. However, theyalso represent the most computational challenge in implementing suchmethods, namely when large-scale optimization problems are used incurve fitting. Some of the computational challenges can be solved byconsidering parallel computing algorithms performed using GraphicalProcessing Units. We propose a tailor-made Cross-Validation criterion function to define optimal bandwidths (two parameters) associated with the risk-neutral estimation problem, defined through an optimization problem with non-convex objective functions. By implementing a grid-search approach within a big data scenario and througha large-scale optimization problem, computational times can be prohibitive for applying these methods in real-time decision-making. Parallel computing methods within a grid-search optimization algorithm
substantially reduce computational times, allowing for more effective
decision-making processes related to risk-neutral density estimationand option pricing.

Nonsmooth optimization and applications,
Part II
Stream: Advances in large scale nonlinear optimization
Invited session

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Tuesday, 10:30-12:00 - Room: 42 (building: 303A)

Chair: Valentina De Simone
Chair: Gerardo Toraldo

Optimal control in environmental economics

1 - Bilevel learning optimization and applications

Stream: Optimal Control Theory and Applications
Invited session

Serena Crisci
Bilevel optimization has gained a lot of attention in recent years as adata-driven learning technique for the estimation of unknown model
operators or for the automatic selection of hyperparameters in highly
parameterized models. Formally, a bilevel problem consists in a nested
optimization problem where a variational model acts as a constraint.
Starting from an outline of the main theoretical concepts of bilevel formulations, in this talk numerical bilevel schemes to address imaging
and portfolio optimization problems will be anaylsed, combined withdifferent strategies to properly derive the gradient of the upper-levelloss, which is one of the major obstacle in bilevel optimization. Numerical tests will be presented to validate the proposed schemes.

2 - Nonsmooth optimization in sparse portfolio selection

Zelda Marino, Stefania Corsaro, Valentina De Simone
In this talk we describe a fused lasso approach for the regularized
multi-period mean variance model, in a Markowitz framework. Multiperiod portfolio selection aims at computing the optimal allocation of
wealth among n assets within a time horizon of m periods. In addition,
the investor can rebalance the portfolio at the beginning of each period.
We define a multi-period minimum variance model with final expected
return and introduce l1-regularization techniques to stabilize the solution process, which is well-known to be ill-conditioned because of
assets correlation. Two l1-penalty terms are used: one on portfolio
weights promotes sparsity in the solution; this allows investor to reduce both the number of positions to be monitored and the transaction
costs. A second l1-penalty term is added to the objective function of

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Chair: Andrea Seidl
1 - Should forests be restored by producers or deforesters?

Fouad El Ouardighi
A non-cooperative model is formulated where a producer and a deforester generate polluting emissions over time. Given the benchmark
provided by the cooperative solution, we investigate two scenarios i)whether the forest’s restoration should be done by the deforester alone(as the forest’s owner) or outsourced to the producer (the forest’s nonowner), and ii) whether non-cooperative strategies for either of these
two scenarios can eliminate history dependency.

2 - Maximizing algal productivity in raceway ponds forbioenergy generation: A mathematical perspective

Aurea Martinez, Lino J. Alvarez-Vazquez, Rodriguez
Carmen, Miguel E. Vazquez-Mendez
We propose a novel mathematical methodology to accomplish the optimal performance of a raceway: an open-channel pond where circulating wastewater is used for the cultivation of algae that will be employed
as source in bioenergy production. Algal productivity maximization isaddressed here by means of optimal control techniques for partial differential equations. So, we first introduce a rigorously detailed mathematical formulation of the control problem, based on a coupling of
the hydrodynamical equations with the system for algal concentration.

EURO 2024 - Copenhagen

TB-36

Then, we suggest a full numerical algorithm for its computational resolution, and finally we show some preliminary results related to thenumerical optimization of the problem.

2 - Coalitional Acceptability Games: A Framework for Riskaverse Stochastic Coalitional Games

3 - A stationary equilibrium model of green technology
adoption with endogenous carbon priceWe study the problem of coalition formation and profit sharing amongrisk-averse agents who jointly undertake a project which requires management decisions and leads to uncertain outcomes. We use acceptability functionals to model the agents’ risk preferences and define thevalue of coalitions by the optimized sup-convolution of their acceptability functionals. We call such games coalitional acceptability games
and provide conditions for the non-emptiness of the core for important
subclasses of games such as network games, bankruptcy games, andgeneralized production games. We show that players agree on ex-antecontracts that are equivalent to a mutual exchange of standard options
contracts. Finally, we illustrate our approach with two numerical examples: a water management application and a detailed case study ofa virtual power plant.

Giorgio Ferrari
We propose and analyze a stationary equilibrium model for a competitive industry which endogenously determines the carbon price necessary to achieve a given emission target. In the model, firms are
identified by their level of technology and make production, entry, andabatement decisions. Polluting firms are subject to a carbon price andabatement is formulated as an irreversible investment, which entails asunk cost and results into switching to a carbon neutral technology. In
equilibrium, we identify a carbon price and a stationary distributionof incumbent, polluting firms, that guarantee the compliance with acertain emission target. Our general theoretical framework is complemented with a case study with Brownian technology shocks, in which
we discuss some implications of our model. We observe that a carbon pricing system alongside installation subsidies and tax benefits forgreen firms trigger earlier investment, while higher income taxes forpolluting firms may be distorting. Moreover, we discuss the role of awelfare maximizing regulator, who, by optimally setting the emission
target, may mitigate or revert some parameters’ effects observed in themodel with fixed limit. This is joint work with Felix Dammann.

4 - Optimal Sustainable Investments in a Multi-Objective
Dynamic Model of the Firm

Andrea Seidl, Richard Hartl, Peter Kort
Reducing pollution is something desireable for a firm, however, sometimes this is only achieveable by means of reducing production which
in turn has a negative impact on revenues. In the present talk, we
consider a simple two-objective optimal control problem. We discuss
the advantage of using the epsilon-constraint method to determine thePareto front as opposed to the conventional weighted-sum approach.
We are able to derive analytic results for our optimal control problem
and discuss their implications.

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Tuesday, 10:30-12:00 - Room: 44 (building: 303A)

Risk Averse and Contextual Stochastic
Optimization
Stream: Stochastic, Robust and Distributionally Robust
Optimization
Invited session
Chair: Guzin Bayraksan
Chair: Francesca Maggioni
1 - ML-based Adaptive Risk Parameters Tuning for Robust
Optimization

David Wozabal, Walter Gutjahr, Raimund Kovacevic

3 - Index policy for multiarmed bandit problem with dynamic risk measures

Ozlem Cavus, Milad Malekipirbazari
The multiarmed bandit problem (MAB) is a classic problem in which a
finite amount of resources must be allocated among competing choiceswith the aim of identifying a policy that maximizes the expected total reward. The classical MAB makes the strong assumption that thedecision maker is risk-neutral and indifferent to the variability of theoutcome. However, in many real life applications, these assumptions
are not met and decision makers are risk-averse. Motivated to resolve
this, we study risk-averse control of the multiarmed bandit problem
in regard to the concept of dynamic coherent risk measures to determine a policy with the best risk-adjusted total discounted return. In respect of this specific setting, we present a theoretical analysis based on
Whittle’s retirement problem and propose a priority-index policy that
reduces to the Gittins index when the level of risk-aversion converges
to zero. We generalize the restart formulation of the Gittins index toeffectively compute these risk-averse allocation indices. Numerical results exhibit the excellent performance of this heuristic approach. Our
experimental studies suggest that there is no guarantee that an indexbased optimal policy exists for the risk-averse problem. Nonetheless,our risk-averse allocation indices can achieve optimal or near-optimal
policies which in some instances are easier to interpret compared tothe exact optimal policy.

4 - A Data-Driven Methodology for Contextual Unit Commitment Using Regression Residuals

Guzin Bayraksan, Ogun Yurdakul
We present a data-driven approach that integrates machine learning
prediction models with optimization under uncertainty in the presence of contextual information by utilizing residuals from the learning models. We discuss an application of this approach for making
unit commitment decisions under uncertain net load conditions to improve short-term power system operations. We propose several enhancements to the basic methodology and assess their effectiveness on
several case studies conducted using real-world data collected from the
California and New York Independent system operators. Results showthat the proposed approach can significantly improve the out-of-sample
performance.

Emanuele Pizzari, Marco Boresta, Diego Maria Pinto
This paper introduces a novel approach that integrates Robust Optimization with Machine Learning (ML) to dynamically estimate optimal risk parameter’ values for uncertain environments. By leveraging
historical data and engineering informative features, we train an ML
model to predict risk protection values tailored to each instance’s characteristics. Unlike static approaches, our method offers adaptability
and enhances decision-making in uncertain domains. Through experimentation, we demonstrate the efficacy of our approach in improving
robustness and performance.

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Tuesday, 10:30-12:00 - Room: 32 (building: 306)

Game Theory, Solutions and Structures VI
Stream: Game Theory, Solutions and Structures
Invited session
Chair: László Kóczy

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1 - Clustering prevention games on signed networks

Maria Serna, Xavier Molinero, Fabian Riquelme
Signed networks are graphs in which each edge has associated a sign.
They were introduced as a simplified model to analyze social relationships. A positive sign in an interaction represents friendship while anegative one means animosity. Central notions in signed networks relate to the possibility of partition the vertices into clusters so that thepositive edges join vertices in the same cluster and the negative edges
join vertices in different clusters. This leads to two relevant notions:structural balance (having such a partition into two clusters) and social equilibrium (having such a partition). We study signed graphswith respect to these two notions from the point of view of cooperative game theory. We first introduce some new families of simple
games on signed network and analyze several parameters and properties. We compare them to the ones known for other subclasses ofsimple games, in particular with social disruption games which wereintroduced regarding social equilibrium. In addition, we give somecomplexity results on the considered properties.
[Supported by Spanish AEI grant MICINN PID2020-112581GB-C21]

2 - Cooperative game theory and cost allocation: the caseof renewable energy

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Tuesday, 10:30-12:00 - Room: 33 (building: 306)

Advances in Continuous Multiobjective
Optimization
Stream: Multiobjective Optimization
Invited session
Chair: Firdevs Ulus
1 - A multiobjective continuation method to compute theregularization path of deep neural networks

Mariam Sangare

Augustina Chidinma Amakor, Sebastian Peitz

A renewable energy community is a non-profit legal entity involving
prosumers which produce, consume, and exchange energy. In thesecommunities, prosumers can cooperate to maximize the community’ssocial welfare. In practice, this naturally raises the question of the community’s social welfare sharing, as the members may have different
contributions to the social welfare. In this presentation, we first empirically highlight the benefits of cooperation for the community and theindividual members. Then, we present some cost-sharing mechanisms(from the literature) that guarantee the stability of the grand coalitionof the prosumers in the community. Finally, we present some results
on instances built with real-world data from our partner’s demonstrator, Smart Lou Quila, located in South France.

Sparsity is a highly desired feature in deep neural networks (DNNs)
since it ensures numerical efficiency, improves the interpretability ofmodels (due to the smaller number of relevant features), and robustness. In machine learning approaches based on linear models, it is wellknown that there exists a connecting path between the sparsest solution
in terms of the L1 norm (i.e., zero weights) and the non-regularized solution, which is called the regularization path. Very recently, there wasa first attempt to extend the concept of regularization paths to DNNs
by means of treating the empirical loss and sparsity (L1 norm) as two
conflicting criteria and solving the resulting multiobjective optimization problem. However, due to the non-smoothness of the L1 norm andthe high number of parameters, this approach is not very efficient froma computational perspective. To overcome this limitation, we present
an algorithm that allows for the approximation of the entire Pareto
front for the above-mentioned objectives in a very efficient manner.
We present numerical examples using both deterministic and stochastic gradients. We furthermore demonstrate that knowledge of the regularization path allows for a well-generalizing network parametrization.

3 - Properties and parameters of social disruption games

Xavier Molinero, Fabian Riquelme, Salvador Roura, Maria
Serna
Signed graphs are extensively used across various real-world social networks, where positive edges represent favourable relationships(friends, colleagues) and negative edges denote unfavourable ones (enemies, adversaries). Some previous research in signed graphs has focused on defining properties to evaluate clustering tendencies in thesense that the positive edges join vertices in the same cluster andthe negative edges join vertices in different clusters. This work gives
theoretical and experimental results of the so-called social disruption
games. These games form a subclass of the simple games defined in
the classical game theory. Social disruption games satisfies that a setof vertices is a winning coalition if and only if the subgraph inducedby the coalition contains a cycle with only one negative edge. In thiswork, we explore properties such as properness, strongness, and decisiveness, among others. We also give some results on parameters likelength, width, strict length, and strict width, etc. Finally, we present
signed graphs that define social disruption games with specific properties and parameters (vetoers, critical players, symmetric players, etc.).
[Supported by Spanish AEI grant MICINN PID2020-112581GB-C21]

4 - One Man, One Vote, One Price

László Kóczy, Balázs R. Sziklai
Originally introduced to allocate parliamentary seats among states orprovinces, apportionment methods can distribute any scarce discrete
resource based on a series of claims or budgets. Here, we focus on theprice of representation: how one can turn the claims, such as the number of voters, into a share of the resource, such as the parliamentaryseats. The D’Hondt method naturally emerges as a competitive equilibrium. We show that the class of parametric divisor methods is fully
characterised by a competitive equilibrium augmented by a uniform
credit or debt with respect to the original claims.

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While in a competitive equilibrium, all pay the same price, states orprovinces are not equally efficient at converting their claims. Optimization apportionment methods such as the Leximin focus on minimizing
price differences. We demonstrate how the D’Hondt and Adams methods can be formulated as optimization methods and highlight their connection to the Leximin method.

2 - Multi-Objective Nonlinear Conjugate Gradient Schemeswith Guaranteed Descent

Manuel Berkemeier, Sebastian Peitz
Various nonlinear Conjugate Gradient (CG) schemes have been observed to improve convergence rates in single-objective optimizationcompared to steepest descent, without requiring second-order derivatives. Pérez and Prudente translated many of the most popular CG
directions to the multi-objective case. However, in order to prove convergence, the classical CG schemes require us to assume Wolfe conditions on the step-size. In some settings, fulfilling Wolfe conditions
might not be possible. Thus, we present several example directions
that provide guaranteed descent in all objective functions, independentof the step-size. This enables us to prove convergence to Pareto-criticalpoints for a simple inexact line-search algorithm with backtracking tosatisfy some modified Armijo-rule. Our special CG directions are designed to be implementable without too much computational cost, andnumerical experiments show the efficacy of the algorithm.

3 - A Bi-Objective Optimization Based Acquisition Strategy
for Batch Bayesian Global Optimization

Pierluigi Mansueto, Francesco Carciaghi, Simone Magistri,
Fabio Schoen
In this work, we deal with batch Bayesian Optimization problems
over a box and we propose a novel bi-objective optimization (BOO)
acquisition strategy to sample points where to evaluate the objectivefunction. The BOO problem involves the Gaussian Process posteriormean and variance functions, which, in most of the acquisition strategies from the literature, are generally used in combination, frequently
through scalarization. However, such scalarization could compromiseEURO 2024 - Copenhagen
the Bayes-Opt process performance, as getting the desired trade-off between exploration and exploitation is not trivial in most cases. We instead aim to reconstruct the Pareto front of the BOO problem based on
optimizing both the posterior mean as well as the variance, thus generating multiple trade-offs without any a priori knowledge. We then propose two possible methodologies to select potentially optimal points
from the Pareto front. Our methodology is finally tested with wellknown acquisition strategies from the literature, in order to highlightits effectiveness on different settings.

4 - Vector exponential penalty E-function method for Edifferentiable vector optimization problems

Najeeb Abdulaleem
In this paper, a new concept of generalized convexity is introducedfor E-differentiable vector optimization problems. The concept of
E-r-invexity is introduced as a generalization of the invexity notion.
Further, a new vector exponential penalty E-function method for Edifferentiable vector optimization problems is introduced. Furthermore, this work presents an investigation into two methods for solving
multiobjective programming problems. First, it explores a sequenceof vector penalized optimization problems utilizing a vector exponential penalty E-function. The convergence of this method is established.
Next, it defines and analyzes the exactness property of a vector exact
penalty E-function method within the context of the introduced vector exponential penalty E-function method. Conditions are provided
to ensure the equivalence of sets of (weak) E-Pareto solutions between
the original E-differentiable optimization problem and the associatedvector E-penalized optimization problem using the vector exact exponential penalty E-function. This equivalence is established for Edifferentiable vector optimization problems involving E-r-invex functions.

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Tuesday, 10:30-12:00 - Room: 34 (building: 306)

Advances in Complex and Real Semidefinite
Programming
Stream: Conic Optimization: Theory, Algorithms, and Applications
Invited session

TB-39

and an arbitrary direction D, what is the maximum t such that X+tD
stays in the SDP cone? This new sub-problem enabled the new algorithm to converge to the optimal solution through a sequence of bothinner and outer solutions (with regards to the SDP cone). The general
Projective-Cutting-Planes logic was first proposed in a purely linear
context [Daniel Porumbel, Projective Cutting-Planes, SIAM Journal
on Optimization, 30(1): 1007-1032, 2020]. We will show that the projection sub-problem can also be solved very efficiently in a new SDP
context. One way to solve it would be to compute the generalized
eigenvalues of X and -D. But we seek maximum speed and we solve itusing certain factorizations suitably tailored to our given context. Results suggest that the overall method may be very competitive (compared to Mosek or ConnicBundle) for matrix sizes larger than 2000 ×2000. It is less competitive if the matrix size is low and if the initialprogram has many linear constraints.

3 - Semidefinite approximations for bicliques and biindependent pairs

Sven Polak, Monique Laurent, Luis Felipe Vargas
Let $G$ be a bipartite graph with vertex color classes $V_1$ and$V_2$. A biindependent pair in $G$ is a pair $(A,B)$, where $A
subseteq V_1$, $B subseteq V_2$ and the union $Acup B$ is an independent set. We consider the following three parameters $alpha(G)$,$g(G)$ and $h(G)$, that are defined, respectively, as the maximum
sum $|A|+|B|$, the maximum product $|A|cdot |B|$ and the maximum ratio $(|A|cdot|B|)/(|A|+|B|)$, taken over all biindependent pairs$(A,B)$ in $G$.
Similar parameters can be defined for bicliques in general graphs,
which can however be reduced to the above parameters in bipartite
graphs. While $alpha(G)$ is easy to compute in bipartite graphs,
Peeters (2003) showed that computing $g(G)$ is an NP-hard problem.
The parameter $h(G)$ was introduced by Vallentin (2021). The parameters $g(G)$ and $h(G)$ have many applications, in particular, tobounding product-free subsets in finite groups, and the nonnegative
rank of nonnegative matrices.
We investigate SDP upper bounds on $g(G)$ and $h(G)$, which canbe seen as variations of the Lov’asz $vartheta$-number. We show
links among them as well as with an earlier parameter by Haemers(2001), and we formulate closed-form eigenvalue bounds. We also
show that computing $h(G)$ is NP-hard, by showing that it is NP-hardto determine if a bipartite graph has a biindependent pair (A,B) with
|A|+|B|=alpha(G) and |A|=|B|. We finally present a conjecture about
the value of $h(G)$ on the hypercube graph.

Chair: Renata Sotirov
1 - Semidefinite liftings for the complex cut polytope

 TB-39

Lennart Sinjorgo, Renata Sotirov, Miguel Anjos

Tuesday, 10:30-12:00 - Room: 35 (building: 306)

We consider the complex cut polytope: the convex hull of Hermitianrank-one matrices xx*, where the elements of the n-dimensional vector x are complex m-th unit roots. These polytopes find applications
in MAX-3-CUT, digital communication, and more generally, complex
quadratic programming. For m = 2, the complex cut polytope corresponds to the well-known real cut polytope. We provide an exact
description of the complex cut polytope for m = n = 3 and investigate
second order semidefinite liftings of the complex cut polytope. For
such second order liftings, we show a method for reducing the sizeof the matrix, without weakening the approximation. We support ourtheoretical findings with numerical experiments.

Stochastic Models in Logistics

2 - Semidefinite programming by projective cutting planes

Daniel Porumbel
We consider the most standard SDP (semidefinite programming) program in a cutting-planes setting, i.e., the program is formulated using
the infinitely-many linear cuts that define the SDP cone. We go beyond
the most classical approach that relies on repeated separation; to separate a matrix X is enough to test if its minimum eigenvalue is above
or below zero. Instead, we upgrade the separation sub-problem to a
projection sub-problem: given a feasible matrix X in the SDP cone

Stream: Stochastic Modelling
Invited session
Chair: Caroline Spieckermann
1 - Inventory Planning with Supply Yield Uncertainty: On
the Optimality of Linear Inflation Rules

Jan Van Mieghem, Riccardo Mogre
We study inventory planning under demand and supply yield uncertainty. We present new optimality conditions and explicit solutions
for the associated single-period “newsvendor” model. In general, thestochastic-optimal order policy is non-linear in the starting inventory
level $x$. The literature has proposed Linear Inflation Rules (LIR)that inflate the classic order-up-to policy. We prove that LIR are only
stochastically optimal in the degenerate setting where either demandor (not and) supply yield is uncertain.
Given the good performance of LIR we investigate their optimality under two robust formulations and provide bounds. We prove that LIR

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TB-40EURO 2024 - Copenhagenare robustly optimal when only the support of the demand and supply yield distributions are known. Yet we also provide the optimal
distribution-free policy when the first two moments of those distributions are known, which again is non-linear. Both models provide novel,
explicit order rules that may be useful for dynamic inventory control.

2 - Analysis of a flexible order quantity contract in a
newsvendor setting

Dimitrios Pandelis, George Kozanidis
We consider a newsvendor model with a retailer facing random demand and a primary supplier characterized by random capacity. The
probability distributions of both the demand and the capacity are assumed to be known. The retailer places an order, which may not besatisfied in full because the quantity that can be delivered is capped by
the capacity of the supplier. In order to mitigate this supply risk, theretailer places an order to a second supplier, which is reliable but more
expensive, under a contract according to which he is allowed to returna portion of the order after the delivery by the primary supplier. We
also assume that this option must be exercised before the realization of
the demand. We characterize the optimal orders to the two suppliers
and present numerical results that provide interesting insights into theeffect of various model parameters on these optimal quantities.

3 - The bullwhip effect with correlated lead times and autocorrelated demand

Zbigniew Michna, Stephen Disney, Peter Nielsen
We quantify the bullwhip effect (which measures how the variance ofreplenishment orders is amplified as the orders move up the supply
chain) when demands constitute a first-order auto-regressive random
process and lead times constitute a possibly temporally correlated stationary sequence of random variables. We assume future demands are
predicted with the minimum mean squared error method and randomlead times are estimated using any method. Under these general assumptions we derive a formula for the bullwhip effect measure as theratio of the replenishment orders variance and demands variance. Using this formula we analyse the impact of auto-correlated demands andauto-correlated lead times on the bullwhip effect. Our investigation of
the impact of the lead time auto-correlation on bullwhip appears to be
unique in the literature. Our analysis focuses on using the naive forecasting method, the moving average method and the minimum meansquared error method for forecasting the lead times. We show how
the bullwhip effect is influenced by demand auto-correlation, lead time
auto-correlation, and number of periods in the moving average forecast of the lead times. We reveal that there exists minima and maxima
in bullwhip effect as a function of those parameters. For the moving average forecasting method of lead times and their negative autocorrelation we observe an even-odd phenomenon. Our theoretical results are confirmed by Monte Carlo simulation.

4 - Explorable Uncertainty in Routing

Caroline Spieckermann, Christoph Kerscher, Stefan Minner
Planning in logistics and transportation is oftentimes complicated by ahigh degree of uncertainty about the actual travel distances, times, or
costs. While stochastic optimization is concerned with making optimal decisions under such uncertainty, it disregards that in many practical applications, uncertainty can be reduced upfront through researchand tests, also known as "explorable uncertainty". However, while uncertainty reduction through exploration can improve decision-making,it oftentimes comes at a cost, and one needs to balance exploration
costs and solution quality. We study the vehicle routing problem withtime windows (VRPTW) and stochastic travel times where uncertainty
about travel times can be reduced by making queries to a traffic data
provider while respecting an overall querying budget. This converts thestochastic VRPTW into a partially deterministic problem that we solve
via point-based approximation and sample average approximation todeal with the remaining uncertainty. We present different methods tomake good and fast querying decisions based on statistical featuresand learning and show their effectiveness in an extensive numerical
study. By assuming different degrees of uncertainty, correlations, andtime-window restrictions, we give detailed insights into the value of
uncertainty exploration in routing.

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 TB-40
Tuesday, 10:30-12:00 - Room: 96 (building: 306)

Reinforcement Learning: Methods andApplications
Stream: Advances in Stochastic Modelling and Learning
Methods
Invited session
Chair: Konstantin Avrachenkov
1 - Influence Maximization in Dynamic Networks Using Reinforcement Learning

Seyedehhaleh Seyeddizaji, Konstantin Avrachenkov
Influence maximization (IM) has been widely studied in recent
decades, aiming to maximize the spread of influence over networks.
Despite many works for static networks, fewer research studies havebeen dedicated to the IM problem for dynamic networks, which creates many challenges. An IM method for such an environment, should
consider its dynamics and perform well under different network structures. To fulfill this objective, more computations are required. Hence,an IM approach should be efficient enough to be applicable for the
ever-changing structure of a network. In this research, an IM methodfor dynamic networks has been proposed which uses a deep Q-learning(DQL) approach. To learn dynamic features from the network and retain previously learned information, incremental and transfer learning
methods have been applied. Experiments substantiate the good performance of the DQL methods and their superiority over compared methods on larger sizes of tested synthetic and real-world networks. Theseexperiments illustrate better performance for incremental and transfer
learning methods on real-world networks.

2 - Online Learning Linear Programming Heuristic for
Weakly Coupled MDP

Alexandre Reiffers
In this talk, we present novel algorithms for online learning of linear programming heuristics for weakly coupled Markov Decision Processes (MDPs). Leveraging two-timescale stochastic approximation
theory, we design simple and efficient algorithms with theoretical guarantees. Furthermore, we show that we can extend our approach tolearn policies for continuous state space weakly coupled MDPs, while
also demonstrating that certain theoretical guarantees are preserved.
Finally, we demonstrate the efficiency of our algorithms on real-world
applications, including load-balancing coupled with autoscaling in parallel queues, and monitoring strategy of slowly varying signals in networks.

3 - Algorithms to solve Risk Sensitive MDPs, Games andApplications

Veeraruna Kavitha, Vartika Singh, Tushar Walunj
There are no computationally feasible algorithms that provide solutions to the finite horizon Risk-sensitive Constrained Markov Decision
Process (CMDP) problem, even for the problems with moderate horizon. With an aim to design the same, we derive a fixed-point equation such that the optimal policy of the CMDP is also a solution. We
further provide two optimization problems equivalent to the CMDP.
These formulations are instrumental in designing a global algorithm
that converges to the optimal policy. The proposed algorithm is basedon random restarts and a local improvement step. Here the local improvement step involves solving a Linear program, and utilizes the solution of the derived fixed-point equation, while, the random restarts
ensure global optimization. Such MDPs are utilized to model and derive robust inventory control.
Further, one may have constrained stochastic games whose objective
and constraints are a combination of linear and risk-sensitive utilities.
The best response becomes a constrained MDP with a combination of
linear and risk-sensitive utilities. We build upon the theory developed
for constrained risk-sensitive MDP and develop a solution techniqueEURO 2024 - Copenhagento numerically solve such combined constrained MDPs and eventually
the game.
Finally, we suggest an online learning algorithm that can potentiallylearn the optimal policy, when the data defining the MDP is not known.

4 - Learning Whittle and LP indices in Average-Reward
Restless Multi-Armed Bandits

Konstantin Avrachenkov
Restless Multi-Armed Bandits (RMABs) are extensively used inscheduling, resource allocation, marketing and clinical trials, just to
name a few application areas. RMABs are Markov Decision Processes
with two actions (active and passive modes) for each arm and with aconstraint on the number of active arms per time slot. Since in general RMABs are PSPACE-complete, several heuristics such as Whittle
index and LP index have been proposed. In this talk, I present reinforcement learning schemes for both Whittle and LP indices withalmost sure convergence guarantee in the tabular setting and an empirically efficient Deep Q-learning variants. Several examples, including
scheduling in queueing systems, will be presented. This talk is basedon joint works with V.S. Borkar and P. Shah from IIT Bombay.

 TB-41
Tuesday, 10:30-12:00 - Room: 97 (building: 306)

Optimization on Manifolds
Stream: Optimization on Geodesic Metric Spaces:
Smooth and Nonsmooth
Invited session

TB-42

referred to as the Lorentz model. In our investigation, we present several innovative properties of the intrinsic kappa-projection into convex sets of kappa-hyperbolic space forms. These properties are crucial for analyzing the method and also hold independent significance.
We discuss the relationship between the intrinsic kappa-projection andthe Euclidean orthogonal projection as well as the Lorentz projection.
Moreover, we provide formulas for the intrinsic kappa-projection into
specific convex sets using the Euclidean orthogonal projection and theLorentz projection. Regarding the convergence results of the gradientprojection method, we establish two main findings. Firstly, we demonstrate that every accumulation point of the sequence generated by themethod with backtracking step sizes is a stationary point for the given
problem. Secondly, assuming the Lipschitz continuity of the gradientof the objective function, we show that each accumulation point of thesequence generated by the gradient projection method with a constantstep size is also a stationary point. Additionally, we provide an iteration complexity bound.

3 - The Riemannian Convex Bundle Method

Hajg Jasa, Ronny Bergmann, Roland Herzog
Within the context of optimization on manifolds, a research directionof particular interest is the investigation of algorithms fit to optimize
non-smooth objectives. This research area is relevant since the need for
optimizing non-smooth objective functions arises in many real-world
problems and applications such as image and signal restoration, denoising, inpainting, etc. In this talk, we introduce the convex bundle
method to solve convex, nonsmooth optimization problems on Riemannian manifolds. Each step of our method is based on a model thatinvolves the convex hull of previously collected subgradients, parallely transported into the current serious iterate. This approach generalizes the dual form of classical bundle subproblems in Euclidean
space. Several numerical examples implemented using the Julia package Manopt.jl illustrate the performance of the proposed method andcompare it to other non-smooth optimization algorithms.

Chair: Sandor Zoltan Nemeth
1 - Constraint qualifications and strong global convergence properties of an augmented Lagrangian method
on Riemannian manifolds

Orizon P Ferreira, Roberto Andreani, Kelvin Couto, Gabriel
HaeserIn the past years, augmented Lagrangian methods have been successfully applied to several classes of non-convex optimization problems,
inspiring new developments in both theory and practice. In this paper we bring most of these recent developments from nonlinear programming to the context of optimization on Riemannian manifolds, including equality and inequality constraints. Many research have been
conducted on optimization problems on manifolds, however only recently the treatment of the constrained case has been considered. In
this paper we propose to bridge this gap with respect to the most recent
developments in nonlinear programming. In particular, we formulate
several well known constraint qualifications from the Euclidean context which are sufficient for guaranteeing global convergence of augmented Lagrangian methods, without requiring boundedness of the setof Lagrange multipliers. Convergence of the dual sequence can also
be assured under a weak constraint qualification. The theory presented
is based on so-called sequential optimality conditions, which is a powerful tool used in this context. The paper can also be read with the
Euclidean context in mind, serving as a review of the most relevant
constraint qualifications and global convergence theory of state-of-theart augmented Lagrangian methods for nonlinear programming.

2 - On projection mappings and the gradient projection
method on hyperbolic space forms

Sandor Zoltan Nemeth, Orizon P Ferreira, Jinzhen Zhu
This talk examines the gradient projection method as a solution approach for constrained optimization problems in kappa-hyperbolic
space forms, particularly for potentially non-convex objective functions. We consider both constant and backtracking step sizes in our
analysis. Our studies are based on the hyperboloid model, commonly TB-42
Tuesday, 10:30-12:00 - Room: 98 (building: 306)

Variational Methods in Vector Optimization
Stream: Variational Analysis and Continuous Optimization
Invited session
Chair: Fernando García Castaño
Chair: Marco A. López-Cerdá
1 - Strongly convex sets with variable radii

Florent Nacry
This talk is devoted to the new class of strongly convex sets with variable radii in Hilbert spaces recently introduced by L. Thibault and theauthor. Such a strong convexity property can be seen as the convex
counterpart of the famous phi-convexity introduced by A. Canino in1988 and thoroughly developed in a 2010 survey by G. Colombo and L.
Thibault under the name prox-regularity with variable thickness (alsoknown in the case of constant radius/thickness as proximally smoothsets, positively reached sets, weakly convex sets, O(2)-convex sets,etc.).
Roughly speaking, a strongly convex set with a variable radius (is obviously convex and) has its curvature locally bounded from below by
an appropriate function. Whenever the latter function is constant, ourstrong convexity coincides with the usual strong convexity of sets (see,e.g., a recent survey by V.V. Goncharov and G.E. Ivanov). Strongly
convex sets have been involved in various mathematics, including:separation properties, minimal time problem, preservation of proxregularity, linear differential games, sweeping processes, etc.
The main aim of the presentation is to provide numerous properties andcharacterizations of strongly convex sets with variable radii through225

TB-43EURO 2024 - Copenhagenthe differentiability of the so-called farthest distance function, the Lipschitz behavior of the farthest point mapping and the strong monotonicity of truncated normals. Perspectives and open questions would
be also provided.

2 - Scalarizations obtained through a property of separation of cones

Fernando García Castaño, Miguel Angel Melguizo Padial,
Giovanni ParzaneseIn this presentation, we will demonstrate that, given a separation property of cones, a $mathcalQ$-minimal point in a normed space is theminimum of a specific sublinear function. This observation provides
sufficient conditions, via scalarization, for several types of proper efficient points. Furthermore, we will explore necessary and sufficientconditions for approximate Benson and Henig proper efficient points
expressed in terms of scalarization. The separation property we consider is a variant of a previously introduced property that had been
applied in the setting of reflexive Banach spaces.

3 - Some New Henig-type Existence and Density Results in
Non-Convex Vector Optimization Problems

Miguel Angel Melguizo Padial, Fernando García Castaño
In this talk we present sufficient conditions for a set and the ordering
cone of a space to ensure the density of the set of Henig proper efficient
points in the efficient line. Our results are stablished in the context ofarbitrary normed spaces and are free of convexity assumptions. We
extend some previous results established under more restrictive conditions.

4 - Measuring the stability. A paradigmatic problen in optimization

Marco A. López-Cerdá
In this talk we focus mainly on linear programming problems, andparticularly on Lipschitz-type properties of the feasible set mapping,
the optimal value function, and the optimal set (argmin) mapping.
Roughly speaking, we aim to compute or estimate the rate of variationof feasible/optimal solutions with respect to the problem’s data perturbations. Some of these properties are local (as Aubin property andcalmness), as far as they concentrate around a certain solution nearby agiven parameter. Some other properties (such as Hoffman stability) areof a global nature, since they tackle global variations of the whole solution set. We emphasize the fact that the quantitative stability measuresprovided in this talk are mainly point-based; thus they are conceptually
implementable in practice.

it is the availability of supply or the increasing demand that drives sustainability transient consumption is often discussed in economic circles. From the complex systems perspective, it is their interaction thatmatters. This article explores how consumer preferences and restaurant menus co-evolve to contribute to a more sustainable food systemand how this co-evolution is affected by different policy instruments.
We use a spatially explicit agent-based model of the catering industry
in Amsterdam as a case study. The model is built using spatial microsimulation to expand on survey data from a discrete choice experiment about restaurants in Amsterdam. We observe that in the absenceof policy interventions, it takes quite a lot of time for changes in dietpatterns to occur. Multiple cities are working on their sustainability.
Based on the generated insights, we expect our research to contributeto the current debate on the policy interventions to achieve sustainableurban food systems and the SDGs, and to be of consequence acrossmultiple cities worldwide.

2 - Unveiling Behavioral Heterogeneity: An Agent-Based
Model Exploration of Farmer Decision-Making in theFace of Climate Change

Paolo Gazzotti, Sandra Ricart, Andrea Castelletti
In the face of climate change threats to agriculture, understanding
farmers’ decisions on crop selection and irrigation practices becomes
crucial, as they tend to be significantly shaped by risk preferences.
Farmers, who all recognize shifts like increased drought severity, exhibit diverse reactions influenced by individual risk aversion, satisfaction, uncertainty, and peer comparisons. Agent-based modeling(ABM) stands out as an essential tool for capturing system dynamics and simulating farmers-environment-climate interactions. Despite
calls for realistic human behavior integration, the prevailing paradigm
still favors rational agents. Here we present an ABM application in
Italy’s Adda River basin, modeling farmers’ decisions on crops and irrigation methods. To understand the system’s response and resilience
to changing climates, we implemented diverse behavioral sub-models.
The first maximizes profit with classic perfect foresight. The second
introduces climate uncertainty and risk aversion variations based on
farmers’ memory. The third, a comprehensive behavioral model, considers reference points and loss aversion, acknowledging decisions beyond profit. It offers a holistic view of farmers’ choices amid uncertainty and risk, calibrated with collected survey data. Preliminary findings uncover significant differences in system dynamics and resilience
across the behavioral setups, providing insights into their efficacy andsuitability.

3 - Resilient national cold chain deployment for addressing food waste and achieving economic prosperity in
the Global South: An agent-based approach

Adam Gripton, Aghdas Badiee, Phil Greening

 TB-43
Tuesday, 10:30-12:00 - Room: 99 (building: 306)

Simulation in sustainability
Stream: Agent-based Models in Management, Economic
and Organisation Sciences
Invited session
Chair: Adam Gripton
1 - Steering sustainable food systems: the complex coevolution of consumer preferences, sustainable restaurants, and policymaking

Angel Lazaro
The transition towards more sustainable food systems is essential
for achieving climate targets and the Sustainable Development Goals(SDGs). Achieving goal 12, responsible consumption and production,in particular, will require changes in consumer behavior, the adoptionof sustainable practices by suppliers, and, most importantly, the implementation of policies that foster such sustainable behaviors. Whether

226

Many Global South countries currently suffer from high degrees ofagricultural food waste due to the warm climate and lack of an integrated national cold chain supply network. This can lead to many societal issues that hinder growth and development, including poor outcomes for population health and nutrition metrics, a "poverty trap" forsmallholder farmers without market options for selling their produce,
and for the national economy through lack of cold-chain traceability
that would allow access to more lucrative export markets. Design ofa national cold-chain network as critical infrastructure requires consideration of a wide range of market forces and behaviours in such atightly-coupled system, as well as dealing with the inherent uncertainty
in such interactions, to ensure that placement and operation of cold
staging facilities can be optimally planned to achieve the desired national outcomes for public health and economic wealth. Agent-basedmodelling (ABM) can assist in capturing these phenomena to allow
a future scenario of cold-chain infrastructure deployment to be tested
for efficiency and resilience in the face of uncertainty and black-swan
events such as extreme weather events caused by climate change. We
present preliminary results, applied to Rwanda as a case study, of abespoke ABM solution designed to show the effects of offering cold
chain as an alternative business model to farmers and showing how
network placement affects national outcomes.

EURO 2024 - Copenhagen TB-44
Tuesday, 10:30-12:00 - Room: 20 (building: 324)

MCDA applications
Stream: Multiple Criteria Decision Analysis
Invited session
Chair: José Rui Figueira
1 - Mapping Energy Poverty: A Multi-Criteria Classification
Framework for the European Union

José Rui Figueira, Madalena Alexandre Lourenço, Carla
Henriques, Miguel Pereira, Carlos Santos Silva
Energy poverty stands as a pressing issue across the European Union(EU), casting shadows over public health and hindering sustainable
development efforts. Despite its prominence, scholarly literature reveals a dearth of studies quantifying the breadth of fuel poverty within
the EU. Remarkably, none have ventured into applying methods fromthe ELECTRE family. In response to this research gap, our study
undertakes the task of classifying energy poverty across EU member
states in the year 2020 by employing the innovative ELECTRE-TrinC method. To further refine our analysis, we supplement this methodwith the Simos-Roy-Figueira (SRF) method, aiding in the assignmentof weights to our selected criteria. Our model integrates nine distinct
criteria to discern the energy poverty levels of 24 EU member states.
Unveiling the findings of our comprehensive study, we observe a distinct trend: Eastern and Southern Europe grapple with elevated levelsof energy poverty, while the Scandinavian nations and Central Europe
demonstrate commendable low levels. Meanwhile, Western Europe
and the Baltic countries fall within the moderate spectrum of energy
poverty. By shedding light on these disparities, our study not only underscores the urgency of addressing energy poverty but also highlights
regional variations that demand tailored interventions and policy considerations. In doing so, we endeavour to catalyse informed discourseand strategic action aimed at alleviating the burdens of energy pove

2 - A Comprehensive Model for Supply Chain Resilience
Measuring

Chien-Wen Chen, Ching-Torng Lin
Business globalization and strategic sourcing have made supply chain
networks become length and complex, which have increased it more
vulnerable and uncertain to disruptions. Geographical wars, such as
the Russia-Ukraine war, have shown the disruptive effect of thesecatastrophes, which inspires me to think that traditional supply chain
management fails to cope with such disruption challenges. To addressthese current and potentially future challenges, scholars and practitioners suggested that supply chain management must incorporate resilience, robustness, and risk management capability. There are several
approaches to assessing a resilient supply chain. Due to the ambiguity of resilience assessment, most measures are described subjectively
using linguistic terms. Abandon the traditional way of assessing resilience, this paper develops a Fuzzy Resilience Index in Supply Chain(FRISC) to measure the level of supply chain resilience. The FRISC
comprises attribute ratings and corresponding weights and is aggregated by a fuzzy weighted average. Finally, an example is presented at
the end of the paper to illustrate the procedure of the proposed method.
The results show that the proposed model can provide more meaningful results to the analyst and can efficiently assist managers in dealing
with both ambiguity and complexity in supply chain resilience auditing.

3 - Unveiling Success in Supply Chain Digital Transformation: A Comprehensive Analysis of Critical Factors for
Resilience and Sustainability in Indian Manufacturing

Jeetu Rana, Yash Daultani, Mohit Goswami, Sushil Kumar
This study delves into the emerging field of Supply Chain Digital
Transformation (SCDT) with a specific focus on fostering resilienceand sustainability within the Indian manufacturing context. Identifying and analyzing the Critical Success Factors (CSFs) underpinning

TB-44

SCDT success is the central theme. Through an extensive literature
review and collaboration with industry experts, fourteen CSFs were
pinpointed. The study employs a unique approach — Grey Causal
Modelling, to unravel the interdependencies and relative importanceof these factors. Two pivotal cause factors, "Vision for Digital Operations" and "Coordination and Collaboration," emerged as crucial
in driving SCDT success. By prioritizing these factors, manufacturing firms can enhance their chances of achieving successful SCDTwith resilience and sustainability objectives. The research not only
contributes valuable insights for organizations navigating the digitallandscape but also sheds light on the intricacies of SCDT in the dynamic environment of Indian manufacturing. The study underscores
the strategic importance of identifying and nurturing specific factors,
offering guidance for firms aiming to thrive in today’s digitally-driven
world.

4 - Addressing Challenges in Multicriteria DecisionMaking: Introducing the Method and User-Friendly Software Called VIKOR-HS

Renata Pelissari, Rafaela Heloisa Carvalho Machado,
Bernardo Reis, Artur Almeida Santos, Samuel Vieira
Conceição
Complex decision-making problems often have specific configurations
such as the need to organize criteria hierarchically and the difficulty insetting importance weights to these criteria. These challenges have notbeen fully addressed yet when it comes to the multicriteria decisionmaking method VIKOR. Our first proposal is to introduce the VIKORHS method to bridge this gap. This method integrates a variant of theMultiple Criteria Hierarchy Process (MCHP) into VIKOR, facilitating
consideration of multiple levels for decision criteria, and incorporates
the Stochastic Multicriteria Acceptability Analysis (SMAA) for eliciting criteria weights across various hierarchy levels. Despite this contribution, a challenge that remains concerns the difficulty in applying
such a method in real-life decision-making scenarios due to its demand
for mathematical and programming knowledge that is often incompatible with that possessed by typical users. Our second contribution addresses this concern by proposing a user-friendly software tailored for
implementing the VIKOR-HS method. To demonstrate the efficacyof the VIKOR-HS method and accompanying software, we applied it
to assess the sustainability of different animal production alternatives
in Brazil, a problem previously explored in the literature. Notably,
the software facilitates robustness evaluation for each alternative whilesubstantially reducing the complexity associated with employing a robust multicriteria approach.

5 - Evaluation of Sustainable Performance Based on Linguistic Entropy and TOPSIS Methods

Chen-Tung Chen, Ke-Fang Chen
In recent years, sustainable development has become a core issue in enterprise management and value creation. In order to ensure the performance of the implementation of the sustainable development goals, theenterprise must evaluate sustainable performance appropriately. Dueto the characteristics of high uncertainty and high complexity, it is an
important issue to apply fuzzy sets theory with multiple experts to construct a sustainable performance evaluation model. It can be regarded
as a group multiple-criteria decision-making problem. The opinions
of experts always are fuzziness and uncertainty, it is reasonable forexperts to use the linguistic variables to express their subjective opinions in the evaluation process. In addition, the TOPSIS (Technique forOrder Preference by Similarity to an Ideal Solution) is one of famous
methods to deal with the decision-making problems. Therefore, thispaper will combine entropy method with TOPSIS based on linguistic variables to construct a systematic evaluation model of sustainable
performance. And then, this paper will propose a linguistic entropy
TOPSIS algorithm (LE-TOPSIS), which will use entropy method to
calculate the weights of criteria and to determine the ranking order ofall enterprises by using TOPSIS. Finally, an example will present in accordance with the LE-TOPSIS algorithm to illustrate the effectivenessof the proposed method.

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TB-45EURO 2024 - Copenhagen TB-45
Tuesday, 10:30-12:00 - Room: 30 (building: 324)

Artificial Intelligence and Machine Learning
for Decision Support
Stream: Decision Support Systems
Invited session
Chair: Stefan Minner
1 - Conversational Decision Support Systems: a telling-tomodeling AI-enhanced approach

Mariusz Kaleta
In the full cycle of the Operations Research methodology, artificial
intelligence is most prominently utilized in the algorithmic aspect.
Meanwhile, modeling is traditionally considered an art exclusive tohuman being experts. What if we could simply tell the computer the
problem and, in return, receive an optimization model and its solution? We propose a new class of Decision Support Systems (DSS),
termed Conversational Decision Support Systems (C-DSS). These systems, empowered by agents based on large language models, performthe art of modeling and elevate human-machine interaction to the interface of problem definition and mathematical modeling, using natural
language as the primary mode of communication. In our concept, adecision-maker interacts with the C-DSS by discussing the problem toobtain decision proposals.
First, we analyze how general-purpose language models handle problems that can be modeled as mixed-integer problems, requiring various modeling techniques such as linearization of convex/concave
functions, logical conditions, and indicator constraints, among others. Additionally, we enhance the general-purpose language modelwith specialized knowledge using the Retrieval Augmented Generation method. The generated mathematical programming models require verification, which can also be facilitated by another language
model-based agent. Therefore, we propose an architecture of C-DSS
powered by several specialized language model-based agents.

2 - Forecasting B2B markets with machine learning andpredator-prey models: empirical evidence from global
Aerospace companies

Dina Litsiou, Chaitanya Kapoor, Konstantinos Nikolopoulos,
Chrysovalantis Vasilakis
In this world of such a volatile, uncertain, and changing environment
and markets, especially in a post-pandemic ’new-normal’ context, it isvery important to be ahead of competitors so as to maintain one’s market share. This is true for all markets, but more than anything for B2B
markets that are affected immediately form supply chain disruptions
and need to employ multiple channels to survive and thrive in turbulent times. Products and services can be sold profitably only if customer needs are satisfied at acceptable (low-ish) prices. Many factors
are involved in making a product cost efficient, one of which is forecasting. Forecasting is one of the most essential parts of almost every
supply chain in the world. This paper contributes towards the improvements in the forecasting methods currently being used by forecasters
in B2B markets, a key area in the early days of Industrial Marketing
Management (70s and 80s) that has been revived recently due to therevolution of big data predictive and prescriptive analytics. This paper is focused on forecasting the competing product life cycles using
Lotka-Volterra predator-prey equations. The objective of this paper is
to find a better version of Lotka-Volterra equations suited for generalmarket products. Various regression analysis techniques have been applied in order to make an attempt to find the best way of forecasting
using Lotka-Volterra equations.

3 - Multi Agent Reinforcement Learning and Graph Neural
Networks for Inventory Management

Niki Kotecha, Antonio del Rio Chanona

228

Current supply chains operate under uncertain environments causing
risks in disruptions and sub-optimal performance due to operational
failure or lack of coordination. The inventory control problem, asequential decision-making problem, is challenged by stochastic andvolatile factors such as lead times and seasonal demand patterns, often
resulting in sub-optimal performance. Reinforcement learning (RL)
remains a promising alternative to enhance decision-making in supply
chains. However, as the supply chain grows in size, the complexity ofdecision-making can grow significantly which may hinder the performance of traditional RL algorithms as they struggle to scale efficiently.
The extension of single-agent RL to multi-agent RL allows for scalability as well as a decentralised decision-making framework of individual entities. Our methodology leverages on the inherent graph structureof a supply chain, developing a multi agent RL framework with Graph
Neural Networks for a multi-echelon multi-product inventory management system. We show the benefits of a collaborative approach by
testing the policies on a series of disruptions. Additionally, the framework moves computational costs from online to offline, ensuring faster
decision making than most efficient optimisation methods. As a result,the methodology proposed shows promising scalability with numberof agents for a decentralised and online decision-making frameworkwhilst still ensuring collaboration between entities.

4 - Understand Your Decision Rather than Your Model: Towards Explainable Deep Learning Approaches for Sustainable Commodity Procurement

Moritz Rettinger, Stefan MinnerHedging against price increases is particularly important in times ofsignificant market uncertainty and price volatility. For commodity
procuring firms, futures contracts are a widespread means of financially hedging price risks. Recently, data-driven decision-support
approaches have been developed, with deep learning-based methods
achieving particularly good results in capturing non-linear relationships between external features and price trends. We employ a prescriptive deep-learning approach modeling hedging decisions as amulti-label time series classification problem. We backtest the performance in two evaluation periods for natural gas, crude oil, nickel,
and copper. The approach performs well in the evaluation period of
the testing framework (2018-2020) yet fails to capture the market disruptions (pandemic, geopolitical developments) in the second evaluation period (2021-2023), yielding significant hedging losses or degenerating into a simple hand-to-mouth procurement policy. We employ
explainable artificial intelligence to analyze the performance for both
periods. The results show that the models fail to perform well undermarket regime switches and disruptive events within the consideredfeature set.

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Tuesday, 10:30-12:00 - Room: 40 (building: 324)

Open Space
Stream: Making an Impact
Invited session
Chair: Ruth Kaufman
Chair: Michele Quattrone
Chair: Sander Van Aken
1 - What’s On Your Mind? Getting What You Want From
This Conference

Sander Van Aken
At a conference like this, we want to interact with, and learn from, eachother. Unfortunately, it’s sometimes just not so easy to bump into andconnect with the right people to discuss the topics you want to learn
about.
In this open space session, it’s the participants who bring in, and discuss about, topics which are on their mind. So you want to learn fromEURO 2024 - Copenhagen

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others about a particular topic related to making an impact with OR inpractice? Join us in this session where you set the agenda. At the start,
we will collect the topics which are on your and others’ mind. Using
this, we split in smaller groups, and you join the one(s) in which you
are interested. As such, it’s you who shape the discussions, and can get
a lot out of it.

3 - The role of Multi Criteria Evaluation Tools to implement
the Circular City ModelNo topic, just curious what’s on others’ minds? There will alreadysome kick-starter points available.

The challenges of sustainable development are mainly concentrated inurban areas. Consequently, cities are key places for the implementationof strategies and initiatives to achieve Sustainable Development Goals.
The implementation of circular cities produces multidimensional benefits in the environmental, economic, social and cultural dimensions.
The circular city requires the activation of integrated and multidimensional urban development policies, capable to involve different disciplines and sectors. The circular city model implementation necessarily requires integrated evaluation tools (multicriteria, multi-group andquanti-qualitative) able to capture the multidimensional impacts that it
is able to produce and considering all the subjects/actors involved in
its implementation. Within the Italian research project PRIN "TReE Supporting the Transition to Ecological Economy in Italian cities Regeneration: circular model tools for reusing architecture and infrastructures" an evaluation framework will be tested to define alternatives
for the reuse of cultural heritage in line with the circular city model.
The objective is the creation of a new Multicriteria Evaluation Framework, based on the integration of different existing methods, to respondto the need to analyze and evaluate the multidimensional impacts generated by adaptive reuse interventions of cultural heritage, under theparadigm of circular and sustainable economy and social innovation.

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Tuesday, 10:30-12:00 - Room: 50 (building: 324)

MCDA and urban planning 1
Stream: Multiple Criteria Decision Analysis
Invited session
Chair: Isabella Lami
1 - A Multicriteria Approach for the Definition of Priorities
of Intervention for Roadway Bridges and Viaducts

Chiara D’Alpaos, Flora Faleschini, Silvia Manarin, Carlo
Pellegrino, Mariano Angelo Zanini
In recent years, the assessment of structural performances and functionality of roadway bridges and viaducts has become a key issue in
Italy and in most European Countries. Large part of the existing stock
exhibits deficiencies due to the aging of the structures and the evergrowing demanding traffic conditions and safety requirements, compared to the original design phase. In 2020, in Italy were issued toassess bridges and viaducts risk exposure and proposed a multilevelapproach based on census, visual inspection, and a preliminary andqualitative risk indicator. To define this risk indicator, four types ofrisks are considered: structural and foundation risk, seismic risk, landslide risk, and hydraulic risk. After this preliminary evaluation, indepth tests and more accurate analyses should be implemented on any
bridge or viaduct exposed to high risk. During the first application ofthese guidelines, several difficulties were detected in finalizing a ranking of the infrastructures included in the same class of risk. This paper
proposes an AHP absolute model to rank roadway bridges and viaducts
by priority on intervention, based on structural and foundation risks. In
detail, to identify criteria, sub-criteria, and rating, and define the hierarchy, an extensive literature review was conducted and a pool of experts
was interviewed via a Delphi survey process. Focus groups were organized to validate the hierarchy by dynamic discussion and identify
local and global priorities.

2 - Argumentation Schemes for Urban Decision Support

Isabella Lami, Marta Bottero, Beatrice Mecca, Gabriella
Pigozzi, Alexis Tsoukias
The decision-making process in urban planning and design projects isinherently complex as it embraces so many different actors, interests,
dimensions and uncertainties. Evaluation plays a key role in supporting such processes, as a guide to prioritising decisions based on theobservation of data derived from analyses, measurements and estimations. In this context, the paper investigates the potential benefits ofargumentation theory aimed at managing the complexity of information and knowledge about the situation under analysis through specificschemes. Indeed, argumentation-based approaches make it possible
not only to identify a ’good’ choice, but also to formulate, through alogic-based formalism, a final recommendation in a graphic and mutually comprehensible form. In this sense, the paper starts from the exploration of an emblematic case study of the city of Turin, which saw
numerous actors, interests and critical points in its decision-making
process, proposing an a posteriori argumentation scheme. The latterhighlights the points of agreement and disagreement that led to a design strategy that was justified and satisfactory to the decision-making
actors. Through application, the potential and criticalities of this approach in decision support in the urban context are observed.

Mariarosaria Angrisano, Marta Bottero, Giulio Cavana,
Antonia Gravagnuolo, Luigi Fusco Girard, Francesco
Fabbrocino

4 - A Compromise Programming Approach for Assessing
the Biophysical Suitability of the Territory: The Case of
the Loulé Municipality

António Xavier, Maria de Belém Costa Freitas, Carla Antunes
Land use fragmentation is an important issue for management and rural development. For the last few decades, the relationship between
mankind and land has become more dynamic though the traditionalcadastral system has been slow in responding to the changing needsof the society. Generally defined, land-use suitability analysis aims
at identifying the most appropriate spatial pattern for future land uses
respecting specific requirements and preferences in what concerns theactivities developed. The analysis of the land use suitability implies
not only to consider the best aggregate solution, but also the balanceamong the different criteria, which calls for the necessity of multicriteria decision analysis techniques. This article proposes a compromise
programming approach for determining the biophysical potential ofland parcels. The approach has 4 steps: 1) Cluster analysis do identify
homogeneous zones within the study area; 2; Compromise programming approach for the suitability index building and ranking construction; 3) Statistical analysis (statistical tests to assess the differencesamong clusters); 4) Spatial analysis of the results. The approach was
implemented in the Loulé municipality, Portugal, in more than 50000
parcels. Results were promising since the approach was able to createa ranking of parcels that have different biophysical conditions, provingthat this approach is relevant and interesting for land management.

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Tuesday, 10:30-12:00 - Room: 60 (building: 324)

DEA applications in Environment andSustainability I
Stream: Data Envelopment Analysis and its Application
Invited session
Chair: Li-Ting Yeh
1 - A cross-efficiency DEA approach for composite indicators: Revisiting Environmental Performance Index

Saransh Tiwari, Sanjeet Singh, Utsav Pandey

229

TB-49EURO 2024 - Copenhagen

To promote and encourage sustainable development in favour of ensuring an egalitarian and environmentally secure future, Yale andColumbia Universities jointly with World Economic Forum developed
the Environmental Performance Index (EPI). The EPI is a composite
index of environmental and sustainability indicators that quantifies andassesses a country’s environmental performance based on three broad
policy objectives: climate change, environmental health, and ecosystem vitality. This paper proposes an approach based on the crossefficiency data envelopment analysis (DEA) for estimating the EPI.
We propose cross-efficiency models for settings where there are only
outputs (without inputs), as in composite indicator construction, thatprovides unique cross-efficiency scores. As cross-efficiency incorporates both self- and peer-evaluation, it offers a more acceptable andreasonable mechanism for computing the EPI. Utilizing the scores obtained via the cross-efficiency DEA approach, we extend our analysis
by employing the regression to determine the relative importance andcontribution of the variables used in EPI construction and find suitableweights that could be assigned to the indicators if the decision-makerdesires to estimate the countries’ EPI scores via fixed weights. The
limitations of the existing methodology for EPI estimation, which isbased on a non-optimization approach with weights assigned to theindicators subjectively, are overcome in our proposed approach.

2 - An Analysis of the Efficiency of European Funds in Sustainable Development in Portugal

Since hydroelectric reservoirs are managed by the centralized administration, Taiwanese policy-makers can pursue the overall performanceof all hydroelectric reservoirs. Conventional centralized data envelopment analysis (DEA) models focus on the reallocation of each resource
input based on the expansion of total outputs. However, these centralized models rarely consider that some resource inputs are closely
related to certain outputs. To bridge this gap, this paper presents a
centralized DEA model with non-separable. The proposed models aim
to address the planning for reallocating water resources among hydroelectric reservoirs in Taiwan.

 TB-49
Tuesday, 10:30-12:00 - Room: M1 (building: 101)

Lot-sizing with energy aspects
Stream: Lot Sizing, Lot Scheduling and Production Planning
Invited session
Chair: Onur Kilic

Sérgio Barreto, Daniel Colares, Gilberto Fernandes
1 - Energy Requirements Planning Problem
Assessing the impact that European Funds have on people’s lives is
an area that has received the attention of researchers and institutions.
On the one hand, citizens demand better results and more information,on the other hand, institutions need methodologies that allow them tomeasure the impact of investments. Considering an investment horizonof 14 years (between 2007 and 2020) and a development assessment
period of 7 years (between 2014 and 2020), this research uses a DEA
methodology to evaluate how the 308 Portuguese municipalities use
financial and human resources (inputs) to achieve sustainable development (output). As a result, each municipality is characterized by a CCR
efficiency measure, being positioned towards the efficient frontier. In
this way, it is possible to identify municipalities with best practices andthe effort required from the rest to achieve them. Graphic maps facilitate the communication of information by illustrating the distributionof efficiency throughout the territory.

3 - Extending Eco-Efficiency Evaluation in DEA through aLens of Strong Sustainability

Xun ZhouNonparametric efficiency analysis methods such as data envelopment analysis (DEA) have been widely employed for evaluating ecoefficiency. However, virtually all published studies in this literature
implicitly assume weak sustainability. This is a very strong assumption that allows for substitution among different forms of inputs and/or
outputs, treating ecological conservation as merely one among several
competing objectives that can be traded off against economic growth.
To broaden the scope of nonparametric eco-efficiency evaluation, thispaper proposes a novel approach based on a strong sustainability perspective. Drawing on a newly introduced method termed Fixed Proportion Constrained DEA, our eco-efficiency evaluation approach restricts the substitutability among inputs and/or transformability among
outputs, highlighting the intrinsic value and irreplaceability of ecosystem services and natural capital. By incorporating this perspective into
eco-efficiency measurement, our proposed method offers a more comprehensive understanding of sustainability and underscores the importance of preserving natural ecosystems for future generations.

4 - Water Resource Reallocation Planning with Integrated
Hydroelectric Reservoirs in Taiwan: Constructing aCentralized Data Envelopment Analysis Model with
Non-separable

Li-Ting Yeh
The uneven allocation of water resources leads to hydropower instability. Therefore, Taiwanese policy-makers must find ways to more
effectively reallocate water resources among hydroelectric reservoirs.

230

Mehmet Onal, Ihsan Yanikoglu
We consider the energy planning problem of a heating plant that serves
to heat a set of houses (flats). Each flat served by the plant has certain
temperature requirements throughout the day. These flats are heatedby radiators that are fed by hot water distributed from and heated in
the heating plant. The plant can remotely control hot water flow rates
through these radiators. For each flat the plant can choose among a discrete set of flow rates. The plant can create energy by running three different boilers with different capacities and running costs. The problem
is to create and energy plan with minimum cost to satisfy temperature
requirements of flats. We analyze the problem within the context of
economic lot sizing problem and present solution approaches to somespecial cases.

2 - A multi-stage stochastic programming model for lotsizing with onsite generation of renewable energy

Ruiwen Liao, Franco Quezada, Céline Gicquel, Safia
Kedad-Sidhoum
One way to achieve energy efficiency in manufacturing is to equip
plants with on-site renewable energy generation system to partially power industrial processes.However, renewable energy sources
are intermittent and the availability is difficult to predict accurately.Therefore,we study an integrated industrial production planningand energy supply problem under uncertain renewable energy availability.We propose a multi-stage stochastic programming model forthis problem. The planning horizon is divided into multiple stages
according to the time at which the PV availability information is updated.The intermittency of PV generation is represented by a scenario
tree.The resulting production and energy supply planning can be seen
as a multi-stage decision process where some decisions are made at the
beginning of the planning horizon whereas the others are postponed tolater decision stages when more information on the uncertain parameters are revealed.At the beginning of the planning horizon, we build aproduction plan for a proportional lot-sizing and scheduling problem ina single-machine multi-item setting. Then based on the available information on PV generation, an energy supply plan is constructed for theupcoming stage, which has to satisfy the energy demand of the system
which has been previously determined by the production plan at the
beginning of the planning horizon. Computational experiments will bepresented to show the practical efficiency of the proposed approach.

3 - Comparison of Discrete Time and Precedence Based
MILP Formulations of Production Scheduling for Furniture Manufacturing

Károly Kalauz, Botond Bertok, Marton FritsEURO 2024 - Copenhagen
Effective production scheduling is essential for optimizing productivity and meeting demand within dynamic production systems. This
study conducts a comparative analysis of two modeling techniques,
Process Network Synthesis (PNS) and Time-Constrained Process Network Synthesis (TCPNS), in the context of mass production. Bothtechniques, rooted in P-graphs, excellent in managing complex andflexible recipes. The precedence-based resource scheduling model employing TCPNS, provides high precision in schedule generation andeffective handling of complex changeover times. However, the required computational effort may exceed practical limitations. In contrast, the discrete time process flow model using PNS offers a fastcomputation through time discretization and a series of combinatorialtechniques to reduce the complexity of the mathematical model of theinitial problem and sub-problems. In addition to the comparative analysis, this study examines algorithmic model generation for both precedence and discrete-time formulation, with multiple resolutions in thelatter case. This nuanced approach provides an enhanced understanding of the practical applicability and computational efficiency of PNS
and TCPNS methodologies, particularly in real-life furniture manufacturing scenarios.

4 - Dynamic lot-sizing for LNG inventories

Onur Kilic
We consider a lot-sizing problem motivated by liquefied natural gas(LNG). LNG is an alternative transportation fuel. It can be supplied
from alternative sources with different quality and price, at different
costs. LNG is subject to quality deterioration. It is yet possible to upgrade its quality by mixing different loads with different levels of quality. LNG is provided to customers via special-purpose facilities. We
address the lot-sizing problem in such a facility. The problem entails
finding a minimum-cost replenishment plan satisfying demands over afinite planning horizon, while meeting a minimum quality level. We
initially formulate the problem as a mixed integer non-linear program,which we then linearize by means of standard linearization techniques.
This model, however, is computationally too expensive to be used inpractice. To overcome the computational challenge, we identify a classof feasible solutions and develop an efficient mixed-integer linear program by which we can find a minimum-cost solution within this specified class of solutions. We numerically illustrate that our approachprovides optimal or close-to-optimal solutions for practical instances
within very reasonable computational times.

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Tuesday, 10:30-12:00 - Room: M2 (building: 101)

Retail Operations and Marketing
Stream: Retail Operations
Invited session
Chair: Winfried Steiner
Chair: Pnina Feldman
1 - Impact of consumers’ environmental awareness on inventory decisions for a retailer in the textile industry

Martin Waitz, Lena Silbermayr, Danja Sonntag
Textile waste is a crucial problem for today’s society. The majority of itis generated by the consumer and does not end up as separate waste, but
is mixed with residual waste and then burned for energy recovery. To
obtain recycled textiles, the operational processes required (e.g., collection and sorting) are still costly compared to using virgin materials,
resulting in higher production costs for greener products. The increase
in recycled materials for textiles therefore depends on consumers’ willingness to pay a premium for more environmentally friendly products.
While some customers may be willing to pay such a premium, others may focus solely on the price of a textile. Understanding thesecustomer segments, particularly a customer’s individual willingness topay for a greener product, will benefit a retailer that offers multiple substitutable products when making inventory decisions. In this work, we

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investigate the influence of consumers’ environmental awareness whenpurchasing textiles and its impact on inventory decisions of a retailer.
Therefore, an empirical study including a choice-based conjoint analysis is carried out to reveal consumers’ preferences and willingnessto-pay for ’greener’ textiles in Austria. These empirical findings areused by a newsvendor model to derive inventory decisions of a retailer,
which are evaluated both economically and ecologically.

2 - Dynamic effects of store promotions on conversion
fraction: Expanding technology applications with innovative analytics

Leonardo Epstein, Ignacio Inostroza-Quezada
In retail settings conversion fractions (CFs) matter to managers because they measure the stores’ effectiveness to convert visitors into
buyers. Despite CFs importance, available methods to evaluate effects
of a given marketing action (MA) on CF are scarce and provide limited
evaluations of these effects. We present an approach that (i) builds amodel for conversion probabilities with data outside the MA period,(ii) uses this model to predict a baseline for conversion probabilities
during the promotional period conditional on the counterfactual hypothesis that the MA had not occurred, and (iii) evaluates MA effects
on CF by comparing this baseline to observed data during the MA period. We illustrate our approach with data from an actual MA consisting on a two-day promotion. The analysis combines arrival data fromvideo images and conversion data from cash registers.

3 - Decision support for product diffusion in retail - The impact of geospatial and temporal access.

Florian Cramer, Christian Fikar
Despite the increasing recognition of local actors as pivotal agentsfor sustainability in food chains, numerous innovative products, particularly from startups, encounter difficulties in reaching consumers.
Offering such products can facilitate consumer interest and position
retailers as enablers of sustainable food systems. However, listing a
new product can not only bring competitive advantages but also leadto risks, e.g., due to potential losses if the product is rejected. Retailers often have to rely on intuition and estimate a product’s potential due to unknown rejection probabilities. This research delves into
the early stages of product diffusion by examining the effectiveness of
traditional stationary brick-and-mortar stores in contrast to the agilityof mobile pop-up stores. Utilizing an agent-based simulation (ABS)model, we replicate the product diffusion process and employ a regression model to determine promising store locations. Through our investigation, we unveil how combining ABS and machine learning procedures can provide decision support for both retailers and MSMEs. Fora limited number of stores, introducing a single mobile store can considerably expedite product diffusion. Generally, using mobile stores
can enable a more pronounced market penetration, whereas stationary
stores yield higher sales due to recurrent purchases. Furthermore, ourfindings show how the probability of rejection influences the shape of
the product diffusion curve.

4 - The Enigma of Ticket Exchanges (and Other ResellingMarkets)

Pnina Feldman
It has long been established in the literature and observed in practicethat sellers can benefit from allowing consumers to purchase in advance of the date of consumption. Because of this advance purchasing,consumers can find themselves either with a ticket that they no longer
want or without a ticket that they wish to have. In the past, scalpers
would facilitate transactions among these consumers for a fee. Sellers historically disliked those practices and actively worked to prevent
them. We obtain a stark finding: an unfettered and efficient resellingmarket eliminates all of the benefits of advance selling, which justifies sellers’ historic hostility to reselling. But now ticket exchanges
are common, growing, and even embraced by sellers. What changed?
We present a theory that demonstrates reselling is actually beneficial
for sellers under one crucial condition: the seller must be able to havesome control over the reselling process, thereby allowing the seller toearn something from each transaction. The old-fashioned paper ticket
did not give such control, but technology now enables electronic tickets, which do. In fact, a seller cannot earn more than what it receives231

TB-51EURO 2024 - Copenhagenfrom a properly designed and efficient reselling market (reselling is an
optimal mechanism). And such a market eliminates the opportunities
for speculators and can also be beneficial to consumers. In sum, ourresults explain why the view towards reselling has shifted dramatically.

 TB-51
Tuesday, 10:30-12:00 - Room: M5 (building: 101)

Electric Busses
Stream: Public Transport Optimization
Invited session
Chair: Twan Dollevoet
1 - Joint Optimization of Electric Bus Scheduling and Fast
Charging Infrastructure Location Planning

Kayhan Alamatsaz, Frédéric Quesnel
Many transit authorities are switching from diesel buses to electricones to address concerns over air pollution, greenhouse gas emissions,and the growing demand for energy. While there are many existingscheduling models for diesel buses, they would not fit electric buses(EBs) because EBs have a shorter range and take longer to charge. This
has led to new research, including this study, which looks at both how
to schedule EBs and where to place fast-charging stations with the objective of minimizing the total system costs. These costs include purchasing and operating of EBs and installing the charging stations. In
our research, we developed two types of mathematical models to findthe best schedules and charging station locations. We created a MixedInteger Linear Programming (MILP) for an arc-based model and an
Integer Linear Programming (ILP) model for a path-based model. To
solve these models, we employed a Cplex solver and a Branch-andPrice algorithm. After testing these methods with various scenarios,
we found that the Branch-and-Price algorithm worked faster than the
Cplex solver. Lastly, we did a sensitivity analysis to find out which
type of electric bus is most cost-effective, taking into account the actual features of different buses. We also looked at how changes in thebuses’ battery sizes and travel ranges would affect the optimum solution.

2 - A matheuristic for a multi-day electric bus assignmentand recharge scheduling problem

Pierre Vendé, Guy Desaulniers, Michel Gendreau, Yannick
Kergosien, Jorge E. Mendoza
To limit greenhouse gas emissions in the public transportation sector, transit authorities are constantly increasing the proportion of electric buses (EBs) in their fleets, creating complex decision problems tocharge the vehicles. In order to facilitate the scheduling of all charging operations, it might be efficient to charge EBs both overnight andat long-term stops during the day, to balance the charged quantities.
In this study, we consider a multi-day electric bus assignment andrecharge scheduling problem which can be defined as follows. Givena set of vehicle blocks (sequences of timetabled bus trips that shouldbe operated by the same bus on a given day), a set of EBs and a set of
chargers, the objective is to assign an EB to each block over multiple
days and to schedule both overnight and en-route recharging operations. Chargers are installed at the depot, and sufficiently long stops.
Each charger has its own charging function represented by a piecewise
linear function. The decision are made ensuring that the EBs neverrun out of energy performing their assigned blocks, the charging capacity at any location should not be exceeded, and the sum of the total
charging costs and long run damages on the battery are minimized. We
first model this problem as a MILP that can be solved with a commercial solver. Then, to yield faster computational times, we propose an
iterative matheuristic based on the MILP.

3 - Charge Scheduling with Renewables for Electric Buses
Using Stochastic Optimization

232

Rito Brata Nath, Madhusudan Baldua, Vivek Vasudeva, Tarun
RambhaWhile electrification of urban transit systems is underway worldwide,
it is essential to integrate it with renewable energy sources to make it
fully sustainable. The intermittent nature of renewables poses a challenge in deciding the required solar panels and battery storage capacity
at charging locations. To address these challenges, we propose a twostage stochastic programming model considering uncertainties in solar
energy generation and bus energy consumption under dynamic timeof-use electricity prices. Specifically, we formulate the problem as amulti-scenario linear program and employ a Benders’ decomposition
approach where the first stage (long-term) master variables determine
the contracted power grid capacity, battery storage capacity, and thearea of solar panels installed at each charging location. The second
stage variables (scenario-specific) associated with each child problem
prescribe the energy transferred to buses from the grid or solar-based
power systems during layovers. We present a case study on the Arlington bus network, US, for 52 scenarios with 3337 trips, where 145
buses and 26 charging locations are required as per a concurrent scheduler algorithm. The solar energy generation data is collected from the
National Renewable Energy Laboratory database. Our results revealthat the Benders’ decomposition is faster compared to the Simplex algorithm, and the scenario-based schedule adapts better to the uncertainties than the average scenario schedule.

4 - An Analytical Approach to Designing an Efficient Strategy for Scheduling Chargers and Electric Charging
Times for a Public Bus Company.

Jaime Miranda, Pablo Chaparro
Amid growing concerns about climate change and the urgency to lower
emissions, there’s a significant shift towards sustainable public transportation. Electric bus fleets emerge as a pivotal solution to decrease
reliance on fossil fuels and reduce greenhouse gas emissions.
This study aims to integrate an electric fleet into a public transit systemtransitioning from diesel to electric propulsion. The goal is to formulate an efficient charging schedule strategy, optimizing charger and bus
usage.
Our methodology involves data collection from a transitioning company, developing an optimization model considering operational constraints like current fleet schedules, charger availability at depots, electric bus range limits, and electrical grid capacity.
Using mixed-integer programming, we aim to create optimal charging
schedules and amounts, reducing the need for numerous chargers anddaily charging, thereby cutting costs, boosting operational efficiency,
extending battery life, and ensuring efficient energy use at depots.
This approach promises significant implications for sustainable transit, showcasing how careful planning and technological solutions canaddress the logistical challenges of electrifying public transportation
fleets.

 TB-52
Tuesday, 10:30-12:00 - Room: 8003 (building: 202)

Mixed Integer Optimization II
Stream: Combinatorial Optimization
Invited session
Chair: Laura Palagi
Chair: Marta Monaci
1 - Cardinality Constraints Meet Large-scale Portfolio

Yuan Chen, Immanuel Bomze, Nikolaus Hautsch, Bo Peng
In financial econometrics, the focus is often on improving covariancematrix estimations rather than addressing optimization problems withconstraints for better portfolio management. This paper argues thatcombining these advanced estimation methods with optimization that
includes specific limits, like cardinality constraints, enhances decisionmaking and investment strategies. Cardinality constraints limit theEURO 2024 - Copenhagen
number of assets in a portfolio, potentially making simpler estimators
like the sample covariance sufficient for investment decisions, especially when dealing with large dimensions that typically introduce significant estimation errors affecting portfolio performance.
We also address the issue of managing portfolios when there are fewer
data points than assets, leading to non-invertible, noisy covariance matrices. Cardinality constraints simplify this challenge, making it possible to aim for a global minimum variance portfolio despite these limitations.
Empirically, we find that smaller portfolios, constrained by cardinality
to include only a subset of available assets, can achieve diversification similar to market portfolios while reducing transaction costs andsimplifying analysis. This suggests focusing on smaller, strategicallyselected portfolios could offer investors efficient and cost-effective outcomes.

2 - Margin Optimal Trees: a novel formulation and a SVMbased decomposition approach

Marta Monaci, Laura Palagi
In recent years there has been growing attention to interpretable machine learning models which can give explanatory insights into theirdecision-making process. Thanks to their interpretability, decision
trees have been intensively studied for classification tasks. Due to theremarkable advances in mixed-integer programming (MIP) of the lastyears, various MIP models for training an Optimal Classification Tree
(OCT) have been proposed. Along the research line of Margin Optimal
Trees [1], we present an alternative mixed-integer quadratic formulation to construct multivariate OCTs. The proposed model defines thedecision rules of the tree as maximum margin hyperplanes following
the Support Vector Machine (SVM) paradigm along the binary tree
structure. Further, we exploit the decomposable structure of the model
to develop a tailored algorithmic approach able to leverage the SVM
nature of each subproblem. First, the algorithm has been tested on nonlinearly separable synthetic datasets in a 2-dimensional feature space toprovide a graphical representation of the maximum margin approach.
Finally, the method has been tested on benchmark datasets from the
UCI repository in order to evaluate its computational efficiency andthe generalization capabilities of the generated trees.
References [1] F. D’Onofrio, G. Grani, M. Monaci, and L. Palagi. Margin optimal classification trees. Computers & Operations Research,
161:106441, 2024.

3 - A novel and effective integer linear programming formulation for the train routing selection problem

Anna Livia Croella, Fabio Furini, Ivana Ljubic, Bianca
Pascariu, Paola Pellegrini, Pablo San Segundo
Given a set of train routes associated with route costs and a set of compatible route pairs associated with pairing costs, the train routing selection problem (TRSP) aims to assign a route to each train, minimizing
the sum of the route and pairing costs while ensuring pairwise compatibility among the selected routes. This problem holds significant
practical importance, particularly in rail traffic management, where it
plays a central role. We propose a binary quadratic programming formulation for the TRSP and employ a linearization technique from theliterature, with a linear number of additional variables and constraints,
to derive a novel and effective integer linear programming (ILP) formulation. Exploiting the specific nature of the TRSP, we devise a technique to enhance the strength of the linear programming (LP) relaxation of the ILP model, resulting in a significant improvement in its
computational performance. We obtain in this manner the first effective exact approach for solving the TRSP with proven optimality. Our
approach is capable of solving real-world instances of the French railway network in very short computational times. These instances havetypically been tackled only by heuristic algorithms in the existing literature. Furthermore, we theoretically and computationally compare
the new linearization against the classical one with a quadratic numberof additional variables and constraints and against the only ILP modelfrom the literature.

TB-53

 TB-53
Tuesday, 10:30-12:00 - Room: 8007 (building: 202)

Toward Climate Neutrality
Stream: Sustainable and Resilient Systems
Invited session
Chair: Sandra Venghaus
1 - Estimating Greenhouse Gas Emissions Through Deep
Learning: A Gradient-Based Analysis of Input Impact

Manizheh GhaemiDizaji, Laleh Behjat, William D. Rosehart
We aim to proposes a novel approach to estimating greenhouse gas(GHG) emissions by leveraging deep learning techniques and conducting gradient-based analysis to assess the influence of various inputs on CO2 emissions. The inputs considered encompass a range ofsocio-economic and environmental factors, including Gross Domestic Product (GDP), transportation modes, and energy generation mix.
By constructing a deep neural network architecture, we aim to capture
the complex relationships between these inputs and GHG emissions,enabling robust estimation and prediction capabilities. Furthermore,
our approach integrates gradient-based analysis to elucidate the relative contribution of each input towards overall emissions output. This
allows for a granular understanding of the impact of individual factors,
empowering policymakers and stakeholders to prioritize interventions
effectively. Through iterative modifications of input values and analysis of resultant emission outputs, we can delineate the share of each input in driving emissions variations. This research presents a promising
methodology for GHG emissions estimation and analysis, offering insights into the intricate interplay between socio-economic drivers andenvironmental outcomes. By combining deep learning with gradientbased analysis, we provide a powerful framework for informing climate policy decisions and facilitating sustainable development strategies.

2 - Environmental Sensitivity and Emission Limits

Salvador Sandoval Bravo
We posit an oligopolistic competition scenario in which two companies compete for an identical product, while consumers have different
preferences for them. One product uses a technology that generates
pollution, while the other uses cleaner technology. Consumers show adegree of environmental sensitivity in favor of organic products overpollutants. The host country’s government sets an optimal emissions
limit for the polluting company. This emission limit is positive when
the discomfort caused by pollution and/or the level of environmental
awareness is high enough. However, the optimal emission limit is zero
if the discomfort caused by pollution and/or the level of environmental
awareness is low enough. The intensity of environmental awarenesshas a negative effect on the optimal limit of permitted emissions.

3 - Toward Climate-neutral Aviation - The Market Adoptionof Sustainable Aviation Fuels using Agent-based Modeling

Sandra Venghaus, Jakob Rohner, Ali Ebadi Torkayesh
The European Union (EU) pursues a progressive policy course towards
sustainable development, reflected in the implementation of a portfolio of targeted policies such as, e.g., the Green Deal, the Fit for 55
Package. While many of the EU’s sectors have experienced declining
emissions, the transport sector, aviation in particular, has shown the
opposite trend. To address this challenge, the EU has launched a political effort to enforce a green transition from fossil fuels to sustainableaviation fuels (SAFs). In 2023, the European Commission adopted the
ReFuelEU Aviation Directive that mandates the uptake of SAFs, including power-to-liquid fuels and advanced biofuels, as part of a larger
effort to achieve sustainable aviation. Analyzing and modeling the effects of such political frameworks is crucial for understanding theirlong-term impact on industry developments and consumer behavior. A
sound system understanding to explore both direct but also indirect effects provides a crucial precondition for effective policy-design. For
this purpose, an integrated approach based on joint System Dynamics

233

TB-54EURO 2024 - Copenhagen

and Agent-based Modeling is used to analyze the economic and environmental impact of the current regulatory framework on market development options of SAFs. Findings indicate that efficient regulatoryinterventions can support the transition and that achieving the green
transition requires a shared responsibility among customers, industry
players, and policymakers.

 TB-54
Tuesday, 10:30-12:00 - Room: S01 (building: 101)

time. A geometric representation of the queue position and row designation of each passenger and a Lorentzian metric enable computation of the asymptotic boarding time when the number of passengers
is large. The analytical asymptotic results are corroborated by simulations with a realistic number of passengers.
We also show how the SBF-policy can be adjusted to include a specified proportion of slow passengers that exceeds the critical upper limit,with limited increase in total boarding time.

4 - Big Data Visualization to Unveil Key Differentiated Service Dimensions of the Airlines Performances in COVID19 Aftermath

Airplane Boarding

Praowpan Tansitpong

Stream: Public Transport Optimization
Invited session

This study explores how operational decisions regarding a qualitydifferentiated product line determines the overall success of a multiproduct planning in airline market by using optimized integrated gradients of global airline industry from 2012-2022. Data visualization
techniques are assigned to define contours and color shades in product and service design due to seat configuration, space allocation, seat
design in time variants. In order to understand product variety dimensions, airline decisions are sliced into multiple cubic dimensions andassigned into different contoured colors, due to constrained in capacity in cabin deck and seat options, the density of colors returns individual decision made by each airline for given origin-destination offlight operations. The results suggested that critical areas of optimal
product line choice are dependent not only on price discrimination andsegmentation strategy, but the airlines with efficient aircraft layout favors service differentiation strategy in order to perform well in the airline market. The study reveals the interconnected relationship between
constraint decisions and operational outcomes, shedding light on how
factors such as space for passenger interactions, efficient seat configuration design, and in-flight seat design collectively impact both market
share and load factors.

Chair: Florian Jaehn
Chair: Simone Neumann
1 - Airplane boarding, multi criteria decision making andthe evolution of the universe

Eitan Bachmat
We will consider airplane boarding as a paradigm in multi criteria decision making. In particular, we will consider ranking according to two
criteria which could be SAT and GPA scores for students or risk andtreatment efficacy for patients. We will relate the process of ranking
students or patients to the process of airplane boarding and see thatthey are both related to the evolution of the universe from initial conditions.

2 - Minimizing the Airplane Boarding Time by PassengerSeat Assignments

Felix J. L. Willamowski
Airline passengers usually have their seats selected or assigned before
arriving at the gate in the airport. We investigate the impact of thepassenger-seat assignment on the boarding completion time, especiallywhen considering a central decision-making process that takes placewhen passengers are already in line. Different seat assignments may
result in different boarding completion times, which directly influence
the turn-around time of an airplane. Identifying seat assignments thatminimize boarding time can therefore provide airlines with significant
cost savings. We introduce the problem of assigning passengers toseats while minimizing boarding time in the context of combinatorial
optimization, study the computational complexity of the problem anddevelop exact, approximation, and heuristic algorithms. In addition toa theoretical analysis, we compare these algorithms in a computational
study. Furthermore, we investigate an online variant of assigning passengers to seats—proposed in Jaehn and Neumann’s section on future
research—and present results on the competitive ratio.

3 - Geometrical optics inspires fast boarding with slow
passengers

Sveinung Erland
Airlines try to reduce the boarding time by letting certain groups ofpassengers enter the queue at the gate in a specific order. Slow passengers use long time to clear the aisle and block other passengers fromreaching their seats. We show that a significant proportion of slow
passengers can be permitted without increasing the boarding time, byusing a simple Slow-in-the-Back-First (SBF) boarding policy that consists of only two groups. The policy is superior to several other group
policies that are based on expected differences in aisle-clearing times,
e.g., due to hand luggage.
The practical SBF policy allows for only slightly fewer slow passengers than a corresponding optimal "lens" policy, which on the other
hand is hard to implement. The lens policy is based on the same principle as an optical lens and is optimal in the sense that no fast passenger
can be replaced by a slow passenger without increasing the boarding234

 TB-55
Tuesday, 10:30-12:00 - Room: S02 (building: 101)

Freight transportation and logistic I
Stream: Transportation
Invited session
Chair: Shafagh Alaei Jordehi
1 - Modeling product allocation problem for split orders
and solving via clustering algorithms

Nihal Erginel
Product allocation problem is a warehouse management that concerns
allocate the products to the storage positions in a warehouse or to thewarehouses in multi-warehouse problem. If the orders split to the several warehouses, the cost of picking them up is a non-added value facility. So, the aim of product allocation problem is to determine which
product should be located to the which warehouse to minimize thecost of handling and transportation activities of the split orders. In
multi-warehouse problem, trucks can visit more than one warehouseto pick up customer orders if an order split to others warehouse. In
this study, we aim that is to minimize the number of visited warehouses for total orders under capacity of warehouse and demand oforders. The assumption is the one product can only storage to the one
warehouse. The mathematical problem is constructed and solved with
GAMS 38.3.0. Also, product allocation problem is a clustering problem in which products can be clustered with respect to the number of
warehouses. In this study, k-means algorithm is used for clustering the
products to the four warehouses. It is shown that the similar results are
obtained from mathematical problem and clustering algorithm.

EURO 2024 - Copenhagen
2 - Design of Novel Resilience and Risk Management
Model for Sustainable Supply Chains

Giray Resat
This study presents a novel solution methodology to cover sustainability objectives by synchronizing risk and resilience impact estimations for multimodal freight transportation industry. Minimization of
total cost, time, and carbon emissions by using augmented [U+03F5]constraint method are outlined under multi-objective mixed integer optimization model. Also, approach is shown as comparative analysis,
after defining some pre-processing, symmetry breaking steps, valid
inequalities, and logic cuts. After identifying fourteen different risk
factors for multimodal transportation system, if the most critical risk
factor occurs, there will almost 380% increase in total logistics costand 210% increase in time, respectively. Decision makers can use alternative Pareto solution sets and information about potential impact ofdifferent risk factors over sustainability in their risk management systems. Therefore, some pro-active action plans can be proposed for dynamic environments and improve the logistics systems reliability under
different supply disruptions.

3 - MCDM Analysis for the Selection of a Location for the
Freight Macro Consolidation Centre - Portsmouth Case

Mohanad AL-Behadili, Djamila Ouelhadj, Andrew Bullock,
Graham Wall
The high rate of population growth in the UK leads to an increase in
freight transportation activities and shortage of urban space. This inturn raises negative impacts in urban areas such as congestion, pollution, etc. In addition, the largest contributor of emissions in urban
areas is transportation, which produced about 24% of carbon emissions in the UK and 22% in Portsmouth in 2020. Freight transportations have negative environmental, economic and social impacts. To
tackle such problems, this paper investigates the potential of the establishment of a macro consolidation centre to consolidate freight flows
between suppliers and customers. This research is part of the Solent
Future Transport Zone project (FTZ) funded by the UK’s Department
for Transport and led by Solent Transport. This paper has developed aMulti-Criteria Decision-Making (MCDM) framework using Analytic
Hierarchy Process and Best Worst Method to determine a suitable location for a macro consolidation centre for the city of Portsmouth. The
three main sustainability criteria proposed for the selection of the location include Environmental, Economic and Social. Each of these criteria has sub-criteria that are designed to meet the needs of the study
area. The results show the efficiency of the MCDM framework andthe sustainability criteria in determining the most suitable site for themacro consolidation centre in Portsmouth.

4 - Examining the Impact of Horizontal Collaboration on
Individual Logistics Service Providers within Synchromodal Networks

Shafagh Alaei Jordehi, Javier Durán-Micco, Cathy Macharis,
Koen Mommens
Horizontal collaboration, alongside with vertical collaboration, is oneof the essential elements of synchromodal transport. Collaboration requires information sharing, among all involved organizations, even if
they are competitors. Yet, implementing collaborative systems encounters significant challenges, such as lack of trust among potential participants and concerns about losing competitiveness. To address this challenge, we develop a simulation model of a synchromodal network to
study the interactions between different players in the network as wellas the impact of horizontal collaboration on individual logistic service
providers. The results show that while collaboration yields benefit forthe overall system, these advantages are not evenly distributed among
all players, and certain entities may even experience losses under specific circumstances. Our model visualizes these disparities and canbe used to design compensation schemes that encourage logistic service providers to participate in collaborative systems. Ultimately, thismodel provides insights into the advantages and obstacles of horizontal
collaboration in synchromodal and in general logistics networks andcan be useful for designing more equitable and sustainable logistics
systems.

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 TB-56
Tuesday, 10:30-12:00 - Room: S04 (building: 101)

Crowdsourcing Logistics
Stream: Transportation
Invited session
Chair: Diana Jorge
1 - Equitable Workload Allocation in Vehicle Routing Problem with Heterogeneous Drivers

Iman Dayarian
We introduce an equitable crowdsourced last-mile delivery model using the Nash Social Welfare (NSW) solution to ensure fairness among
drivers. In the private logistics sector, fairness considerations are growing due to pressures to improve workload equity. Our model aims tomaximize driver equity and efficiency while ensuring company efficiency by putting a cap on the deviation of the company’s cost fromthe least-cost solution value. We formulate the problem as a variantof the vehicle routing problem with a nonlinear objective function inspired by NSW’s approach. A column generation method is developed
to solve the problem, and a computational study evaluates the model’s
behavior in terms of company cost, drivers’ profit, and equity.

2 - An Approximate Dynamic Programming Approach for a
Crowdshipping Platform

Emma Innocente, Jean-Sébastien Tancrez
Spurred by the growth of online shopping, parcel delivery has grown
rapidly, driving a need for cost-effective last-mile delivery. Amidst thisgrowth, sustainability concerns are gaining prominence. Crowdshipping leverages crowdsourcing for the personalized delivery of freight,
turning ordinary citizens into couriers for the distribution of small
items. In this collaborative delivery system, individuals already traveling from an origin to a destination take charge of all or part of thedelivery, taking a package along with them and making a stop along
the way to drop it off, potentially reducing freight trucks and enhancing sustainability. To fully leverage the potential of crowdshipping,real-time matching of crowdshippers to parcels is crucial, considering
spatial and temporal uncertainties in crowdshippers’ and parcels’ availability. Due to its size, the dynamic assignment problem consideredin our article cannot be solved with conventional dynamic programming methods. Using approximate dynamic programming with valuefunction approximation, our algorithm learns value functions offline
through a training horizon, enabling efficient decision-making. This
adaptive learning algorithm provides nonmyopic behavior yet requires
only solving sequences of assignment problems no larger than would
be required with a myopic algorithm. Initial results demonstrate its
superiority over myopic solutions in reducing delivery distances.

3 - Optimizing the dynamic heterogeneous order-courier
assignment problem for instant deliveries

Diana Jorge, Tomás Rocha, Tania Ramos
Instant delivery services gained popularity for delivering meals to citydwellers, and the range of products available for delivery has significantly expanded. These platforms utilize crowdsourced couriers who
operate their own vehicles, leading to a diverse courier fleet. In the faceof great competition to attract and retain both customers and couriers, it is crucial for companies to develop advanced decision support
tools. Such tools must be capable of generating real-time assignmentsthat fulfill the expectations of service providers, customers, and couriers. To address the dynamic and complex nature of matching orders
with couriers, considering the varied fleet and specific order-vehicle
requirements, a time-driven simulation-optimization decision support
tool was created. This tool is designed to manage incoming orders,couriers’ availability, real-time courier location updates, and factors
like dynamic traffic congestion and different regional speed limits fordifferent vehicle types. The efficacy of the developed tool was validated through tests using instances from existing literature, where it
demonstrated its capacity to effectively tackle the assignment problem. Furthermore, when applied to a real-world case study, the proposed tool achieved a reduction of roughly 4.5% in total delivery235

TB-57EURO 2024 - Copenhagentimes—from the moment an order is placed to its arrival at the customer—compared to the original assignments.

4 - Parcel delivery planning with multiple parcel services
and crowdworkers

Dominik Stampa, Stefan Nickel
There are several more or less established options for transporting
parcels. Classic parcel delivery is usually done by van, with only a few
parcel services available. These parcel services operate in large areas,e.g., across an entire country. Looking at recent developments, there
are services that only operate in small areas, e.g., a city, and deliverparcels, for example, by bike. In recent research, delivery per drone
or autonomous vehicle is investigated, usually restricted to smaller areas like cities or city districts. In addition to these dierent types ofdelivery services, we want to consider commuters as a resource forparcel transportation. The inclusion of commuters could open up previously untapped transport capacity potentials. Commuters with car
could make their transport capacity available on the way to work andtransport parcels as crowdworkers. If we integrate all these types ofdelivery services and crowdworkers into a general delivery network,
we can split parcel deliveries among several parties. A single parcel
can then be transported by several delivery services or crowdworkers. We present an algorithm, based on a shortest path approach, that,given a customer’s delivery order, computes a transport from sender torecipient independently of other orders. With this approach, we canimmediately provide the customer with transportation proposals basedon dierent objectives such as delivery time, emissions, or price.

3 - A constrained simulation approach to robust risk management of derivative products.

Bertrand Tavin
This paper considers the problem of assessing and hedging the risk carried by a portfolio of non-standard derivative products managed witha parametric model. We first formalise the problem and define theframework in which it can be solved by using a constrained simulation
approach with respect to the model parameters. Our approach is suitable for an agent who may be agnostic with respect to a prior modeland who wish to account for expert views on the range of possible models. Instead of breaking them into several subproblems, the proposed
methodology has the important advantage of answering the risk measurement and hedging question in one step. Namely, the agent needs
to run the simulation just once to get the desired answers. We present
numerical results obtained with recent market data when applying themethod to a portfolio of variance swaps and forward-start options valued with stochastic volatility models. In this project we apply classic
Operational Research techniques, namely constrained simulation, in anovel manner to risk measurement and hedging of trading portfolios.

 TB-58
Tuesday, 10:30-12:00 - Room: S07 (building: 101)

Public Transport
Stream: VeRoLog - Vehicle Routing and Logistics
Invited session

 TB-57
Tuesday, 10:30-12:00 - Room: S06 (building: 101)

Risk management and valuation of financial
contracts
Stream: Modern Decision Making in Finance and Insurance
Invited session
Chair: Eric André
Chair: Bertrand Tavin
1 - Insurer’s management discretion: Self-hedging participating life insurancePeter Hieber
The majority of articles on participating life insurance contracts withguarantees assumes a deterministic and exogeneously given investment
strategy for the insurer’s assets, for example a geometric Brownian motion. However, the insurer may strategically adjust the risk of its asset investments depending on liability values. We look at the optimal
design of such investment strategies with the aim to reduce insurer’s
risks. In the extreme case, the insurer may hedge its liabilities ("selfheding"). This is joint work with Karim Barigou (Université Laval,
Canada).

2 - Merged LSTM-MLP for option pricing

Rita Pimentel, Morten Risstad, Erlend Stegavik Rygg, Jacob
Vinje, Sjur Westgaard, Cassandra Wu
This paper proposes a new merged LSTM-MLP deep learning approach for option pricing. The model has the advantage of combining
time series data with static data. On the one hand, it learns from historical asset returns, as well as considers the option characteristics atthe pricing date. This configuration does not need an estimation for an
explicit volatility measure, improving upon existing methods. We test
it on S&P 500 European call options from 2015 to 2022. The model
excels in accuracy and risk-adjusted returns, showcasing its usefulness
in real-life applications.

236

Chair: Krissada Tundulyasaree
1 - Integrated Planning of Train Path and Rolling Stock Assignment in Railway Freight Transportation

Louis Fourcade, Stéphane Dauzere-peres
The optimal use of critical resources, particularly train paths androlling stock, plays a pivotal role in enhancing the operational efficiency and cost reduction of railway freight companies. Determining
the right timetables for train paths is essential when integrating theselection of train paths and the assignment of the rolling stock. In
this research, we explore heuristic approaches for this integrated problem where practical operational constraints such as maximum load,
maximum weight, delivery time and freight car shunting in classification yards are taken into account. We propose an Adaptative Large
Neighborhood Search (ALNS) heuristic based on a Pickup and Delivery Problem with Time Windows and Transfers (PDPTWT) to define
the train path catalog that is required for the routing decisions of therolling stock. A column generation approach is then used for the assignment of individual demands to train paths. Real-world data on the
French railway network is used to validate the proposed approach, andgood solutions are obtained in reasonable computational times. Our
integrated approach is also compared with a sequential approach.

2 - Planning of Inductive Charged Passenger Buses on Airport Aprons

Niklas Pöch
Given the increasing importance of sustainability in the aviation sector
due to climate change, electrifying ground vehicles on airport aprons
is one way to reduce emissions. Dynamic inductive charging, which
wirelessly charges vehicles while moving, is especially suitable for
airport apron vehicles as it eliminates downtime for charging electricvehicles compared to conductive charging. We focus on schedulingelectric passenger buses on airport aprons that use an inductive charging infrastructure to charge their batteries. Specifically, we investigate
which vehicle should perform each service trip, whether it is transporting passengers from a gate to an airplane or from an airplane to a gate.
We aim to ensure the reliable operation of these buses and avoid delays
and breakdowns due to empty batteries. We present a formulation of
the problem using a mathematical model. With this model, we want toevaluate different inductive charging infrastructures.

EURO 2024 - Copenhagen
3 - Policy Setting for Integrated Freight and Passenger
Transportation

Krissada Tundulyasaree, Layla Martin, Rolf Van Lieshout,
Tom van Woensel
Public authorities use policies to encourage freight forwarders to shift
towards more sustainable modes of transportation. In an integrated
freight and passenger transportation setting, freight forwarders canchoose between unimodal (truck) or multimodal (truck and scheduledtrain/barge) transport, minimizing their operational costs. To promote
more sustainable transport, public authorities levy taxes on road transport and subsidize scheduled services. In this study, we propose a bilevel model where the authority sets road-access taxes and rail/wateraccess charges while freight forwarders select the modes of transportation. Due to the inherent computational complexities, we solve theproblem using a bi-section algorithm for the upper level and Adaptive
Large Neighbourhood Search for the freight-forwarder’s lower levelproblem. We find that it is usually optimal to make the scheduled line
free and to set the tax sufficiently high to stay within a predefined budget. However, increasing subsidies and taxes gives marginal returns
on modal shift and mileage reduction. Due to decreased routing efficiency, a budget-neutral policy results in considerable cost increases
for the freight forwarders. The policy maker can allocate a budget tokeep tax at an acceptable level while still promoting modal shift.

4 - One Million ... and Beyond! Solving Huge-Scale VRPs
in a Handful of Minutes

Daniele Vigo, Luca Accorsi
We propose a new dataset of Capacitated Vehicle Routing Problem instances which are up to two orders of magnitude larger than those in
the currently used benchmarks. Despite these sizes might not have an
immediate application to real-world logistic scenarios, we believe they
could foster fresh new research efforts on the design of effective and efficient algorithmic components for routing problems. We provide computational results for such instances by running FILO2, an adaptationof the FILO algorithm proposed in Accorsi and Vigo (2021), designed
to handle huge-scale CVRP instances. Solutions for such instances
are obtained by using a standard personal computer in a considerably
short computing time, thus showing the effectiveness of the acceleration and pruning techniques already proposed in FILO. Finally, resultsof FILO2 on well-known literature instances show that the newly introduced changes improve the overall scalability of the approach withrespect to the previous FILO design.

TB-60

via a column generation algorithm. The result can be shown to provide
both a tighter bound than previously known as well as a simple heuristic for the control problem. To allow for more general basis functions
and possibly good value function approximations and corresponding
heuristics, we also discuss simulation-based approximate dynamic programming (sb-ADP). In a numerical study, we show that our sb-ADP
heuristic typically obtains expected revenues close to our theoretical
upper bound and better than a simple ALP-based policy. The quality ofthe sb-ADP, however, highly depends on the allotted computation time.
For increasingly complex customer preferences, the time required forstrong performance can be prohibitive.

2 - Airline Cancellation, Loadfactor, and Weather

Fredrik Odegaard
Airlines occasionally either delay or completely cancel scheduled
flights. Often the explicit reason is inclement weather, however, otherfactors, including loadfactor, might influence the delay/cancel decision. This paper presents an empirical analysis of US airlines flightcancellations considering loadfactor and weather at origin and destination. The data spans May 2022 to June 2023.

3 - Boosting Hotel Profits: The Power of Enhanced Cancellation Predictions

Ryusuke Kimura, Kimitoshi Sato
The hotel industry faces the uncertainty of reservations and cancellations. Although dynamic pricing provides appropriate discounts forlast-minute cancellations, it is not always possible to fill vacancies unless there is sufficient demand. Therefore, it is important to know how
the accuracy of cancellation forecasts affects the results of dynamic
pricing in practice. In this study, we analyze the relationship between
forecast accuracy and expected returns using actual data on room reservations and cancellations in a region in Japan. The results show that a
10% improvement in forecast accuracy results in a 2% improvement inrevenue. We also show that this improvement simultaneously improves
consumer surplus by lowering the average sales price and improving
the load factor. Furthermore, the improvement is more effective during
the peak season when the number of potential customers is higher.

4 - Reserved product pricing with dependent and normally
distributed reservation prices

Thomas Archibald

 TB-59
Tuesday, 10:30-12:00 - Room: S08 (building: 101)

Pricing and Capacity Management
Stream: Pricing and Revenue Management
Invited session
Chair: Thomas Archibald
1 - Dynamic Pricing of Empty Seats in the Transportation
Industry under the Nested Logit Model

Reserved product pricing (RPP) is a strategy used by firms selling two(or more) products to consumers. Under this strategy, products are offered for sale individually and consumers who purchase only one of the
products is later offered a discount on the other product. Prior analysis
has shown that RPP can be more profitable than other bundling strategies under the common assumption of independent and uniformly distributed reservation prices. We use a simulation model to investigate
the performance of RPP with more general assumptions about reservation prices. The results show that RPP remains an attractive strategy
provided not too many customers anticipate the discount and modify
their behaviour to purchase only one product initially. The main finding is that the optimal RPP strategy is relatively robust to the correlation in reservation prices.

Davina Rauhaus, Christiane Barz, Jochen Gönsch
We suggest a dynamic pricing model for selling "empty seats" - seatreservations for extra space in addition to regular reservations. Suchextra space tickets share the main product’s resources and are a significant revenue-generating opportunity when coaches, trains, or airplanes
frequently depart with many empty seats. We formulate the problem
of a transportation company that sells tickets in the same compartment(1) without a seat reservation, (2) with a seat reservation, and (3) witha seat reservation and extra space as a Markov decision process. To address the resulting curse of dimensionality, we follow two approaches.
First, we use approximate linear programming (ALP) with an affine
value function approximation. We solve the resulting linear problem

 TB-60
Tuesday, 10:30-12:00 - Room: S09 (building: 101)

Projects, risk and law
Stream: Project Management and Scheduling
Invited session
Chair: Femke Boelens

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TB-61EURO 2024 - Copenhagen

1 - Risk assessment framework for project’s activities withconditional expectation

Ken-ichi Suzuki, Tetsuo Iida

Femke Boelens, Richard Boucherie, Anne Zander

Evaluating the impact of an activity on a project is essential for risk
management, Therefore, over time, various researchers have proposed
different risk indicators, including criticality index (Van Slyke, 1963),
significance index (Williams, 1992), Crucially index (Williams, 1992),
management-oriented index (Madadi & Iranmanesh, 2012), criticalityslack-sensitivity index (Ballesteros-Pérez et al., 2019), and more.
However, while each indicator conveys a certain aspect of the activity’s behavior, they have faced criticism for sometimes presenting anevaluation that is counter-intuitive to actual practice, and the debate
on the choice of risk indicators is ongoing. In this presentation, we
first discuss the desirable properties that risk indicators should possess. These include consistency with an overall risk measure, linkage
to a risk-reducing action, and risk decomposition. Based on these properties, we propose a risk assessment framework that uses conditional
expectation. In the proposed framework, we measure the overall risk
with the expected delay to a reference time point and define the risk
indicator by an activity’s expected duration conditioned with the delay
and criticality of the activity. Finally, we demonstrate the effectiveness of the new indicators by applying them to the cost allocation forstochastic CPM and the buffer sizing for the buffer allocation problem.

Efficient planning of judicial capacity is crucial for ensuring a timely
resolution of court cases. Accurate predictions of demand are essential for capacity planning. The demand of the court consists of casesthat need to be resolved, either by one or multiple hearings or in writing. We analyze the arrival process of cases to the court and formulatea negative binomial regression model to predict the court demand on
a weekly basis, taking seasonality into account. We test our prediction model on real-life data of the Dutch judiciary. Subsequently, weformulate an optimization model for planning capacity for one futureyear based on these predictions, considering capacity constraints, aiming to minimize the waiting time. We model the problem as a nonhomogeneous queue with a time-dependent arrival rate and a service
rate that is subject to control. By incorporating insights from a transient
analysis of the queueing model, we solve the optimization model bystochastic programming, compare our capacity planning to the statusquo, and run experiments to test several choices of capacity planning.

2 - An efficient linear reformulation for project portfolio optimization problems

 TB-61
Tuesday, 10:30-12:00 - Room: S10 (building: 101)

Ming-Hua Lin, Jung-Fa Tsai

Nonlinear Location Optimization

With the rapid evolution of technology and the constant changes inthe business environment, many enterprises assign different tasks as
projects. Therefore, project management performance is critical to
the success of the enterprise. There are usually multiple projects executed concurrently in the organizations. Project portfolio management significantly influences project management performance dueto the limited available resources and the interdependency between
projects. Project portfolio selection is one of the most critical issues
in the project portfolio management process. Allocating available resources to an optimal set of projects will result in the most benefits.
This study considers a project portfolio selection problem that discusses selecting an optimal subset of projects from a set of candidate projects for maximizing benefits with an effective and efficient use
of limited resources. The original mathematical model of the project
portfolio selection problem is a nonlinear integer programming problem that is hard to solve for finding a globally optimal solution. This
study utilizes a novel linearization approach that efficiently transforms
the project portfolio selection problem with cross-product terms as amixed-integer linear program solvable for finding a globally optimal
solution. Compared with current methods, the proposed method uses
much fewer continuous variables to linearly reformulate the problem.
Numerical examples are presented to demonstrate that t

Stream: Locational Analysis
Invited session

3 - Analyzing Delay Litigation Cases of Public Building
Construction Using Text Mining Techniques

Ying-Mei Cheng, Wei-Jie Huang
In long-term construction projects with complex work interfaces, disputes that arise during construction often leave contractors unable tocomplete the work within the contractually agreed timeframe. This results in varying degrees of loss, compelling the contractors to seek aresolution of responsibility through litigation to safeguard their rights.
Construction project delays have been extensively studied, with researchers often delving into analyses of factors influencing projectschedules. The current study examines legal documents from litigation
cases to observe the relationship between factors influencing projectschedules and the outcomes of disputes commonly seen in litigation.
This involves consolidating Supreme Court rulings on project schedule
extensions and utilizing text mining tools to analyze and explore thecause-and-effect relationships of schedule-influencing factors in construction dispute litigation cases. The aim is to provide insights into
whether resorting to the courts is a valuable option during contract disputes in construction projects.

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4 - Analysis of demand and planning of capacity for the judiciary

Chair: Pawel Kalczynski
1 - The minimum covering Euclidean ball problem: review
and report

Mark Cawood, Lin Dearing
Efficient primal and dual algorithms have been developed recently by
the authors for the n-dimensional minimum covering Euclidean ball
problem of several types. These include the minimum covering ballof a given finite set of distinct points, the minimum covering ball of agiven finite set of balls each with different radius and center, and theminimum covering ball of a set of distinct points with weighted Euclidean distance between the points and the center of the covering ball.
Each problem type corresponds to an equivalent one-center location
problem.
The common approach for each algorithm is a search-path method,where the search path is determined by the intersection of bisectors ofpairs of points. Each problem has a different structure for the bisectors of pairs of points (hyperplanes, hyperboloids, and hyperspheres,
respectively). The step size along the search path is computed exactlyat each iteration. Each point on the search path is primal (dual) feasible
and satisfies complementary slackness for the primal (dual) algorithm.
Example problems are presented for each problem type and illustrate
the bisectors, their intersection, and the optimality conditions. Computational results are presented.
Current work is presented on the combined problem of finding the minimum covering Euclidean ball of a given set of balls with weighted
distance. The bisector of a pair of points is a quartic surface.

2 - Congested Hub Location Problem with Price-dependent
DemandAta Jalili Marand, Pooya Hoseinpour
This study investigates a congested hub location problem with the aimof optimizing a service provider’s profit by determining hub locations,capacities, and prices for serving commodities. The study incorporates a price-dependent demand function with a general functional
form and models hub operations as M/G/1 queueing systems to addresscongestion effects. The problem is initially formulated as a mixedinteger nonlinear program, which is subsequently transformed into a

EURO 2024 - Copenhagen
mixed-integer second-order cone program, demonstrating solvability
with standard off-the-shelf solvers. Through numerical analysis, thestudy evaluates the influence of different demand functional forms andservice time constraints on the optimal network configuration and theservice provider’s profitability.

3 - The Planar Obnoxious Facilities p-Center Problem

Pawel Kalczynski, Zvi Drezner
The p-center problem is one of the classical location analysis problems. It involves locating one or more facilities (in the plane or on the
network) among multiple demand points while minimizing the largest
distance between a facility and a demand point. Recent papers present
novel solution techniques that allow for solving large-scale planar pcenter problems. However, p-center models involving obnoxious facilities have yet to be investigated. Obnoxious facilities emit or inflict nuisance, such as noise or pollution, on the demand points. This nuisance
propagates "by air," so it can be reduced by locating the facilities further away from the demand points. However, this new property makes
p-center models nonconvex and more challenging to solve. We formulate the planar obnoxious p-center problem as a general non-linear
program and reformulate it as a nonconvex quadratically constrained
optimization problem. We present two solution techniques: an optimal(within tolerance) one involving the linearization of non-linear constraints and one involving a multi-start approach with a local solver(without the optimality guarantee). The effectiveness and efficiencyof our techniques are demonstrated on both generated and commonlyused instances of the p-center problem. In addition, we investigate theimpact of the obnoxious property on the planar p-center problem difficulty and solution quality.

 TB-62
Tuesday, 10:30-12:00 - Room: S12 (building: 101)

Energy Management in Ports and Shipping I
Stream: OR in Port Operations
Invited session
Chair: Cagatay Iris
1 - Fleet deployment and slot exchange in liner shipping
alliance with carbon emission trading system

Yadong Wang, Shenghui Zhu, Cagatay Iris
Shipping alliance has become a prevalent cooperation form amongmajor container shipping companies in recent decades. By forming
an alliance, shipping companies can better satisfy the shipping demand and improve capacity utilization and profitability. At the sametime, the shipping industry has become an important source of global
greenhouse gas emissions. Faced with the emerging worldwide maritime carbon emission trading system, the shipping alliance needs toredesign its fleet to control carbon emissions and reduce its negative
effects on the profit of the alliance members. This paper considers the
fleet deployment and slot exchange problem in shipping alliance withthe consideration of carbon emission trading system. A mixed integer
nonlinear programming model is first proposed to simultaneously optimize the fleet deployment, the ship sailing speed and schedule for eachshipping service, the slot allocation and exchange for each alliancemember, and the container routing, with the objective of maximizing
the alliance total profit (freight revenue minus the ship operating costand emission trading cost) and minimize the variation of the profit rate
among alliance members. A tailored spatial branch-and-bound algorithm is proposed subsequently considering the structure of the problem to obtain the global optimal solution. Numerical experiments also
demonstrate the effectiveness of the model in emission reduction andprofit improvement for a shipping alliance.

2 - Proposal to reduce carbon footprint throgh the use of
h2v in tugboats in ports

TB-62

Lorena Bearzotti, Luciano Nuñez, Maria Isabel Vega, Javier
Maturana-Ross
The importance of transportation in society and its significant contribution to polluting emissions from internal combustion engines using
fossil fuels highlight the necessity of transitioning to a renewable energy matrix. Green hydrogen emerges as a viable option to mitigate
this impact. This transition aims to reduce emissions in maritime transportation progressively. The maritime sector plays a crucial role inglobal trade and transportation, accounting for a substantial portionof fossil fuel consumption and associated emissions. The adoptionof cleaner and sustainable technologies in maritime transport not only
aids in emission reduction but also fosters innovation and the development of eco-friendly solutions. Enhancing research and development in energy-efficient technologies and environmentally sustainable
practices within maritime transport is essential for combating climatechange on a global scale. Transitioning towards green hydrogen andother renewable energy sources in maritime transport signifies a significant step towards a cleaner and more sustainable future. This shift
prioritizes emission reduction and environmental protection as key objectives. By leveraging green hydrogen and renewable energy sources,
the maritime industry can pave the way for a greener, more efficient,and environmentally conscious approach to transportation, setting new
standards for sustainability and emission reduction in the sector.

3 - Column generation algorithm for smart-split stacking
strategy at automated container terminals

Yuanjun Feng, Dongping SongContainer stacking strategy determines the positions of incoming containers in the storage yard, which plays a vital role in improving theefficiency and productivity of container terminals and reducing the energy consumption of equipment and vehicles. The smart stacking strategy for import containers aims to create relocation-free stacks - named
smart stacks, by utilizing customer information, thus reducing the retrieval time of import containers. There are two variants of stacking
policies under the smart stacking strategy: split policy and non-split
policy. The split policy, where containers from the same customer areallowed to be split between smart stacks and non-smart stacks, is superior to the non-split policy. This paper investigates the import container stacking problem under the split policy. The problem is first
formulated as a mixed-integer programming (MIP) model to minimize
the total working time of the yard crane. By enhancing the representation of certain variables, we then develop an improved MIP model,
which is computationally more efficient than the original model whenusing CPLEX as a solution tool. We also propose a column generation algorithm to solve the improved model. Extensive computational
experiments are conducted to demonstrate the effectiveness of the proposed models and the efficiency of the proposed algorithm.

4 - Optimising electric vehicles charging and energy management in port microgrids

Cagatay Iris
Seaports, as substantial consumers of energy, aim to adopt energy management systems in response to rising energy costs and a commitment
to sustainable operations. Many ports are shifting to electricity as theirprimary energy source, opting for fully electrified equipment like electric vehicles (e.g. battery AGVs) instead of carbon-intensive alternatives. This paper presents a mixed integer linear programming model
designed to address the integrated planning and energy management
challenges faced by seaports utilizing a port microgrid. The planning
module of the model determines the allocation of electric vehicles andrelated equipment, as well as the scheduling of EV charges, specifying
the energy usage for each one-hour period. Additionally, the planning
module decides the berthing duration for each ship, influencing thehourly energy consumption of the port. The energy management aspect involves aligning energy demand and supply, considering various
energy pricing schemes, bidirectional energy trading among different
sources (such as the utility grid, renewable energy sources), and integrating energy storage systems. The study’s findings suggest thatthe implementation of smart grid technology, represented by the port
microgrid, can yield substantial cost savings compared to traditional
setups. Notably, efficient charge planning and battery management
within microgrid systems enable ports to achieve significant cost reductions.

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TB-63EURO 2024 - Copenhagen TB-63
Tuesday, 10:30-12:00 - Room: S14 (building: 101)

Risk Management and Cryptoassets
Stream: OR in Banking, Finance and Insurance: New
Tools for Risk Management
Invited session
Chair: Gianna Figà-Talamanca
Chair: Giacomo Gaggero
1 - Pricing Options with a Compound CARMA(p,q)-Hawkes
model

Andrea Perchiazzo, Lorenzo Mercuri, Edit Rroji
Recently a novel self-exciting point process has been introduced in theliterature, featuring a continuous-time autoregressive moving averageintensity process. Such a model, named CARMA(p,q)-Hawkes, extends the traditional Hawkes process by integrating a CARMA(p,q)
framework instead of an Ornstein-Uhlenbeck intensity. As a matter of fact, the proposed model maintains the same level of mathematical tractability as the Hawkes process (e.g., infinitesimal generator, backward and forward Kolmogorov equations, joint characteristic
function); but it shows enhanced capability in reproducing complex
time-dependent structures evident in several market data. Based on
this framework we propose a compound CARMA(p,q)-Hawkes model
incorporating a random jump size independent of both the counting
and intensity processes, which serves as a key component for a new
option pricing model. We conduct an analysis to assess the effectiveness of this pricing model in replicating the volatility surface observedin market option data.
References: Mercuri, L., Perchiazzo, A., & Rroji, E. (2024). A
Hawkes model with CARMA (p, q) intensity. Insurance: Mathematics
and Economics.

2 - Forecasting the worst: is implied volatility forwardlooking enough?

Carlo Confalonieri, Paola De Vincentiis
The paper backtests the accuracy of 1-day Value-at-Risk (VaR), computed with variance-covariance methodology, using various alternative
methodologies for estimating the risk factor’s volatility. The aim is togauge whether using option-implied volatility (IV) produces superior
results compared to other time-series based measures. In particular,
we want to test if a forward-looking estimate - reflecting the sentimentof market investors - is more capable of capturing the tail behaviourof returns distribution during crisis periods, characterized by volatility spikes. The empirical analysis is performed on the S&P500 Stock
Index, using both the VIX Index and VIX-1day Index, recently introduced by the Chicago Board Options Exchange. The crisis analysed
are the pandemic period and the months following Russia’s invasion of
Ukraine. Our results contradict our initial hypothesis, showing that IVbased VaR tend to be underestimated during market turbulences, bothin terms of frequency of realized losses exceeding the threshold and interms of average magnitude of the excess losses. However, quite a different picture emerges when we use the new VIX1D index, instead of
the traditional VIX. The preliminary evidence indicates that the 1-day
implied volatility derived from near term options may be effective for
estimating VaR and superior to alternative methodologies.

3 - Value-at-Risk of an Option Portfolio Under Different
Scenarios. A Proposal of a More Reliable Market Measure

Giacomo Gaggero, Pier Giuseppe Giribone, Duccio Martelli,
Sanmoy Mukherjee
Value at Risk is one of the most widely implemented methods in the financial sector for assessing the traditional market risk associated with aportfolio. We focus our attention on nonlinear pay-off derivatives suchas options, proving the effectiveness of the proposed methodology also
for non-standard options. To calculate the future projections of the analyzed portfolio, we implement a full-repricing forward-looking Monte

240

Carlo method. Based on data from the last five years, we compute foreach of the eleven stocks, on which options are written, the volatility implementing six different techniques (Close-to-Close, High-Low,
High-Low-Close, EWMA, GARCH(1,1) methods and Implied Volatility), the dividend using two alternatives (Historical and Implied measures) and the drift using both traditional econometric approaches like
ARIMA and innovative like LSTM technique. Subsequently, having
estimated the volatility and the drift, we have implemented those forsimulating the underlying prices for the next periods using a Geometric Brownian Motion. Doing this, we have used only the most prudential scenarios for the projection of the assets. We have consequently
considered all the possible combinations for these inputs in the option
pricing model, estimating the most reliable forward-looking fair value
distributions of each option over the next days through 20,000 daily
simulations. Value at risk has then computed taking the percentiles ofthis distribution.

 TB-64
Tuesday, 10:30-12:00 - Room: S16 (building: 101)

Arc Routing
Stream: VeRoLog - Vehicle Routing and Logistics
Invited session
Chair: Isaac Plana
1 - Lagrangian and Other Relaxations for the Urban Snow
Removal Problem

Kaj Holmberg, Roghayeh Hajizadeh
Snow removal problem in cities is a challenging task in nordic countries. Doing the snow removal efficiently is important. Since theamounts of snow vary a lot from day to day, and from year to year,
fixed plans are not the best. Optimization of the snow removal tours
could save much time and money.
We study the multi-vehicle urban snow removal problem from a mixed
integer programming perspective. It is a very hard problem, and obtaining the exact optimum seems to be out of reach. Therefore, we
study relaxations of the problem. Our goal is simply to find the best
bounds for the optimal objective function value that is possible in limited time.
Since the problem has many sets of constraints with complicated structures, using Lagrangian relaxation might be beneficial. We discussdifferent possibilities of relaxing sets of constraints and develop a Lagrangian heuristic which consists of a suitable Lagrangian relaxationof the problem, a subgradient optimization method for solving the Lagrangian dual, and procedures for obtaining feasible solutions. The
heuristic has been implemented and applied to artificial and real life
city networks.
Extensive computational tests show that the lower and upper bounds
can be improved.

2 - A Branch and Price for the Min Max Multi-Trip Location
Arc Routing Problem

Teresa Corberán, Isaac Plana, Jose Maria Sanchis
In this work, we address the Min Max Multi-Trip Location Arc Routing Problem (MM-MT-LARP). We consider a depot from which a setof P trucks, each one carrying a drone, must travel to P out of D available points, where the drone is launched. Each drone has a limited
autonomy which allows it to fly a maximum time L before having to
get back to the launching point to change its battery so that it can startanother route. Once the drone has completed all its routes, the truck
goes back to the depot. The goal of the MM-MT-LARP is to determine the launching point for each truck and find a set of drone routes
for each truck, each one starting and ending at its launching point andwith flight time not greater than L, in such a way that all the drones’routes jointly traverse all the given edges and the largest total time ofEURO 2024 - Copenhagenall the trucks (time of traveling to the launching point, flight time ofthe drone and time of traveling back to the depot) is minimized. In
this talk, we present a route-based formulation for the problem anda branch-and-price algorithm to solve it, as well as some preliminary
computational results on a set of instances with different characteristics.

3 - On the Chinese Postman Problem With Load-Dependent
Costs

Paula Segura Martínez, Isaac Plana, Jose Maria Sanchis
The Chinese Postman Problem (CPP) is a classical arc routing problem whose objective is to find a minimum cost tour on a connected
undirected graph G that traverses each edge of G at least once. An
extension of this problem introduced in the literature is the Chinese
Postman Problem with load-dependent costs (CPP-LC), in which thecost of traversing an edge depends on its length and also on the weightof the vehicle at the moment the edge is traversed. This is motivated by
the intention of reducing pollution in transportation, since the level ofemissions from a vehicle is influenced by factors beyond the distancetraveled, such as its load. Two mathematical programming formulations and two metaheuristic algorithms have been proposed in the literature for the CPP-LC solution. We present in this work a new mixedinteger linear programming formulation for the CPP-LC, and propose
some valid inequalities to reinforce it. We also develop a study of theincompatibilities of a subset of variables from the formulation and describe all the cliques of the underlying intersection graph, categorizingthem into five families of inequalities that are valid for the problem.
We design a branch-and-cut algorithm for the CPP-LC solution basedon this formulation that incorporates the separation of all the valid inequalities proposed. Some computational results obtained with our
new exact procedure are presented and compared with those provided
in the literature.

4 - The Covering Arc Routing Problem

Isaac Plana, Mercedes Landete, Juanjo Peiró, Jose Maria
SanchisTransporting valuable or dangerous goods may require constantsurveillance to prevent potential thefts or incidents. Such supervision can be carried out by installing cameras or hiring security guards,
which entails a monetary investment. In this work, we introduce theCovering Arc Routing Problem (CovARP), that combines arc routing
and location problems. Let us consider an undirected graph with a costassociated with the traversal of each edge and a subset of edges (required edges) that must be traversed to perform some kind of service.
Additionally, there is a set of potential monitors that can be activatedor not. Each monitor has an activation cost and covers a certain set ofedges if it is activated. The CovARP consists of both finding a closed
path (tour) that traverses all the required edges at least once and determining a set of monitors that should be activated, so that all the edges
traversed by the tour are covered by at least one active monitor, while
minimizing the sum of the tour cost and the activation cost of the monitors. We present an integer linear formulation for this problem anda branch-and-cut algorithm. This algorithm has been tested on a randomly generated set of instances. We have analyzed three different
situations: when the activation cost of the monitors is clearly higher
than that of the tour, when the cost of the tour outweighs the activation
cost of the monitors, and the balanced case in which both costs arecomparable.

TC-03

Tuesday, 12:30-14:00
 TC-01
Tuesday, 12:30-14:00 - Room: Sportshallen (building: 101)

Milos Kopa
Stream: Keynotes
Keynote session
Chair: Milan Hladik
1 - Stochastic dominance in decision making under uncertainty

Milos Kopa
Stochastic dominance is a statistical tool developed for comparing therandom variables among each other. In financial applications, theserandom variables usually represent random returns of the consideredassets or portfolios. The paper focuses on portfolio selection problems
with stochastic dominance constraints for various orders of stochastic dominance relations. Firstly, the tractable necessary and sufficientconditions for particular probability distributions are discussed. Secondly, these conditions are employed in the static and dynamic portfolio selection problems. Finally, the extensions for the case of vector
comparisons are presented. The theoretical results are accompanied byempirical examples.

 TC-03
Tuesday, 12:30-14:00 - Room: 1005 (building: 202)

Optimization and Machine Learning:
Methodological Advances
Stream: Data Science Meets Optimization
Invited session
Chair: Michael Römer
1 - Mixed Integer Programming and Heuristics Approaches
for Clustering with Local Feature Selection

Cem Iyigün
In this study, we work on a clustering problem where it is assumed thatthe features identifying the clusters may differ for each cluster. Number of clusters and number of relevant features in each cluster are given
in advance. A center-based clustering approach is proposed. Finding the cluster centers, assigning the data points and selecting relevant
features for each cluster are performed simultaneously. A non-linear
mixed integer mathematical model is proposed which minimizes thetotal distance between data points and their cluster center by using theselected features for each cluster. Different linearization methods havebeen used for solving the problem. Besides, two different heuristic algorithms have been developed by taking into account the nature of thementioned problem. Experimental results have been presented.

2 - Anytime algorithm configuration

Elias Schede, Kevin Tierney
Optimization algorithms contain parameters that greatly influence their
behavior, such that finding good parameters with automated algorithm
configuration (AC) tools has become a critical component in the algorithm design process. Optimization algorithms often possess theanytime property, meaning they can be stopped at any time during
their execution and provide a feasible solution. However such algorithms are not specifically targeted by current AC techniques. Setting
the parameters of anytime algorithms is especially difficult, as the parameters ought to provide robust performance across varying execution times. Traditional AC methods address this challenge by finding241

TC-04EURO 2024 - Copenhagena one-size-fits-all parameter configuration, however finding a scheduleof configurations, each targeted to a different runtime, can lead to betteroverall performance. We introduce a novel AC method for configuring anytime algorithms that produces viable configuration schedules
that assign different configurations to different runtimes. Our proposed
method harnesses an early termination mechanism for unpromisingconfigurations using a machine learning model, and uses two novel
MIP formulations to discard configurations and to create the configuration schedule, respectively. The resulting schedules can be usedby decision makers to choose a suitable configuration given a specificruntime for an anytime target algorithm.

3 - Using Clustering to Strengthen Decision Diagram
Bounds for Discrete Optimization

Michael Römer, Mohsen Nafar
Offering a generic approach to obtaining both upper and lower bounds,
decision diagrams (DDs) are becoming an increasingly important tool
for solving discrete optimization problems. In particular, they provide
a powerful alternative to other well-known generic bounding mechanisms such as the LP relaxation. A standard approach to employ DDs
for discrete optimization is to formulate the problem as a Dynamic
Program and to compile a DD top-down in a layer-by-layer fashion.
To limit the size of the resulting DD and to obtain bounds, one typically imposes a maximum width for each layer which is then enforcedby either merging nodes (resulting in a relaxed DD providing a dualbound) or by dropping nodes (resulting in a restricted DD providing
a primal bound). The quality of the DD bounds obtained from thistop-down compilation heavily depends on the heuristics used for theselection of the nodes to merge or drop. While it is sometimes possible to engineer problem-specific heuristics for this, a generic approachrelies on sorting the layer’s nodes based on objective function information. In this talk, we propose a generic and problem-agnostic approachthat relies on clustering nodes based on the state information. In a setof experiments with different knapsack and scheduling problems, weshow that our approach outperforms the classical generic approach,and often achieves drastically better bounds both with respect to thesize of the DD and the time used for compiling the DDs.

This result unifies and significantly extends the known tractable classesof BPO. The generality of our technique allows us to deal also with extensions of BPO, where we enforce extended cardinality constraints on
the set of binary points, and where we seek k best feasible solutions.
We also extend our results to the significantly more general problem
where variables are replaced by literals. Preliminary computational results show that the resulting algorithms can be significantly faster thancurrent state-of-the-art.

3 - Generalized convexity applied to branch-and-bound algorithms for MINLPs

Ksenia Bestuzheva
One of the key properties of convex problems is that every stationary
point is a global optimum, and nonlinear programming algorithms thatconverge to local optima are thus guaranteed to find the global optimum. However, some nonconvex problems possess the same property.
This observation together with empirical evidence that local searchsolvers often find global optima of nonconvex problems such as optimal power flow and pooling has motivated research into generalizations of convexity. This presentation proposes a new generalization which we refer to as optima-invexity: the property that only one
connected set of optimal solutions exists. We discuss conditions for
optima-invexity and its applications within spatial branch-and-boundalgorithms for mixed-integer nonlinear programs.

4 - M-Decomposable Sets in Integer Programming

Diego Moran
An M-decomposable set is a closed convex set which is the sum of acompact convex set and a closed convex cone. We present propertiesof these sets in the context of integer programming. In particular, we
present properties of their integer hull, some cutting plane closures andsubadditive duality.

 TC-06
Tuesday, 12:30-14:00 - Room: 1013 (building: 202)

 TC-04
Tuesday, 12:30-14:00 - Room: 1001 (building: 202)

Recent Advances in MINLP
Stream: MINLP
Invited session
Chair: Silvia Di Gregorio
1 - Modelling of piece-wise linear concave constraints incontinous covering problems

Liding Xu, Claudia D’Ambrosio
We study continuous set covering on networks. The covering condition
has a representation as a piece-wise linear concave constraint, which
we formulate as a disjunctive system. We propose three MILP reformulations based on indicator constraint, big-M, and disjunctive programming techniques to formulate the disjunctive system. We classify
new and old formulations and conduct experiments to compare them.

2 - A Knowledge Compilation Take on Binary Polynomial
Optimization

Silvia Di Gregorio
The Binary Polynomial Optimization (BPO) problem is defined as the
problem of minimizing a given polynomial function over all binary
points. The main contribution of this paper is to draw a novel connection between BPO and the problem of performing model counting overthe (max, +) semiring on Boolean functions. This connection allows us
to give a strongly polynomial algorithm that solves BPO with a hypergraph that is either beta-acyclic or with bounded incidence treewidth.

242

Advancements of OR-analytics in statistics,
machine learning and data science 14
Stream: Advancements of OR-analytics in statistics, machine learning and data science
Invited session
Chair: Nina Kajiji
Chair: Gordon Dash
Chair: Gerhard-Wilhelm Weber
1 - A comparative study of large-margin strategies for Active Learning

Annabella Astorino, Antonio Fuduli
Training a classifier is a challenging task as it typically requires a significant amount of labelled data, while most available samples are unlabeled. For instance, in the field of diagnostic imaging, labelling medical images requires the expertise of a specialist. In this case, Active
Learning (AL), a machine learning approach that aims at minimizing
the number of samples used in the learning phase, can assist the decision maker. A performing AL algorithm selects the most informative
data points based on a defined metric and passes them to a human labeler, who progressively adds the labeled data to the training set. The
motivation for adopting such a strategy is related to the significant effort required in terms of costs and man-hours to label the data.
The concept of active learning is based on the consideration that not alldata points are equally important for training a classifier model. Therefore, AL algorithms select only the most valuable data instances. The
key point is how this selection is made. This work focuses on possible sampling based on large-margin strategies of the Support Vector

EURO 2024 - Copenhagen
Machine (SVM) type. The distance to the separating hyperplane canbe considered a reliable metric for measuring the model confidence or
certainty on unlabeled data samples. In this study, the role of samples
close to the classifier boundary is investigated by comparing supervised and semi-supervised approaches. Preliminary numerical experiments are reported.

2 - Utilising decision-making methods and online tools
to accelerate sustainable renovation investments: Insights from a stakeholder engagement approach

Aikaterini Papapostolou, Ioanna Andreoulaki, Vasilis
Kotrogiannis, John Psarras
The building sector is the largest consumer of energy in Europe. Energy efficiency is fundamental for the cost-effective transition towards
a decarbonised energy system. The renovation rates of buildings revealthat there is an urgent need for action by leveraging private capital ininvestments. To this end, multiple actors of the value chain need to beengaged to enhance communication among them, overcome barriers
and increase chances for renovation projects to be financed. The involvement of key stakeholders is useful to get alternative perspectives,
which stimulates shifting the focus from project activities to intended
outcomes in the early stage, and to clear communication of the scope ofan energy efficiency project in terms of objectives, milestones and outputs. The specific paper introduces a stakeholder engagement process
analysing the impact that an online marketplace promoting energy efficiency investments in buildings would have to the energy and financing
community. This is done through a concrete methodological approachbased on an institutionalised engagement process of the relevant stakeholders. Key findings, which have emerged from a dedicated survey
and a training workshop with regards to the market needs and criticaluncertainties affecting the building renovation investments popularity,
support the co-creation of an energy efficiency marketplace that enables project aggregation and matchmaking with financing institutions
in a standardised manner.

3 - Performance comparison of machine learning models in detecting the best-performing methods for the
resource-constrained single-and multi-project scheduling problem

Weikang Guo, Mario Vanhoucke, José Coelho
Priority rules are the most used methods in many commercial softwaretools for scheduling projects under limited resources because of theirease of implementation, intuitive working, and fast speed. The branchand-bound (B&B) procedure is the most common way to deal withthe resource-constrained project scheduling problem to find the optimal solution in a finite number of steps. Meta-heuristics are the mosteffective methods to solve project scheduling. Although these methods
have been developed, no single method has been shown to be the best.
To fill this gap, we rely on machine learning classification and ranking
models to find the relation between the characteristics of the project
instances and the performance of solution methods.
This study not only aims to compare the performance of different machine learning models but also proves that machine learning can beused to solve the resource-constrained multi-project scheduling problem (RCMPSP). The contribution is threefold: First, we extend two
types of machine learning models to solve the multi-project schedulingproblem. Second, we extend both machine learning models to priority
rules, branch-and-bound procedures, and meta-heuristics, respectively.
Third, we compare the performance of two types of machine learningmodels on the three categories of solution methods. An extensive computational experiment is set up to compare the performance of machinelearning models with the existing methods from the literature.

4 - Assignment optimization with decision-dependent
completion times for trademark application evaluation

Shabnam Kilic, Ozlem Karsu, Taghi Khaniyev
In this work, we consider an assignment problem with decisiondependent completion times which is motivated by a real-life application in trademark evaluations. We formulate the problem of assigningevaluations to agents as a Mixed Integer Programming (MIP) model

TC-07

to help decision-makers better assign workers to evaluation tasks, considering their expertise and current workload. The aim is to minimize
the backlog level of the office, which is quantified by the total number of tardy jobs. We provide two alternative formulations and explore
their computational performance on a large number of test instances.
We integrate both linear and neural network prediction models by using machine learning algorithms and evaluate the improvements in the
total number of tardy jobs.

 TC-07
Tuesday, 12:30-14:00 - Room: 1019 (building: 202)

Behavioural OR meets Information systems
Stream: Behavioural OR
Invited session
Chair: Michael Leyer
1 - The intention to work in the metaverse concerning thequality of working life: A PLS-SEM approachThomas Paetow
The Quality of Working Life assesses the quality of the relationship between employees and their work environment, reflecting the well-beingof employees concerning their professional lives. The metaverse, as a
new form and location of work, is poised to alter the work environment significantly. Considering these developments, examining individuals’ intentions to work in the metaverse is crucial. This study aims
to analyze the intention to work in the metaverse and its effects on the
Quality of Working Life. A designed questionnaire will be used togather empirical data to test the hypotheses. The analysis will use Partial Least Squares Structural Equation Modeling (PLS-SEM) to modeland verify relationships between variables. The results are intended toprovide insights into employees’ intentions to work in the metaverse
against the context of Quality of Working Life. Anticipated influential factors include work-life balance and psychosocial well-being in
the metaverse. The PLS-SEM analysis should validate our theoreticalmodel and facilitate the quantification of relationships. This research
provides theoretical and practical implications, e.g., for designing digital work environments, and lays the foundation for future studies inthis area.

2 - Trust, Risk, and the Decision to use Smart Contracts: A
Dual-Study Analysis

Wieland Müller
This study examines the influence of trust and risk on individuals’
decision-making regarding the adoption of smart contracts. Two quantitative studies were conducted in the United States to conduct an indepth analysis of trust, risk, and the decision to use smart contracts.
The first study presents a trust model for smart contracts based on Hoffand Bashir’s (2014) dimensions of trust and Lemieux et al.’s (2019)
findings on trust towards distributed ledgers. The second study builds
on the findings of the specific trust dimensions, using Mayer et al.’s(1995) trust model and Stuck et al.’s (2021) role of risk in automation.
The results highlight the importance of situational social trust and trustin technology for individuals’ risk behavior and the decision to adopt
smart contracts. It also shows how perceived risk is moderated and how
risk-taking propensity positively influence usage intentions. The study
emphasizes the importance of trust dynamics and risk considerationsin the adoption of smart contracts and makes a valuable contribution
to the field with its original findings and comprehensive analysis. It
contributes to theory by providing a novel, multidimensional modelof trust and an integrative approach to examining the relationship between trust and risk behavior and its influence on decision making inan organizational context. In practice, this study provides insights forthe design and implementation of smart contracts in various industries.

243TC-09EURO 2024 - Copenhagen

3 - Behavioural Real Options Analysis of Projects using
Machine Learning

Marius Wittke
Investment projects typically involve flexibility of action, also known
as managerial flexibility. Managerial flexibility has a value that can bequantified using Real Options Analysis. In order to exercise managerial flexibility, information is required. Unlike financial options, there
is typically no objective market information available for real options.
Instead, the decision maker usually has noisy information. This meansthat the current value and relevant factors affecting the project have tobe estimated and therefore decisions have to be made on the basis ofimperfect information. These estimates are subjective and can be affected by cognitive biases. Cognitive biases are systematic errors injudgement and decision making. As a result, cognitive biases can leadto poor decisions, which can reduce the value of managerial flexibility. In this context, the question arises as to how wrong decisions due
to cognitive biases can be avoided. Since cognitive biases can be interpreted as a deviation from rational action, it would be helpful to beable to identify an optimal policy as a benchmark. However, this canbe challenging, especially with noisy information. Machine learningalgorithms could be useful in deriving an optimal policy. The basic
idea is that wrong decisions lead to opportunity costs that can be minimised by an algorithm based on a simulation model. Neural Networks
and Reinforcement Learning could be used to make optimal decisions.

4 - The Effect of Overconfidence on the Use of Recommender and Decision Support Systems

Ayşegül Engin, Rudolf Vetschera
To benefit from digitization, decision support systems and recommender systems find wide business-to-business applications with theincreasing digitization. To ensure that organizations benefit from implementing such systems, how employees interact with them as users
is crucial. Certainly, modifications to system elements, such as design
elements, can improve usability by mitigating the gap between what
the user can work with and what the system offers. However, cognitive biases such as the hindsight bias and the overconfidence bias
affect the usability of the system and cannot be mitigated through asystem element modification. The present study focuses on the effectof overconfidence as a consequence of hindsight bias on the use of personalized recommender systems and a more general decision support
system. This study aims to contribute to the literature on recommendation systems by showing that users benefit from recommender systems
depending on their cognitive biases differently. Using an experimental approach, the study looks into the use of recommender systems
and general decision support in repeated decisions with higher order
structures. Because of the higher-order structure, the designed decision situation for this study entails uncertainty. To disentangle decision
behavior from outcome, which is affected by the uncertainty posed bydecision situation, the study uses a reinforcement learning model todescribe decision-making behavior of users.

 TC-09
Tuesday, 12:30-14:00 - Room: 10 (building: 116)

Market-based analyses in long-term energy
system models
Stream: Energy Markets
Invited session
Chair: Jonas Finke
1 - Assessing the Energy and Socio-Economic Impacts ofRenewable Energy Expansion in Austria by 2030

Robert Gaugl, Sonja Wogrin
To evaluate the impact of increased shares of renewable energy sourcesin electricity (RES-E) on wholesale electricity prices and demand in

244

Austria by 2030, we linked a bottom-up electricity system model (ATLANTIS) with a top-down macroeconomic model (DYNK). This integrated approach enables a comprehensive analysis encompassing both
energy and socio-economic dimensions, factoring in the continental
European electricity grid constraints through ATLANTIS rather thanrelying on simplified representations. The study reveals the growing
importance of gas-fired power plants in balancing the intermittent output from RES-E, indicating that these plants will continue to influenceelectricity market prices. It highlights the potential socio-economic
benefits, including an 18% GDP growth by 2030 (compared to 2017)
under Austria’s renewable expansion act (EAG) scenario, driven by
population growth, export expansion, and increased total factor productivity, alongside a 20% rise in final electricity demand. A sensitivity analysis around CO2 pricing suggests higher electricity costs
and reduced GDP growth, hinting at a slight dip in electricity demandversus the EAG scenario. The findings underscore the necessity forenhanced renewable integration and storage solutions to fully decarbonize Austria’s electricity system, emphasizing the critical role offlexible gas-fired generation and the potential economic implicationsof transitioning to a more sustainable energy mix.

2 - Screening Curves in Net-Zero Energy Systems: Insights
and Limitations

Maximilian Walde, Julian Keutz, Oliver Ruhnau
Various dispatchable power generation technologies can complement
each other due to their diverse economic characteristics. Screening
curves have been used to determine optimal mixes of power plant capacity from load duration curves since the 1970s. Starting with only
fossil and nuclear generation technologies, recent applications have incorporated variable renewable energy sources by considering residualload duration curves. However, the applicability of screening curves
to net-zero energy systems with growing shares of renewables remains
untested. Here, we aim at filling this gap by creating net-zero screening curves and comparing the derived optimal capacity with the resultsof a linear model minimizing total power system costs. In a first step,
we exploit the screening curve approach to identify cost-optimal decarbonized dispatchable technologies as a function of renewable energy shares and across a wide range of cost assumptions. In a second
step, we process the linear model and investigate aspects that are neglected in the screening curve model, i.e. the deployment of short-term
storage and time-variant prices of synthetic fuels. Preliminary results
show consistent technology choice for both the screening curve andlinear model. However, the optimal capacity differs with rising sharesof renewables, particularly for the technologies with lowest and highest
variable cost. This raises concerns about the applicability of screening
curves in net-zero energy systems.

3 - Heterogenous investors in energy markets

Christoph Weber
With decarbonization, numerous and heterogenous new generation
units are installed. Decisions on such investments depend on multiple, partly not observable factors. Many long-term energy models areyet formulated as linear programs and are therefore prone to abruptchanges in primal variables ("penny-switching"). Discrete choice models deal with heterogeneity in individual decisions and avoid pennyswitching. Here an approach is presented to incorporate such decisionmodelling in a non-linear, convex optimization problem, along withtechnological and system constraints. The problem is reformulated using the entropy function of information theory and transformed to an
essentially linear program with a few exponential cone restrictions. A
first application investigates wind and PV investments across Germany
based on the novel approach and compares the outcomes to the resultsof standard LP formulations. It is shown that the regional spread ofinvestments increases when unobserved heterogeneity is taken into account.

4 - Linking modelling to generate alternatives and market
equilibria - On the economic interpretation of near-costoptimal solutions in energy system models

Jonas Finke, Christoph Weber, Valentin Bertsch

EURO 2024 - Copenhagen
The energy transition requires complex decisions, which can be supported by energy system models. Modelling to generate alternatives(MGA) is a method to generate diverse, near-optimal solutions and address the structural and parametric uncertainties of such models. Inenergy planning, MGA generates almost cost-minimal but societallymore feasible model outcomes. However, it neglects the economic
feasibility resulting from (potentially lacking) profitability of investments based on market revenues, which in contrast is guaranteed for
unconstrained cost-minimal solutions under certain conditions. Therefore, this paper aims at developing a market-economic interpretation ofnear-cost-optimal alternatives. Methodologically, we use results frommulti-objective optimisation and the first-order Karush-Kuhn-Tucker
conditions to show equivalence between MGA and cost minimisations
with additional cost terms. On the way, we augment MGA to generate only Pareto-optimal solutions, i.e. to avoid costs that do not contribute to higher diversity between alternatives. Economically, the introduced cost terms can be interpreted as capacity-based subsidies or
penalties that, as an intervention to an otherwise perfect market, leadto the same outcome as found with MGA. Such an intervention guarantees profitability and hence market-based feasibility. Practically, wedemonstrate and validate our theoretical findings for the case study ofa multi-national power system model.

 TC-10
Tuesday, 12:30-14:00 - Room: 11 (building: 116)

Kidney Exchange I

TC-11

that there always exists an optimal KEPRA solution in which every
cycle contains at most one RA, and use that property to develop an
effective variable reduction procedure for each model. We finally usethat property to extend the well-known position indexed chain-edge
formulation to the KEPRA and show that it clearly outperforms all theother ILP models.

3 - Strategyproofness of Credit Mechanisms for Kidney Exchange

Bart Smeulders
Increasing the scale of kidney exchanges leads to improved outcomesfor patients, as it increases the possibility of compatible donors being
present. One way to increase scale is by collaboration between different hospitals or countries. However, it has been shown that in single
period settings, no strategyproof mechanisms exist to organize suchcollaborations. This has led to an interest in credit mechanisms whichfunction over multiple time periods. Individual agents reveal pairs to
the collaboration, and receive credits in return. These influence whichtransplants are selected, where more credits lead the choice of solutions that are more beneficial to the agent. Based on the transplants
received, credits are subtracted. Such mechanisms can lead to fairer
outcomes, but also potentially escape the impossibility of strategyproof
mechanisms in single period settings. In this talk, we look in particular at credit mechanisms proposed by Klimentova et al. (2021) andBiro et al. (2020). These mechanisms divide credits equal to the number of transplants obtained in an optimal solution, and each transplants"costs" one credit. We discuss the strategyproofness of their proposedmechanisms, and more in general of all mechanisms sharing some important properties with these.

4 - Local stability in kidney exchange programs

Stream: OR in Health Services (ORAHS)
Invited session
Chair: Joel Joris Van de Klundert
Chair: Twan Dollevoet
1 - Novel immunological and optimization strategies to optimize Saudi Kidney Exchange

Michal Mankowski
Living donors are often incompatible with their intended recipients.
Kidney exchange matches one patient and his or her incompatible
donor with another pair in the same situation for an exchange. We studied novel immunological such as applet matching to optimize the Saudi
National Kidney Exchange Program (SNKEP) involving a number ofSaudi transplant centers and their pool of patients. Cross-hospital kidney exchanges allow an increase in the likelihood of finding the matching pairs, improve the qualities of the matches, and provide. We modeled the impact of new immunological matching strategies on overall
graft survival.

2 - Integer linear programming formulations for the kidneyexchange problem with reserve arcs

Marie Baratto, Yves Crama, João Pedro Pedroso, Ana Viana
Kidney exchange programs allow patients with end-stage renal disease
to receive a kidney transplant from a living donor. The maximizationof the number of transplantations within a pool of potential donors
and recipients is a combinatorial optimization problem that has generated abundant literature over the past 20 years. Several authors havepointed out the relevance of the classical literature on stable matchingsin this context. However, the optimization of stable kidney exchanges
has not been widely investigated. We extend recent work on stable exchanges by introducing and underlining the relevance of a new conceptof locally stable exchanges. We show that locally stable exchanges in
a compatibility digraph are exactly the so-called local kernels of an
associated blocking digraph (whereas the stable exchanges are the kernels of the blocking digraph). We prove that it is NP-hard to determine whether a graph has a nonempty local kernel. Next, we propose
several integer programming formulations for computing a locally stable exchange of maximum size. These formulations can be viewed asmodeling the maximum local kernel problem in the blocking digraph.
We conduct numerical experiments to assess the quality of our formulations and to compare the size of maximum locally stable exchanges
with the size of maximum stable exchanges. It turns out that nonempty
locally stable exchanges frequently exist in digraphs which do not haveany stable exchange.

Maxence Delorme, Wendy Liu, David Manlove
We are interested in the kidney exchange problem with reserve arcs
(KEPRA), an extension of the classical kidney exchange problem inwhich one is allowed to select arcs that do not appear in the compatibility graph (called reserve arcs, or RA). We study two optimization
problems: (i) what is the maximum number of transplants given an upper bound on the total number of RA that can be used, and (ii) whatis the minimum number of RA that is necessary for every recipient toreceive a kidney.
This problem is motivated by a recent breakthrough in the field of kidney transplant reported by the newspaper "The Guardian" stating that
"researchers have successfully altered the blood type of three donor
kidneys in a gamechanging discovery that could significantly improvethe chances of patients waiting for a transplant finding a match".
We show that, even though existing integer linear programming (ILP)
formulations can easily be extended to the KEPRA, solving these models with a state-of-the-art ILP solver requires a long computation time
due to the fact that the compatibility graph is complete. We then show TC-11
Tuesday, 12:30-14:00 - Room: 12 (building: 116)

Behavior in group decision-making
Stream: Behavioural OR
Invited session
Chair: Femke Bekius
1 - Behavioural consequences of supporting open strategizing with OR methods

Jonan Raaijmakers, Femke Bekius, Vincent De Gooyert,
Etienne Rouwette245

TC-12EURO 2024 - Copenhagen

Over the last two decades, strategizing developed from an exclusive
endeavour towards a more decentralized approach that aims to empower stakeholders in strategic decision-making. This new stream instrategizing is named ’open strategy’ and transforms the traditional exclusive strategic decision-making into a more inclusive and transparent
process. Although open strategy is beneficial, e.g., increased diversity
or quality of ideas, it also brings new challenges and dilemmas that asof now are unresolved by contemporary open strategy tools. We study
open strategy processes in the Dutch energy, road, and drinking watercontext. From a literature study, we found that operational research(OR) methods can contribute to open strategizing, by resolving someof its dilemmas. Therefore, we apply participatory OR methods to support inter-organizational stakeholders in making strategically aligned
decisions to tackle challenges that transcend organisational boundaries.
We present findings from an ongoing study that aims to understand the
behavioural consequences of OR-supported open strategy processes.
We approach this study by investigating model-supported interactions
on an individual level, how individuals add, remove, and change model
elements, and, on a group level, how these interactions shape the path
towards a group decision. Furthermore, we investigate the use of theoutcomes from the intervention in the broader multi-organisational
context, with evaluative interviews.

2 - Playing a card game in a workshop to generate decisionobjectives: pretesting the evaluation

Alice H. Aubert
Game-based approaches are increasingly used in participatory
decision-making. Yet, rigorous evaluation of their effectiveness is often lacking. Typical challenges include small sample sizes, no control treatment, and no control for the potentially confounding factors.
Sometimes, claims about potential benefits of the game are unalignedwith the evaluation. I will present a framework for promoting a morerobust evaluation of game-based approaches, based on an illustrativecase. The illustrative case focuses on evaluating the effectiveness ofplaying a card game in a workshop designed to generate decision objectives for sustainable wastewater management. Specifically designed
for non- experts, such as municipality council members, the card gametargets the diverging phase of generating objectives and incentivize
participants "collect" diverse criteria. Guided by the call for complementary data types in BOR and principles from the educational science, we concurrently developed a comprehensive evaluation process
and the card game-based workshop. Our pre-test of the evaluation process with 10 students highlighted the complementary nature of quantitative and qualitative data types. Yet, challenges such as sample sizeand confounding factors remained. We suggest collaborative efforts
among researchers to address these issues. The framework and insightsfrom our illustrative case contribute to developing robust approaches toevaluate participatory interventions.

3 - Evaluating the impact of supporting group decisionmaking with participatory OR methods

Femke Bekius
Achieving sustainable transformations, in areas such as climatechange, mobility, agriculture and food, health and ageing, has become
a imperative goal of societies worldwide. Participatory OR methods
are often used to support group decision-making in the context of sustainable transformations. It brings multiple stakeholders with different
incentives and aims together and potentially results in a shared vision,
perspective or decision. However, evidence about the impact of participatory OR methods on sustainability transformations is scarce. Evaluation of participatory OR methods happens in various ways and isgenerally performed in an ad-hoc manner. Moreover, evaluation takes
place at various levels including the behavior of the individual, dynamics of the group, consequences for the organization, or impact on
the larger system in which the transformation takes place. In this talk, I
present a number of concepts which can be used to evaluate the processof decision-making in reaching sustainable transformations. Hereby,
I distinguish between the different levels (individual, group, process,system) at which assessment can take place. In particular, I focus on
the question: How do these concepts assess the "transformative potential" of OR-supported decision-making processes? To illustrate this I
use case studies from different domains where participatory OR methods are used to support sustainable transformations.

246

4 - Learning to facilitate team discussions

Etienne Rouwette, L. Alberto Franco
Building an operational research model together with the team responsible for the issue at hand helps to elicit knowledge and create commitment to joint action. In these facilitated modelling interventions
the facilitator has a central role. How can an analyst perform the roleof facilitator? Furthermore, what is effective team facilitation? Understanding the rationale behind tasks helps the facilitator to translate
generic advice on what to do, to the specific situation encountered.
Our assumption is that the more the facilitator’s behaviour is in linewith these attitudes, the more effective they will be in supporting ateam decision process. We describe five facilitator attitudes (helping,neutrality, inquiring, relational engagement, self-reflexivity), as wellas the facilitator tasks aligned with each of these attitudes.

 TC-12
Tuesday, 12:30-14:00 - Room: 13 (building: 116)

DSS in Agriculture
Stream: OR in Agriculture and Forestry
Invited session
Chair: Aida Huerta-Barrientos
1 - Integrated Production, Storage and Transportation Optimization in the Compound Feed Industry

Jasper van Doorn
Production scheduling and vehicle routing are both well known andwidely studied problems within the field of operations research. However, in many practical settings a supply chain consists out of a sequence of different stages that all require some scheduling or routing.
Rather than just optimizing the planning within each stage separately, agood synchronization between the different stages is crucial to prevent
inefficiencies or infeasibilities. Especially when there is none or limited storage capacity between subsequent stages, bad synchronisation
might lead to delays or even deadlocks. An integrated solution method
is required to prevent this and obtain good synchronization over theentire supply chain. We propose a general framework for modellingsuch multi-stage problems and compare different integration strategies
The issue of multi-stage scheduling problems with limited intermediate storage especially arises for industries that are making a transition
from make-to-stock towards make-to-order. A good example is thecompound-feed industry, where storage is usually designed for large
quantities of universal products. Due to changing customer requirements, the production changes more and more towards small quantities of many different personalized products instead. Since intermediate storage is not suited for this, this increases the need for good synchronization between production and transportation. We also provide
results based on realistic data from this industry

2 - Vehicle routing problem to optimize pig deliveries to theabattoir

LluisM Pla, Yun Bao, Pol Llagostera Blasco
Nowadays, pigs are raised in large industrial-scale operations. The fattening farms operates under an all-in-all-out management policy. In
general, the fattening period depends on the starting weight, batch homogeneity, feeding system, growth curve, reward system and chain
management policies. Although pigs are fed with the same diet, not
all of them reach market weight at the same time. Hence, partial sales
produce better economic rewards. Once the old batch leaves, and before the new batch arrives, the facilities are cleaned and sanitized. The
viewpoint of an abattoir collecting fattened pig from many different
farms belonging to the same supply chain has not yet been considered.
Farms vertically integrated sell their entire production to a single abattoir in agreement with the supplier contract signed with the integrator
company in a long-term relationship. The routes to collect pigs andEURO 2024 - Copenhagen
the arrival to the abattoir need to be balanced over time and scheduled beforehand with uncertainty in pigs’ liveweight to avoid workload peaks and a fair operation for the PSC. The problem is a vehicle
routing problem with uncertain demand since the deliveries depend on
the inventory of pigs. Thus, the objective is to formulate a mixedinteger linear-programming (MILP) model to optimize daily routes oftrucks and transports of fattened pigs to the abattoir. A metaheuristic
algorithm is proposed to approximate the solution of the model and toefficiently plan transportation.

3 - A Simulation Study for Agricultural Production Optimization

Zehra Kamisli Ozturk, Gurkan Ozturk
The aggravation of agricultural land misuse, alongside climate change
and population growth, intensifies the pressure on agriculture to fulfill
food demand. To address this challenge, urgent action is needed to implement sustainable land and forest management practices, expand cultivation areas, and enforce effective agricultural policies. Agriculture
has historically been pivotal in sustaining livelihoods and economies,
necessitating optimization in production planning and productivity to
unlock its full potential.
There is limited research on agricultural production planning, particularly in Turkey. In this study, the focus is on conducting a 2-3 year
simulation study to maximize revenue from agricultural products in a
specific region. Using mixed integer programming, the study designs
crop patterns for selected parcels and plans production accordingly.
The GAMS software is utilized to solve the model.
In conclusion, addressing agricultural challenges through optimization
models and simulation studies is crucial for fostering sustainable andefficient agriculture. This study contributes to optimizing agricultural
production in Eskisehir, Turkey, underscoring the importance of customized solutions in agricultural planning and management.

4 - Using Expert Systems for simulation modelling of Hassavocado farming

TC-15

1 - The ’je ne sais quoi’ of algorithms that foster humancentric analytics design

Christina Phillips
There have been developments recently in interpretable and explainable AI as well as moves to understand ways to do human centred
analytics design. As technology and the use of algorithms in businessbecome more ubiquitous we need to understand what parameters in algorithm design can facilitate human centred approaches. Ways to do
this ethically and sustainably become ever more pressing as businessmoves into a world of ESG reporting and increasingly cybernetic systems.
This talk initially outlines the parameters (defined by prior research)needed by algorithms and AI/ML techniques that make them amenable
to integration with human practice. We then take a quick look at thephilosophical underpinnings needed by research into cross paradigm
work. This sets the stage for a tour of some currently available mathematical tools and techniques that can work with human practice andthe contexts they are likely to work under.

2 - Augmenting Leadership Development with Generative
Artificial Intelligence: Perspectives, Research Methods,
and Future Directions

Nuoya Tan, Leroy White
Since the release of ChatGPT in 2022, Generative Artificial Intelligence (AI) has been transforming people’s work lives. There is increasing research interest in leveraging Generative AI on human resource management, change management, innovation management,
and management education. However, integrating Generative AI andleadership, especially leadership development, needs more attention.
Previous work reviewed AI in learning and development and studied
gender bias of AI-generated content in leadership training. To further existing research, we want to explore the potential of augmenting
leadership development by leveraging Generative AI on leadership intervention and organizational context.

Aida Huerta-Barrientos, Aidee Luz, Alma Elia Vera-Morales
In recent decades, the avocado fruit has gained popularity so many
countries including United States, area among the biggest importer ofthis fruit, which implies a growing demand for this market. In Mexico
context, micro and small producers are the ones who sustain most of
the avocado exports in Michoacán. Decision making for the management of avocado production is a complex issue due to the large numberof variables associated with developing a feasible production function
and business plan. Although studies have been conducted by authors,
the use of Expert Systems to modelling and simulation the farmer decision making process on Hass avocado production is still insufficiently
explored. This paper addresses the need to design and implement anexpert system for simulation modelling of Hass avocado farming forsolving complex issues affecting the avocado production in Mexico
context. First, we present the literature review about the use of Expert
System tool for modelling and simulation the farmer decision making
process on Hass avocado production. Second we describe the formulation and implementation of an Expert System for simulation modellingof Hass avocado farming using Netlogo software. In particular, we discuss a case study in Mexico. Then, the main results of our analysis are
showed. Finally, we present our conclusions.

 TC-13
Tuesday, 12:30-14:00 - Room: 15 (building: 116)

Impact of AI on Soft OR - B
Stream: Soft OR and Problem Structuring Methods
Invited session
Chair: Nuoya Tan

 TC-15
Tuesday, 12:30-14:00 - Room: 18 (building: 116)

Staffing and workforce planning andscheduling
Stream: OR in Health Services (ORAHS)
Invited session
Chair: Paula Antón Maraña
1 - Optimizing adaptive medical staff scheduling using recourse decisions amidst fluctuating patient demandMarkus Schüller, Jan Schoenfelder, Jens Brunner
Scheduling of medical staff represents a time-consuming task, evenduring periods of relatively stable patient demand and personnel availability. Since rosters are built based on long-term patient loads, significant discrepancies between expected and actual patient emergences
may occur, especially in times of high volatility like the COVID-19
pandemic. To ensure adequate care in short-term, recourse decisions,such as extra shifts or transferring patients are necessary to cover highworkload peaks in terms of demand. Nevertheless, exact forecasts
about number of patients cannot be made for more than a few daysin advance. This makes it nearly impossible for schedulers to anticipate unexpected demand when creating the upcoming schedule. In
this work, we provide a Mixed-Integer Program (MIP) to (re-)allocate
medical staff and therefore ensure appropriate care of all patients. Our
MIP consists of a two-stage optimization model, which at first performs the initial schedule while on the second stage applying recourse
decisions to cover realized patient demand. The expected workload isdetermined on forecasts about patient emergence and the acuity level247

TC-16EURO 2024 - Copenhagenof patients by partners of the PROGNOSIS consortium. Our temporal
scope encompasses a time horizon of one week. As patient demand issubject to numerous uncertainties, we evaluate scenario-based rosters
by applying Sample-Average Approximation and provide generalized
insights about short-term adjustments in personnel scheduling.

2 - Flexible shift scheduling of healthcare workers using
column generation

David Ajit Kirpekar-Sauer, Jens O. Brunner
We consider a tactical shift scheduling problem, consisting of assigning health care workers in a hospital to shifts for handling a given number of tasks spread out over a fixed time horizon. We will implement a
branch-and-price algorithm with the master problem assigning schedules to workers and the subproblem generating columns (shift schedules) to add to the master problem. The master problem will minimize
the total cost of the chosen shift schedules and assigning outside workers to handle understaffing. The novelty in our approach is the use of
a network flow model in the shift subproblem. Instead of formulating
the subproblem as a MIP, we will make use of a novel network formulation. By traversing the network from the starting period to the end
period a path, corresponding to a valid shift schedule, will be achieved.
As such the subproblem can be solved, and a maximum reduced costschedule can be found, by solving the shortest path problem for thegraph. We will implement Dijkstra’s algorithm with a labelling andreadout step to find the shortest path through the network. In addition
we will evaluate the possibility of scheduling flexible and in-flexible
workers to achieve a greater flexibility in the generated schedules. The
fraction of flexible workers will be evaluated with varying degrees offlexibility to ensure a feasible application of the method. The method
will be applied and tested on a number of test instances generated fromreal-world data.

3 - Optimizing physician allocation in an imaging diagnostic company

Maristela Santos, Vladimir Yuri Tasso, Caio Tomazella,
Thiago Mariotti
Imaging diagnostic companies primarily rely on specialized medical
workforce to deliver their services. However, there has been a stagnation in the number of specialist physicians over the years, making theworkforce scarcer and more expensive. Another factor negatively impacting imaging diagnostic companies is health insurance providers’current financial status due to the sector’s costs. Given this scenario,
optimizing the allocation of medical resources in shifts is mandatory
to achieve optimal outcomes, maximizing revenue and reducing costs
associated with idleness. Idleness occurs when the demand is low for
one examination unit, and physicians are over-allocated. In contrast,
the demand for examinations in another unit can be high, but there
is a need for more physicians. An integer linear optimization model
was developed to allocate physicians in shifts and medical units, allowing physicians to be allocated in nearby units with available rooms.
Additionally, two objective functions were analyzed: minimizing thenumber of unmet examinations, maximizing the lowest coverage considering units and shifts. Instances from an imaging diagnostic company were used for computational tests. When comparing the results
obtained with data from the company, the model demonstrated a significant reduction in unmet demand and superior overall coverage, proving to be a practical approach to optimizing the allocation of medical
resources.

4 - Labour scheduling for healthcare workers using efficient strategies

Paula Antón Maraña, Joaquín Pacheco, Julio Cesar Puche,
Silvia Casado
Labour scheduling takes on special relevance in the healthcare field as
most professionals work alternate shifts, which makes it difficult to balance work and personal life. One of the common practices is the allocation of schedules according to workers’ preferences for each planning
period. This results in models where professionals are assigned shifts
in order to maximise their preferences while taking into account labour
constraints. Usually these models include such constraints in their formulation, which results in excessively complex models. Thus, they248

can only be applied in very small instances and require heuristic andmetaheuritic techniques to solve real instances such as Variable Neighbourhood Search, Simulated Annealing, Tabu Search, Iterated Searchand Genetic Algorithms, or even hybrid approaches, mate-heuristic or
hyperheuristic strategies. In this work we propose a strategy applied toa real case of an elderly people’s home, which consists of the following
steps: a) to generate all possible sets of shifts ("patterns") that can be
assigned to a professional that meet the labour constraints; b) to propose a model for assigning professionals to patterns. The labour constraints are satisfied, even if they do not appear explicitly, and the resulting model can be solved exactly on real instances of large size. Thisefficient strategy maximises the satisfaction of healthcare staff preferences, improving their productivity and the quality of patient care.

 TC-16
Tuesday, 12:30-14:00 - Room: 19 (building: 116)

Football analytics
Stream: OR in Sports
Invited session
Chair: Nobuyoshi Hirotsu
1 - A Neural Network Approach to Evaluate Expected Goals
in Football

Giuseppina Dello Ioio, Stefania Corsaro, Zelda Marino
The sports industry has a significant global reach, with a value of $480
billion in 2023, which is expected to increase to $629.81 billion by
2028. These data reveal various profit potentials, particularly for the
betting industry, which generated a total turnover of EUR 1.41 trillion
in 2022, of which EUR 730 billion was attributed to football alone.
Due to its predominant role in the global sporting landscape and its
significant economic impact, this work focuses on football, specifically on Expected Goals (xG). The accuracy of predicting xG plays acritical role in the financial and strategic decisions of football teams,
as well as for investors and bookmakers, as it allows them to assess ateam’s performance and identify value bets more accurately. This work
proposes an innovative Neural Network approach to predict Expected
Goals in the main Italian league, Serie A, with the aim to provide a significant advancement in the application of predictive methods to football, opening new perspectives for analyzing and optimizing sportingand financial performance. Implementing this methodology could improve on-field performance and optimize team investments. Additionally, it could provide important insights for investors and bookmakers,
enabling them to make more informed and profitable decisions in thecontext of professional football. This work is part of a larger research
project that aims to evaluate financial contracts indexed to the performance of professional athletes.

2 - Some novelty on the xG model for Football Analytics

Rodolfo Metulini, Mattia Cefis
Within the realm of football analytics, an objective is to enhance thepredictive capabilities and exploit the emerging tool known as the expected goal (xG) model for pratical needs. The xG model aims toclassify each shot, predict the final score of a game and evaluate strikers and goalkeepers performance. In previous works we proposed tomatch event data and composite indicators of player performance derived through Partial Least Squares - Structural Equation Model (PLSSEM). The aim of this work is twofold: leveraging a dataset comprising tracking data from the Italian "Serie A" related to 2022/2023 season, we introduce to the existing model some original features, such as
the Kos angle. Our results showcase intriguing findings in comparison
to a benchmark (Understat), particularly in certain metric indices. Furthermore, the significance of performance composite indicators from
PLS-SEM and specific tracking variables for the xG model is established. Afterwards, we exploit the performance of the model proposing
a combination of machine learning and game theoretical approaches toassess the marginal contribution of single players in determining theEURO 2024 - Copenhagen
expected goal and the final game score. In particular, to achieve thislatter goal, we adopt our xG as the cohesion function of a (generalized) Shapley value. By doing so, an instrument to jointly estimate
the probability to score the goal and evaluate the role of each player indetermining such probability is proposed.

3 - Expected Shots Concept for Football

Jirka Poropudas, Ville-Pekka Inkilä
In football analytics, various metrics for teams’ offensive efficiency
have been presented. As the objective of the game is to score goals, thenumber of goals scored and the number of shot attempts are the mostobvious efficiency metrics. The commonly used expected goals (xG)
metric estimates the quality of shot attempts by assigning them a probability of scoring based on shot location and other factors. However,
all these metrics share a common weakness as they focus solely on
possessions where shots are attempted, neglecting the broader context
of play. This presentation proposes an approach where this limitation
is avoided using event data consisting of the locations and times of various ball touches such as passes, dribbles, and shots. The data are thenused to divide the match into possessions, i.e., continuous sequences oftouches by one team, and to estimate the probability that a given touchon the ball is a shot attempt. These probabilities are used to calculate
the shot probability for each possession, i.e., the possession’s expectedshots value (xS). This metric allows one to measure the quality of possessions and teams’ ability to create scoring chances without restricting
the analysis to shot attempts that actually took place. The proposed approach is compatible with various extensions such as the skill level andtendencies of individual players as well as a more detailed descriptionof the game state as provided by player tracking technologies.

4 - Modelling tactical changes in association football usinga Markov game

Nobuyoshi Hirotsu, Yuki Masui, Yu Shimasaki, Masafumi
Yoshimura
We model tactical changes in association football as a Markov game.
The pitch is discretised into nine zones and the states of the Markov
game are defined according to the zone in which the ball is locatedin play, the team in possession, and the score. We first model tactical changes in a Markov decision process framework, wherein one
team maximises their probability of winning. Then, we model tacticalchanges in a two-person zero-sum Markov game framework, whereinboth teams maximise their probability of winning. Fundamental to ourmodelling is the notion that tactical changes impact upon transition
rates. We verify the models using data from matches in a season of
the Japan Professional Football League. We define a change in transition rates that can be realised by changes in tactics, and illustrate
an example of optimal tactical changes when both teams can vary theirtactics. The models we develop in the paper can support managers whoare considering important decisions about substitutions and changes toformation, for example, when a match is in-play.

TC-18

Since the late 1960’s, Uruguay has developed an important system of
housing cooperatives, which are an affordable option with many advantages over traditional options such as buying or renting a house orapartment.
A housing cooperative in Uruguay usually involves between 20 and
80 members. After the buildings are completed, a assignment is performed, giving to each member (as long as he/she continues to participate in the cooperative) the right to live in the assigned unit. The
assignment is most usually performed by a random draw; which does
not take into account individual preferences of the members over thedifferent units.
In this presentation, we discuss the use of optimization methods toperform assignments which take into account the preferences of thecooperative members. Working with some Uruguayan cooperatives,
we found that the total satisfaction, the minimum individual satisfactionwere important criteria. Based on these insights, we developed asystem employing a a two-stage Mixed Integer Linear Programming(MILP) optimization model taking into account the minimum individual satisfaction and the total satisfaction. This system has already been
applied in more than 30 cooperatives, involving more than 1000 habitational units, obtaining large improvements in comparison to randomdraws. Further work is being performed to include other criteria suchas minimizing the difference between individual satisfactions.

2 - Sustainable and optimal transport corridors from China(Asia) to Europe reflecting geopolitical tensions in theregionNahid Jafari
Building resilient and sustainable supply chains of goods and energy
between Europe and Asia is a key challenge because of long distance,
political risks, economic sanctions, environmental impacts, weather
conditions, geoeconomics, and etc. In spite of existing routes passing Russia (the Norther Corridor) or Iran (the Silk Road), the European Union (EU) and Turkiye are more interested in initiating a newroute in the middle of the two, bypassing Russia and Iran, is so-called
the Middle corridor. In the Middle corridor, Azerbaijan, located at
the intersection of the North-South and East-West transport routes, isrecognized as an important transport and logistics hub. Meanwhile,the establishment of the Zangezur corridor by ending the conflict ofArmenia-Azerbaijan may enhance diversifying energy supplies to Europe and beneficial for the global trading. The aim of this study isconducting a route optimization (intelligent, agility, cost-effective) followed by a data-driven analysis of influential factors and explore thevarious instruments of geoeconomic competition, then design a sustainable supply chains network.

3 - The Kerkenes eco-center in Anatolia: an interdisciplinary OR project

Gerhard-Wilhelm Weber, Francoise Summers

 TC-18
Tuesday, 12:30-14:00 - Room: 42 (building: 116)Developing green and sustainablecommunities (EWG-ORD Workshop 1)

At previous EURO conferences, we presented, considered and contributed to the Kerkenes Eco-Village Project on Turkish rural countryside. At EURO 2024 2024 we will inform about new aspects and related developments and view this project also from the overall and national aspects of urbanization, energy supply and education in Turkey.

Chair: Nina Kajiji
Chair: Gordon Dash
Chair: Gerhard-Wilhelm Weber

Kerkenes Eco-Center, established by G. and F. Summers and supported
by S.T Elias-Özkan, aims to promote sustainability through environmental studies. It pursues the following objectives: - To advocate theuse of renewable sources of energy; - To act as a stimulus and a catalyst for environment-friendly building with appropriate materials andenergy efficient designs; - To act as a dynamic experimental base fortesting designs, materials and activities suitable for viable and sustainable village life. - To encourage village development and income generating activities that might halt and even reverse migration from rural
areas to the cities.

1 - Preference-based unit assignment for Uruguayan housing cooperatives

We will relate the project with overall needs in Turkey, be it in the ruralcountryside or in the big cities, with regard to the demand for natural
gas and for better primary school education in all social classes.

Stream: OR for Development and Developing Countries
Invited session

Hector Cancela249TC-19EURO 2024 - Copenhagen

4 - Transformative approaches to urban sustainability: acase study of Bozcaada, Canakkale, Turkey, and circular economy strategies in developing countries

Eylul Sadanoglu
This research pioneers a transformative path towards urban sustainability in developing countries, employing a Theory of Change (ToC)
framework. Focused on Bozcaada, Canakkale, the study integrates circular economy principles, emphasizing a dynamic approach to sustainability assessment. Through the lens of the ToC, this research explores
the anticipated impact of strategic interventions on the urban sustainability landscape.The methodology involves a multifaceted approach,incorporating sustainability assessment, network analysis, and the development of a novel circular economy framework. Findings highlight
responsible resource use, waste reduction, and innovation management within the sustainability context, all evaluated through the lensof the ToC. Hence, utilises network analysis to uncover key relationships, collaborations, and influential stakeholders, presenting opportunities for strategic partnerships that align with the envisioned impactof the ToC. The comprehensive framework developed herein serves asa guide for practical interventions and strategic resource management
in Bozcaada, fostering circular economy principles. Stakeholders anddecision-makers can leverage this research to assess and enhance urban
sustainability, aligning with the Theory of Change impact, in developing countries.

 TC-19
Tuesday, 12:30-14:00 - Room: 44 (building: 116)

OR in Heating Systems
Stream: OR in Energy
Invited session
Chair: Sandra Venghaus
1 - Planning of Power-Water-Heat Systems Using an
Integrated, Extremes-Aware, Clustering-Optimization
Framework

Jefferson Riera, Ricardo Pinto de Lima, Omar Knio
The rising shift towards renewable energy sources to decarbonize thepower sector highlights the need to create advanced optimization toolsthat capture variability and complementarity of renewable resources
and incorporate infrequent but critical extreme weather conditions
planning models. A clustering-optimization framework is introduced
to address extreme weather conditions when planning for a multisector (including power, water, and heat) system, aiming to mitigate theeffects of detailed variability. This framework was applied to assess thefinancial and resilience outcomes of adding concentrated solar power(CSP) and geothermal energy into a system already utilizing wind, solar photovoltaics, and batteries. When planning for fully renewable energy systems, extreme weather conditions necessitated higher capacity
investments to reduce reliance on external power sources. Whereas
technologies like CSP and geothermal have been shown to lower costs
across the system, the inclusion of CSP could lead to decreased reliability during extreme weather events. This underscores the importanceof designing energy systems that not only embrace renewable technologies but also robustly withstand and adapt to the challenges posedby extreme weather conditions, ensuring both sustainability and reliability in the face of climate change.

2 - Integrated flexible heating and cooling under climate
uncertainty in a European energy systems modelDavid Huckebrink, Leonie Sara Plaga
To inform the urgently needed energy transition, models are used tofind the optimal design and operation strategy of future energy systems. Since the focus of studies in this field has largely been on the250

power sector, synergies between coupled sectors have not been sufficiently researched. The integration of heating is important because of
three reasons: First, heating is mostly fossil fuelled and offers a large
decarbonisation potential. Second, decarbonisation achieved throughelectrification is a mix of both a burden through increased demand anda benefit through greater flexibility to the power sector. Third, heating and cooling demands depend on the weather, which is subject touncertainty in the future. Therefore, we propose an integrated modelof the European power system and heating and cooling demands thatcan be met flexibly while taking different climate projections into account. The methodology can be split into three steps. The first step
is data clustering and merging of the European electricity transmission
and generation capacities, heating and cooling demands and respective
technologies and weather timeseries. Second, heat demands are used
to derive aggregated buildings’ multi-node representations. Finally,
the model will be optimised using Backbone and decarbonisation isachieved through GHG emissions limits. The results of this work allow for the electricity and heating sector to be analysed in an integrated
fashion and will be publicly available.

3 - Reduction of Fossil Fuel Usage for Hydrogen Production in District Heating Networks by Implementing PV
EnergyKarina Medwenitsch, Lukas Gnam, Markus Schindler
In order to achieve the goals of the European Green Deal set by theEuropean Commission, a reduction of the usage of fossil fuels is necessary. This also applies to the sector of district heating, where gas isstill widely used, specifically in Austria. The district heating network
in Neusiedl am See in the Austrian Burgenland region comprises a gas
boiler, a biomass plant as well as four high-performance heat pumps,
which are fueled by energy generated in the local wind park, as heatproduction components. Although the share of gas used for the heatproduction is already low with only a few per cent, depending on thecurrent electricity prices, the gas boiler is still necessary for the operation of the district heating network. Additionally, an electrolyzer was
integrated into the system in 2023 in order to use the surplus energy
from the wind park for the production of hydrogen. However, when
operating the district heating network most economically, the hydrogen production leads to a significant increase in the gas share, withthe percentages ranging between 17 and 21 %. The objective of thiswork is to reduce the usage of gas in the network while also operating
the electrolyzer by implementing additional energy from a hypothetical PV plant nearby. The results show that the gas share is cruciallyreduced to a range of 4 to 10 %.

4 - Reducing Greenhouse Gas Emissions through Strategic Subsidy Allocation for Building Heat Technologies:
A bi-level Optimization Approach

Sebastian Lubjuhn, Sandra Venghaus
The global population is battling with the urgent need to transition
towards sustainable and efficient energy systems. With 15% of thetotal emissions in Germany, the heating sector is one of the largest
greenhouse gas-emitting sectors. Subsidies are key policy instruments
governments employ to incentivize the adoption of such technologies.
However little research has been done on optimizing these subsidies
to achieve the best emission reduction on a long-term horizon. This
challenge is compounded by the colliding interests of the German government, which aims to minimize total emissions through subsidy allocation, and property owners, who seek to minimize costs through investments in heating technologies. While certain heating technologies
may offer short-term sustainability benefits, others hold greater potential for long-term emission savings. To address this issue, we proposea bi-level optimization model utilizing a modified grid search at theupper level and linear programming at the lower level to optimize subsidy distribution decisions. Preliminary findings indicate that subsidies
targeting both heating pumps and biomass heating show promise for
maximizing emission reduction. Nonetheless, it is observed that additional subsidies for biomass heating entail substantial costs relative
to their marginal impact. This underscores the importance of carefulsubsidy allocation strategies to achieve optimal outcomes in emission
reduction efforts.

EURO 2024 - Copenhagen TC-20
Tuesday, 12:30-14:00 - Room: 45 (building: 116)

Integrative Approaches in Health andDisease: From Molecular Structures to
Clinical Outcomes
Stream: Computational Biology, Bioinformatics andMedicine
Invited session
Chair: Agnieszka Rybarczyk
1 - Quality assessment of 3D RNA structures using graphneural networks

TC-21

challenging. Here, computational testing, like in silico analysis, becomes crucial for identifying potential therapeutic targets effectively.
In our study, we used a Petri net model to identify key network nodes
whose blockade could impede atherosclerosis in hyperglycemia. Ourfindings suggest that inhibiting isoforms of protein kinase C in diabetic patients could help slow the progression of atherosclerosis. Additionally, we observed that inhibiting aldose reductase could decelerate
atherosclerosis advancement and reduce PKC expression in DM. Targeting oxidative stress by inhibiting the AGE-RAGE axis emerges as
a promising therapeutic strategy for managing hyperglycemia-induced
atherosclerosis. While blocking NADPH oxidase, the primary enzyme
responsible for generating reactive oxygen species (ROS) in blood vessels, only marginally impeded atherosclerosis development, it effectively halted the increased production of mitochondrial ROS associatedwith mitochondrial dysfunction. These findings lay the groundworkfor more in-depth studies.

Maciej Antczak, Bartosz Adamczyk, Marta Szachniuk
General quality assessment of 3D RNA structures predicted in silico
is crucial to identify native or near-native 3D RNA folds. Nowadays,
there are many, freely available computational methods for 3D RNA
structure prediction, however without the reference - experimentally
determined 3D RNA structure - it is difficult to reliably rank them interms of practical usefulness. Here, we propose to apply graph neural networks to precisely infer the interatomic relationships commonly
observed in 3D RNA structures and therefore reliably predict the quality of RNA structures. We started with the preparation of a highquality training dataset based on experimentally determined structures
retrieved from a non-redundant 3D RNA structures repository. Due tothe scarcity of the available structures, we had to extend the dataset
by 3D RNA models predicted using all state-of-the-art methods for 3D
RNA structure prediction. We confirmed that graph NNs can harness
a wide range of conformational space. We also solved the problem ofvariable-length 3D RNA structure representation crucial for the proper
application of machine learning techniques by constructing local 3D
motifs and applying an ensemble of local quality scores to assess thewhole 3D RNA structure quality. A novel general quality assessment
approach that does not need the reference 3D structure to reliably rank
RNA 3D structures, we believe will be a breakthrough in the field of
RNA structural bioinformatics.

2 - A Large Neighborhood Search Matheuristic for the Balanced Minimum Evolution Problem

Henri Dehaybe, Daniele Catanzaro, Allan Sapucaia
The Balanced Minimum Evolution Problem (BMEP) is a hard combinatorial optimization network design problem arising in the field ofphylogeny estimation. In this framework, the phylogeny is represented
by an unrooted binary tree where the leaves are the individuals (thetaxa), and the internal nodes represent speciation events. Given a matrix of estimated distances between each pair of taxon, the aim is toreconstruct the most accurate phylogeny according to the minimum
evolution principle. We propose a Large Neighorhood Search (LNS)
procedure to improve locally optimal solutions initially obtained by
the state-of-the-art method (FastME). This LNS procedure works byiteratively destroying a subset of the incumbent unrooted binary tree,
and then reconstructs it with a matheuristic-based repair operator. Dueto its local search nature, this LNS procedure provides a scalable wayof obtaining more accurate phylogenies for large datasets by directly
improving the solutions found by the state-of-the-art method.

3 - Identifying Key Therapeutic Targets for Treating
Hyperglycemia-Induced Atherosclerosis Using a Petri
Net-Based Model

Agnieszka Rybarczyk, Dorota Formanowicz, Piotr
Formanowicz
Persistent high glucose levels, a hallmark of diabetes mellitus (DM),lead to widespread cellular damage. Atherosclerosis, occurring alongside glucose metabolism disturbances, significantly complicates thecondition and accelerates in DM, making it critical to slow its progression. Identifying intervention targets within the complex networkof molecules and processes, without disrupting essential functions, is TC-21
Tuesday, 12:30-14:00 - Room: 49 (building: 116)

Efficiency, equity and fairness inhumanitarian operations
Stream: OR in Humanitarian Operations (HOpe)
Invited session
Chair: Zehranaz Donmez
1 - Vulnerability Based Prioritization in Disaster Response
Efforts

Halenur Sahin, Feyza Sahinyazan, Irfan Mahmutogullari
Humanitarian logistics literature commonly uses Equity, Efficiency
and Effectiveness (3E) objectives. The equity objective aims to minimize differences in individual treatments by assuming equal impact
for everyone in a disaster. Efficiency measures aim to reduce the costsof aid programs, while effectiveness focuses on the quality of humanitarian aid, measured by factors such as response time, reduced risk, or
human suffering. 3E objectives presume beneficiary homogeneity, yet
it is crucial to recognize that disasters disproportionately impact socioeconomically disadvantaged individuals. Vulnerable groups—lowincome, elderly, disabled, or marginalized—face distinct challenges
and heightened risks in disasters. Any measure assuming homogeneous demand neglects the intersectionality of vulnerable communities. This study presents an alternative approach to prioritizing vulnerable populations in disaster response, striving for a more inclusiveand compassionate disaster management strategy. To assess the performance of this approach against traditional 3E measures, we analyze the emergency assembly point allocation problem using Istanbul’s
neighborhood-level vulnerability and population characteristics in our
computational study. Our results show that the vulnerability-based prioritization approach can single-handedly achieve more inclusive results for vulnerable populations without significantly deteriorating 3E
objectives and non-vulnerable population outcomes.

2 - Efficient and equitable transportation of casualties by
Online Optimization in mass casualty incidents

Fatemeh Zare Bidaki, Davood Shiri, Sibel Salman
After a mass casualty incident, numerous victims endure severe injuries with different chances of survival, requiring timely rescue operations and prioritization in transportation. We develop a Mixed-Integer
Linear Programming (MILP) model for a multi-period routing andscheduling problem for a heterogeneous rescue fleet with the objective of maximizing the expected number of survivors. Recognizing
the uncertainties inherent in such situations, such as fluctuations in thevictim population and their conditions over time, we propose an online optimization algorithm. This allows for real-time adjustments to
the rescue operations based on incoming data. Our framework favors
equity in healthcare service delivery through various fairness metrics.

251

TC-22EURO 2024 - Copenhagen

Validation of proposed approaches is achieved through computational
tests and offline comparisons with real-world scenarios.

3 - Relief Supply Chain Planning: Fair allocation of in-kind
donations in post-disaster phase

Zehranaz Donmez, Bahar Yetis Kara, Ozlem Karsu, Francisco
Saldanha-da-Gama
In this paper, we study the relief logistics problem that emerges in
the response phase after a disaster. We consider a case in which a
set of demand nodes must be supplied with some urgent commodity
over a planning horizon. The supply for this relief item is irregularand time-dependent and often not enough for the demand since, forinstance, it may depend on voluntary donations. As observed in previous disasters, donations lead to a high level of uncertainty in the
distribution network as the amount depends on the choice of volunteers. We assume that supply information reveals at the beginning ofeach period, upon processing the collected in-kind donations. This uncertainty poses a major challenge when it comes to fairly distributing
the available amount. The problem of interest can be cast as a multiperiod location-inventory problem and formulated using multi-stage
stochastic programming. Unlike most of the existing literature, location decisions take place in each stage of the planning horizon as theinformation gradually reveals. The mathematical model is tested using instances built from real data available in the literature, using a setof scenario trees with different supply levels. Computational tests are
made on these benchmark instances with various sizes of network andscenario trees. The results show that the new modeling framework can
be extremely useful when it comes to finding fair solutions for supplying a relief item with uncertain availability.

4 - Equitable location-allocation in multistage humanitarian settings applied to Brazilian disasters

Jie Bao, Douglas Alem, Aakil Caunhye
This research develops a novel three-stage stochastic locationallocation model for a preparedness-response disaster relief problem
within a scenario-based stochastic programming setting. In the mainstream academic literature, preparedness-response activities are commonly represented within a two-stage stochastic programming framework assuming there is a single moment when all uncertain parameters
are revealed. Grounded on practical disaster operations, our approachconsiders two moments within the disaster timeline in which different
related uncertain parameters are unveiled: victims’ needs and donation. The main rationale is to represent practical disaster aftermath situations in which right after the disaster strikes a given region, victims’
needs become known and donation campaigns start collecting relief
items. The precise and available donation amount is finally revealed
when the donation campaign ends and it is very much dependent on
the victims’ needs. In this three-stage model, the initial stage involves
facility location decisions about establishing distribution centres andrelief prepositioning, while the next two stages involve last-mile distribution decisions about prepositioned and donated supplies. And we
fully account for deprivation costs arising from unmet victims’ needs
in our modelling. Our results are based on real disasters in Rio deJaneiro, Brazil.

 TC-22
Tuesday, 12:30-14:00 - Room: 81 (building: 116)

Stochastic models in energy systems
planning and operations
Stream: Energy Management
Invited session
Chair: Yifu Ding
1 - Will hydrogen remain green? Supply chain optimization
for green hydrogen transportation considering production uncertainty

252

Yifu Ding, Thomas MagnantiProduced from renewables, green hydrogen can be used as a clean fuelin the transportation and power generation sectors. According to theInternational Energy Agency, the annual production of low-emissionhydrogen could reach 38 million tons in 2030. However, hydrogentransportation across a large system produces significant costs andemissions, undermining its decarbonization purpose. This talk will
present a supply chain optimization model that addresses variability
and uncertainty in green hydrogen and ammonia production. Specifically, the optimization model will consider the production and storageof green hydrogen and ammonia and their transportation to different
destinations via tube trucks, trucks, rail, or ships. The uncertaintyaware formulation will model the variability and uncertainty of renewables during hydrogen and ammonia production.

2 - Design a robust renewable electricity system for Europe
considering extreme weather conditions

Xiaoming Kan
Following sustained cost reductions and rapid diffusion into the power
generation mix, renewable energy technologies such as wind powerand solar PV may serve as the cornerstone for the future electricity system. An electricity system dominated by wind and solar is susceptible
to uncertainty in weather conditions, particularly long periods of low
wind and sunlight, known as "Dunkelflaute." Previous studies investigating how to design a renewable electricity system typically employ adeterministic approach, which assumes perfect foresight about future
weather conditions. In this study, we develop an adaptive robust optimization model to analyze a future renewable electricity system for Europe while considering the uncertainties in weather conditions. We analyze 40 years of historical weather data using the Finkelstein-Schafer
statistical method to identify extreme weather conditions for wind andsolar power. Our results show that considering "Dunkelflaute" events
for the entire Europe may increase the installed generation capacity by
16% for wind power and 36% for solar PV, compared to the base caseresults calculated with typical weather data. Additionally, the transmission grid is more than doubled, while the hydrogen storage capacity iseight times greater than the base case. Consequently, the electricity
system cost may increase by more than 20%. These results highlightthe importance of accounting for weather-related uncertainties in designing future electricity systems.

3 - Uncertain Unit Commitment For The Operation Of Hybrid Power Plants Under Uncertainty

Nouha Dkhili, Vincent Leclere, Marco Marchese, Anna
Robert, Vitor Luiz Pinto de Pina Ferreira
The increased deployment of renewable energies introduces intermittency in power generation that was reliably dispatched in the past. The
conventional Unit Commitment problem in energy system management consists in optimizing the operation schedule of thermal units
to meet the load at minimum cost and CO2 emissions. However, Uncertain Unit Commitment (UUC) models extend this framework by
integrating uncertainties stemming from generation, load fluctuations,pricing, etc. These extensions require decomposition-based computational methods to solve these problems efficiently and accurately. TotalEnergies collaborates with Ecole des Ponts (CERMICS lab) to focus
on an isolated industrial energy-intensive site, initially with a known
load. The energy demand must be met by internal resources: thermal
generators, PV panels, and batteries. The complexity of this problem stems from the interplay of factors like storage and ramping constraints, interstage coupling, binary variables, and the intermittency ofsolar energy. Ensuring continuous load fulfillment remains imperative
for safety and reliability. Yet, an overly cautious approach can resultin high production costs and CO2 emissions. We study the risk-neutral
and risk-averse approaches via the stochastic and robust settings, respectively. By separating the information structures behind binary andcontinuous variables, we can decouple them in the optimization problem and use cut-generation algorithms to solve both problems.

EURO 2024 - Copenhagen
4 - Operational optimization of energy flows in energybased industrial symbiosis

Daniela Guericke, Alessio Trivella, Devrim Murat Yazan
Industrial symbiosis (IS) is a concept of circular economy, where one
company utilizes waste, residue, or by-products of another company
to replace otherwise needed resources. Companies can also engage
in energy-based IS. Here they either utilize waste energy streams directly in energy cascades, or they use waste products for fuel replacement and bioenergy production. Examples are the utilization of waste
heat from industrial processes in district heating systems or the usage of industrial or urban waste for energy production. The conceptof energy-based IS is often applied in so called hubs for circularity(H4C), i.e., industrial parks where co-located companies collaborate toclose energy and resource loops. In this work, we present a modelling
approach to optimize the operational planning of the energy flows ina H4C utilizing energy-based IS. Since the waste (energy) streams are
by-products of other main production processes, they are not plannable
and, therefore, uncertain and volatile. To address this uncertainty, weapply stochastic programming. We present results for a case study
using industrial and public data and input from literature. In our experiments, we investigate the benefits of energy-based IS for the energy
cost and energy mix of the hub.

 TC-23
Tuesday, 12:30-14:00 - Room: 82 (building: 116)

Optimization for the Circular Economy
Stream: Circular Economy, Remanufacturing and Recycling
Invited session
Chair: Gerardo Perez Valdes
1 - Mixed sustainable supply chain design: a case of therecovery of end-of-life tires and batteries

Andrea Espinoza, Jeremías Vásquez, Pavlo Santander
The increasing amount of waste generated, combined with a lack ofproper management, has resulted in the development of policies to encourage the recycling of end-of-life (EOL) products. Extended producer responsibility (EPR) has been one of the most common regulations, holding producers accountable for the management of the products they manufacture. End-of-life tires (ELTs) and batteries are twoof the most common EOL products covered by EPR laws. Different
supply chain designs have been proposed in the literature to address
the treatment of both products, but they address them separately. The
study presents a multi-objective optimization model for the development of an integrated supply chain considering three different objectivefunctions to assess economic, environmental, and social perspectives.
The proposed model is applied to a case study in Chile’s Metropolitan
Region. The e-constraint methodology yields a Pareto front used to
calculate a compromise solution. The supply chain exhibits a unique
distribution compared to previous mono-objective iterations, featuringa mix of different technologies installed at both the centers and reprocessing plants. The system processes 33.17% of the total available
EOL products. This total fulfills the minimum requirements established by the EPR law in Chile of 3% for batteries and 25% for ELTs.

2 - Formalising e-waste Supply Chain for Sustainable Development

Sheeba Pathak, Wolfgang Garn, Christopher Turner
The e-waste management market is a major business opportunity witha projection to grow to USD 137.60 bn by 2029 being a key motivator to undertake research in this area. A formal, methodical, organised sector is essential to ensure the best possible collection ofe-waste as well as its proper disposal or recycling. While the UN
SDG Goal No. 12 aims for responsible production and consumption,

TC-23

businesses are still looking into ways to guarantee that e-waste collection, recycling, or disposal is not an expensive endeavour. This implies that a variety of activities throughout the supply chain would
be involved, not only in the collection, disposal, and recycling of
e-waste, but also in the establishment of a system for its management. This paper builds on the novel Present-Unique-RepeatableEnvironmentally-friendly (PURE) framework underlying the introduction of makerspaces and Centre for Refurbishment and Recycling ofElectrical and Electronic Waste (CReW) & addresses quantitatively
how the network flow should be to achieve sustainable development
goals of minimising cost and emissions. Also generates employment
opportunities while tackling health concerns for those residing within
the proximity of landfills. Therefore it ensures a leaner process that canbe adopted horizontally across industries while also coining electronic
reverse logistics into e-waste’s forward logistics as a formal supply
chain design

3 - A Scenario-Based Stochastics Programming Framework to Enhance Viability: Aligning R-imperatives,
Product Development, and Supply Chain Design

Sobhan Mostafayi Darmian, Juan Pablo Morande, Fabio
Sgarbossa, Mohammad Fattahi
Recent research highlights that supply chains (SC) need to be resilient
against disruptions and adaptable to long-term shifts in demand andsupply environment. Moreover, there is a growing recognition of theneed to transition towards a circular economy to protect the environment while promoting economic growth. This has led to a call fordeveloping SCs that are sustainable, resilient, and adaptable, called viable SCs. Motivated by a manufacturing company in Norway as thecase study, this study proposes a scenario-based stochastic programming framework to measure resilience and adaptability through qualitative scenarios. It also suggests integrating the core principles of thecircular economy, which are called R-imperatives, into product development processes and supply chain design decisions to make SCs viable. Our results reveal the crucial role of implementing R-imperatives
into the integrated PD and SC design for establishing the foundationsof design for viability.

4 - Optimisation and Macroeconomic models in Circular
Economy analyses

Gerardo Perez Valdes, Amira Rachah, Kirsten S. Wiebe
In this presentation, we showcase a range of projects that integrate optimization and macroeconomic modeling to explore the circular economy in Norway.
The first project focuses on utilizing input-output modeling to evaluate
the impacts of building renovation policies in Oslo, with a specific emphasis on energy efficiency measures. The findings of this work havebeen published in peer-reviewed journals and serve as a foundation
for future analyses that combine detailed technical information withmacroeconomic analysis.
The second project, Synagri, employs supply-use modeling to assess
the effects of biotechnology advancements in the agricultural sector
and their benefits for farmers and other stakeholders near the beginning of the value chain. In particular, the project considers and models
the circularity and reuse of byproducts of the animal rearing and foodprocessing.
Lastly, the European project Treasource employs value-chain optimization in the context of recycling plastics, batteries, and bio-based
streams in Northern Europe. The developed model focuses on waste
generated from industrial processes and consumption, exploring its recycling potential.
All of the aforementioned projects prioritize data-driven circular economic analysis and cover diverse topics, each presenting their own
unique challenges and requirements. Some of them have concluded,
while others are ongoing work.

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TC-24EURO 2024 - Copenhagen TC-24
Tuesday, 12:30-14:00 - Room: 83 (building: 116)

Agri-Food Supply Chains
Stream: Sustainable Supply Chains
Invited session

food waste. In this paper, We examine the capability of agri-food supply chains to incorporate ugly veg to reduce food waste and financiallyempowering the growers who are often the weakest link. We model theinteractions between the players in the chain and compare and contrast
the common spot contracts with the so called “whole-crop contracts".
We examine the players’ incentives to up-cycling the ugly veg, and assess the potential to reducing waste and protecting growers against low
wholesale prices.

Chair: Deniz Besik
1 - Price Subsidies with or without Physical Procurement:
Impact on Quality, Profits, and Welfare

Aysajan Eziz, Nagesh Gavirneni, Omkar D. Palsule-Desai
Newly introduced price subsidy programs without physical procurement have resulted in an increased (vis-à-vis the subsidy programswith physical procurement) preponderance of deliberate quality degradation for certain crops by Indian farmers. The physical procurementof the crop from farmers provides an alternate sales channel to farmers
enabling the government to subdue the competition between farmers
in the open market. The farmers’ deliberate quality degradation may
moderate the benefits of the altered competitive structure. The viability of such programs has been questioned, and policymakers are looking for guidance. Using a multi-stage incomplete information-based(Bayesian) game-theoretic model, we comprehensively characterize
the farmers’ strategic production and selling decisions. We demonstrate that the government price support with multiple sales channels
created by physically procuring the crop improves the producer surplus
when the farmers’ landholdings are highly disparate and the qualitybased competition is lower. A higher minimum support price (MSP),a higher landholding for a farmer producing a high-quality crop, anda lower high-quality crop price premium in the open market improvethe consumer surplus, the social surplus, and the average quality of thecrops supplied by farmers when the government does not procure thecrop physically v/s when it does. We provide guidance to the government exercising caution in selecting MSP and farmer subsidy program.

2 - Strategic Decisions and Sustainability in Competitive
Agricultural Supply Chains: A Triple Bottom Line Perspective

Pritha Dutta, Deniz Besik, Sara Saberi, Rodrigo Mercado
Fernandez
This paper presents a mathematical modeling framework of a competitive multiperiod agricultural supply chain network under the triple bottom line sustainability, comprising financial, environmental, and social
aspects. In our competitive supply chain modeling framework, agrifood firms strategically determine the flows of agricultural products todemand markets, the scale of farmland used for sustainable and conventional farming practices, and the allocation of labor for both sustainable and conventional farming practices. Profits determine the financial sustainability component, while environmental sustainability isassessed based on the total greenhouse gas emissions generated and theamount of water consumed. Social sustainability is measured througha social benefit function that considers the labor employed for bothsustainable and conventional farming practices, as well as agri-foodfirms’ overall economic contribution through agricultural productionin the regions. The competition between agri-food firms is studied
through game theory, where the governing Generalized Nash equilibrium conditions correspond to a variational inequality problem. We
use the Euler method as the algorithm to analyze a numerical study
focusing on avocado supply chains. We draw attention to the potential
impacts of climate change and deforestation in our numerical study.
Given the trade-offs, our findings highlight the importance of achieving the triple bottom line.

3 - Battling Waste and Empowering Growers with Ugly Veg:
The Role of Contracts

Behzad Hezarkhani, Guven Demirel, Yann Bouchery
The production process of fruit and vegetable often generates producethat does not conform to the aesthetic standards established by the retail sector. The so called “ugly veg” is a major contributors to global

254

 TC-25
Tuesday, 12:30-14:00 - Room: 011 (building: 208)

Graph and network optimization
Stream: Combinatorial Optimization
Invited session
Chair: Enrico Malaguti
1 - A survey on ILP formulations for the kidney exchange
problemMathijs Barkel, Rachael Colley, Maxence Delorme, David
Manlove, William Pettersson
Kidney exchange programs facilitate life-saving transplants by allowing patients with willing but incompatible donors to exchange theirdonors either through cycles, or through chains initiated by nondirected donors. In each matching run, one must identify a maximum
weight packing of cardinality-constrained vertex-disjoint cycles andchains on a compatibility graph. This computational problem, known
as the Kidney Exchange Problem (KEP), is both theoretically and empirically hard.
We conduct a survey of existing integer linear programming formulations and associated model-related enhancements for the KEP, extending upon prior work by including the most recent advances. Our key
contribution lies in addressing a gap in the literature: whereas models
for only cycles have been extensively studied, models for both cycles
and chains have received less attention. We demonstrate that all cycle
models can be adapted to model chains, thereby enabling a comprehensive approach to modelling cycles and chains by merging any cycle model with any chain model. Furthermore, we explore alternative
strategies, such as modelling cycles and chains concurrently using ashared set of variables.
Additionally, we conduct extensive computational experiments on generated instances to evaluate and compare the performance of the different model combinations for varying instance parameters, providing
valuable insights for practitioners and researchers alike in effectively
addressing the KEP.

2 - Uncertainty-affected graph problems solution throughBulk Optimization

Paolo Paronuzzi, Christoph Buchheim, Enrico Malaguti,
Michele Monaci
This work explores graph optimization problems, focusing on bulk optimization setting where potential link failures in different scenarios introduce uncertainty. Bulk optimization, a recently introduced problem
class in the literature, recognizes that in many practical cases, uncertainty affects only a small portion of the system. This contrasts with robust optimization, often considered overly conservative. Accordingly,
the number of scenarios and the number of edges per scenario, although not necessarily low, are finite and limited. Given a graph and acost for each edge, our goal is to activate a subset of edges of minimum
cost, ensuring that the resultant graph presents some desired structure(i.e., a perfect matching or a spanning tree) in each scenario. Our study
develops a Benders’ decomposition approach where the master problem encapsulates variables determining which links to activate, while
the subproblems verify the feasibility of the master solution in eachscenario. The transformation of the subproblems into an efficiently

EURO 2024 - Copenhagensolvable flow problems enhances computational tractability. Although
the bulk optimization paradigm has been studied for what concerns its
theoretical properties, and some approximation algorithms have been
proposed, we are not aware of any computational approach. The proposed approach demonstrates promising results and applicability, offering a robust and scalable solution algorithm for graph problems in
the presence of uncertainty.

3 - Lower and upper bounds for resources allocation inlightpath communication networks

Enrico Malaguti, Francesco Cavaliere, Federico Michelotto,Michele Monaci, Daniele Vigo
We consider the problem of allocating wavelengths of an optical network in order to serve a set of connection requests. In order to provide
a robust allocation, communication must be ensured even in case of
failure of some link, which is obtained by defining, for each connection
request, two disjoint paths in the network. Each connection uses its
working path by default, and is rerouted to its unique recovery path incase the working path is unavailable. As uniquely assigning both paths
to a single connection may be highly inefficient, we adopt a shared protection policy, in which the same wavelength and path can be shared
among different connections, provided that no collision arises. We discuss alternative relaxations for the problem based on Mixed-Integer
Linear Programming formulations, and introduce a metaheuristic algorithm based on the Ruin-and-Recreate paradigm. Computational experiments on realistic data show that our algorithms are able to produce
feasible solutions with tight optimality gap.

 TC-26
Tuesday, 12:30-14:00 - Room: 012 (building: 208)

Sustainability in Distribution andTransportation
Stream: Combinatorial Optimization
Invited session
Chair: Francisco Saldanha-da-Gama
Chair: Sina Rastani
1 - Enhancing Efficiency and Sustainability through Reconfigurable Delivery Systems

Fatima Ezzahra Achamrah, Sabine Limbourg
Funded by the Grantham Centre for Sustainable Futures, this research
explores the development of a dynamic system for urban last-mile delivery, aimed at enhancing efficiency, resilience, and sustainability inurban logistics. Focusing on integrating real-time traffic data and predictive analytics, the study extends a previous work on a consolidationbased multi-modal delivery model, grounded in Life Cycle Assessment
methodology.
Central to this research is the incorporation of real-time traffic datafrom platforms like Waze. This data is crucial for dynamically reconfiguring delivery routes in response to changing traffic conditions,
thereby maintaining operational efficiency and resilience. Alongside,
predictive analytics, utilising machine learning algorithms and demand
forecasting models, plays a significant role. Focused on Sheffield area,
the study also leverages data from platforms like UK Open Data for informed and automated territory reconfiguration processes. We further
use Global Mapper software and clustering algorithms for dynamic adjustment of delivery territories.
The implementation strategy included a comprehensive simulation
phase using AnyLogic. This phase was instrumental in modelling urban traffic and delivery scenarios, allowing for an effective comparisonof the dynamic system against traditional static models. Key performance indicators such as operational costs, delivery times, and environmental impacts were analysed, providing valuable insights.

TC-27

2 - A Matheuristic for the Optimal Placement of EV Charging Stations in an Urban Environment

Bryan Coulier, Hatice Çalık, Thijs Becker, Greet Vanden
BergheIn response to global environmental concerns and policy initiatives,
the automotive industry is currently undergoing a significant transformation towards sustainable transportation. As EVs become a major
component of the transition to a greener, more electrified future, governments are investing heavily in charging infrastructure. The rapid
rise of EV adoption poses significant challenges to conventional power
grids. Intelligent solutions are necessary in order to ensure reliable andefficient charging infrastructure.
Current studies concerning EV charging infrastructure often overlook
the underlying power distribution network and the accessibility ofcharging stations for EV users. The majority of these studies are
small in scale, employing metaheuristics without validating their results using exact solutions. Moreover, researchers have predominantly
focused on fast chargers, while slow chargers play an equally crucialrole in urban settings.
In an effort to address these significant gaps, our research focuses on
the placement of slow charging stations in urban areas while taking
into account (i) the distribution network, (ii) customer demand, and(iii) accessibility. We propose a novel Mixed-Integer Linear Programming (MILP) model to determine the optimal placement, quantity, andtype of charging stations. To enhance scalability and computational
speed, we also introduce a multi-stage matheuristic for solving largescale instances by decomposing the grid network.

3 - Improving Route Planning of Electric Vehicles by Considering Road Gradients and Regenerative Braking

Sina Rastani, Merve Keskin, Tugce Yuksel, Bülent Çatay
With the increasing greenhouse gas emissions, the use of electric vehicles (EVs) in logistics has increased. In contrast to conventional vehicles, EVs use batteries to store energy, and they often stop at stations to recharge. The energy consumed per unit distance traveled depends on several factors including vehicle load, speed, and road gradient. Traversing an arc with a positive gradient requires more energy
compared to an arc on a flat network. This is further amplified when
the EV carries a heavy load while moving uphill. Conversely, if thedriver presses the brake pedal to maintain a constant speed while moving downhill, it can regain energy through regenerative braking technology. The impact of road gradients and regenerative braking may
be significant for companies that carry heavy loads in hilly regions.
Therefore, an approach that incorporates both cargo weight and road
network terrain may improve the route planning of EVs. With thisaim, this study addresses an extension of the Electric Vehicle Routing Problem with Time Windows considering a comprehensive energy
consumption function that incorporates the cargo load, road gradient,and regenerative braking. We formulate the problem as a mixed integer
linear program, solve small-size instances with Gurobi, and develop a
Large Neighborhood Search to tackle the large-size instances. Numerical results show that considering the road gradient along with the cargoweight can significantly influence the routing decisions.

 TC-27
Tuesday, 12:30-14:00 - Room: 047 (building: 208)

Mathematical Optimization for Trustworthy
Machine Learning
Stream: Mathematical Optimization for XAI
Invited session
Chair: Connor Lawless255

TC-28EURO 2024 - Copenhagen

1 - Slowly varying regression under sparsity

Vassilis Digalakis
I will present the framework of slowly varying regression under sparsity, allowing sparse regression models to exhibit slow and sparse
variations, through an application in energy consumption prediction.
First, I will formulate the problem of parameter estimation as a mixedinteger optimization problem; then, I will demonstrate that it canbe precisely reformulated as a binary convex optimization problem
through a novel relaxation technique, convexifying the non-convex objective function while matching the original objective on all feasible binary points. I will develop a highly optimized implementation of a cutting plant-type algorithm, a fast regularization-based heuristic methodthat guarantees a feasible solution, and a practical hyperparamrter tuning procedure relying on binary search that, under certain assumptions,is guaranteed to recover the true model parameters.

2 - Improving pricing strategies on a branch-and-price algorithm for the hyper-rectangular clustering problem
with axis-parallel clusters

Clustering is an unsupervised learning task that aims to partition data
into a set of clusters. In many applications, these clusters correspond toreal-world constructs (e.g. electoral districts) whose benefit can only
be attained by groups when they reach a minimum level of representation (e.g. 50% to elect their desired candidate). This paper considers
the problem of performing k-means clustering while ensuring groups(e.g. demographic groups) have that minimum level of representation
in a specified number of clusters. We show that the popular k-means
algorithm, Lloyd’s algorithm, can result in unfair outcomes where certain groups lack sufficient representation past the minimum threshold in a proportional number of clusters. We formulate the problem
through a mixed-integer optimization framework and present a variant of Lloyd’s algorithm, called MiniReL, that directly incorporates
the fairness constraints. We show that incorporating the fairness criteria leads to a NP-Hard sub-problem within Lloyd’s algorithm, but weprovide computational approaches that make the problem tractable foreven large datasets. Numerical results show that the approach is able tocreate fairer clusters with practically no increase in the k-means clustering cost across standard benchmark datasets.

Diego Delle Donne, Javier Marenco, Eduardo Moreno
Given a set of points in the space of an arbitrary dimension, the hyperrectangular clustering problem with axis-parallel clusters (HRC-AP)
consists in grouping the points into clusters, each cluster being determined by a hyper-rectangle in the corresponding space. HRC-AP has
been proposed as a model for explainable clustering, as it is straightforward to describe the clusters by the bounds defining each hyperrectangle: if each coordinate corresponds to a relevant parameter in
the application generating the points, then a cluster is specified by its
bounds on each parameter. A variant of HRC-AP considering the existence of outliers (called HRC-APO), allows a certain number of points
to be discarded and not included in any cluster. In previous works,
we explored integer programming techniques for HRC-APO proposing
several formulations and algorithms. We showed that extended formulations (solved via branch-and-price) outperformed classical compact
formulations. Nevertheless, these techniques fall short as soon as thenumber of points to be clustered is increased to medium-sized reallife instances. In this work we propose an innovative strategy to solve
the pricing problem by modeling it as a flow problem. This allows
us to develop a polynomial-time routine for the pricing, qualitatively
improving the performance of previous approaches. We compare theefficiency of the new method against the existing ones by providing an
exhaustive computational experimentation.

3 - An Optimization-Based Order-and-Cut Approach for
Fair Clustering of Data Sets

Hrayer Aprahamian
Machine learning algorithms have been increasingly integrated into applications that significantly affect human lives. This has spurred interest in designing algorithms that train machine learning models to minimize the training error while imposing a certain level of fairness. In
this paper, we consider the problem of fair clustering of datasets. In
particular, given a set of items, each associated with a vector of nonsensitive attribute values and a categorical sensitive attribute, our goal
is to find a clustering of the items that minimizes the loss (i.e., the clustering objective) function while imposing fairness measured by Rényi
correlation. We propose an efficient and scalable in-processing algorithm, driven by findings from the field of combinatorial optimization,that heuristically solves the underlying optimization problem and allows for regulating the trade-off between clustering quality and fairness. The approach does not restrict the analysis to a specific loss
function but instead considers a more general form that satisfies certain
properties. This broadens the scope of the algorithm’s applicability.
We demonstrate the effectiveness of the algorithm for the specific caseof k-means clustering, as it is one of the most extensively studied andwidely adopted clustering schemes. Our numerical experiments revealthat the proposed algorithm outperforms existing methods by providing a more effective mechanism to regulate the trade-off between loss
and fairness.

4 - Fair Minimum Representation Clustering

Connor Lawless256

 TC-28
Tuesday, 12:30-14:00 - Room: 065 (building: 208)

Advancements of OR-analytics in statistics,
machine learning and data science 6
Stream: Advancements of OR-analytics in statistics, machine learning and data science
Invited session
Chair: Adil Bagirov
Chair: Gerhard-Wilhelm Weber
Chair: Sona Taheri
Chair: İsmail Özcan
1 - Geometry and deep learning-based methods comparison for segmentation of trees in aerial laser scan point
cloudsPovilas Treigys, Kasparas Karlauskas
Laser scanning technology has facilitated advanced analysis of complex outdoor environments by capturing 3D point clouds. Identifying
points within a point cloud corresponding to individual objects of aparticular class, semantic instance segmentation is of great relevance innumerous fields. In the context of forest point clouds, in particular, semantic instance segmentation proves valuable for forestry applications
such as tree inventorization and infrastructure contexts such as transmission line corridor surveys, where the positioning of trees is used
for safety considerations. While recent developments in deep learning algorithms for point cloud processing have yielded solutions forsemantic instance segmentation in terrestrial laser scans of forests, applying such methods to lower spatial resolution aerial laser scans stillneeds to be explored. Compared to their terrestrial counterparts, aerial
laser scans present an attractive prospect for large-scale forest analysis.
This paper introduces a benchmark dataset for forest semantic instancesegmentation created from publicly available aerial laser scans. The
dataset is an evaluation platform for standard geometry-based classical tree instance segmentation algorithms. Additionally, it is used toevaluate a deep learning model for tree instance segmentation. A comparison between the different methods is made in the context of aerial
laser scans of forests.

2 - The shortest path problem with path constraints

Nan Xu, Zhang Miaomiao, Yicong Qin, Hao Zhang
Motivated by the practical aircraft routing problem in civil flight planning, we consider a variant of the shortest path problem with inclusionary and exclusionary constraints, specifying whether node Y must or
must not be visited after node X is visited. The problem is defined on a
directed graph G, with a source node S and a sink node T. We introduce
an algorithm that generates an augmented graph G’ from the original

EURO 2024 - Copenhagengraph G, ensuring compliance with the inclusionary and exclusionary
constraints. This transformation reduces the constrained shortest path
problem in G to a classic shortest path problem in the augmented graphG’. For each inclusionary constraint, we modify the original graph toensure that all paths from X to T pass through Y. For each exclusionary constraint, we modify the original graph so that all paths from X
to Y are redirected to a duplicate node Y’, which has no outgoing arcs,
ensuring that no path extended from X to T through Y/Y’. It need to
be proved that: (1) There are no paths violating the constraints in G’;(2) For each valid path in G, there exists a corresponding path in G’;and (3) For each path in G’, there is a corresponding path in G. We
demonstrate the algorithm’s validity through proofs by contradiction.
Numerical experiments demonstrate the efficiency of our algorithm.
Furthermore, the algorithm has been successfully integrated into ourflight planning system, indicating its practical utility.

3 - A framework for modelling view change in Byzantine
fault-tolerance network

Yifei Xie, Btissam Er-Rahmadi, Xiao Chen, Tiejun Ma, Jane
HillstonIn the rapidly evolving field of machine learning, the increasing complexity of models necessitates larger datasets and greater computational resources. This demand for scalability and enhanced computational capabilities has driven the study of distributed optimization to
the forefront. Byzantine fault-tolerance (BFT) consensus is a fundamental building block of distributed systems. BFT is the property ofa system that can resist the class of failures which can continue operating even if some of the nodes fail or act maliciously, thanks to thecapacity to prevent the no more than one third adversaries from gaining
a consensus and the mechanism of view changes for replacing faulty
nodes. Our research introduces a novel mathematical programming
framework aimed at optimizing the performance of BFT algorithms.
Our approach is twofold: firstly, we optimize node network distribution strategies to maximize throughput; secondly, we investigate mechanisms for selecting backup nodes to replace the leader during view
change phases when failure occur. To validate our framework, we conducted experiments on a real-world testbed. The results demonstratesignificant improvements in BFT algorithm performance, showcasing
our method’s potential in optimizing throughput and ensuring robust
fault tolerance during view changes. This study contributes to the development of more resilient and efficient distributed systems, supporting the growing demands of machine learning applications.

4 - Civil aviation route-planning method based on improved restricted searching area algorithm

Zhang Miaomiao, Nan Xu

In order to address the large calculation scale and slow route-planningspeed problems caused by the large global aviation network and complicated restrictions, this paper proposed an improved algorithm forroute-planning within hybrid restricted searching area. Firstly, a machine learning algorithm was used to optimize the major axis parameter of the ellipse in the ellipse restricted searching area algorithm byanalyzing the historical flight plan data. Then, based on the rectangle
restricted searching area algorithm and the ellipse restricted searching area algorithm, the proposed algorithm considered restrictions on
the search direction to further reduce the search scale and improve thesearch efficiency. Experiments show that compared with ellipse restricted searching area algorithm, the hybrid restricted searching areaalgorithm reduces the search size by about 50% and the total execution time by 34% on average, which verifies the effectiveness of themethod.

TC-29

 TC-29
Tuesday, 12:30-14:00 - Room: 157 (building: 208)

Exact Algorithms and Formulations for
Network Optimization Problems
Stream: Combinatorial Optimization
Invited session
Chair: Ivana Ljubic
Chair: Martina Cerulli
1 - Anonymizing networks via mathematical programming

Carmine Sorgente, Raffaele Cerulli, Ivana Ljubic
Social network data sets are often published and used for analysis purposes in many application domains. This may raise privacy issues,as simply hiding the identity of the users does not prevent an adversary from tracing back to the real-world entities associated withcertain nodes of the network having sufficiently distinguishable features from others. To protect individuals’ privacy, networks are commonly anonymized before publication. In this context, the k-degree
anonymization problem, originally introduced by Liu and Terzi in
2008, aims to identify the smallest set of modifications of the edge
set of a network needed to make it k-degree anonymous, meaning thateach user of the anonymized network will have the same number ofconnections as at least other k-1 users. In such a definition, different
k values reflect different privacy requirements w.r.t. the vertex degree
feature. This work addresses different settings of the problem allowingedge insertions and/or deletions. We compare different families of integer linear programming formulations of the problem on benchmark
social network instances and additionally derive some properties andvalid inequalities that we exploit in the solution process.

2 - Mixed-integer linear programming formulations and column generation algorithms for the Minimum Normalized
Cuts problem on networks

Diego Ponce, Justo Puerto, Francisco Temprano Garcia
The normalized cut function in networks was introduced by Shi andMalik in 2000 to solve some issues concerning the interpretability ofthe minimum cut problem, which is a classical problem in graph theory whose aim is to provide the bipartition that minimizes the numberof edges between nodes from different subsets, applied to partitioning
and districting problems. The minimum k-way normalized cut problem (MkWNCP) tries to minimize the external edge density of eachsubset of a k-partition, also considering the number of internal edges.
We develop several reformulations using mathematical programming
tools to exactly obtain the minimum k-way normalized cut of a graph.
This is a challenging task since the normalized cut function is nonlinear because it is defined by the ratio of two linear functions of integer variables. We also propose a branch-and-price procedure for the
MkWNCP which scales better than the compact formulations. Extensive computational experiments assess the usefulness of these methods
to solve the k-way normalized cut problem on large graphs and random graphs. In addition, all methods have been analyzed and studied
in order to try to improve them as much as possible.

3 - Estimating Optimal Split Delivery Vehicle Routing Problem Solution Values

Shuhan Kou, Bruce Golden, Luca Bertazzi
Motivated by Basel and Willemain’s (2001) work on estimating optimal tour lengths in the Traveling Salesman Problem (TSP), we identify
an intrinsic relationship between the distribution of the feasible solution space and the optimal solution value in combinatorial optimization
problems. Our goal is to use regression modeling to estimate optimal
solution values. We begin with a model that uses standard deviation
as a predictor and we explain why this makes sense. We study TSP
instance sets, both Euclidean and non-Euclidean. Next, we add themean and the minimum in order to obtain very high quality results. We
extend our experiments to include more than 10,000 vehicle routing
problem (VRP) instances. Again, we obtain excellent results. Finally,257

TC-30EURO 2024 - Copenhagen

we focus on the more difficult Split Delivery Vehicle Routing Problem(SDVRP) by combining the mean and standard deviation predictors
with two topological features. In testing on 95 diverse benchmark instances from the literature, our regression model successfully estimatesthe optimal SDVRP solution value to within about 3%, on average.

4 - Exact approaches for the colorful components problems

Martina Cerulli, Claudia Archetti, Carmine Sorgente
Given a vertex-colored graph G, any connected component of G is said
to be colorful if all its vertices have different colors. We address threeproblems related to these particular components of a graph, motivatedby comparative genomics: the Colorful Components (CC), the Maximum Edges in transitive Closure (MEC), and the Minimum Colorful Components (MCC) problems. In social networks, where nodes
represent individuals and edges represent connections, all three problems can be used to determine the most influential connections linking
distinct (colorful) and cohesive (connected) communities. By solving
the CC problem, the risk of echo chambers is mitigated. Addressing the MEC problem means promoting the propagation of information through the community. Instead, the MCC problem helps preventthe isolation of users within small disconnected groups. When instead
considering cyberspace networks of devices with various types of connections (e.g., permissions, data flow), identifying a subset of edges toremove while ensuring that the network remains in connected colorfulcomponents helps optimize the network’s resilience to cyber threats.
We formulate the three problems as integer nonlinear programs, linearize them and propose families of valid inequalities. Furthermore,
we provide bounds on the maximum number of colorful components
at optimality and exploit them to reduce the size of the models. Benchmark instances are tested to evaluate the performances of the proposedmodels.

 TC-30
Tuesday, 12:30-14:00 - Room: 064 (building: 208)

Modeling tools
Stream: Software for Optimization
Invited session
Chair: Paul Kerr-Delworth
1 - Integrating Machine Learning with GAMSPy

Hamdi Burak UsulGAMSPy is a powerful mathematical optimization package which
integrates Python’s flexibility with GAMS’s modeling performance.
This combination opens doors to previously challenging applications,notably in bridging the worlds of machine learning (ML) and mathematical modeling. While GAMS excels in indexed algebra, ML predominantly relies on matrix operations. To enable applications in ML,
our work introduces essential ML operations such as matrix multiplications, transpositions, and norms into GAMSPy. In this talk, we showcase the use of these additions by generating adversarial images foran optical character recognition network using GAMSPy. We highlight GAMSPy’s versatility and its potential to be used in ML researchand development. We delve into future prospects, show how GAMSPy’s approach differs from existing alternatives and discuss innovative methods where mathematical modeling intersects with machinelearning.

2 - Introducing the new Object-Oriented Xpress Solver API

Adam Ferreira Da Costa
A fast, scalable, and easy-to-use API is an essential component whenworking with commercial high-end optimization solvers. FICO Xpress
has been offering Solver APIs for more than two decades for an evergrowing set of programming languages (C, C++, Java, C#, Python,
R).

258

The new FICO Xpress Solver API for Java and C# is designed as an
object-based layer ensuring a memory-efficient and reliable experienceto the user. With the new API, the use of Solver features such as callbacks becomes easier, and it gives access to the full set of problem
types available with Xpress Solver.
Among the key features of the new Xpress Solver API are the ability
to use modern programming concepts such as Collections, Streams,
Lambdas, and operator overloading to build expressions and constraints.
The new Solver API offers seamless integration with the Solver andaccess to cutting-edge features, and we observed model building times
speedup by a factor of 8 on models with several millions of variables
and constraints compared to previous APIs.
In this talk, we focus on design and architecture challenges when implementing such a new API layer. Furthermore, we will cover new features available in the augmented API, as well as examples and guidelines how to implement optimization models in object-oriented languages.

3 - Hexaly Studio: a no-code platform for mathematical optimization

Thierry Benoist, Frédéric Gardi
Hexaly Optimizer is a new kind of global optimization solver. Its
modeling interface is nonlinear and set-oriented. It also supports usercoded functions, thus enabling black-box optimization and, more particularly, simulation optimization. In a sense, Hexaly APIs unify modeling concepts from mixed-linear programming, nonlinear programming, and constraint programming. Regarding performance benchmarks, Hexaly distinguishes itself against the leading solvers in themarket, like Gurobi, IBM Cplex, and Google OR Tools, by delivering
fast and scalable solutions to problems in the spaces of Supply Chain
and Workforce Management like Routing, Scheduling, Packing, Clustering, and Location. For example, on notoriously hard problems likethe Pickup and Delivery Problem with Time Windows or Flexible Job
Shop Scheduling with Setup Times, Hexaly delivers solutions with agap to the best solutions known in the literature smaller than 1% ina few minutes of running times on a basic computer. In addition to
the Optimizer, we provide an innovative development platform called
Hexaly Studio to model and solve rich Vehicle Routing and Job Shop
Scheduling problems in a no-code fashion. The user can define its
problem and data, run the Optimizer, visualize the solutions and key
metrics through dashboards, and deploy the resulting app in the cloud- without coding. This web-based platform is particularly interestingfor educational purposes; it is free for faculty and students.

4 - Recent Modeling and Solver Improvements in MATLABPaul Kerr-Delworth
The problem-based workflow is now an established interface making
it much easier to model and solve optimization problems in MATLAB.
Two new improvements to the problem-based workflow will be presented. First, techniques for enhanced analysis of problems will beoutlined, allowing for significant performance gains on certain problems. Next, a new graphical interface for problem-based modeling in
MATLAB will be introduced. Usage of the problem-based workflow
for industrial applications will be illustrated with an example. Finally,
key improvements to the linear, mixed integer and least squares MATLAB optimization solvers will also be shown.

 TC-31
Tuesday, 12:30-14:00 - Room: 046 (building: 208)

Analytics and the link with stochastic
dynamics III
Stream: Analytics
Invited session
Chair: Gerhard-Wilhelm Weber
Chair: Gordon DashEURO 2024 - Copenhagen
1 - A distributionally robust optimisation approach to faircredit scoring

Pablo Casas, Huan Yu, Christophe Mues
Optimization under uncertainty has been a topic of large debate in Operations research (OR) and Machine Learning (ML) communities for along time. Recently, many authors have turned to distributional robust
optimization (DRO) in search of a solution; however, fewer studies
have focused on enhancing fairness under uncertainty. This is of greatinterest in those applications of ML that can have a direct damaging
impact on the population, credit scoring (CS) being one of the mostpotentially damaging fields according to regulatory bodies.
The paper presented explores the effects of using a DRO-based logistic regression (LR), one of the most commonly used classifiers in CS,
across multiple CS datasets. This study will show how robustness has
a greater impact on fairness than the fairness constraint, and that the
impact on performance is negligible and, in some datasets, positive.
We will also provide an empirical analysis of the effect of the different hyperparameters that are unique to DRO-based LR. Furthermore,
we will argue that the level of robustness narrows the dispersion of theprobability of default distribution and that the parameters in charge ofthe ground metric have an unnoticeable impact. On a side note, wesuggest traditional fairness metrics used in credit scoring are not best
suited for the task.

2 - Advancing NLP classification with disentangled embeddings: a post-processing approach to separate style
from content

Nico Hambauer, Michelle Fribance, Patrick Zschech, Mathias
Kraus
In the field of natural language processing (NLP), identifying cheap
talk remains a challenge. Boiling down the sole content from stylized text is one approach to tackle this problem. Therefore, this study
translates a recent approach known as PISCO from the computer vision (CV) field into the NLP field. This method post-processes andthereby disentangles embeddings into distinct style and content latentvector coordinates. We then isolating the chunk of neurons that only
encodes the content of a text and further use this information for aclassification head. Thereby, we improve the objectivity of NLP applications by removing the confounding influence of stylistic variations.
With a focus on financial disclosure analysis, we aim to improve thepredictive quality of sentiment and specificity downstream classification tasks. We demonstrate the technique’s applicability by applying
this method upfront these final classification tasks. Our initial findings suggest a promising enhancement in predictive performance forboth downstream tasks. This demonstrates the potential of disentangled embeddings as a novel intermediary step towards improving NLP
classification tasks. Our advances enable more accurate and evidencebased decision-making and insight generation, contributing to a morereliable and, to a certain extent, interpretable use of recent rapid advances in NLP models.

3 - Using integer programming, Markov logic networks andinference to help find missing shareholders in social
networks for a New Zealand Māori indigenous incorporation

Andrew J Mason, Karl Zhu, Anthony Downward
Parininihi ki Waitotara (PkW) is a New Zealand indigenous organisation lead by Māori that manages land and other assets on behalf ofthousands of shareholders. Many of these shareholders are "lost" inthe sense that PkW has no contact details for them. As a consequence,
PkW has millions of dollars of dividends waiting to be paid if theseshareholders can be found. We have developed a system to collectpublic social network data and search this for the missing PkW shareholders. This involves solving the entity resolution problem to determine whether multiple document records refer to the same person or
people. Traditional entity resolution approaches treat all matching decisions independently and only compare attribute similarities between
reference pairs. However, this independence assumption omits valuable relational information in situations where the references describe
a rich network of relationships between people. Collective entity resolution - where entities are resolved jointly - incorporates this previously

TC-32

ignored information and can predict matches with greater accuracy.
We use Markov logic networks to convert our domain knowledge andevidence data into a Markov network. Prediction is made by performing most probable explanation (MPE) inference on the network using
integer programming formulations and a mixed IP solver. We reportexperiments that demonstrate how our collective entity resolution approach is able to resolve complex relational information.

4 - Energy-aware robotic multi-sided assembly line balancing problem

Abdolreza Roshani
Multi-sided assembly line balancing problems typically arise in plants
producing large-sized, high-volume products such as automobiles. The
difference between these assembly lines and traditional ones lies in
the ability to install multiple single stations at each position along theline, enabling operators (whether workers or robots) to simultaneously
perform various tasks on the same product. This paper addresses therobotic multi-sided assembly line balancing problem. Here, it is assumed that robots can be selected from a set of candidate models differing in speed and energy efficiency. Consequently, they can executeassembly tasks at varying speeds while consuming different levels ofenergy. The objective is to allocate robots to workstations and tasks tominimize the total cost of installing a new assembly line for a given
cycle time, encompassing fixed robot costs, workstation installation
costs, and energy costs. To tackle this optimization problem, a mixedinteger programming formulation is proposed. Given the problem’s
NP-hard nature, a metaheuristic algorithm based on the variable neighbourhood search approach is developed to address real-size instances.
Computational experiments are conducted to evaluate the performanceof both the proposed model and the heuristic.

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Tuesday, 12:30-14:00 - Room: 41 (building: 303A)

Algorithms for machine learning and inverse
problems: zeroth-order optimisation
Stream: Advances in large scale nonlinear optimization
Invited session
Chair: Silvia Villa
Chair: Luca Calatroni
Chair: Cesare Molinari
1 - Zeroth-order implementation of the regularized Newton
method with lazy approximated Hessians

Geovani Grapiglia
In this work, we develop a zeroth-order (derivative-free) implementation of the Cubically regularized Newton method for solving general non-convex optimization problems. Specifically, we consider theclass of unconstrained problems whose the objective function is twice
continuously differentiable with Lipschitz continuous Hessians. The
proposed method employs finite difference approximations of gradientvectors and Hessian matrices. We use a special adaptive search procedure in our algorithms, which simultaneously fits both the regularization parameters of the models and the stepsizes of the finite difference
approximations. Thanks to this procedure our scheme do not require
the knowlege of the actual Lipschitz constants. Additionally, we equip
our algorithm with the lazy Hessian update, meaning that the method
reuse a previously computed Hessian approximation matrix for several iterations. In terms of worst-case complexity, we prove that the
proposed method is able to find an $epsilon$-approximate stationary
point using at most $mathcalO( n3/2 epsilon-3/2 )$ function evaluations, where $n$ is the dimension of the problem. This complexity
bound improve existing bounds in terms of the joint dependence on $n$and $epsilon$ for derivative-free methods applied to the minimizationof functions with Lipschitz continuous Hessians. Illustrative numericalresults confirm our theoretical findings.

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2 - Accelerating Randomized Adaptive Subspace TrustRegion Derivative-Free Algorithms

Stefan M. Wild, Kwassi Joseph Dzahini, Xiaoqian Liu

 TC-33
Tuesday, 12:30-14:00 - Room: 42 (building: 303A)

Optimal control and resilience
This talk presents an algorithm for solving deterministic derivative-free
optimization problems with thousands of decision variables. We employ a randomized dimension reduction technique based on JohnsonLindenstrauss transforms and the trust-region framework of Cartis andRoberts. A key ingredient in our work is to extend the affine spacewithin which the algorithm operates to include information learned in
the course of the optimization. By adapting this space and judiciously
using information gained in the course of the optimization, we obtainsignificant performance benefits on a class of large-scale test problems
without sacrificing convergence guarantees.

3 - An Optimal Structured Zeroth-order Algorithm for Nonsmooth Optimization

Cesare Molinari
Finite-difference methods are a class of algorithms designed to solve
black-box optimization problems by approximating a gradient of thetarget function on a set of directions. In black-box optimization, thenon-smooth setting is particularly relevant since, in practice, differentiability and smoothness assumptions cannot be verified. To cope with
nonsmoothness, several authors use a smooth approximation of the target function and show that finite difference methods approximate its
gradient. Recently, it has been proved that imposing a structure in the
directions allows improving performance. However, only the smoothsetting was considered. To close this gap, we introduce and analyze
O-ZD, the first structured finite-difference algorithm for non-smooth
black-box optimization. Our method exploits a smooth approximationof the target function and we prove that it approximates its gradienton a subset of random orthogonal directions. We analyze the convergence of O-ZD under different assumptions. For non-smooth convex functions, we obtain the optimal complexity. In the non-smooth
non-convex setting, we characterize the number of iterations needed
to bound the expected norm of the smoothed gradient. For smoothfunctions, our analysis recovers existing results for structured zerothorder methods for the convex case and extends them to the non-convex
setting. We conclude with numerical simulations, observing that ouralgorithm has very good practical performance.

4 - Stochastic derivative-free optimization algorithms using random subspace strategies

Kwassi Joseph Dzahini, Stefan M. Wild
We propose trust-region and direct-search frameworks for large-scale
stochastic derivative-free optimization by introducing the STARS andStoDARS algorithms. While STARS achieves scalability by minimizing random models that approximate the objective in low-dimensional
affine subspaces thus significantly reducing per-iteration costs in termsof function evaluations, these goals are achieved by StoDARS throughthe exploration of the decision space by means of poll directions generated in random subspaces. These subspaces and their dimension arechosen via Johnson–Lindenstrauss transforms such as those obtained
from Haar-distributed orthogonal random matrices. The quality of thesubspaces and sets of poll directions, as well as the accuracies of estimates and models used by the algorithms are required to hold with
sufficiently high, but fixed, probabilities. Convergence and complexity results are obtained for both methods using martingale theory. In
particular, by leveraging the ability of StoDARS to generate a dense
set of poll directions, its almost sure convergence to Clarke stationary
points is established. Moreover, the analysis of second-order behavior of the well-known mesh adaptive direct-search algorithms using asecond-order-like extension of the Rademacher’s theorem-based definition of the Clarke subdifferential (so-called generalized Hessian) is
extended to the StoDARS framework, making it the first in a stochastic
direct-search setting to the best of our knowledge.

260

Stream: Optimal Control Theory and Applications
Invited session
Chair: Stefan Wrzaczek
1 - Open-loop production control under stochastic conditions and environmental considerations

Konstantin Kogan, Dmitry Tsadikovich, Matan Shnaiderman,
Beatrice Venturi
We consider a manufacturer that produces in response to a stochastic
demand and emits pollution during the production process. Industrial
pollutants released into the air are characterized by spatial variability and heterogeneity, and the precision of instruments for measuring stochastic pollution stocks varies widely across pollutants. Consequently, commitment (open-loop) strategies to control production
and associated emissions are considered more practical than contingent(feedback) approaches when manufacturers are concerned about environmental consequences. In this paper, we derive an optimal, openloop control policy over an infinite time horizon and show a condition
that ensures that such a policy induces asymptotic convergence of expected inventory stock and pollution stock trajectories to unique steadystates. Moreover, we find that the variance in the pollution stock also
converges asymptotically to a unique steady state, which is critical for
prevention of irreversible environmental consequences. We show thatenvironmental uncertainty affects inventories, leading in the long run
to lower expected steady-state inventory stocks. We compare feedback and open-loop production policies and show that though there
are losses associated with the inability to track inventory and pollution
stocks accurately and thus applying the open-loop control instead of
feedback, the expected long-term inventory and pollution stocks are
not affected.

2 - Offshoring and reshoring under social and economic
uncertainty

Maddalena Muttoni, Alessandra BurattoThis study examines a firm adopting offshoring, the strategic relocation of business processes abroad. Motivated by cost benefits likecheaper labor and regulatory advantages, offshoring may enhance competitiveness and market access. However, in today’s dynamic market,
the strategic advantage of offshoring can quickly transform into a liability for firms. As consumers grow more discerning and sociallyconscious, factors like the MADE IN effect (the preference/aversion
toward products based on their country of origin) alongside rising ethical concerns about labor exploitation and environmental sustainability,
may challenge the efficacy of offshoring. Moreover, unforeseen circumstances such as war, pandemics, or social unrest can disrupt operations abroad, prompting companies to reassess their production strategy. Given these factors, we consider an external disruption of social,economic, or geopolitical origin, occurring unexpectedly and leading
to a regime shift within the firm’s operational environment. The objective of this research is to investigate how the firm should adjust its
offshoring strategy in anticipation of and in response to such regime
shifts. The problem is structured and addressed as a 2-stage optimal
control problem with a stochastic switching time. The study aims toprovide insights into proactive measures firms can employ to adapt
their offshore operations effectively, mitigating risks and maintaining
competitiveness in a dynamic global environment.

3 - Building resilience: a dynamic approach

Lijue LuIn recent years we have witnessed various supply chain disruptions
that severly affect the normal flow of goods and profitability. The
purpose of this paper is to analyze a dynamic game that involves a
supply chain dealing with a normal-disruption cycle, which is characterized by a random and unpredictable environment. The supplier
can invest in building resilience to minimize the potential disruption’s

EURO 2024 - Copenhagen
impact. We derive the feedback equilibrium strategies and compare
the policies and payoffs under different supply chain structures (centralized/decentralized). Finally, we propose a cost-sharing scheme toachieve coordination.

4 - A model-based measure for the resilience of resource
use under the risk of disruption

Stefan Wrzaczek, Michael Kuhn
We study a simple economic-ecological framework of resource extraction that explicilty incorporates (i) a regimechanging shock and (ii) the
possibility a resistence threshold/Skiba-point to lead to divergent behaviours towards either systemic recovery or system collaps following
the shock. This structure allows us to model resilience (in the senseof regime-changing shocks either being avoided or systemic recovery being possible and optimal) and its behavioural implications in ameaningful way. Specifically, we propose a model-based measure ofresilience that upon proper calibration of the model can be employedin numerical assessments of the implications for resilience of different
extraction policies. We also study a simple example and show that anticipation of shocks is leading to precautionary behaviour that boosts
systemic resilience.

 TC-34
Tuesday, 12:30-14:00 - Room: 43 (building: 303A)

Portfolio optimization
Stream: Stochastic, Robust and Distributionally Robust
Optimization
Invited session
Chair: Milos Kopa
Chair: Sebastiano Vitali
1 - Simulation-optimization for dynamic bank asset allocation

Pedro Júdice
Bank asset allocation is a major determinant of banks’ long-term performance and sustainability. Therefore, it is one of the most criticalproblems in bank management. Current bank asset allocation models fail simultaneously to use realistic scenarios and produce statedependent asset allocations, so do not take advantage of the changes
in business cycles. Using a multi-objective genetic algorithm, we develop a simulation-optimization method that determines dynamic asset
allocation rules based on environment variables such as interest rates.
Performance analysis on an independent testing set indicates that thealgorithm outperforms other established methodologies, namely optimizing time-independent static allocations or choosing equal-weightpolicies. In addition to performance, we emphasize interpretability,
developing an algorithm that allows results analysis and interpretation,and can thus be used in practice to support banks’ senior management
dynamic decisions.

2 - Bilevel models in portfolio selection problems

Monika Kal’atová, Milos Kopa

TC-35

assumption can be made about variables in the upper level. In a moving window analysis, these variables can vary or remain the same for
all windows. Therefore, two linear bilevel optimization problems withmultiple followers are considered. One of them can be split in time,
the other one not. The results for these two cases will be presented.

3 - Measures of Stochastic Non-DominanceJana Junova, Milos Kopa
We introduce measures of stochastic non-dominance to provide insights in situations when stochastic dominance between two randomvariables does not hold. They quantify how far a given random variable is from dominating a benchmark random variable by a specified
order stochastic dominance. They are found by solving an optimization problem that searches for an optimal random variable, which is as
close as possible to the given one but dominates the benchmark. The
measure of stochastic non-dominance is then the Wasserstein distance
between the optimal and the given random variable. Depending on
the assumptions imposed on the optimal variable, we distinguish thegeneral and the specific measures of stochastic non-dominance.
We derive closed-form expressions of the measures of stochastic nondominance under various parametric assumptions. Moreover, we
present relations to selected types of almost stochastic dominance.
Finally, a formula is derived for the computation of the measures ofstochastic non-dominance for discrete distributions with equiprobableatoms. It is applied to portfolio optimization problems in the empiricalpart.

4 - Investments in transmission lines and storage units
considering second-order stochastic dominance constraints

Sebastiano Vitali, Miguel Carrión, Ruth Dominguez
Increasing intermittent renewable generation to meet the climate goals
entails a deep transformation of current power systems. The transmission system must adapt to ensure a rapid and flexible response to thechanges in the energy flows. Flexibility can be provided by reinforcing
the interconnection among all the market agents and/or by installing
facilities with fast response to the changes. In this work, we study theinvestment problem of a central planner that seeks to expand the transmission network and install storage units considering decarbonizing
measures. We propose a two-stage stochastic problem with uncertainty
on the demand growth and including representative days to characterize the hourly demand and renewable power variability. To obtain better expansion strategies according to a limit on the carbon emissions,second-order stochastic dominance constraints are imposed. Numerical analyses based on the power system of the Canary island (Spain)
are provided. The results show that to install storage units is key to efficiently integrate renewable generation. The investments in the transmission system are mostly in lower-voltage lines. The formulation
with stochastic dominance constraints results in higher second-stage
investments, allowing a better adaptation to the demand growth evolution.

 TC-35
Tuesday, 12:30-14:00 - Room: 44 (building: 303A)

Stochastic Optimization withDecision-Dependent Uncertainty

It is a common practice that an investor makes a decision based on not
only in-sample efficiency, but also takes into account out-of-sample
performance of the portfolios. To combine in-sample efficiency with
out-of-sample optimality, one can employ a bilevel stochastic optimization problem. In our paper, the lower level of the problem consistsof the mean-risk optimization model, where Conditional Value-at-Risk
as a frequently used risk measure is applied. The upper level searches
for in-sample efficient (lower level) portfolios with maximal out-ofsample performance (out-of-sample return).

Stream: Stochastic, Robust and Distributionally Robust
Optimization
Invited session

The reformulation of the problem assuming a discrete probability distribution yields to a linear bilevel optimization problem. Additional

Rosario Paradiso, Angelos Georghiou, Said Dabia, Denise
Tönissen

Chair: Giovanni Pantuso
1 - Exact and Approximate Schemes for Robust Optimization Problems with Decision Dependent Information
Discovery261

TC-36EURO 2024 - Copenhagen

Uncertain optimization problems with decision-dependent information
discovery allow the decision maker to control the timing of information discovery, in contrast to the classic multistage setting where uncertain parameters are revealed sequentially based on a prescribed filtration. This problem class is useful in a wide range of applications,however, its assimilation is partly limited by the lack of efficient solution schemes. In this work, we study two-stage robust optimization problems with decision-dependent information discovery where
uncertainty appears in the objective function. The paper’s contributions are twofold: we develop an exact solution scheme based on anested decomposition algorithm, and we improve upon the existing
K-adaptability approximation by strengthening its formulation using
techniques from the integer programming literature. We use the orienteering problem as our working example, a challenging problem fromthe logistics literature which naturally fits within this framework. The
complex structure of the routing recourse problem forms a challenging
test bed for the proposed solution schemes, in which we show that theexact solution method outperforms at times the K-adaptability approximation, however, the strengthened K-adaptability formulation can provide good quality solutions in larger instances while significantly outperforming existing approximation schemes even in the decision independent information discovery setting.

2 - Stochastic Queuing-Based Optimization Problems for
Network Design

Miguel Lejeune

Nonetheless, we show that, for some classical endogenous distributions, the proposed method yields mixed-integer linear or convex programs with exogenous uncertainty. We validate this method by applying it to a network design and facility-protection problem, considering distinct decision-dependent distributions for the random variables. Whereas the original formulation of this problem is nonlinear
nonconvex for most endogenous distributions, the proposed method
transforms it into mixed-integer linear programs with exogenous uncertainty. We solve these obtained programs with the sample averageapproximation (SAA) method.

 TC-36
Tuesday, 12:30-14:00 - Room: 32 (building: 306)

Game Theory, Solutions and Structures VII
Stream: Game Theory, Solutions and Structures
Invited session
Chair: Thomas Lidbetter
1 - Discontinuous Game and Nash Equilibrium

Inese Bula
We study stochastic queueing-based optimization models to design
networks in which the objective is to minimize the response time. The
networks are modelled as collections of interdependent queueing systems in which the service times and the arrival of requests are randomvariables whose distribution parameters are determined endogenously.
The optimization models take the form of nonconvex MINLP problems with fractional, exponential, and polynomial terms for which we
propose a convex integer reformulation framework (MILP or convex
MINLP).

3 - Solution of Two-Stage Stochastic Programs underDecision-Dependent Uncertainty

Giovanni Pantuso, Mike Hewitt
We present an exact solution algorithm for two-stage stochastic programs under decision-dependent uncertainty. In such problems, firststage decisions determine the probability distribution of second-stage
uncertain parameters. Particularly, we focus on a broad class of problems where the number of potential probability distributions is finite
but exponentially large. The proposed method extends the well-known
L-Shaped method and is applicable also to two-stage stochastic program with integer variables at both stages. We show that the new version converges finitely. In addition, we present results from a computational study based on facility location problems under endogenous
uncertainty. The results provide promising evidence of efficiency andscalability.

4 - Solving Two-Stage Stochastic Programs with Endogenous Uncertainty via Random Variable Transformation

Maria Carolina Bazotte Corgozinho, Margarida Carvalho,
Thibaut Vidal
Real-world decision-making problems involve decision-dependent uncertainty, where the probability distribution of the random vector depends on the model decisions. However, few studies focus on twostage stochastic programs with this type of endogenous uncertainty,
and those that do lack general methodologies. We thus propose ageneral method for solving a class of these programs based on random variable transformation, a technique widely employed in probability and statistics. The random variable transformation converts astochastic program with endogenous uncertainty (original program)
into an equivalent stochastic program with decision-independent uncertainty (transformed program), for which solution procedures are
well-studied. Moreover, endogenous uncertainty usually leads tononlinear nonconvex programs, which are theoretically intractable.

262

We consider convex game with discontinuous payoff functions. A good
overview of some key results in the literature on the existence of Nashequilibria in discontinuous games is given in [3].
We will look at the discontinuity in a certain form: we will look at
w-continuous functions, which means that the discontinuity measure
is not greater than the specified positive number w. Functions of thistype can be approximated by continuous functions ([1,2]). If the approximation is also a concave or quasi-concave function, then a Nashequilibrium exists in the game. This equilibrium can be thought ofas a quasi-Nash equilibrium with respect to the discontinuous payoff
function of the original game. We would prefer that in the case wherea Nash equilibrium exists in the original game, it is also found in aquasi-sense. If no equilibrium exists in the original game, then one
would want the quasi-Nash equilibrium to provide the best possible
solution. We will look at examples and conditions under which this ispossible.
[1] Bula, I. On the stability of Bohl-Brouwer-Schauder theorem. Nonlinear Analysis, Theory, Methods, and Applications, V26,
1859—1868, 1996. [2] Bula, I. Discontinuous functions in Galeeconomic model. Mathematical Modelling and Analysis, V.8(2),93—102, 2003. [3] Reny, P. J. Nash Equilibrium in Discontinuous
Games. Annual Review of Economics, V.12, 439—470, 2020.

2 - An enhanced gradient algorithm for computing generalized Nash equilibrium

Fellipe Santos, Adriano Lisboa, Douglas Vieira, Rodney
SaldanhaThis paper introduces an enhanced algorithm for computing generalized Nash equilibria in multiple-player nonlinear games. The algorithm degenerates into a gradient algorithm for single-player games(i.e., optimization problems) or potential games (i.e., equivalent tominimizing the respective potential function), similar to the Rosen gradient algorithm. Analytical examples demonstrate that it has similar
theoretical guarantees of finding a generalized Nash equilibrium whencompared to the relaxation algorithm, while numerical examples showthat it is faster. Furthermore, the proposed algorithm is as fast as, butmore stable than, the Rosen gradient algorithm, especially when dealing with constraints and non-convex games. The proposed strategy istested in benchmarking game theory problems and real-world applications.

3 - Strategic Behaviour in Priority Queues with Deadlines.

Jesse Nagel, Joris Slootweg

EURO 2024 - Copenhagen
In many queueing systems customer satisfaction is determined moreby the meeting of a deadline (missing a flight, service level agreements, etc.) than waiting time. We study the effect of such deadlines
on strategic behaviour in priority queues. We model a queueing gamewhere customers have a choice to pay a toll for priority access, allowing them to skip part of the queue. In this model, the meeting of thedeadline is the most important factor in the utility functions of the customers. When looking at symmetric Nash equilibria in this model, we
find Evolutionary Stable Strategy equilibria which do not exist in themodel without deadlines. In addition, we model a heterogeneous situation where different groups of costumers have different deadlines andstudy how this influences social welfare and revenue.

4 - Optimal Pure Strategies for a Discrete Search Game

Thomas Lidbetter
Consider a two-person zero-sum search game between a Hider and a
Searcher. The Hider chooses to hide in one of n discrete locations (or"boxes") and the Searcher chooses a search sequence specifying which
order to look in these boxes until finding the Hider. A search at box itakes t_i time units and finds the Hider - if hidden there - independently
with probability q_i, for i=1,...,n. The Searcher wants to minimize theexpected total time needed to find the Hider, while the Hider wants
to maximize it. It is shown in the literature that the Searcher has an
optimal search strategy that mixes up to n distinct search sequenceswith appropriate probabilities. This work investigates the existence of
optimal pure strategies for the Searcher - a single deterministic searchsequence that achieves the optimal expected total search time regardless of where the Hider hides. We identify several cases in which the
Searcher has an optimal pure strategy, and several cases in which such
optimal pure strategy does not exist. An optimal pure search strategy
has significant practical value because the Searcher does not need torandomize their actions and will avoid second guessing themselves if
the chosen search sequence from an optimal mixed strategy does notturn out well. This is joint work with Thuy Bui and Kyle Lin.

 TC-37
Tuesday, 12:30-14:00 - Room: 33 (building: 306)

Multiobjective Mixed-Integer Nonlinear
Optimization
Stream: Multiobjective Optimization
Invited session
Chair: Aly-Joy Ulusoy
1 - Using Dual Relaxations in Multiobjective Mixed-Integer
Convex Quadratic Programming

Gabriele Eichfelder, Marianna De Santis, Daniele Patria, Leo
Warnow
We present a branch-and-bound method for multiobjective mixedinteger convex quadratic programs that computes a superset of efficient integer assignments and a coverage of the nondominated set. The
method relies on outer approximations of the upper image set of continuous relaxations. These outer approximations are obtained addressing the dual formulations of specific subproblems where the values ofcertain integer variables are fixed. The devised pruning conditions anda tailored preprocessing phase allow a fast enumeration of the nodes.
Despite we do not require any boundedness of the feasible set, we areable to prove that the method stops after having explored a finite number of nodes. Numerical experiments on a broad set of instances with
two, three, and four objectives are presented.

2 - A development of a nonconvex and combinatorial biobjective programming for molecular design problems

Lauren Ye Seol Lee, Amparo Galindo, George Jackson, Claire
AdjimanTC-37

Multi-objective optimization (MOO) is a technique widely used in engineering to balance different, often conflicting decision criteria thatare difficult to compare directly. However, common methods for solving MOO problems, including the weighted sum, normal boundary intersection (NBI), and sandwich methods, often struggle in addressing
the complexity of nonconvex and discrete decision variables in engineering designs.
In our research, we introduce a robust approach, referred to as SDNBI,
that blends the sandwich algorithm with a modified version of the NBI
method (mNBI). This new method is particularly effective in navigating the complex, non-linear parts of the decision space, and in quickly
identifying areas where no further optimal solutions can be found. Our
study explores the theoretical interplay between mNBI and the sandwich algorithm, focusing on three key aspects: the accuracy of its approximations, the decomposition of objective space based on Pareto
front convexity, and its efficiency in circumventing redundant searches
in disconnected Pareto segments. The effectiveness of this combined
approach is benchmarked against existing MOO methods using standard literature problems and further validated in real-world scenarios
such as solvent design for CO2 capture and the design of working fluids in Organic Rankine Cycle processes.

3 - Bi-objective valve placement and control in an operational water distribution network using a hybrid
evolutionary-deterministic method

Aly-Joy Ulusoy
We consider the valve placement and control problem, which consistsin simultaneously determining the location and settings of new pressure control valves in water distribution networks for the optimizationof performance (here, leakage reduction) at minimum cost. We present
a hybrid method combining the complementary advantages of nonlinear programming solvers and evolutionary algorithms to solve theresulting non-convex bi-objective mixed-integer non-linear program.
The performance of the proposed algorithm is first evaluated on small
case study networks from the literature. Our results show that the
proposed hybrid method converges quickly to a good approximationof the Pareto front of the corresponding valve placement and control
problems. Next, the hybrid method is applied to a large-scale operational network from the UK and obtained solutions are compared to
the locations of existing pressure control valves (determined based onengineering judgement).

4 - Automated Grid Expansion and Reinforcement Planning Considering Multiple Objectives

Hendrik Maschke, Lars-Peter Lauven
The transition towards renewable energy sources presents increasing
challenges in operating, maintaining, and planning electrical grids.
With the growing complexity of planning tasks, various planning criteria impact the decisions on specific measures for grid reinforcement and expansion. To address such a growing number of criteria, it becomes necessary to develop suitable models and simulation
tools. Therefore, an existing automated power grid expansion algorithm based on pandapower is enhanced to handle multiple objectives.
So far, using stochastic local search algorithms like hill climbing or
iterated local search, a solution has been found to minimize cost as
the sole objective. In addition to cost, other objectives like reliability, security, sociology, or environment are often considered as well.
A decision-support approach in grid planning is therefore proposedthat considers these additional criteria. We propose a methodologythat aims to optimize grid reinforcement and expansion by considering multiple objectives. The genetic NSGA-II algorithm is employed
to find solutions that balance these considerations effectively. The approach aligns with sustainable development principles and fosters resilient and sustainable grid planning. Pareto front solutions, representing trade-offs between different objectives, offer a range of alternatives
that can be considered in grid planning processes and therefore address
the complex challenges of energy grid planning.

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TC-38EURO 2024 - Copenhagen TC-38
Tuesday, 12:30-14:00 - Room: 34 (building: 306)

large scale problems, and can outperform classical graph algorithms
like network simplex and cost scaling.

Modern techniques for network optimization
Stream: Conic Optimization: Theory, Algorithms, and Applications
Invited session

 TC-39

Chair: Jacek Gondzio
Chair: Filippo Zanetti

Inventory Models

1 - LEMON - an Open Source C++ Library for Solving Network Optimization Problems

Tuesday, 12:30-14:00 - Room: 35 (building: 306)

Stream: Stochastic Modelling
Invited session
Chair: Zhen Chen

Alpár Jüttner, Péter Madarasi
LEMON (Library for Efficient Modeling and Optimization in Networks) is a generic open source C++ library of graph and network
algorithms and related data structures. It is a package of highly efficient and versatile tools with simple and convenient interface, targeting
both computer scientists and the operations research community, bothfor academic and commercial purposes.
This talk will outline the basic design concepts, features and performance of LEMON, with a focus on the recent additions to the library.
Then, two case studies are presented. The first one demonstrates how it
may support basic research in Combinatorial Optimization, while thesecond one shows an example of industrial application.

2 - Efficient exact recomputation of linear modifications of
the constraint matrix in Linear Programming

Guillaume Derval, Bardhyl Miftari, Damien Ernst, Quentin
Louveaux
In this work we focus on linear parametric programming, where theconstraint matrix is an affine function of an external parameter lambda;
the goal is to assess the impact of lambda on the objective value of the
optimal solutions.
Computing the objective for multiple values of lambda is usually cumbersome and involves solving the related linear program in full multiple times.
We show that by reformulating the problem in term an optimal basis
for a given lambda*, we can use an eigendecomposition and Schur’s
decomposition to obtain an upper bound for the objective value forother values of lambda. Moreover, the bound is actually tight when thebasis stays optimal.
Given a solution for lambda*, the bound can be computed in quadratic
time (in the number of constraints) for another lambda. The range
for the bound is exact can also be computed in quadratic time: thisopens the way to an iterative algorithm for computing the full function, changing base each time the current one becomes invalid.

3 - A regularized interior point method for sparse optimaltransport on graphs

264

1 - Competition and Cost in a Dual Sourcing Supply Chain

Jizhou Lu
In a supply chain with a manufacturer ordering from two competing
suppliers, this study examines how information quality affects information sharing’s value and supplier competition. Market demand follows a time-series model. All members use periodic inventory reviews, order-up-to policy, and MMSE forecast method. We analyze
various combinations of three information-sharing situations and four
information-quality levels. Results show: 1. Supplier demand rises
with competitive power, but increased orders mean higher inventory
costs and potential savings through sharing. Suppliers should balancethese to find optimal competitiveness and gain advantage. 2. Different
information errors impact supplier competition. For instance, transmission errors might give one supplier an edge when the manufacturer
shares data with both, while source errors do not. 3. Differentiation,
including information status and quality, leads to competitive advantage. Suppliers should negotiate for exclusive sharing when no errors
or only source errors exist. For transmission errors, enhancing information quality management is advised.

2 - Multi-product stochastic inventory problem with business overdraft

Zhen Chen
We consider a multi-product stochastic inventory problem in which
the retailer faces cash constraints but it can leverages business overdraft to deal with unexpected cash shortage. We propose a stochastic
programming formulation for this problem and apply stochastic dual
dynamic programming (SDDP) to solve it. Some of the improvementsof SDDP are considered and developed to make the algorithm morecomputational efficient. Finally, numerical experiments are conducted
to evaluate the performance of SDDP and obtain managerial insights.

 TC-40

Filippo Zanetti, Jacek Gondzio, Stefano Cipolla

Tuesday, 12:30-14:00 - Room: 96 (building: 306)

In this talk, we address the solution of optimal transport problems using Interior Point Methods (IPM). We propose two approaches which
exploit the expected sparsity of the optimal solution to enhance the efficiency of the numerical linear algebra required to solve the Newton
system. The first approach deals with classical optimal transport overdense bipartite graphs, enforcing sparsity of the intermediate approximations by means of a column-generation-like approach. The numerical experiments show that this method can achieve scalable results forvery large problems. The second approach deals with optimal transport
over sparse graphs, using a proximal stabilized interior point method.
The induced primal-dual regularization allows to use sparsified versions of the normal equations to inexpensively generate IPM search
directions. The proposed method, despite a potential high level of inexactness in the Newton direction, retains the polynomial complexityof standard IPMs, for suitable choices of the sparsification parameters.
Numerical results show that this approach is efficient and robust for

Data mining biomedical applications
Stream: Advances in Stochastic Modelling and Learning
Methods
Invited session
Chair: Javier Cabrera
Chair: Michael Katehakis
Chair: Nuria Diaz-Tena
1 - A Natural Hermite dissimilarity index randomization forthe design of pre-clinical studies, based on a genetic
algorithm.

Sofia Weigle

EURO 2024 - Copenhagen
Algorithms for randomization of animal studies such as Irini (Weigle
et al 2023) are used to allocate the subjects of a study at random intoexperimental groups. This paper presents a new method that offers
an alternative to Irini random allocation a new criterion based on the
Natural Hermite index. The idea is that instead of comparing variables
across samples individually, the new method compares the multivariate
density estimators of the populations. A new method called differential
Natural Hermite index was introduced in order to measure the dissimilarity among the multivariate density estimators of the various populations, and it was minimized by applying a genetic algorithm. The
genetic algorithm implements a process similar to the Theory of Evolution, for minimizing the differential Natural Hermite dissimilarity
for partitions of a dataset into balanced subgroups. Simulations were
performed for comparing the new dissimilarity to IRINI and to random allocation via exhaustive search. The simulations demonstrated
the performance superiority of minimizing a fully multivariate dissimilarity such as the Natural Hermite one instead of the combination of
univariate dissimilarities implemented by the Irini criterion. The Natural Hermite index that was able to find better partitions than the othertwo algorithms at a very comparable computational expense.

2 - Principal Phrase Mining: An Automated Method for Extracting Meaningful Phrases from Text

Ellie Small
Technological advances have resulted in an explosion of information in
all aspects of society. Much of this information is unstructured in theform of text. Being unstructured means that machine learning and artificial learning techniques cannot be applied. The text mining field allows the extraction of frequent words from collections of texts in orderto obtain summary information in a more structured format. Machinelearning techniques and artificial intelligence may then be applied tothis structured data. However, as useful as it is to obtain a collection ofcommonly occurring words from texts, more specific information may
be obtained from texts in the form of commonly occurring phrases. Despite this need, extracting frequent phrases is not commonly done due
to inherent complications, the most significant being double-counting.
Double-counting occurs when words or phrases are counted when they
appear inside longer phrases that themselves are also counted, resulting in a large selection of mostly meaningless phrases that are frequent
only because they occur inside frequent super phrases. Several papers
have been written on phrase mining that describe solutions to this issue; however, they either require a list of so-called quality phrases to
be available to the extracting process, or they require human interaction to identify those quality phrases during the process. In addition,
those methods are often very time-consuming.

3 - Forecasting high dimensional Tensor with relatively few
observations.

Nuria Diaz-Tena
When studying excess Covid deaths in the 50 US states, the responses
is a tensor time series of 50 states by 15 major causes of deaths and theoverall. This data is observed monthly since 2015 before covid until
2023 and the objective is to estimate the excess deaths during covid
years. We propose a model that combines a non-linear trend modelthat explains the over-all structure of the data, followed by a seasonality model, and a tensor autoregressive model for the residuals. The
seasonality model consists in a combination of sinusoidal trends at different frequencies. The results give clear patters of excess deaths bystate and disease during covid time that can be communicated and interpreted with data visualization.

 TC-41
Tuesday, 12:30-14:00 - Room: 97 (building: 306)

Subgradient-based methods
Stream: Nonsmooth Optimization
Invited session

TC-41

1 - A Subgradient Methods for general invex non-smooth
vector optimization problems.

Arunima Kumari
Vector Optimization problems have a wide range of applications in various fields, like economics, decision theory, game theory, information
theory and optimal control theory. In this paper, unlike the generalscalarization techniques, a sub gradient method without a scalarization approach is proposed for minimizing a non-differentiable invex
function which works directly with vector valued functions. The proposed method includes regularization and the interior points variantsof Newton’s Method. The proposed algorithm generates a sequence ofefficient points in the interior of epigraph of objective function which
satisfy Karush-Kuhn-Tucker conditions.

2 - Subgradient Methods for Nonsmooth Fractional Optimization Problems over the Fixed-Point Contraints

Mootta Prangprakhon, Nimit Nimana
We consider a class of nonsmooth fractional optimization problems
over the fixed-point constraints, where the numerator is a convex function and the denominator is a concave function. To solve this problem,
we propose iterative algorithms whose idea is to perform subgradientsteps with respect to the convex numerator and the concave denominator separately. Under certain assumptions, we obtain the convergenceproperties of the proposed methods. Furthermore, to tackle large-scale
optimization problems, we propose an incremental subgradient algorithm to deal with the case where the objective function is a sum-ofratios fractional optimization problem.

3 - Calculating of weak subgradients and radial epiderivatives and applications in nonconvex and nonsmooth
optimization

Refail Kasimbeyli
This work presents some calculus rules and new properties of the weak
subgradients and the radial epiderivatives. The weak subgradient andthe radial epiderivative concepts are based on the conical supporting
methodology and therefore are able to characterize global behavior ofa function under consideration and hence are used as a conical underestimation for nonconvex functions satisfying the Lipschitz condition from below. These generalized derivatives are used to establish
global optimality conditions and to develop solution methods in nonconvex and nonsmooth optimization. Although the radial epiderivative
concept can behave as the classical directional derivative in particularcases, it can be used to characterize and generate optimal solutions formany optimization problems that are impossible to handle with classical ones. In this work we present illustrative examples and demonstrate
how the weak subgradients and the radial epiderivatives can be calculated for special classes of functions. The work presents applications
for some classes of nonconvex and nonsmooth optimization problems.

4 - A bundle-type method based on descent-ascent directions for solving nonsmooth DC programs

Giovanni Giallombardo, Manlio Gaudioso, Giovanna
Miglionico
We present a bundle method for the unconstrained minimization ofnonsmooth difference-of-convex functions, which is based on the calculation of descent-ascent directions. In fact, we define a descentascent direction as a direction which is expected to simultaneously provide a descent for the minuend component function, and an increase forthe subtrahend component function. The algorithm only requires evaluations of the minuend component function at each iterate-point, and it
can be considered as a parsimonious bundle method, as accumulationof information takes place only in case the descent-ascent direction
does not provide a sufficient decrease. Hence, bundle-resets take place
every time a sufficient decrease in the objective function is achieved.
No line search is performed, while proximity control is pursued independent of whether the decrease in the objective function is achieved.
Computational performance of the algorithm, which is proved to terminate at a point satisfying a weak criticality condition, are reported
relative to a set of benchmark DC instances, ranging from small tolarge size.

Chair: Puya Latafat265

TC-42EURO 2024 - Copenhagen TC-42

 TC-43

Tuesday, 12:30-14:00 - Room: 98 (building: 306)

Tuesday, 12:30-14:00 - Room: 99 (building: 306)

Variational Analysis and Subdifferential
techniques

Simulation in transportation and logistics

Stream: Variational Analysis and Continuous Optimization
Invited session

Stream: Agent-based Models in Management, Economic
and Organisation Sciences
Invited session

Chair: Abderrahim Hantoute

Chair: Lena Hörsting

1 - The Geometry of Sparsity-Inducing Balls

Michel De Lara, Jean-Philippe Chancelier, Lionel Pournin,
Antoine Deza
Sparse optimization seeks an optimal solution among vectors withat most k nonzero coordinates. This constraint is hard to handle,
and a strategy to overcome that difficulty amounts to adding a norm
penalty term to the objective function. The most widely used penalty isbased on the l1-norm which is recognized as the archetype of sparsityinducing norms. In this talk, we present generalized k-support norms,
generated from a given source norm, and show how they contribute toinduce sparsity via support identification. In case the source norms are
the l1- and the l2-norms, we analyze the faces and normal cones of
the unit balls for the associated k-support norms and their dual top-k
norms.

2 - Partially non-convex minimax theorem and applications
to remoteness of sets and functions

Mohammed Ibrahim Abdelkayoum Ghitri, Abderrahim
Hantoute
We propose two new variants of the well-known Fan’s minimax
theorem, weakening the assumptions of convexity and lower semicontinuity used in the convex-concave standard version. Namely, theconvexity and lower semi-continuity are only required on a smaller set
which does not have to be convex or compact. In this case, the bifunction may take extended values on the given pair of convex sets. A
second variant is then given when the bi-function is finite on these sets,
removing the lower semi-continuity condition.These results are next
used to study the remoteness of sets and functions in Hilbert spaces.
Key words: Minimax theorem, convexity, remotal sets and functions.

3 - Advances on the sharp Hoffman constant of the argmin
mapping

Jesús Camacho, Maria Josefa Cánovas, Helmut Gfrerer, Juan
Parra
The aim of this talk is to introduce an exact point-based formula (involving only the problem data) for the Hoffman constant of the argmin
mapping in linear optimization, which can be considered as the sharp
Lipschitz constant restricted to its domain. The work is developed in
the parametric context of right-hand side perturbations of constraint
systems. To achieve our goal, we develop a new concept of its own
interest, called well-connected piecewise convex mappings. Indeed,
optimal set mappings fall into this new classification of mappings, forwhich we provide a recursive construction to derive a crucial equality
between the Hoffman constant and the supremum of the calm moduli.

4 - Non-active constraints in convex infinite optimization

Abderrahim HantouteWe provide optimality conditions for general convex infinite optimization problems in absence of compactness assumptions. Hence, no prerequisites are considered on the constraints index set and no continuitylike behavior is assumed on the dependence of these constraints withrespect to the parameters. The resulting KKT and Fritz-John conditions involve only the objective and the constraint functions, enlightening the different roles played by the (almost) active and non-active
constraints. Namely, the Lagrange multipliers associated with nonactive constraints can be made very small. The main ingredient is new
representations of both the subdifferential of the supremum function
and the normal cone of its efective domain.

266

1 - Spatio-temporal pricing of battery-swapping tasks onmobility-sharing platforms using proximal policy optimization

Minjeong Kim, Ilkyeong Moon

Battery-powered mobility-sharing companies face operational challenges in managing low-battery mobilities due to the dockless service,
allowing pickups and drop-offs anywhere. To tackle this, such companies engage independent contractors to recharge these mobilities, offering them per-task compensation. Given the contractors’ liberty to
dictate their work schedules and volumes, it is essential to devise apricing strategy to motivate their participation. The core tradeoff involves balancing the wages paid to workers against the penalty costs
from unsatisfied customers due to incomplete charging tasks. To thisend, this study proposes a spatio-temporal pricing strategy that assigns
tasks with differentiated regional prices by time interval, aiming to attract workers to areas with a scarcity of available workers. We use
a reinforcement learning approach with proximal policy optimization
to handle the high dimensionality of the problem. A simulation environment mimics dynamic worker participation and task reservation in
response to price updates made by the RL agent. Computational experiments demonstrate the effectiveness of reducing overall platform
costs under varying task and worker distributions. Moreover, the findings indicate that the RL-driven pricing policy mitigates the spatial andtemporal discrepancies between task demand and worker availability.
Our study is applicable to other spatial crowdsourcing platforms which
necessitate resolving spatio-temporal imbalances.

2 - Multi-Agent Airline Integrated Planning and Scheduling
Under Demand Uncertainty

Mahdi Noorafza, Bruno Filipe Santos, Alexei Sharpanskykh

Integrated planning and scheduling are crucial aspects of airline management, especially when dealing with uncertain passenger demand. A
common strategy to tackle this challenging approach is to develop partially integrated optimization models. While existing research deploys
these partial models in stages, this study introduces a framework basedon a multi-agent system model that combines fleet planning, network
planning, and flight scheduling into a single model. Our frameworkprovides a comprehensive solution to the challenges arising from thecomplex interactions between fleet acquisition decisions, network design, and flight timetables in an airline. The planning and schedulingare carried out through computational agents representing departments
and individual decision-makers within an airline. A demand scenario
tree is utilized to model demand uncertainties in the future. Nodesin the scenario tree contain similar computational agents and receive
demand input associated with that node. The final plan is generatedusing an innovative collaborative multi-agent planning algorithm thatallows information exchange between agents within each node and thenodes themselves. The framework can accommodate detailed operational constraints of the airline, enabling the tailored identification andevaluation of potential operational improvements. Finally, we demonstrate the application and potential improvements of the framework inseveral cases.

EURO 2024 - Copenhagen3 - Exploring the trade-off between efficiency and equity ofurban public charging investment: an agent-based simulation study

Tanxiaosi Luo, Yanqiu Song, Junlin Chen
The allocation of urban public charging resources plays a crucial role infacilitating EV diffusion. As a public resource, electric vehicle charging infrastructure (EVCI) also faces the conflict between equity andefficiency for its distribution, necessitating consideration of how to balance these two allocation principles. However, current research lacks
in-depth exploration of the trade-off between equity and efficiency in
the EVCI layout. Therefore, this paper uses an agent-based model(ABM) to depict the complex dynamic relationship between EVCI
investment and EV diffusion, aiming to capture the evolution of EV
uptake under different EVCI distribution principles. Empirical datafrom Beijing is further applied to calibrate and validate the model before conducting simulation experiments to analyze which principles
should be prioritized with limited resources to fully realize the role of
EVCI in promoting transportation electrification. Findings suggest theefficiency-oriented allocation strategy is clearly superior to the equityoriented allocation strategy at the innovation and growth stage of EVs,
and both are better than the baseline scenario. It is indicated that efficiency should be prioritized for EVCI allocation at the beginning stageof EV diffusion, which can eventually achieve a trade-off between efficiency and equity when the EV market maturates.

4 - A Stakeholder-Based View of Business Cases for Integrated Urban Transport

Lena Hörsting, Catherine Cleophas
Integrating urban parcel transport into a public passenger transport system is a promising concept to reduce traffic congestion and to use resources more efficiently. However, the last decade saw several promising pilot projects failing due to the lack of long-term support and profitability. Existing reviews show that financial viability is an important
aspect for the long-term success of such projects. Integrated transportrelies on a range of stakeholders, which differ in their incentives andwant to profit from the system. For example, partially financing the
public transit system with the revenue gained from parcel transportation may contribute to the success of a shared transport system from thepoint of view of public transit providers. In this talk, we first describe
the views of relevant stakeholders based on a review of the literatureand case studies. Further, we propose performance indicators to evaluate a shared passenger and freight system from the perspective of theindividual stakeholders. Secondly, we conceptualize an agent-based
simulation model to evaluate business cases for a shared passenger andfreight network.

 TC-44
Tuesday, 12:30-14:00 - Room: 20 (building: 324)

Pairwise comparisons and preference
relations 1
Stream: Multiple Criteria Decision Analysis
Invited session
Chair: Matteo Brunelli
Chair: Konrad Kułakowski
1 - Applying MCDA to Determine Eligibility for Pardon

Irit Talmor
During certain national festivities, nations occasionally grant massprisoner pardons based on specific standards. In this study, we introduce a novel technique to determine which prisoners qualify for early
release using a multi-criteria decision analysis (MCDA). The criteria
used were gender, age, crime severity, proportion of sentence completed, and behavior while in jail. Criteria weights were calculated using the analytical hierarchy process (AHP), based on data provided by
the authorities. The model produced transparent and impartial results,
offering decision-makers a convenient and efficient tool.

TC-44

2 - AHP and Nonlinear Optimization to Improve MarketValue Appraisal of Real Estate Assets

Sergio Copiello, Carlo Grillenzoni, Pietro Bonifaci
In real estate appraisal, the market value of a property is often identified based on the principle of price equilibrium; thus, the prices ofsimilar, recently traded real estate assets are the basis for the assessment. Under this framework, a technique widely used by practitioners
and appraisers goes under the name of sales comparison adjustment
grid. The property to be assessed (the so-called subject) is compared
to ten or so transactions (the so-called comparables). Adjustments are
made to the comparables’ prices to reflect their differences with respect
to the subject. To that end, the properties’ main attributes - namely, thefeatures the market players on the demand and supply side value themost - must be listed and weighted. The weighting of the attributes usually based on the appraisers’ local real estate market knowledge - isamong the most prominent weaknesses of the appraisal technique. We
propose to improve it by deriving the weights from the data itself usinga model combining the methodological setting of the Analytic Hierarchy Process (AHP) and nonlinear optimization. On the one hand, aranking of the comparables is derived by the pairwise comparison of
their prices. On the other hand, another ranking is derived by the pairwise comparison of their attributes. The weights of the attributes arethen iteratively searched as those that minimize the distance between
the two rankings.

3 - Comparing two MCDM Approaches to Evaluating Corporate Governance

Kao-Yi Shen
This study aims to analyze corporate governance using Rough Set Theory (RST), highlighting its significance in both academic discourse andindustrial practices. Corporate governance plays a pivotal role in ensuring trust and protection for investors, effective risk management,enhanced corporate performance, fulfillment of social responsibility,
and adherence to legal and regulatory compliance. This study compares a bipolar model based on RST with a traditional multi-attributedecision model. The objective is to evaluate the effectiveness and distinguishing characteristics of the RST-based bipolar model compared
to conventional multi-attribute decision models. The RST-based model
introduces a unique perspective that deviates from the traditional approaches of multi-attribute decision-making. Through this comparative analysis, the study aims to provide valuable insights into the application of RST in decision-making frameworks, contributing to a
deeper understanding of its practical implications in decision science.
Through rigorous analysis, the research identified distinct characteristics and performance metrics of the RST-based bipolar model. It assessed its efficacy in decision-making contexts—key findings shedding
light on the model’s strengths and potential areas for improvement.
The comparative analysis enhances our understanding of the practical
implications of employing RST in decision science, providing valuable
implications for academia and industry.

4 - Computation of the Maximum Eigenvalue of the Pairwise Comparison Matrix in the Analytic Hierarchy Process

Shunsuke Shiraishi, Tsuneshi Obata
This talk deals with a pairwise comparison matrix of the analytic hierarchy process. We show the convergence results of Newton’s andsecant methods for finding the maximum eigenvalue of the pairwise
comparison matrix. In recent years, we have shown similar results forlower-order (3-rd and 4-th order) pairwise comparison matrices. The
proof was based on our representation formula for characteristic polynomials. In this talk, we will report that we can extend the result to
the general order of the pairwise comparison matrix. The proof relies
intensely on the Gauss-Lucas’ theorem on the zeros of polynomials
and their derivatives. We also give computational simulation resultsfor 4-th order pairwise comparison matrices. We randomly provide
pairwise comparison matrices of Saaty’s discrete scale and count theiteration numbers of Newton’s, secant, and bisection methods, respectively. The simulation gives insight into the superiority of Newton’s
method compared to the secant and bisection methods.

267

TC-45EURO 2024 - Copenhagen TC-45
Tuesday, 12:30-14:00 - Room: 30 (building: 324)

Integer Programming for Decision Support
Stream: Decision Support Systems
Invited session
Chair: Janusz Miroforidis
1 - A Multiple Criteria Mixed Integer for the Parallel Machine Scheduling Problem Programming Model: Common Sourcing

proposed. It combines features of interactive and a posteriori methods. Before the decision-making process begins, a finite two-sided
Pareto front approximation is derived with the help of a MIP solver.
It is used to calculate the so-called interval representations of Pareto
optimal outcomes designated, directly or not, by weights of Chebyshev scalarization. Decision-makers can use them to reveal their preferences without additional optimization calculations. The use of the
proposed method is demonstrated with selected large-scale instancesof the MOMIP problem with two and three objective functions. Its
limitations and possible enhancements are also discussed.

4 - Enhancing Decision-Making in Agricultural Policies: A
Four-Step SWOT Protocol

Janja Rudolf, Andrej Udovč

Tugce Yucel
Companies have to use all their resources in an optimum way in order to be successful in a competitive environment. One of the important approaches in recent years is the effective use of the workforce and the effective use of human resources in order to achieve
efficiency.Different in industry for production environments, widely
parallelized to minimize production time in different production environments machine scheduling problems are used.In this study, it wasapplied in a defense industry company in Turkey. A multi-criteria
mixed integer programming model has been developed for more efficient planning on two identical NC machines.The aim of the model
is to minimize the total delay time and total completion time of thejobs.The aim is to ensure that many parts of different projects are correct on the cutting benches minimizing total delay and total completiontime according to prioritization and under certain constraints.In addition,an iterative heuristic approach based on a mathematical model has
been developed for the problem size for which the mathematical model
is not sufficient. The methodology used is to increase the efficiency ofthe company by improving the total time to completion, maximum latency, total latency and number of delayed jobs.

2 - Decision Support Model for Incomplete Risk-Seeking
Preferences

The protocol underwent rigorous testing in the agricultural sector, focusing on collective agri-environmental and climate schemes. Whilethese initiatives hold potential (Pe’er et al., 2020), they often lack numeric data and standardized indicators, hindering comparisons (Rudolf
in Udovč, 2022). Consequently, decision-makers face challenges in assessing their impact on the environment and public goods. The protocol consists of four steps. In first step, SWOT results from various casestudies are gathered, focusing on factors that impact the same goal.
These results are then grouped into common themes and subthemes
using the Stirling method (2001), creating a hierarchical decision treeof attributes. In second step, analytical hierarchical process is used
to calculate the weights of SWOT factors and attributes, incorporating
input from multiple professionals. In third step, a customized formis created for decision-makers to list SWOT factors for their specificcase, following the decision tree of attributes. This form is linked to a
tailored transformative formula, converting qualitative SWOT analysis
into quantitative values. Building on the SWOT scorecard technique(Rudolf and Udovč, 2022), this formula adjusts using the individual
weights assigned to SWOT factors in step two. The fourth step arranges numeric codes into groups, representing levels on a Likert scale.
These levels are recognized within a model, aiding in the evaluation ofcase studies.

Peng Xu, Tri Tran
Risk-seeking behavior has been extensively documented across various research fields such as decision sciences, economics and finance,
psychology. In this research, we propose a decision support model
based on reverse second-order stochastic dominance (RSD) to accommodate incomplete risk-seeking preferences. Specifically, we establish
dominance conditions according to the RSD criterion in discrete statespace. We then develop a stochastic optimization model that enables
to identify an optimal decision alternative whose dominance over a
pre-specified benchmark is robust for all risk-seeking decision makers. Furthermore, we demonstrate that RSD-based optimization model
can be formulated as a mixed-integer linear programming problem togenerate decision recommendations. The developed decision supportmodel is well-suited to support data-driven decision analytics problems, including production and operations management, logistics andsupply chain management, and healthcare management, particularly inthe presence of incomplete preference information.

3 - A hybrid decision-making support method for largescale multi-objective mixed integer programming problems

Janusz Miroforidis
Many practical multi-criteria decision-making problems are formulated using Multi-Objective Mixed Integer Programming (MOMIP).
Among many methods of multi-criteria decision-making, interactive
ones are considered the most rational. In interactive methods, Chebyshev scalarization and MIP solvers can be used to derive efficient outcomes. However, for a large-scale instance of the MOMIP problem,
even commercial solvers may not provide the optimal solution of its
scalarization within a time limit, and its existence is justified in practice. In such a case, the applicability of interactive methods becomes
questionable because this interactivity is lost when the time limit ison the order of many minutes. In this work, for large-scale instancesof the MOMIP problem, a hybrid decision-making support method is268

 TC-46
Tuesday, 12:30-14:00 - Room: 40 (building: 324)

From Academia To Practice: Reflections
From People Who Have Taken The Leap
Stream: Making an Impact
Invited session
Chair: Joao Fonseca
Chair: Rubén Ruiz
Chair: Rodrigue Fokouop
1 - From Academia To Practice: Reflections From Those
Who Have Taken The Leap

Making An Impact, Joao Fonseca, Martina Fischetti
Just how different is it to work within an industry or a commercial consultancy, if you have been used to working from within academia? In
this 90’ session, a panel of people who have made the move will share
their experiences and lessons learned. The differences may not be theones you expect.
The latest details of panel members can be found on the
Making an Impact page on the main conference website(euro2024cph.dk/programme/making-an-impact-2024); the panel is
expected to include Ruben Ruiz (Amazon, and a keynote speaker atthis conference), Rodrigue Fokouop (Air Liquide), Martina Fischetti(ex-Vattenfall), chaired by João Paiva Fonseca (Portchain)

EURO 2024 - Copenhagen TC-47
Tuesday, 12:30-14:00 - Room: 50 (building: 324)

MCDA and urban planning 2
Stream: Multiple Criteria Decision Analysis
Invited session
Chair: Marta Bottero
1 - Enhancing Local Sustainable Development: A Digital
Spatial Decision Support System to Recovery Marginal
Areas in Italy

Maurizia Pignatelli
Urban areas are undergoing a digital transition of spatial planning systems. This transition implements advanced infrastructure technologies
to define future scenarios efficiently. Spatial support system tools supporting sustainable development and help local governance processes
to make better decisions. However, spatial planning systems at the
local level often fail to adapt to small-scale and local social innovations, particularly in marginalized areas, highlighting the need for improved integration of digital technologies. The study aims to create aninteractive web-GIS dashboard, serving as a digital Multicriteria Spatial Decision Support System (MC-SDSS), to assist local authorities
in fostering a responsive environment for future local policy actions
aimed at recovering sustainable local development. The methodological framework of this study is structured in two parts: (I) Define anevaluation framework based on the set of Key Performance Indicators(KPIs) and perform the spatial impact assessment in four pilot casestudies (II) Develop an interactive web dashboard consisting in KPIs
through the use of GIS for setting targets and strategies through theimplementation of Multicriteria Analysis (MCA) and a Social Design
System Thinking (SDST) approach. The results demonstrate the operability of the web dashboard and the key findings of its implementationin the case studies, providing a set of suitable strategies and actions torecover sustainable marginal areas.

2 - Reviewing Micromobility Modes of Paris City by novel
MCDM Approach: Extracted Framework Element Effect(EFEE)

TC-48

include insufficient land supply, population growth, economic development, investment demand, and other factors. Several surveys showthat average house prices are rising faster than average wages, causing many families to face difficulties in house purchases. In Taiwan,
people generally feel the pressure of house purchases. High housing
prices relative to average wages require residents to face longer repayment periods and higher loan interest rates during the process, exacerbating the difficulty of house purchase. In addition, unequal development among regions has also led to great different considerations
for house environment in different regions. House purchase decisionsof different age groups are affected by many variables. The number ofyears and amount of the loan directly affect the repayment pressure andquality of life, while the average transaction price per square meter ofhouses is an important factor affecting the choice of living area. This
paper takes Taiwan as an example. We focus on different districts in
Taipei City of Taiwan. The views and difficulties faced by people onhouse purchase are discussed. Based on survey data, including housing prices and average salaries, the different decision-making models
between old, middle-aged, and young generations of dual-income couples in house purchase are presented.

4 - Evaluating social impacts of adaptive reuse of publicbuildings: a Multi-Criteria Decision Analysis approach

Marta Bottero, Alessandra Oppio, Danny Casprini, Giulio
Cavana, Federico Dell’Anna
Adaptive reuse in architecture and urban planning refers to the repurposing of existing buildings for new uses. Several and diverse sustainability benefits are associated with this practice, including preventionof soil consumption and natural resources depletion, limiting construction and demolition waste production, and saving costs. With specificreference to social outcomes, adaptive reuse of buildings can generate
positive effects such as revitalizing local communities, valorizing cultural heritage, providing new services for the inhabitants, creating newjobs, to name a few. These effects are even more relevant in the caseof reuse projects concerning public buildings, where there is a strong
need to guarantee the legitimacy of the decisions made according toa social purpose. The present contribution explores the evaluation ofsocial impacts in adaptive reuse of public buildings, and it proposesa multidimensional framework based on Multi-Attribute Value Theory(Keeney and Raiffa, 1976) for the construction of a synthetic impact
score to rank alternative projects.

Esra Çakir, ÇaĞri Tolga
The assessment of micromobility in Paris is essential for aligning urban transportation practices with the climate targets set by the European Union, fostering sustainable mobility solutions to mitigate greenhouse gas emissions and promote environmental stewardship.This
study presents an innovative approach to evaluating micromobility
modes in the urban context of Paris through the application of theExtracted Framework Element Effect (EFEE) methodology within a
Multi-Criteria Decision Making (MCDM) framework. The EFEE approach analyzes the impact on overall evaluation when specific criteria
are systematically extracted, revealing the relative importance of eachcriterion in Euclidian environment. Various micromobility options,such as e-scooters, bike-sharing, and emerging alternatives, undergoscrutiny based on criteria relating to their environmental impact, accessibility, safety, and efficiency. Moreover, the research delves into
assessing the alignment of these evaluated micromobility modes with
the European Union’s 2050 climate targets, with a particular emphasis
on reducing greenhouse gas emissions and fostering sustainable transportation practices. The findings provide insights into the efficacy of
micromobility modes in mitigating climate change and advancing sustainable urban mobility. Ultimately, the study aims to inform policymakers, urban planners, and stakeholders in Paris about the role ofmicromobility in achieving the EU’s long-term climate objectives.

3 - Decision-Making Models of House Purchase Among Different Age Groups

Chia-Wen Liao, Min-Jun Wu
The rapid rise in real estate prices around the world has attracted
widespread attention. The reasons for the rise in real estate prices

 TC-48
Tuesday, 12:30-14:00 - Room: 60 (building: 324)

DEA applications in Environment andSustainability II
Stream: Data Envelopment Analysis and its Application
Invited session
Chair: Tomas Balezentis
1 - Electric Vehicle and Charging Infrastructure Growth inIndian States: Assessment using a modified PCA- DEA
Application

E Sandhiya, M. S. GajanandIn recent years, the rapid proliferation of economic activities in India
has been accompanied by a surge in the demand for electric vehicles(EVs). Following the Paris Agreement, India has set ambitious targets
for reducing carbon emissions, including the "30@30" goal. However,with varying economic and demographic characteristics in India, EV
adoption presents unique challenges and hinges on the development ofa robust, equitable, and strategically planned charging infrastructure.
This is because, while the global average indicates that we need to haveat least one charger per 10 EVs, the current EV to public charging infrastructure in India is 181:1.In this study, we analyse the growth of EV
ecosystem across Indian states. Based on the literature, we first identify the various factors that affect the EV ecosystem. Using Principal
Component Analysis (PCA), we construct a comprehensive measure269

TC-49EURO 2024 - Copenhagenof these factors by grouping them into similar themes and then evaluate the performance of the Indian states using a modified Data Envelopment Analysis (DEA) approach. This comprehensive approach
will not only provide valuable insights for policymakers but also pave
the way for targeted interventions aimed at accelerating the transition
towards a sustainable electric mobility future. Additionally, we suggest targeted interventions for states with lower scores, focusing on
policy enhancement and infrastructure investments to accelerate theirprogress in the electric vehicle ecosystem.

2 - Measuring the efficiency of humanitarian supply chainsin face of disasters using a network data envelopment
analysis

Majid Azadi
Over the past few there have been so many disasters around the globe
mostly due to climate change and pandemics. To deal with naturaland man-made disasters efficient humanitarian supply chains have attracted significant attention. To benefit from efficient humanitarian
supply chains there has been focus on efficiency measurement usingnovel systems, frameworks and models. In spite of the significanceof measuring efficiency in humanitarian supply chains, there are stillsome key barriers with the current efficiency measurement using novel
systems, frameworks and models. The goal of the current study isdeveloping a new network data envelopment analysis (NDEA) modelthat contains a number of distinctive advantages. In this paper the divisional and overall efficiency of humanitarian supply chains are measured using our novel model correctly. Moreover, our developed model
can address all types of data in the process of measuring the efficiencyof humanitarian supply chains. We presented a case for our developedmodel along with some managerial insights.

3 - Coastline Sensitivity Analysis for Oil Spill Mitigation:
A Not-oriented Slack-based Data Envelopment Analysis
Approach

Danijela Tuljak-Suban, Valter Suban
Oil spills are a constant threat to marine ecosystems and coastal communities. One of the critical aspects of oil spill response is the placement of oil booms. The decision of where to place oil booms is complex as it depends on several factors, including the location of thespill, the nature of the coastline and the potential for environmental
and economic damage, especially to the local community. In this paper, a not-oriented slack-based data envelopment analysis (NO-SBMDEA) approach is proposed to evaluate coastal sensitivity to oil spills.
The NO-SBM-DEA approach considers the key aspects of coastal sensitivity: geomorphology, socio-economic and environmental aspects.
The specifics of all variables are taken into account when calculating
an overall performance score so that there is no need to switch between input and output orientation. The proposed approach provides acomprehensive and objective assessment of coastal sensitivity that canguide the placement of oil booms in the event of an oil spill. All data
originate from studies carried out within the North Adriatic Incident
Response System (NAMIRS) project. The proposed method was also
applied to a case study in the northern Adriatic Sea to demonstrate
its potential as a valuable tool for global oil spill mitigation efforts
worldwide. The method provides an objective and transparent tool forassessing the sensitivity of coastal areas and can help ensure that oilbooms are placed in the most effective locations.

4 - Shadow Pricing of Nitrogen Surplus in Lithuanian Family Farms

Tomas Balezentis, Vida Dabkiene
We use the slacks-based data envelopment analysis model to obtainthe shadow prices of nitrogen surplus in a sample of Lithuanian family
farms over 2014 and 2019. The variable returns to scale weak disposability technology is assumed. The results indicate that the shadow
price obtained for Lithuanian cereal farms is relatively low comparedto the earlier literature. This implies that the environmental performance of Lithuanian cereal farms needs to be improves (especially, thefertilizing regime).

270

 TC-49
Tuesday, 12:30-14:00 - Room: M1 (building: 101)

Lot-sizing with game theory aspects
Stream: Lot Sizing, Lot Scheduling and Production Planning
Invited session
Chair: Wilco van den Heuvel
1 - Mechanism design for job reassignments in collaborative manufacturing

Margaretha Gansterer, Dominik Zehetner
The remarkable success of sharing economy platforms in the B2C
sector has led to increased attention from both practitioners and researchers seeking to apply these models to the B2B sector. Studies
have already demonstrated that combined with cloud manufacturing
and additive manufacturing, these concepts can considerably reduce
costs while the responsiveness supply chains is increased. However,there is a need to design resource allocation mechanisms such that desirable properties are to be achieved. Promising approaches for suchan environment are decentralized combinatorial auction frameworks
that can be used to exchange jobs to decrease production costs ofoperations efficiently and effectively. In our approach, machines autonomously select jobs from an existing production plan to forward
them to other suppliers that can produce these parts for lower costs.
An auctioneer creates promising part bundles and manufacturing machines autonomously place bids on the packages via a combinatorial
second price reverse auction. Costs of the reallocated bundles areshared throughout a Shapley value-based approach without the need todisclose critical information. We provide analytical and numerical insights on this approach and show if and how desirable properties suchas budget balance, incentive compatibility, and individual rationality
can be achieved.

2 - Supply contracts with investment cost sharing under arbitrary bargaining powers

Pyung-Hoi Koo, Young Jin Kim, Chang Seong Ko
In this study, we propose a novel coordinated supply contract scheme
integrating bargaining power (BP) considerations with capacity planning. Our scheme empowers manufacturers to design supply contracts
conducive to channel coordination under arbitrary BP scenarios. It
comprises two supply contracts: capacity cost sharing (CCS) and surplus capacity compensation (SCC) contracts. In the CCS contract,
the manufacturer shares a portion of the supplier’s capacity investment
cost in a revenue-sharing framework. Conversely, the SCC contract entails the supplier bearing the cost of unused capacity. These contracts
incentivize the supplier to invest in production capacity in alignment
with the overall supply chain capacity, regardless of the BP scenarios.
We demonstrate that our contract scheme can satisfy any BP structure.
Specifically, the SCC contract facilitates channel coordination underhigher supplier BP, while the CCS contract leads to coordination underlower supplier BP. We also analyze the interrelation between contract
parameters for channel coordination and establish the relationship between bargaining power and optimal supply contracts. Our findings
reveal that revenue sharing and capacity cost sharing fractions are positively proportional to each other in the coordinated CCS contract, andthe compensation rate for the surplus capacity increases as capacity
cost sharing rate increases to achieve a coordinated SCC contract.

3 - Cost Allocation Mechanisms in a Capacitated Economic
Lot-Sizing Game

Basak Altan
In this paper, we consider a capacitated economic lot-sizing problem
where companies determine the optimal joint order plan in order tomeet their demands in the most cost effective manner for a given planning horizon. After determining the minimum cost solution of the collaboration, the resulting joint-cost is to be allocated among the collaborators appropriately in order to sustain the collaboration. We firstprove that the core of the corresponding cooperative game may be

EURO 2024 - Copenhagen
empty. Next, we establish the necessary and sufficient conditions forthis game to have a non-empty core and show that when these conditions are satisfied, we can obtain a core allocation by solving a dual
problem of a tight LP-relaxation. We also establish the equivalencyof the dual optimal solution set and the core allocations when they
exist and satisfy equal treatment of equals. Finally, we propose fivedifferent cost allocation mechanisms: (i) marginal allocation, (ii) dual
linear programming based mechanism, (iii) the Shapley Value, (iv) the
Nucleolus, and finally (v) pattern-based mechanism. Based on an extensive computational study, we observe that the pattern-based allocation mechanism significantly outperforms the other mechanisms withrespect to the fairness of the cost allocations.

4 - Exact and Heuristic Methods for Multi-agent Schedulingof Shared Manufacturing Systems

Ege Duran, Cemalettin Ozturk
The progress of digitization and the emergence of resource-sharing
business models have brought manufacturing companies with new possibilities to enhance their competitiveness and resilience. Shared manufacturing systems consist of agents such as manufacturing resources,
and customer orders which pursue their individual objectives. Hence,
despite its benefits, shared manufacturing systems come with computational challenges for effectively planning and scheduling this multiagent ecosystem. Our study addresses this issue by introducing novel
mixed integer programming (MIP) and constraint programming (CP)models as exact methods for representing and solving the schedulingof shared manufacturing resources. Additionally, a greedy constructive heuristic is developed. The effectiveness of the exact and heuristic
methods is demonstrated through benchmarking with randomly generated instances.

 TC-50
Tuesday, 12:30-14:00 - Room: M2 (building: 101)

Assortment Management

TC-51

an integrative optimization model to address assortment planning problem in a make-to-stock environment, leveraging customer substitution
behaviors to mitigate supply and demand uncertainties. Our framework addresses the challenge of aligning product offerings with supply chain capabilities and customer preferences to maximize expected
profit, while considering procurement and production capacity constraints. We formulate the problem as a multi-stage stochastic programwith uncertain and time-varying demand, represented by multi-period
look-ahead forecasts, as commonly observed in practice. Results arepresented using data from an e-commerce furniture manufacturer andretailer.

3 - Data-Driven Dynamic Assortment Optimization with
Product Returns

Recep Bekci
We consider assortment optimization in retail and online marketplaces.
Our research integrates the dynamics of product returns into the process of assortment planning, proposing a data-driven approach that refines existing optimization practices, aiming to improved revenue management and reduced waste.

4 - Large-scale dynamic assortment planning problem: aheuristic approach

Mozart Menezes, Lijue Lu, Hamed Jalali
Single-period dynamic assortment planning refers to the retailer’s
problem of deciding the set of products to offer and their initial inventory levels with stochastic demand and dynamic substitution. The
goal of the retailer is to maximize its expected revenue subject to a capacity constraint on the total number of items offered. This problem isnotoriously difficult to solve. We propose an efficient heuristic for thisproblem considering an online retailer that has hundreds of possible
SKUs to choose from and has the possibility to stock thousand units intotal. We impose no assumptions on preference lists and substitution
patterns. Compared to the most recent heuristic, our algorithm is competitive in the average revenue obtained but it is significantly fasterespecially for large instances. We show that our approach performs
well in a variety of settings.

Stream: Retail Operations
Invited session
Chair: Hamed Jalali

 TC-51

1 - Pricing and Inventory Decisions for an Assortment under a Generalized Nested Logit Choice

Tuesday, 12:30-14:00 - Room: M5 (building: 101)

Bacel Maddah, Hussein Tarhini
We consider joint inventory and pricing decisions of substitutableproducts in an assortment that are differentiated by some primary andsecondary attributes captured by a nested logit consumer choice. The
choice model we consider is characterized by nests having different
price elasticity parameters and dissimilarity indices, which generalizes
recent work. On the supply side, we consider a newsvendor-type setting under Normal demand which is generated from an approximation
to a Poisson arrival process. Motivated by results in the literature on
the “riskless" case, which ignores inventory costs, we assume that theprices of all products in the assortment can be written as a function ofa single decision variable. We verify that this simplifying assumption
yield near-optimal results based on a detailed numerical study. We thenanalyze the properties of the expected profit at optimal inventory levels
as a function of the pricing decision variable.

2 - Assortment Planning Optimization under Uncertainty
with Customer-driven Product Substitution

Jisoo Park, Walid Klibi, Benoit Montreuil
The evolving retail landscape, marked by increasing production costs
and resource constraints, highlights the need for assortment planning
decisions made with supply chain considerations. Retail product manufacturers typically have their assortment decisions led by the marketing department, yet these are intricately linked to operational decisions
across procurement, production, and inventory planning. We propose

Network Design and Line Planning for
Public Transportation 1
Stream: Public Transport Optimization
Invited session
Chair: Rowan Hoogervorst
1 - Row and column generation for line planning undercongestion

Rolf Van Lieshout, Yahan Lu, Layla Martin, Lixing Yang
This paper considers the problem of determining lines and frequencies
in a public transport system. In comparison to existing approaches,
we explicitly consider congestion and assume that passengers may
choose different routes to reduce discomfort due to crowding. Our
solution approach targets at finding a system-optimal solution by generating passenger routes in a dynamic fashion, whilst also adding cutting planes to deal with the non-linearity introduced by the congestion
terms. Since passengers may deviate from system-optimal routes, line
plans are evaluated by computing a user-equilibrium routing based on
Wardrop’s principle. Case studies demonstrate that incorporating congestion leads to fundamentally different line plans, that achieve a lower
perceived travel time both for the system-optimal routing and for theuser-equilibrium.

271

TC-52EURO 2024 - Copenhagen

2 - An exact solution method for solving the line planningand frequency setting problem

DLN can significantly speed up all TNDP algorithms that solve the
PAP over and over when designing a public transport network.

Siv Marie Cartland Hansen, Evelien van der Hurk, Rowan
Hoogervorst
The line planning problem consists of finding a set of lines to serve as
well as which frequencies to operate in a public transport network. Including frequency-setting gives an indication of a timetable and allows
for a better approximation of the operator costs and passenger service,
however, it makes the problem difficult to solve for real-life instances.
In this talk, we present an exact, more scalable solution method forsolving line planning problems. The problem is modelled using amixed integer programming formulation, which minimizes both operating costs and passenger costs subject to budget and capacity restrictions. The passenger cost is the perceived travel time considering
frequency-dependent transfer and waiting time costs. The proposed
algorithm starts with a reduced representation of the problem. It iteratively adds frequencies to the model by utilizing the internal trade-off
between the two conflicting objectives in the problem. We present
a computational study based on both well-known artificial and realworld problems. Our results show that the algorithm is able to findsolutions faster and without explicitly creating the complete formulation.

3 - A Genetic Algorithm with Several Fitness Functions forLine Planning

Maarten Wens, Pieter Vansteenwegen
Most algorithms solving the transit network design problem (TNDP) or
Line Planning Problem (LPP) focus only on one objective function andthe provided input parameters, e.g. the distance matrix. By changing
these parameters or the objective, algorithms can escape from local optima. In this work, a genetic algorithm is created that includes several
alternative fitness functions across a population, focusing on different
aspects of a solution. This can range from putting a focus on lowdemand areas to putting a focus on minimising the transfers. Our implementation introduces the concept of sub-populations, which workin parallel and aim for optimal solutions for slightly different fitness
functions. These sub-populations share their found improvements, allowing a synergy between them. When this idea is implemented in analgorithmic framework for network design, the results improve the current literature for all Mumford networks, a commonly used benchmark
instance. For these benchmark instances, the objective function value,considering the average travel time of passengers, is reduced by (1, 0.5,
3, 2.5)%. Not only does this algorithm improve current benchmark results, but the output contains a set of high-quality, yet substantiallydifferent networks, which is interesting for transport operators and forthe follow-up research we have in mind. Finally, this algorithm is successfully used in a case study in cooperation with the main public bus
operator of Flanders, Belgium.

4 - An alternative network representation to speed up network design

Pieter Vansteenwegen, Dilay Aktas
Given the infrastructure network and an origin-destination demand matrix, the set of lines of a bus, train or tram system is optimized with theTransit Network Design Problem (TNDP). When addressing the TNDP
with passenger travel time in mind, the passenger assignment problem(PAP) needs to be addressed. Due to the complexity of the TNDP,
typically bi-level optimization models and/or metaheuristics are used,
where the PAP is solved every time a line plan is evaluated. In doing
so, the transit network needs to be expanded, typically with a so-called’Change-and-Go’ (CNG) network with dummy transfer nodes, in orderto model transfer penalties as part of the passenger travel time. Then,the all-pairs shortest path problem needs to be solved for this expanded
network representation, which is by far the most time consuming partof the algorithms. To overcome this, we present a much more efficient
network representation, the ’Direct Link Network’ (DLN), where additional edges are added instead of additional nodes. We compare thecomputation time required to solve the PAP by using CNG and DLN on
the most commonly used benchmark networks and several real-life networks. The results show that with the DLN representation, the PAP canbe solved on average 350 times faster than with CNG. Consequently,272

 TC-52
Tuesday, 12:30-14:00 - Room: 8003 (building: 202)

Exact methods in combinatorial
optimization (Contributed)
Stream: Combinatorial Optimization
Invited session
Chair: Liudmyla Koliechkina
1 - The McNugget-Knapsack Connection: Refining Upper
Bounds on the Frobenius Number with Operational Research Applications

Aled Williams, Daiki Haijima
We study the classical Frobenius problem, which seeks to find thelargest integer that cannot be represented as a nonnegative integral
combination of given relatively prime positive integers, termed the
Frobenius number. The Frobenius problem is known by other names
within the literature such as the money-changing problem, the coinproblem, the chicken McNugget problem and the Diophantine problem of Frobenius.
The Frobenius number is an important quantity in operational researchwhere, from a geometric viewpoint, it is the maximal right-hand side
such that the corresponding knapsack problem is integer infeasible.
Further, given that the knapsack problem is NP-hard, there is a richhistory on this problem. Note that computing the Frobenius number ingeneral is NP-hard and, as such, there has been a much research into
producing upper bounds on the Frobenius number.
The main contributions are observations regarding a previously known
upper bound. In particular, we observe that a previously presented
argument features a subtle error that alters the value of the bound. Despite this, we show that the error does not impact upon on the bound’svalidity, although it does impact on its tightness. We compare the relative tightness of the corrected upper bound with the original. In particular, we show that the updated bound is tighter in all but only a relatively "small" number of cases using both formal and simulation-based
techniques. This is joint work with Daiki Haijima.

2 - Evaluating approximate symmetries in complex brain
networks

David Hartman, Anna Pidnebesna, Aneta Pokorna, Jaroslav
HlinkaComplex networks representing structural properties of natural systems exhibit characteristics such as small-world or scale-free. However, the symmetry of such networks has not been studied much until recently, although some networks, such as the human brain, havesymmetries directly embedded in their structure, or such symmetry is
an indicator of their specific state. In a recent study, however, it was
shown that non-trivial symmetries based on graph automorphisms exist
in general complex networks representing real-world systems. Nevertheless, due to the uncertainty of determining the edges of such a
network, it is necessary to consider approximate symmetries, as wasshown in a recent study by Liu 2020, where the image of the resulting
automorphism may show inconsistencies for several pairs. However,the proposed optimization method has shortcomings, such as, for example, the instability of the results, relatively small radius of search,and not considering fixed points. In this paper, we consider adopting the method recently developed for treating the Graph Matching
Problem and propose an alternative method that addresses some of theshortcomings of the approach mentioned above. In addition, we propose a scheme for testing the approximate symmetry methods using
different random networks reflecting the approximate symmetry in the
network representing the complex networks with emphasis on the human brain.

EURO 2024 - Copenhagen
3 - Promoting Student Wellness through Maximally Diverse
Grouping with Acceptance Relation

Lior Aronshtam, Tammar Shrot, Hadassa Daltrophe
The problem of partitioning objects into different groups according topredefined attributes has been extensively studied in OR research. A
field in which grouping is frequently referenced is education, wherestudents need to be divided into groups for collaborative learning. A
major factor contributing to successful learning is student wellness,
which is influenced by the group’s social structure. Hence, the abilityof the student to choose their group mates is crucial in the grouping
process. Yet, students’ wellness is not the only important factor inbuilding groups. An additional important factor is the diversity of thegroup members in various features. Many studies in OR approached
the grouping problem using the known setting of the maximally diverse grouping problem (MDGP), which does not concern student social preferences.
In this study, we consider a new version of the MDGP, in which each
student declares a set of acceptable students, and the goal is to find a
maximal diversity partition to groups in which every student is groupedwith at least one of its acceptable mates. We formally defined the
problem, studied its complexity, and found that a solution does notalways exist and it is NP-complete. Driven by real-world challenges,
we used students’ acceptable choices to structure mid-school classes
and project groups in academic courses using an integer programming
solver. We analyzed the existence of a solution in each case and its
quality.

4 - Algorithm for Solving Combinatorial Optimization Problems with Fractional-Linear Objective Functions

Liudmyla Koliechkina
This annotation proposes an algorithm for solving combinatorial optimization problems with fractional-linear objective functions. Suchproblems are ubiquitous in diverse domains including operations research, scheduling, and network optimization, where the goal is to findan optimal arrangement of elements from a finite set. The proposed
algorithm uses the properties of combinatorial configurations of permutations and their graphs. The essence of the algorithm is to performthe following steps: the coefficients of the numerator and denominator of the fractional linear function are determined, the elements ofthe permutation are specified; for a selected pair of adjacent permutations, the difference in the values of the linear fractional function is
calculated; based on the calculated coefficients and signs of the differences, a structured permutation graph is formed, which allows you
to analyze changes in the values of the objective function at the vertices of the graph; for the selected elements of the permutation in thegraph, a Hamiltonian path is formed along which the values of theobjective function change; extreme values of the objective function arecalculated, which are achieved at the extreme points of the permutationgraph.
This algorithm allows you to solve combinatorial optimization problems with fractional linear objective functions by analyzing the function values for various permutations and constructing a structured permutation graph.

 TC-53
Tuesday, 12:30-14:00 - Room: 8007 (building: 202)

Sustainability in Consumer Systems &Industry
Stream: Sustainable and Resilient Systems
Invited session
Chair: Jitender Madaan
1 - Millets for Sustainability: Consumer Segmentation using Stated Preference Analysis

TC-53

Adya Pandey, Nomesh Bolia
Millets can significantly contribute to achieving certain Sustainable
Development Goals (SDGs) namely SDG 2 (Zero hunger), SDG 3(Good health and well-being), SDG 12 (Ensure sustainable production
and consumption pattern), and SDG 13 (climate action). Traditionallya part of the Indian food basket, millets lost their share drastically since
the Green Revolution due to the incentivization of other food grains.
However, due to increasing malnutrition and environmental degradation, the Government of India is undertaking numerous initiatives topromote large-scale consumption of millets. To ensure a sustainablesupply of millets, it is important to tackle the consumption barriers
faced by the masses. The present study utilizes a stated preference survey to identify the consumption barriers and understand respondents’
likeliness of increasing millet consumption if these barriers are eliminated or reduced. An online survey was conducted in March 2023
for the same. Based on the survey responses, consumer segmentation is done using non-parametric tests viz. Kruskal-Wallis and MannWhitney test followed by Post hoc Dunn Bonferroni’s test. The results
indicate demographic subgroups that have significantly higher likeliness of increasing their millet consumption when different strategies
are implemented. The results of the study can serve as a baseline formillet-specific industry players and help them develop marketing tools
incorporating features that help eliminate the barriers.

2 - Flexibility Focused Sustainable and Resilient Product
Recovery Network Models

Jitender Madaan, Ekachidd Chungcharoen, Sanjeet Singh
Decision makers for adopting sustainability in their operations did notconventionally realize the environmental and socio-economic advantages of returns management. However, studies have shown sustainable investments in returns produce ecological improvements that canhave considerable financial advantages (Win-win situation). However,comprehensive research is still required to achieve this sustainability
to achieve economic and environmental paradigms. Research must
focus on providing resilient models that demonstrate the competitive
advantage companies can build by extracting value from product returns. It has also been realized that to make Enterprise Systems (ES)more sustainable, responsive, and competitive, considerable research
effort is required to demonstrate performance improvement forward(i.e. flow of materials, products, and information from producer toconsumer) as well as in reverse flows (referring to both recovery andreturn of products and information from consumer to producer). The
tradeoffs between the performance objectives of both flows necessitate the development of enough process capabilities that can improve
performance simultaneously. Flexibility can be seen as one such capability to enhance the resilience of value recovery flow systems. Thisis an effort to study and demonstrate economic performance through aresilient product recovery system under various scenarios, which is theprimary subject of the paper.

3 - Examining the Influence of Sustainable Operational
Practices on Corporate Performance: A Study on Automotive Sector

Vaishnavi Pandey, Vijayta Fulzele
This paper applies Institutional theory to benchmark sustainability (via
Return-To-Scale) and identify key factors enhancing market capitalization and shareholder equity through Panel Data Analysis. This
study uses benchmarking analysis- to study the Return to Scale of 16
global automobile firms, specifically utilising Data Envelopment Analysis (DEA-VRS) using MAXDEA software and Panel Data Regression
Analysis using STATA software for Years 2019-2022. The findings
suggested that Volkswagen (1464.09) has the highest and positive return to scale, followed by Kia (39.19), Ford (19.14), Volvo(13.73),
Maruti Suzuki (7.97), Mercedes (4.60), Honda (2.06), Tata Motors(1.83) respectively. On the other hand, Nissan (0.60), Hyundai(0.48),
and Toyota (0), respectively, have constant Return to Scale. Furthermore, General Motors (-0.06), Mahindra and Mahindra (-0.13),
Isuzu(-0.69), BMW(-0.77), and Stellantis(-1.09)have negative Return
To Scale. Panel Data Regression highlights that GHG emissions, Energy Consumption, Waste Generation, Total Assets, R&D, and Operating costs significantly affect Automobile firms’ Market Capitalization. Gender diversity is entirely insignificant. GHG emissions, waste273

TC-54EURO 2024 - Copenhagengeneration, social contribution, total assets, and operating costs significantly affect Shareholder equity in the firm. Energy consumption andLost-Time accidents minimally affect shareholder equity. Water Consumption, Gender Diversity, and R&D investment are insignificant for
Shareholder Equity.

4 - Green Shopping Online:
Boosting Eco-Friendly
Choices Through Integrated Product Strategies

Raziyeh Reza-GharehbaghThis study presents a novel approach to online retail sustainability,
embedding eco-labeled products alongside conventional offerings tostimulate sustainable consumer behavior. Inspired by the EU eco-label
strategy, our framework aims to empower consumers while promoting
sustainable purchasing decisions. We examine the nexus between consumer utility, environmental awareness, and online retailers’ marketing
efforts, strategically enhancing the visibility of eco-labeled products.
Central to our analysis is the certification landscape, where retailers
navigate between developing their own certifications and utilizing external standards. Employing game theory, we analyze retailers’ optimal strategies under different certification policies. This research not
only introduces a new paradigm in online retail sustainability but also
provides strategic insights crucial for future sustainability initiatives inthe digital marketplace.

 TC-54
Tuesday, 12:30-14:00 - Room: S01 (building: 101)

Disruption management and recovery
Stream: Public Transport Optimization
Invited session
Chair: Marjan van den Akker
1 - A Strong Formulation for the Train Dispatching Problem
and its Solution

Maik Schälicke, Karl Nachtigall
Disruptions in the operational flow of rail traffic can lead to conflicts between train movements, such that a scheduled timetable canno longer be realised. This is where dispatching is applied, existing
conflicts are resolved and a dispatching timetable is provided. In theprocess, train paths are varied in their spatio-temporal course. Thisis called the train dispatching problem (TDP), which consists of selecting conflict-free train paths with minimum delay. Starting froma path-oriented formulation of the TDP, an IP is introduced. The IP
selects the best of all potentially possible train paths via binary decision variables, which results in a big number of variables, so column
generation is used to solve the problem. Instead of modelling pairwise
conflicts, a stronger formulation is achieved by cliques formulated overthe complete train path. The resulting pricing problem is a hard problem, because the shadow prices of the conflict cliques must be taken
into account. When constructing a new train path, it must be determined whether this train path belongs to a conflict clique or not. This
problem is tackled by a MIP. The methodology is tested on instancesfrom a dispatching area in Germany. Numerical results show that thepresented method achieves acceptable computation times with goodsolution quality while meeting the requirements for real-time train dispatching.

2 - A real-world locomotive scheduling problem withstochastic travel times and buffers

Kanchan Joshi, Jan Fabian Ehmke
Railways, offering environmental efficient transport services, are a potential alternative to roadways. However, the increasing complexity ofrail networks and interactions between infrastructure, resource planning, particularly locomotive scheduling, and personnel deployment,
poses significant challenges. Railway companies use optimization
techniques to generate circulation plans, promising efficient resource274

utilization. Nonetheless, these plans are often deterministic and ignore information on disruptions arising from delayed crew, crowded
infrastructure and breakdowns. As a consequence, ad hoc short-term
rescheduling and adjustments are necessary, resulting in economic
penalties and a negative impact on perceived service quality. In this paper, we aim to determine reliable locomotive schedules that minimize
the number of locomotives and empty-run distances using a time-space
network formulation. To generate reliable plans, we design and include
buffers considering stochastic travel times. We evaluate the effect of inserting these buffers on the number of locomotive and empty-run distances for real-world Austrian railway use cases. At the same time,
we assess schedule reliability through feedback from simulations. The
trade-off between reliability, the number of locomotives, and emptyrun distances can offer valuable insights to planners seeking to address
disruptions without stressing already constrained infrastructure.

3 - Simulated Annealing to Solve the Integrated Airline
Fleet and Crew Recovery Problem

Philip de Bruin, Marjan van den Akker, Kunal Kumar,
Lisanne Heuseveldt, Marc Paelinck
Airline operations are prone to delays and disruptions, since the schedules are generally tight and depend on a lot of resources. Since the
COVID pandemic, most airlines also have shortages of these resources,
making the problem worse. In case of disruptions, such as aircraft
breakdowns or crew sickness, the schedule needs to be adjusted. Thismeans either changing resource assignments or delaying or cancelling
flights. Such adjustments need to resolve a disruption while minimizing overall costs and impact on passengers. For practical use, thisneeds to be done as close to real-time as possible.
We focus on both the aircraft and crew schedules. Resolving disruptions for these can be done sequentially, but decisions in one problem
may lead to more conflicts in the other. Thus, we take an integrated
approach. Such an approach comes with computational challenges,
especially because of the runtime required for practical use.
For this, we develop a local search algorithm based on simulated annealing. Where, in order to not spend time on making changes in unaffected parts of the schedule, we test different neighbour generation andselection approaches that steer the local search into resolving these
disruptions more directly. The work is done in collaboration with andwith data from KLM Royal Dutch Airlines.
We show that our approach is very fast, achieving runtimes well below
a minute. Furthermore, we also show that the disruptions are resolvedin a cost-efficient manner.

4 - Disruption Management in Airline Luggage Handlingusing Local Search

Lisanne Heuseveldt, Marjan van den Akker, Ali
Poursaeidesfahani, Philip de Bruin, Kunal Kumar
Flight times are influenced by many factors. Examples are weather
conditions or congestion at the airport, leading to ATC delays or limited gate availability. These factors influence the arrival time at thegate, which can thus vary considerably.
Since COVID, ground personnel for loading and unloading luggage toand from an aircraft are scarcely available, so we need to deploy themefficiently. Because of this scarcity, the process is highly sensitive tothe variation of arrival times. To deal with this, a fast algorithm to(re)schedule the allocation of the luggage handlers is required.
We consider the assignment of luggage handlers to tasks arising fromarriving and departing aircraft on the day of operations. We aim to
make real-time updates that solve disruptions and are cost-efficient.
This problem can be considered as a technician routing and scheduling problem (TRSP), where we consider teaming, skill and time window constraints in a dynamic setting. We have to synchronise multiple
tasks corresponding to the same flight, which is a new aspect in regard
to TRSP literature. Our algorithm proposes delays or cancellationsof flights; these levers are often unexplored from a ground operations
perspective.
We solve these disruptions using a simulated annealing algorithm, withthe original solution as a starting state. This solves the problem in realtime. We will present computational results from experiments withreal-life data provided by KLM Royal Dutch Airlines.

EURO 2024 - Copenhagen TC-55
Tuesday, 12:30-14:00 - Room: S02 (building: 101)

Freight transportation and logistic II
Stream: Transportation
Invited session

TC-56

logistics with two echelons is proposed, which can be used both independently and in combination with alternative means of transportation.
An Adaptive Large Neighborhood Search is presented to analyze realistic urban delivery areas, and insights into its performance and thepracticality of integrating combined fleets are provided. A case study
from Hamburg, Germany, shows that labor costs are a key factor influencing the effectiveness of delivery vehicles. In addition, the strategic
value of integrating robots and drones into delivery systems becomes
clear, even if only a proportion of customers opt for these new means oftransportation. These findings provide valuable recommendations forstakeholders considering the introduction of innovative parcel delivery
systems tailored to urban environments.

Chair: Frank Meisel
Chair: Barbara Himstedt
1 - Impact of Foldable Container Implementation on Vehicle Operations: Focusing on Folding Time

Koichi Shintani, Hikaru Takashima, Kenichiro Nagaiwa,
Etsuko Nishimura
The introduction of foldable containers is anticipated to reduce thenumber of vehicles required for repositioning empty containers. However, when using foldable containers, folding time (FT) is necessary inaddition to waiting time (WT) until the same number of containers as
the stacking tier accumulates. These factors could potentially decrease
the operational efficiency of vehicles and containers. Therefore, thisstudy focuses on the relationship between FT, WT, and the operational
status of an inland depot, employing a stochastic simulation approachto verify how efficiently foldable containers can facilitate empty container repositioning by vehicles. The results of numerical experiments
reveal that the variation in FT duration significantly impacts the economic viability of foldable containers.

2 - Hub-and-spoke container shipping network design under uncertainty

Cihan Butun, Sanja Petrovic, Luc Muyldermans
Containerized shipping has recently been deeply affected by events
such as Covid-19 pandemic and regional wars. These events led to
unexpected slumps or surges in demand, hampered cargo handling capacity of ports, and compelled liners to streamline their services tomitigate operational difficulties and logistical hurdles. In this paper,
we address the design of a hub-and-spoke container shipping networkby assuming that the values of origin-destination cargo demands andport capacities are imprecise. The imprecision in these parameters isrepresented with trapezoidal possibility distributions. The problem involves the location of transshipment hub ports, allocation of non-hub
ports, and routing of cargo flows in the resulting network so as to minimize the expected network cost. We formulate the problem as a possibilistic mixed integer linear programming model and convert it to achance constraint model using a fuzzy measure. To solve the chanceconstraint model, we develop a Tabu Search algorithm. In addition, we
design an upper bound procedure that decomposes the problem into
three subproblems and solves them iteratively. The computational experiments using the data of 50 ports in the Mediterranean show that the
proposed solution method finds optimal or near optimal solutions in avery short computational time. In addition, analysis of the resulting
networks provides important insights into the impact of uncertainty on
the network cost and number of hubs.

3 - Optimizing Urban Parcel Logistics: An ALNS-based approach to combine tradition and technology

Barbara Himstedt, Frank Meisel
In the past, urban parcel delivery was dominated by diesel-powered
vans. However, with society’s growing awareness of sustainability,
parcel delivery services are now encouraged to adopt innovative andenvironmentally friendly systems. As a result, cargo bikes, robots, anddrones are investigated as potential alternatives. But relying on a single delivery system may not always be practical, as not all customer
locations are suitable for these new technologies. However, it could
prove beneficial to combine them with the traditional method to ensure an efficient and cost-effective delivery process. To address theseissues, a mathematical optimization model for managing urban parcel

 TC-56
Tuesday, 12:30-14:00 - Room: S04 (building: 101)

Advancing mobility towards sustainable
solutions I
Stream: Transportation
Invited session
Chair: Eloisa Macedo
Chair: Jorge Bandeira1 - One-to-Many Stable Matching for Integrating Ridesharing and Public Transit on a Mobility-as-a-Service PlatformYating Wu
Mobility-as-a-Service (MaaS) is an innovative user-based mobility
system that offers users a one-stop personalized service by seamlessly
integrating multi-modal transport services. MaaS has become increasingly popular and is reshaping travel behavior. However, MaaS is essentially a decentralized user-centric system, which is challenging tooperate since both service providers and travelers are self-interested.
We address this problem by studying a MaaS system that integrates
one-to-many ridesharing and public transit through a digital platformthat flexibly matches riders with drivers and/or public transit. We generalize the classic one-to-many stable matching concept to incorporate
the users’ preferences in the planning of daily operations. The stablematching problem formulated as an integer program typically includes
a huge number of matches and complex stability constraints. We develop a computationally efficient algorithm that integrates Lagrangian
relaxation with column-and-row generation procedure to solve themodel. Theoretical properties of the model and algorithm are investigated. We also test the algorithm on a real-world case and show thatthe algorithm converges quickly. Extensive computational experiments
reveal various new managerial insights into social welfare, transit usage, users’ savings, matching rates, and travel detour.

2 - The electric dial-a-ride problem with capacitated charging stations, multiple depots, and customer rejection

Yumeng Fang, Tai-yu Ma, Francesco Viti
Integrating mass transit with demand-responsive service is challenging
due to joint optimization of bus routes and multimodal customer trips.
State-of-the-art mixed-integer-linear programming (MILP) approaches
can solve the problem exactly for less than 10 requests (Posada et al.
2017). This is due to the cumbersome modeling of partial routes ofthe mass transit network, where the number of arcs expands rapidly
with the network size. Besides, using electric vehicles in this problem leads to additional complexity because of charging scheduling andcapacitated charging station constraints. This study proposes a novel
MILP formulation of an electric integrated dial-a-ride problem withmultiple depots and capacitated recharging stations to minimize overall system costs considering transfer synchronization and customer rejection. To address the abovementioned issues, we use time-dependent
shortest paths on transit networks to substantially reduce redundant decision variables. With a four-hour computational time limit, we test the275

TC-57EURO 2024 - Copenhagenmodel up to 20 requests with different initial battery levels of vehicles.
Results show that this model can solve the problem optimally around
95% faster and to a larger problem size if compared to Posada et al.
(2017). A more compact arc-based formulation is developed concerning capacitated charging stations. The numerical results can reduce
up to two-digit computation time compared to the state-of-the-art arcbased method.

3 - Enhanced analysis for more sustainable traffic systems
through Two-Step-SDP

Eloisa Macedo, Paulo Fernandes, Jorge Bandeira
The EMBRACER Project focuses on improving the interconnection
with urban and rural areas and achieve seamless intelligent climateresilient regional and local intermodal mobility. Under this framework, digitalisation and traffic/transport data are essential to promote
more sustainable mobility alternatives and satisfy citizens mobility
needs. Data-driven knowledge allows for more informed and effective decision-making, both at a strategic and operational level. In thiscontext, data analysis tools play a relevant role. We will focus on thepotential of the Two-Step-SDP methodology for analysing vehicle dynamics data and gaining insights regarding traffic-related impacts. The
technique is suitable for performing data clustering and dimensionality reduction, has shown superior performance to analogous methodologies, and it is implemented as an open-source tool. In particular, itconsiders the clustering problem of objects and attributes formulated
as nonlinear SDP-based models and, due to their nature, it requires theuse of Linear SDP relaxations and an approximation algorithm to obtain their solution. This methodology was applied to different sets ofvehicle dynamics data and it revealed to enable a more detailed analysis of the traffic-related impacts. Analising traffic-related data allows
policymakers to make evidence-based decisions to improve the sustainability of transport systems and can then, implement targeted interventions to more sustainable mobility solutions.

4 - Traffic Distribution for Sustainable Intercity Corridors:
A Mixed Conventional and eCAV Scenario

Jorge Bandeira, Luis Pereira, Eloisa Macedo
In the scope of the EMBRACER Project, the study explores integration of connectivity and automation in transport, emphasizing thechallenges of managing the coexistence of conventional vehicles andconnected and automated vehicles (CAVs). Optimising traffic flow
in a mixed environment (different vehicle classes, automation levels,
propulsion technologies, and kinematics) is crucial to realize the fullpotential of CAVs. Intercity corridors, with parallel routes, offer a controlled environment to examine the complex interplay of traffic flow,
demand, network capacity, and demand management strategies. The
study explores how optimising traffic distribution across parallel interurban routes can reduce travel time and environmental impacts, considering current traffic scenario and projecting effects of a 30% penetration of electric CAVs. The study employs field data (traffic volume,
fleet composition, travel time, GPS data), microscopic traffic modeling to simulate existing conditions and incremental demand, and VSP
methodology for emissions modeling. External costs are monetizedbased on greenhouse gas emissions and air pollution, enabling multiobjective optimisation. Results show a nonlinear impact of CAV integration, with benefits influenced by route characteristics and traffic
demand. While widespread electric vehicle adoption significantly reduces emissions, optimised traffic distribution is essential for minimising traffic-related externalities in intercity corridors.

 TC-57
Tuesday, 12:30-14:00 - Room: S06 (building: 101)

Market dynamics and implications for
portfolio decisions
Stream: Modern Decision Making in Finance and Insur-

276

ance
Invited session
Chair: Jörn Sass
1 - Investor activity in EFSF/ESM secondary bond markets

Peter Schwendner
This study analyzes the secondary market dynamics of the European
Financial Stability Facility (EFSF) and European Stability Mechanism(ESM) bonds from 2014 to 2020. Findings reveal increased activity ofprivate sector investors, notably fund managers, with significant market risk exposure. The study examines the secondary bond market response to the initial impact of the COVID-19 crisis, observing investor
behavior aligning with previous models of capital repatriation and subsequent re-entry. Additionally, it highlights a trend towards heightened
activity in long-term market segments, spurred by a low yield environment, leading to the normalization of long maturities as liquid instruments. The paper also delves into the digitalization and automationof fixed income markets through electronic trading platforms, noting
a gradual increase in electronic trading adoption, particularly amongsmaller ticket sizes. Despite this trend, central banks and public entities exhibit lower electronic trading shares, suggesting less pressure or
motivation for adoption. Finally, the study explores the transaction network of primary dealers, revealing regional and institutional patterns
that support liquidity maintenance strategies, emphasizing the role oflocal banks in stabilizing turnover volume and enhancing investor diversity across different trading platforms and countries.

2 - A Novel Approach to Thematic Portfolio Construction:
Patent-based Investment Decision Making

David Jaggi, Alexander Posth, Carsten C. Guderian
In thematic investing, investors rely on long-term trends to detect andsubsequently invest in what they assess as innovative companies withpromising technologies. The established method for investors is to
identify such companies via qualitative assessments or business sector classifications. Due to the established links between innovations,(new) technologies and patents, patent analytics has recently become
an information source in thematic investing. However, there seems to
be a mismatch between business sector- and technology-based classifications, which potentially affects (1) assessments in which technologies companies are active in and (2) the grouping of companies withsupposedly similar technology profiles. With our research, we addressthese issues by analyzing the patent portfolios of the companies attributed to two Global Standard Classification Standard sectors. We
examine the patent attribution profile vector of the companies in these
sectors and compare them via the cosine distance metric and use clustering to group companies of high technological similarity and compare these to the S&P sectors. Further, we employ artificial intelligence
tools to identify names for these newly generated clusters to ease comparability and interpret the results with a Sankey diagram analysis anda network graph analysis. Our findings yield implications for various
decision-makers involved in thematic investment, including investors,
corporate management, and policymakers.

3 - Stochastic dynamic optimization in strategic financial
management

Antti Korhonen
Financial institutions play a major role in the economy and the society
as a whole. Securing their stability and success is utmost importantin the current unpredictable financial markets. Recent bank failures
have demonstrated that financial crises tend to move fast in the global
economy. Managing uncertainty is the key issue in volatile operating
environments.
The talk will compare stochastic dynamic optimization methodology
with simulation-based approaches which are still widely used in financial institutions. Mathematical optimization offers great benefits insolving complex real world strategic financial management problems.
Only mathematical optimization is able to prepare institutions for diverse economic and financial market scenarios simultaneously and produce concrete decision recommendations. Simulation methodologies
are insufficient for today‘s business decision making.

EURO 2024 - Copenhagen
4 - Utilizing realized covariance estimators in finding safe
haven evidence

Josip Arneric, Tea Šestanović
In the recent literature, an extensive application of realized covariancehas been documented, which plays a key role in optimal allocation ofvarious assets within contemporary portfolio management. The advantage of realized covariance, against alternative measures, is utilizing intraday high-frequency data observed over short intervals. Using
this approach unknown integrated covariance matrix of a multivariate
diffusion process can be estimated. This research is focused on modeling realized covariance between the EUROSTOXX50 index of Eurozone and gold, aiming to explore the characteristics of gold as a safe
haven during crisis periods (including the COVID-19 pandemic and the
Ukrainian war). In this context, the primary goal is to determine data
properties, such as asynchronicity and the presence of microstructuralnoise and price jumps of return series. Subsequently, a suitable synchronization scheme and optimal sampling frequency will be chosen
to eliminate these issues. To determine which estimator provides anunbiased and consistent estimation of realized covariance between the
EUROSTOXX50 index and gold for investigating the safe-haven property, six covariance estimators will be compared: realizied covariance(RCOV), realized bipower covariance (RBPCOV), realized thresholdcoavraiance (RTCOV), realized outlyings weighte covariance (ROWCOV), robust realized two times sacled coavraiance (RRTSCOV) andHayashi-Yoshida realized covariance (HYRCOV).

 TC-58
Tuesday, 12:30-14:00 - Room: S07 (building: 101)

Waste Collection
Stream: VeRoLog - Vehicle Routing and Logistics
Invited session
Chair: Annarita De Maio
1 - Model for the Hospital Waste Collection Problem Considering Shared Load

Danna Manrique-Saavedra, Javier Arias-Osorio, Edgar E.
Córdoba-Sarmiento
Hospital institutions navigate daily through critical risk activities, giving rise to the substantial generation of medical waste. These activities
involve tailored control and monitoring procedures, encompassing logistical planning for the disposal and elimination of waste as an integral
part of achieving their mission.
This ethos extends universally across industries and supply chains associated with healthcare. Consequently, this study delves into thepharmaceutical supply chain from the perspective of conscientious disposal practices. It categorizes waste into hazardous and non-hazardous
types, linking them to designated treatment centers, whether those berecycling hubs or remanufacturing facilities. This classification takes
into account the lifespan parameter for non-hazardous waste and mandates direct incineration for hazardous materials.
Finally, the study introduces an optimization model to scrutinize various hospital institutions as prolific sources of waste. These institutions would be visited by a diverse fleet of waste collection vehicles,
equipped to handle both hazardous and non-hazardous waste. The
model aims to address the primary queries of the Inventory Routing
Problem (IRP): 1) When is the optimal time for visits? 2) What volume
of waste should be collected? and 3) Which route should be pursued?
Additionally, it incorporates an analysis of shared load considerations
in each collection, particularly for smaller-scale instances.

2 - Tactical Planning Considering Different Levels of Flexibility for a Waste Collection Routing Problem

TC-58

The planning of waste collection operations typically involves a series of tactical and operational decisions. This work is concerned withstudying tactical decisions related to waste collection schedules andthe use of intermediate facilities for unloading waste, while optimizing
route planning at the operational level. The real-world inspired waste
collection problem at hand can be modelled as a periodic vehicle routing problem with intermediate facilities. Vehicles based at a central
depot collect waste from different locations and visit incinerators enroute, when they are full, and at the end of each tour to replenish theircapacity. The planning horizon is several days or weeks and, as plans
should be repeatable, a periodic plan is established. In order to provide
decision support to practitioners at both operational and tactical levels, we investigate more flexible collection schedules and flexibility inthe use of intermediate facilities and their allocation to vehicle routes,
considering minimum quotas and maximum capacities. The aim is tominimize overall transportation costs while avoiding overflows of thewaste bins. The problem is solved using an adaptive large neighborhood search with problem-specific operators and a local search strategy. We present computational results on benchmark instances fromthe literature and on a set of real-world inspired instances from the cityof Vienna showing the effects of allowing more flexibility in planning.

3 - A Roll-On Roll-Off Vehicle Routing Problem for Industrial Waste Collection: A Case Study in Northern Italy

Annarita De Maio, Francesca Vocaturo, Stefano Bortolomiol,
Antonio Napoletano
This work tackles the issue of waste collection for a company situatedin the Northen Italy. The project aimed to streamline waste collection
planning through the implementation of a dedicated optimization engine. This problem involves collecting various types of waste frommultiple customers, which then need to be transported to specific landfills. Compatibility constraints arise from the nature of the waste, thevehicle type, or the container loaded onto the vehicles. The collection process can unfold in various ways: containers may be loaded
directly onto the waste management company’s trucks and then returned empty to the original customer, or the loads (always equivalent
to full containers) can be transferred onto the containers attached tothe trucks and transported to the landfill. Moreover, the service must
adhere to specific time windows associated with depot, customers, andlandfills. The problem is defined as a roll-on roll-off pick-up and delivery problem with time windows. Given its complexity, a three-phase
covering heuristic is developed. In the first phase, the algorithm clusters customers into subgroups; in the second phase, route generation
takes place, while in the third phase, a modified version of the Chvátal
heuristic is implemented to determine a feasible solution, with the aimof minimizing costs and ensuring the maximum coverage of served
customers. The approach was tested using real data instances provided
by the partner company.

4 - The Twin Rural Postman Problem

Marcos José Negreiros, Augusto Palhano, Nelson MaculanThis work shows a new arc routing problem where it is given a strongly
connected weighed mixed graph and its required set of links (edges andarcs) and vertices, and a departure/end vertex. The Twin-Rural Postman problem (TRPP) is formed by at least two routes that departs andend at the same vertex v0, in such a way that both cover with minimum
cost (distance) all the required links and vertices, and one of the RPP’sroute is performed in such a way that the other twin is no more than amaximum distance from the other, once both movements are desirable
to be synchronized. This problem appears in the waste collection insuburban areas of Brazilian cites, as in country cities, where a truck
collects in a waste generator sector some streets continuously and alaborer perform a walking tour collecting and joining waste in special
points where the vehicle will pass. We present the problem as a multiobjective combinatorial optimization problem, propose a procedure tosolve it, and evaluate solutions considering what is doing in the field,
what can be planned without algorithm support, and finally with algorithm support for the sectors of the cities of Andradina, Jales and São
José do Rio Pardo/São Paulo-Brazil.

Christina Hess, Alina-Gabriela Dragomir, Karl Doerner277

TC-59EURO 2024 - Copenhagen TC-59
Tuesday, 12:30-14:00 - Room: S08 (building: 101)

Pricing Strategies
Stream: Pricing and Revenue Management
Invited session
Chair: Yeqing Zhou
1 - Optimal Pricing with Customer Heterogeneity in
Duopoly Markets: Insights from Network Effects

Kimitoshi Sato, Nako Iwaji
This study considers a pricing problem in a duopoly market where
customers have different preferences with respect to congestion. Customers choose a product based on the quality and price of the productsold by each firm, as well as the degree of congestion (aggregate demand) for that product. Customers who prefer congestion are more
likely to choose a store if the store is more congested in order to obtain
utility. Such a phenomenon is called a positive network effect (customer type P). On the other hand, customers who do not like congestion will use a store when it is less congested. This is called a negative
network effect (customer type N). In this study, we formulate a pricecompetition model under a linear demand function in order to clarify
how the intensity of the network effect and the ratio of customer types
affect competition among firms. Our analysis reveals that The equilibrium price increases with the number of type P, and the maximum
revenue of both firms increases accordingly. The higher the intensity
difference between positive and negative network effects, the higher
the profit of the firm with good quality increases and that of the firmwith poor quality decreases. Furthermore, comparing the case where
both firms jointly determine prices and the competitive situation, weshow that when the ratio of type N and the negative network effect arestrong, the profit from competition exceeds that of joint.

2 - Optimizing Monopoly Airline Markets: An Integrated
Approach to Scheduling, Fleet Management, and Dynamic Pricing

Soheil Sibdari
This study introduces a sophisticated model designed to optimize operational efficiency and revenue management for airlines operating
within monopolized markets. Unlike previous models that separately
address scheduling, fleet assignment, and pricing strategies, our approach integrates these components into a unified framework. The
model leverages advanced algorithms to simultaneously determine optimal flight schedules, fleet allocation, and pricing strategies, taking
into account demand elasticity, competitive dynamics, and operational
constraints. We incorporate real-time data analytics to dynamically adjust the model parameters, ensuring the airline’s ability to respond tomarket fluctuations and passenger preferences effectively.

3 - Empirical and Theoretical Results on Bertrand Duopoly
with Interaction

Arik Sadeh
The Bertrand competition is an arena where firms compete in the pricemarket. In Cournot games, firms compete by setting their quantities
in the market. The attractiveness of Bertrand’s games in economics isthe visibility of prices to all competitors. Games are commonly usedin markets with a few decision makers where the playing firms see
downward demand functions. In this study, we analyze two firms in a
duopoly market. The firms sell nonidentical products that satisfy thesame customers’ needs. The demand function for each firm is based on
its price and the competitor’s price, along with an interaction of bothprices on each demand. We show that there is more than one Nashequilibrium point that leads to stability questions, The role of the costfunction, its functional form, and parameters impact the number of potential equilibrium points. This is an essential issue in markets withtechnological and innovative products. The dynamics of Bertrand’s
games can serve as a mechanism to reveal the costs of the competition.
Along with the optimal paths for the players under varying circumstances, we developed a simulator to handle various scenarios relevant
to research.

278

4 - Simple Policies for Joint Pricing and Inventory Management

Yeqing Zhou, Adam Elmachtoub, Harsh ShethWe study the fundamental joint pricing and inventory control problem,
where the customers are price sensitive. In the continuous review infinite horizon setting, the optimal policy is known to be an (s, S, p)
policy, and the optimal price changes for every inventory state. We
consider policies with only a small number of prices offered and compare these policies to the optimal policy. When customer valuations
follow an MHR distribution, we provide policies with theoretical guarantees for revenue and costs. In the lost sales case, we show that there
exists a single price policy that achieves at least as much revenue as
the optimal dynamic policy while incurring costs at most sqrt(1+lnS)
times more. The cost ratio is improved to 1.225 when the valuation
distribution is uniform. When backlogging is allowed, we show that athree-price policy can achieve similar guarantees.

 TC-60
Tuesday, 12:30-14:00 - Room: S09 (building: 101)

Advanced heuristics for machine scheduling
Stream: Project Management and Scheduling
Invited session
Chair: Léa Blaise
1 - Flight test scheduling: An integer programming modeland a large neighborhood search algorithm

Guopeng Song, Hanqiao Tao, Roel Leus
Flight tests play a critical role in the research and development of new
aircraft as they help verify the airworthiness and capabilities and expose design and manufacturing defects. During the test course, a large
number of test tasks need to be scheduled appropriately so that theflight tests can be completed with minimum cost and high efficiency.
Therefore, there is a strong need for developing an efficient methodthat can generate high-quality test schedules. In this paper, we study
the flight test scheduling problem to minimize the number of required
test flights, thereby decreasing the cost and time required during theentire test course. We establish a mixed-integer programming model
to formally describe the problem, propose several computationally efficient lower bounds to help verify the quality of obtained solutions,and develop a large neighborhood search algorithm for generating ahigh-quality solution of the flight test scheduling problem effectively.
Comprehensive computational experiments are performed to demonstrate the efficiency of our proposed algorithm. We report some generalmanagerial insights based on the obtained computational results.

2 - More powerful energetic reasoning using redundant resources for the Cumulative Scheduling Problems

Kristina Kumbria, Jacques Carlier, Antoine Jouglet,
Abderrahim Sahli
In this presentation, we are dealing with the Cumulative Scheduling
Problems(CuSP). In the CuSP-decision, we are given a set I of n nonpreemptive tasks, meaning that once a task begins execution, it cannot
be interrupted until completion. A constant amount m of a given resource is available over the time horizon to process the tasks. Each taski has a release date r_i, a processing time p_i, and a deadline d_i. The
task i must be executed within its time window [r_i, d_i], and throughout its execution it requires a constant known amount c_i of resources.
The objective of this decision problem is to detect infeasibility. Until
now, for this problem, the most efficient algorithm is based on energy
reasoning. We have already proposed a more powerful energetic reasoning by solving a tripartition problem and built a checker by using a
Dynamic Programming method. In the CuSP-optimization, the deadline d_i is replaced by the tail q_i which is equal to C_max-d_i andthe objective is to find the minimal makespan C_max. The energetic

EURO 2024 - Copenhagenlower bound corresponds to the minimal value for which infeasibility
may not be detected. We can solve this problem by using the checker
already proposed for the CuSP-decision. We also improved the efficiency of the method by introducing redundant resources. Computation results confirm the efficiency of the proposed methods.

3 - Scheduling in Offshore Wind: Developing a Simulation
Tool Representative of Stochastic Weather

Hans Schwartzlose, Kong You Liow
Scheduling of activities and resources occurs across various sectors,
especially those susceptible by uncertainty, where budgets are significantly impacted by delays, and a need for sudden reprioritization. AtØrsted Wind Power operations, we face challenges due to weather conditions that affect accessibility of wind farms for multiple types of operation & maintenance. To address these challenges, we have createda Scheduling Engine designed to integrate weather uncertainties into
our planning systems. We are aiming to deploy the engine to planningproblems ranging from tactical asset planning over major component
exchange campaign planning, to operational weekly planning. Embedding the engine into our planning systems enables the creation ofmultiple scenarios and assists in understanding the potential impacts
of weather conditions durations. Building upon its generic foundation, with the capability of custom adaptation to the specific schedulingproblem, we aim to extend the Scheduling Engine to become the go-to
tool for all planning and scheduling needs within Ørsted Wind PowerOperations. This presentation outlines some of the complexities in designing the generic scheduling engine and developing it for real-world
application.

4 - Disjunctive scheduling using interval decision variables
with Hexaly Optimizer

Léa Blaise
Hexaly Optimizer, formerly known as LocalSolver, is a "model andrun" mathematical optimization solver based on various exact andheuristic methods. The presentation will introduce the different
components of Hexaly Optimizer’s local search through disjunctive
scheduling problems. We will first show how its modeling formalism can be used to express various academic and industrial scheduling problems using interval and list decision variables. These models are very compact, which enables the solver to handle even largescale problems. Detecting non-overlap constraints in the model provides the solver with valuable information, which can be exploited
through various scheduling-specific movements implemented in Hexaly Optimizer’s local search. However, due to the tightness of precedence and non-overlap constraints in good solutions to disjunctive
scheduling problems (Job Shop Scheduling Problem, for example),such a small-neighborhood search alone struggles to obtain good performance. Hexaly Optimizer overcomes this issue by reinforcing its
local search component with a solution repair algorithm based on constraint propagation. When a move renders the solution infeasible, it isgradually repaired, one constraint at a time, by heuristically shifting
the variables just enough to repair. To extend the local transformation
rather than cancel it, and to ensure the procedure is fast, we impose
never to backtrack on a previous decision to increase or decrease avariable’s value.

 TC-61
Tuesday, 12:30-14:00 - Room: S10 (building: 101)

Location with Multiple Actors
Stream: Locational Analysis
Invited session
Chair: Boglárka G.-Tóth
1 - A systematic literature review and research agenda on
Multi-Objective Facility Location

TC-61

Serena Fugaro, Antonino Sgalambro
Facility Location problems have been studied intensively due to theirwide range of practical applications in various sectors such as Humanitarian Aid Logistics, Healthcare, Public Transport. In general, thedecisions involved in Facility Location are inherently strategic, leading to the definition of problems characterised by many, potentiallyconflicting, objectives. The literature on Multi-Objective Facility Location problems is extensive and growing rapidly, yet there is a lack ofcontributions that comprehensively illustrate the state of the art. For
this purpose, we conducted a thorough systematic literature review by
analysing a sample of 193 relevant papers published in international
peer-reviewed journals between 2011 and 2023. Indeed, we focusedon modelling and algorithmic aspects, as well as on the managerial
context underlying the arising problems and yielding to their inherent Multi-Objective nature. This study has identified several researchgaps at the modelling and methodological level, but also in terms ofprominent potential applications not addressed so far from a managerial and practical perspective. Accordingly, we present some findings
from our analysis and propose a research agenda outlining some future
research directions in Multi-Objective Facility Location for both researchers and practitioners, and signposting how theoretical advancements could enable provision of services and design of systems thatare more inclusive, resilient and robust.

2 - Extensions to Competitive Facility Location with Multipurpose Trips

Malgorzata Miklas-Kalczynska
Competitive facility location models involve locating one or more newfacilities on the market where competing facilities already exist. The
goal is for the added facilities to capture the maximum possible market share. Recent extensions to the classic competitive facility location
problem introduce multi-purpose shopping for complementary or substitute goods. The idea comes from the observation that customers often shop for different goods and visit multiple locations during a single
trip. The facilities (called clusters) offering these other goods exist but
do not compete with our chain. The multi-purpose, multi-stop models existing in the literature thus far are, in fact, two-purpose, whichmeans that they assume a customer makes either a single stop or two
stops before returning to the point of origin.
We formulate an extension to a two-purpose continuous competitive
facility location model, introducing the possibility of more than two
stops when shopping for complementary goods or services. Our problem is non-linear and non-convex, thus difficult to solve. We provide asolution technique and run experiments on instances incorporating 100
to 20,000 demand points and up to 20 facilities. We compare the solutions obtained by our model to the single-purpose and the two-purpose
models. We show that introducing the possibility of more than two
stops increases the market share captured by our chain.

3 - A bilevel gradual covering facility location model withcustomer preferences and different facility types

Hande Kucukaydin
We address a gradual covering competitive facility location problem
where two competing retailers must decide on opening various types
of facilities by taking into account customer preferences represented
by probabilities for visiting different facility types. It is assumed that
customers at a demand point are entirely willing to travel to a specific type of facility when it is located within a full coverage radius.
However, if a facility is positioned beyond this full coverage distance,
customers are either partially covered or not covered at all. It is also
assumed that customers at a demand point can visit several facilities
of different types of a retailer. However, among facilities of the same
type, customers at a demand point can visit more than one facility ofthe same type if each facility belongs to a different retailer’s chain. In
such a case, the captured proportion of the buying power by a single
facility is determined by both its own coverage extent and the total
coverage provided by all facilities, which may lead to a loss of somebuying power. Thus, the revenue generated by a facility depends not
only on the visiting probabilities but also on the coverage strength in
the presence of competitors. We formulate a nonlinear integer bilevel
programming model and propose tabu search heuristics for the solution.

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TC-62EURO 2024 - Copenhagen

4 - A Stackelberg location problem on the plane using delivered price equilibria

Boglárka G.-Tóth, Jose Fernandez
We study the problem of finding the optimal location and price equilibria of a Stackelberg location game when price equilibria are used.
First, the leader and then the follower choose their locations and thenset delivered prices to maximise their profits. Previous work has solved
problems of location-price equilibria on different settings, showing thatthe minimiser of the social cost gives a location equilibrium. Any solution to the Stackelberg problem is also a location equilibrium, andit can be shown that there are location equilibria which are not minimizers of the social cost. Our research question is whether there may
exist a Stackelberg solution which is not an equilibrium provided by
the social cost. Assuming that firms set the equilibrium prices after
the locations are fixed, the problem can be modelled as a bilevel optimization problem. We propose an exact interval branch-and-boundalgorithm, suitable for relatively small problems, to obtain rigorous
solutions. Our main goal is to see how the Stackelberg solution candiffer from the location-price equilibria provided by the minimizationof the social cost.

3 - Iterated Greedy for the Yard Crane Scheduling Problem

Hongtao Wang, Rubén Ruiz, Eva Vallada, Fulgencia Villa
Yard crane scheduling problems (YCSPs) arrange requests for loading
containers by yard cranes on terminals. Of the scheduling nature andoperational complexity is a real-world YCSP with the assignments of
input/output (I/O) points that serve as dynamic buffers to receive andsend containers. The YCSP schedules storage and retrieval containers coordinated with their time parameters at the I/O points, which arealso limited and need to be assigned during the scheduling process. Instead of proposing hybrid or complicated approaches, we introduce aseries of simple Iterated Greedy (IG) methods upon a classical vanilla
IG. New destruction, reconstruction and local search operators are designed for task schedules and I/O assignments. It results in 3 IG algorithms which require little variation from the vanilla IG. Extensive
computational campaigns have shown a statistically wide margin of the
proposed IG methods over several state of the art approaches.

 TC-63
Tuesday, 12:30-14:00 - Room: S14 (building: 101)

 TC-62
Tuesday, 12:30-14:00 - Room: S12 (building: 101)

Container Stacking and Yard Planning II
Stream: OR in Port Operations
Invited session
Chair: Christopher Expósito Izquierdo
1 - Container relocation and retrieval tradeoffs minimizing
schedule deviations and relocations

Robert Klar, Anders Andersson, Anna Fredriksson, Vangelis
Angelakis
Ports are striving to improve operational efficiency in the face of everincreasing trade volumes. In this context, the operation of port terminal
storage yards is key, as complexity and poor coordination lead to containers being stacked without regard to retrieval schedules, resulting
in time- and energy-consuming reshuffling operations. This problem,
known as the block relocation (and retrieval) problem (BRP), has recently received considerable attention. While there are promising solutions to the BRP, the literature considers the problem in isolation,
optimizing a single operational parameter for one of the many port
stakeholders. This often leads to efficiency losses, since port processes
involve different stakeholders and port parts. In this work, we explicitly
focus on the scheduling of trucks for hinterland distribution: schedules
are often shifted to minimize reshuffling, which causes losses to transport forwarders and reduces the competitiveness of the port. We discuss the trade-off between minimizing the number of container reshuffles and minimizing the deviation from the scheduled time windows forcontainer retrieval by introducing a multi-objective optimization problem. Given the complexity of the problem, we present a greedy heuristic. Our results indicate that the number of schedule deviations can be
reduced without significantly affecting the number of relocations.

2 - Container Space Allocation using a Dominance-BasedRough Set Approach (DRSA)

Jana Ries, Salem Chakhar, Rosa G. González-Ramírez
Considering growing number of TEUs shipped globally, there continue
to be increasing resource constraints in container ports. In addition,changing and uncertain exogenous factors impact predictability andefficiency of container port operational planning. A DRSA approachis introduced, integrating a multi-criteria classification problem into
the solving approach of the container space allocation problem. The
approach is discussed in the wider context of port operations, emphasising the sensitivity of the problem solution on operational planning
strategies within a port system.

280

Advanced Options Strategies Using O.R.
and Machine Learning
Stream: OR in Banking, Finance and Insurance: New
Tools for Risk Management
Invited session
Chair: Ilkay Boduroglu
1 - A Machine Learning Model for Options Traders that
Predicts the Magnitude but not the Direction of PostEarnings Stock Price Jumps

Ilkay Boduroglu
US companies typically announce their earnings report after the stock
market has closed. Their stock price usually experiences a significant
move, up or down, the following day. Predicting the direction of thismovement can be tricky, as the stock price may go down even after agood earnings report and vice versa.
As a result, market players focus on non-directional options strategies,
such as Iron Condors, to capture a quick profit overnight. Even then, if
the magnitude of the post-earnings price move (in either direction) is
too large, this strategy ends up losing money. Therefore, it is crucial topredict the magnitude of the post-earnings price change successfully.
A highly successful predictive model could make a massive difference
in a hedge fund’s performance.
This paper aims to develop a Machine Learning model that predicts
the magnitude of the post-earnings stock price change. We first noticed that Estimize.com is an acclaimed data provider in the financeindustry, and it publishes consensus predictions for earnings reports
based on the predictions of its best competing analysts. Also, Thomson Reuters, one of the two major data providers in finance, publishes
Estimize’s consensus predictions the day before earnings releases. We
then decided to record the variance of the most confident analysts’ predictions at Estimize. We then analyzed the relationship between thisvariance and the magnitude of the post-earnings stock price change.
We have promising results.

2 - Creating Optimal Speculative Portfolios for 0-DTE (ZeroDays-To-Expire) SPY Options

Yusufcan Ozkan, Ilkay Boduroglu
The daily notional volume of 0-DTE SPY options is over 1 billion
USD. We created an Integer LP model to take advantage of this market every day. Our out-of-sample backtests produce Sharpe ratios thatexceed 3. We include commissions, slippage, and the margin requirement, without which the results would have been unrealistic.

EURO 2024 - Copenhagen
Our model, similar to Papahristodoulou’s (2003), is formulated so thatthe model’s optimal solution will determine the optimal speculative
portfolio. The integer decision variables are the quantities of available 1-DTE SPY options contracts. We assume we can trade thesequantities a very short time before the closing bell, the day before theexpiration date. So, technically, we trade 0-DTE SPY options contracts. In the future, we shall experiment with SPX options, which are
cash-settled, unlike SPY options.
We use constraints that limit all significant portfolio Greeks, delta,
gamma, theta, rho, and vega. Since there is only one underlying instrument, SPY, and one specific expiration date, all Greeks are additivein each portfolio. Our objective function maximizes the sum of differences between theoretical prices and market prices of available options,net of commissions and slippage. We compute theoretical prices as inPapahristodoulou (2003). The gist of our paper is the way we calculate
the theoretical prices of options and the validity of this computation.

3 - Deep learning-based allocation for financial strategy
replication in incomplete market.

Johan Macq, Yannick Malevergne, Marc Senneret, Patrice
Abry
We develop and implement a model-free, deep learning-based approach to tracking the performance of any chosen financial strategy
in the presence of market frictions. Our method provides the optimal dynamic allocation over a - possibly time-varying - set of trading instruments. To achieve the optimal replication strategy, we introduce a novel architecture called Universal Allocators, inspired byTransformer networks. Its number of parameters is independent of thedimension of the investment universe and the length of the training
path, allowing it to handle large portfolios without additional computational cost. We implement an adversarial training scheme using the
Wasserstein-1 distance to solve the replication task. To the best of ourknowledge, this is the first time that an adversarial training scheme using the Wasserstein-1 distance has been used to solve such a problem.
To illustrate our method, we replicate a highly non-linear strategy, similar to a collar index, which provides the daily performance of an underlying asset whose values have been clipped below and above a given
threshold. The replication set consists of a large number of vanilla options whose strike prices and expiration dates are defined over a discrete and sparse lattice, making the market incomplete. Using both
simulated and S&P500 data, we show that our method provides financially meaningful and interpretable allocations for replicating such a
strategy at moderate cost.

4 - Portfolio Optimization and Parameter Uncertainty

Anton Vorobets
Portfolio optimization has a mixed reputation among investment managers, with some being so skeptical that they believe it is almost useless due to the inherent parameter uncertainty. It is undeniable that
portfolio optimization problems are sensitive to parameter estimates,
especially the expected returns that are arguably also the hardest parameters to estimate. However, most practitioners still attempt to build
mean-risk optimal portfolios, albeit in implicit ways. Resampled optimization is a popular mathematical heuristic to tackle the parameter
uncertainty issue. It computes optimal portfolios using sampled parameter estimates and calculates a simple average of the portfolio exposures across samples. The unsatisfactory aspect of the resampled
approach is that there is no mathematical justification for using the
average of portfolio exposures, it just works well in practice. This article provides perspectives for understanding the resampling approachby analyzing the portfolio exposure estimation process from a biasvariance trade-off. We show that the traditional resampled optimization
corresponds to a naive version of stacked generalization. Finally, we
introduce a stacked generalization approach that can be used to handle
both parameter uncertainty and combine optimization methods in fullgenerality. We coin the new method Exposure Stacking.

TC-64

 TC-64
Tuesday, 12:30-14:00 - Room: S16 (building: 101)

Dynamic Vehicle Routing 1
Stream: VeRoLog - Vehicle Routing and Logistics
Invited session
Chair: Karl Doerner
1 - Dynamic Routing and Scheduling Optimization of Teleoperated Car-Sharing Service

Gideon Gottschalg, Arne Strauss, Marlin Wolf Ulmer, Jarmo
Haferkamp
Teleoperated vehicles are a promising concept for increasing the attractiveness and profitability of car-sharing services. Such vehicles can beremotely steered by an operator to the location of an on-demand vehicle requesting user. It eliminates the need for users to walk to a
car-sharing vehicle and the need for providers to relocate vehicles withdrivers on site to meet the temporal and spatial vehicle demand. To
ensure an acceptable service level, an effective utilization of the vehicle fleet and the number of operators is required. The decision process
involves deciding which vehicle should be steered next by which operator to fulfil which user request. This is challenging as the future
demand and the future availability of vehicles are uncertain, since therental duration and return location are unknown. We tackle this problem with a two-step allocation algorithm that combines a cost function
approximation with the prediction of future vehicle returns to minimize the waiting time for current and potential future users. The firststep identifies effective vehicle-user matchings which balance waitingtimes and driving efforts. In the second step, the sequence of identified matchings to be executed by the operators is determined, anticipating potential improvements by predicted future vehicle returns.
We demonstrate the merits of our approach in comparison to intuitive
benchmark policies in a computational study highlighting the characteristics of this novel problem.

2 - A Rollout Algorithm for Dynamic Stochastic Purchasing
Routing With Perfect Information Model Estimation

Daniel Cuellar-Usaquen, David Álvarez-Martínez, Marlin
Wolf Ulmer, Camilo Gomez
This paper addresses the dynamic stochastic purchasing routing problem faced by a purchaser in negotiating with multiple suppliers to meetits demand. The purchaser contacts suppliers sequentially and must decide whether to buy based on purchase prices, available quantities, androuting costs. Each supplier can be contacted only once, with uncertain
price and quantity information revealed upon contact. The purchaser
faces a maximum number of contacts within a planning horizon. This
problem is challenging because of the dynamic stochastic componentsof the negotiation process. The purchaser must weigh the information revealed by the current supplier against the remaining suppliers’
behaviors and route consolidation to make decisions. We propose arollout algorithm that combines dynamic programming with a perfect
information model to determine whether to purchase from the current
supplier and which supplier to contact next. Our method samples the
supplier behavior scenarios and solves independent deterministic problems to estimate the opportunity cost of not purchasing from remaining suppliers or the impact of purchasing from the current supplier.
We introduce a reverse discount factor to reduce the optimism in perfect information model estimation. Our method is validated against
benchmark policies using real procurement data from an e-commerce
platform. Results confirm the superior performance of the rollout algorithm and provide insights into benchmark policy effectiveness.

3 - The On-Demand Delivery Problem: Online Assignmentof Orders to Warehouses and CouriersPeter Dieter, Philipp Speckenmeyer, Guido Schryen
The surge in customers’ preference for online shopping has spurred
the growth of on-demand delivery services, exemplified by companies like Getir, Gorillas, and Flink. These companies promise nearinstantaneous deliveries, typically within a few minutes. To be able281

TD-01EURO 2024 - Copenhagento fulfill this promise, multiple micro-warehouses and a courier fleet
using e-bikes are employed. To address the assignment problem, thecurrent practice of logistics companies is to statically define spatial areas as polygons for each micro-warehouse and assign all customers
within this polygon to the respective warehouse. However, such astatic assignment neglects real-time information that might be used toachieve a better workload balance of orders between warehouses. In
this work, we suggest a dynamic and data-driven assignment of orders
to warehouses and couriers based on the current workload and previously assigned orders to the warehouses. The problem is formalized asa sequential decision problem, as customers arrive dynamically overtime. The goal is to minimize total tardiness. We develop methods tosolve the considered problem and apply them to problem instances ona stylized grid as well as to instances derived from real-world data of
Chicago. Our methods are benchmarked to current practices from theindustry, showing that a dynamic assignment can substantially reducetardiness.

Tuesday, 14:30-16:00
 TD-01
Tuesday, 14:30-16:00 - Room: Sportshallen (building: 101)

Veronica Piccialli
Stream: Keynotes
Keynote session
Chair: Elisabeth Köbis
1 - Optimizing machine learning: Enhancing interpretability and performance through mathematical optimization

Veronica Piccialli
This presentation highlights the interplay between optimization andmachine learning, examining two pivotal examples. First, we will explore how mathematical optimization can significantly enhance the interpretability of machine learning models, with a focus on classification problems. Interpretability is a crucial requirement for the practical
deployment of machine learning models across diverse fields. Optimal decision trees are fundamental tools for building robust and interpretable classification models. However, the challenge lies in their inability to ensure optimality for large datasets. Recently some efficientapproaches have been introduced for building optimal decision treesthat require all the features to be binary. We will describe an efficient
optimization-based approach for deriving a supervised discretizationof the original dataset. Thanks to the use of optimization, the proposed
technique allows to control the importance of the features extracted,
and hence the granularity of the discretization. This novel technique
not only facilitates the training of a robust and interpretable optimal
decision tree but also allows to address the complexities associatedwith larger datasets. Finally, we’ll transition to a pragmatic application in process engineering, showcasing the successful integration of
optimization and machine learning to solve an important real-world
problem.

 TD-02
Tuesday, 14:30-16:00 - Room: Glassalen (building: 101)

New Tools in Insurance Risk Management
Stream: OR in Banking, Finance and Insurance: New
Tools for Risk Management
Invited session
Chair: Massimiliano Menzietti
Chair: Giorgio Rizzini
Chair: Rita D’Ecclesia
1 - Modelling and pricing of multi-region catastrophebonds

Krzysztof Burnecki
The insurance-linked securities (ILS) market, as a form of alternativerisk transfer, has been at the forefront of innovative risk-transfer solutions. The catastrophe bond (CAT bond) market now stands for almost
half of the whole ILS market and is steadily growing. Since CAT bonds
are often tied to risks in different regions, we follow this idea by constructing different pricing models that incorporate various scenarios ofdependence between catastrophe losses in different areas. Namely, we
consider independent, proportional, and arbitrary two dimensional distributions cases. We also derive normal approximations of the prices
and compare them with prices obtained via bootstrapping. We findsignificant difference between those approaches. We believe that thesefindings can be helpful in modelling and pricing of other ILS tied tonatural disasters. For illustration purposes we analyse Property Claim
Services data.

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EURO 2024 - Copenhagen
2 - Ruin probability of the insurer-reinsurer quota-sharemodel with phase-type distributed claims

Marek Teuerle
In this work, we consider a bivariate risk process describing the capitals
over time of two insurance companies that share premiums and claims
with fixed proportion. Such a model can be used in modeling of thecapital for the insurer-reinsurer system with proportional reinsurance[1-5]. Our main results include a formula for the ruin probability of insurer or reinsurer in the infinite time for phase-type distributed claims[5]. Additionally, we will introduce two alternative methods for approximating the aforementioned ruin probability: De Vylder-type anddiffusion approximations [2,4]. The accuracy of our results is validated
using both synthetic and empirical data [2-5].
[1] F. Avram, Z. Palmowski, M. Pistorius (2008). A two-dimensionalruin problem on the positive quadrant. Insurance: Mathematics and Economics 42(1), 227-234 [2] K.Burnecki, M.Teuerle,
A.Wilkowska (2019). De Vylder type approximation of the ruinprobability for the insurer-reinsurer model, Mathematica Applicanda
47(1), 5-24 [3] K.Burnecki, M.Teuerle, A.Wilkowska (2021) Ruinprobability for the insurer-reinsurer model for exponential claims,
Risks 9(5), 86 [4] K.Burnecki, M.Teuerle, A.Wilkowska (2022).
Diffusion approximations of the ruin probability for the insurerreinsurer model driven by a renewal process. Risks 10(6), 129 [5]K.Burnecki, Z. Palmowski, M.Teuerle, A.Wilkowska (2023). Ruinprobability for the quota share model with phase-type distributed
claims.https://arxiv.org/abs/2303.07705

3 - Explainable AI methods for early warning system of financial crisis prediction

Jurgita Cerneviciene, Audrius Kabasinskas
Accurately predicting financial crises holds immense significance forthe national economy and the financial sector. The market’s instability
has significant implications for decision-making processes, emphasizing the need to accurately predict and anticipate economic fluctuations
at the earliest possible stage. Early warning methods can be a valuable
tool for financial risk managers in reducing the likelihood of extreme
losses. With the advent of modern technology and the availability ofreal-time data, it has become feasible to create forecasting models thatcan provide almost instantaneous predictions of financial crises. In thisstudy, we present a technique that combines Artificial Neural Network,
Random Forest, and XGBoost to analyze and predict the possibility ofa financial crisis based on various performance indicators of pension
funds, such as return on investment, risk level, and other related ratios.
By employing the rolling window technique and methods of Explainable Artificial Intelligence (XAI), such as SHAP (Shapley Additive
explanations) and LIME (Local Interpretable Model-agnostic Explanations), a comprehensive analysis was conducted. According to our
results hidden market regime (forthcoming financial crisis) within one
to ten days can be predicted with a high degree of accuracy. The riskadjusted performance metrics, such as the Bernardo and Ledoit ratio,
Sterling Ratio, Sortino ratio, and Pain index, turnout to be the mostimportant measures.

4 - Modelling shock propagation and resilience in financial
temporal networks

Giorgio Rizzini, Fabrizio Lillo
We provide a model for shock propagation and resilience in a temporal
network. Each node is a bank and two banks are connected throughan arc if a bank lends money at least once to another bank in a period.
We assume that each node is associated with two latent parameters, i.e.
the fitnesses, describing its propensity to create incoming and outgoing links at any time. At each time, the presence of an arc in the network is an independent realization of a Bernoulli random variable withprobability specified by a link function of the fitness of two nodes. We
assume that the fitness vector evolves accordingly to a VAR(1) throughwhich it is possible to model the lagged interactions between fitness. In
this framework, our aim is to study the resilience of a temporal networkby proposing a modification of the impulse response analysis, which,in the network case, is a non-linear function of the shock intensity. We
study on how a shock on a node of a temporal network propagates andhow the system relaxes back to the equilibrium state. We also propose

TD-03

a novel econometric estimation model that combines the Maximum
Likelihood Estimation method and the Kalman filter to estimate thefitness’dynamics. We test our methodology to synthetic networks andthen we apply it to the electronic Market of Interbank Deposit network. The analysis is suitable to detect evolutionary features and therelationships among financial institutions highlighting which are thecritical banks in shock spreading TD-03
Tuesday, 14:30-16:00 - Room: 1005 (building: 202)

Data science meets strongly NP-Hard CO
Stream: Data Science Meets Optimization
Invited session
Chair: Dimitri Papadimitriou
1 - Provably convergent algorithm for free-support Wasserstein barycenter of continuous non-parametric measures

Qikun Xiang, Zeyi Chen, Ariel Neufeld
We propose a provably convergent algorithm for approximating
Wasserstein barycenter of continuous and non-parametric measures.
Our algorithm is inspired by the fixed-point scheme of ÁlvarezEsteban, del Barrio, Cuesta-Albertos, and Matrán (2016), which begins
with an initial measure and iteratively updates it via its pushforward by
the mean of optimal transport (OT) maps to the input measures. However, the convergence of their scheme relies on obtaining exact OT
maps which is computationally intractable for general non-parametricmeasures. Hence, we develop consistent estimators of OT maps via
shape-constrained least squares regression, and replace the OT maps
in the fixed-point scheme with their estimated counterparts. This gives
rise to a stochastic fixed-point algorithm which is provably convergent to the true barycenter. Specifically, in order to obtain consistent
OT map estimators, we develop tailored approximation techniques forpreserving the regularity of both the true OT maps and the estimatedOT maps throughout the iterations. Moreover, our algorithm does not
restrict the support of the barycenter and can be implemented in a distributed computing environment. These properties make it suitable forlarge-scale information aggregation problems, e.g., multi-expert consensus in recommender systems and distributed sensor networks. In
our numerical experiment, we showcase the practicality and efficiencyof our algorithm for expert consensus on probabilistic forecasting.

2 - A Novel Criterion for Batch Size Adaptation in Stochastic Gradient Methods

Marco Boresta, Alberto De Santis, Stefano Lucidi
In this work, we introduce a novel interpretation of the variance associated with batch gradient estimates in machine learning model training. By reevaluating this variance, we introduce a novel strategy forthe dynamic adjustment of batch sizes throughout the training process.
This approach diverges from traditional approaches by prioritizing amore detailed representation of variance behavior, which can lead tomore efficient and effective training regimes. We test our methodology
against a series of problems, benchmarking it against current state-ofthe-art methods to demonstrate its advantages and applicability.

3 - Data-Driven Submodular Set-function Optimization:
Theory and Applications in Assortment Planning andRecommender Systems

Jigar Patel
Recommender systems and informed assortment planning facilitate
the display of goods or services tailored to individual customer
needs/characteristics, promote the visibility of assorted products, andrevenue growth. We address the problem of optimal assortment anddisplay of online search results for goods or services. The objective
is to maximize the platform’s revenue and customer engagement byleveraging the menu of displayed search results to users, subject to283

TD-04EURO 2024 - Copenhagencatering to their individual search criteria or characteristics. Our analysis is based on a detailed data set from a leading online platform.
Assortment planning involves optimization over a utility set-function.
For example, a supermodular utility function is related to complementary goods, and a submodular utility function is related to substitutes.
By analyzing our data, we identify properties of our utility function
and implement the proper optimization algorithm tailored to individual
users. Recent advances in the discrete optimization field has enabled
us to provide optimal or near-optimal algorithms in polynomial time totackle the difficult problem of submodular function optimization

 TD-04
Tuesday, 14:30-16:00 - Room: 1001 (building: 202)

Robust and Multi-Level Optimization

Because Sigma-2- and Sigma-3-hardness proofs are usually tediousand difficult, not so many complete problems for these classes are
known. This is especially true in the areas of robust and bilevel
optimization (we focus in this talk on min-max-regret, interdiction,
most vital vertex, and two-stage robust optimization problems). Even
though these areas are well-researched for over two decades and one
would naturally expect many (if not most) of the problems occurring inthese areas to be complete for the above classes, almost no completeness results exist in the literature. We address this lack of knowledge
by introducing over 70 new Sigma-2-complete and Sigma-3-complete
problems. We achieve this result by proving a new meta-theorem. Thismeta-theorem applies to most classical problems, like clique, vertex
cover, knapsack, TSP, facility location and many more. In summary,our work reveals the interesting insight that a large amount of NPcomplete problems have the property that their min-max versions are’automatically’ Sigma-2-complete.

4 - A Branch-and-Bound Scheme for a Class of Partial Inverse Combinatorial Optimization Problems

Stream: MINLP
Invited session

Maximilian Merkert, Eva Ley

Chair: Maximilian Merkert

We consider partial inverse optimization problems, which are bilevel
optimization problems in which the leader aims to incentivize the follower to include a given set of elements in the solution of their combinatorial problem. For solving partial inverse combinatorial optimization problems with only weight increases, we present a new branchand-bound scheme. In this talk, we focus on the partial inverse shortest path problem with only weight increases. Branching on follower
variables, the scheme relies on two different methods that are basicallycomplete inverse shortest path problems on similar graphs, which are
known to be solvable in polynomial time. Computational experimentssuggest that for dense graphs our branch-and-bound scheme outperforms an MPCC reformulation as well as a decomposition scheme.

1 - On the Complexity of the Bilevel Shortest Path Problem

Dorothee Henke, Lasse Wulf
We introduce a new bilevel version of the classical shortest path problem and completely characterize its computational complexity with respect to several problem variants. In our problem, the leader and thefollower each control a subset of the edges of a graph and together aim
at building a path between two given vertices, while each of the twoplayers minimizes the length of the resulting path according to theirown edge lengths. We investigate both directed and undirected graphs,
as well as the special case of acyclic directed graphs. Moreover, wedistinguish two versions of the follower’s problem: Either he has tocomplete the edge set selected by the leader such that the joint solution is exactly a path, without any additional edges, or he is allowed
to include only a subset of the leader’s selection into the final path. In
general, the bilevel problem turns out to be much harder in the former
case: We show that the follower’s problem is already NP-hard here andthe leader’s problem is even hard for the second level of the polynomial hierarchy, while both problems are one level easier in the lattercase. Interestingly, for acyclic directed graphs, this difference turns
around, as we give a polynomial-time algorithm for the first version of
the bilevel problem, but it stays NP-hard in the second case.

2 - Robust Optimization Under Controllable Uncertainty

Eva Ley, Rebecca Marx, Maximilian Merkert, Tim Niemann,
Sebastian Stiller
Applications for optimization with uncertain data in practice often feature a possibility to reduce the uncertainty at a given query cost, e.g.,
by conducting measurements, surveys, or paying a third party in advance to limit the deviations. To model this type of applications, we
introduce the concept of optimization problems under controllable uncertainty (OCU). For an OCU we assume the uncertain cost parameters
to lie in bounded, closed intervals. The optimizer can shrink each ofthese intervals around a certain value called hedging point, possibly
reducing it to a single point. Depending on whether the hedging points
are known in advance or not, different types of OCU arise. Moreover,the models may differ with respect to when the narrowing down, theunderlying optimization, and/or the revelation of true data take place.
In the talk, we discuss two example problem settings - one with known
and one with unknown hedging points - in more detail, where we handle the remaining uncertainty by the paradigm of robust optimization.
For both cases, we give conditions under which a single-level reformulation is possible. Throughout the talk, we use shortest-path problems as underlying optimization problem for illustrating specifics ofand phenomena arising in OCU.

3 - A large and natural Class of Sigma-2- and Sigma-3complete Problems in Bilevel and Robust Optimization

Lasse Wulf

284

 TD-06
Tuesday, 14:30-16:00 - Room: 1013 (building: 202)

Advancements of OR-analytics in statistics,
machine learning and data science 15
Stream: Advancements of OR-analytics in statistics, machine learning and data science
Invited session
Chair: Paul Harper
Chair: Jinal Parikh
1 - A methodology of predict-interpret-suggest for used car
pricing

Qihao Wu, Yong-Hong Kuo
Pricing used cars is a complicated process that involves various influencing factors. This study deploys a series of interpretable machinelearning models to predict used car prices with elaborate feature engineering. A comparative analysis reveals that ensemble tree algorithms
can achieve the highest performance, with an R-squared value exceeding 0.93. Through examining the interpretable feature attributes,
the used cars’ age, make, and engine capacity are ranked as the top
three most significant determinants of price. Furthermore, our wrapper
method explores the heterogeneity of features and datasets, by which
the performance of neural networks with numerical features on more
specific data is highlighted. Finally, the treatment effects of significant
binary variables are summarized as suggestions for consumers. By
integrating predictive modeling, interpretability, and causal inference,
this study presents a novel Predict-Interpret-Suggest framework for theused car market.

EURO 2024 - Copenhagen
2 - A network-based model for dynamic analysis of thebanking sector

Riccardo Bastianutti
This paper introduces an innovative model to interpret the dynamics ofbanking sector, moving away from the traditional reliance on individual firm variables. Instead, it proposes a network approach to capture
information from inter-firm interactions. The model utilizes Principal
Component Analysis (PCA) to extract the first principal component,interpreted as a proxy for each firm’s weight within the banking sector. A graph is constructed, with the first principal component acting
as the weight for links between firms, and the firm with the highest
weight serving as the network’s center. Applied to a dataset of banking sector firms, the model effectively captures sector dynamics. Active nodes, those with an indegree greater than zero, are identified andanalyzed over time. Results are compared with the S&P 500 index,
revealing a strong correlation (above 0.91). Additionally, a predictivemodel is implemented on the probability density function (PDF) of
the first principal component to analyze sector evolution. Predictivemodels demonstrate an error of less than 5%, highlighting the model’srobustness. The proposed model boasts several advantages over existing approaches. Firstly, it provides a more comprehensive view ofthe banking sector by considering inter-firm interactions. Secondly,
it identifies key players in the banking sector and their impact on dynamics. Lastly, it offers predictive capabilities for the banking sector’s
future evolution.

3 - Source Condition Double Robust Inference on Functionals of Inverse Problems

Nathan Kallus
We consider estimation of parameters defined as linear functionals ofsolutions to linear inverse problems. Any such parameter admits a doubly robust representation that depends on the solution to a dual linear
inverse problem, where the dual solution can be thought as a generalization of the inverse propensity function. We provide the first source
condition double robust inference method that ensures asymptotic normality around the parameter of interest as long as either the primalor the dual inverse problem is sufficiently well-posed, without knowledge of which inverse problem is the more well-posed one. Our result
is enabled by guarantees for novel iterated Tikhonov regularized adversarial minimax estimators based on minimax (robust) optimization
over general hypothesis spaces.

 TD-07
Tuesday, 14:30-16:00 - Room: 1019 (building: 202)

Behaviour and decision support
Stream: Behavioural OR
Invited session
Chair: Ilkka Leppanen
1 - Do people willfully ignore decision support? Evidence
from an online experiment

Stefan Haeussler, Sebastian Bachler, Katharina Momsen,
Matthias Stefan
The increasing usage of decision support systems typically hinges
on the assumption that objective information provision can improve
decision-making. This premise, however, disregards the potential forhumans to deliberately ignore information in a manner that serves theirown interests. We study willful information avoidance in the usage ofdecision support with the help of an online experiment where participants solve a task of high cognitive complexity, i.e. a knapsack problem. We indeed find first evidence for motivated reasoning in our experiment in one of two dimensions: First, we do not find evidence thatparticipants willfully ignore decision support when it might suggest
revising their initial choices. Yet, we find evidence that they willfully

TD-07

ignore information that might tempt them to act selfishly. Thus, participants use decision support to self-commit to more pro-social choices.
Contrary to that, participants do not avoid decision support information to behave egoistically while maintaining a positive self-image.
Although our results seem like good news, they show that even objective information provision in decision-support is no guarantee thathumans acquire it in an unbiased way.

2 - Shaping Programmer Practices: Effective Strategies for
Mitigating Bias in Machine Learning Algorithm Development

Seyedmohammadreza Shahsaheni, Osman Alp, Justin
Weinhardt, Alireza Sabouri
In recent years, increased awareness of gender bias in machine learning has led developers to focus more on gender equality in modeloutcomes. This study investigates how fairness norms and accuracy
thresholds impact developers’ model choices and reporting decisions.
The experiment design in this study replicates the machine learning algorithm development process. Results from 604 programmers participating in this experiment show that fairness norms led to the selectionof fairer models (Hypothesis H1). Moreover, developers in companies
valuing accuracy less prioritized fairness when informed of its norm
(Hypothesis H2). Interestingly, we found that high accuracy thresholds with fairness norms encouraged participants to prioritize fairness,even at the expense of personal benefits. As an exploratory analysis,
we study the reporting provided by programmers and the effect of theirbackgrounds and demographics on their models’ performance. This
study highlights the importance of fairness norms in guiding machinelearning development and reporting, aiding gender equality in AI applications.

3 - Workload and Operational Performance in Digital Control Rooms: The Dual Role of Human-automation Interaction

Changyu Men, Marijn Verschelde, Maud Van den Broeke,
Bart Roets
In today’s business context, employees increasingly experience workload pressure, which impacts operational performance. To alleviate theworkload burden and mitigate its potentially negative effects, automation is frequently seen as a solution. However, the mere implementation of automated systems is insufficient, and it is essential to understand employees’ behavior in terms of how they interact with digital
automation systems, a topic still understudied in current literature. Our
study aims to expose the key role of human-automation interaction in
relation to workload and consequential operational performance. For
this purpose, we analyze a rich dataset of digital control rooms at amajor European rail system operator. This unique and novel setting
allows us to examine the impact of controllers’ automation intervention willingness on their workload and operational performance. On
the one hand, we find that a higher workload increases train delays andthat employees who have a tendency to adjust the decision from theautomation system experience a significant increase in workload. On
the other hand, we find that employees with a tendency to adjust the
automated system’s decisions exhibit greater resilience to mitigate thenegative effects of workload on train delays. These findings underscore
the necessity to consider the dual effect of human-automation interaction in relation to workload management and operational performanceand the value of studying new settings.

4 - Unveiling the credit nature of Buy-Now-Pay-Later

Mee Chi So
Consumers often see buy-now-pay-later (BNPL) as a convenient payment mechanism allowing them to spread the purchase cost over aninterest-free period. Many consumers do not realise it is a form ofcredit and may result in late charges being incurred. This lack ofunderstanding is one of the contributing factors that has led many
BNPL users to overspend and accumulate debt. This project aims totransform our understanding of how BNPL impacts consumers’ spending behaviour and inform information provision practices so that consumers can make better informed decisions. A series of experiments
will be conducted to evaluate how BNPL should be presented so that285

TD-09EURO 2024 - Copenhagenconsumers are given appropriate and adequate information prior to instigating a contract. The results will inform financial regulators as they
introduce regulation on BNPL offers, whilst helping protect consumers
and ensure that BNPL is sustainable.

 TD-09
Tuesday, 14:30-16:00 - Room: 10 (building: 116)

Long-term energy system planning
Stream: Energy Markets
Invited session
Chair: Kenneth Bruninx
1 - The role of CfDs in future Electricity Markets

Lena Kitzing
Against the backdrop of an ongoing debate on reforming the European electricity market, leading voices in academia and EU institutions
agree that electricity markets must be supplemented with additional
long-term options, including Contracts-for-Difference (CfDs). A key
design question for renewable support schemes in general, and CfDs
in particular, is how to prevent electricity market distortions and preserve short-term, operational market integration without jeopardising
the effectiveness of the schemes in leveraging investment of private
capital for renewable energy deployment. Different experts advocate
for different versions and implementation options of CfDs, including generation-based and generation-independent design approaches.
Generation-based two-sided CfDs have so far been used in more than
200 auctions across ten European countries, namely Denmark, France,
Greece, Hungary, Ireland, Italy, Poland, Portugal, Spain, and theUnited Kingdom. We show that generation-based CfDs in existing
designs can be non-distortive for day-ahead markets. Remaining issues arise through spill-over incentive effects across market segments(e.g. towards intraday, balancing and futures markets). We discuss
generation-independent CfDs, which have theoretical advantages overgeneration-based designs, in particular in relation to intraday and balancing markets, but feature unresolved implementation challenges andwould imply rather significant changes in the market.

2 - Capacity Expansion Planning under Uncertainty subject to EENS Constraints

Marilena Zambara, Daniel Avila, Anthony Papavasiliou
We present a method for solving a large-scale stochastic capacity
expansion problem which explicitly considers reliability constraints,
in particular constraints on expected energy not served (EENS).
Our method tackles this problem by relaxing the EENS constraints.
We solve the relaxed formulation in an iterative manner, using a
subgradient-based method. Each iteration requires the solution of astochastic capacity expansion problem, for which we implement subgradient and cutting plane decomposition schemes that have recently
been introduced in the literature and have proven very effective insolving large instances of such models. We implement the proposed
methodology on the Economic Viability Assessment (EVA) model thatis used by ENTSO-e in the annual European Resource Adequacy Assessment (ERAA). The EVA is extended to include reliability constraints. We are able to solve this "extended" EVA and obtain theleast-cost capacity expansion plan for meeting specific reliability requirements per zone, fully accounting for uncertainty. Our approachalso allows us to attain the shadow price of load shedding by zone
(through the Lagrangian multipliers of the relaxed constraints) which
can serve as an indication of the zone-specific VOLL or price cap that
could in principle deliver the appropriate level of investments within
an energy-only market.

Recently, challenges related to climate change have increased the needfor ever more complex energy system models, covering more sectors,
more technological detail, as well as high spatial and temporal resolution. Furthermore, step-wise decarbonisation goals (e.g., for theheat sector) may require contradictory system designs for different future years. Similarly, anticipated, but only slowly implemented, developments of the hydrogen system may necessitate certain transition
technologies. With the inherent risks for lock-in effects or stranded assets, the timing of infrastructure investment decisions has increasingly
gained importance. Current energy system models often focus either
on designing this sector-coupled energy system in great detail, lacking
knowledge of the pathway to achieve it, or on designing pathways toa decarbonised energy system, lacking a detailed consideration of thesystem’s operation.
We give an overview of algorithms that help depict complex transition
paths together with high operational details, e.g., adaptive temporal
resolutions and ways to account for seasonal storage, classical decomposition algorithms (e.g., Benders), closely related algorithms such as
stochastic dual dynamic programming (including extensions), as well
as approaches based on simulation. We further discuss requirements
and features, potential (dis)advantages, and present test cases to illustrate how these algorithms can be applied to a wide range of energy
system models.

4 - A multi-objective optimization approach to assess thetrade-off of potential energy and material supply risks
in the energy transition

Gianvito Colucci, Valentin Bertsch, Valeria Di Cosmo, Jonas
Finke, Laura Savoldi
This work proposes a methodology for the combined assessment ofenergy and material supply risks (SRs), using a multi-objective optimization (MOO) approach in energy system models (ESMs). Indeed,
the transition from fossil fuels to low-carbon sources is decreasing theenergy import dependency of many countries, while increasing the riskof potential bottlenecks along the entire supply chain of low-carbon
technologies, leading to an energy security trade off. As policymakers
are promoting new devoted policies, ESMs represent suitable tools totest their effectiveness. However, despite the studies evaluating futurematerial requirements are increasing in number, they are usually done
ex-post starting from already available energy transition scenarios. In
that way, such energy scenarios are not affected by potential materials
and technology supply chain bottlenecks. That makes the analysis presented here among the first-of-a-kind assessments of energy and material SRs in a multi-objective energy system optimization framework.
The proposed methodology involves the consistent definition of the energy and material SRs for a reference energy system as two separate
objective functions to be used in a MOO. The trade-offs between suchSRs, costs and CO2 emissions are studied through MOO optimization
for a case study developed within the open-source Temoa framework,
providing insights about technology competitiveness in terms of energy security.

 TD-10
Tuesday, 14:30-16:00 - Room: 11 (building: 116)

Kidney Exchange II
Stream: OR in Health Services (ORAHS)
Invited session
Chair: Joel Joris Van de Klundert
Chair: Twan Dollevoet
1 - Evaluating policy changes to Eurotransplant’s kidney
allocation rules with Discrete Event Simulation

Hans de Ferrante, Bart Smeulders, Frits Spieksma
3 - Modeling long-term energy transition pathways: Algorithmic approaches and their properties

Stefan Strömer, Kenneth Bruninx, Stefan Pfenninger

286

Eurotransplant (ET) prioritizes candidates for deceased-donor kidneytransplantation with the ET Kidney Allocation System (ETKAS) algorithm. ETKAS is a points-based system which awards points forEURO 2024 - Copenhagen
patient and donor attributes, including dialysis time, HLA match quality, candidate age, relative location of the candidate w.r.t. the donor,
and chances of finding a donor with at most 1 HLA mismatch in the
ET donor pool.
The ET Kidney Advisory Committee (ETKAC) regularly points outthat ETKAS is outdated. For instance, ETKAS still assigns equal priority to matches on HLA-A, -B and -DR loci, ETKAS does not include
age matching, and ETKAS inadequately compensates immunized candidates for having a restricted donor pool. Despite recognition of such
issues, the weights assigned to ETKAS attributes has changed little
since ETKAS was introduced in 1996.
To facilitate ETKAC discussions on revising ETKAS, we developed
the ETKAS simulator, which is a discrete event simulator based on
ET registry data simulating ET kidney allocation. We find that thesimulator accurately describes candidate dialysis times at transplant,
mortality rates, and transplantation rates in ET member countries. To
demonstrate utility of the ETKAS simulator for policy evaluation, we
map the impact of assigning extra points to HLA-DR matching relative to HLA-A and HLA-B matching, as well as directly prioritizing
immunized candidates by assigning points for virtual Panel Reactive
Antibodies (vPRA).

2 - Wanted, Dead or Alive: Leveraging Deceased Donors
for Enhanced Patient Outcomes in Kidney Exchange
Programs

Roby Cremers, Twan Dollevoet, Joel Joris Van de Klundert,
Michal Mankowski
Kidney transplant is crucial for end-stage renal disease patients, yet demand for kidneys surpasses supply. Organs can be obtained from bothdeceased and living donors, with compatibility between patient anddonor being a pivotal factor in the matching process. Kidney Exchange
Programs (KEPs) aim to address the kidney shortage issue by facilitating exchanges between living incompatible donor-recipient pairs, either through cycles or through chains initiated by a living donor. To
boost transplant rates, the use of deceased donor kidneys to trigger
transplant chains has been proposed, which involves the last donor in
the chain donating back to the deceased donor waiting list. This study
investigates when to use deceased donors to initiate chains, aiming tomaximize the total number of transplants, while ensuring patient types
on the deceased donor waiting list are not disadvantaged. First, we use
an offline Integer Linear Programming framework to assess the efficacy
of using deceased chain-initiating kidneys (CIKs) within the existing
practice. Building on this foundation, we introduce a stochastic modelusing Sample Average Approximation to address the dynamic and uncertain nature of the problem. Through simulation, using secondary
data from the National Kidney Registry between 2012 and 2016, weshow that deceased CIKs can potentially enhance patient outcomes interms of the number of transplants and wait times, but the results are
sensitive to the characteristics of the KEP.

3 - Half-Cycle: A new formulation for modelling kidney exchange problems

David Manlove, Mathijs Barkel, Maxence Delorme, William
Pettersson, Tom Smeets
A patient who requires a kidney transplant, and who has a willing but
incompatible donor, may be able to ’swap’ their donor with that of another patient, who is in a similar situation, in a cyclic fashion. Also,
non-directed donors can trigger ’chains’ of transplants involving multiple donor-recipient pairs. In this talk we introduce the Half-Cycle
Formulation (HCF), an integer programming (IP) model for finding
optimal sets of exchanges involving cycles and chains among incompatible donor-recipient pairs. In HCF, a cycle (i.e., set of kidney swaps)
is represented by two compatible halves, and we also demonstrate how
the model can be extended to incorporate chains. After giving several
algorithmic enhancements for HCF, we show through computational
experiments with an IP solver that our model is competitive in relation
to other formulations for certain cycle and chain size limits.

4 - Synthetic data generation for kidney exchange programme simulation

William Pettersson, David Manlove

TD-11

Kidney exchange programmes (KEPs) increase the potential for living
kidney donation, often using operational research techniques, and drastically improving the quality of life of recipients. This is achieved by
identifying an optimal set of transplants that can be performed withina pool of donor-recipient pairs via exchange cycles and chains. However, determining appropriate optimality criteria to use is not trivial,and only with historical real data or realistic synthetic data can simulations show how different criteria affect the performance of the KEP.
However, historical datasets can be difficult to obtain.
In this talk we walk through the process of producing a realistic synthetic data generator from real-world data using state-of-the-art techniques. We include full Jupyter notebooks that can easily be followedby researchers associated with real-world KEPs. We then follow up
by demonstrating how the data generator can be used to simulate theeffects of a number of different optimality criteria on the KEP.

 TD-11
Tuesday, 14:30-16:00 - Room: 12 (building: 116)

BOR in public policy and environmental
decisionsStream: Behavioural OR
Invited session
Chair: Judit Lienert
1 - Analysing the compensatory properties of the outranking approach PROMETHEE

Sebastian Schär, Jutta Geldermann
Compensation in multiple criteria decision aggregation procedures iscommonly understood as allowing a gain in one criterion to offset aloss in another criterion. The compensatory behaviour depends on theselected multicriteria approach but can for some approaches also be
steered by their specific design in terms of preference modelling andchoice of aggregation logic. In domains such as environmental or public policy decision-making compensation is not always desired. Theremay be impacts that pose a loss too serious to be counterbalanced byany kind of good performance on other criteria. Furthermore, compensation may overshadow the existence of strong minority views. It may
therefore be necessary to limit the extent to which an aggregation procedure can allow for compensation or explicitly acknowledge it. The
PROMETHEE methods are increasingly applied in environmental andpublic policy decision making due to their comprehensiveness and explainability. However, there are different statements in the literature
about their compensatory properties. Furthermore, guidelines for applicants and analysts which explain how to control and steer the degreeof compensation are apparently rare and opaque. We analyse the compensatory behaviour of outranking approaches with particular emphasis on the PROMETHEE methods and compare it to that of multiple
attribute value theory approaches with the aim of providing guidelines
for application and preventing unwanted compensation behaviour.

2 - Preferences and risk attitudes in Swiss pesticide governance

Milena Wiget, Judit Lienert, Karin Ingold
Negative effects on human health and the environment call for ambitious policies to reduce the risks of agricultural pesticide use. Yet,
strategic uncertainty exists, which is often neglected. New policies
must be of utility to a range of stakeholders to be implemented. We
studied how preferences for achieving multiple conflicting objectives
and domain-specific risk attitudes towards uncertain consequences influence the support of new policy alternatives. To this end, we combined concepts from policy analysis and Multi-criteria Decision Analysis. We collected data on preferences, risk attitudes, and policy support from 24 key stakeholders and their collaboration partners in Swiss
pesticide governance. Analyzing the data with Bayesian ordinal logistic regression models, we found specific relations between the support of certain policies and preferences, risk attitudes, or both. In
wicked socio-environmental decision problems with great uncertainty,

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TD-15EURO 2024 - Copenhagen

we thus suggest considering risk attitudes additionally to preferences
to develop ambitious policies with potential for a sustainable transition.
Our contribution is threefold: Theoretically, we present an innovative
combination of the Advocacy Coalition Framework and Multi-attribute
Value/Utility Theory. Methodologically, we describe a standardized
approach to elicit preferences and risk attitudes, novel to policy analysis. Practically, we discuss the potential of pesticide risk reduction
policies in the case of Swiss agriculture.

3 - Advancing BOR: Structuring challenges and (inter)disciplinary approaches in the energy transition

Marta Lopes, Carlos Henggeler Antunes
The energy transition is a global priority to address climate change
and ensure energy security. While technological developments play
an instrumental role in enabling this transition, the importance of human behaviour cannot be understated. People’s energy consumption
practices significantly impact energy demand patterns. Additionally,
the adoption of renewable energy technologies depends on public acceptance, and energy projects often affect communities. Operational
research (OR) models and methods have been successfully employed
to address several complex problems in the energy sector, such as thelocation of infrastructures, the energy sharing in energy communities
and market design, involving multiple stakeholders, including utilities,
governmental agencies, and consumers. The objective of this study
is to organise the relevant operational research challenges related tothe energy transition, identify the associated behavioural aspects, andexamine several disciplinary approaches used to address these challenges. Ultimately, the aim is to identify research gaps, contributing to
the advancement of the behavioural OR discipline.

4 - Time and behavioral implications are neglected in applied environmental decisions

Judit Lienert, Fridolin Haag, Philipp Beutler, Martijn KullerTime can play a decisive role in practical applications of Multi-Criteria
Decision Analysis (MCDA) aiming to support environmental and otherpublic policy decisions. When we decide about options that affect environmental (or societal) systems, we often address long time ranges.
An overview of applications of MCDA published in non-OR journals(Operational Research) indicates that time is heavily neglected. Whatabout the OR literature? A systematic search of OR journals indicates that the temporal aspect of decision-making in MCDA is also
neglected in our field. In this talk, I will present some data to support this claim. Additionally, I shortly present insights from own research projects, where we have started addressing temporal aspects ofdecision analysis. This includes a prominent question in economics
that has not been thoroughly treated in our literature: do preferenceschange over time; and does it matter? Furthermore, using an example
from wastewater infrastructure planning, I demonstrate how we canimplement MCDA in complex real-world environmental projects andinclude time aspects. We used a very pragmatic approach to evaluate
decision options over time with MCDA, which nevertheless seemeduseful to support decision-making. Various mathematical approaches
to deal with temporal decision-making have been proposed, but what
about behavioral aspects? I conclude by proposing research questions
that could be tackled by the BOR community.

 TD-15
Tuesday, 14:30-16:00 - Room: 18 (building: 116)

Nurse rostering
Stream: OR in Health Services (ORAHS)
Invited session
1 - An indirect time study for nursing tasks in Belgian hospitals: methodological aspects and optimal sampling

Stijn De Vuyst, Kurt De Cock, Dieter Claeys288

In this presentation we report on a large-scale time study to estimate
the expected duration of a set of common nursing tasks in 25 Belgian
hospitals involving over 600 nurses. A direct time measurement bystarting and stopping a timer at the beginning and end of every executed task proved to be too invasive and complicated if done by thenurse itself and too expensive if done by an outside expert. We therefore used an indirect method by equipping each nurse with a smart
phone running an application that beeps at irregular time intervals. After every beep, within a time-out interval, the nurse registers what task
type she was executing. In addition, after the nurse’s work shift is finished, she is presented with a list of 10 task types and asked to tally
how many tasks of each type were executed during the shift. For acertain task type, the beeps allow us to estimate the fraction of time anurse spends on tasks of this type, while the tallies estimate their rate,i.e. how many tasks of this type occur per time unit. The ratio of thefraction and rate is the expected duration.
We provide approximations of the bias and the variance of the resulting estimator using the Delta method leading to reliable confidence
intervals and optimal strategies for deciding which task types to tallyin future nurse shifts. In addition, we investigate the quality of ourapproximation by comparing to Monte Carlo simulations.

2 - Identifying the key factors of job satisfaction of thesecond-career certified nursing assistants in long-termcare

Chien-Hui Wang, Ru-Ying Li

The aging population and declining birth rates have become social issues in many Asian countries, especially Taiwan. To alleviate the significant shortage of manpower, hiring second-career certified nursing
assistants has emerged as a new trend in care institutions. This study
combines interviews and the Analytic Hierarchy Process to explore
the job satisfaction and key factors influencing turnover decisions ofsecond-career care workers. The findings of this study will contributeto understanding the factors affecting job satisfaction among secondcareer care workers and serve as a reference for training programs inlong-term care institutions.

3 - A study of flexibility and the chaining principle in the
mid-term nurse scheduling problem

Diego Fiorotto, Maria Paula Padilla, Karim Martínez

The scheduling of personnel is a common problem that healthcare institutions frequently face worldwide. It consists of assigning nurses
to shifts to satisfy a series of constraints relative to the workforce’s
skills, employee preferences and legal regulations. The current healthcare landscape, characterized by rising demand and a decreasing workforce, together with the effect that scheduling has over aspects likecosts, turnovers, and job satisfaction, has become a great motivation
for studying this problem. Cross-training, which consists of training
nurses to cover additional units that require distinct skills, has beenstudied in different sectors from manufacturing to healthcare as a strategy to add flexibility to the healthcare systems and improve its responsiveness. In this paper, we study existing forms to include this typeof flexibility and propose a mathematical formulation to incorporate
it. Computational experiments over different scenarios show that the
proposed extensions are functional and can produce solutions for the
mid-term nurse scheduling problem. From the experiments, we derive some interesting insights about cross-training flexibility. First, ourresults show that it is more beneficial to invest in intensity, which isthe number of cross-trained nurses, than investing in breadth, i.e., thenumber of additional units a nurse is trained to. Furthermore, there is atrade-off between flexibility and the amount of dedicated working timefor each unit.

EURO 2024 - Copenhagen TD-16
Tuesday, 14:30-16:00 - Room: 19 (building: 116)

Performance and scouting in football
Stream: OR in Sports
Invited session
Chair: Felix Sage
1 - Top Players Prediction in Football: Interesting Facts on
Data Preparation and Analysis

Senthil Murugan Nagarajan
In the dynamic landscape of football, predicting the emergence andsuccess of top players is a crucial aspect that has gained momentum
with the advent of advanced analytics. This presentation explores thefascinating world of forecasting top players in football, focusing on the
intriguing facts surrounding data preparation and analysis. By delving into the intricacies of collecting and processing vast datasets, thispresentation unveils the methodologies employed to identify key performance indicators and potential breakthrough talents. Leveraging
cutting-edge analytics, teams and analysts aim to unravel patterns andtrends that transcend traditional scouting methods. This talk highlights
the synergy between technology and talent evaluation, offering insights
into the challenges, breakthroughs, and captivating facts that define thelandscape of predicting top players in football.

2 - Data science models for football scouting: the Racingde Santander case study

Manuel Duran, Diego Brunetti, Sebastián Ceria, Guillermo
Durán, Andrés Farall, Nicolás Marucho, Pablo Mislej
The use of data to improve the performance and operation of sports
institutions is increasingly expanding. Related to scouting, the mostrenowned case is that of Moneyball, which summarizes the successful
use of data to improve the performance of a US professional baseballteam that had a much smaller budget than that of most of its opponents.
In this work we apply different data science techniques to improve
the scouting of a professional soccer team. In particular, we attack
the problem of similarity between players, so that a sports manager
can have information about who is the player most similar to someone he has to replace, or who he cannot access due to a budgetaryissue. Various similarity schemes were employed to compare players
by considering their specific individual characteristics as covariates.
Some approaches adopted a non-supervised perspective, while others
utilized supervised models that incorporated valuable information obtained from the players’ known positions on the field as a target for supervision. The disparity between these contrasting approaches yieldedcomplementary results. We take as a case study that of the Real Racing
Club de Santander, from Spain Second Division,

3 - TOPSIS++: A New Approach for Player Recruitment andSelection in Team Sports

Mark Connor
Player recruitment and selection is one of the most important factors
in determining the success of a team. The recruitment process is acomplex task that involves considering various factors (form, fitness,etc) whilst being constrained by the team’s budget and the availabilityof players in the market. The recruitment process is further complicated by the fact that the performance level of a player is not static andcan change over time. Therefore, it is important to consider both theplayers current form and the player’s future form in recruitment and selection decisions. In this paper, we propose a new approach for player
selection and recruitment in team sports. TOPSIS++ is an extensionof the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS) method that is widely used in decision-making problems.
TOPSIS++ is designed to address the limitations of the original TOPSIS method by considering not just a player’s current form but also
their previous and future predicted form. We demonstrate the effectiveness of TOPSIS++ by applying it to the problem of player selection
and recruitment in football. We compare the performance of TOPSIS++ with the original TOPSIS method and show that TOPSIS++

TD-18

performs better in terms of identifying the best player to recruit whenevaluated against the actual performance of the players in proceeding
seasons. Our results show that TOPSIS++ is a promising approach forplayer selection and recruitment in team sports.

4 - Should he stay or should he go? Head Coach turnoverand cultural disparities in the market for managers - Empirical Evidence from European Professional Football

Felix Sage
When a football team is experiencing a sporting downturn, there arelimited short-term options available from the club’s management perspective. Building a better-performing squad in the short run is not
feasible. Another potential adjustment is to change the team’s headcoach. This analogy can be transferred to leaders managing any organization. We focus on the question of which characteristics are attributed
to a successful long-term leader. The paper investigates the impact ofteam performance and individual characteristics of the coach on thecoach’s tenure. For this purpose, we use various data from the top
five European leagues (first and second division) from the 2012/13 to
the 2022/23 season. In order to adequately analyze this impact, we use
elaborate competing risk models to identify the effect of cultural differences on various reasons of coach dismissals. The data is gathered at
the matchday level and thus obtained a dataset covering 43,985 matchdays. To our knowledge, we are the first to simultaneously examine
these effects across ten different European leagues over a decade. Just
like the modern cooperation, most football teams consist of employees(players and head coaches) of different cultures producing language
barriers which can particularly lead to communication and transaction costs. The unique dataset allows us to include these cultural andlinguistic effects that could impact team performance, coach’s performance, and ultimately, the coach’s tenure.

 TD-18
Tuesday, 14:30-16:00 - Room: 42 (building: 116)

AI and ESG for the small economy SDG
agenda (EWG-ORD Workshop 2)
Stream: OR for Development and Developing Countries
Invited session
Chair: Nina Kajiji
Chair: Gordon Dash
Chair: Gerhard-Wilhelm Weber
1 - A reinforcement learning mechanism for trading wind
power futures

Bruno Kamdem, Moustapha Pemy
Recent developments in Europe have disrupted gas supplies andreignited the urgency of moving towards electricity production fromwinds. But, the narrow predictability of wind and the intermittency ofelectricity generation can complicate decisions within trading desks.
To alleviate the issue at hand, an attempt in this paper is to provide an
optimal training mechanism by accounting seasonalities in wind productions as electricity sells in the exchange. We formulate the problem
from a standpoint of an institutional investor concerned with ESG factors. For the trader, we define his reward at each future price after
a number of trades. We introduce a parametrized approximate valuefunction using deep convolutional neural network in which we specify the weights of the Q-network at each iteration. We derive somekey implications for the trader as to the best time to buy wind power
futures and at what price to do it.

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2 - Generative South African ESG indexes and multi-target
neural complexity in return prediction

Gordon Dash, Nina Kajiji, Bruno Kamdem
A company’s dedication to environmental, social, and governance principles (ESG) has led to an interest in developing and evaluating firmlevel ESG scores. Despite the considerable interest in assessing firmlevel scores, investment analytics lack standardized country-specific
ESG indexes to comprehensively represent sustainability while facilitating asset predictability. This deficiency is noticeable in the emerging
BRICS capital markets (i.e., Brazil, Russia, India, China, and South
Africa (SA)). Focusing on SA, the primary objective of this study is
to formulate an innovative algorithmic framework to identify distinct
dynamic ESG factors capable of assessing sustainability risk in securities traded on the Johannesburg Stock Exchange (JSE). The proposed
alternative data procedure utilizes asset returns in the exploratory factor analysis to generate maximum validity factor scores to formulate
JSE-specific sustainability factors. The effectiveness of these factors
is assessed through a machine-learning model operating within a complex information space that considers the interconnections of valuation
signals. Employing an explainable AI technique (XAI), we determinethat the newly created S-factor emerges as the primary feature for explaining next-period returns. Following closely behind is the E factor,
the SA volatility index, and a precious metals factor. XAI reveals thatthese factors excel at discerning the varying emphasis South African
firms place on their ESG signals.

3 - Optimizing post-pandemic recovery: a comparative
analysis of machine learning techniques for tourism
SMMEs revenue predictive modelling

Helper Zhou, Gordon Dash, Nina Kajiji, Mabutho Sibanda
The COVID-19 pandemic dramatically affected global tourism, especially South African tour operating Small, Medium, and Micro Enterprises (SMMEs). These enterprises are now leveraging machinelearning (ML) to make informed decisions using predictive models.
However, choosing the right ML technique is challenging for these operators due to their limited technical expertise. This study compares
shallow and deep learning methods for predicting tour operator performance, using a dataset from 650 South African SMMEs, spanning2019-2021. The enhanced radial basis function (K4-RANN), a shallow
learning model, outperformed deep learning models, showing better
prediction accuracy with lower error rates and higher R’ values. Therefore, the study recommend that tourism SMMEs adopt the K4-RANN
model for more accurate booking and revenue projections, thereby improving decision-making and efficiency post the Covid-19 pandemicera.

4 - Investors’ perception of firms’ ESG focus: Evidence
from staggered implementations of Benefit Corporations law in the US

Laura Ferraro, Devendra Kale, Anis Triki
Benefit Corporations (BC) pursue a dual purpose: maximization of
profits and generating societal and environmental benefits. BCs are
becoming popular across the globe, with BC laws being passed in different countries. To date, BC laws have been passed in 43 US statesand several countries in Europe and South America. Using the staggered adoption of BC law across US states, we examine how investors
reacted to this law. We find that investors reacted negatively to thepassing of the law. Our results are robust in using alternate measuresof abnormal returns and excluding firms in the financial services sector. Moreover, our results are robust regarding entropy balancing thethree moments. This suggests that investors in the US do not favor corporations adopting a dual purpose (as opposed to a solo focus on profit
maximization). We further find that the negative reaction is predominantly for smaller and loss-making firms. Since most of the BCs in the
US are, in fact, small in size, investors expect smaller firms to adopt
the BC status. We also find that investors’ negative reactions softened
after the onset of the COVID pandemic when the focus on ESG saw
an exponential increase. As an additional analysis, we are replicating
our work in the Italian context, as it was the first European country toadopt the BC law. Our results are significant for stakeholders, including investors, boards, regulators, and analysts.

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Tuesday, 14:30-16:00 - Room: 44 (building: 116)

Decomposition techniques applied toenergy problems
Stream: OR in Energy
Invited session
Chair: Angela Flores
1 - Decomposing stochastic energy system optimization
models - time-splitting in Benders Decomposition vs.
PIPS-IPM++

Shima Sasanpour, Manuel Wetzel, Karl-Kiên Cao, Andres
Ramos

Energy system optimization models (ESOMs) are a helpful tool to design future energy systems. Although uncertainties in the input dataassumptions can significantly influence the structure of the energy system, they remain unconsidered in many cases. Through the applicationof stochastic programming (SP) it is possible to obtain expansion decisions that offer risk hedging with regard to the uncertainty of future
developments. ESOMs become large in size when a broad regional
scope and technological diversity for sector coupling are considered,even without taking uncertainties into account. Therefore, speed-up
techniques are needed to keep the models solvable, especially if SP
is additionally considered. Benders Decomposition (BD) is a widelyused method to solve SP models since the second stage can be solved
in independent subproblems, allowing for a parallelization along thestochastic scenario dimension. However, the cardinality of the scenario
set is usually much smaller than the number of time steps. In our analysis we expand the parallelization potential of stochastic ESOMs byadditionally splitting the scenarios along the time dimension. We apply
this decomposition technique to two methods: First, we consider timesplitting in BD in combination with MPI. This is then compared to theparallel high-performance computing solver PIPS-IPM++, which wasrecently extended to also consider stochastic optimization, allowing a
decomposition along the scenario and time dimension.

2 - Analyzing decomposition approaches for large-scaleenergy system models

Lene Marie Grübler, Felix Muesgens

The share of renewable energy generation capacities will rise substantially in the upcoming decades, which requires the expansion of power
grids and storage solutions. Current research therefore often aims at
jointly optimizing expansion and operation of generators, storages andgrid infrastructure, resulting in complex optimization problems to besolved. Their structure reveals decomposability on the temporal aswell as on the spatial scale, which allows the application of decomposition techniques to keep them computationally tractable. While investment decisions spanning over multiple time steps represent complicating variables on the temporal scale, constraints describing interregional power exchanges represent complicating constraints on thespatial scale. Previous decomposition approaches in energy systemmodelling have either been performed on the temporal or on the spatial scale, but combined approaches have not yet been investigated.
As energy system models hold complicating variables and constraints,
there exist numerous possible decompositions of the original problem,
which may be solved by different decomposition techniques. In thiswork we analyze which techniques are most efficient in optimizing
large-scale multi-regional energy systems. We deliver a case study
for the comparison of different decomposition strategies for a specificproblem type and contribute to the open question of which decompositions are most suitable for which type of optimization problems.

EURO 2024 - Copenhagen
3 - Hybrid decomposition method for power system planning under uncertainty

Angela Flores
Integrating both short-term flexibility requirements and long-term uncertainty into a unified planning framework results in large-scale multistage stochastic mixed-integer programming problems. Consequently,
efficient solution methodologies are needed to enable the planning ofrealistic-size power systems. This work investigates possible ways ofcombining column generation, lagrangian relaxation, and Benders decomposition in a hybrid decomposition method, aiming to enhance
convergence by leveraging the strengths of diverse decomposition
techniques. The proposed hybrid decomposition is implemented using
distributed computing, allowing parallel initiation of each decomposition approach with the potential for exchanging bounds and solutions.
Solutions from Benders decomposition and the bundle method at each
iteration can be used to generate new columns for the column generation approach. Likewise, the columns obtained by column generation
can be used to derive new cuts for Benders decomposition and the bundle method. The performance of the hybrid decomposition is analyzed
through case studies applied to the NREL 118-bus power system.

 TD-20
Tuesday, 14:30-16:00 - Room: 45 (building: 116)

Advancements in AI and Genomics:
Bridging Technology and Biology for Future
Healthcare Solutions
Stream: Computational Biology, Bioinformatics andMedicine
Invited session
Chair: Aleksandra Swiercz
1 - AI-powered lab-on-a-chip platform for rapid bacterial
detection

Felipe Tetsuo Yamada, Flávia Barbosa, Luis Guimarães
Antimicrobial resistance (AMR) presents a serious threat to publichealth by undermining the efficacy of antibiotic treatments, leading
to prolonged illness and heightened healthcare costs. Nearly 5 million deaths occur annually due to drug-resistant infections, with theEuropean Union recording over 33,000 fatalities yearly, coupled withan economic impact exceeding EUR 1.5 billion annually in healthcare
expenses and productivity loss. The widespread misuse of antibiotics
across various sectors, including human medicine, animal husbandry,
and agriculture, coupled with inadequate infection prevention and control measures, fuels the proliferation of resistant bacteria, driving the
AMR crisis. Addressing AMR in bacteria requires concerted actions
to promote judicious antibiotic usage, enhance infection prevention
and control protocols, develop new antibiotics, and bolster surveillance systems to monitor resistance trends effectively. Utilizing molecular detection techniques, this study employs a microfluidic lab-on-achip device with DNA and bacteriophage proteins as bio-recognition
molecules. The device generates unique signals for different fluid compositions. The aim is to develop a machine learning model capableof analyzing these signals, thereby aiding in the detection and monitoring of bacteria and AMR genes during infections. Ultimately, thiswill improve antibiotic prescribing decisions in both human and animal
healthcare.

2 - Genomic Map of Poland versus artificial intelligence opportunities, challenges and threats

Piotr Lukasiak, Aleksandra Swiercz, Maciej Majchrzak, Jacek
Blazewicz
Computing, together with computational tools supported by machinelearning, plays a key role in the creation of national genomic maps by

TD-21

enabling the analysis, interpretation and use of vast amounts of genetic
information from multiple sources. Operations research methods are
essential for understanding the genetic diversity of populations and foradvances in personalised medicine and disease inheritance research.
They influence the modelling processes of genome sequencing, comparison with reference genomes, identification of genetic variants andassessment of their functional consequences. Data processing enables
the integration of genetic information from different sources, enabling
the understanding of the relationships between genetic variation andphenotypic traits and diseases, as well as the identification of population structure and the analysis of genetic relationships between individuals in a population. The Genomic Map of Poland is a set of unique
databases of genomic sequences reflecting the genetic picture of thePolish population, together with computational and visualisation toolsthat allow the analysis and interpretation of large amounts of genetic
data and the integration of different sources of information, leading
to an understanding of the genetic diversity of the population and theidentification of relationships between genes and phenotypic traits anddiseases.

3 - Assesment of the quality of de novo assembled genome

Aleksandra Swiercz, Alicja Dzik, Piotr Lukasiak, Jacek
Blazewicz
The first draft of the human genome project was published more than
two decades ago. It was incomplete, especially in the centromeric andtelomeric regions, which are highly repetitive. With the developmentof third-generation technology, it has become possible to fill most of
the gaps in the reference genome (T2T consortium). However, longread technologies can cover large portions of the genome, but this involves higher error rates. Thus, de novo sequencing projects often
use different sequencing technologies to, on the one hand, obtain long
DNA fragments and, on the other hand, improve the sequence quality
with short reads.
In the Genomic Map of Poland project, we used a pipeline to construct de novo a diploid human genome based on the trio: mother, father and child. We used several technologies in the pipeline: shortreads, PacBio HIFI, Hi-C and ultra-long Nanopore. The resulting
chromosome-wide scaffolds were compared to reference genomes(GRCH38 and CHM13). We then assessed the quality of our reference
genome assembly by analyzing the consistency of k-mers appearing in
the genome sequence and in short and long reads. It was also checked
whether k-mers specific to only one of the parents (mother or father)occur in the copy of the chromosome inherited from one parent (eachcopy of the chromosome contains small differences in the sequence,
specific to the individual). Various k-mer features indicate the highquality of the assembled genome.

 TD-21
Tuesday, 14:30-16:00 - Room: 49 (building: 116)

Complex societal problems
Stream: OR in Humanitarian Operations (HOpe)
Invited session
Chair: Anna Nagurney
1 - Towards Effective Assignment Decision-Making in
Refugee Resettlement

Jésica de Armas, Valérie Bélanger, Marie-Ève Rancourt,
Gilbert Laporte
The number of asylum applications in the European Union (EU) has
significantly increased during the last years, highlighting the need for
effective decision-making in the resettlement process. This research
focuses on the refugee status, the most common outcome among asylum seekers, and the resettlement process that transfers refugees froman asylum country to a receiving country. Integration plays a crucialrole in the success of resettlement, benefiting both refugees and receiving countries. Efforts from all parties, including refugees’ adaptation and receiving communities’ readiness, are necessary for successful integration. The National Integration Evaluation Mechanism291

TD-22EURO 2024 - Copenhagenproject evaluates the integration outcomes of refugees in 15 EU member states, providing a comprehensive overview through a set of integration indicators. The priorities of receiving countries and the preferences of refugees also influence the assignment process. However, nocomprehensive optimization framework exists that combines these dimensions for decision support. This study proposes a decision-making
system that incorporates integration potential, receiving countries’ priorities, and refugees’ preferences, considering quotas and analyzing
different scenarios. The system aims to aid organizations in making informed decisions and could be integrated into existing information systems. The research contributes to addressing the complex challengesof refugee resettlement and has potential societal impact.

2 - Decentralization Approach for Global Relocation Planning under Climate Change

Kash Barker, Buket Cilali, Andres Gonzalez, Ahti Salo
The upcoming climate crisis is a consequential driver for displacementof communities, a little-studied problem in operations research. Its
effects would strike people worldwide, causing displacement in great
quantities. The unique characteristics of this problem - including consideration of a long-term planning horizon, future uncertainties, andheterogeneous populations and numerous locations under different climate change impacts at varying levels - require a different approachto handle this problem. For these reasons, our approach can assist in
developing relocation plans to manage climate-change-induced movements with a long-term outlook while using the resources effectively
and protecting the well-being and dignity of both displaced and receiving communities. The first step of these plans is the high-level planning of where the displaced people should go and when. The second
step reflects the mixed decision-making structure resulting from the involvement of many local authorities and the necessity of looking after
global humanitarian interests. We suggest a consensus structure thatcompromises the global outcomes and local interests using a global
optimal model, self-optimal local models, and a final global model thatuses the outputs of the global and self-optimal models and iterates byadjusting the decisions until fairness among all participating hosts isachieved.

3 - Health Product Availability in the Presence of Cash Constraints: A Study of Community Health Entrepreneurs in
Rural Kenya

Lisanne van Rijn, Harwin de Vries, Lucas Veelenturf
Access to essential medicines and other health products remains a challenge in low- and middle-income countries, particularly in rural areas.
Community health entrepreneurs (CHEs) play a key role in bridging
this gap by selling health products in small rural stores. However,
CHEs frequently experience stock-outs due to cash constraints, leading to lost sales. How to relieve cash constraints in rural areas is currently unclear. We address this question by conducting a field experiment that tests two interventions with 458 CHEs in Kenya in collaboration with social enterprise Healthy Entrepreneurs. The first intervention involved setting up stock-hubs. These are are small consignment
stock locations where CHEs can replenish their inventory on-the-spot,
thereby reducing the need for cash-on-hand. The second intervention
is a cashflow game designed to help CHEs realize the value of retaining money in their business, thereby improving their cashflow resulting
from increased sales. Although both interventions yielded positive effects on CHE sales, they were not statistically significant. The cost ofaccessing the hub and informational barriers limited CHEs’ ability to
fully utilize the stock-hubs. The results of the cashflow game highlighted the need for additional measures beyond raising awareness toaddress cash constraints effectively. This study provides valuable new
insights into addressing cash constraints in areas with high resupply
costs and long resupply intervals.

4 - Quantification of International Trade Network Performance Under Disruptions to Supply, Transportation,
and Demand Capacity, and Exchange Rates in Disasters

Anna Nagurney, Dana Hassani, Oleg Nivievskyi, Pavlo
Martyshev

292

Both sudden-onset and slow-onset disasters are causing disruptions to
global trade, impacting the availability and affordability of commodities from agricultural to mineral ones. In this paper, we develop amulticommodity international trade network equilibrium model underdisaster scenarios with distinct probabilities of the occurrence of thedisasters and their impacts on the capacities associated with production, transportation, and consumption. The disaster scenarios can also
affect the exchange rates. We state the governing equilibrium conditions and derive the variational inequality formulation in commodity
path flow variables and Lagrange multipliers associated with the capacity constraints. For each disaster scenario, we construct an international trade network performance measure, an international trade network performance measure, followed by a unified performance measure that includes all the disasters and their probabilities. Robustness
is then quantified as the difference between the network performance
under no disruptions and the unified performance measure. An international trade network component performance indicator is also given
to assess the impacts of the complete removal of trade network supply
markets, demand markets, and/or transportation routes. The modelingframework is then illustrated through a series of numerical examples,
motivated by Russia’s war on Ukraine. The work is of relevance todecision-makers and policy-makers.

 TD-22
Tuesday, 14:30-16:00 - Room: 81 (building: 116)

Distributed energy systems
Stream: Energy Management
Invited session
Chair: Juana Lopez Redondo
1 - Multi-objective optimization for a microgrid energy
management system: Controlling battery lifetime andeconomic cost.

Juana Lopez Redondo, Luis Polanco, Víctor M. Ramírez, José
Luis Torres, J.d. Álvarez
Batteries are the element of the microgrid with the shortest and limited
lifetime, characterized by a finite number of charge/discharge cycles.
By optimizing charge/discharge cycles, the system can extend battery
life, contributing to overall sustainability. However, in microgrids, it
is often in the interest of minimizing their economic operating cost,which is a contradictory objective to maximizing battery lifetime, as
batteries can save energy from renewable energy sources for use during periods of high energy demand instead of using energy from themain grid. Therefore, the energy management system must addressthese objectives to ensure efficient operation of the microgrid. This
research presents a novel energy management system designed to simultaneously minimize energy costs and battery charge/discharge cycles in a microgrid. Our methodology employs a multi-objective optimization algorithm to identify solutions that satisfy both economic
and operational objectives. The result is a set of equally valuable solutions, giving decision makers the flexibility to choose the option thatbest fits their current preferences. This adaptability allows users or operators to change solutions as conditions evolve, eliminating the needto re-solve the optimization problem. The effectiveness of the energy
management system is demonstrated through its application in simulation to the microgrid located in a bioclimatic building at the Universityof Almería.

2 - Empowering Energy Exchange: A Tri-Level Decentralized Coordination Framework for TSOs, DSOs, andDERs

Tolga Karabas, Meltem Peker, Mustafa Kemal TuralWith the proliferation of Distributed Energy Resources (DERs) in
power grids, the transition to bidirectional power flow challenges traditional unidirectional energy systems. The uncertain nature of renewable DERs further escalates the complexity of energy management.
Despite these challenges, DERs offer valuable energy and flexibility

EURO 2024 - Copenhagenservices to power grids. This study proposes a novel tri-level decentralized transactive energy market framework to address these complexities. In this framework, Distribution System Operators (DSOs)
are positioned as key distribution-level local energy market coordinators and serve as crucial intermediaries, facilitating communication
between Transmission System Operators (TSOs) and DERs throughnetwork-aware pricing and transactive control mechanisms. By allowing DER participation through optimized bidding strategies in local energy markets (and indirectly in wholesale markets via DSOs),
the framework aims to optimize system operations while accounting
for uncertainty through distributionally robust optimization methods.
Thus, it presents a promising avenue for enhancing grid security andreliability through the proposed distributionally robust unit commitment and optimal power flow problems. By considering the dynamic
interactions among TSOs, DSOs, and DERs, the proposed coordination approach aims to lay the groundwork for a more resilient and efficient energy system management with optimized grid operations.

TD-23

 TD-23
Tuesday, 14:30-16:00 - Room: 82 (building: 116)

Remanufacturing and refurbishing
operations
Stream: Circular Economy, Remanufacturing and Recycling
Invited session
Chair: Neil Geismar
1 - The impact of supply shortage and uncertainty on product design for remanufacturing in a closed-loop supply
chain

Tanja Mlinar, Maud Van den Broeke, Roya Tat
3 - Improving the Battery Model for Energy Management
Systems in Microgrid Applications

Cesar Cerda, Rodrigo A. Carrasco
The adoption of non-conventional renewable energy sources (NCRE)
is essential to achieve the Paris Agreement’s objectives. This transformation does not only involve changing the primary energy sourceof large-scale electricity generation but, its effective adoption requires
changes at the level of the energy transportation and distribution network, including NCRE distributed generation and microgrids systems
to provide stability to the grid. Since NRCE are generally variable, energy storage and energy management systems (EMS) has become key
allies on this process. In this talk we present how improvements onmathematical model for battery storage in EMS for microgrid applications can produce better decisions, reducing the microgrid operation
costs significantly. For this purpose, a new battery model (MLPP-A) ispresented, which incorporates the nonlinearities of the battery charging process through a piecewise linear approximation. Our MLPP-A
model is benchmarked against a linear battery charging model (MLB),to get the parameters for both models experimentally, and to compare
their adjustment with real system, a microgrid test bed was implemented. To evaluate the EMS performance with both battery models,under same solar irradiance conditions, on different seasons of the year,
a computational simulation was implemented, which showed an average economic benefit of 6.33% once MLPP-A model is used instead of
MLB model, without significant additional computing time required.

4 - Incorporating two-stage stochastic programming in the
optimal scheduling of an offshore off-grid power-to-X
systemMaximilian Fey, Lorenz Häfele, Kai Hufendiek
Offshore off-grid power-to-X (PtX) processes are gaining research interest due to their potential in unlocking untapped renewable energy
sources and meeting the growing demand for green hydrogen and its
derivatives. However, the inflexibility of the chemical processes involved, coupled with the stochastic nature of wind power production
as the sole energy source, poses significant operational challenges. Despite the buffering function of energy and material storages included in
the system, the inherent uncertainty in wind power generation complicates the assurance of safe and continuous operation, given the narrow
operational windows and low ramp rates. Commonly proposed predictive control solutions, such as model predictive control (MPC), often
overlook the uncertainty from wind power forecast deviations, whichis particularly significant offshore. We propose a novel MILP-based
MPC framework that incorporates two-stage stochastic programming
to address this uncertainty. The framework generates scenarios using
an ARMA model based on deterministic wind forecasts and reported
statistical error characteristics. This approach aims to enhance the operational efficiency of off-grid PtX processes, thereby increasing theirload factor, product yield, and competitiveness. It is expected to facilitate wide-scale deployment of these processes, contributing in thetransformation towards a fossil-free economy.

Nowadays, companies are increasingly confronted with shortages anduncertainty in supply of raw materials. At the same time, in light ofsustainability, supply chains are increasingly interested in returning
used products from customers and remanufacturing them. We develop
an analytical model in a closed-loop supply chain context to study how
shortages and uncertainty in supply impact different supply chain decisions. We focus on a supply chain with a (re)manufacturer and collector as decision-makers. The manufacturer offers products (produced
from scratch or through re-manufacturing) and then sells them to thecustomers (i.e., forward flow). They also make product design investment decisions which affect remanufacturing costs, and set the products’ selling prices. The collector incentivizes consumers to returnused products, collects them (i.e., return flow), and sells them to the(re)-manufacturer who uses them for the purpose of remanufacturing.
We provide managers with insights on these decisions under a decentralized and centralized setting under supply shortage and uncertainty,
and conduct a sensitivity analysis with respect to various parameters.

2 - Optimal Processing and Pricing Policies in a Mobile
Phone Refurbishment System

Osman Kulak, Martin Grunow
The market for refurbished phones provides significant opportunities
for efficiently reusing scarce resources and grows rapidly. In sucha circular supply chain system, purchasing, refurbishment, and sales
are directly affected by a highly dynamic environment with fluctuating
prices and demands. To maximize the long-term average profit in the
refurbishment system, determination of the optimal purchasing, processing, and sales strategies is required. We propose a linear programming formulation for a Markov Decision Process that considers the arrival of multiple-raw materials and investigate how the sales price andpurchasing price variations, the correlation between sales and purchasing prices, and limited capacities for semi-finished and finished goods
inventory and for refurbishment affect the considered supply chain forrefurbished mobile phones. Our numerical experiments use industry
data and lead to critical managerial insights for our industry partner.

3 - How Marginal Value of Time Influences Optimality when
Remanufacturing to Multiple Generations

Neil Geismar
Because a product’s value decays over time, recovered items (cores)
cannot regain their original value when remanufactured—even if kept
in pristine condition. We consider a Remanufacturing Original Equipment Manufacturer who can remanufacture recovered cores to their
original configuration or upgrade them to current technology. He sells
new and remanufactured items concurrently. Each production period
signifies an upgrade of the product’s features to a new technological
generation.
We use our novel modeling of Marginal Value of Time (MVT) to perform a rigorous structural decomposition over all potentially optimalvalues of costs, consumers’ valuations, core availability, and MVT todetermine conditions under which cores should be restored to their
original configuration and those conditions under which cores should
be upgraded to current technology. We also show that for only avery small region of the parameter space (1.58% of remanufacturing293TD-24EURO 2024 - Copenhagen

instances) does concurrently offering both generations of remanufactured units provide greater profit than does remanufacturing to the optimal single generation. Furthermore, the average improvement in these
instances is negligible (0.42%).

 TD-24
Tuesday, 14:30-16:00 - Room: 83 (building: 116)

Sustainable Food and Health Care Logistics
Stream: Sustainable Supply Chains
Invited session
Chair: Caroline Prodhon
1 - Simulation Optimization-based Decision Support System to Enable More Local Food in the Gastronomy Sector

Suad Saliju, Christian Fikar, Christine Mendoza Pardo,
Claudia Paciarotti
Recent calls for more sustainable practices in the food industry, eating
healthier, and the desire to support regional farmers and rural communities, have seen an increase in the demand for local food. Particularlyin the gastronomy sector, many restaurants have started offering more
local food as a result of such trends. Despite various potential benefits of offering local food, real-world implementation settings often
fail due to challenges in logistics operations. To enable efficient andsustainable logistics operations in the sale of local food in the gastronomy sector, this work investigates innovative and sustainable logistics
concepts through the development of a simulation and optimizationbased decision support system (DSS). Using the DSS, various logistics
concepts are implemented and tested on two pilot areas in Bavaria,
Germany and one pilot area in Marche, Italy. Preliminary results highlight potential benefits translated into shorter distances, better vehicle
utilization, improved food quality, and lower lead times.

2 - The Value of Speed and Equity - Should Drones Be Used
to Deliver Goods or are Better Solutions Available?:
Case Studies from the UK

Andy Oakey, Antonio Martinez Sykora, Tom Cherrett
The UK’s NHS is actively looking at how more sustainable and innovative transport modes can be used alongside traditional logistics
systems to support their target of reaching net-zero emissions by 2040.
Uncrewed Aerial Vehicles (UAVs, or drones) are increasingly being
trialled in health care systems outside the UK, though evidence fromprevious studies and trials is suggesting that they may not be the complete answer to many of the industry’s challenges. Using on-goingcase study investigations of local NHS same-day logistics networks in
the UK region, this paper evaluates the benefits and costs of introducing mixed-mode logistics with UAVs to existing operations. Covering
dense urban areas and sparse rural island areas, real-world datasets andrealistic operating conditions are assessed using a novel savings algorithm and bin packing algorithm. Implications and recommendations
for the wider logistics industry are subsequently given, including suggestions of where UAVs may be most applicable, and the best mode
selections for different objectives and environments. The research is
being undertaken as part of the and EPSRC e-Drone project.

3 - Should the Governments Charge a Carbon Tax from
Servitizing Firms?

Mehmet Alegoz
Carbon tax is one of the widely used emission policies to encouragecompanies to decrease their emissions. Although numerous attempts
have been made to investigate the effects of a carbon tax policy on
the traditional selling business model, the effects of this policy on theservitization business model, in which the firms sell the use or the outcome of a product, remain as an important research gap. Motivated294

by this fact, this study proposes Stackelberg Game models to investigate the economic, environmental, and social effects of a carbon tax
policy on the servitizing firms. Equilibrium results reveal that carbon
tax yields an increase in the usage fee, and thus aggregate usage inthe market decreases as a result of working under a carbon tax policy.
This decrease in aggregate usage, on the one hand, yields a decreasein the environmental impact, and on the other hand, deteriorates theservitizing firm’s profit.

4 - Sharing deliveries in a short food logistic context

Adrien Callico, Pierre Desport, Caroline Prodhon,
Jean-Charles Billaut
A food supply chain qualifies as "short and local" when it is characterized by a short physical distance between producers and consumers,
with no more than a single intermediary between them. Such a configuration is referred to as the "short local food supply chain" (SLFSC).
Our focus lies in examining the logistical problems associated with the
SLFSC. Producers often set up organizations to simplify their logistical operations. For instance, they create producers’s stores, design
nodal points for third-party logistics operators to collect goods, or engage in reciprocal assistance. Our study focuses on the latter type ofcooperation. We consider that a producer is authorized to transport
goods from one another to customers. This practice is now possible inFrance for short distances, specific products and limited quantities. In
the case where a customer C orders products from producers A and B,
two collaborative cases may arise: producer A delivers to customer C
after retrieving goods coming from producer B, or producer A drops
off his own goods to producer B, and the latter delivers customer C. We
model this new delivery sharing problem and present our first results.

 TD-25
Tuesday, 14:30-16:00 - Room: 011 (building: 208)

Applications of Machine Learning in
Optimization
Stream: Combinatorial Optimization
Invited session
Chair: Jan van Vuuren
1 - Enhancing Wi-Fi Fingerprinting through the Use of an
Automated Supervised Autoencoder

Tiny Du Toit
Indoor localisation is an essential area with vast implications for navigation, emergency services, and a range of Internet of Things contexts.
The challenge lies in the intricate nature of indoor spaces and the limitations inherent in current technologies for precise indoor localisation.
This research addresses these issues using a supervised autoencoder(SAE) approach on the UjiIndoorLoc dataset, encompassing Wi-Fi fingerprint data from various buildings and levels at Jaume I University.
An AutoML search was employed to identify the best hyperparameters, including learning rate, number of epochs, layers in both the autoencoder and multilayer perceptron, nodes per layer, and dropout rate.
Bayesian optimisation, an efficient search strategy, was used to test different hyperparameter combinations and neural network layer configurations to find the most effective models. The findings demonstrate
the model’s high accuracy rates: 99.91% for building identification,
91.45% for floor identification, and an overall accuracy of 91.18%.
The SAE model’s performance showcases its capability and competitive edge over a standard model and various methods, underscoring
its potential to overcome the difficulties of complex indoor localisation
tasks.

2 - Artificial intelligence techniques for designing conformal cooling channels for injection moulding using
Moldex3D

Antonio Gaspar-Cunha, João Melo, Tomás Marques, António
PontesEURO 2024 - Copenhagen
Designing moulds for injection moulding is a decisive step given thecomplexity of some plastic parts produced by this process. The correct
design principles must be followed to prevent the existence of defects
that make the parts worthless. This includes short shots, flash, sinkmarks, warpage, weld lines and air traps, which can be avoided orminimized by the adoption of various measures, including optimizing
the process parameters and using high-quality moulds. However, thepresence of a high number of design variables related to the different
phases of the process makes this a difficult task, principally because
these variables are of different types: i) operating conditions; ii) mould
design, including filling channels, number of gates and cavity; and iii)
variables that depend on the machine used. Therefore, the use of numerical modelling programs was usually adopted to obtain relevant
information on the injection moulding process and to enable the engineer to increase the speed of the development process. Given the highquantity of data generated and the high number of decision variables
and objectives involved more sophisticated data analysis is required.
The objective of this work is to develop and implement data mining
techniques and surrogate models based on Artificial Neural Networks
to optimize the cooling phase. Different methodologies will be tested
and the hyperparameters of the methods used will be assessed to selectthe best model to use.

3 - Neural style transfer inspired optimisation

TD-26

2 - A time-space network model for a truck and drones delivery system

Dell’Olmo Paolo, Lavinia Amorosi, Justo Puerto, Carlos
Valverde
We present a bi-objective multi-period mixed integer linear programming formulation based on a time-space network for a delivery problem with an automated mothership and a fleet of drones. The proposed
formulation integrates not only the routing and the scheduling problems underlying the delivery, but also the charging cycles of drone
batteries with the movements of the mothership. The two objectivefunctions are the minimization of the mothership and UAVs energy
consumption and the maximization of the number of served customers.
Indeed, it is assumed that not all customers can be served within thetime horizon under consideration. Experimental results on a testbed ofartificial instances will be presented.

3 - Developing a Sustainable Walk-and-Drive Tourism Recommendation System for Sports Mega-Events

Elham Jelodari, Sami Benjabeur

Stephan Nel
In the case of traditional neural style transfer, the aim is to transfer
the style embedded within an image, song, or literary work to that ofanother, e.g. to transfer the art style of Van Gogh’s Starry Night toanother image. Contemporary approaches have demonstrated notableefficacy in respect of capturing and translating embedded properties
within complex data representations. In this study, an investigation iscarried out into the application of neural style transfer to optimisation
algorithms. More specifically, the extent to which neural style transfer can capture and transfer salient features of high-quality solutions toother solutions is investigated. The proposed approach entails various
modifications to different facets of conventional neural style transfer,
such as data set construction, solution encoding, neural architecture,and loss functions. Different benchmark optimisation problems are
considered in respect of evaluating the utility of the proposed approach.

 TD-26
Tuesday, 14:30-16:00 - Room: 012 (building: 208)

Routing for hybrid fleets of vehicles
Stream: Combinatorial Optimization
Invited session
Chair: Lavinia Amorosi
1 - Optimizing Seal Pup Counting Operations: A Novel
Drone Routing Application

Annunziata Esposito Amideo, Lavinia Amorosi, Daire
Carroll, Paula Carroll
Effective wildlife conservation relies on our ability to documentchanges in species abundance. Drones are commonly used to monitor wildlife numbers, such as the number of seals born during annual
breeding aggregations. Within this context, we address harbour seal
population monitoring as the Drone Routing Problem (DRP). Given a
set of islands (as locations) and an equal technology drone fleet, we
aim to minimize the travel energy cost associated with routing while
complying with drone energy limitations (i.e., battery consumption).
We model the DRP as a Mixed Integer Linear Program (MILP) resulting in a variant of the well-known Capacitated Vehicle Routing Problem (CVRP) model accounting for the relevant energy requirements.
We also consider different drone cruising velocities and potential wind
uncertainty. We solve the proposed MILP and present preliminary experimental results for a real-world application: monitoring seal pup
production in Kosterhavet National Park, Sweden.

Participating in sports events has a rich history, from the ancient
Olympics to today’s global mega-events. In real-world scenarios,
tourists attending sports events also desire to visit attractions during
their stay. Tourist planning systems often integrate road and pedestrian
networks to minimize transportation costs and greenhouse gas (GHG)
emissions. Indeed, reducing annual tourism carbon emissions is a challenging objective due to infrastructure lock-in effects. In our research,
tourists move to various Points of Interest (PoIs) or sports events, either
by walking or driving, according to their preferred periods, considering time windows, and then are picked up from nearby locations. Thisvariant of the delivery and pickup problem allows tourists to switch between walking and driving in different periods. Our contribution aims
to develop a decision support system for tourists attending sports megaevents, offering opportunities to explore PoIs and participate in sports
events. We present a mixed-integer linear programming formulation
for the multi-period green delivery and pickup problem with time windows, aiming to maximize profit and minimize GHG emissions. We
solve this problem using a new Hybrid ALNS algorithm and evaluate
its performance against NSGA-II on real tourism data for Paris, focusing on the 2024 Paris Olympics. Finally, we offer insights for tourist
agency managers through various analyses.

4 - On the Hierarchical Directed Capacitated Arc Routing
ProblemThu Huong Dang, Minh Hoang Ha, Trung Thanh Nguyen,
André Langevin
The Hierarchical Directed Capacitated Arc Routing Problems (HDCARP) is a variant of the Capacitated Arc Routing Problems (CARPs),
in which the arcs in a graph are partitioned into priority classes. However, unlike traditional CARPs that aim to minimise total time, theHDCARP focuses on minimizing the maximum completion time ofeach priority class in a hierarchical fashion. Practical applications of
the HDCARP include snow plowing, salt spreading, street cleaning,
and waste collection. In this study, we explore two variants of the HDCARP. The key difference between these variants lies in the consideration of precedence relations between classes within routes. We propose
MILP formulations and matheuristics for both HDCARP variants. The
MILP formulations enable us to find optimal solutions for small-scaleinstances and evaluate the quality of matheuristics. Our matheuristics
are based on decomposing the problem into multiple sub-problems, resulting in faster running time for large-scale instances. We conduct extensive computational experiments to assess the performance of theseapproaches and present our findings.

295

TD-27EURO 2024 - Copenhagen TD-27
Tuesday, 14:30-16:00 - Room: 047 (building: 208)

Feature attribution and selection for XAI
Stream: Mathematical Optimization for XAI
Invited session
Chair: Vanesa Guerrero
1 - Shapley value analysis for revealing molecular signature in Raman Spectra

Marco Piazza, Enza Messina, Mauro Passacantando
Raman Spectroscopy is a technique based on the inelastic scatteringof a monochromatic light beam used to observe low-frequency modes
in a target molecular system. The scattering pattern can be viewed
as a sort of "fingerprint" that encodes information about the chemicalcomposition of a given target, including presence, concentrations, andinteractions among molecules. Recently, Raman spectra of biofluids
have been used for medical diagnosis, and Machine Learning and Deep
Learning models have been proposed for the development of noninvasive tests for early diagnosis. However, the lack of interpretability ofthese models makes decoding and interpreting the Raman spectral fingerprint a challenging task. In this work, we explore the application ofeXplainable Artificial Intelligence (XAI) methods to extract explanations aimed at the possible identification of new biomarkers. Amongthe possible approaches, feature attribution methods seem to be themost promising for analyzing spectroscopic data, and we will focus
our attention on Shapley value methods based on cooperative gametheory. The computational results of a comparative analysis will bepresented and discussed.

2 - Unboxing Tree ensembles for interpretability: A hierarchical visualization tool and a multivariate optimal rebuilt tree

Giulia Di Teodoro, Marta Monaci, Laura Palagi
The interpretability of models has become a crucial issue in Machine
Learning because of algorithmic decisions’ growing impact on realworld applications. Tree ensemble methods, such as Random Forests
or XgBoost, are powerful learning tools for classification tasks. However, while combining multiple trees may provide higher prediction
quality than a single one, it sacrifices the interpretability property resulting in "black-box" models. In light of this, we aim to develop aninterpretable representation of a tree-ensemble model that can provide
valuable insights into its behavior. First, given a target tree-ensemble
model, we develop a hierarchical visualization tool based on a heatmap
representation of the forest’s feature use, considering the frequency ofa feature and the level at which it is selected as an indicator of importance. Next, we propose a mixed-integer linear programming (MILP)
formulation for constructing a single optimal multivariate tree that accurately mimics the target model predictions. The goal is to provide
an interpretable surrogate model based on oblique hyperplane splits,
which uses only the most relevant features according to the defined
forest’s importance indicators. The MILP model includes a penalty on
feature selection based on their frequency in the forest to further induce
sparsity of the splits. The natural formulation has been strengthened to
improve the computational performance of mixed-integer software.

3 - Feature Selection for Regression Neural Network using
Mathematical Programming

Georgios Liapis, Sophia Tsoka, Lazaros Papageorgiou
In recent years, a growing body of literature has focused on training
sparse deep neural networks or refining already trained ones towards
making them sparser. The strategic reduction of complexity of the
network has shown to effectively mitigate overfitting, improve overall generalisation performance and enhance explainability. This work
introduces a feature selection approach tailored for regression tasks intrained neural networks, utilising Rectified Linear Unit (ReLU) activation functions. The methodology simplifies the network by identifying and retaining only the most important features for the input layer.

296This problem is mathematically formulated as a Mixed-Integer Linear Programming (MILP) model. This model formulates the ReLU
operator with binary variables, enabling the application of big-M constraints, while limiting the number of active features. The objective of
the model is to identify and eliminate the features that do not significantly contribute to the prediction quality of the neural network, sothat it can be re-trained using only the rest of them. A binning strategy
is employed on the output variable in order to assess the importanceof features across the entire spectrum of the output. Through a number of real-world datasets, it is demonstrated that the complexity of the
network is reduced, while maintaining good predictive performance.

4 - Selecting variables in additive models as linear or nonlinear terms

Vanesa Guerrero, Manuel Navarro García, Maria Durban,
Arturo del Cerro
In an era when the decision-making process is often based on the analysis of complex and evolving data, it is crucial to have systems whichallow us to incorporate human knowledge and provide valuable support to the decision maker. In this work, statistical modelling andmathematical optimization paradigms merge to address the problemof feature selection in additive models while deciding if the variables
are linear or nonlinearly related with the outcome. We assume that thesmooth functions involved in the additive model are defined through areduced-rank basis (B−splines) and fitted via a penalized splines approach (P−splines). A mixed-integer quadratic optimization model is
proposed to address the variable selection model and a matheuristic
approach, based on the Large Neighborhood Search paradigm, is developed to solve larger instances. The proposed methodology is tested
in both simulated and real datasets and it is shown to be competitive
against other approaches in the literature.

 TD-28
Tuesday, 14:30-16:00 - Room: 065 (building: 208)

Advancements of OR-analytics in statistics,
machine learning and data science 7
Stream: Advancements of OR-analytics in statistics, machine learning and data science
Invited session
Chair: Hector Cancela
Chair: Gerhard-Wilhelm Weber
1 - A new centrality measure for misinformation diffusion

Safiye Şeyma Kaya Gezmiş, Zeynep Aksin, Barış Yıldız
Social networks offer the capability to spread inspiring ideas and adapt
innovations at unprecedented speed and convenience. Although suchan information spreading capability is invaluable, social networks canalso rapidly disseminate misinformation to a large number of people,
with dire consequences for impacted individuals and societies. In thisstudy, we focus on the spread of a specific type of misinformation:acute rumors (AR), which we define as misinformation with the potential to quickly spread in the network and mobilize individuals to takeharmful actions. We present a new diffusion model for AR, which extends the classical linear threshold model by considering the base ideaof the individuals regarding the topic of the rumor and the emotional
impact its content creates on them. Based on this diffusion model, we
propose a new centrality measure that can accurately detect individuals
with a high potential to enhance the spread of AR. In a comprehensive
numerical study, we evaluate the performance of our proposed centrality measure by comparing it to existing traditional centrality measuresin the literature. Our results attest to the superior performance of ourcentrality measure not only in terms of detecting the individuals withthe highest potential to increase the reach of AR but also in finding thecorrect ranking among the individuals in the network regarding theirpotential to contribute significantly to the dissemination of acute rumors.

EURO 2024 - Copenhagen
2 - Limited memory bundle DC algorithm for sparse pairwise kernel learning

Napsu Karmitsa, Kaisa Joki, Antti Airola, Tapio Pahikkala
Pairwise learning is a specialized form of supervised learning that focuses on predicting outcomes for pairs of objects. Data with pairwise
observations naturally arise, for instance, in recommender systems, information retrieval, drug-target interaction (DTI) prediction, and link
prediction in social networks. Here, we formulate the pairwise learning problem as a difference of convex (DC) optimization problem using the Kronecker product kernel, L1- and L0-regularizations, and various possible nonsmooth loss functions. Our aim is to create an efficient learning algorithm, SparsePKL, that produces accurate predictions with the desired sparsity level. In particular, we aim to solve
the realistic form of the DTI problem called zero-shot learning, whichcorresponds to predicting labels for drug-target pairs where neither the
drug nor the target is encountered during the training phase. In addition, we propose a novel limited memory bundle DC algorithm (LMBDCA) for general large-scale nonsmooth DC optimization and apply
it as an underlying solver in the SparsePKL. The performance of theSparsePKL algorithm is studied in seven real-world drug-target interaction data, and the results are compared with those of the state-of-theart methods in pairwise learning. Further, we evaluate the LMB-DCA
as a standalone optimization method by comparing it with the wellknown DCA.

3 - Beyond the Pearson’s correlation coefficient: a generalpurpose measure for quantifying the dependency between random variables

Rob van der Mei, Guus Berkelmans, Joris Pries, Sandjai
Bhulai
When analyzing a data set, typical questions are: ’Do underlying relationships exist?’, ’Are some variables redundant?’, and ’Is some target
variable Y highly or weakly dependent on variable X?’. Interestingly,
despite the evident need for a general-purpose measure of dependency
between RV’s, common practice of data analysis is that most data analysts use the Pearson correlation coefficient (PCC) to quantify dependence between RV’s, while it is well-recognized that the PCC is essentially a measure for linear dependency only. Although many attempts
have been made to define more generic dependency measures, there is
yet no consensus on a standard, general-purpose dependency function.
In this talk, we will discuss and revise the list of desired properties andpropose a new general-purpose dependency function provides data analysts a powerful means to quantify the level of dependence between
variables.

4 - Data envelopment analysis with machine learning in
healthcare efficiency: a prematurity study

Fernando Marins, Elias Carlos Aguirre Rodríguez, Elen
Yanina Aguirre Rodríguez, Diego Eduardo Quagliato Scarelli
Cava, Luiz Fernando Costa Nascimento, Aneirson Franciscoda Silva, Anderson Rodrigo de Queiroz
Prematurity (birth before the 37th week of gestation) is a leading causeof neonatal mortality and heightened complications in newborns. In
Brazil, the annual cost of premature births is approximately R$8 billion, with newborns spending an average of 51 days in intensive care,
totalling a yearly cost exceeding R$15 billion, so improving the healthsystem for mothers and premature births is essential. Data Envelopment Analysis (DEA) is a vital tool to evaluate healthcare systemefficiency across territories, but measuring efficiency over time requires re-implementing DEA models. Consequently, Machine Learning (ML) emerges as a viable solution for predicting efficiency scores
through supervised learning. Therefore, this study integrates DEA,
specifically the Constant Returns to Scale (CRS) model with input
orientation, with ML techniques to develop a predictive model for
healthcare system efficiency based on prematurity and incorporating
indicators such as the number of physicians, beds, healthcare establishments, and per capita health expenditure (R$) in Brazil’s microregions. The DEA analysis assesses each microregion’s readiness tocare for premature newborns, providing inverted efficiency indicators
where Decision-Making Units (DMUs) with values near or equal to
one suggest lower performance. This approach enables determining

TD-29

efficiency for new DMUs through ML, based on DEA indicators andresults, while identifying sectors needing improvement in the healthcare system.

 TD-29
Tuesday, 14:30-16:00 - Room: 157 (building: 208)

Exact Algorithms and Formulations for
Combinatorial Optimization Problems
Stream: Combinatorial Optimization
Invited session
Chair: Ivana Ljubic
Chair: Martina Cerulli
1 - Bi-objective Algorithms for a Vehicle Replacement
Problem including Budget Constraints and CO2 Emissions Minimization

Giulia Caselli, Joseph Hartman, Manuel Iori, Carlo Alberto
Magni, Giorgio Zucchi
The transportation sector, encompassing road (the primary source),sea, and air transport, accounts for 14% of global emissions over thelast decade and around 25% of total greenhouse gas emissions in theEuropean Union. This underscores the imperative for companies toprioritize transitioning to cleaner and greener modes of transport intheir medium- to long-term vehicle investment plans. In this work, we
address an original vehicle replacement problem with environmental
concerns. A keep-or-replace decision must be made for every assetof the fleet in every time period while meeting budget constraints tominimize total discounted costs and CO2 emissions. Multiple options
for replacement are given for every family of assets in such a waythat fuel-based vehicles may be replaced by, for instance, electric andhybrid vehicles. The problem is inspired by a real-world industrial application in Italy and, because of its generality, can be used to model alarge variety of further applications. We provide a dynamic programming model and integer programming formulations for the problem.
Then, we compare the formulations and propose different bi-objective
approaches to solve the problem in practice.

2 - A new MILP model for the Simple Assembly Line Balancing Problem with Power Peak Minimization

Paolo Gianessi, Xavier Delorme
The constantly-increasing concern on environment and sustainability
and the recent events which have pushed energy prices to alarming levels have led manufacturing systems to try to achieve higher standardsof energy efficiency. This is the reason behind the growing interest of
the research community in the study of optimization problems in production systems that deal with energy-related constraints and/or criteria. Among them is the Simple Assembly Line Balancing Problem
with Power Peak Minimization (SALB3PM). The problem attempts
to assign the tasks of a production process to the workstations of apaced straight machining line, and to determine their starting times so
as to minimize the peak of the overall power consumption profile. This
work focuses on the SALB3PM and proposes a new Mixed-Integer
Linear Programming formulation in which a set of binary three-index
variables models the core assignment and trigger decisions. We showthat the proposed model outperforms the reference 01LP model on abenchmark instance set. We then test the model on new instances inwhich tasks have non constant power profiles and we propose somevalid inequalities and preprocessing. Numerical results are presented
and discussed.

3 - Stochastic facility location problem with outsourcing
costs

Eduardo Moreno, Ivana Ljubic

297TD-30EURO 2024 - Copenhagen

Stochastic facility location problems with outsourcing costs (SFLPOC)
optimize facility placement and customer assignment under demand
uncertainty. Excess demand beyond a facility’s capacity incurs outsourcing costs. This work addresses SFLPOC, aiming to minimize
overall expected costs (installation, servicing, and outsourcing).
We model SFLPOC as a two-stage stochastic program. While priorwork focused on specific assumptions or small scenario sets, we
present methods suitable for general probability distributions. For discrete scenario sets, we improve upon classic Benders decomposition
by exploiting the second-stage subproblem’s structure.
To handle general distributions, we partition the probability space, enabling the computation of expected values with fewer scenarios. Coupled with Benders cuts, this provides an exact solution method forcommon distributions (e.g., Bernoulli, Gaussian).
Additionally, we introduce a compact formulation specifically for i.i.d.
demand distributions, allowing us to solve even continuous distribution problems to optimality. Computational experiments on establishedbenchmarks demonstrate that our compact formulation consistently
finds optimal solutions, while the Benders approach provides strong
solutions with proven optimality gaps for general distributions, outperforming sample average approximations.

4 - Locally Stable Roommate Problem

Elise Vandomme, Marie Baratto, Yves Crama
In 1962, Gale and Shapley initiated the study of stable matching problems, introducing the Stable Marriage Problem (SMP). The aim is tomatch men and women based on their preferences for all members of
the opposite gender. A matching is deemed stable if there are no pairsof unmatched individuals who prefer each other to their partners in thematching. We say that there are no blocking pairs. A natural generalization of SMP is to consider non-bipartite models. For instance,
consider a set of n individuals where each one ranks all the others inorder of preference. In this context, a matching can be assimilated
to pairing individuals to share living spaces, leading to the formulation of the Stable Roommate Problem (SRP). While there always exists a stable matching in SMP, it is not the case in some instances of
SRP. However, Irving provided a polynomial algorithm that, for any
instance of SRP, determines whether a stable matching exists or not. If
it exists, it produces one. We focus on a different definition of stability,
namely local stability, recently introduced in the context of kidney exchange programs (KEPs). The decision problem’s complexity for nonempty locally stable exchanges remains open. When the exchange is amatching, this is equivalent to a local version of SRP. Here, a matchingachieves local stability if no blocking pairs intersect with it. In thistalk, we explore the trade-offs between global and local stability anddiscuss the complexity of local SRP.

PyPSA is used in several projects, such as PyPSA-Eur, which focuseson the European energy system. PyPSA-Eur automates the creation ofhighly detailed models of the European transmission networks of several energy carriers, incorporating renewable energy potentials, conventional power plants, and demand data across different sectors. This
enables researchers and policymakers to conduct extensive analyses of
network expansion, storage needs, and sector coupling strategies essential for achieving carbon neutrality.
This presentation will explore the functionalities, applications, and recent advancements of PyPSA and PyPSA-Eur. We will explore casestudies demonstrating their use in optimising energy systems for costeffectiveness and sustainability. Attendees will gain insight into thepotential uses of these software tools in energy system planning andoperational research.

2 - GAMSPy: The Best of Both Worlds - Integrating Python
and GAMS

Justine Broihan
Optimization applications combine technology and expertise frommany different areas, including model-building, algorithms, and datahandling. Often, the gathering, pre/post-processing, and visualizationof the data is done by a diverse organization-spanning group that sharesa common bond: their skill in and appreciation for Python and the vastarray of available packages it provides. For this reason, GAMS offers
a new comfortable way to integrate with Python on the data-handlingand modeling side. In this talk, we will explore the benefits of our
Python library GAMSPy.

3 - Convenient and Efficient Implementation of Analytic
Python Apps with Xpress Insight and WorkbenchAlexander Biele
In this talk we aim to illustrate the efficient reduction of development
time and enhancement of the developer experience for fully fledged
analytic Python applications, utilizing FICO Xpress Workbench andXpress Insight. Our discussion spans across all stages of the development cycle, encompassing model implementation, scenario analysis,
and the crucial feedback loop closure with business users.
Our initial focus involves showcasing the seamless deployment of ananalytic Python model, transforming it into a fully operational application within Xpress Insight. Then we demonstrate the latest enhancements for Python language support in our IDE Xpress Workbench. We
will showcase how advanced debugging features lead to an improved
and integrated development experience. Beyond local debugging, we
will spotlight remote debugging capabilities tailored for scenarios executed in Xpress Insight. This becomes particularly significant as production data might be inaccessible on a developer’s laptop due to security considerations.

4 - Matrix-friendly modeling with gurobipy

 TD-30
Tuesday, 14:30-16:00 - Room: 064 (building: 208)

Python Modeling Tools
Stream: Software for Optimization
Invited session

Robert Luce
Gurobipy makes it very easy to build optimization models that are
naturally formulated with multi-dimensional constraints. In particular you can use familiar concepts from NumPy like dimensions, shape,
vectorization and broadcasting in combination with gurobipy’s matrixfriendly objects to construct your optimization model. In this talk wewill walk you through the functionality, discuss performance aspects,
and present best practice code patterns.

Chair: Robert Luce
1 - An Introduction to PyPSA: Exploring Open-Source,
High-Resolution Energy Planning

Elisabeth Zeyen, Iegor Riepin
PyPSA (Python for Power System Analysis) is an open-source toolbox to simulate and optimise modern power and energy systems. It
encompasses various functionalities, such as conventional generatorsand links with unit commitment, variable wind and solar generation,
storage units, coupling to other energy sectors, and mixed alternating
and direct current networks. PyPSA is graph-based and designed toscale well with large networks and long time series.

298

 TD-31
Tuesday, 14:30-16:00 - Room: 046 (building: 208)

Analytics for Combinatorial Problems from
Health Care to the Food Industry
Stream: Analytics
Invited session
Chair: Patrick De Causmaecker

EURO 2024 - Copenhagen
1 - How should volunteers be dispatched to out-of-hospital
cardiac arrests?

Caroline Jagtenberg
Survival for out-of-hospital cardiac arrest (OHCA) can be significantly
improved through bystander efforts. To shorten the time to goodquality cardiopulmonary resuscitation, some emergency call centers
use mobile phone technology to rapidly locate and alert nearby trained
volunteers. Several such community first responder (CFR) systems areactive worldwide, for example GoodSAM, which operates in the UK,
Australia and New Zealand.
GoodSAM sends so-called phased alerts: they notify increasingly
many volunteers with built-in time delays. The policy that defines
these delays affects (1) response times - which have a direct relation
to survival - (2) CFR workload and (3) the number of redundant CFR
arrivals. We start by comparing policies through Monte Carlo Simulation, in which we use bootstrapped values from historical GoodSAM
responses, estimating the three KPIs above. CFR app managers canuse those results to identify a policy that displays a desirable trade-off
between the performance measures.
We continue by using machine learning to predict the best policy touse, given where the volunteers are observed in relation to the patient.
We do this by formulating the problem as a multiclass classification
problem, for which we train a tree on the results from the simulations
above. We compare the performance of the tree against a policy designed by dynamic programming. Finally, we look into optimal trees
which go beyond the heuristic nature of machine learning algorithms.

2 - Unlocking the Value of Extensive Data: Estimating spatial cardiac arrest risk to guide resource allocation decisions

TD-32

4 - Manufacturing Process Optimisation and Predictive Analytics: Leveraging Data to Minimise Giveaway in
Cheese Production

Giwa Reagan Iziomo, Ozren Despic, Jiabin Luo, Viktor
Pekar, Eliseo Vilalta-Perdomo, Tim Fisher
This study addresses the challenge of high giveaway in Soft Cheese(British Brie) manufacturing, aiming to standardise weights and increase yields. Soft Cheese is produced by placing wet curds into
moulds to form specific shapes and weights, which cannot be modified post-formation. Initial hypotheses suggested that weight variations were due to differences in properties between the outer and innercheeses within a mould, caused by design issues. The project employed optimisation techniques and designed experiments to collectintricate data from the production process. This data aimed to capture variations in causes (production steps) and effects (weight inconsistencies), generating analytical datasets which were examined using
descriptive visualisations, hypothesis testing, and predictive analytics,
including machine learning. Causal inference methods were applied
to quantify the impact of different factors on weight variations. Key
recommendations included the introduction of a standardised mould
filling system and improved mould trays to address intra-tray effects.
For inter-tray effects, mainly linked to changing curd properties during
production, a software-driven solution was implemented to automatically adjust for these changes. These measures reduced giveaways
from 21% to 9%, resulting in substantial savings. This interdisciplinary initiative showcases the integration of optimisation techniques
and predictive analytics to solve a critical issue in food manufacturing.

Derya Demirtas, Robin Buter, Remy Stieglis, Hans van
SchuppenOut-of-hospital cardiac arrest (OHCA) is a significant public healthproblem with notably low survival rates. Early defibrillation is crucial
for survival, highlighting the importance of nearby automated external
defibrillators (AEDs). Current AED placement strategies often relyon historical OHCA data, which are limited in availability. Publicly
available demographic/socioeconomic data are often easily available
and shown to have correlations with OHCA risk. This study aims to 1)
estimate spatial cardiac arrest risk using demographic/socioeconomic
data alone 2) compare AED location models based solely on estimatedrisk with those incorporating historical OHCA data to inform demand.
Machine learning techniques were applied to a comprehensive dataset
spanning multiple municipalities. Predicted OHCA incidence of each
district were used to optimize AED locations, alongside AED optimization models that used smoothed out historical cardiac arrest data
as demand. Results on several municipalities underscore the valueof an OHCA registry. Nonetheless, in its absence, machine learningmodels leveraging demographic and socioeconomic data offer a viablemeans to substantially enhance coverage.

3 - Data-driven optimization of wildfire resource deployment

 TD-32
Tuesday, 14:30-16:00 - Room: 41 (building: 303A)

Algorithms for machine learning and inverse
problems: optimisation for neural networks
Stream: Advances in large scale nonlinear optimization
Invited session
Chair: Silvia Villa
Chair: Luca Calatroni
Chair: Cesare Molinari
1 - Differentiating Nonsmooth Solutions to Parametric
Monotone Inclusion Problems

Antonio Silveti-Falls

Mostafa Rezaei, Yasser Zeinali, Ilbin Lee
Wildfires impose significant health, environmental, and social costs
annually. This paper presents the development of a predictive andprescriptive framework for deploying three wildfire suppression resources: air tankers, helicopters, and firefighters. Leveraging fifteen
years of historical data on wildfires in Alberta, Canada, we initiallytrain machine learning models to predict both the daily number of fireoccurrences and the required hours of suppression resources. Subsequently, we utilize the forecasts in an optimization procedure to ascertain the necessary number of suppression resources at various bases,
aiming to minimize response time. The optimization procedure employs a queueing model to compute the response time probabilities for
fires. Given resource shortages, the historical level of resource usage
serves as a lower bound for the required resources. To address this, weincorporate survival analysis techniques into the forecasting models toaccount for data censoring. Our results can guide wildfire managers
and decision-makers in enhancing the acquisition and allocation of fire
suppression resources.

Understanding the differentiability and regularity of the solution toa monotone inclusion problem is an important question with consequences for convex optimization, machine learning, signal processing,and beyond. Past attempts have been made either under very restrictive
assumptions that ensure the solution is continuously differentiable orusing mathematical tools that are incompatible with automatic differentiation. In this talk, we discuss how to leverage path differentiability
and a recent result on nonsmooth implicit differentiation calculus togive sufficient conditions ensuring that the solution to a monotone inclusion problem will be path differentiable and provide formulas forcomputing its generalized gradient. Our approach is fully compatible
with automatic differentiation and comes with assumptions which areeasy to check, roughly speaking: semialgebraicity and strong monotonicity. We illustrate the scope of our results by considering threefundamental composite problem settings: strongly convex problems,dual solutions to convex minimization problems and primal-dual solutions to min-max problems.

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TD-33EURO 2024 - Copenhagen2 - Inexact Restoration trust-region algorithm with random
models for unconstrained noisy optimization

Simone Rebegoldi, Benedetta Morini

Svetlana Asmuss, Natalja Budkina

We consider unconstrained smooth optimization problems where theevaluation of both the objective function and its gradient is subject
to errors. Particularly, we assume that the function and gradient estimates are random and sufficient accuracy in the estimates can be
guaranteed with sufficiently high probability. Following the popular
Inexact Restoration framework, we reformulate the original problem
as a constrained problem, where the constraint h(y)=0 represents theideal case in which the function and the gradient are evaluated exactly,
being y the noise level and h a non-negative function whose value is related to the accuracy that the estimates can achieve in probability. We
show that our problem setting is viable for well-known optimization
problems and then design a new trust-region algorithm that employs
first-order random models. We analyze the properties of the algorithm
and provide the expected number of iterations required to reach an approximate first-order optimality point. We also validate our proposed
algorithm on a collection of least-squares problems, showing that itachieves comparable or lower noiseless values on average with respect
to a state-of-the-art competitor.

The aim of this research is to develop a fuzzy logic based technique for
optimal control of dynamical systems under vague conditions whichallows, among other, to solve optimal trajectory planning and contour
modelling problems with uncertainty in location constraints. The goal
is to find a control law that will drive the output trajectory in such a
way that vague constraints are satisfied in the fuzzy logic sense. In order to adapt methods for control under specific types of uncertainty, wedevelop and apply fuzzy logic based mathematical tools such as fuzzy
optimization, fuzzy metrics, etc. Fuzzy relations of a special design areused to incorporate uncertainty of constraints into the classical settingof the optimal control problem under consideration. The proposed approach is illustrated with examples, the numerical part of the analysisof which is reduced to quadratic programming.

3 - Conservation laws for gradient flows

Sibylle Marcotte
Understanding the geometric properties of gradient descent dynamics is a key ingredient in deciphering the recent success of very large
machine learning models. A striking observation is that trained overparameterized models retain some properties of the optimization initialization. This "implicit bias" is believed to be responsible for somefavorable properties of the trained models and could explain their good
generalization properties. In this talk, I will first rigorously expose
the definition and basic properties of "conservation laws", that define
quantities conserved during gradient flows of a given model (e.g. ofa ReLU network with a given architecture) with any training data andany loss. Then I will explain how to find the exact number of independent conservation laws by performing finite-dimensional algebraic
manipulations. In the specific case of linear and ReLu networks, thisprocedure recovers the conservation laws known in the literature andshows that there are no other laws.

4 - (Automatic) Iterative Differentiation: some old (& new)results

Samuel Vaiter
In this presentation, I will introduce some convergence results concerning the automatic differentiation of iterative algorithms, i.e., differentiating through the iterations, whether they are "smooth" or not. While
the asymptotics of smooth problems are well understood (Gilbert 1992,
Beck 1994), mainly thanks to the use of a fixed point theorem, the nonsmooth case presents more difficulties. I will show how the frameworkof conservative Jacobians can achieve such results. I will also illustrate
a strategy to adopt when the operator is not contracting, taking the differentiation of the Sinkhorn-Knopp algorithm as motivation. Finally, I
will discuss one-shot differentiation, a method that combines the simplicity of automatic differentiation with the performance of implicit
differentiation (in some cases). This approach, particularly suitablefor fast algorithms, will be illustrated through superlinear optimization methods and bi-level optimization scenarios. Work in collaboration with Jérôme Bolte (Toulouse School of Economics) and Edouard
Pauwels (Toulouse School of Economics).

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Tuesday, 14:30-16:00 - Room: 42 (building: 303A)

Optimal control theory
Stream: Optimal Control Theory and Applications
Invited session
Chair: Stefana-Lucia Anita

300

1 - Fuzzy logic based approach for optimal control of dynamical systems under uncertainty

2 - Measurized Markov Decision Processes

Alba V Olivares-Nadal
In this talk, we introduce a framework that facilitates the analysis of
discounted infinite horizon Markov Decision Processes (MDPs) by visualizing them as deterministic processes where the states are probability measures on the original state space and the actions are stochastic
kernels on the original action space. More specifically, we provide a
simple general algebraic approach to lifting any MDP to this space ofmeasures; we call this to measurize the original stochastic MDP. We
show that measurized MDPs are in fact a generalization of stochastic
MDPs, thus the measurized framework can be deployed without lossof fidelity. Lifting an MDP can be convenient because the measurized
framework enables constraints and value function approximations thatare not easily available from the standard MDP setting. For instance,
one could add restrictions or build approximations based on moments,
quantiles, risk measures...etc. In addition, measurized MDPs are deterministic and are particularly beneficial when managing large populations. A reason is that high-dimensional weakly coupled MDPs canbe reduced into a unidimensional MDP in the space of distributions
when the state-components are independent and identically distributed.
This implies that solving the measurized problem only requires aggregated information, eliminating the impractical assumption of constantly having detailed and updated information for each component inhigh-dimensional contexts.

3 - Regularity in optimal control and applications

Vladimir Veliov
The talk is devoted to an extension of the property of strong metric subregularity of mappings. We focus on the so-called optimality mapping
associated with the system of first order necessary optimality conditions for ODE optimal control problems. Problems that satisfy the
Legendre-Clebsch condition and affine optimal control problems will
be considered separately.
Two applications will be presented: (i) existence of Lipschitz continuous optimal feedback control; (ii) convergence with error estimates of
discretization and Newton-like methods for optimal control.

4 - Optimal control of a generalized Fokker-Planck equation

Stefana-Lucia Anita
Our talk concerns an optimal control problem (P) related to a generalized Fokker-Planck (FP) equation. First we establish some properties of the solutions to the generalized FP equation using a semigroup
approach in an appropriate Sobolev space. Problem (P) is proven to
be deeply related to a stochastic optimal control problem (PS) for a
McKean-Vlasov equation. We prove next the existence of an optimal control for the deterministic problem (P) and the existence of an
optimal control for an approximating optimal control problem (Ph) related to a backward Euler approximation of the generalized FP equation (with a constant discretization step h). Finally, we show that under
additional hypotheses "(Ph) converges to (P)" in a certain sense. First
order necessary optimality conditions for (Ph) are derived as well.

EURO 2024 - Copenhagen TD-34

TD-35

Chair: Frauke Liers
Chair: Marc Goerigk

set of distributions (ambiguity set). We present an approach for DRO
over time that uses online learning and scenario observations arriving
as a data stream to learn more about the uncertainty. Our robust solutions adapt over time and reduce the cost of protection with shrinking
ambiguity. For various kinds of ambiguity sets, the robust solutions
converge to the SO solution. Our algorithm achieves the optimization and learning goals without solving the DRO problem exactly at
any step. We also provide a regret bound for the quality of the online
strategy and also discuss how to perform a scenario reduction with approximation guarantee. We illustrate the effectiveness of our procedureby numerical experiments from popular benchmark libraries and givepractical examples stemming from telecommunications and routing.
Our algorithm is able to solve the DRO over time problem significantly
faster than standard reformulations.

1 - Robust Spare Parts Inventory Management with Emergency Shipment

This talk is based on joint work with K. Aigner, A. Bärmann, K. Braun,
S. Pokutta, O. Schneider, K. Sharma, S. Tschuppik. Parts of the talkare published in INFORMS J Optimization, 2023.

Tuesday, 14:30-16:00 - Room: 43 (building: 303A)

Trends and Open Problems in Robust
Optimization
Stream: Stochastic, Robust and Distributionally Robust
Optimization
Invited session

Zhao Kang, Ahmadreza Marandi, Rob Basten, Ton de Kok,
Joan Stip
We develop an Adaptive Robust Optimization (ARO) model for managing spare parts inventory in the early stages of a product’s life cycle. This model takes into account demand uncertainty and emergency
shipments. To obtain the exact solutions of the ARO model, we establish its equivalence to a deterministic counterpart. We prove that thedeterministic counterpart can be decomposed into two subproblems.
Based on this decomposition, we develop an efficient algorithm to obtain near-optimal solutions for thousands of stock-keeping units.
We conduct a case study at ASML, highlighting the practical implications of our ARO model for spare parts inventory in the semiconductor
industry. Our results show that the ARO solution is more cost-effective
than that of the conventional stochastic optimization model. Additionally, the adaptability of the ARO model to parameter variations, suchas the emergency shipment time and cost, demonstrates its superiority
in providing flexible and economically viable solutions.

2 - Multistage Sales and Production

Dominic Keehan, Andy Philpott, Andrew J Mason
We study a multistage sales and production problem. At each stage amarket price is observed, sales decisions are made, and resources areavailable for a production decision which yields products to be soldat later stages. The prices of each product evolve randomly. To address model ambiguity, we study two distributional robustifications of
the sample average version of this problem. The model is applied toa problem arising in New Zealand’s dairy industry. We first compare
the out-of-sample performance of the sample average policy to that ofa model predictive control policy which is shown to be distributionallyrobust with an ambiguity set of distributions with matched means. We
then compare this with a distributional robustification using discrete
Wasserstein distance-based ambiguity sets. The models are solved using stochastic dual dynamic programming in SDDP.jl.

3 - Open Problems in Robust Combinatorial Optimization

Marc Goerigk, Michael Hartisch
Robust combinatorial optimization has come a long way over the last
25 years. While many variants of decision criteria and uncertainty sets
have been proposed, some cornerstones have emerged. These are minmax, min-max regret, two-stage, and recoverable robust problems withdiscrete, interval, continuous budgeted, and discrete budgeted uncertainty. In this talk, I take stock of the current state-of-the-art in theseareas and point out the challenges that we face. This includes a list ofpromising open problems that I encourage the audience to consider.

4 - Computationally Efficient Data-Driven Distributional
Robustness Over Time

Frauke Liers, Kevin-Martin Aigner, Andreas Bärmann,
Kristin Braun, Sebastian Pokutta, Kartikey Sharma
Stochastic Optimization (SO) typically requires knowledge about theprobability distribution of uncertain parameters. As the latter is often unknown, Distributionally Robust Optimization (DRO) provides astrong alternative that determines the best guaranteed solution over a TD-35
Tuesday, 14:30-16:00 - Room: 44 (building: 303A)

Location and transportation problems under
uncertainty
Stream: Stochastic, Robust and Distributionally Robust
Optimization
Invited session
Chair: Francesca Maggioni
Chair: Francesca Vocaturo
1 - Resilience enhancement of distributions networks withrobust optimal sizing and location under uncertainty
and random contingencies

Pascal Quach, Yiping Fang, Anne Barros
The resilience of power systems is challenged by increasingly frequentand severe environmental events. Anticipation of threats and vulnerabilities is crucial to ensure adequate response and recovery from extreme events. In this context, the deployment of local generation canhelp absorb the impact of system disturbances. Size and location of
distributed energy resources are critical factors in determining theireconomic and technical viability. In this paper, we develop robustand distributionally robust optimization models among others, underload and supply uncertainty, and random contingencies. An illustrative case study is presented to demonstrate the models’ performanceon a benchmark distribution network with synthetic data combining avariety of representative days and failure scenarios in order to explore
the trade-offs between reliability, costs, and resilience. System performance assessed under in-sample and out-sample scenarios constitutethe resilience metrics.

2 - Polynomial-time algorithms to compute violation in therobust vehicle routing problem with time windows andbudget uncertainty

Igor Malheiros, Michael Poss, Artur Pessoa, Anand
SubramanianThis research focuses on the robust vehicle routing problem with time
windows (VRPTW) under uncertain travel times. More specifically,
we assume all travel times belong to a known budget uncertainty set.
To solve large-scale instances, one often resorts to local search algorithms. These algorithms involve checking whether a route is feasible regarding the time window; if it is not, evaluate its infeasibility.
Our goal is to study the computation of the infeasibility of the robust
VRPTW with budget uncertainty for two classical cases of functions.
We prove that the computation of both cases is polynomially solvableby providing dynamic programming algorithms to solve them.

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3 - A two-stage stochastic programming approach for anelectric fleet composition and mix vehicle routing problem.

Paolo Beatrici, Francesca Maggioni, Sebastian Birolini, Paolo
Malighetti
In this talk we consider a fleet composition and delivery routing problem, performed by a heterogeneous fleet of both conventional and electric vehicles under uncertain demand. Multi-trips for recharging operations of the electric vehicles to the central depot are considered. The
problem is modeled as a two-stage stochastic mixed integer program
where the first stage decisions are related to the selection of the vehicles which compose the fleet while second stage decisions concern
the routing to satisfy the uncertain customer demand. The aim is theminimization of total operation costs due to the initial acquisition offleet components, the travel time of each vehicle and an extra penalty
cost in case of unserved customers. A tradeoff between cost and emissions is evaluated to analyze the impact of the selection of electricvehicles in the delivery fleet. Scenarios representing the stochasticity
in customers’ demand are generated through a kernel density estimation approach. Computational experiments are carried out on instances
based on real data of a large Italian delivery company. The impact
of stochasticity on demand is examined through stochastic measures.
Some managerial insights are finally discussed.

4 - Preventive maintenance in fixed interval schedulingproblems under endogenous uncertainty - a stochastic
optimization approach and exact decomposition algorithm

Martin Branda
Fixed interval scheduling deals with the problem of assigning jobs
with fixed processing intervals to available machines. We consider
the problem where the finishing times are subject to random delays.
We elaborate the problem with expected number of overlaps objective
which penalizes the number of additional machines needed to process
the jobs when the delays appear and some jobs cannot be processedby the assigned machines. We consider preventive maintenance operations which can improve machine reliability and positively influence
the distribution of the random delays of the jobs assigned to the samemachine. This leads to a two-stage stochastic optimization problem
with endogenous uncertainty for which we propose an extended robustcoloring reformulation. We design a Benders decomposition algorithm
to solve larger instances to optimality and perform a numerical study
on simulated instances.

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Tuesday, 14:30-16:00 - Room: 32 (building: 306)

Game Theory, Solutions and Structures VIII
Stream: Game Theory, Solutions and Structures
Invited session
Chair: Panos Lorentziadis
1 - A Game Theoretic Approach for Solving Lot Scheduling
Problems

to providing misleading information for personal gain. We investigate
five fundamental objective functions in lot scheduling: (i) minimizing
the completion time of the last job exiting the system, (ii) minimizing
the total completion time, (iii) minimizing the total weighted completion time, (iv) minimizing the number of tardy orders, and (v) minimizing the total weighted number of tardy orders. Notably, we showthat problems (i) to (iv) can be efficiently solved in polynomial time,
while problem (v) is solvable in pseudo-polynomial time. Our researchadvances the understanding of decentralized scheduling, where agents’behavior and information gaps affect decision-making. It underscores
the need to address real-world complexities in scheduling theory andprovides insights for designing adaptable algorithms.

2 - Parallel axiomatizations ofegalitarian Shapley values

generalized

weighted-

Hyungkyu Cheon, Dong Gu Choi
Joosten (1996) attempted to reconcile marginalism and egalitarianism by proposing the egalitarian Shapley values, which is a convex
combination of the Shapley value and the equal division values. Abe
and Nakada (2019) generalized the equal division values of egalitarian
Shapley values to propose weighted-egalitarian Shapley values. Recently, Cheon and Choi (2024) introduced a new solution by generalizing the Shapley values to the weighted Shapley values within thisweighted-egalitarian Shapley values framework. In this study, we further advance the generalization of TU-values. This research delineates
the axiomatic properties of solutions resulting from the convex combination of four classes of TU-values. These classes include convex
combinations of the weighted division values with weighted Shapley
values, positively weighted Shapley values, random order values, andthe Harsanyi set. This research has been influenced by the parallel axiomatization of Besner (2020). Besner (2020) axiomatized weighted,
positively weighted, and multiweighted Shapley values, random ordervalues, and the Harsanyi set, presenting the axiomatization of all individual classes as a single theorem for all classes. In our study as well,within the main theorem, the axiomatization of adjacent two classes
differs by only one axiom, and the process of generalization of classesof solutions and weakening of corresponding axioms progresses in thesame direction.

3 - For how long should bids be valid in a procurement auction?

Panos Lorentziadis
Bids in procurement auctions are typically required to remain valid fora certain period of time. If the underlying cost changes in time, as
for example due to inflationary economic conditions or changes of the
production input prices, the eventual profit when awarding takes place
may be dramatically lower than anticipated. To address this risk, bidders can impose an upper bound constraint on the probability that the
final mark-up stays below a pre-determined level. In this setting we derive the equilibrium bidding strategies when the cost change is realized
continuously or in discrete steps. We determine the effect of the validity time to the expected bidder profit and show how bidders can profit
if the risk is correctly managed. On the other hand, we demonstratethat the contracting authority always suffers for a prolonged validity
time in terms of the expected procurement project cost and we develop
methods to determine the optimal time within which bids are valid.
We explore and compare methods of price adjustments which can partially or totally compensate against cost change. We conclude with a
discussion of the findings and recommendations for both bidders andauctioneers on the efficient management of this kind of risk.

Shani Alkoby, Baruch Mor
Lot scheduling problems are a significant focus in scheduling theory
due to their broad applications and effect on operational efficiency.
Traditionally, research in this field assumes a knowledgeable scheduler and truthful agents. However, this study delves into a more realistic scenario where these assumptions are challenged, proposing agame theory approach to handle the complexities arising from incomplete information and strategic behavior. We examine lot schedulingscenarios with uniform capacities for lots comprising orders of varying sizes. Departing from the conventional paradigm, we introduce
the concept of a scheduler with limited information and agents prone302

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Tuesday, 14:30-16:00 - Room: 33 (building: 306)

Applications of Multiobjective Optimization
Stream: Multiobjective Optimization
Invited session
Chair: Aly-Joy Ulusoy

EURO 2024 - Copenhagen
1 - Optimizing Charging Station Infrastructure Deployment: A Multi-Objective Stochastic Programming ApproachBüşra Alptekin, Sule Itir Satoglu
Electromobility plays a pivotal role in achieving Net Zero emissions
targets globally, with transportation accounting for 15% of emissions,
72% of which stem from road transportation. However, the widespread
adoption of electric vehicles (EVs) poses significant challenges. EV
penetration and CS network establishment are intertwined, resemblinga chicken-and-egg problem. If EV demand stagnates, CS utilization
remains low, discouraging investment from charging point operators(CPOs). Conversely, inadequate CS infrastructure worsens user concerns like range anxiety, hindering electrification. While existing studies often focus on one-time CS location decisions, developing a welldistributed CS infrastructure requires a holistic approach in decisionmaking. To address this, our objective is to formulate a multi-period
CS network deployment plan focused on the most frequented highways. We propose a multi-objective stochastic programming approachto optimize sustainability dimensions and identify new CS locations toaugment the existing network. This model embraces the Triple Bottom Line (TBL) approach, considering economic, ecological, and social aspects from the perspectives of charging point operators (CPOs),
government entities, and EV users, respectively. Factors such as location, charger quantity, load capacity, grid expansion needs, and energy source types—be they transformer upgrades or renewable installations—are assessed across various EV penetration scenarios.

2 - A Multicriteria and Multi-Objective Framework for InHouse and Outsourced Technological Developments byOpen Banking

Vinícius Dezem, Swati Sachan, Marcelo Macedo, Andre
Longaray
Open Banking is an innovative data-sharing framework designed fordata interoperability among diverse financial and non-financial organizations, operating within a country’s regulatory confines. Interoperability works through the deployment of application programming interfaces (APIs), which enable a two-way exchange of banking data with customers’ consent. This scenario is poised to reshapethe decision-making process of financial institutions, necessitating therefinement of strategies to implement customer-centric business models and augment profitability through the exploration of novel revenue
streams. Facing the complexity of the banking market it’s paramountto structure the decision context considering the multiple stakeholders
and variables involved. This research presents a case study on a retailbank where stakeholders encountered challenges in structuring actionplans for digital transformation following the launch of Open Banking.
It introduces a Hierarchical Multi-criteria Decision-Aid (H-MCDA)
framework for engaging stakeholders at various levels and proposes a
Multi-objective Optimization (MOO) approach to assess and compare
the effectiveness of multiple strategic plans, which aligns with technological innovation, customer interests, and the bank’s long-term goals.
Our findings present a comparison between two strategic action plan
scenarios: the bank’s intrinsic in-house technological developments
and the outsourcing of technical expertise.

3 - An Iterative Optimization Framework for Streamlining
Crude Oil Scheduling in Refinery Operations

İrem Marttin, Özgür Kabak, Melike Kamuran Onat, Büşra
Aydın, Ahmet Nomer, Oğuz Şüyün
Refineries are complex industries that take crude oil then fractionate
and crack it into valuable products. Feed continuity and quality arevery crucial for the production, so the petroleum scheduling is a key
step for a stable operation. This study aims to develop an optimization
tool addressing operational limits encountered practical contexts to facilitate adept decision-making in crude oil scheduling. To this end, ascheduling model was devised, covering the unloading of crude oil received via pipeline to multiple storage tanks, intra-tank crude oil mixing, and charging the resultant mixture to the crude distillation unit.
An event-based continuous-time Mixed Integer Linear Programming
formulation was created to effectively navigate the intricacies of thescheduling problem. Given the nonlinear problem stemming from the

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homogenous crude oil mixture in the tanks, an iterative solution approach was designed. This approach breaks down the problem into
manageable sub-problems based on periods of constant tank compositions. The viability of the proposed model has been evaluated throughempirical assessments conducted on real refinery cases. The findings
revealed satisfactory results within a brief interval, especially given theindustrial scale and complexity of the problem covering 4 tanks witharound 10 crude oil types. In this regard, the model aims to notably
shorten scheduling time while enhancing efficiency through steady operations facilitated by prompt decision-making.

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Tuesday, 14:30-16:00 - Room: 34 (building: 306)

Semidefinite Programming andimplementations, Quantum Information
Theory and other applications
Stream: Conic Optimization: Theory, Algorithms, and Applications
Invited session
Chair: Stefano Cipolla
Chair: Jacek Gondzio
1 - Quantum relative entropy optimization

Hamza Fawzi
Many problems in quantum information are formulated as convex optimization problems involving the quantum relative entropy function.
These problems cannot be directly expressed as semidefinite programs.
In this talk I will discuss various tools to deal with such optimization
problems. In particular, I will present a self-concordant barrier with
optimal parameter for the quantum relative entropy cone. This barrier
function can be used with interior-point schemes to solve convex optimization problems with the quantum relative entropy function. Basedon joint work with James Saunderson (arXiv:2205.04581).

2 - Proximal-Stabilized Semidefinite Programming

Stefano Cipolla, Jacek Gondzio
In this talk, we will present a regularized version of the primal-dualInterior Point Method (IPM) for the solution of Semidefinite Programming Problems (SDPs). Leveraging on the proximal point method, anovel Proximal Stabilized Interior Point Method for SDP (PS-SDPIPM) is introduced. The method is strongly supported by theoreticalresults concerning its convergence: the worst-case complexity result isestablished for the inner regularized IPM solver. Moreover, the new
method demonstrates an increased robustness when dealing with problems characterized by ill-conditioning or linear dependence of the constraints. Extensive numerical experience is reported to illustrate the advantages of the proposed method when compared to the state-of-the-art
solver.

3 - Efficient Quantum Relative Entropy Programming withSDP Preprocessing

Mehdi Karimi, Levent Tuncel
Optimization over the quantum relative entropy (QRE) cone has many
applications in quantum information processing, for example, calculating the key rates for quantum key distribution (QKD) protocols.
Recently, a new version of our convex optimization software package
Domain-Driven Solver (DDS) was released with modified numericalapproaches for solving QRE programming problems, potentially combined with many other convex function/set constraints. In this talk, we
propose a preprocessing and two-phase approach for QRE programming to reduce the size and improve the conditioning of a given instance. Phase-I of this two-phase approach is a well-behaved convex
optimization problem, such as minimizing a self-concordant barrier oran SDP. The reformulated QRE programming problem can be solved

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TD-39EURO 2024 - Copenhagen

more efficiently and faster by DDS or other optimization algorithms.
We finish the talk by presenting numerical results of using DDS for
solving many classes of problems, including calculating the QKD rate
for different protocols.

 TD-39
Tuesday, 14:30-16:00 - Room: 35 (building: 306)

Analysis of Stochastic Models I
Stream: Stochastic Modelling
Invited session
Chair: Cristian Pelizzari
1 - Interarrival Distribution of a Periodically-Observed
Time-Homogeneous Poisson Process

Sheldon Jacobson, Zach Hornberger, Douglas King

 TD-40

Despite the prevalence of Poisson process modeling in social science,little research has explored how the periodic collection of data affects
the applicability of this model. Our research studies how discretization impacts the Poisson process model’s interarrival distribution andintroduces the periodically-observed time-homogeneous Poisson process (PTPP) model as a viable alternative. The long-run behavior of
the PTPP is quantified and compared to the analogous Poisson process. We show the PTPP consistently provides a good approximationof process outputs regardless of discretization, extending the capabilities of the traditional model. The applicability of the PTPP model isdemonstrated through multiple case studies.

Tuesday, 14:30-16:00 - Room: 96 (building: 306)

2 - The Clustered-State Markovian Arrival Process: A
Framework for Dependent Recurrence and Mortality
ModelingÁlvaro Díaz Pérez, Rosa Elvira Lillo Rodríguez, Pepa
Ramírez-Cobo
The Clustered-State Markovian Arrival Process (CS-MAP) is introduced as an extension of the Markovian Arrival Process (MAP). Thisnew model can be employed to model marked point processes witha finite mark space, but our focus lies in its application for modeling recurrent processes with terminal events, which are prevalent in
the biomedical context where temporal sequences of recurrences areobserved, preceded by the death of the patient. We present novel results regarding this stochastic process, including explicit expressions
for the marginal and joint densities and for the moments and correlations of the inter-event times, as well as for the probability mass function of the number of recurrences before death. Furthermore, we provide an explicit expression for the likelihood function, incorporatingright-censoring, which is common in survival analysis. Maximizing
the log-likelihood function poses a challenge due to the large numberof parameters in the model; however, we propose a heuristic approachfor this purpose and we employ an appropriate local maximization algorithm. Additionally, we introduce some methods to enhance computational efficiency, such as a simplification of the likelihood function.
Finally, we demonstrate the effectiveness of the proposed maximum
likelihood approach using simulated data and apply it to model real
data concerning patients with oncological diseases.

3 - Markov Chain Bootstrapping and Simulation for a
Quadrivariate Stochastic Process in Energy Market Scenario Generation

Cristian Pelizzari, Enrico Angelelli, Paolo Falbo, Alessandra
RuffiniResearch in energy markets often requires jointly considering multiple sources of uncertainty. Among all players in the energy markets,
typical sources of profit uncertainty are supply costs, weather conditions, sale prices, and demand levels. Typically, these components are
not independent of one another. Moreover, the dependencies are often

304

non-linear. In turn, this makes investment and management decisions
and risk assessment complex. In the methodological field, an approachthat has proven particularly effective in modelling linear and non-linear
dependencies among multiple sources of uncertainty was advanced in
Cerqueti et al. (2017). The method is based on the approximation ofstochastic processes through Markov chains of order k, with k belonging to the set 1,2,.... The present work is concerned with modelling
the quadrivariate stochastic process of natural gas price, electricity demand, electricity price, and solar radiation based on the method of in
Cerqueti et al. (2017). The novel aspect of the present method compared to its previous applications consists in the management of thedifferent temporal frequencies of the four components (gas prices havea daily frequency, while the others have an hourly frequency). The
method, based on the choice of a few parameters compared to othermethods, allows us to bootstrap and simulate the quadrivariate stochastic process. Statistical tests are applied to the generated scenarios toassess their goodness of fit.

Tools and algorithms for equilibrium
detection
Stream: Interfaces Between Optimization, Hierarchical
Problems and Equilibrium Detection with Applications
Invited session
Chair: Laura Rosa Maria Scrimali
1 - Quasi-variational inequalities and applications: newchallenges on the constraints set-valued map

Domenico Scopelliti
In 1982, Chan and Pang introduced the quasi-variational inequality,that is, a Stampacchia variational inequality where the constraints set
is subject to modifications depending on the considered point. Nowadays, it provides a broad unifying setting for the study of several mathematical problems and real-world applications. The classical way to
get the existence of solutions consists of requiring that the constraints
set-valued map is a self-map; in addition, suitable continuity assumptions and properties on its values are required. However, to deal withthe challenges arising from the applications, sometimes it is needed toremove some of these requirements. With this spirit, the talk aims todiscuss some recent developments on the topic.

2 - Computing quasi-perfect equilibria: from a characterization to a differentiable path-following method

Chuangyin Dang, Yiyin Cao
As a different paradigm from Selten’s perfection for rationality on
strategy perturbation, the concept of quasi-perfect equilibrium wasformulated by van Damme through backward induction for finite
extensive-form games with perfect recall. The admissibility of quasiperfect equilibrium gains itself an advantage over Selten’s perfect equilibrium. Nevertheless, the formulation provides insufficient information on how to find such an equilibrium so that its existence was derived from the existence of a normal-form proper equilibrium (sufficient but unnecessary). To address this issue, this paper develops witha separation technique an equivalent definition of quasi-perfect equilibrium through the introduction of epsilon-quasi-perfect equilibriumand establishes directly the existence of a quasi-perfect equilibrium.
To demonstrate its computational effectiveness, we illustrate with simple examples how one can employ the definition to analytically find
all the quasi-perfect equilibria. As a byproduct, we acquire an equivalent definition of sequential equilibrium. A further application of thedefinition leads to a differentiable path-following method to computequasi-perfect equilibria.

EURO 2024 - Copenhagen
3 - Heuristics and optimization-based methods for solving
generalized Nash games: a comparison

Monica-Gabriela Cojocaru
In this paper, we introduce three heuristic algorithms to investigate
their in soilving Generalized Nash Equilibrium Problems (GNEP). The
first is an evolutionary-inspired algorithm which utilizes competitive
selection and (linear & nonlinear) regression to motivate generationsof new points. The second involves stochastic gradient descent of the
Shadow Point function across mass numbers of agents to find gamesolutions. Last but not least, we present an ANN-based model withintelligent agents who solve a GNEP. These algorithms are evaluatedon 2 and 3 player games in 2 and 3 dimensions, with both linear andnon-linear shared constraints. The success of these algorithms is discussed and compared vis-a-vis existing, optimization-based methods(such as VI/QVI and KKT-based methods), and the limitations of thealgorithms are explored. Finally, we introduce two measures of performance for all the algorithms in terms of their capability of finding anddescribing the entire solution set of a GNEP.
This is joint work with: Benjamin Benteke, Kira Tarasuk, Nick Hoover
and Mihai Nica (at U of Guelph)

4 - Equilibrium selection via approximation and penalization

Giancarlo Bigi
The selection of equilibria is a central issue in the management ofmulti-agent systems that can be partially controlled. Once the system has been modelled, the selection can be performed through an hierarchical program whose lower-level describes the equilibria of thesystem and the upper-level explicitly addresses the selection criterion
through a suitable objective function. These hierarchical programs are
simpler than more general bilevel structures as the lower-level problems are non-parametric with respect to the upper level variables. In
order to tackle them, suitable approximated versions are introduced.
On the one hand, the approximation does not perturb the original [exact] program too much and allows for some additional flexibility in the
choice. On the other hand, it allows relying on suitable exact penalty
schemes by recovering those regularity conditions that the original
problems do not satisfy. These penalization approaches are addressedin detail and their convergence properties are established.

 TD-41
Tuesday, 14:30-16:00 - Room: 97 (building: 306)

Lower-order composite optimization
problems

TD-41

The significant advantage of the IMTC is that it has a closed-form expression and low storage requirement, and promotes the mix sparse
structure of the solution. Numerical results on simulated data indicate that the IMTC has a strong promoting capability of the mix sparse
structure and outperforms

2 - On Convergence of Iterative Thresholding Algorithms
to Global Solution for Nonconvex Sparse Optimization

Yaohua Hu
Sparse optimization is a popular research topic in applied mathematics and optimization, and nonconvex sparse regularization problems
have been extensively studied to ameliorate the statistical bias and enjoy robust sparsity promotion capability in vast applications. However,puzzled by the nonconvex and nonsmooth structure in nonconvex regularization problems, the convergence theory of their optimization algorithms is still far from completion: only the convergence to a stationary
point was established in the literature, while there is still no theoretical evidence to guarantee the convergence to a global minimum or atrue sparse solution. This talk aims to find an approximate global solution or true sparse solution of an under-determined linear system.
We will propose two types of iterative thresholding algorithms withthe continuation technique and the truncation technique respectively.
We introduce a notion of limited shrinkage thresholding operator andapply it, together with the restricted isometry property, to show thatthe proposed algorithms converge to an approximate global solution ortrue sparse solution within a tolerance relevant to the noise level andthe limited shrinkage magnitude. Applying the obtained results to nonconvex regularization problems with SCAD, MCP and Lp penalty andutilizing the recovery bound theory, we establish the convergence of
their proximal gradient algorithms to an approximate global solutionof nonconvex regularization problems.

3 - Avoiding strict saddle points of nonconvex regularized
problems

Hao Wang
In this paper, we consider a class of non-convex and non-smooth
sparse optimization problems, which encompass most existing nonconvex sparsity-inducing terms. These problems, moreover, are probably non-lipschitz around sparse solutions. We propose an damped iterative reweighted l1 algorithm to solve these problems. The algorithm
is guaranteed to converge only to local minimizers when randomly initialized. By deriving a second-order sufficient optimal condition under
the non-degenerate assumption, we show that the strict saddle property
is generic on these sparse optimization problems.

4 - An Inexact Projected Regularized Newton Method for
Fused Zero-norms Regularization Problems

Stream: Nonsmooth Optimization
Invited session

Yuqia Wu, Shaohua Pan, Xiaoqi Yang

Chair: Xiaoqi Yang
Chair: Yaohua Hu

In this talk, we are concerned with structured $ell_0$-norms regularization problems, with a twice continuously differentiable loss function and a box constraint. This class of problems have a wide range
of applications in statistics, machine learning and image processing.
To the best of our knowledge, there is no effective algorithm in theliterature for solving them. In this paper, we first obtain a polynomialtime algorithm to find a point in the proximal mapping of the fused$ell_0$-norms with a box constraint based on dynamic programming
principle. We then propose a hybrid algorithm of proximal gradientmethod and inexact projected regularized Newton method to solve
structured $ell_0$-norms regularization problems. The whole sequence generated by the algorithm is shown to be convergent by virtueof a non-degeneracy condition, a curvature condition and a KurdykaLojasiewicz property. A superlinear convergence rate of the iterates
is established under a locally H"olderian error bound condition on asecond-order stationary point set, without requiring the local optimality of the limit point. Finally, numerical results highlight the features ofour considered model, and the superiority of our proposed algorithm.

1 - Mix Sparse Optimization: Theory and Algorithm

Xiaoqi Yang
In this talk, we consider the $ell_0$ regularization problem for mix
sparse optimization and investigate its mathematical theory and algorithm. In the theoretical part, we first introduce the notions of sparse
eigenvalue conditions, one of the weakest regularity conditions in theliterature, and then establish the oracle property without any regularity
condition and provide a recovery bound for the mix sparse optimization problem under the weak assumption of sparse eigenvalue condition. Moreover, an asymptotic analysis is provided to advance theunderstanding of the convergence of the $ell_p$ regularization to the$ell_0$ regularization. In the algorithmic part, we propose an iterative mix thresholding algorithm with continuation technique (IMTC)
to solve the mix sparse optimization problem and present its global
convergence theorem and linear convergence rate to a local minimum.

305

TD-42EURO 2024 - Copenhagen TD-42 TD-43

Tuesday, 14:30-16:00 - Room: 98 (building: 306)

Tuesday, 14:30-16:00 - Room: 99 (building: 306)

Variational techniques in conic optimizationand mean field games

Market Design 1

Stream: Variational Analysis and Continuous Optimization
Invited session

Stream: Market Design
Invited session
Chair: Aleksandrs Smilgins

Chair: Hector Ramirez
1 - Successive incentives
1 - A dual proximal-gradient approach for variational mean
field games

Luis Briceño-Arias, Francisco José Silva Alvarez, Xianjin
Yang
In this talk, we provide a new algorithm to approximate equilibria ofvariational mean field game systems (MFG) with local couplings. Under suitable conditions on the coupling function, the dual of the variational formulation of the MFG reduces to the minimization of thesum of a proper convex lower semicontinuous function and a differentiable convex function whose gradient satisfies a locally Lipschitztype condition. In this context, we provide a generalization of theproximal-gradient (or forward-backward) splitting algorithm for tackling the dual problem. We prove the convergence of our method andderive a linear convergence rate when the differentiable function is locally strongly convex. We recover classical results in the case when
the gradient of the differentiable function is globally Lipschitz continuous and an already known linear convergence rate when the function
is globally strongly convex. Compared with some benchmark algorithms to solve these problems, our numerical tests show similar performances in terms of the number of iterations but an important gainin the required computational time.

2 - Real roots of real cubics and optimization

Manish Krishan Lal
The solution of the cubic equation has a century-long history; however, the usual presentation is geared towards applications in algebra
and is somewhat inconvenient to use in optimization where frequently
the main interest lies in real roots. In this note, we present the rootsof the cubic in a form that makes them convenient to use and we also
focus on information on the location of the real roots. Armed with
this, we provide several applications in optimization where we compute Fenchel conjugates, proximal mappings, and projections.
This is a joint work with Heinz Bauschke and Shawn Wang.

3 - Extensions of Constant Rank Qualification Constraint
Condition to Nonlinear Conic Programming

Hector Ramirez
We present new constraint qualification conditions for nonlinear conic
programming that extend some of the constant rank-type conditions
from nonlinear programming. Specifically, we propose a general andgeometric approach, based on the study of the faces of the cone, fordefining extensions of (relaxed) constant rank CQ and constant rankof subspace components condition to the conic context. We then compare these new conditions with some of the existing ones, including
the nondegeneracy condition and Robinson’s constraint qualification.
The main advantage of the latter is that we are able to recast the strong
second-order properties of the constant rank condition in a conic context. In particular, we obtain a second-order necessary optimality condition that is stronger than the classical one obtained under Robinson’sconstraint qualification, in the sense that it holds for every Lagrange
multiplier, even though our condition is independent of Robinson’s
condition.

Jens Leth Hougaard, Lars Østerdal, Juan D. Moreno-Ternero,Jens Gudmundsson
We study the design of optimal incentives in sequential processes. An
agent initiates a value-creating sequential process through costly investment with random success. If unsuccessful, the process stops. If
successful, a new agent thereafter faces a similar investment decision,and so forth. For any realization of the process, the total value is distributed among the agents using a reward rule. Such rules induce agame among the agents. The domain of the rules is very rich, yet weare able to show equilibrium existence. We characterize rules that yield
the highest welfare created in equilibrium. We also characterize rulesthat yield the highest possible payoff for the initiator in equilibrium.
A canonical class of rules are those in which an agent’s reward (apartfrom the initiator) is affected by her own success but not the successor failures of others. Our findings show that such simple reward rules
invoking short-run individual incentives are sufficient to meet our longrun systemic objectives.

2 - Saving Millions in Government Procurement Through
Data Science and Market Design

Gabriel Weintraub, Marcelo Olivares, Daniela Saban, Piero
ZanoccoFramework agreements (FAs) are procurement mechanisms used in
private and public organizations by which a central procurement
agency selects an assortment of products, typically through auctions,and then affiliated organizations can purchase from the selected assortment as needs arise. In Chile’s central procurement agency (ChileCompra), FAs accounted for 23% of the procurement expendituresduring 2018-19. However, descriptive analysis of purchase transaction data suggests that some FAs exhibited low levels of competitionin the auctions used to select suppliers, which could potentially result
in larger government expenditures. We collaborated with ChileCompra to redesign FAs to enhance competition introducing two importantchanges: (i) standardize the product catalogue using natural language
processing algorithms; (ii) use this product standardization to induce
more competition in the auctions to select of suppliers. These changes
were implemented through an experimental design in the new Food FA
to measure its impact, showing that inducing more intense competitionin the auction stage reduced transaction prices by 8%. This pilot study
ultimately led ChileCompra to implement a similar design in all of its
FAs, and many of the improvements in the design of the FAs were included in the new regulation on government purchases. If we were to
extrapolate the savings from our pilot re-design to all of these FAs, thetotal savings would amount to around US$64 million in 2022.

3 - Bidding Strategies in Treasury Auctions

Sasa Pekec, Aleksandrs Smilgins
We use data from Danish and Swedish treasury auctions to analyze
differences in bidding strategies and auction performance. Our analysis suggests that underreporting true valuations is more pronounced
under discriminatory auction format than under uniform auction format. Analogously, underreporting of true demand quantity is more
pronounced under discriminatory auction format than under uniform
auction format. We find no significant difference in the expected government profit (relative to the market prices).

4 - Managing cascading network disruptions through optimal liability assignment

Jens Gudmundsson, Jens Leth Hougaard, Jay Sethuraman306

EURO 2024 - Copenhagen
Interconnected agents such as firms in a supply chain make simultaneous preparatory investments to increase chances of honouring theirrespective bilateral agreements. Failures cascade: if one fails theiragreement, then so do all who follow in the chain. Thus, later agents’investments turn out to be pointless when there is an earlier failure.
How losses are shared affects how agents invest to avoid the losses in
the first place. In this way, a solution sets agent liabilities depending on
the point of disruption and induces a supermodular investment game.
We characterize all efficient solutions. These have the form that lateragents—who are not directly liable for the loss chain—still shoulder
some of the losses on the premise that they might have failed anyway.
Moreover, we find that such indirect liabilities are necessary to avoid
unbounded inefficiencies. Finally, we pinpoint one efficient solution
with several desirable properties.

 TD-44
Tuesday, 14:30-16:00 - Room: 20 (building: 324)

Pairwise comparisons and preference
relations 2
Stream: Multiple Criteria Decision Analysis
Invited session
Chair: Konrad Kułakowski
Chair: Matteo Brunelli
1 - Pairwise Comparisons: Gauge Theoretical Aspects andBeyondJean-Pierre Magnot
I will review the existing links between pairwise comparisons andgauge theory in physics. These links already exist in portfolio management since 1999 but are highlighted only recently in pairwise comparisons, even if the mathematical structures are the same. After aindroduction in which the necessary notions of connexion, holonomy
and gauge are refreshed for the audience, I will give an in-depth description of the mathematical analogy between priority vectors with
Yang-Mills fields, both in a deterministic and a stochastic way. In thispicture, the notion of inconsistency indicator will have a plurality ofnatural meanings in terms of mathematical concepts. At the end of
this short talk, I hope to have the time to discuss perspectives both fordecision theory from physic, in particular with the controversal use of
the theory of Lie groups but also with a minimal setting for pairwise
comparisons in the framework of categories and also with quantization
procedures, and for physics from decision theory, in particular withinconsistency reduction for scalar gauge fields (e.g. electromagnetic
fields).

2 - A Simulation Analysis of Inconsistency Indices Values
for Transitive Pairwise Comparison Matrices

TD-45

simulations for various CIs have been designed. The intent is to discuss the result of that examination, in particular certain characteristicsof CIs distribution obtained for transitive PCMs. It is believed that,on the bases of these results, one can more credibly classify PCM as
enough or not enough consistent.

3 - Performance Assessment of Sumo Wrestlers from
Match Results as Incomplete Pairwise Comparison with
Consideration of Match Time

Tsuneshi Obata, Shunsuke Shiraishi
In most competitive sports, one of the main interests of the audience isto know who is truly strong. Various tournaments are held to determine
the strongest champions, but the winners depend on the tournament
regulations to no small degree. It is not always sure that the champion
is the highest-performing team/player. The outcome of a competitive
sports match, which wins, can be analogous to a pairwise comparison
in decision-making. In many competitive professional sports leagues,
all teams belonging to a league fight each other in a round-robin tournament, and the team with the most wins is honored as the champion. However, in Japan’s professional sumo league, Ozumo, about
40 sumo wrestlers, rikishi, compete in a limited schedule of 15 days,
so not all combinations of rikishi compete against each other. Using amethod that handles incomplete pairwise comparisons, we propose amethod for obtaining the values representing the actual performance ofteams/players from the results of a semi-round-robin tournament suchas Ozumo. For the construction of a pairwise comparison matrix, we
consider match time.[U+3000]We also provide applications to the actual data of Ozumo.

4 - Extending AHP with Belief Degrees for Hesitant Evaluations

Ozge Sahin Zorluoglu, Özgür Kabak
The Analytic Hierarchy Process (AHP) method, developed by Thomas
Saaty, is structured around pairwise comparisons to derive priority
weights for decision criteria and alternatives. In this study, a new
approach is presented by extending AHP method to be expressed interms of belief degrees (BD). In AHP method decision maker (DM)may prefer to use different types of preference formats for each criteria
to provide information. They may have hesistancy in expressing theirevaluation by a single expression and they can use several linguistic
expressions to evaluate alternatives. Therefore, hessitant fuzzy linguistics terms can be used. In this study we develeop a new method to deal
with problems where criteria weights are given as fuzzy preference relation and evaluations of DM is given as hesitant fuzzy sets. To aggregate different preference formats we transformed criteria weights andevaluations of DM to BD without any loss of information. Weighted
Cumulative Belief Degree (WCBD) operator is employed for aggregation of alternative evaluations and criteria weights expressed with
BD. First, a satisfaction level s_r, q∈0,. . . ,6 is determined for aggregations of evaluations by problem owner. By use of WCBD operator one
can integrate two belief structures by considering t satisfaction levelof problem owner. The developed method has been applied to various
problems and has yielded satisfactory results.

Tomasz Starczewski, Pawel Tadeusz Kazibudzki
In the Analytic Hierarchy Process (AHP), a Consistency Index (CI)is used for detecting inconformity of a Pairwise Comparison Matrix (PCM). An original idea of T. Saaty is to compare value of
CI with an average value of CI obtained for a large amount (e.g.
100,000) of random PCMs. The inventor of AHP idea has specified percentage thresholds for CI values, which should not be exceeded. However, as A.Z. Grzybowski has shown, very small number of random PCMs is even close to consistent in Saaty’s sense(https://doi.org/10.1016/j.eswa.2012.04.051). Furthermore, according
to Grzybowski’s research, for PCMs of the size larger than 5, the probability of gaining PCM, that satisfies Saaty’s consistency conditionis close to 0. Hence, evaluation of the relationship between random
PCMs and consistent PCMs is rather pointless. However, much higher
percentage of randomly matrices are consistent, when transitive PCMs
are taken into account. Moreover, PCMs given by real decision makers
usually are transitive (despite of their inconsistency). Therefore, it was
decided to investigate mostly that kind of PCMs. Thus, Monte Carlo TD-45
Tuesday, 14:30-16:00 - Room: 30 (building: 324)

Decision Support for Sustainable
Operations
Stream: Decision Support Systems
Invited session
Chair: Shaofeng Liu
1 - WasteLogs: a decision support tool for strategic waste
collection

Jérôme De Boeck, Vera Fischer, Meritxell Pacheco Paneque307

TD-46EURO 2024 - Copenhagen

Waste collection management has seen an increasing interest in the OR
community these past years. This is due to the wide range of Capacitated Vehicle Routing Problems (CVRP) studied and the growing trend
in studying sustainability-related problems.
For waste collection companies, it can be challenging to identify thetype of strategy most suitable for a given situation. On the one hand,the complexity of the state-of-the-art algorithms presented in the literature; on the other hand, the data needed for these algorithms can bechallenging to obtain and encode.
In partnership with Alpenluft, a Swiss waste collection consulting
company, and the Innosuisse agency supporting R&D projects, we developed the WasteLogs application, a user-friendly strategic waste collection decision tool. The application offers interfaces allowing the encoding of the collection points, the amounts of waste to collect, and thecollection strategy in different features that can be combined to generate a routing for collection vehicles. There are currently three state-ofthe-art collection strategies implemented in the tool. Each algorithm
minimizes the CO2 emissions through heuristic methods; the user canthen identify what collection strategy is the most suitable and extract
the information needed to import them into GPS systems. WasteLogs
also allows importing existing collection tours to evaluate whether they
can be improved.

2 - Optimizing Industrial Order Fulfilment: A Decision Support System for Efficient Intra-logistics

Marco Taccini, Giulia Dotti, Manuel Iori, Anand
SubramanianThis study addresses the improvement of order fulfilment in industrial settings with centralized order consolidation and distributed warehouses. To ensure homogeneity and customer satisfaction when transporting products to the central facility, logistics operators need to makedecisions about warehouse management, transportation modes, stock
unit configurations, and product features. Many companies rely on
manual information flow to achieve these objectives, resulting in costly
and time-consuming operations. Here we propose a Decision Support
System (DSS) that integrates information flow digitalization and material flow optimization. Optimization is accomplished through an integer linear programming model that considers all the factors listed
above. Digitalization relies on a scalable software infrastructure that
periodically gathers commercial information, checks product availability, runs the optimization model, and integrates decisions into the enterprise resource planning system. The DSS was developed in collaboration with an international ceramic tile company. Compared tomanually implemented operations, the results reveal a significant reduction in transportation costs, ranging from 24% to 40%, along withestimated time savings of about 120 hours per month. Therefore, theimplementation of the DSS offers substantial cost savings by optimizing stock transfers and maximizing valuable working hours.

3 - Managing Transportation and Inventory Simultaneously: A Dual-Objective Approach in Supply Chain Networks with Multiple Distribution Networks

Lokesh Vijayvargy, Saumyaranjan Sahoo
In order to meet customer needs efficiently, logistics plays a crucialrole within the broader supply chain by managing the forward and reverse flow, as well as storage, of products, services, and associateddata. This study addresses the supply chain network problem, focusing
on determining the optimal allocation of orders from multiple plants,
warehouses, and distributors. The goal is to minimize total transportation and inventory costs while simultaneously optimizing locations,flows, shipment composition, and shipment cycle times. The formulated problem is treated as a multi-objective programming challenge
and is solved using the value function approach to obtain the optimalorder allocation of products. A case study on ABC Company’s supply
chain is conducted to assess the effectiveness and applicability of thedeveloped multi-objective model. The results indicate optimal transportation and inventory costs for ABC Company, along with an optimal distribution network within India. These findings hold significant
relevance for the manufacturing sector, especially for those grapplingwith logistics challenges in their supply chain networks. The developed model equips researchers and managers to navigate various uncertainties and logistics risks associated with supply chain networks.

4 - Exploring the Role of Peer Learning in Supporting Decisions for Food Sharing Organisations

Shaofeng Liu, Xinyi Li, Dulekha Kasturiratne, Huilan Chen
More and more individuals and families have been driven into the danger zone of going hunger recently. To rescue the situation, food sharingorganisations (e.g. charities) have worked untiringly. One of the key
challenges facing the food sharing organisations is the lack of decision
support to help improve their operations efficiency and community ofpractice. This paper aims to investigate the role of peer learning in thecreation of closed-loop social value networks, subsequently to support
food sharing organisations and communities.
A Systematic Literature and Practice Review (SLPR) has been undertaken. Based on the SLPR, a comprehensive peer learning frameworkfor food sharing has been developed consisting of four core components: peer learning agents, peer learning modes, peer learning practice, and peer learning outcomes and impacts. The impacts of peer
learning on the creation of closed-loop social value networks will beassessed via four stages of the network, including social value propositions, value beneficiaries, value delivery channels, and value capture
and feedback. The peer learning framework contributes to new knowledge and practice in a number of aspects: (1) it defines relationships
among the four peer learning components; (2) it establishes the main
impacts of peer learning on four stages of social value networks; and(3) it provides recommendations to food sharing organisations in making efficient decisions.

 TD-46
Tuesday, 14:30-16:00 - Room: 40 (building: 324)

Success, Failure And The Factors That
Influence Outcomes
Stream: Making an Impact
Invited session
Chair: Sander Van Aken
Chair: Dennis Huisman
Chair: Frédéric Gardi
1 - Success, Failure And The Factors That Influence Outcomes

Making An Impact, Sander Van Aken, Frédéric Gardi, Dennis
Huisman, Alberto Delgado
The speakers on this panel all have wide experience of working withstakeholders to deliver projects putting OR into practice. In the firstpart of the session, each speaker will talk about their experiences andlearnings, and how these have shaped their own views on which factors influence whether an application could be (or will be?) a success,a failure or somewhere in-between. We will then invite audience questions, and open up the discussion: do these sound familiar? What are
your own experiences? What lessons would you like to share on how
to address them?
For final details of speakers, please see the ’Making an Impact’ pageon the main conference website.

 TD-47
Tuesday, 14:30-16:00 - Room: 50 (building: 324)

MCDA and urban planning 3
Stream: Multiple Criteria Decision Analysis
Invited session
Chair: Francesca Abastante308

EURO 2024 - Copenhagen
1 - A MCDA Model to Support Territorial Planning at the Regional Scale: the Case of Negotiated Planning Tools in
the Lombardy Region

Marta DellOvo, Alessandra Oppio, Marco Rossitti, Francesca
Torrieri
Nowadays, public territorial planning is called to properly orient public funding toward interventions that ensure long-term sustainability
while contributing to collective well-being. For this reason, resorting to integrated planning tools, opened to public-private partnerships,
becomes crucial, together with considering their coherence with thereference planning frame. Based on these premises, the research reflects on the evaluation models’ role in addressing this coherence issue
and supporting territorial planning by implementing negotiated planning tools in the Lombardy Region. Indeed, negotiated planning isa means to address public interest toward territorial development bypromoting public-private partnerships. It is ruled by the R.L. 19/2019,
which provides an evaluation clause to understand the tools’ contribution to achieving regional objectives. Thus, the research aims to define
an evaluation model to support the regional administration in ensuring the effective implementation of negotiated planning tools and theircompliance with regional strategies. The multi-dimensional nature of
the considered issue calls for resorting to Multi-Criteria Analysis. After describing the methodological process to trace the law’s provisions
into a multi-criteria frame, the evaluation model is tested on some casestudies of implemented Agreements in the Lombardy Region. Such atest better highlights the model’s opportunities, limits, and improvement room.

2 - A GIS-MCDA Approach for Spatial Assessment of Real
Estate Dynamics: a trial in Rotterdam, the Netherlands

Eugenio Muccio, Giuliano Poli, Hilde Remoy, Maria Cerreta
According to the United Nations, while housing is an integral part of7 SDGs, it makes direct and indirect contributions to all Agenda 2030
goals. In particular, increasing access to affordable housing remains
essential for SDG11, addressing the requirements of low-income, vulnerable, and marginalized households while advancing housing affordability for progress towards sustainable communities. The multiattribute nature of housing is reflected in its market value, determined
not only by economic variables (e.g., price, income, GDP) but also
by non-economic factors influencing real estate dynamics, specificallyin tourism contexts. Establishing a monitoring framework for residential real estate urban dynamics at the neighborhood level is usefulfor decision-makers in addressing planning uncertainty associated with
uneven spatial development, housing affordability, and gentrification.
This contribution aims to experiment with spatial composite indicators
produced through MCDA aggregating methods to rank urban districtsof Rotterdam, the Netherlands, concerning the three above-mentioned
phenomena. A set of monetary and non-monetary indicators has been
implemented as proxy variables to assess the characteristics of housing
stock. Assessing housing values using GIS-MCDA aggregation procedures is still a low-explored field which the authors aim to stress toadvance research in the assessment of real estate spatial dynamics at
the urban scale.

3 - The grammar of constraints for a things-based justice.
Urban text(ure)s and the ulteriority of the local identities

Salvatore Giuffrida, Maria Rosa Trovato, Vittoria Ventura,
Cheren Cappello, Ludovica Nasca
Project evaluation derives from operational research method and tools
for formalizing decision-making processes. Among these, the Appraisal and valuation discipline privileges the ones aimed at measuring
the added value, an aspect of the progress of social communities that
implies considerations of distributive justice. Although not universallyendorsed, the appeal to the civic engagement of evaluation is increasingly compelling since the succession of economic crises and environmental fluctuations enlarges the frontline of countering the distress ofterritories and communities. This paper addresses the redevelopment
processes of historic urban fabric by proposing a "scenario-generating
model" based on a structured and limited set of constraints (rules) able
to outline a potentially unlimited number of consistent layouts. On
the basis of two different experiences carried out with reference to a

TD-48

building-urban fabric in Italy, some critical reflections are proposedon: 1. the "grammatical" framework of this system, which bases its internal coherence on the "morphological," "semantic," and "syntactic"
levels; 2. the projection of the "ethics of constraint" from the particular dimension of "what is owed" to things and people, to the generaldimension of "what is owed" to earth and communities.

 TD-48
Tuesday, 14:30-16:00 - Room: 60 (building: 324)

DEA applications in Policy Making and
Planning I
Stream: Data Envelopment Analysis and its Application
Invited session
Chair: Stavros Kourtzidis
1 - The international Efficiency Gap Analysis of Mobile Network Operator markets

Norihiro Hayakawa, Yasufumi Saruwatari
The objective of this study is to clarify the impact of market characteristics of the mobile telecom market in Japan, the Republic of Korea (South Korea), the United Kingdom (UK), and the United States(USA) on the efficiency of management of the Mobile Network Operators (MNOs) in each market. The MNOs are said to be affected by theirgovernment policies. In comparison to the efficiencies of MNOs internationally, it is necessary to consider not only the external factors that
the MNO can NOT control but also the internal factors that the MNO
can control. This study employs "meta-frontier" to separate those factors and clarify the efficiency gap (i.e., the Technology-Gap) between
the countries. The Technology-Gap reveals a range of characteristics
across international mobile network operator markets: the platform migration strategy (3G to 4G, 4G to 5G), the mobility of customers, thesmartphone (device) sales and customer acquiring strategy, the government regulations, and so on. One of the results shows that the global
MNO markets can be divided into two categories given the mobility ofcustomers. The markets that have high mobility of customers migrate
their generation aggressively and sell smartphones (devices) at lower
prices to acquire new customers. The markets that have low mobilityof customers tend to cut their service costs and lower their service fees
to retain customers.

2 - Analysing the performance of Business Incubators inIndia using a DEA-Decision Tree approach

M S Karthicanand, Prakash Sai Lokachari
Startups drive economic growth through technological innovations,market expansion, and increased production. This trend is evident
globally, with countries like India fostering entrepreneurship throughwell-funded support structures, notably business incubators. Incubator performance assessment is challenging due to existence of diverse
frameworks and objectives. Current literature often overlooks improvement pathways for individual incubators. To address this, we employ Data Envelopment Analysis (DEA) for relative performance evaluation, identifying improvement opportunities without preconceived
assumptions. A Classification Decision Tree based on DEA outcomes
distinguishes incubator profiles, notably highlighting differences between university-based units and others. Mentoring, infrastructure, andtechnology support emerge as crucial, especially for Deep Tech startups, which require distinct assistance due to their capital intensity andtechnological uncertainty. Our study assesses 53 Indian non-profit incubators and reveals variations in profiles that are discussed in the context of supporting Deep Tech startups, making it vital for policymakers
and incubator managers navigating the evolving entrepreneurial landscape.

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TD-49EURO 2024 - Copenhagen3 - Including a Public Input in the Additive Two-Stage Network DEA Model: The Case of Quantitative Easing inJapan

Stavros Kourtzidis, Giannis Karagiannis, Nickolaos G.
Tzeremes
The traditional additive two-stage network DEA model under constantreturns to scale has non-increasing decomposition weights (i.e. theweight assigned to the first stage is not less than the weight assigned tothe second stage), which also has a direct impact on the stage efficiencies. Previous research has revealed that adding external input/s in thesecond stage provides the necessary conditions under which there canbe a reversal in the decomposition weights for the two stages. In thispaper we are investigating the case of an external public input in thesecond stage, which is shared across DMUs but not allocatable, andits effect on the decomposition weights. The empirical investigation isundertaken using a dataset of Japanese Regional Banks. Bank of Japan
has a long tradition with quantitative easing as a monetary policy instrument that could help boost economic activity, avoid deflation, andovercome the problems of the liquidity trap. Given that quantitativeeasing provides all banks with liquidity, we model it as a constant input in the second stage of the model. This generalised structure of themodel with an external single constant input in the second stage tend to
produce results which do not suffer from the issues under investigation.

 TD-49
Tuesday, 14:30-16:00 - Room: M1 (building: 101)

Production planning problems
Stream: Lot Sizing, Lot Scheduling and Production Planning
Invited session
Chair: Christian Almeder
Chair: Daryna Dziuba

used for medical diagnosis and treatment. The RC production and delivery process consists of five stages: cyclotron, synthesis, vial filling,delivery to the hospital(s), and injection. The produced isotope decays exponentially throughout all these stages. The demand is ordereddaily by each hospital, specifying each treatment’s dose and time of
injection. The research focuses on the basic case of a single cyclotron,
single material, and single hospital, which represents many existing radiopharmaceutical production and delivery systems. We develop andanalyze a relaxed model for this case, together with a solution scheme
for a detailed discrete injection plan. The proposed model is a novel
relaxed optimization model with a convex cost function and a set of operations management constraints. The solution determines the number
and sizes of the daily cyclotron batches to meet the hospitals’ demand
while minimizing the costs of production and inventory holding, considering the radioactivity decay. The proposed solution process derives
both symmetric solutions (of identical batches) and nonsymmetric solutions. A set of best feasible solutions are presented for the choice ofthe decision maker.

3 - Optimal Order Quantity for Subcomponent: An Extension of EOQ Model

Yash Aneja
Some components used to assemble a final product are assembled inhouse in separate assembly lines. Subcomponents are used to assemble
components. Some subcomponents are purchased outside or produced
outside the plant and delivered. We want to decide the order quantityof subcomponent which is used in the production of component. As
an example, in many auto manufacturing plants, vehicle engine (component) is assembled in-house and used in vehicle (final product) assembly line. Major components (subcomponents) of engine are engine
block, crank shaft, piston. . . . Some auto manufacturers assemble engine in-house and make engine block outside the vehicle manufacturing plant and deliver it. We want to find the optimal delivery batch
size (order size) of engine block to vehicle manufacturing plant. We
develop a mathematical model for the problem and obtain a simple
optimal poicy.

4 - Solving capacitated lot sizing problems using a combination of fast construction heuristic, metaheuristic andimprovement heuristic

Daryna Dziuba, Christian Almeder
1 - Production planning in the automotive industry withArtelys integrated scheduler service

Hugo Chareyre, Maxime Dufour
Efficient and accurate production planning is crucial in the automotive
industry. Artelys has developed a scheduler service for Toyota Motor
Europe that creates plannings for post-production workshops operations across Europe. The problem consists in scheduling operations on
vehicles on different production lines with available resources. Thiscase study details how this industrial problem differs from the classicalResource Constrained Project Scheduling Problem (RCPSP) with its
additional operational constraints (multiple shifts, preemptive breaks,
sequence constraints, etc.) and its multi-objectives (minimizing latetasks, late vehicles, maximizing efficiency and workers ergonomics,etc.). Artelys has worked closely with Toyota to deploy this scheduler
service as micro-service in order to solve this complex problem in areal-time context and as a flexible decision-aid software. The associated constraint programming model is implemented with Fico Mosel
modelling language and the model is solved using Artelys Kalis solver.
This micro-service has replaced a manual tedious task that was taking
place differently in all workshops by delivering faster, higher quality
solutions.

2 - Integrated Production and Delivery of Rapidly Decaying
Products

Ron Weitzman, Inessa Ainbinder, Gavriel David Pinto, Daniel
Lifshitz, Gad Rabinowitz
The goal of this research is to develop models for planning andscheduling production systems of rapidly decaying products. The F18 radiopharmaceutical cyclotron (RC) production and its delivery isa specific and important example of such a system. F-18 isotopes are

310

The capacitated multi-item lot-sizing problem with setup times isknown to be NP-hard. In our recent work, we proposed the 2-step
construction heuristic (2-SCH), which delivers competitive solution
quality within a short computational time and can be applied to various problem extensions with minimal modifications. It was shownthat problem instances with high capacity utilization and high average time between orders are particularly challenging and, in the worst
case, only solutions that use overtime capacity could be constructed.
Leveraging the structure of the 2-SCH, we explore opportunities toenhance solution quality as well as counteract the possible use of overtime by incorporating the 2-SCH into a metaheuristic and designing
an improvement heuristic that relies on information from the 2-SCH
solutions.

 TD-50
Tuesday, 14:30-16:00 - Room: M2 (building: 101)

Omni-Channel Retailing
Stream: Retail Operations
Invited session
Chair: Alexander Hübner
1 - Navigating Omnichannel Grocery Retailing: Bridging
Customer Insights for Strategic Decisions

Daniela Fernandes, Pedro Amorim, Jan C. Fransoo, Fábio
MoreiraEURO 2024 - Copenhagen
In response to the growth tendency of online sales, grocery retailers
have embraced e-commerce, and committed to the required investments. It is a recognized fact that profitability is hard to attain given thecosts associated with order fulfillment and last-mile distribution, aggravated by customers’ unwillingness to pay additional fees for these
services. We consider the case of an omnichannel grocery retailer, thatprovides home deliveries for a delivery fee, leveraging its Brick-andMortar network. Improving the operational efficiency of online orderfulfillment is important, but larger opportunities lie in effectively managing demand within and across channels. We use customer-level activity over time to infer the influence of online channel features, such asdelivery fees and assortment size, and of the store network on customer
behavior. Our findings reveal that customers strategically adjust their
shopping habits based on factors like fees, product variety, household
needs, and proximity to physical stores. Building on these insights,
we model customer behavior regarding store selection, shopping frequency, and expenditure patterns. By considering the cost to serve
online orders and the value customers present to the retailer acrosschannels, we prescribe solutions that optimize the retailer’s profits. In
a series of numerical studies, we draw managerial insights on the pathto omnichannel profitability.

2 - Optimal Policies for Handling Returned Products in the
Reverse Supply Chain

Nizar Zaarour, Emanuel Melachrinoudis, Sining Chai
Product returns are an essential part of conducting business today,
due to the emergence of e-commerce and the proliferation of generous return policies. Such policies, however, can backfire due to theircostly operations and unpredictability. To minimize the adverse impact, we develop and compare different collection policies to minimize
the combined inventory and transportation costs in a three-echelon network consisting of (a) customers returning products to initial collection points (ICPs), (b) the ICPs, and (c) the centralized return centers(CRCs), that receive the products shipped from the ICPs after they are
aggregated into larger shipments to leverage economies of scale. Collection policies are developed for the cases of a single product and asingle ICP and CRC; multiple products and a single ICP and CRC;
and multiple products with a single ICP and multiple CRCs, by using
individual shipment policy where ICPs ship out products to individual CRCs and a combined shipment policy that allows shipping androuting from an ICP to multiple CRCs. Mathematical models are developed to determine the collection period at the ICPs to minimize
the combined inventory and transportation costs for these policies. An
efficient algorithm is developed, and results are presented with an experimental dataset.

3 - Plan Ahead, Act in Real-Time: Optimization of Driver
Capacity Allocation in the Omnichannel Grocery Sector

Javier Rubio-Herrero, Pedro Lopes Gerum, Moonwon Chung,Matteo Giaretti
We collaborate with Glovo, a European technology company in the
e-commerce grocery sector, to optimize hourly driver capacity allocations across their stores. We do this by introducing decisions at thestrategic, tactical, and operational levels in a multi-tiered frameworkin which long-term decisions are employed to improve short-term decisions. Our suggested framework demonstrates a considerable improvement over Glovo’s current operations when it comes to revenue
and delivery costs.

4 - Optimizing Profit and Minimizing Waste: A Bi-Objective
Dynamic Programming Model for Inventory Control andPricing of Perishable Products

Melda Hasiloglu Ciftciler, Onur Kaya
When considering the prevention of food waste, it is worth examining the strategies of perishable food retailers. Inventory management
and sale price strategies of perishable food products are essential for
retailers to maximize profit and minimize food waste. In our study,
we examine the challenge faced by a retailer who simultaneously sells
a single type of food product with different ages (old and new), allowing for demand shifts between these products based on sale prices
and consumer behaviors. We develop a bi-objective dynamic programming model and find the optimal discounted price, sale price and order

TD-51

quantity of food products to maximize the retailer’s profit and minimize food waste. We formulate four commonly practised static and dynamic pricing policies and quantify the advantage of dynamic pricing
and price differentiation between old and new products by consideringboth profit and waste. Our findings indicate that significant improvements can be obtained by dynamically determining the order quantity
and the old product’s sale price, considering the current inventory athand. Moreover, for our bi-objective function, we use the weightedsum method and we analyze the results of different weight combinations of profit and waste in the objective function. The findings emphasize the significance of waste and sustainability issues, highlightthe balance between waste and profit, and offer valuable insights tocompanies aiming to enhance their system results.

 TD-51
Tuesday, 14:30-16:00 - Room: M5 (building: 101)

Network Design for Public Transport
Stream: Public Transport Optimization
Invited session
Chair: Anita Schöbel
1 - Ferry network design and scheduling: an exact optimization model for analysis of pathway alternatives

Michael Sederlin, Tomas Lidén
Owing to the lack of land-based infrastructure connecting islands,
ferry-based public transport is often the only feasible means of reliable
mass transportation. Ferry transport operating in populated archipelagos consists of a combination of fixed and flexible services, highlyvariable stopping patterns within lines, a lack of regular operating cycles and a small heterogeneous vehicle fleet.While water bodies separating islands create barriers, the unique geographic layout with manyislands in close proximity also offers large routing freedom.Taking
advantage of novel pathways may require cost-inducing efforts suchas depth surveys, dredging or marking shallows. We present a joint
method for ferry routing and scheduling based on a multi-trip vehicle routing problem approach. The proposed model incorporates manyof the observed characteristics of real systems. Traffic requirements
are included as minimum stop visits over the scheduling period and as
window-constraints.Evaluation of novel pathways is supported in theform of arc-bundles with fixed opening costs. The computation time isshown to be sensitive to the number of window constraints. Full system solutions with an optimality gap between 10 and 15 % are obtained
within a time-scale reasonable for strategic planning. Our results showthat pathway evaluation and operational assessment need to consider
the traffic requirements and fleet characteristics at hand.

2 - Scenario-driven Algorithm Selection for Transportation
Planning Problem

Zhang Jiarui, Xiaojie Luan, Li Haiying, Yifan Zhang
Countless algorithms have been developed to tackle transportation
planning problems. However, there is no one-size-fits-all algorithm
that can ensure absolute superiority in all scenarios. To improve solving resilience across various instances and demands, we take Service
Network Design Problem (SND) as a starting point. A Convolutional
Neural Network-based method is proposed to distill and represent thefeature of the instance and demand. Meanwhile, historical solving data
are used to train a machine learning module for selecting the best candidate algorithm. This module is embedded into an algorithm framework, employing several algorithms to solve diverse instances undervarious solving demands. Numerical experiments reveal that the proposed method has superior solving resilience compared to individual
and random algorithms utilization.

311

TD-52EURO 2024 - Copenhagen3 - Optimizing stopping patterns in public transport

Anita Schöbel, Ricardo Reicherz, Sven Jäger
In order to increase the travel speed of trains or buses in public transport, it may be beneficial to skip some stops on their routes. In suchskip-stop services, a line is divided into two sublines (let’s say a green
and a red one) which are operated alternatingly. The stations are also
divided into two sets with usually non-empty intersection, a set ofgreen and a set of red stations. The green line only stops at green
stations and the red line only stops at red station. With such a pattern,
it is possible to increase the travel speed of the trains. However, there
now may exist pairs of stations which have no direct connection any
more forcing passengers to transfer between the red and the green line.
It may even happen that passengers have to take a detour to travel froman origin to a destination.
We consider the problem of designing skip-stop patterns from apassenger-oriented point of view. Given a number p of stops to beleft out (together in both lines) and a set of origin-destination pairs
representing the passengers’ demand, we aim at finding stopping patterns that minimize the number of transfers, the number of passengers
who need a detour, or the traveling time of the trains. For all threeobjectives we provide IP formulations and characterize optimal solutions. We then combine these objectives and show which of them are in
conflict and which of these goals can be reached simultaneously. The
results are illustrated at examples.

4 - Comparative analysis of a Stackelberg model and amulti-objective model for the design of urban transportation networks

Mauricio Cepeda Valero, Jose Fidel Torres Delgado, Andres
Gonzalez, Luis Felipe Jimenez Sanchez
Urban transportation network design is a crucial area of research.
However, there is a lack of comparative studies between different
optimization techniques, primarily due to the unique nature of eachmethod, adapted to specific problem structures. This study aims toaddress this gap by evaluating two models—Stackelberg and multiobjective—for urban transportation network design. In the Stackelberg
model, users lead transport system usage decisions, while the transportation provider follows. Conversely, the multi-objective model considers both user and provider objectives simultaneously. These models
are represented as multi-layer network flow problems, and they aim
to optimize network expansion and flow distribution, balancing theprovider’s cost minimization objective with user objectives of minimizing travel time and network accessibility time. The evaluation ofthese models involves measures such as betweenness, edge flow, accessibility, and demand compliance. The models are applied to fourtypes of topological networks representing city transportation systems:
mesh, hub and spoke, linear, and tree structures. The results indicatethat the mesh network modeled by the Stackelberg model achieves thehighest accessibility and cost efficiency. Thus, the study contributes tothe understanding of urban transportation network design by providing insights into the performance of different optimization models andtheir applicability in various network structures.

 TD-52
Tuesday, 14:30-16:00 - Room: 8003 (building: 202)

Integer Programming and Combinatorial
Optimization: Complexity Questions andAlgorithms
Stream: Combinatorial Optimization
Invited session
Chair: Sergei Chubanov
1 - Some results on an extension of the vertex cover problem

312

Juan F. Correcher, Mercedes Landete, Juanjo Peiró, Hande
Yaman
The relationship between locations and their connections can be modeled by using some extensions of the vertex cover problem. In this talk,we revisit some of these extensions and provide new ideas that can beused in solution methods. The applicability of our proposals is tested
in a branch-and-cut algorithm on several computational experiments,
the results of which will also be discussed.

2 - Assessing Network Cohesion Using the Collapsed kCore

Domenico Serra, Martina Cerulli, Carmine Sorgente, Claudia
Archetti, Ivana Ljubic
In social network analysis, the size of the k-core, representing thelargest subgraph with minimum degree k, is often used to measurecommunity cohesion. The Collapsed k-Core Problem aims to identify a subset of crucial users, whose removal results in the smallest kcore. We formulate this problem using mathematical programming forthe first time. One approach employs an Integer Linear formulation,modeling deletion rounds. Another approach presents two bilevel programs, differing in their formulation of the k-core identification. We
reformulate the first bilevel model as a single-level sparse model using
a decomposition method. For the second model, we provide a linear
formulation for k-core identification and dualize it, resulting in a compact single-level Mixed-Integer Nonlinear problem. We also outline
preprocessing steps and valid inequalities for all formulations. Performance comparison is conducted against a state-of-the-art solver onbenchmark instances.

3 - On finding dissimilar paths with different dissimilarity
weights: a comparative approach

Marta Pascoal, Maria Teresa Godinho
The resilience of the solution for routing problems may be affected if
the solutions share many network resources. The problem of finding
sets of K paths between given origin and destination nodes in a network
which minimize a total cost function and maximize the paths dissimilarity intends to promote the search for low cost solutions that simultaneously have little in common to one another. That problem wasaddressed recently based on bi-objective single-commodity and discretized flow formulations which model the similarity based on two assumptions: the minimization of the number of arc reuses and the minimization of the number of pairwise arc overlaps. In the present work
we review the bi-objective linear integer formulations and compare
this bi-objective approach with single-objective formulations where
the similarity measure is weighted differently depending on the sensitivity of the area of the network where arcs may be shared.

4 - On the complexity of optimizing black-box functions inoracle models

Sergei Chubanov
We discuss algorithmic complexity of general discrete nonlinear optimization models with augmentation, verification, and evaluation oracles. We show that in many situations where at least a verification
oracle is available, which verifies whether a given solution is optimal,
an optimal solution for the respective discrete optimization problem
can be found in strongly polynomial oracle time. Also, we discuss algorithms based on machine-learning methods with performance guarantees for practical black-box optimization scenarios of large-scale
scheduling problems and optimization problems with partial differential equations where only evaluation oracles are available.

 TD-53
Tuesday, 14:30-16:00 - Room: 8007 (building: 202)

Resilient Networks
Stream: Sustainable and Resilient Systems
Invited session
Chair: Miriam Sarkis

EURO 2024 - Copenhagen
1 - Securing Network Resilience: Leveraging Node Centrality for Cyberattack Mitigation and Robustness Enhancement

TD-54

constructed via the -constraint method to support decision-makers allocate resources to reliable manufacturing systems.

Essia Hamouda
In response to the dynamic and ever-evolving landscape of network attacks and cybersecurity, this study aims to enhance network security
by identifying critical nodes and optimizing resource allocation withinbudget constraints. We introduce a novel approach leveraging node
centrality scores from four widely-recognized centrality measures. Our
unique contribution lies in converting these centrality metrics into actionable insights for identifying network attack probabilities, providing
an unconventional yet effective method to bolster network robustness.
Additionally, we propose a closed-form expression correlating network
robustness with node-centric features, including importance scores andattack probabilities. At the core of our approach lies the developmentof a nonlinear optimization model that integrates predictive insights
into node attack likelihood. Through this framework, we successfully
determine an optimal resource allocation strategy, minimizing cyberattack risks on critical nodes while maximizing network robustness.
Numerical results validate our approach, offering further insights into
network dynamics and improved resilience against emerging cybersecurity threats.

2 - Capacity planning to manage the operation of redundancy installations

Flávia Barbosa, Mariana Casalta, Ana Camanho, Jaime
Gabriel SIlva
One of the biggest challenges in efficiently managing any organisation is drawing up a conscious strategic capacity plan. Therefore, it isessential to draft a mid-term tactical capacity plan with singular andmeasurable objectives. This work focuses on the business activity ofurban water utilities, which is often faced with economic, operational,
and institutional constraints, and it is fundamental to optimise theirbusiness activity. In this context, the water-energy nexus is importantto these utilities, as it describes the relationship between the water used
for energy production, including electricity and fuel sources, and theenergy consumed to capture, treat, and supply water. The energy represents one of the highest financial costs for these organisations. We
we propose a methodology to minimise energy expenditure in urban
water supply service. It includes a mid-term tactical capacity planning
for a Portuguese water utility over three years. A water supply network
usually has five groups of infrastructure: catchments, pipelines, reservoirs, pumping stations, and water treatment plants, which can haveredundancy installations. The energy consumption can be minimisedby managing the operation of redundancy installations. In this way, it
is possible to make the most use of the organisation’s assets, considering its energy costs and the forecast of the average demand for waterby the region’s population.

3 - Decision-support tool for resilient biopharmaceuticalsupply chains

Miriam Sarkis, Nilay Shah, Maria Papathanasiou
Supply chain resilience is a growing priority in the biopharmaceutical sector. In recent years, the industry has seen the market boom ofnext generation therapies against life-threatening diseases and the urgent demand for vaccines during the pandemic. Manufacturers catering for these markets reported shortages and delays due to unforeseendemand trends combined with uncertainty in manufacturing capabilities for platforms still under development. We present a decisionsupport tool that can help assess supply chain resilience a priori for
networks that require rapid scale-up to commercialisation and operate
under supply-demand uncertainty. Firstly, the propagation of underlying manufacturing uncertainty to scale dependent throughput-related
and cost-related KPIs of manufacturing at the investment level is assessed via uncertainty analysis and global sensitivity analysis. Secondly, a stochastic mixed-integer linear problem is formulated for the
optimisation of investment planning under process and demand uncertainty. Finally, we introduce a scenario-based supply-demand chanceconstraint, which quantifies manufacturing supply robustness in meeting target demands. Solution feasibility is tested via Monte Carlo based
simulation. Candidate investment plans for single-product and multiproduct systems are assessed and cost-resilience Pareto frontiers are TD-54
Tuesday, 14:30-16:00 - Room: S01 (building: 101)

Demand-responsive public transport 3
Stream: Public Transport Optimization
Invited session
Chair: Maximilian Schiffer
1 - Rerouting Heuristic for the Congestion-Aware Dial-aRide-Problem Considering Intersection Capacities

Paul Rieger, Arturo Crespo Materna, Andreas Oetting
Given recent social and technological developments, the consideration of the Dial-a-Ride-Problem (DARP) in large-scale ridepooling has
gained more importance as a sustainable and increasingly viable alternative to personal motorized vehicles. In reality, routing decisions
for DARPs in large-scale ridepooling effectively influence traffic conditions and the resulting travel times in the network. Since routing
decisions are mostly based on travel times, a mutual dependency between routing decisions and travel times emerges. Recent research on
DARPs in large-scale ridepooling provides solutions for congestionaware routing and vehicle assignment while the modelling of infrastructure capacity is based on traffic dynamics within links. The mainfeature of the presented approach is the modelling of infrastructure capacity and its impact on driving and waiting times, extending beyond
links to include intersections with adaptive signal control. The noveltyof the suggested approach is twofold. Firstly, a new analytical approach for modelling infrastructure and its capacity is provided whileconsidering mutual dependencies between flow capacities within intersections in case of adaptive signal control. Secondly, a heuristic isderived to optimize a given initial solution of a DARP with the aim
to optimize capacity consumption of infrastructure while maintaining
constraints of a typical DARP. The results are shortly demonstrated
within a small case study.

2 - Insertions with lookahead for dynamic ridepooling services

Arne Schulz, Christian Pfeiffer
Due to the required reduction of emissions, modern mobility concepts
are rapidly evolving. Ridepooling is one of these concepts. Beside
the reduction of emissions due to electric vehicles, ridepooling services promise to reduce traffic due to pooling and to increase mobility
access especially in suburban areas. In practice, ridepooling services
receive customer orders dynamically and thus have to integrate themin the vehicles’ tours. In this talk, we discuss an efficient procedure toinsert new customer requests into given tours while incorporating possible future customers with the objective to serve as many customer
requests as possible over the time horizon.

3 - Online routing for Demand Adaptive Systems

Maximilian Schiffer, Benedikt Lienkamp
As urban populations continue to grow, traditional static and fixedroute transportation systems face unprecedented challenges to meetsocieties’ dynamic and diverse mobility needs. Demand adaptive systems (DAS), which combine traditional fixed bus lines and demandresponsive systems, have emerged as a promising paradigm in public
transportation to address these challenges. DAS provides traditionaltransit-line service for a set of compulsory stops. These compulsory
stops are bound to a schedule with fixed time windows during which
the vehicle serving the line has to leave each compulsory stop. Passengers may issue requests at optional stops, inducing detours in thevehicle routes. Focusing on the operational phase, where we adjust
vehicle routes and schedules in response to user requests, we formulate and solve a variant of the dynamic single-vehicle routing problem
with time windows and profit. We propose an algorithmic framework
based on a set of dynamic user requests containing both optional and313

TD-55EURO 2024 - Copenhagencompulsory stops. We present a machine learning enriched integer
programming framework that enables a DAS operator to select a subset of requests to serve while maximizing its expected profit. Here, we
determine a route serving all selected requests, respecting compulsory
stops’ time windows. With this algorithmic framework, we compare
traditional fixed-route transportation systems with DAS in Munich andpresent managerial insights from the operator’s and user’s perspectives.

4 - A Framework for building Last Mile Connectivity to Urban Public Transit Systems in India

Murari Srinivasa, Rajluxmi V. Murthy, Reshma
ChandrasekharanMultimodal public transit is an integral part of urban passenger transport in rapidly urbanizing modern economies. Public transit systems
need strong, tightly-coupled last mile connectivity for continued patronage. In this paper, we deploy a spatial analytics and optimizationbased approach to create a replicable framework which identifies andaddresses gaps in last-mile connectivity to a planned urban public transit system such as the metro rail. We draw on classic transportation literature on the feeder network design problem and more recent researchon last mile transportation systems, while adding to optimization literature on NP-Hard variants of the generic spanning tree problem foran undirected graph to provide an optimal set of last-mile stops androute structures. We demonstrate our framework through a systematic
deconstruction of publicly available spatial/demographic data on an urban agglomeration in India (Bengaluru, Karnataka) and its metro rail
infrastructure under development. Our findings show that the proposed
long-term metro rail covers about 49% of the current population of 5.8
MM who reside within a walking distance from the proximal station.
We identify stops and potential route structures for a fixed last-mile
feeder network system which can potentially add a further 33% to thecoverage. We also identify zones and segments of the city and its population for consistent planning of the built environment to facilitate
better last mile connectivity.

 TD-55
Tuesday, 14:30-16:00 - Room: S02 (building: 101)

Freight transportation and logistic III
Stream: Transportation
Invited session
Chair: Mikele Gajda
1 - Consistent Collaborative Vehicle Utilization

2 - Continuous approximation model for estimating average tour length in prize collecting traveling salesman
problemDaisuke Hasegawa, Yuichiro Miyamoto, Toshio Nemoto

The prize collecting traveling salesman problem (PCTSP) is to findroutes and subset of nodes that minimize a tour distance with the constraint to be less than given rewards by visiting nodes. This study estimates the PCTSP travel distance using a continuous approximation
model. Continuous approximation model does not search for routes,
but it estimates the travel distance using the area of the region and thenumber of nodes. In our proposed model, the size of the delivery zone
and the number of nodes is given, and the spatial distribution of thepenalty is represented as a demand function. It replicates the concentration of population and economic value in the center of cities. The
function makes it possible to determine the range of the penalty andthe average trip distance within the zone. In addition, we propose amethod for estimating the PCTSP travel distance in multiple delivery
zones. It takes into account the distance between regions and the relative balance of demand and distribution. The method is expected to be
applied to the evaluation of priority areas where delivery services suchas home delivery, food delivery, and carpooling.

3 - The Full Truckload Pickup and Delivery Problem with
Truck Platooning

Yilin Wang, Junlong Zhang

Truck platooning is a promising technology for reducing energy consumption, increasing vehicle safety, and improving traffic efficiency.
In this research, we examine the cost-effectiveness of truck platooning
from the perspective of a freight company performing full truckload
pickup and delivery tasks over a transportation network. We propose
two model formulations for this problem: a direct delivery model andan indirect delivery model, where the indirect delivery model allows
trucks to visit intermediate locations during deliveries to facilitate theformation of platoons. We devise an improved dynamic discretization
discovery (DDD) algorithm to solve the two models exactly. Throughextensive computational experiments, we find that (1) the improved
DDD algorithm can increase solution accuracy with much less computational effort compared with the original DDD algorithm; (2) the
cost-saving effect of truck platooning is favorable, with an average of
80.55% trucks being followers in platoons; and (3) for freight companies operating on small transportation networks, using the direct delivery model may be more appropriate.

Sahand Asgharieh Ahari
Carriers are driven by the dual objectives of minimizing delivery costs
and maximizing customer satisfaction. Collaboration can reduce cost,
while service consistency enhances customer satisfaction. To addressthese objectives concurrently, we introduce a multi-period collaborative model which we term "consistent collaborative vehicle utilization"framework. In this framework, carriers can borrow trucks from eachother, facilitating deliveries to their respective customers, all while adhering to defined consistency measures. In our framework, the borrowed truck first departs from the lender’s depot to the borrowing carrier’s and then visits delivery locations based on optimal routing decisions. The consistency measures implemented ensure that customers
are served exclusively by their designated carrier and at approximately
the same time during periods when demand exists. Carriers selectdelivery times from a set of options specified by customers which is
known as time window assignment problem. We formulate the consistent collaborative vehicle utilization problem as an integer programming problem and develop a branch-and-price algorithm to solve it.
Additionally, to address larger instances, we propose two heuristics
employing column generation technique. Our experiments shows thatthe consistent collaborative vehicle utilization framework can lead to aremarkable reduction of up to 43% in the number of vehicles, accompanied by a substantial profit increase of up to 15%.

314

4 - The multi-product pickup and delivery problem withtime windows and alternative destinations.

Mikele Gajda

We introduce and investigate the multi-product pickup and delivery
problem with time windows and alternative destinations. In this problem, we consider a fleet of vehicles with a predetermined capacity forserving multiple customer orders simultaneously. Each order consistsof a collection of products, possibly with different pickup locations,and a set of potential delivery points with specific time windows forcustomer service. To comprehensively investigate the problem, weconstruct an extensive set of instances that build upon the well-known
CVRPLIB dataset. We provide a formal problem description formulated as a MILP and obtain optimal solutions using a branch-andbound approach. Finally, considering the problem’s inherent complexity, we propose a hybrid adaptive large neighborhood search. Our algorithm demonstrates effectiveness in obtaining satisfactory solutions
for medium and large-scale instances, which typically pose challenges
for exact methods.

EURO 2024 - Copenhagen TD-56
Tuesday, 14:30-16:00 - Room: S04 (building: 101)

Advancing mobility towards sustainable
solutions II
Stream: Transportation
Invited session
Chair: Eloisa Macedo
1 - Linking freight transport and energy system models forcoherent strategic investments decisions

Celine Pagnier, Steffen Bakker, Pernille Seljom
The decarbonization of the transport sector is deeply intertwined withthe evolution of the energy system. While modifications of the transport energy demand are often put forward in transport models, there
is seldom any discussion on the long-term effects of the newly neededenergy-related infrastructures. To investigate this issue, we perform
a quantitative analysis of the reciprocal dependencies between modaland fuel shift in freight transportation and energy systems, including
energy infrastructure investments.
A soft-link is established between IFE-TIMES-Norway, an energy system model based on linear programming, and STraM, a freight transport model whose focus is on long-term infrastructure investments decisions in a multi-stage stochastic mixed-integer linear program framework. STraM generates an energy demand for the transport sector
based on endogenously computed modal and fuel splits, which is fed
into TIMES. Reciprocally, TIMES enhances STraM with its economic
representation of technologies and evolution of fuel mix. We provide
adaptable methods to reconcile disparities in resolution and aggregation between those models.
Then, comparative analyses allow to demonstrate the framework’s improvement over the use of a single model and offer insights into theintricate dynamics of the transport-energy nexus. Finally, the gained
knowledge is applied to a Norwegian case study to analyze coherent
strategic investments decisions for the next few decades.

2 - High-resolution Platoon Prediction for CoordinatedTraffic Control along Urban Arterials

Zakir Farahmand, Oskar Eikenbroek, Eric van Berkum
Smooth progression of platoons of vehicles along urban arterials cansignificantly contribute to environmental objectives, especially whenheavy-duty vehicles are involved. Generally, signal controls suchas fixed-time, vehicle-actuated and adaptive aim to minimise delays,
which may conflict with reducing the number of stops to improve
air quality and GHG emissions. Overcoming this challenge requires
dynamically synchronising the signal timings of a series of intersections with approaching vehicles to simultaneously reduce delays andthe number of stops. A fundamental prerequisite of such controls is
real-time prediction of platoon dynamics including forming, dispersion, stopping and arrival after departing from upstream. Existing
coordination methods usually focus on flow estimation at aggregate
levels, losing critical information for multi-criteria signal operations.
Our study introduces a novel method for predicting platoons’ dynamics
with high resolution under mixed-traffic environments. Utilizing various data sources, including loop detectors, vehicle-to-infrastructure(V2I) communication and floating car data, we employ attention-basedgraph neural networks to predict platoon arrival and stop times whileconsidering current and anticipated signal phases. Experiments conducted on real-world datasets from an arterial in Enschede, the Netherlands, reveal the suitability of our approach for coordinated signal controls to reduce the number of stops and enable green waves.

3 - A space-time-energy flow-based Iinteger programming
model to design and operate a regional SAEV systemand corresponding charging network

Gonçalo Santos, Sebastian Birolini, Gonçalo Correia

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Shared automated vehicles are expected to be part of the supply oftransportation systems in the future. Parallel to this evolution, there is
the rapid penetration of battery electric vehicles (BEVs). The limitations in battery capacity and charging speed of BEVs can influence theplanning and operation of shared automated electric vehicle (SAEV)
systems. The design of such systems needs to include these limitations
so that their viability is properly estimated. In this paper, we develop a
space-time-energy flow-based integer programming (IP) model in support of the strategic design of a regional SAEV system. The proposed
approach optimizes the fleet (size and composition) and charging facilities (number and location), while explicitly accounting for vehicle
operations in aggregated terms (including movements with users, relocations, and charging times). The model is used to assess the impact ofvehicle range and different types of chargers in the optimal design ofan interurban SAEV transport system in the center of Portugal. Link
to paper: https://doi.org/10.1016/j.trc.2022.103997

4 - Aligning the Public Charging Infrastructure to the EV
Driver: Lessons from Brussels and San Francisco

Simon Weekx, Ona Van den bergh, Lieselot Vanhaverbeke,
Gil TalThe transition towards Electric Vehicles (EVs) is an important step toreduce greenhouse gas emissions and local air pollution. An important obstacle for the large-scale adoption of EVs is the availability ofsufficient public charging infrastructure. To determine how much infrastructure is needed, scholars often rely on EV adoption data or transactional charging data at existing stations. However, a major drawbackof these studies is that they extrapolate the charging behavior of theearly EV adopters, which may not be representative for the entire population.
In this study, we present a two-step approach to analyze the impact
of different behavioral charging scenarios on the required number ofchargers. Based on two large-scale datasets of charging transactionsin Brussels and San Francisco, we first employ K-means clustering
to find segments of EV drivers that exhibit similar charging behavior
in terms of where, when, and how much energy they charge. Second, we develop a Discrete Event Simulation model to determine howmuch additional charging infrastructure is needed to accommodate anincrease in charging activity in each of the behavioral segments. Our
results indicate that, depending on the behavioral type, up to 8 times
more infrastructure is needed to accommodate the same increase indemand. This highlights the potential for behavior-steering policies
such as dynamic- and penalty pricing to make more efficient use of theavailable charging infrastructure.

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Tuesday, 14:30-16:00 - Room: S06 (building: 101)

Large-scale network optimization andinventory management
Stream: Optimization at Amazon
Invited session
Chair: George Iosifidis
Chair: Daniel Guimarans
1 - Large Scale Topology and Connectivity Design with InNetwork Processing. Faheem, Onur Can Saka, George Iosifidis, Amit Kumar
Topology and connectivity design with in-network processing (sortation, cross docking) is a critical component for Middle Mile transportation network design to achieve operational efficiency, costeffectiveness, and delivery speed. In this paper, we consider a middle
mile transportation network that consists of warehouses, sortation centers and delivery stations. Given a warehouse to delivery station volume forecast, our goal is to design cost and speed optimal transportation network. We formulate the topology and connectivity design problem as a Mixed Integer Linear Programming (MILP) problem, which315

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is NP-hard. We describe the key characteristics of the problem andanalyze the arising modeling and optimization challenges. To solve
large scale real-world problems (>100 million variables), we present
our optimization-based matheuristic pipeline, which comprises preprocessing and path pruning strategies, local neighborhood search, andincremental model solving. We show the efficacy of our model usinga range of operational datasets. We conclude with potential researchthreads that can further improve the solution quality and runtime.

2 - A scalable solution to joint routing and sorting problems

Charupriya Sharma, George Iosifidis, Daniel Guimarans,
Amit Kumar, Georgios Paschos
We study the joint routing and sorting problem in Amazon’s middle
mile network, which optimizes the routing of packages from fulfilment
centers to delivery stations by using intermediate hubs to sort and consolidate flow. Packages are sorted into pallets, and packages in thesame pallet are routed over the same path unless the pallet is dismantled in some intermediate hub. While the problem resembles a multicommodity flow optimization problem, the operational constraints related to connectivity and sorting capacities and decisions create additional complications for routing that are not present in the literature.
We present a scalable, edge based model to solve this problem using a
combination of search space reduction via edge pruning, a warm-startheuristic based on combinatorial optimization and a tight MIP formulation which uses a destination based aggregation of commodities. We
observe significant improvements over path-based formulations in bothrun time and overall costs.

3 - Inventory Decentralization Tailored to Product Demandin Delivery Speed-Sensitive Logistics Networks

Katja Meuche, Yuan Li, Amitabh Sinha, Benoit Montreuil
Large retailers such as Amazon offer a vast array of products, encompassing millions of unique items. The multitude of products creates acomplex demand landscape where high-demand products are classified
as "head," moderate-demand as "body," and low-demand as "tail." To
control complexity and achieve efficiency in the fulfillment network,
Amazon regionalized its operations in the US. This process involved
defining regions, assigning fulfillment centers accordingly, and aiming to meet customer demands using the inventory of the fulfillment
centers within each region. This prompts the crucial question: Whatlevel of inventory decentralization is economically beneficial for eachproduct category when considering the customer’s sensitivity to delivery speed? In this talk, we report on a large-scale case study focusing
on the 48 contiguous US states. We present our methodology, whichleverages adapting the Newsvendor model to incorporate transportation costs and order-to-delivery-time sensitivity. We finally present
key insights from the study, notably that (1) profit maximization calls
for decentralization of inventory for head and body products and more
centralization of tail product inventory, and (2) a nuanced approach toinventory decentralization, and hence, region definition based on the
product category, is essential.

 TD-58
Tuesday, 14:30-16:00 - Room: S07 (building: 101)

MILPs for Vehicle Routing 1
Stream: VeRoLog - Vehicle Routing and Logistics
Invited session
Chair: Roberto Baldacci
1 - Logic-Based Benders Decomposition for a Capacitated
Vehicle Routing Problem With a Zone Tariff

Niklas Tuma, Alexander Hübner, Manuel Ostermeier316

For the cost-efficient supply of stores, it is common in retail practice
to rely on third-party logistics providers (3PL) instead of their an fleet.
A 3PL carries out the retailers’ delivery tours and commonly bills according to a zone-based tariff. This tariff assigns each store to one zonethat reflects the travel distances for the delivery. Stores further from thedepot are assigned to higher zones. The cost of a tour depends on thefurthest zone visited and the volume, subject to discounts. Additionally, 3PLs limit the detour of a tour to ensure economic feasibility. The
detour limitation prevents excessive driving within the zones visited.
While using 3PLs and their zone tariffs reduces the complexity for retailers, the question arises of how retailers can plan cost-minimal tours
that are economical for 3PLs. We address this issue and formalize theproblem as a Capacitated Vehicle Routing Problem with a Zone Tariff. The nonlinear tariff and the non-monotonically increasing detour
drive the complexity. We provide the first mixed-integer formulation(MIP) for the problem, which we strengthen with valid inequalities.
We develop a logic-based Benders decomposition algorithm (LBBD)
to solve larger instances and improve it with problem-specific acceleration techniques. In our numerical experiments, we solve a real-worldcase and adapted instances from the literature. The LBBD approach
reduces the runtime compared to the MIP by up to one order of magnitude.

2 - The Cumulative School Bus
Polynomial-Size Formulations

Routing

Problem:

Farnaz Farzadnia, Jens Lysgaard, Tolga Bektas
This article challenges the conventional notion that minimizing shortest paths guarantees the fastest passengers arrivals at destinations. Instead we present a fresh perspective in the school bus routing problem. This article introduces the cumulative school bus routing problem, which the objective of the problem is to select a drop-off point foreach student among potential locations within a certain walking distance and to generate routes such that the sum of arrival times of all
students from their school to their homes is minimized. The article describes six polynomial-size mixed integer linear programming formulations based on original and auxiliary graphs, and the formulations are
numerically compared on real instances. The article reports the resultsof computational experiments performed to evaluate the performanceof the proposed models.

3 - An Exact Approach For The Time Window Assignment
Traveling Salesperson Problem With Stochastic Travel
Times

Francesco Cavaliere, Matteo Fischetti, Roberto Roberti,
Domenico Salvagnin
This study centers on the Time Window Assignment Traveling Salesperson Problem with Stochastic Travel Times, marking a shift froma cost-focused strategy to a more balanced approach that considers
both cost and service quality aspects in delivery operations. This research aims to address the challenge of integrating real-world uncertainties, particularly stochastic travel times, into the optimization process. When treated as a two-stage stochastic problem, the first stage
involves making decisions about routing and time window assignment
using binary and possibly continuous variables. This is followed byhandling continuous variables associated with time window violationsin the subsequent stage.
This work contributes to the ongoing exploration of vehicle routing,emphasizing the critical role of time window assignment in the optimization framework. Existing literature provides limited methodologies for this specific problem (Celik et al. 2023). In response, we introduce a novel formulation inspired by the Fox et al. three-index formulation for the Time-dependent Traveling Salesperson Problem (Foxet al. 1980). Employing Benders Decomposition as a versatile solution
framework, our approach exhibits substantial advancements over priorstate-of-the-art solutions for this problem.

4 - An Enhanced Dynamic Discretization Discovery Algorithm for the Continuous-Time Service Network Design
Problem

Roberto Baldacci, Shengnan Shu, Zhou Xu

EURO 2024 - Copenhagen
Service Network Design Problems (SNDPs) are prevalent in the freightindustry, holding significant practical value in their solutions. Whilethe classic SNDP is often defined on a planning horizon discretized
into a finite number of integral time units, the Continuous-Time Service Network Design Problem (CTSNDP) operates on a continuoustime planning horizon to avoid errors from discretization. Existingalgorithms for CTSNDP and its variants primarily adopt a Dynamic
Discretization Discovery (DDD) solution framework. This framework
iteratively refines a finite set of integral time units, constructs a partially time-expanded network based on the discretization, and leverages the network to derive relaxations and feasible solutions for the
problem. However, DDD algorithms face limitations in computational
performance, especially when handling instances characterized by ahigh-cost ratio of vehicle-based costs over flow-based costs and hightime flexibility. Consequently, a substantial portion of these instances
remains unsolved. In this study, we enhance the DDD method for CTSNDP by introducing a new initial discretization, a novel exact methodfor deriving upper-bound solutions, and an improved refinement strategy. The computational study demonstrates that, with these innovative
components, the enhanced DDD algorithm exhibits exceptional performance.

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Tuesday, 14:30-16:00 - Room: S08 (building: 101)

Pricing and applications
Stream: Pricing and Revenue Management
Invited session
Chair: Vienna Klein
1 - A Dynamic Compensation Strategy to Leverage Occasional Drivers in Last-Mile Delivery

Rouven Schur, Kai Winheller
Amid the rapid growth of online retail, last-mile delivery faces significant challenges, including the cost-effective delivery of goods to all
customers. Accordingly, the development and improvement of innovative approaches thrive in current research. Our work contributes to thisstream by applying dynamic pricing techniques to effectively model
the possible involvement of the crowd in fulfilling delivery tasks. The
use of occasional drivers (ODs) as a viable, cost-effective alternative to
traditional dedicated drivers (DDs) prompts the necessity to focus on
the inherent challenge posed by the uncertainty of ODs’ arrival times
and willingness to perform deliveries. We introduce a dynamic programming framework that offers individualized pairs of delivery task
and compensation to ODs as they arrive. This model, akin to a reversed form of dynamic pricing, accounts for ODs’ decision-making
by treating their acceptance thresholds as a random variable. Thereby,
our model addresses the dynamic and stochastic nature of OD availability and decision-making. We analytically solve the stage-wise optimization problem, outline inherent challenges such as the curses ofdimensionality, and present structural properties. Designed to copewith these challenges, our approximation methods aim to accurately
determine avoided costs, which are a key factor in calculating optimalcompensation.

2 - Pricing Decisions with Loading Guarantee Service in
the Container Shipping Industry under Capacity and Demand Uncertainties

Tigar Putri Adhiana, Dongping Song, Shu Guo
In container shipping industry, container rollover (i.e. the container
cannot be loaded onto the vessel that has been booked) is a common
phenomenon, which is largely caused by uncertainties in demand, supply and operations. For example, in 2021, the global container rollover
ratio was in the range of 30% 50% due to the COVID-19 disruption
and the capacity crunch. The rollover phenomenon has a significant
negative impact on shippers because their shipments will be delayed,

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and their operations will be disrupted. Recently, some shipping lines
such as Maersk and Hapag-Lloyd have introduced an additional service
called loading guarantee to shipping to differentiate them from othershipping lines and gain competitive advantage. In this study, we investigate the load guarantee service pricing problem in the container shipping industry. Specifically, we formulate the optimization problem of
the pricing decisions of both loading guarantee service and freight rate
for an ocean carrier. To examine the effects of capacity and demand
uncertainties on the pricing decisions, we model the uncertainties inthree scenarios: (i) pricing decisions considering capacity uncertainty,(ii) pricing decisions with demand uncertainty, and (iii) pricing decisions considering both capacity and demand uncertainties. Analyticalanalysis and numerical experiments are conducted to demonstrate theeffectiveness of the models and results.

3 - From approximation errors to optimality gap - exploiting
structural knowledge of opportunity cost in integrated
demand management and vehicle routing problems

Vienna Klein, David Fleckenstein, Robert Klein, Claudius
Steinhardt
The widespread adoption of digital distribution channels both enables
and forces more and more logistical service providers to manage booking processes actively to maintain competitiveness. As a result, theiroperational planning is no longer limited to solving vehicle routing
problems. Instead, demand management and subsequent vehicle routing problems are integrated to steer the booking process with theaim of optimizing the downstream fulfillment operations. The resulting integrated demand management and vehicle routing problems (iDMVRPs) can be modeled as Markov decision process and, theoretically, solved via the well-known Bellman equation. Unfortunately,
the Bellman equation is intractable for industry-sized instances. Thus,in the literature, i-DMVRPs are often addressed via opportunity cost
approximation approaches. However, the overall performance of therespective approaches largely varies between different instance structures. Furthermore, to the best of our knowledge, there is neither
a structured procedure to analyze the corresponding root causes nor
general guidelines on when to apply which class of approximation
approach. In this work, we address this gap by proposing a structured method to analyze, explain and compare the performance impact
of different opportunity cost approximation-based solution approaches
for i-DMVRPs. Further, we identify common patterns in approximation errors and derive general guidelines for an informed algorithm
development process.

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Tuesday, 14:30-16:00 - Room: S09 (building: 101)

Resource constrained scheduling
Stream: Project Management and Scheduling
Invited session
Chair: Joanna Józefowska
1 - An FPTAS for scheduling with resource constraints

Bo Chen, Vitaly Strusevich
We study a basic scheduling problem with resource constraints: A
number of jobs need to be scheduled on two parallel identical machines to minimize the makespan, subject to the constraint that jobs
may require a unit of one of the given renewable resources during their
execution. For this NP-hard problem, we develop a fully polynomialtime approximation scheme (FPTAS). Our FPTAS makes novel useof existing algorithms for the subset-sum problem and the open shop
scheduling problem.

2 - A bi-objective production scheduling problem with reentrant flows and labour resource constraints

Johanna Mlekusch, Richard Hartl

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This work introduces a production problem inspired by a real-world
company, where the manufacturing process reflects a re-entrant flexible flow shop scheduling problem. Notably, in addition to the reentrant feature, the challenge involves allocating additional resources,
namely, workers, to specific production steps. This scenario can becharacterized as a scheduling problem with dual-resource constraints.
A key aspect of the problem is the consideration of a cross-trained
workforce, allowing workers to transition between stages during production. Furthermore, we consider a heterogeneous workforce, where
workers can be differentiated through their skill level, which is definedby the number of stages they can operate on. The objectives of the
problem are to minimize the maximum completion time and production costs influenced by employee salaries, reflecting their skill level.
The payment structure assumes that each worker is paid for the period starting from their earliest operation starting time until the latest
completion time including idle intervals in between. The bi-objective
problem is solved using constrained programming and lexicographic
optimization. To provide a comprehensive overview of solutions, prioritizing the makespan objective, the epsilon-constraint technique isemployed to identify the set of non-dominated solutions. Preliminary
findings suggest a substantial potential for cost reduction while maintaining the same throughput.

3 - Bi-objective based scalable balancing of a reconfigurable assembly line

Siwar Arbi, Audrey Cerqueus, Evgeny Gurevsky, Alexandre
Dolgui
Balancing an assembly line involves assigning a required set of tasks
to a given set of workstations, subject to task precedence constraints
and achieving some production goals. The two studied goals are antagonistic. The first seeks to maximize line productivity, which means
minimizing the load on the busiest workstation. The second aims tomaximize line robustness, expressed by a specific measure, and lies in
the potential presence of tasks with uncertain processing time. A reconfigurable environment enables an already provided assembly line
balancing (or LB) to adjust or improve its level of productivity or robustness if necessary, by allocating extra resources to workstations. As
their number is limited, it is important to use them as efficiently as
possible, and therefore to keep only such allocations, called efficientreconfigurations (or ER), which are non-dominated in the Pareto sense
for two objectives studied. Different LB options provide different varieties of ER. In order to compare them, we introduce the so-calledmeasure of scalability, which is computed into two steps. At first, foreach set of ER sharing the same number of used extra resources, thehyper-volume of the image of this set in the objective space is calculated. Then, the sum of all these hyper-volumes is viewed as thescalability measure for the studied LB. To find an LB with the greatest value of its scalability measure, a simulated annealing is developed
and tested on instances of different size.

both break-down and build-up scheduling into a unified network design model.

 TD-61
Tuesday, 14:30-16:00 - Room: S10 (building: 101)

Location in Logistics and Supply Chain
Management
Stream: Locational Analysis
Invited session
Chair: Giuseppe Bruno
1 - From isolated core facilities to interrelated service regions: understanding dependencies in supply chain
network design

Hannah Bakker, Stefan Nickel
In supply chain network design (SCND), facility location decisions
play a pivotal role. Yet, the individual role of and interdependence between facilities constituting an optimal solution remain inadequately
understood. This lack of contextual information hinders decisionmakers’ confidence in these models, resulting in a gap between academic research and practical implementation. Our study evaluates
optimal and near-optimal solutions for a given instance, introducing
concise measures to characterize commonalities in the decisions andthe criticality and interdependence between individual location decisions. We relate these characteristics to the spatial distribution of candidates and customers and show that contrasting academic perception,
the underlying spatial patterns yield little explanatory value for the stability of location decisions. We propose a pattern-recognition-based
approach to show that well-performing solutions to an instance often
share an implied division of the facility customer space, which not
only explains the criticality and relationship between individual facilities but also allows anticipating how minor perturbations will affectthe network. Our research offers a deeper understanding of facility location dynamics within SCND. This may empower decision-makers totrust and implement these models in real-world settings.

2 - Integrating location and inventory decisions in healthcare supply chains: review and first model

Inez Puttemans, Kris Braekers, An Caris

4 - Scheduling ULD loading operations in air cargo hubs

Ricardo Euler, Ralf Borndörfer, Christian Puchert, Tuomo
TakkulaAir cargo typically moves through airports that serve as hubs. A container or pallet, usually referred to as a unit load device (ULD), arriving
at such a hub often holds shipments bound for various destinations.
Consequently, its contents must be unloaded and moved into storage(ULD break-down). Once enough shipments for a single destination
have accumulated, a new outbound ULD is loaded (ULD build-up).
Both the build-up and break-down procedures need to be executed ona limited number of workstations and within the strict timeframe dictated by the flight schedule. Resource conflicts, therefore, need to beprevented by a careful scheduling of build-up and break-down processes.
We explore the structure and challenges of these interdependent
scheduling problems, both from a mathematical and industry viewpoint, and discuss appropriate solution strategies. We find that breakdown scheduling is easily solved by simple mixed-integer programming (MIP) models on realistic datasets. The situation differs for buildup scheduling, where a desire to build up ULDs in batches introduces
an additional objective. Here, we discuss a tailored approach basedon logic-based Benders decomposition that decomposes the time horizon based on cargo availability. Finally, we discuss how to integrate

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Healthcare institutions, including hospitals and retirement homes, facevarious challenges. They struggle with tight budgets due to government savings and rising costs. Moreover, they deal with a shortage ofcare staff due to high workloads, forcing them to make an effort to relieve non-care tasks (i.e., logistics tasks). These challenges put significant pressure on healthcare institutions. To address these challenges,
hospitals aim to reduce costs and improve healthcare logistics while
ensuring high-quality care. One key opportunity to improve healthcare
logistics is consolidating inventory across healthcare institutions. Thisentails healthcare institutions within a network pooling their inventory
from individual warehouses into one or a few central care hubs. Decision support for inventory pooling is facilitated by integrated decisionmaking on location and inventory. Adopting this integrated decisionmaking can cut costs and enhance healthcare logistics while at least
maintaining quality of service. This research aims to develop innovative models and solution algorithms, providing decision support toimprove healthcare logistics by integrating location and inventory decisions. This talk will discuss key findings from a new literature review
on integrating these decisions, covering 44 papers across contexts likemanufacturing, healthcare, spare parts, etc. Moreover, a first mathematical model focusing on integrating these decisions in a healthcare
context will be proposed.

EURO 2024 - Copenhagen
3 - Dynamic spaces in layout planning - a new concept to
introduce time-dependent layouts

Niklas Braun, Rebekka Volk, Frank Schultmann
Current dynamic layout models only consider changes in the placement of temporary facilities, like workstations and material storages,
without considering changes in the spatial structure of the layout itself. While they are practical to account for repositioning of the facilities and the resulting changing material flows, these models assume a static layout structure over time. To address this limitation,
we present a novel optimization model introducing ’dynamic spaces’.
They accommodate the changing availability of rooms or areas representing the layout over time. This innovation finds application in
many cases, such as optimizing deconstruction projects where partsof a building are taken down and can’t be used as locations anymore
midway through the project or when safety protocols require certain
areas of a factory to be sealed off temporarily during remodeling. Additionally, it can also be used to represent expanding layouts e.g., ifsurrounding areas are temporarily used during a construction project.
In this contribution we analyze the difference in the optimization process compared to classical formulation and show the strength of the
new model in numerical case studies.

4 - Strategies for the redefinition of postal collection anddelivery services in urban areas

Giuseppe Bruno, Antonio Diglio, Carmela Piccolo, Eduardo
Pipicelli
The rapid increase in the volume of parcels to be delivered pushed logistics providers to explore innovative solutions to streamline the morecritical and expansive stage of the supply chain: the last-mile logistics.
Among the solutions, the most explored is self-collection, by whichcustomers autonomously collect parcels from manned or unmanned
facilities. However, the success of the strategy depends on two main
aspects: the individuation of the determinants affecting the customers’
choices; the location of such facilities in relation to the positions of thepotential customers.
In this work, we explore the possibility of describing these aspects
through the formulation of mathematical models for the optimal location of self-collection facilities with the objective of maximizing theattractiveness of this system, as alternative to home delivery. In particular, the mechanism of patronization of potential facilities is regulatedby an attractiveness function in which some determinants are considered.
Thanks to the cooperation with the principal Italian logistics provider,
we apply the model to some real case studies with the objective of designing efficient and robust self-collection networks in densely populated contexts. The obtained results suggest insightful managerial
implications and prove that the model can be used as an appropriate
tool to support the decision-making process concerning the design of aself-collection network in an urban context.

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Tuesday, 14:30-16:00 - Room: S12 (building: 101)

Sustainable freight transportation
Stream: OR in Port Operations
Invited session
Chair: Claudia Caballini
Chair: Mahnam Saeednia
1 - Optimal multi-period policy for empty container transferring between two ports under uncertainty

Dongping SongIn container shipping, ports often face uncertain demand and supplyof empty containers over time. Effectively and efficiently managing

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empty containers between ports in multiple time periods is challenging. In this paper, we consider two neighbouring ports that are facing independent and uncertain supply and demand of empty containers
from shippers. We seek the optimal empty container transfer policy between two ports over a multi-period planning horizon to minimize thetotal expected cost, consisting of empty container transferring costs between two ports, inventory holding costs, and container leasing costs
at both depots. The problem is formulated as a stochastic dynamic
programming model. The local properties of the value function, suchas the first and second derivatives on a region-wise basis, are analysed. The region-wise properties of the value function enable us toestablish the structural characteristics of the optimal empty containertransfer policy over multiple time periods. Specifically, the entire state
space is divided into three control regions by two monotonic switching curves. The asymptotic behaviours of the switching curves areanalysed analytically. The structural properties of the optimal policy
and the asymptotic behaviours of the switching curves are then used toconstruct simple near-optimal and easy-to-operate policies. Numericalexamples are provided to demonstrate the analytical results.

2 - HafenPlanZen - Port Master Planning through Simulation, Optimization, and Visualization

Larissa Timm
Maritime transport is vital to global trade, with ships carrying over
80% of the world’s goods in volume. Efficient port planning is criticalto the functioning of intermodal hubs like the Port of Hamburg, but
long concessions of 20-30 years make port planning a complex andchallenging task. A port is constantly evolving due to technological,
economic, political, and environmental factors, requiring innovative
approaches to port planning. Our project, HafenplanZen, is funded by
the IHATEC program and is a collaborative effort between the Hamburg Port Authority, Hamburg Port Consulting, and the University of
Hamburg. The Port of Hamburg already has a high degree of digitalization and employs multiple digital twins to simulate and optimize
various aspects of port operations. HafenplanZen leverages this digital infrastructure and serves as a support tool for port planners, integrating data from multiple digital twins to provide a comprehensive
overview of the port’s behavior and efficiency. One of the project’s key
focuses is the development of a sophisticated 3D visualization tool thatuses simulation to evaluate the port’s performance. HafenplanZen’s
simulation-based approach enables port planners to test various improvement strategies and make informed decisions about optimizing
port performance. The tool also provides improvement suggestions
based on simulation results, allowing planners to make data-driven decisions about how to improve the efficiency of port operations.

3 - A Simulation-Based Optimization Framework for Adaptive Disruption Management Strategies in Synchromodal Transport

Mahnam Saeednia, Saiedeh Razavi, Bilge Atasoy, Siyavash
Filom, Muhammad Satrya Dewantara
Disruptions that impact transport operations are an inevitable realitythat pose significant challenges. These disturbances, escalating inboth frequency and impact, have far-reaching implications on seamless freight transport operations. From unexpected weather conditions
to unplanned maintenance of the transport network, each disruption
brings with it a ripple effect that can drastically alter the planned movement of freight. This, not only negatively impacts the immediate transport operations but also has profound implications on the broader supply chain landscape, necessitating innovative solutions to ensure resilience and reliability. This paper proposes a simulation-based optimisation framework for adaptive disruption management strategies.
It incorporates real-time events dynamically to facilitate selection ofmodes, routes, and handling points. It leverages an event-based model
to capture the dynamics of the system at the event of a disruption. The
stochastic optimisation model lying at its core generates disruptionaware reactive solutions to minimizes their potential impact. These solutions are informed by optimal disruption management policies suchas rerouting, transshipment, or holding, thus ensuring enhanced operational resilience and efficacy in response to disruptions. This adaptive
approach to disruption management facilitates more resilient, robust,
efficient, and sustainable supply chain operations in the face of increasing uncertainties and disruptions.

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4 - Port-hinterland transport: addressing sustainability
with cooperation and electric trucks

Claudia Caballini, Erika Olivari, Simona Sacone, Mahnam
Saeednia
Vehicle electrification represents one of the most promising solutions
to significantly reduce road transport emissions, paying attention to thefull energy cycle. However, truck electrification has downsides suchas economic investments, limited battery life and charging time requirements. Cooperation is another lever for increasing sustainability:
sharing transport orders allows the reduction of the number of trucks
needed to meet the transport demand, resulting in increased profits andreduced CO2 emissions. This paper proposes an optimization modelfor the cooperative planning of road transport operations, matchingtrips owned by different carriers and assigning them to a fleet of diesel
and electric trucks. The final objective is to maximize the total carriers’ profit while reducing emission costs. Time windows, haulers’
driving constraints, maximum duration of electric batteries, charging
times and emissions costs are considered. A compensation mechanism
is introduced to motivate carriers to share their trips. The problem
is solved using exact approaches and evaluated using a real data-set
related to the port of Genoa and its hinterland. An experimental campaign is carried out, making sensitivity analysis on some significant
factors of the problem. A numerical analysis is realized to evaluate thecomputational effort necessary to solve large-size case studies and an
appropriate heuristic approach is designed for large problem instances.

 TD-63
Tuesday, 14:30-16:00 - Room: S14 (building: 101)

New Challenges for Risk Management
Stream: OR in Banking, Finance and Insurance: New
Tools for Risk Management
Invited session

The circular economy encourages a move away from the traditional
linear corporate approach to production and consumption in favor of
integrated systems with external connections and in which materials
circulate in a closed circular loop leading to substantial improvements
in resource efficiency, energy savings and achievement of sustainability goals. In this transitional process, access to bank financing for firms
is a key challenge not only in the perspective of circular businesses but
also in the perspective of banks which must consider new factors that
may influence the creditworthiness evaluation process with an impact
on risk management. Consequently, circular businesses and banks arelinked in a new systemic and complex approach both in practice and inacademic literature. The aim of this research is to explore this twofold
area and understand its main characteristics in order to develop future
research on this topic. To achieve this objective we developed a conceptual framework that guided a literature review based on a selectionof 42 articles explored through a bibliometric and thematic analysis.
Our results confirm the complexity of the relationship between circular businesses and banks: in the implementation process circular businesses are mainly faced with financial barriers; banks do not consider
the impact of circularity factors in assessing credit risk and default
probability of circular businesses; there is a need for banks to shift
from a shareholder vision.

3 - Evolution of IRB models through new data sources andmachine learning

Federico Avataneo
The data available on SMEs (small and medium-sized enterprises) has
recently experienced a sharp increase as new sources have emerged,
providing value added to improve the risk management models ofbanks, which can be processed using advanced ML techniques. The
characteristics of this segment make the adoption of ML models particularly well-suited and strategic, supporting process automation anddigitalisation. To achieve these goals, the rating model has been updated, making it available online and in real time, without easing risk
management standards. ML algorithms provide a tool to complement
pre-existing approaches enhancing their breadth and accuracy.

4 - On the Adverse Selection in longevity risk Transfer

Chair: Mariacristina Uberti
Chair: Simone Landini

Valeria D Amato, Maria Carannante, Massimiliano Menzietti,
Steven Haberman

1 - The estimation risk under the IRB approach: an analytic
way-out

The literature finds evidences of adverse selection all insurance businesses. Nevertheless, these analyses remain inconclusive as regards
the economic consequences for the insurance industry, particularly forinstitutions bearing longevity by transferring the longevity risk exposure. The present research gives a contribution to the assessment of theadverse selection in the context under consideration and evaluate thechange in the cost of transferring longevity to capital market, which
can be pointed out as a sort safety loading, consisting in an optimalhedge, i.e. "the equilibrium hedge", since it contains the market inefficiencies due to the adverse selection effects. For estimating. thelongevity exposure, the projections are based on a more informative
stochastic model which is the frailty-based model proposed by the authors.

Simone Casellina, Simone Landini, Mariacristina Uberti,
Patrick Zoi
The Internal-Ratings Based (IRB) approach is aimed at providing ameasure of the maximum loss that a credit portfolio could generateover a year and with a given confidence level. As most of the standard risk measures, such as the Value-at-Risk (VaR), or the Expected
Shortfall (ES), also the IRB measure depends on some parameters thatmust be estimated. The usual plug-in approach, consisting in substituting in the theoretical formulas the parameters with their estimates,
does not consider the effect of the additional uncertainty generated by
the estimation error. In this paper, we develop an analytical correction
to the IRB formula that enables us to correct for the parameters uncertainty, using the theoretical setting developed by Gourieroux and Zakoïan (2013) and, to our knowledge, this is the first application of thatapproach to credit risk. This approach provides an approximated correction that depends on the variance-covariance matrix of the estimatedparameters and does not require specific assumptions on their prior distribution, avoiding also computationally intensive Monte Carlo simulations. We show the validity of our correction on simulated data andshow that our results are consistent with Tarashev (2010) who adopts
Bayesian methods. We argue that our approach is more flexible andsuited to be extended to the estimation of other parameters of the IRB
formula. We show a practical application of our approach relying onreal data.

2 - Financing the circular economy: effects on bank risk
management

Nadia Ricci, Caterina Lucarelli, Pietro Vozzella320

 TD-64
Tuesday, 14:30-16:00 - Room: S16 (building: 101)

Dynamic Vehicle Routing 2
Stream: VeRoLog - Vehicle Routing and Logistics
Invited session
Chair: Pirmin Fontaine
1 - Dynamic Routing Problem With Time Windows in a Signalized Road Network

Yi-Fen Huang, Tsung-Sheng Chang, Yen-Yu ChenEURO 2024 - Copenhagen
The ability to guide vehicles through the best routes to their destinations according to dynamic traffic information is called for in transportation and logistics operations. However, it is well known that the
dynamic routing problem is very difficult to tackle due to dynamic
nature of road networks, traffic evolution, and time-sensitive delivery constraints. It is noted that existing route planning methods often fail to adapt to non-recurrent traffic congestion and especially unexpected events such as accidents. That is, effective route planning
methods must be equipped with dynamic strategies for dynamic adaptation to traffic changes. Therefore, our research intends to develop a
dynamic route planning method by leveraging dynamic traffic data andpredictive modeling techniques. More specially, this method timely
adjusts vehicle routes based on changing traffic conditions and time
constraints. Note that the proposed dynamic route planning method
focuses on signalized urban road networks. We use simulation technique to verify the proposed method and carry out sensitivity analysis.

2 - Multi-Trip Vehicle Routing Problem With Multiple Time
Windows and Dynamic Starting Locations

Evren Guney

TD-64

to the low demand. One potential solution is on-demand buses, which
operate on flexible schedules and routes that depend on the actual demand. Still, the low demand in these regions remains a challenge to
PTPs. They want to maximize the served passengers but are forced
to accept or reject customer requests immediately within the dynamic
booking process and without knowing the true impact of the total number of server passengers due to potential future requests. This raises thequestion of predicting the customer’s fit within a passenger maximizing dial-a-ride problem (PM-DARP).
To address this problem, we introduce the request fit predictor (RFP)
framework that allows for fast decisions about the acceptance or rejection of customer requests. Within this framework, we model therequest fit as a binary classification task and learn the fit with supervised machine-learning models from a static PM-DARP using historical data.
The framework is tested on real-life data, and the results show that the
PTP can serve 7.48% more passengers using the RFP compared to acheapest insertion benchmark and more than 25% more than the statusquo. As a side effect, we also reduce the driven distance by 6.68%.

Vehicle Routing Problem with Time Windows (VRPTW) is a wellstudied challenging combinatorial optimization problem. In this work,
we focus on an extension of VRPTW where each vehicle can travel
multiple routes, each customer has multiple time windows and eachvehicle can start their route from their latest destinations. These kindof problems usually arise in marine routing problems, where a fleet ofships need to visit multiple customer and supplier ports and the trip
may end up in any of the ports. Then, their next trip will start in thelast port they have visited in their previous trip. Usually the optimization problem is defined over a planning horizon, however the routing
scheme and the initial/final locations of the ships are dynamic depending on the planning horizon cut-off dates.
A mixed integer linear programming formulation is developed to represent the problem and various small-sized instances are solved exactly,
using commercial solvers. For larger instances various heuristic andmeta-heuristic methods are being developed and tested.

3 - Dynamic Location-Routing With Modular Production
Facilities and Resource Collection: A Variable Neighborhood Search Approach

Julia Erdmann, Tristan Becker
In the transition to climate neutrality, hydrogen is expected to playa vital role as a clean and versatile energy carrier. However, largescale deployment of hydrogen requires adequate production and transportation infrastructure. A promising option to produce renewable hydrogen involves the utilization of biomass gasification. To efficiently
bridge local biomass potentials and hydrogen demand, decentralized,
small-scale production networks with mobile production units present
an opportunity. In this context, we recognize a new optimization
problem that integrates the dynamic location-routing problem withthe traveling purchaser problem, accounting for relocatable modular
production facilities. We propose a novel integer programming formulation, considering resource management, production, transportation, and demand fulfillment. To address the computational challenges
arising from practical applications, we develop a Variable Neighborhood Search (VNS) heuristic, which consists of sequentially optimizing location-allocation, resource selection, and routing aspects, using
multiple neighborhood structures and random perturbation strategies.
Extensive computational experiments illustrate the effectiveness of our
VNS approach across a large set of problem instances. Furthermore,
we present a case study focusing on local biomass-to-hydrogen production in Germany, leveraging straw as a feedstock and demonstrating
the practical applicability of our proposed methodology.

4 - A Supervised Machine Learning Framework to Predict
the Request Fit for Dynamic Dial-a-Ride Problems

Pirmin Fontaine, Simon Mader, Stefan Voigt
In the transformation towards sustainable transportation, a good public transport offer is a crucial element. However, in rural areas, public
transport providers (PTPs) face challenges in introducing a good offer using classical line-based services within a restricted budget due

321

TE-01EURO 2024 - Copenhagen

Tuesday, 16:30-17:30
 TE-01
Tuesday, 16:30-17:30 - Room: Sportshallen (building: 101)

Pascal van Henteryck
Stream: Plenaries
Plenary session
Chair: Anita Schöbel
1 - Fusing Learning and Optimization for Engineering

Pascal Van Hentenryck
The fusion of machine learning and optimization has the potential todeliver outcomes for engineering applications that the two technologies cannot achieve independently. This talk illustrates this potential
with the concept of an optimization proxy that can produce, in realtime, feasible and near-optimal solutions to classes of optimization
problems. The talk reviews the theoretical foundations underlying optimization proxies and demonstrates its practical practical on applications in power systems, supply chains, and mobility.

322

EURO 2024 - Copenhagen

Wednesday, 8:30-10:00
 WA-01
Wednesday, 8:30-10:00 - Room: Sportshallen (building: 101)

Peter BiroStream: Keynotes
Keynote session
Chair: Boglárka G.-Tóth
1 - Optimality and fairness in kidney exchange programmes

Péter Biró
In this talk we survey some recent developments on the OR aspects ofkidney exchange programmes (KEPs). These programmes have been
established in most of the Western countries in the last two decades tofacilitate the exchange of living kidney donors for those recipients who
have willing but immunologically incompatible donors. In the firstpart, we describe the European practices including the hierarchic optimisation used in the matching runs of the KEPs for computing optimal
solutions. These IP-based methods have been implemented in the simulator software developed in the ENCKEP COST Action (2016-2021)
and subsequently incorporated in the KEPSOFT software that provides
a common IT-platform for European applications. In the second part,
we describe an alternative solution concept based on the individual
fairness notion of stability. We explain how this classical cooperative
game theoretical concept can incentivise the recipients to register valuable donors or multiple donors in KEPs. Finally, in the third part, we
present new results on international KEPs, where credit-based compensation schemes have been proposed to balance out the benefits ofthe countries when merging their national pools.

 WA-02
Wednesday, 8:30-10:00 - Room: Glassalen (building: 101)

New Advances in Italian Energy Markets
Stream: OR in Banking, Finance and Insurance: New
Tools for Risk Management
Invited session

The high short-term volatility of renewable energies raises supply security issues that can be addressed through energy storage technologies. The idea of introducing a long term energy storage market is thesolution for two problems in the investment of storage technologies:
high costs and high volatility of energy prices. The main goal of thispaper is the modeling and solution of a profit maximization problem
for a risk-averse energy producer, who owns an hydroelectric pumping storage facility, and aims to seize the opportunity to finance theinvestment through an energy storage market. We modeled the facility owner’s decisions through an optimization problem. The ownerprofits from both selling storage capacity to a counterpart and participating in speculation within the energy spot markets and ancillary services. Our contribution to literature lies in the joint optimization ofgeneration/pumping and the decision sell part of the storage capacity.
One of the main profit opportunities for the plant owner is to fulfill
customer orders through accounting rather than physically. This introduces a significant complication: it is necessary to consider water
movements from both an accounting and a physical perspective. The
decision maker risk’s aversion has been formulated including the Conditional Value-Risk as a risk measurement in the objective function.

3 - Efficient Incentive Policies of Renewable Energy Communities

Alessandra Ruffini, Paolo Falbo, Carlos Ruiz
In 2018, the EU issued a Directive on Renewable Energy Communities(REC). REC implementation will increase RES-based electricity supply and decrease GHG emissions. National Governments are financing
ecological transition with incentives, and a relevant amount of funds isreserved for REC. Anyway, the success of these investments depends
on people’s, firms’ and municipalities’ choice to join a REC. A direct
economic advantage of joining a REC is that members can share an incentive tariff calculated on self-produced and self-consumed electricity. In our work, we face the problem of maximizing self-consumption
to promote RECs diffusion. For this purpose, we develop a bi-levelproblem with a policymaker and one REC. We consider a particularkind of REC composed of households (HS), that can install photovoltaic plants (PV), and a biogas plant (BG), because these are themost common technologies in agricultural and urban contexts. At theupper level, the central authority maximizes REC’s self-consumption
by financing investment in RES plants. At the lower level, the BG and
HS interact in the same REC, maximizing their profits subject to capacity and budget constraints. We developed two versions of this model tohave results independent from the iteration order and to reach a Nash
Equilibrium. Results are obtained through historical scenario simulation using Italy’s data from 2017 to 2021 and developed in Pyomo with
Gurobi Optimizer.

Chair: Cristian Pelizzari WA-03

1 - Is storage a business? A test on the Italian day-ahead
electricity market

Wednesday, 8:30-10:00 - Room: 1005 (building: 202)

Stefano Zedda, Simone Sbaraglia, Alessandro Fiori Maccioni
Energy storage systems emerge as key elements in optimizing sustainable resource use, ensuring a steady flow of energy even when primarysources, such as sun or wind, are intermittent. In this scenario, thetechno-economic integration of storage technologies becomes a key
building block in the transition to a more resilient and efficient energy system. In this paper, we considered the possibility of using theelectricity storage as a business, buying electric energy from the Italian electric day-ahead market when prices are low, and selling it backwhen prices are high. The theoretical modeling and the resulting simulations show that the storage can actually be a profitable business, andthat its results are highly variable over time. In particular, the recent
gas prices crisis changed the framework, showing very interesting revenue opportunities.

2 - Optimizing hydro-pumping plants management in the
new storage market

Diana Tratta, Paolo Falbo, Carlo Filippi

WA-03

Data Science and Optimization
Stream: Data Science Meets Optimization
Invited session
Chair: Ender Özcan
Chair: Andrew J. Parkes
Chair: Chang Liu
1 - Integration of Operations Research (OR) and Machine
Learning (ML) - A Literature Review

Francis Miranda
There is an increasing trend of combining Operations Research (OR)with Machine Learning (ML) techniques. Both disciplines are alreadyvery beneficial when used independently. Combining both techniques
will even enhance the benefits. The author starts with a brief historical
overview on Operations Research (OR) and Machine Learning (ML),tracing the important milestones in each field. Then the author talks323

WA-04EURO 2024 - Copenhagen

about the different categories and techniques used in Operations Research and Machine Learning, highlighting the diverse methodologies
such as linear programming, mixed integer programming and network
optimization in OR, together with supervised, unsupervised learningand reinforcement learning in ML. Next, the author talks about thepractice of Operations Research and Machine Learning, including the
educational background and job roles or functions of practitioners in
the field, the software tools and the most common techniques or algorithms used. The author also discusses the different applications invarious industries. The author will highlight the benefits and limitations of Operations Research (OR) and Machine Learning (ML) whenused independently. Then, the author explores the ways both techniques are integrated based on the literature such using ML then OR,
using ML in OR, and using OR in ML. Finally, the author will mention
some case studies from the literature how companies leveraged on bothtechniques to enhance their operational decision-making process.

2 - Optimizing Post-Disaster Recovery Strategies
Telecommunication Network Infrastructure

inracial minority groups. To establish the innovation channel, we demonstrate a higher correlation between corporate workplace equality andconsumer spending among more innovative industries and show that
corporate workforce equality can predict innovation efficiency in termsof patent quantity, quality, originality, and generality.

 WA-04
Wednesday, 8:30-10:00 - Room: 1001 (building: 202)

Algorithms for Mixed-Integer Nonlinear
Programming and Nonconvex Optimization
Stream: MINLP
Invited session

Mohamed Saâd El Harrab, Michel Nakhla

Chair: Jan Kronqvist

In the wake of an increasing number of natural disasters such as storms
and cyclones, overhead electricity and telecommunications infrastructures are exposed to significant risks, with damage to pylons, antennas
and poles disrupting essential services.

1 - Warm Starting of Outer Approximation for Biobjective
MINLPsThis study introduces a hybrid model that integrates machine learning and discrete optimization to optimize emergency response. By
analyzing storm damage to electricity and telecoms networks, we apply machine learning to cluster affected areas based on storm severity,
customer reports and wind speed. This data-driven insight informs a
unique mixed-integer programming model, treating the challenge as avariant of the capacity expert routing problem. Our solution employs
computational experiments and the Branch and Price algorithm, witha detailed case study of a storm event in France serving as a practical
illustration.
The results demonstrate the effectiveness of our hybrid approach in optimizing emergency team deployment strategies, adapted to the varying
requirements of different storm severities. This contribution enriches
the emergency operations management and disaster relief domain, presenting a novel perspective on combinatorial optimization for crisis
mitigation.

3 - Unit Commitment Predictor

Farzaneh Pourahmadi
The system operators usually need to solve large-scale unit commitment problems within limited time frame for computation. In this talk,we will discuss how by learning and predicting the on/off commitment
decisions of conventional units, there is a potential for system operators
to speed up their computation significantly. Additionally, we propose
a data-driven unit commitment model enabling the system operator toutilize available contextual information in the unit commitment model
to enhance decision-making efficiency. We explore whether, and if so
to what extent, our proposed data-driven model outperforms stochastic
models.

4 - Rise of Conscious Consumers: Impacts of Corporate
Workplace Equality on Household Spending

Erik Tamm, Jan Kronqvist
Multiobjective mixed-integer nonlinear programming is a challenging
type of problem to solve even when the continuous relaxation is convex. This work presents two new methods which attempts to solve
convex biobjective MINLPs efficiently. This is done by considering theso-called epsilon-constraint method for a biobjective problem. Since
the set of nondominated points is in general not convex due to integrality constraints, the classic weighted sum method is not suitable.
The epsilon-constraint method solves a sequence of similar convex
MINLPs to approximate the set of nondominated points. Each subproblem can be solved using outer approximation, where a polyhedral
approximation is obtained. The main contribution of the work is thetwo methods presented of how to reuse the polyhedral approximationfrom a solved subproblem when solving the next one. The methods
are derived from two observations. First, the subproblems are by construction increasingly restrictive, which means that a valid cut will bevalid for all the following subproblems. Second, the problems are only
slightly changed, which means that the integer combinations needed to
be visited should be similar between subproblems. Preliminary results
show that there is a noticeable improvement in both solution time andthe number of iterations needed to solve each subproblem.

2 - Mixed-Integer Semidefinite Programming by Outer Approximation

Jan Kronqvist
We consider different methods for generating cuts and solving mixedinteger semidefinite programming (MISDP) instances within an outerapproximation framework. In fact, the main components of the classical outer approximation algorithm for convex mixed-integer nonlinear
programming can easily be tailored towards MISDP such that similar
convergence properties are obtained. We propose some new methods
for generating cuts that have desirable theoretical and computational
properties and we present a numerical comparison.

Chang Liu
3 - Generating cuts with phantom branching
We first construct a measure of corporate workplace equality based on
textual analysis of equal employment opportunity (EEO) statements inonline job postings. We validate this measure by demonstrating its predictive power for future employment discrimination lawsuits and identifying the costs that deter low-equality firms from mimicking highequality peers. We then show a positive correlation between corporate workplace equality and consumer spending and propose two economic channels: preference and innovation. To establish the preference channel, we demonstrate that the correlation between corporate
workplace equality and consumer spending increases when consumer
preferences experience exogenous shocks in major social events, suchas the MeToo Movement in 2017 and the Black Lives Matter Movement in 2020. This effect is particularly strong among gender and

324

Yehor Blokhin, Jan Kronqvist
We consider a strategy for generating cuts for set partitioning and set
packing constraints. We perform an exploitative wide branching to detect infeasible and suboptimal integer combinations and tighten continuous relaxation. The idea originates from results that even restarting
branch-and-bound solver can be beneficial fo overall performance.

4 - Polynomial Optimization:
Tightening branch-andbound schemes with conic constraints

Brais González Rodríguez, Raúl Alvite Pazó, Samuel Alvite
Pazó, Bissan Ghaddar, Julio González-Díaz

EURO 2024 - Copenhagen
Conic optimization has recently emerged as a powerful tool for designing tractable and guaranteed algorithms for non-convex polynomial optimization problems. On the one hand, tractability is crucial
for efficiently solving large-scale problems and, on the other hand,strong bounds are needed to ensure high-quality solutions. In this research, we consider constraints based on linear, second-order cone, andsemidefinite programming and study their impact when used to tighten
the baseline relaxations of a given global optimization algorithm. We
describe how to design these conic constraints and their performancewith respect to each other and with respect to the standard RLT relaxations for polynomial optimization problems. Our finding is thatthe different variants of nonlinear constraints (second-order cone andsemidefinite) are the best performing ones in around $50%$ of the instances in widely used test sets. Additionally, we present a machinelearning approach to decide on the most suitable constraints to add toa given instance. The computational results show that the machinelearning approach significantly outperforms each of the individual approaches.

 WA-06
Wednesday, 8:30-10:00 - Room: 1013 (building: 202)

Advancements of OR-analytics in statistics,
machine learning and data science 16
Stream: Advancements of OR-analytics in statistics, machine learning and data science
Invited session
Chair: Helper Zhou
Chair: Bruno Kamdem
Chair: Gerhard-Wilhelm Weber
1 - Applying multiple indicators to assess the relationshipbetween finance and human factors

Betül Kalaycı
There are various empirical phenomena relating to individual investors’ behavior, such as how their emotions and opinions influence
their choices. Sentiment refers to all of these emotions and opinions. In
finance, stochastic changes can occur in response to investor sentimentlevels. Machine Learning methods are well-known and helpful tools
for prediction problems, and they have already been applied successfully to handle a wide range of financial problems. In this study, we
focus on the behavior of financial difficulties based on the sentimentlevels of investors, rather than pure financial problems. The goal ofthis study is to evaluate sentiment index prediction performance using
two-stage MARS-NN, MARS-RF, RF-MARS, RF-NN, NN-MARS,
and NN-RF hybrid models. We further prepare to describe people’s
sentiments toward the economy according to their level of confidence.
For this objective, we employ HMM to estimate the underlying statechange of the Consumer Confidence Index (CCI) and investigate its relationship with some macroeconomic indices (CPI, GDP, and currencyrate) at monthly intervals. The purpose is to monitor and comprehend the transitions between these phases, as well as to chart a course
through them. We also plan to apply volatility models to each subgroup
we receive in HMM to see if we can improve our predicting results.

2 - Identifying high-risk job advertisements on social media to help mitigate labour exploitation.

WA-07

collaboration with a non-profit organisation. Using these features, wethen train a predictive model to distinguish high-risk adverts from others. We validate our model using a different set of job adverts which
are unseen to the model. Through this work we demonstrate the use ofmachine learning to help vulnerable job seekers by facilitating timely
intervention. This work represents a crucial step towards mitigating
the risk of labour exploitation in the digital age and safeguarding therights of workers.

3 - Employing artificial intelligence in healthcare: a systematic approach for the detection of mental disorders
via social media analysis

Qing Yin, Yunya Song, Xian Yang
The rising prevalence of mental disorders significantly strains public healthcare services. The rapid growth of social media has created a solid foundation for research into public mental health. Artificial Intelligence (AI)-assisted methods have been developed to detect mental disorders in social media posts. However, existing studies
have primarily focused on a limited array of predefined mental disorders, overlooking the continuous emergence of unknown mental disorders. This study presents an innovative method by establishing acomprehensive system based on pre-trained language models (PLMs),
termed as systematic PLMs (sPLMs). This system is adept at detecting
known/in-domain (IND) and unknown/out-of-domain (OOD) mental
disorders, and it further specifies the names of the mental disorders
detected. Comprehensive experimental assessments in a public social
media dataset, the Reddit dataset, demonstrate that sPLMs outperform
pure PLMs (pPLMs) in detecting OOD mental disorders, achieving
a 16.54% higher average F1 score. Furthermore, sPLMs exhibit a91.58% accuracy in generating names for OOD mental disorders. This
superior performance, both quantitatively and qualitatively, paving theway for advanced AI applications in healthcare domain.

4 - Stochastic scheduling in the presence of side data case: surgery planning

Ricardo Otero, Erik Demeulemeester
In this research, we focus on the stochastic Advance Scheduling Problem with Downstream Capacity Constraints (ASPDCC) in the casewhere the decision maker has access to side data. We begin formulating the stochastic ASPDCC as a two-stage stochastic program usinga sample average approximation (SAA) approach. Unlike the common
method to characterize uncertainty, which assumes a lognormal distribution as the true data-generating process for each surgical specialty,
we propose a data-driven framework that integrates a machine learning algorithm (decision trees) to estimate the conditional probability
distribution for the surgery durations and the length of stay (LoS), in
the presence of side data. Finally, to speed up the solution of the SAA
model, we use a benders decomposition incorporated into a branchand-cut framework. To justify the value of using side data, we perform
extensive experimentation using real data from a reference hospital in
Bogota, Colombia. Results show that our algorithm generates schedules that outperform the traditional method of fitting a lognormal distribution for each surgery specialty.

 WA-07
Wednesday, 8:30-10:00 - Room: 1019 (building: 202)

Behaviour and decision processes
Stream: Behavioural OR
Invited session

Preeti Sharma, Mahnaz Hosseinzadeh, Sajid Siraj

Chair: Ilkka Leppanen

Millions of individuals are subjected to exploitative labour practices
worldwide. This project focusses on intervention at the recruitment
stage, where deceptive job advertisements often proliferate unchecked.
We propose a machine learning approach to identify high-risk online
job adverts potentially leading to labour exploitation. With the helpof natural language processing techniques such as word embeddings,
we extract relevant features from a sample of job adverts, obtained in

1 - Robust determination of certainty equivalents

Bernhard Nietert
Certainty equivalents are a core concept in Finance/Economics. Nevertheless, their experimental detection as outlined in papers like
Holt/Laury (2002) and ensuing publications is not robust and not reliable enough given its role as a core concept: it consists of a one-off325

WA-08EURO 2024 - Copenhagenchoice between a risky lottery and a riskless payoff and does not consider further conditions that might exert influence. For that reason, thispaper uses on the one hand the PEST procedure advocated by Luce.
PEST analyzes repeated (and not just one-off) choices and determines
certainty equivalents from the steady state, i.e., when decision makers
no longer switch between the riskless payoff and the risky lottery. On
the other hand, conditions are specified as: (i) expected value is given
as additional in-formation next to the state-dependent outcomes of therisky lottery; (ii) different starting values of the riskless payoff in PEST.
This paper finds: (i) The condition "expected value" leads to higher
average reaction times and requires more trials in the PEST procedurecompared to the benchmark case "only state-dependent outcomes". (ii)
The starting value of the riskless payoff in the PEST procedure exerts
massive influence on certainty equivalents. (iii) The number of fixation
points (derived from eye tracking) and reactions times can (partially)
explain why conditions influence certainty equivalents.

obtained from six small-sized transportation companies in the Netherlands. A meta-analysis across these companies suggests that (i) theprioritization of important customers and (ii) the level of complexity ofthe planning problems affect the extent to which planners deviate fromthe recommended routes. Planners learn which customers are more
important using customers’ history of orders and take that into consideration in route planning. However, the impact of customer priority on
routing decisions varies depending on the complexity of the planningtask. Planners increasingly adhere to the recommended routes when
faced with more complex routing problems. This can possibly be explained by the fact that prioritizing important customers, and simultaneously minimizing the distance of the route, requires more cognitive
load. As a result, planners deviate less from the tool’s recommendations for complex routing problems, even when such routes serve ahigh number of important customers.

2 - Process tracing and attention in preferential choice

Ilkka Leppanen
Decision making among options that consist of many attributes or that
contain risk is commonplace in various everyday contexts, and understanding how these decisions are made is highly valuable for researchers and practitioners. Here I develop scroll tracking, a novel
process tracing method that builds on a simple mobile web application where the decision maker scrolls between options and the app
tracks their response dynamics in pixel-time coordinate space. I thenuse scroll data obtained from two studies, a consumer choice problem and an risky decision making problem, to study predictions madeby different attentional evidence accumulation models. The information obtained from the app can be used to successfully predict different attention-choice relationships and to predict subjective value. I
also compare several evidence accumulation models, equipped withthe scroll data, in their predictive ability. All the models are able tobenefit from the rich variety of data that scroll tracking provides. Thesefindings demonstrate the potential of novel, naturalistic process tracing
tools that can be used to covertly study latent decision processes.

3 - How, when and why human agents act in a simulation?
The PRIAGE architecture.

Miguel Baigorri, Fermin Mallor
Modeling human behavior is a highly complex task because humans
don’t always act rationally, and their actions can be influenced by awide variety of stimuli that are difficult to measure. However, accurately modeling human behavior is one of the main challenges in developing useful agent-based simulations that can be applied to real-life situations. We propose a new architecture called PRIAGE (Pursued, Requested, and Induced Actions for reaching Goals in the Environment),
which offers an innovative approach to the influence of an agent’s internal state on their actions. In the majority of existing human-behavior
paradigms the agent’s internal state is used only to determine which
action to choose from a set of possible actions. PRIAGE focuses on
describing the different reasons that can trigger an action: agents’ actions are either requested by the environment, induced by the agent’s
internal state, or actively executed by the agent to achieve goals. A
second novelty is that the internal state is not only used to determine
what action to do next, but may also influence how well the action is
performed, creating performance measures that can modify the environment and the agent. After describing the new proposed architecture, we apply it to a case study analyzing physicians in an emergency
department. We focus on how the internal state of the agent influences
two performance measures: the treatment time and treatment qualityof patients.

Wednesday, 8:30-10:00 - Room: 1020 (building: 202)

Theory of Knowledge, Technology, andInnovation
Stream: Knowledge, Technology, and Innovation
Invited session
Chair: A. D. Amar
Chair: Santanu Roy
1 - Supplier reputation and Supplier-sourced innovative
new technologies adoption

Rupak Rauniar, Steve Zhou
Rooted in the theories of transaction cost economics and contingency
theory for technology transfer, this study is being undertaken to examine supplier reputation aligned with the competitive needs of anorganization as an important criterion for the selection of suppliersourced innovative new technologies. Competitive advantage for an
organization through supplier-sourced innovative technologies would
be empirically investigated by i) analyzing the relationship of supplier
reputation fit with the competitive advantage of the organization, andii) analyzing the relationship between competitive advantage and thesuccessful adoption of supplier-sourced innovative technology in theorganization. As such, the study aims to make an important theoretical contribution in the area of buyer-supplier innovative technology
diffusion, transfer, and integration. Further, as more organizations areundertaking digital transformation initiatives, the proposed study canprovide empirical evidence for the managers on the importance of supplier reputation as a critical selection criterion for supplier-sourced innovative new technology.

2 - Cutting off the offcuts

Arti Amar, Jim Bamford, Will Gibson, Simon BarransThis paper describes a case study of the application of Lean Thinking
in a Small-Medium Size(SME) manufacturing industry. AssociatedUtility Supplies (AUS) is in North of England and supplies (amongst
other products) industrial steel work for Electrical, Rail, and Telecom
sectors since 1998. It employs 54 staff with an annual turnover of ’9
million.

Marieh Kadivar, Sarah Gelper, Tom van Woensel

A programme of digitisation was implemented and one of the issues
initially identified as a pilot was to address the "offcut issue". During
production excess raw material of varying lengths are left over and historically these are stored and are used in an ad hoc manner involving
manual inspection and measurement of the offcuts before orders areplaced with suppliers. This was an issue that management had beenaware for some time but had been unable to successfully resolve.

Despite the significant advancements in optimization models for route
planning and their implementation in planning tools, planners often
deviate from the recommended solutions offered by these tools. To detect the factors that influence such deviations, we analysed field data

Utilising Lean improvement techniques this process was mapped out
and improvements suggested following 5S and 7 Wastes thinking, inaddition as much of the process as possible was digitised and incorporated into the update of the organisation’s ERP system.

4 - The effect of customer priority and planning complexity
on deviations from model-generated recommendations
in route planning326

 WA-08

EURO 2024 - Copenhagen
This novel approach has resulted in a 40% increase in the productivityof the process and greater stock visibility. More importantly for theorganisation, it has provided a successful case study of the applicationof a Rapid Improvement Cycle and upskilling of key operational staff
in Lean improvement techniques, which has paved the way for further
digitisation of the production planning.

3 - Hexaly Studio: no-code modeler for routing andscheduling problems

Lucas Ligny
Hexaly Studio is a no-code SaaS platform for mathematical optimization. Hexaly No-Code Modeler allows users to create a mathematicaloptimization model based on a business description of its characteristics. The verticals currently handled by Hexaly No-Code Modeler
are Vehicle Routing and Production Scheduling. Consequently, using
Hexaly Studio, software developers and data scientists can build rich
Vehicle Routing or Production Scheduling apps without writing a lineof code. This no-code approach is particularly powerful for fast prototyping.
For each vertical, the user can describe the business problem - decisions, constraints, objectives - by selecting the appropriate businessfeatures along a straightforward workflow. Then, the Hexaly No-Code
Modeler builds a Hexaly model, together with sample data inputs and adashboard with appropriate widgets to visualize the solutions provided
by Hexaly Optimizer. The widgets can be texts, tables, maps, (Gantt)charts, etc. Having explored the solution using sample data, the user
can feed the Hexaly model with real-world data and adjust the model
constraints and objectives in no-code, or directly by customizing theHexaly model.

 WA-09
Wednesday, 8:30-10:00 - Room: 10 (building: 116)

Decarbonized energy systems & markets
Stream: Energy Markets
Invited session
Chair: Thomas Klatzer
1 - Fully Flexible Temporal Resolution for Energy System
Optimization

Zhi Gao, Germán Morales-Espana, Diego Tejada, Matteo
Gazzani
To facilitate the transition towards climate-neutral energy systems,
many decision-makers rely on Energy System Optimization Models(ESOMs). However, the complexity associated with operational decisions in the numerous time steps can bring a tremendous computationalburden. So far, most efforts have been dedicated to creating typicalperiods to represent the full horizon, with repetitions in a uniform resolution. On the other hand, owing to the various natures of operational
decisions, a uniform resolution might not be necessary. In fact, it has
been proposed to designate separate resolutions for certain sections of
the system. In that method, temporal period lengths have to be multiples of each other. Taking this to a new level, we propose a concept
and mathematical formulation of fully flexible temporal resolutions,where energy flows and balances can have independent and dynamically varying resolutions. Furthermore, we present a case study of the
optimal operation of the Northwest EU power system to demonstrate
its advantages, utilizing TulipaEnergyModel.jl, an open-source ESOM
tool. Results show that compared to using traditional uniform resolution, a simplification enabled by the fully flexible resolutions gives
a relatively more accurate solution while incurring less computationalcost. The time spent formulating the optimization problem also drops
proportionally.

WA-09

2 - Bet on horses, not unicorns - The impact of technology
optimism on the decarbonization of the European energy system

Jacob Mannhardt, Giovanni Sansavini
The European and global energy systems need a drastic shift toward
low-carbon technologies to achieve deep decarbonization across all
sectors. Technological innovation is crucial for facilitating an economically and ecologically viable transition. However, a widespread concern exists that overly optimistic anticipation of emerging low-carbon
technologies, i.e., "unicorn technologies", may delay effective emission reductions today. In this study, we quantify the impact of technology optimism emerging from various forecasting errors, e.g., infinitely
fast capacity expansion, on the feasibility and cost of the European energy transition. To this end, we describe the decision-making process
as a myopic planner who plans their investment and operation based ona foresight horizon with an overly optimistic technology forecast. Using the linear cost-minimization framework ZEN-garden, we optimize
the transition of the sector-coupled European energy system, including
the electricity, residential heating, transport, and industry sectors. We
investigate the dependency of the energy transition on such "unicorn
technologies" and quantify the change in the investment strategy when
the decision-maker plans with erroneous forecasts. We find that theoverly optimistic forecast leads to a delayed investment in mature lowcarbon technologies and, therefore, increased emissions in early years
because the decision-maker wrongfully assumes to be able to remove
or abate them in the future.

3 - A Tight Approach on Piecewise Linear Pipeline Gas
Flow Modeling with Linepack

Thomas Klatzer, Germán Morales-Espana, Diego Tejada,
Sonja Wogrin
Integrated power and gas energy system optimization models (ESOMs)
can be a valuable tool for planning decarbonized energy systems. In
this context, pipeline gas transmission is a particularly complex problem due to the nonlinear and nonconvex relation of (bidirectional) gas
flows and gas pressure and the pipeline’s capacity to be used for shortterm gas storage (linepack). One method to deal with this complexity is piecewise linearization, which implies formulating the ESOM as
mixed-integer linear program (MILP). In the literature, several generalpiecewise linearization methods with the desirable characteristics oftight formulations have been applied. However, these methods are nottailored to this specific problem and thus tightness is harmed. Against
this background, we propose a novel piecewise linearization method
for the pipeline gas transmission problem with linepack. By computing the tightest possible polyhedra equivalent to its linear systems of
constraints, we show that the proposed formulation is tight per se whileothers are not. The resulting computational advantages are demonstrated in a case study of an integrated 24-bus power and 12-node gas
system interlinked by gas-fired generators (based on discrete unit commitment decisions).

4 - Modelling competetive pumped hydropower

Erik Bjørnerem
Balancing increasing shares of variable renewable generation requires
flexible technologies such as hydropower. However, hydropower representation is often oversimplified in current power system investment
models, leading to an overestimation of the flexibility potential. Moreaccurate representations are therefore required to correctly map theavailable resources. A common simplification is reservoir aggregation, which removes complexity added by reservoir couplings. This
information becomes increasingly important for pumped hydropower
operation, to account for individual reservoir capacity limits and flow
constraints between reservoirs. Additionally, deterministic formulations do not account for any uncertainty or variation in inflow or theavailability of wind and solar resources. This is crucial to include
for systems with a high share of generation from variable renewablesources.
This work aims to combine techniques from power system investment
models with hydropower operational models to more accurately represent the flexibility from pumped hydropower storage. This framework327

WA-10EURO 2024 - Copenhagenis then to be used to compare the viability of large-scale pumped hydropower storage in Europe to other storage solutions, such as battery
or hydrogen storage, under different policy scenarios for carbon emissions.

 WA-10
Wednesday, 8:30-10:00 - Room: 11 (building: 116)

ED logistics
Stream: OR in Health Services (ORAHS)
Invited session
Chair: Laura Boyle
Chair: Melanie Reuter-Oppermann
1 - Patient reallocation for waiting time reduction in emergency departments within multi-hospital networks

Dario Nicola Marchese, Sebastian Birolini, Mattia Cattaneo
The timely provision of emergency care has emerged as a significant
challenge due to a consistent rise in the number of visits to emergency
departments each year. However, there has been no corresponding expansion in hospital resources and infrastructure, resulting in a highlevel of overcrowding. One of the primary reasons can be attributed to
non-urgent patient visits, which account for 80% of the total accesses.
This work focuses on the patient reallocation strategy, illustrating howtransferring non-urgent patients between EDs within the same multihospital network can effectively decrease waiting times. To implement
this approach, we devise a novel multi-objective optimization modelwith an intrinsic two-stage structure, where arriving patients can beeither admitted or diverted to another hospital according to system capacity and congestion, and subject to consistent vehicle routing andavailability. The proposed model is applied to a real-case study involving a multi-hospital system in northern Italy, attaining an average
reduction of over 35% in daily waiting times. Extensive numerical experiments are conducted to demonstrate the scalability of the modeland quantify its benefits in various settings. Based on the experimental
outcomes, we develop a machine learning algorithm which serves as atool to quickly assess the potential of the reallocation in a given hospital network with minimal information and provides insights on therequired fleet size.

2 - Optimizing Patient Flow and Personnel Welfare in Emergency Departments: A Multi-objective Approach

Burak Pac
Emergency room overcrowding remains a significant challenge inhealthcare systems worldwide, often involving several-hour-long waiting times for patients and creating a stressful, intense environment forphysicians and nurses. Traditional strategies, such as redirecting arriving ambulances to alternative emergency departments, have limitations
in fully optimizing patient flow and resource allocation. This study
presents a novel approach to address this issue by systematically redirecting patient demand to specific emergency departments, integrating
real data analysis with system modeling. The primary objectives include minimizing system time for patients, ensuring equitable emergency room crowding levels, and maximizing emergency physician
and nurse welfare by accommodating their preferences regarding location and shift schedules across a network of public emergency medical
centers within the city. By considering factors such as travel times,
patient volume, and service durations, the proposed methodology aims
to optimize system performance using multi-objective computing budget allocation for non-dominated sorting particle swarm optimization.
Additionally, the study explores the use of genetic programming as
an alternative approach. This research offers a comprehensive solution to the complexities of patient flow management in emergency departments, with promising implications for practical implementationin healthcare systems.

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3 - Capacity Balancing Algorithm for Placement of Observation Patients: Assessing the Effect on ED Crowding

Vera Tilson, Dessi Pachamanova, Shiya Xiao, Keely
Dwyer_Matzky
We examine the queueing performance of a real-time capacitybalancing (CB) algorithm for routing patients from the Emergency Department (ED) to an Observation Unit (OU) or to an inpatient ward.
We study the significance of relative bed capacities between the ward
and the OU. OUs are designed for specific patient types, with shorter
expected hospital stays. By redirecting patients and reserving OU capacity for those who benefit most, overall hospital stay durations are
reduced. However, high ward utilization may cause longer ED waitingtimes. To prevent ED crowding, implementing a CB algorithm requires
careful analysis of ward and OU long-run utilization.

4 - Dependence between patient arrivals and service times
in emergency department data

Laura Boyle
Hospital emergency departments are frequently modelled componentsof healthcare systems, owing to their importance in primary healthcare access and to their scrutiny in politics and the media. Queueing
models are routinely used to model emergency departments and it iscommonly assumed that service time distributions are independent ofsystem state. This talk will explore a motivating emergency department dataset to discuss the dependence between patient arrivals andservice times. A method for detecting the source of the dependence
will be presented and the implications for developing accurate queuing
models of hospital systems will be discussed.

 WA-11
Wednesday, 8:30-10:00 - Room: 12 (building: 116)

Scenarios and foresight practices:
Behavioural issues I
Stream: Behavioural OR
Invited session
Chair: Martin Kunc
1 - Behavioural aspects of system dynamics use with scenarios

Martin Kunc
System dynamics (SD) is a methodology to generate qualitative andquantitative models. SD has two main concepts that are highly suitable
to use with scenarios: feedback processes that define the structure ofsociotechnical systems and accumulation processes that are responsible for the dynamic behaviour of systems over time. Kunc (2023) proposes framework that integrates methodologically scenarios and SD
where the integration can take multiple forms depending on the useof SD for creating or supporting scenarios. Lane (2017) proposes a
behavioural focus for SD in terms of its contributions related to mental models, encountering the world, building and implementing formal
models, and content of formal models. This work evaluates how the behavioural focus of SD can support overcome some of the behaviouralissues in scenario development process such as cognitive biases, effective group facilitation and overcoming business-as-usual thinking(Bryson et al, 2016)

2 - Accuracy, Awareness, and Acceptance: How Attention
Drives People’s Use of Point versus Interval Forecasting
Algorithms

Bas van Oudenhoven, Rob Basten
Many operations rely on forecasts of future events, often determining
the allocation of significant sums of money. Fortunately, algorithmic
forecasts achieve high levels of accuracy, but many decision-makers
reject algorithms upon observing they are not perfectly accurate. What
determines whether decision-makers perceive an algorithm as inaccurate, and how are these perceptions related to their willingness to use

EURO 2024 - Copenhagenalgorithms when forecasting future events? We propose that the tendency to dismiss algorithmic advice is partially due to the common
practice of presenting forecasts as single points rather than intervals.
In Study 1, we find that people perceive equally imperfect forecasts
as more accurate when these forecasts are presented as intervals rather
than points. In Study 2, we examine how these differential accuracy
perceptions affect people’s willingness to use algorithms. Here, wefind that people do not necessarily favor interval over point forecasts
when deciding whether to rely on the algorithm’s forecasts. Instead,such preference only occurs when people are explicitly prompted toevaluate the algorithm’s accuracy before deciding whether to commit
to its forecasts. In sum, these findings underscore the complex processes underlying people’s perceptions of algorithmic errors and theirsubsequent reliance on algorithms. Moreover, they show how managers can apply interval forecasts to effectively increase human forecasters’ reliance on forecasting algorithms.

3 - Causal Machine Learning: A Methodological Shift in Explainable AI for Decision Making

Rosa Taghikhah

Explainable AI (XAI) aims to enhance the transparency and interpretability of AI systems, a crucial need in decision analytics where understanding AI decision-making processes can significantly influence
trust and operational adoption. Traditional XAI models often focus on
one-to-one mappings between inputs and outputs, providing localized
explanations for individual predictions. This approach, while useful,falls short in capturing the complex, interconnected relationships inherent in many real-world data sets. It particularly struggles with dynamic
environments where feedback loops and interactions among variables
play critical roles in shaping outcomes. These limitations highlight theneed for more sophisticated methods that can unravel the intricate webof relationships and their causal effects on predictions. Causal machine learning offers a promising avenue for advancing explainability
by focusing on the identification and understanding of causal relationships rather than mere correlations. Unlike traditional XAI models,causal approaches seek to model the entire system of variables andtheir interactions, including feedback loops. This enables a more comprehensive view of how changes in one part of the system can ripple
through and affect other parts, providing insights into the mechanisms
driving predictions. This enables insights into the mechanisms driving
predictions.

4 - Scenario Simulation and Forecasting of Circular Chemical Economy: Assessing Policy Impacts on Investor
and Consumer Behaviour for Policy Decision-Making

R. M. Kazakov, Bing Xu

This paper proposes the development of a decision support tool aimed
at evaluating the impacts of regulatory measures within the circularchemical economy framework. Building upon previous research in
the field and addressing identified barriers and challenges, a system
dynamics simulation tool is being developed. This tool will enable
stakeholders to assess the effects of regulatory interventions on investor
and consumer behaviours, aiding policy decision-making processes.
By employing simulation scenarios that consider different regulatory
frameworks, the tool facilitates the evaluation of potential shifts in investment patterns and consumer preferences, offering insights crucial
for policy formulation and implementation. The transition towards
a circular chemical economy necessitates proactive policy measures
to promote sustainable practices and mitigate environmental impacts.
However, understanding the multifaceted impacts of regulatory interventions on investor and consumer behavior remains crucial for effective policy decision-making.

WA-12

 WA-12
Wednesday, 8:30-10:00 - Room: 13 (building: 116)

YW4OR_1
Stream: WISDOM - Women in OR
Invited session
Chair: Dilek Gunnec
1 - Upgrading arcs in the covering tour problem

Marta Baldomero-Naranjo, Andrea Mancuso, Adriano
Masone, Antonio Manuel Rodriguez-Chia, Claudio Sterle
In this study, we present the Covering Tour Problem with Arcs Upgrade (CTPAU). This problem is an extension of the Covering Tour
Problem (CTP) that considers the possibility of enhancing the networkby reducing the length of some arcs, i.e., upgrading them. Hence, upgrading an arc means reducing its length, usually within certain limits,
at a given cost that is proportional to the extent of the upgrade.
The CTPAU is formulated with three different sets of nodes, V, W, and,T that is a subset of V. Two decisions have to be made simultaneously:i) identify the tour of minimum length that passes through a subset ofV, ensuring that all nodes of set T are included in the tour, and thateach node in W is within a given coverage distance from a node on thetour, ii) decide with connections to upgrade.
Therefore, the CTPAU seeks to identify the minimum length tour while
integrating arc upgrading and a budget constraint. In this context, we
present some MILP formulation and compare them to illustrate the potential and limitations of each one.

2 - Building Resilience of Critical Infrastructures in Timesof Crisis

Emilia Grass
In times of crisis, healthcare systems and aid agencies face unprecedented challenges, necessitating robust resilience strategies to ensure
uninterrupted service delivery. This talk emphasizes the importanceof preparedness, adaptability, and the strategic use of Operations Research (OR) techniques within these critical sectors. For healthcare,building infrastructure resilience involves enhancing cyber security, securing supply chains, and integrating advanced decision support tools
to maintain care continuity. OR techniques play a crucial role in optimizing resource allocation, forecasting demand, and planning emergency responses. Similarly, disaster management and aid agencies
must focus on agile response strategies, leveraging OR for data analytics and informed decision-making, and fostering partnerships acrosssectors to mobilize resources swiftly. Central to these efforts is a holistic approach that combines physical infrastructure enhancements withsystemic resilience through OR, ensuring services can withstand, adapt
to, and recover from disruptions. By prioritizing resilience and applying OR methodologies, healthcare and aid organizations can safeguard
vulnerable populations during crises, contributing to societal stability
and well-being.

3 - A methodology to tackle multi-objective linear programming problem

Mariana Mesquita-Cunha
Multi-objective decision-making often poses challenges due to thecomputational complexity of generating and analyzing the Pareto
front. To address this, we propose a framework integrating two strategies to alleviate the burden on decision-makers (DMs). Firstly, we
introduce algorithms based on the epsilon-constraint method to derivea Pareto front representation balancing coverage, uniformity, and cardinality, thereby reducing the number of non-dominated vectors to beconsidered. Secondly, we incorporate DM preferences to refine thefeasible region. We do so initially using criteria ranking and selecting a region of interest of the Pareto front to focus the representation.
Then, after generating the initial representation, through pairwise comparisons we construct convex cones and eliminate inferior regions fromconsideration. We demonstrate the efficacy of our framework throughits application to pharmaceutical supply chain design, illustrating its

329

WA-13EURO 2024 - Copenhagen

adaptability aid in tactical and strategic decision-making contexts. Thismethodology offers a practical approach to multi-objective optimization challenges, providing DMs with manageable decision support
while maintaining focus on the proposed strategies

4 - An overview of Arc Routing Problems with drones

Paula Segura Martínez
In the last decade, the emerging technology of unmanned aerial vehicles, commonly known as drones, has provided new opportunities forpractitioners in urban logistics. Drones present attractive advantages
compared with standard ground vehicles, such as avoiding the congestion on road networks, eliminating the risk of personnel in difficultaccess operations or getting higher measurement accuracy in infrastructure inspection. The use of drones to perform the service in Arc
Routing Problems (ARPs) involves significant changes in the traditional way of modeling and solving them. While in traditional ARPs
the ground vehicles are limited to travel through the edges of a network (following the local infrastructure), drones have the flexibility totravel off the network as well. These aerial vehicles may service only
part of an edge and then travel in a straight line to any point of anotheredge, without following the links of the network. This consideration
makes Drone ARPs continuous optimization problems with an infinite
and uncountable number of feasible solutions. In this talk, I summarize these ideas and review some variants of ARPs with drones that wehave addressed and studied during my doctoral thesis.

 WA-13
Wednesday, 8:30-10:00 - Room: 15 (building: 116)

Food Supply Chains

The Public Distribution system (PDS) distributes essential food andnon-food items to the economically disadvantaged population. The
distribution is often facilitated through a network of state warehouses
and fair price shops. Each year, the Government of India spends ahuge amount of money on the distribution of these grains across thenation, from surplus to deficient regions, which puts a heavy burden
on financial resources. The present study aims to optimise the PDS operations to improve logistical decision-making and thereby reduce thecost to the government. The mathematical model is developed using
mixed integer linear programming, which cost. The constraints considered in the model are demand constraints, supply constraints, andcapacity constraints. The parameters includes capacity, demand, geocoordinates, and district name. The outputs are optimal tagging andassociated amounts of food commodities to be transported and optimalclusters for storage facilities significant. The above model has been
implemented for 3 months in one of the states where the state has reported a reduction in the cost to the government. Efforts are underway
to scale the exercise to all other states.

3 - Facilitating System Dynamics to Analyze Digital Logistics Platforms for Regional Food Supply

Christine Mendoza Pardo, Christian Fikar
In our work, we use system dynamics to investigate the influence ofdigital solutions aiming to enhance the regional food supply for thehospitality industry and communal catering. A systems thinking approach is applied to identify feedback structures and underlying interdependencies. Therefore, findings from literature and two model
regions were collected. With the help of causal loop diagrams, the results get visualized. They indicate that digital solutions can help toovercome key obstacles in such systems, namely insufficient logistics
solutions as well as a lack of communication and transparency.

4 - Decentralized and circular plant-based protein supply
chain networks for the future[U+202F][U+202F]

Ege Naz Ozer, Sander De Leeuw, G.D.H. (Frits) Claassen

Stream: Secure & Sustainable Food Supply
Invited session
Chair: J.c. Gerdessen
1 - Food network design with quality deterioration considerations: an application to a perishable food supply
chain

Junhan Zhang, Peter Kirst, Sander De Leeuw, G.D.H. (Frits)
Claassen
Given the increasing demand for freshness, the need to reduce foodwaste, and to ensure food safety, it is crucial to consider quality deterioration processes in the (re)design of food supply chains. Past
developments in food supply chains focussed foremost on efficiency
and economic profitability, resulting in large-scale, centralized processing facilities. This has become a liability as efficiency in largescale processing limits product differentiation and flexibility. Decentralised, (upstream) pre-processing, and improved pre-treatments canaffect product deterioration in an early stage and will result in morediverse and intermediate product flows that can be used in existing andnew value chains and provide new markets. To identify the benefits ofalternative supply chain structures, we propose a generic model basedon mixed-integer linear programming. The modelling structures areapplicable to a broad variety of different (food) supply chains. Both,network design aspects and gradual quality deterioration processes areincorporated. We illustrate the applicability of the concept from anexisting fruit supply chain in China. The results show that decentralized pre-processing can mitigate quality deterioration processes. In
addition, a decentralised supply chain leads to improved sustainability from a social point of view since smallholder farmers in emerging
economies can increase their income without affecting the profits ofdownstream stakeholders.

2 - OR based solution for distribution of grains within a
state

Shallu Bhasin, Nomesh Bolia

330

Global protein consumption is shifting towards plant protein sources
due to population increase, resource depletion, requirements for diversifying diets, and the need to reduce environmental impact. This
study aims to redesign the European Union (EU) plant protein processing supply chains for the current and the future state of the plantprotein food market.[U+202F]In this study, this network design problem is formulated as a multi-objective mixed integer linear programming (MILP) model, incorporating decentralized processing operations and the re-use of waste streams across various echelons for sidestream valorizations and to form closed loops. The Pareto front is obtained for the minimization of environmental impact and maximizationof economic gain. Numerical experiments for technology development
and replacement of animal protein with plant protein production andconsumption demonstrate that the current supply chain framework can
be extended to cover the shift towards plant protein sources, but the
network structure depends on the processing technology capabilities
and available pathways of value recovery.

 WA-15
Wednesday, 8:30-10:00 - Room: 18 (building: 116)

Home Health Care and Operating Room
Scheduling
Stream: OR in Health Services (ORAHS)
Invited session
Chair: Kris Braekers
1 - Quantifying the trade-off between costs and nurse satisfaction in home healthcare planning decisions

Arne Delaet, Kris Braekers, Katrien Ramaekers, Patrick
HirschEURO 2024 - Copenhagen
Home healthcare (HHC) nurses in the Flemish context face a significant challenge: the irregularity of work rosters in successive rostering
periods, which disrupts their ability to plan personal activities. This issue affects their job satisfaction and contributes to high turnover rates
within the industry.
Existing literature predominantly addresses nurse satisfaction in HHCthrough single-objective models using a weighted sum, often failing tocapture the complex trade-offs involved. In addition, decision-makers
struggle to assign appropriate weights to each factor in this function.
This study proposes a bi-objective model to balance costs and care
worker satisfaction, defined by roster regularity in successive planning
periods. Our novel approach utilizes a multi-directional local search(MDLS) framework with an embedded matheuristic to approximate
the Pareto frontier between the conflicting objectives (i.e., costs andcare worker satisfaction). This matheuristic constructs multi-day planning in an integrated manner by rostering nurses, assigning patients tonurses, scheduling patient visits and constructing routes. Our model
offers decision-makers valuable insights for optimizing rostering practices by presenting multiple solutions that highlight trade-offs. Finally,
the effectiveness and performance of our solution approach will bedemonstrated by discussing the results of some empirical experiments.

2 - Home healthcare routing and scheduling with tasksplitting

Loek van Montfort, Markus Leitner, Wout Dullaert
This study introduces the concept of task-splitting into home healthcare routing and scheduling, focussing on the design of routes andtime-tables for caregivers providing services at patients their homes.
Task-splitting entails the division of a (long) patient visit into two separate visits, which can be performed by different caregivers at different times. Splitting a visit can increase the planning flexibility ofhome healthcare (HHC) providers, as the resulting split tasks may havelower requirements related to the caregiver qualifications, relaxed visiting time-windows, or a shorter combined duration. However, the inclusion of task-splitting together with the temporal dependencies thatarise between the split parts also presents a computational challenge.
To support the incorporation of task-split decisions into the planning
process and explore the impact for HHC providers, we first introduce
different mixed integer linear programming formulations. Next to deciding which tasks to split, these formulations are capable of handlingvarious types of synchronisation constraints between the visits. Subsequently, we propose pre-processing routines and heuristic procedures
to improve the computational performance of the resulting solution algorithm. Finally, we present results from a computational study that
demonstrates the potential benefits of task-splitting, including a reduction on the staff requirements and decrease of operations costs among
the instances considered.

3 - Collaborative Scheduling and Routing of Home Healthcare Service across Multiple Communities

Wenjie Hu, Yang Wang, Jin-Kao Hao, Jianguang Feng
As an important home health care mode in China, the homecommunity service mode focuses on the impact of healthcare centers ineach community and coordinates the medical resources between communities. In this paper, we propose a new collaborative home healthcare routing and scheduling problem in multiple service centers (CHHCRSP). In addition to the traditional routing and scheduling decisions, C-HHCRSP also assigns a working center for each highly qualified caregiver who can provide health services across multiple communities. This problem is challenging due to the presence of complex,
realistic constraints such as mandatory lunch breaks, synchronizedvisits, and downgrading of services. We first formulate the problem
as a mixed integer programming model, which is solved by CPLEX.
Then, we propose an adaptive large neighborhood search (ALNS) algorithm that integrates new problem-specific destroy and repair operators. To further improve the performance of ALNS, we propose two
post-optimization techniques, which are based on heuristic strategies
and a set partitioning model, respectively, resulting in the enhanced
algorithms ALNS-HS and ALNS-SP. Tested on 104 benchmark instances, ALNS-HS shows a competitive performance with CPLEX on
small instances. For large instances, we compare our algorithms with

WA-16

a classic large neighborhood search algorithm to demonstrate their superiority. Additional analyses are performed to verify the roles of thecomponents of our algorithms.

4 - A two-layer heuristic for patient sequencing in theoperating room theatre considering multiple resource
phasesBabak Akbarzadeh, Broos Maenhout
In this paper, we study the surgery scheduling problem in the operating
room theatre. The problem considers the sequencing of patients andcalculation of their start times with splitting of surgeries into resource
phases to facilitate the efficient use of different types of resources. We
propose a dedicated two-layer heuristic to compose an operational patient and resource schedule. The first optimisation layer applies an evolutionary heuristic to devise patient schedules while considering thescheduling of the operating surgeons and rooms. This step employsa machine-learning mechanism predicting the feasibility of chromosomes, which improves the algorithm’s efficiency and effectiveness,and relies on novel local search operators to find high-quality solutions. The second layer devises the schedule of the other resources using a decomposition-based heuristic. Computational experiments are
conducted to show the performance of the proposed two-layer heuristic
and validate its design choices. We benchmark the proposed algorithmwith other optimisation procedures and show the contribution of considering multiple resource phases for real-life decision-making.

 WA-16
Wednesday, 8:30-10:00 - Room: 19 (building: 116)

Fairness in sports
Stream: OR in Sports
Invited session
Chair: Laszlo Csato
1 - Analysis of Perception Differences in Football Video
Refereeing

Cristian Dobre
The Video Assistant Referee (VAR) is introduced in football to guarantee consistency and accuracy in the appliance of the official playing
rules. The VAR must avoid appraisals that are prone to interpretability,
and must ensure fair play. However, as with every subjective judgmentprocedure, differences in perception might arise. This work has gathered statistical evidence that even with use of the VAR, conclusions on
the same game situations will still differ among more referees judging
it behind the VAR screens. This was achieved by studying the performance of the Dutch football referees who are asked to judge game situations based on video replays. For all game situations, the referees arerequested to classify the violation of the fouls rules into 11 ordered categories (from 0 = no violation of the rules up to 10 = strict violation of
the rules resulting in a red card). The discrete scores given by the referees are transformed into continuous scores, as required by the Analysis
of Variance models, using linear optimization techniques. The statistical methods show enough evidence that there is difference in perception between several referees. Subsequently, groups are formed suchthat each group contains referees that have similar perception for more
uniformity in decision making.

2 - How to organize a fair rogaining competition?

David Van Bulck, Joonas Pääkkönen, Benjamin Jacquet,
Dries Goossens
Orienteering is a cross-country running sport where competitors use a
map and compass to visit control points as quickly as possible. In a
special variant called rogaining, each control point is assigned with ascore, competitors are allowed to visit the given controls in the order of
their own preference, and there is a time limit that prevents competitorsfrom visiting all control points. Hence, it is important for the competitors to carefully plan which controls they are going to visit (like in the
knapsack problem) and in which order (like in the traveling salesman331WA-18EURO 2024 - Copenhagenproblem). Thus, winning a rogaining competition not only requires
physical but also planning and navigational skills.
Unlike most operations research literature, we approach the rogaining
problem from an organizers’ perspective: how to assign scores fairly,
balancing physical and strategic skills? In practice, this typically translates to designing rogaining competitions such that brute physical skills
are not overly favored. We follow a bi-level optimization approach
where the outer-layer assigns scores to control points and the innerlayer simulates a portfolio of pre-defined competitors. The result is a
Pareto front of possible score assignments with distinct trade-offs between physical and strategic skills. Our method’s efficacy is shown in
its application to the 2023 rogaining world championships.

3 - The allocation of FIFA World Cup slots based on the
ranking of confederations

Laszlo Csato
Qualifications for several world championships in sports are organised such that distinct sets of teams play in their own tournament for apredetermined number of slots. This paper provides a reasonable approach to allocate the slots based on historical matches between thesesets of teams. We focus on the FIFA World Cup due to the existence of
an official rating system and its recent expansion to 48 teams. Our proposal extends the methodology of the FIFA World Ranking to compare
the strengths of five confederations. Various allocations are presented
depending on the length of the sample, the set of teams considered, as
well as the frequency of rating updates. The results show that more European and South American teams should play in the FIFA World Cup.
The ranking of continents by the number of deserved slots is different
from the ranking implied by FIFA policy. We recommend allocating atleast some slots transparently, based on historical performances, similar to the access list of the UEFA Champions League.

 WA-18
Wednesday, 8:30-10:00 - Room: 42 (building: 116)

OR for Medical Services in Developing
Countries
Stream: OR for Development and Developing Countries
Invited session
Chair: Sadia Samar Ali
Chair: Elise del Rosario

Merin Mathew, Malavika Krishnakumar, Radhika Menon,
Sreesankar Ajayan, Raghu Raman, Prema Nedungadi, Georg
Gutjahr
Improving menstrual hygiene and reproductive health is one of theUnited Nations’ Sustainable Development Goals. Intervention programs target girls in developing countries and provide them withknowledge and material to manage menstruation. This paper considers the cost-effectiveness of such programs with a case study from arural area in Madhya Pradesh, India. A sequence of maximal-covering
location problems (MCLPs) was solved to select a set of villages as
optimal locations to conduct multiple interventions in the study area.
Pre- and Post-surveys with participants are used to estimate the proportion of girls who adopted safe and hygienic menstrual practices. Interviews with 528 adolescent girls from nearby villages are employed
in a diffusion model to estimate the indirect reach of the intervention
across the neighboring villages. Health benefits are measured in termsof Disability-Adjusted Life Years (DALYs). The cost-effectiveness ratio of a menstruation intervention is estimated to be about $28.5 per
DALY. These programs also provide additional benefits such as an increase in awareness, a reduction in school absenteeism, and an increasein the self-confidence of girls.

3 - Prevalence and costs of delayed discharge in a tertiary
Indian hospital

Georg Gutjahr, Merin Mathew, Malavika Krishnakumar,
Rejitha P S, Preetha P, Binil Babu, Ajitha Aniyappan, Merlin
Moni, Dipu T. S.
In India and other low and middle-income countries, delays in the approval of medical procedures from insurance companies often lead toextended hospital stays. Patients can not be discharged until the insurance company approves all procedures.
In this talk, we study the prevalence of the problem and the financial
implications by analyzing all discharge summaries from 2021 to 2023
in a tertiary hospital in Kerala, South India. Unnecessary hospital stays
will cause substantial economic loss to the hospitals. They will also
lead to patient dissatisfaction and reduced bed availability for patientswith medical needs.
If the hospital allocates additional resources, it can improve dischargeplanning through system-level approaches. In this talk, we investigate
the implications of different hospital policies. We use counting processes to model patients’ arrival on each day, survival models for thelength of hospital stays, and a Markov decision process to model theeffects of different policies. The decision variables can be optimized byapproximate dynamic programming. We find that policies that prioritize a timely discharge should be recommended. Beyond costs, thesepolicies also increase patients’ satisfaction and the overall efficiency ofthe health care system.

1 - Modelling of emergency medical services in Indonesia

Paul Harper, Sarie Brice, Mark Tuson, Geraint Palmer, Daniel
Gartner
Our research programme is working with Indonesian emergency ambulance services and the Indonesian Government to help them makecritical decisions on the optimal types, capacities and geographical locations of response vehicles. Such factors directly impact on the probability of patient survival, ability to respond to major disasters, and theoverall quality of care provided. There are however many challenges
faced in the country, including vast geographical areas, traffic congestion, inadequate numbers of ambulances and a lack of a co-ordinatedservice.
This talk will describe findings and impact of the research projectto date, including an analysis of ED survey data that we undertook
across Jakarta hospitals, and an approach to consider EMS allocations
for both heterogeneous populations (multiple medical needs) using aheterogeneous fleet (consideration of multiple vehicle types each withdiffering travel speeds). This has been achieved via the creation of an
optimisation and simulation decision-support framework.

2 - Cost-effectiveness of a menstrual hygiene program inrural India332

 WA-19
Wednesday, 8:30-10:00 - Room: 44 (building: 116)

Towards sustainable development
Stream: OR in Energy
Invited session
Chair: Shamim Homaei
1 - Towards an optimum carbon backbone: A Bilevel Model
Approach for Efficient CO2 Pipeline Design and Price
Policy in Germany

Vladimir Stadnichuk, Stephan Bogs, Ali Abdelshafy, Grit
Walther
The pressing challenge of climate change and the need to reduce thelevels of anthropogenic carbon dioxide emissions (CO2) have madeCarbon Capture and Storage (CCS) an increasingly critical area of research. CCS represents a vital technology in the pursuit of mitigatingclimate change impacts by capturing CO2 emissions at their sourceand storing them underground. Recognizing the importance of thisEURO 2024 - Copenhagentechnology, our study delves into the optimization of CCS infrastructure, specifically focusing on the integrated design of CO2 pipeline
networks and the development of effective price policies. Through abilevel model tailored for the CCS context, we examine the potentialof price policies to enhance the efficiency of CO2 pipeline networks
in Germany. Herein, our findings illuminate the dual nature of CCS:
on one hand, it offers a pathway to lower CO2 prices for emitters, thus
promoting environmental welfare. On the other hand, it encounterschallenges in market design, particularly in addressing the complex
issue of preventing monopolistic control by pipeline operators . Additionally, we recognize the ’distance-to-sink’ effect as a significant
barrier, disadvantaging emitters situated far from storage sites.

2 - Green Skies Ahead: Streamlining the Sustainable Aviation Fuel Supply Chain in New ZealandHadi Vatankhah Ghadim, Ali Ebadi Torkayesh, Jannik Haas,
Rebecca Peer, Grit Walther, Sandra Venghaus
Presently, the domestic aviation sector in New Zealand emits approximately 825 kilotons of CO2 equivalents, positioning the country
among the top 10 OECD nations with the highest per capita CO2 emissions from domestic flights. Therefore, transitioning away from conventional fossil fuels to more sustainable alternatives, notably Sustainable Aviation Fuels (SAF), is inevitable. Enabling an effective transition to SAFs requires understanding the share of different SAFs to meet
future demand, the timing and location of production plants, fuel transport between different regions and airports, the resource use for SAF
production, and all associated economic investments. To address thisgap, we present a mixed-integer linear programming model to support
the development of SAFs in New Zealand. The proposed framework
optimizes the SAF supply chain, considering different production processes, to provide an evidence base to support the transition towards
SAF integration. In this first case study, we consider the domestic
aviation industry and domestic SAF production using indigenous resources. Ultimately, integrating these fuels will lead to decarbonizationin the aviation sector and contribute to the country’s environmental andclimate change goals.

3 - Sustainable power generation pathways:
model insights for European countries

EMPIRE

Shamim Homaei, Rahul Anantharaman, Stian Backe, Simon
Roussanaly, Asgeir Tomasgard
As global efforts intensify to meet the targets of the Paris Agreement,
the necessity to reduce emissions from the power sector becomes increasingly evident. This study explores the role of different power generation technologies in achieving emission reduction targets. For thispurpose, the European Model for Power System Investments with Renewable Energy (EMPIRE) is used to optimize power generation pathways across Europe by 2050. The primary focus is on understanding
the impact of CCS technology, exploring the balance between different
generator types (renewables, biomass, nuclear, and fossil-based generators) and their collective contribution to decarbonizing the power sector. Notably, the study considers high capture rate CCS technology toreveal its potential to reshape the energy mix. The research also develops various pathways associated with CCS and without CCS technology, offering a comprehensive understanding of how these pathways
contribute to the decarbonized power system. Moreover, the study illuminates the impact of these pathways on electricity prices and provides valuable insights into potential changes. Our findings emphasize
the crucial role played by CCS in transitioning towards a green power
sector and shaping sustainable power generation pathways. In addition
to offering practical insights for policymakers, the study illustrates the
dynamics of the power system transition and proposes a roadmap for amore sustainable and resilient energy future.

4 - A TEMOA-Europe-based scenario analysis to examine
crucial strategies for decarbonization

Daniele Lerede, Valeria Di Cosmo
Europe still has a strong dependence on fossil fuels, and natural gas
in particular, despite the claimed commitments towards carbon neutrality by 2050. The REPowerEU Plan is reducing the huge amountof gas imported from Russia. Nonetheless, the main strategy to guarantee security of supply mainly envisages long-term agreements with

WA-21

more reliable partners for gas and a larger support to the developmentof renewables. At the same time, a carbon-neutral energy generation
technology nuclear fission is undergoing a substantial phase-out, andthe use of fossil fuels for energy production is discouraged through the
adoption of growing carbon tax levels in the form of excise duties andthe Emissions Trading System. This work presents a scenario analysis carried out using the open-software and open-framework energy
system optimization model TEMOA-Europe. Taken for granted theabsence of Russian gas imports starting from 2027, the examined alternative scenarios envisage: 1) a business-as-usual scenario; 2) a scenario with increasing carbon prices and constraints on nuclear capacity
to simulate the current phase-out trend; 3) a scenario with increasing
carbon prices and the possibility to increasingly rely on nuclear electricity; 4) a net-zero emissions by 2050 scenario. Energy costs, theprofitability of renewable energy projects and the role of gas are evaluated for each of the mentioned scenarios providing useful insights forthe development of the European energy system.

 WA-21
Wednesday, 8:30-10:00 - Room: 49 (building: 116)

Post-Disaster Relief Distribution
Stream: OR in Humanitarian Operations (HOpe)
Invited session
Chair: Lars Päsler
1 - Scheduling of Unmanned Aerial Vehicles for Disaster
Response

Arya Misiç, Mert Kara, Selin Özpeynirci
Unmanned aerial vehicles (UAV) are becoming an increasingly prominent alternative to traditional methods in disaster response, providing
rapid aid to regions where transportation becomes difficult after natural
disasters. Optimizing the use of UAVs becomes even more important,especially considering the crucial role of rapid response and limited
resources. This study focuses on developing efficient scheduling algorithms for UAVs that will be used in the distribution of emergency aid
packages after natural disasters.
The goal of this study is to minimize the total weighted arrival time ofaid packages to be distributed to the disaster area from multiple warehouses with UAVs. Given that UAVs can carry only a single package
at a time, this problem becomes a scheduling problem. Within thescope of this study, the methods to be developed for the scheduling of
UAVs to deliver aid packages to the disaster area will be tested on reallife scenarios designed for a potential earthquake in Istanbul, a region
known for its high seismic risk.
By developing a mixed integer linear programming model, it is aimed
to reach the optimal solution for small data sets. A heuristic approachis developed in order to find near-optimal solutions in reasonable time
for large-sized instances of these problems, which are known to be NPhard. The solution methods are applied on the İstanbul example, andinsights are provided for disaster management planning.

2 - Post-disaster Relief Distribution Optimization Framework in Salgar, Colombia

Juana Jaramillo-Rios, Paula Penagos, Carlos A.
Gonzalez-Calderon
Humanitarian logistics is crucial in crisis and disaster scenarios, enhancing system resilience and facilitating rapid restoration. Postdisaster relief distribution is a critical challenge, requiring efficient andeffective relief to minimize suffering. The complexity of relief distribution involves navigating damaged infrastructure, coordinating withstakeholders, and adapting to dynamic conditions. The social impact ofrelief distribution, especially on vulnerable populations, is paramount.
Ensuring accessibility to essential resources and efficient connectivity can mitigate suffering. Colombia, a developing country, is mostvulnerable to natural disasters, climate-related risks, and economic instability. The 2015 flash flood in Salgar, Antioquia, caused 93 deaths
and affected over 1,263 people. The community faces challenges in333

WA-22EURO 2024 - Copenhagenpost-disaster relief distribution, including damage assessment, accessibility, resource allocation, and efficient node placement. This research
proposes an optimization framework for transportation networks to improve relief distribution efficiency and effectiveness in disaster-affectedareas, leveraging a mathematical model. The framework uses multidisciplinary methods, including qualitative data analysis from humanitarian logistics experts and optimization algorithms for quick decisionmaking. It optimizes aid distribution to maximize coverage, minimize
response times, and allocate resources.

3 - Hybrid simulation-optimization approach for planning
relief-aid distribution

Mohanad Rezeq, Tarik Aouam, Frederik Gailly

During times of armed conflict, a number of security check-points are
set up by authorities to regulate the movement of commercial and relief trucks into and out of the war region. These security check-points
have become highly utilized, because of complex security procedures
and increased truck traffic, which heavily slows down the delivery ofrelief aid. Our research aims to improve the process at the security
check-points by redesigning the current process to reduce processing
times and relieve congestion at check-point entrance gates. These efforts are then integrated with the proposed decision-support tool (theClearing Function Distribution Model, CFDM) to minimize the effects
of security check-point congestion on the entire humanitarian supply
network using a hybrid simulation-optimization approach. Employing
business process simulation, the current and re-engineered processes
are both simulated, and the simulation output is used to estimate theclearing function (capacity as a function of the workload). Our efforts
will contribute to improving the planning of the humanitarian network
experiencing congestion at security check-points by minimizing theimpact of congestion on the delivery lead time of relief aid to the finaldestination. For this, the Kerem Abu Salem security check-point in
the south of GAZA is used as a case study. The results show that the
CFDM tool works better when the Re-engineered-Clearing function’s
output is applied.

4 - Fleet Composition and Scheduling for Provision of EnRoute Relief Aid Using an Adaptive Large Neighborhood Search with Decisions on the Intensive Margin

Lars Päsler, Merel Groen, Lisanne van Rijn

Refugees need periodic relief aid along their routes to safe places. To
this end, continuation of service is crucial to alleviate their mental andphysical burden. It has been shown that employing mobile facilities
over fixed facilities to provide relief aid can reduce costs. The challenge in coordinating the number and schedules of mobile facilities
has been formulated in the multi-period mobile facility location problem with mobile demand (MM-FLP-MD). Our research aims to determine the impact of using a fleet of different facility types and introducing capacity and duration constraints for facilities. We compare solutions from a Mixed Integer Linear Programming model and an Adaptive Large Neighborhood Search (ALNS). A prior proposed ALNS isenriched with additional operators and we exploit the problem’s structure to permit a two-step repair procedure. Moreover, we discuss theapplication of an ALNS for decisions on the intensive margin, i.e. tomake decisions from a non-binary decision space. This methodology
is applied in a case study of the Western Balkan route during the 2015
European refugee crisis. We find that some of our operators provide
similar improvements at a lower computational cost compared to thebest operators in previous literature and some decision variables lie
indeed on the intensive margin. An insight for humanitarian organizations is to use more smaller facilities in optimistic cost scenarios andfavor larger facilities in more pessimistic scenarios.

334

 WA-22
Wednesday, 8:30-10:00 - Room: 81 (building: 116)

Renewable Energy Challenges
Stream: Energy Management
Invited session
Chair: Giovanni Masala
1 - Sustainable Energy Transitions: Addressing Variable
Renewable Energy Challenges with the AD-MERGE Integrated Assessment Model

Kamyar Amirmoeini, Olivier Bahn, Kelly de Bruin, Hamed
Kouchaki-Penchah, Pierre-Olivier Pineau
In response to rising climate change challenges, the evolution of Integrated Assessment Models (IAMs) has been essential for a sustainable future. IAMs integrate environmental, technological, and socioeconomic insights, to provide insight to sustainable planning. The recent significant cost reductions in Variable Renewable Energy (VRE)
technologies have enhanced their adoption. However, VREs introduce
complexities in modeling concerning system flexibility and grid stability. This study aims to assess the interplay between technologicaladvances, grid integration, and market dynamics of VREs—elements
fundamental to the decarbonization of the power sector. To achieve thisaim, we incorporate a wider spectrum of power generation technologies into the AD-MERGE IAM, including updated options for hydro,
fossil fuels, and nuclear, with a special focus on refining the integration and dynamics of VREs. Additionally, more energy storage solutions are introduced to enhance the model’s technological representation. The improved storage options can enable the mitigation of thevariability of VREs, ensuring a more reliable and stable energy supply.
By evaluating the impacts of these advancements within the Shared
Socioeconomic Pathway 2 scenario, our research addresses VRE integration challenges, seeks to overcome intermittency, and supports
decarbonization targets.

2 - Data analytics for the impact of renewable energy impact on decoupling economic growth from carbon emissions

Ting Wu, Tao Wen, Yuwang Chen
Renewable energy contributes to reducing carbon emissions and attaining the general goal of energy sustainability, security and affordability
in a balanced way. However, there exist complex causal relationships
between renewable energy development, carbon emissions and economic growth, which makes it extremely difficult to reach consensus
on key actions to address climate change across the globe. For example, in recent Climate Change Conferences, one of the key decision problems was how to mobilize climate finance and investment torenewable energy from developed countries in support of climate actions in developing countries. This work aims to develop data analytics
to support the impact evaluation and strategy development of renewable energy, which helps to strike an informed balance between carbon reduction and economic growth in both developed and developing
countries. The causal relationships between traditional and renewableenergy consumptions, carbon emissions and economic growth in theworld’s major economies and carbon emitters are analysed both analytically and visually. The analytical results can be used to support
data-driven decision making on net-zero strategies.

3 - Advancing Sustainable Mining: Integrating Renewable
Energy through Optimization and Simulation

Boda Li, Chen Song, Ahmed Hajji, Diana Marcela Valencia
LuceroThe mining industry, recognized for its high energy consumption andcarbon emissions, faces pressing demands from customers for moresustainable practices. This work outlines a comprehensive approach tointegrating renewable energies, into the mining sector to address thesechallenges. While the potential benefits of such integration include reduced energy consumption and emissions, it also presents significant

EURO 2024 - Copenhagen
operational complexities. Mining equipment operates under intricate
dynamics, and the fluctuating nature of renewable energy can complicate these operations further. Moreover, the mining industry’s needfor a continuous power supply necessitates that renewable energy integration is reliable and capable of meeting energy demands. To tackle
these challenges, we propose the construction of optimization problem tailored to the complex operational dynamics of mining equipment
and corresponding process, aimed at minimizing energy consumption
while maximizing the use of renewable energy. Given the uncertainties inherent in renewable energy production and mining operations,
we employ a rolling horizon optimization approach. Additionally, we
integrate discrete event simulation to describe the whole process ofore processing to overcome the issue of missing parameters, enabling
more accurate estimation of critical variables in the optimization problem. Preliminary results from this integrated approach demonstratesignificant promise for the mining industry.

 WA-23
Wednesday, 8:30-10:00 - Room: 82 (building: 116)

Game theory for the circular economy
Stream: Circular Economy, Remanufacturing and Recycling
Invited session
Chair: Petr Fiala
1 - Product recovery strategies considering competition:an analytical modelSong Liu, Maud Van den Broeke, Tanja Mlinar, Stefan
Creemers
The rapid increase in waste from used products, coupled with growing
environmental awareness, has encouraged original equipment manufacturers (OEMs) to collect and properly treat these used products
through recycling, remanufacturing, or hybrid recovery strategies.
Concurrently, independent remanufacturers (IRs) have begun entering
the market to remanufacture used products. However, the effect of suchindustry competition on OEM’s recovery strategies, consumer surplus
and overall product returns remains unclear. In this study, we addressthese research questions by employing a stylised model in which an
OEM faces competition from an IR, aiming to characterise optimal solutions for both parties. Our findings indicate that the entry of an IR
positively influences the overall recovery of used products. Additionally, after the IR enters in the market, the OEM will be more reluctant
to recover used products and less frequently opt for hybrid recovery
strategies. Finally, through numerical analysis, we explore the variations in OEM’s and IR’s profits, and consumer surplus, across different
scenarios.

2 - Delegate or Not? When Divisional Conflict Exists in Remanufacturing Operations

Qin Zhou, Fangsheng Ge
Alongside the practice of offering new and remanufactured products
through distinct marketing channels, manufacturers have implemented
diverse internal channel structures upon establishing a dedicated remanufacturing division. Some firms, like Caterpillar and Volvo Group,
opt for a decentralized structure by delegating remanufacturing-related
decisions to the dedicated division. In contrast, companies like General
Electric and John Deere choose to retain decision-making at the firmlevel, encompassing both manufacturing and remanufacturing, following an integrated structure. Motivated by the observed industrial practices, we analytically investigate the manufacturer’s internal channel
structure decision within a manufacturer-retailer supply chain. Usinga game theoretic model, our analysis demonstrates that while the decentralized structure encourages manufacturers to enter remanufacturing, the optimal approach is for manufacturers to choose the integrated
structure when the production costs of new products are moderate. In

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contrast, when the production costs of new products are polarized (i.e.,either low or high), it is advantageous for decision-making authority
to be delegated to the remanufacturing division. A triple-win outcome
for the manufacturer, retailer, and remanufacturing division is unattainable because the retailer can never benefit from the manufacturer’s decisions made under the decentralized structure.

3 - The optimal green product promotion strategy for omnichannel brand merchants

Wenting Han, Jianfeng Cai
Electronic coupons are a conventional tool used by brands to gain market share. However, with the rise of e-commerce live streaming, more
and more brands are adopting AI live streaming as a new promotional
method. In the context of sustainable development, AI live streaming will promote green products beyond geographical and language
barriers. Therefore, integrating AI live streaming into product promotion has become a new direction to reveal the trade-offs between allchannel brands in AI live streaming and electronic coupon strategies.
We develop a co-opetition model to compare the different promotion
methods. In this model, we considered the potential impact of network externalities and consumer social responsibility. We found that
although AI live-streaming promotion is widely used, its advantages
are only evident when network externalities are significant. It is noteworthy that the intelligence level of AI anchors is closely related tothe investment level of brand merchants, and differences in consumer
social responsibility sensitivity may limit brand merchants’ choices
in AI live-streaming promotion strategies. In such cases, brand merchants may be more inclined to adopt electronic coupons, to increase
sales revenue. Finally, we further examine the impact of logistics costs
and the mixed promotion strategy on the sales revenue of supply chain
members to show the robustness of the main findings.

4 - Closed-loop supply chain coordination with a depositrefund system

Petr Fiala, Petr Pokorny
Traditional supply chain (SC) focuses on the management of forward
flows going from upstream members to downstream units. The ClosedLoop Supply Chain (CLSC) also manages the backward flows fromdownstream members to upstream units. The integration of upstream
and downstream activities into one system has three main objectives: tomeet environmental objectives, to create new economic opportunities
and to provide competitive advantages. This paper analyses a CLSC
consisting of a manufacturer producing new products and selling themthrough a retailer. The manufacturer recycles the products at the end of
their use (used packaging) and generates unit production cost savings if
it uses recycled material instead of new material. The retailer sells the
products to the final customer, collects the used packaging and returns
the deposit to the customer under a guaranteed buyback contract. Several models are proposed for this situation and the operating conditions
are analysed.

 WA-24
Wednesday, 8:30-10:00 - Room: 83 (building: 116)

Sustainable Supply Chain Management
Stream: Sustainable Supply Chains
Invited session
Chair: Selçuk Karabatı
1 - Cooperative Adoption of Supply Chain Traceability

Sanjith Gopalakrishnan, Devpriyo Ray, Sriram
Sankaranarayanan
Traceability — the ability to track products from origin to consumption— is viewed as an essential first step in effective control and compliance relating to environmental and labour obligations along the supply
chain. Despite such benefits, the widespread adoption of traceability
technologies in supply chains is hindered by challenges including the335

WA-25EURO 2024 - Copenhagenhigh costs of adoption and the misalignment between entities incurring these costs and those reaping the benefits. We consider a network
game-theoretic model of traceability technology adoption with possibly overlapping supply chains of multiple consumer-facing firms. We
characterize the network-optimal adoption strategy with a tractable integer linear formulation. We then develop a cost sharing mechanism
implementable via transfer payments to upstream suppliers. The cost
sharing mechanism satisfies certain formal fairness properties, and,
when efficient, supports the network optimal adoption strategy, andwhen inefficient, supports an equilibrium welfare which can be lessthan the network optimal welfare, and the ratio (a measure analogous
to the price of anarchy for an associated non-cooperative game) is examined numerically using real-world supply chain structure data. A
central theme of our work is to advance cost sharing, implementable
via simple upstream transfer payments, as a complementary solution to
the seeding strategy to initiate the diffusion of traceability technology
in supply chains.

2 - Optimization of supply chain planning considering faircarbon emissions reduction and profit distributions

Chang Lyu, Songsong Liu
Facing the global challenge on climate change, how to maintain economic growth with effective carbon emissions reduction, has become
an important and urgent issue. However, the related studies only focus
on the unilateral optimization of total supply chain carbon emissions
reduction, which will lead to uneven distribution of carbon emissions
reduction among the supply chain members and seriously undermine
the stability of the supply chain. To fill this research gap, this study
establishes a trading path of carbon quota inside and outside the supply chain, and constructs a mixed integer linear programming (MILP)
model for carbon emissions reduction based on the carbon cap-andtrade policy considering dual fairness. The production, distribution
and capacity planning strategies, as well as carbon trading decisions,of all supply chain members are optimized. Using the Nash bargaining
method and the -constraint method, the carbon emissions reduction
and profit of each supply chain member can be fairly distributed. A
industrial case based numerical example is introduced to demonstrate
the applicability and effectiveness of the proposed model and solution
method. The results show that Nash bargaining method could lead to afair distribution of profit and carbon emissions reduction. In addition,
when some members have insufficient carbon emissions allowance, thecap-sharing mechanism plays a better role in fair coordination.

3 - Product Line Design under Sustainability Incentives

Selçuk Karabatı, Burak Gokgur, Gunes Biliciler, Asli Gurler
KandemirWe examine the product line design and pricing problems faced bya firm when the standard and green products exhibit markedly different environmental performances. We assume that consumer valuationsof the green product are influenced in part by their level of ’environmental awareness.’ The market comprises two segments with varying levels of awareness. We introduce a utility-based consumer choicemodel and validate it through experimentation. Initially, we study thefirm’s profit maximization problem, and then integrate it into the regulatory agency’s incentive design problem. The regulatory agency aims
to achieve a predetermined reduction in the realized use-phase environmental impact of products. Through computational analyses, wehighlight the significant impact of segment sizes and their levels of environmental awareness on both the product design decisions of the firm
and the incentive design mechanism of the regulatory agency.

4 - Learning the effects of different buffering strategies insupply chain risk management: A regression approach

Emre Kara, Ali Tamer Unal
In today’s globalized world, supply chains have global characteristics
as well. Instead of geographically separated supply chains, there exists
a global single supply chain in the world. Because of this circumstance, disruptions even at geographically distant locations can havesevere impacts on all businesses globally. This nature of businessincreases the importance of supply chain risk management (SCRM)
more than ever. Manufacturing plants require accurate tools to measure and manage risks effectively and profitably. In this study, an enhancement to Material Requirements Planning (MRP) type production

336

planning is proposed such that supply chain risk can be measured precisely and risk management policies can be developed accordingly. Initially, supply chain disruptions are defined and categorized and buffers
used against these uncertainties are introduced to the MRP optimization model. With this new model and a risk function defined in termsof backlogs, an SCRM framework in the form of buffer management
is proposed. Afterwards, the performance of the model with different settings is observed with simulation experiments and the statisticalanalysis is conducted on the data. Although the most significant buffering policy was found to be the supplier lead times, many findings werediscovered on the effects of buffers on the supply chain (SC) risk andthe profitability of the business.

 WA-25
Wednesday, 8:30-10:00 - Room: 011 (building: 208)

Topics in Integer Programming I
Stream: Combinatorial Optimization
Invited session
Chair: Issmail El Hallaoui
1 - Local Branching techniques to strengthen Logic-Based
Benders Decomposition

Ioannis Avgerinos, Yiannis Mourtos, Stavros Vatikiotis,
Georgios Zois
This work focuses on the integration of Local Branching into LogicBased Benders Decomposition (LBBD) schemes. Given a MILP formulation for scheduling on unrelated parallel machines, it is noticed
that certain k-OPT neighbourhoods could implicitly be explored byregular local search operators. After enumerating such neighbourhoods and obtaining their local optima, a local branching cut (applied
as a Benders cut) eliminates all their solutions at once, thus avoiding
an overload of the master problem with thousands of Benders cuts.
To guarantee convergence to optimality, the constructed neighbourhood should be exhaustively explored - this procedure is accelerated
by domination rules or selectively implemented on nodes which are
more likely to reduce the optimality gap. In this study, we implement
internal swaps of jobs, i.e. swaps between jobs on the same machine,over the solution of the master problem, to construct formulationspecific 4-OPT neighbourhoods. The experimentation on two challenging scheduling problems (i.e., the minimisation of total completion
times and the minimisation of total tardiness on unrelated machineswith sequence-dependent and resource-constrained setups) shows considerable reduction of optimality gaps or acceleration of convergence,in comparison with regular Benders cuts. As our approach is easilytransferrable to different optimisation problems, it provides a promising prospect to improve the performance of regular LBBD algorithms.

2 - Primal Benders Decomposition

Issmail El Hallaoui, El Mehdi Er Raqabi, Francois Soumis
Benders decomposition has been applied significantly to tackle largescale optimization problems with complicating variables, which, when
temporarily fixed, yield problems significantly easier to solve. Still, in
its standard form, Benders decomposition shows several shortcomings,
for instance upper bound zigzagging (for a minimization problem). We
propose the Primal Benders Decomposition where the upper bound decreases at each iteration. It fully profits from primal information generally available in practice. As consequence, the number of cuts needed
to converge is drastically reduced. Tests on large scale academic andindustrial problems will be presented.

3 - Polynomial-size Formulation for Edge Coloring Problems on Forests

Emiliano Lancini, Michele Barbato

EURO 2024 - Copenhagen
An edge coloring of a graph G is an assignment of colors to the edges
so that two incident edges receive distinct colors. In particular, one isoften interested in determining an actual edge coloring with the minimum number of colors so to explicitly assign tasks to machines overtime. In the general case, this problem is known to be NP-Hard, however, if G is bipartite, one can solve this problem efficiently, throughconstructive algorithms. One drawback of this “algorithmic” approachis that it often involves sophisticated implementation. Moreover, finding edge coloring that minimize a generic objective function on bipartite graph is known to be NP-Hard.
The edge coloring Polytope of a graph G is the convex hull of all thepossible edge coloring of G. In this work, we prove that a natural formulation of the edge coloring polytope on forests is polynomial in size
and is described by a totally unimodular matrix. The interest towards
this result is twofold. On one hand, it allows to use linear programming techniques to solve edge-coloring problems. On the other, it
gives a polynomiality proof for all edge coloring problems on forests.
Our results give an alternative proof to different known results such as
the max-edge coloring problem on forests and the minimum-sum edge
coloring on forests and multicycles.

4 - Formulations and Algorithms for the Edge Improvement
Problems

Esra Koca, Burak Pac
We explore the edge improvement problem, relaxing the fixed edge
traversal times assumption in traditional network flow problems. We
consider two versions: discrete edge improvement, limited to a discrete set, and continuous edge improvement, where values can vary
within a given range. Initially, we analyze both versions on a treeshaped network, discussing their computational complexity. For more
complex networks, we develop mixed-integer programming (MIP) formulations for both versions. This study is the first to propose and compare different formulations for the discrete edge improvement problem
and to present one for the continuous version. Since our models underperform for medium and large problem instances, we introduce a
Benders decomposition algorithm to solve the discrete problem. Additionally, we use it heuristically to find high-quality solutions for thecontinuous edge improvement problem efficiently. We also devise an
MIP formulation to determine lower bounds for the continuous problem, leveraging McCormick envelopes and optimal solution properties.
Our experiments show that the Benders decomposition algorithm outperforms other formulations for the discrete edge improvement problem, while the heuristic method proposed for the continuous problem
provides quite good results even for large instances.

 WA-26
Wednesday, 8:30-10:00 - Room: 012 (building: 208)

Optimization problems in scheduling

WA-26

solving all tasks. We will also analyze a range of ’what if’ scenarios,
where we relax common problem constraints such as technicians having to finish their current task before starting another. Computational
results are reported for simulated scenarios with a varying number oftechnicians, resolution locations, and tasks, along with different spatialconfigurations of the resolution locations for each task.

2 - Resource constrained project scheduling with durational uncertainties and maximum time lags

Evelin Szögi, Péter Györgyi, Tamas Kis
We study a resource constrained project scheduling problem. A set ofjobs arrive in real-time and each of them is a sequence of tasks that
require several resources for execution. There are minimum and maximum time lag constraints among the consecutive tasks, which may
have uncertain durations. The objective is to minimize the total waiting time so that we guarantee the feasibility of the resulting schedule.
The problem has a great practical interest in small-scale biomanufacturing systems, such as laboratory for drug design, where there canbe large uncertainties in the duration of the tasks. For instance, theproduction of CAR T cells for personalized gene therapies of patientswith a serious disease. The production process requires several steps
including cell expansion where millions of CAR T cells are grown in acell culture. The variance of cell expansion time may be several days,
while the other production steps are deterministic and take a couple ofdays. We have the following results: 1) We provide theoretical bounds
about the quality of solutions we may expect. 2) We propose a polynomial time reactive scheduling policy for inserting tasks into a schedule.
This method (i) can handle large instances, (ii) outperforms a simple
heuristic with the feasibility guarantee, and (iii) starts each job as earlyas possible relative to the previously scheduled jobs.
Ack.: This work has been supported by the H2020 project AIDPATH,
grant agreement number 101016909.

3 - The LPT heuristic for minimizing total load on a proportionate openshop

Gur Mosheiov, Enrique Gerstl
The LPT heuristic for minimizing total load on a proportionate openshop
Enrique Gerstl and Gur Mosheiov
School of Business Administration, The Hebrew University, Jerusalem
91905, Israel
Abstract
We study the problem of minimizing total load on a proportionate
openshop. The problem is proved to be NP-hard. A simple LPT(Longest Processing Time first)-based heuristic is proposed, and abound on the worst-case relative error is introduced. The proposedbound is significantly smaller than the classical bound on the relative error of LPT when minimizing makespan on parallel identical machines. The algorithm is tested numerically and is shown to produce
extremely close-to-optimal schedules.

4 - Optimizing paths and schedules in crowded events

Stream: Combinatorial Optimization
Invited session

Valentina Morandi, Gianfranco Guastaroba, M. Grazia
Speranza

Chair: M. Grazia Speranza

In large events, improving visitor experiences involves a personalizedplanner for individual preferences. Crafting unique itineraries, it often overlooks suggestions given to others visitors. This is crucial inlimited-capacity locations, as myopic decisions may lead to extended
queues. We study a situation where a large number of people visit
a popular venue (e.g., an art gallery, a mall, a theme park, or an exhibition) where points of interest are located (e.g., paintings, shops,
attractions, or pavilions). Visitors have a maximum time available forthe overall experience. The points of interest have a binding capacity
and, whenever the turnout of visitors exceeds such capacity, queues
occur. Given the maximum time available along with the time spentqueuing, a selection of the points of interest may become necessary.
Visitors usually act as autonomous decision-makers and do not take
into account their interaction with other visitors. This leads to remarkable inefficiencies that could be, to a certain extent, overcome througha coordination of the paths and schedules of the different visitors. The
resulting optimization problem is modeled on a time-space network

1 - Technician Routing and Scheduling for Tasks with Uncertain Resolution Locations

Siv Sørensen, David Pisinger
We introduce a new extension of the Technician Routing and Scheduling Problem (TRSP), where tasks are associated with multiple potential resolution locations, each with a distinct probability of success.
Our objective is to efficiently resolve all tasks while minimizing resource consumption, such as driving distance or time. We investigate
various routing and scheduling strategies and compare their performance against a known brute force approach employed in the telecommunication sector. Additionally, we model the problem as a Markov
Decision Process (MDP) to find an optimal decision making policy forthe technicians, that minimizes the expected resource consumption for337

WA-27EURO 2024 - Copenhagenas a Mixed-Integer Linear Program (MILP), where the goal is to minimize a weighted combination of the points of interest not selected
and the time spent queuing. Computational results show the benefits
that can be achieved by using the model proposed as a tool to support
decision-making.

the institutional LMS. Results show that the enhanced algorithm improves both accuracy and recall, allowing professors to identify thesegment of lower-performance students before the course concludes,
allowing for proactive measures to be taken.

3 - A network-based recommender system to enhancelearning in educational games

 WA-27
Wednesday, 8:30-10:00 - Room: 047 (building: 208)

Learning Analytics using Mathematical
Optimization and XAIStream: Mathematical Optimization for XAI
Invited session
Chair: Nidia Guadalupe Lopez Flores
Chair: María Óskarsdóttir
Chair: Anna Sigridur Islind
1 - Where am I in the course? Student-facing dashboard
for Conceptual Modeling Education

Elena Tiukhova, Charlotte Verbruggen, Tinne De Laet, Bart
Baesens, Monique Snoeck
Modern technologies make it possible to collect and analyze large
amounts of educational data. Learning Analytics (LA) uses this data
to improve learning. To deliver LA insights to the educational stakeholders, Learning Analytics Dashboards (LADs) aggregate learning
metrics into a single screen using a variety of visualization techniques. A learner must be provided with a frame of reference forproper data interpretation, with peers being the most popular choice
in modern LADs. However, it carries the risk of negatively impacting learners by encouraging competition rather than mastery of knowledge, thereby widening the gap between high-performing and struggling learners. This problem is exacerbated in conceptual modelingeducation, where mastery orientation is crucial to learn to solve complex task of modeling. This research presents a student-facing LAD,
grounded in achievement goal orientation theory, with the primary goalof supporting mastery orientation (task completion/understanding).
The LAD also supports performance goal orientation (social comparison) through an optional peer comparison feature. By monitoring dashboard usage, student activity, and academic performance, our study investigates (RQ1) the added value of the LAD to student activity andperformance in conceptual modeling education, (RQ2) the relationship between dashboard usage and learning orientation, and (RQ3) the
added value of the performance-oriented component to student activity
and performance.

2 - Sequence mining algorithm for students failure classification in multiple blended courses scenarios

Joaquín Roa, Cecilia Saint-Pierre, Thomas Peet
Over the past decade, the use of learning management systems (LMS)
for online and blended courses has become widespread in higher education institutions, resulting in a high increase in the available data.
The unprecedented volume of data has allowed researchers to develop
new and improved algorithms for monitoring the learning process, becoming a useful tool for teachers, given the absence of face-to-faceinteraction with the students. In our work, we developed a new iteration of a classification method that uses sequence mining from eventlog data to predict lower-performing students during the realizationof a course. Previous versions of this algorithm were based on noncontinuous scenarios, using data available for various courses during
a single semester. In the present study, we made optimizations to thealgorithm, considering a greater diversity in the available training data,fine-tuned using performance metrics that minimize false negatives,and delimited a new, more general methodology, for present and future
use cases. We tested our enhanced method in a second-year undergraduate blended course from the Engineering School at Universidad
de Chile, with online content, activities, and assessments supported in

338

Eva Ósk Gunnarsdóttir, Anna Ingólfsdóttir, Antonio Puron,
María Óskarsdóttir, Sebastian Maldonado
Educational games hold particular significance in elementary school
education, as they serve as a popular tool for engaging children inlearning. However, a common challenge faced by these platforms is therisk of children becoming disinterested. To mitigate this, it becomesdesirable to sustain their engagement with the platform to foster continuous learning and retention. This raises the crucial question: Whichgames should children play? While the approach may be effective inthe classroom, children who engage with these games outside of school
settings have unrestricted access to all available games, often without knowing which ones are best suited to their needs. Consequently,
if they struggle, they are prone to becoming bored or frustrated, ultimately leading them to disengage. To enhance engagement and optimize learning outcomes, it is essential for children to play games where
they perform adequately yet are still challenged enough to promotelearning. In this work, we develop state-of-the-art recommender systems based on graph-based neural networks for an educational gameplatform for children called Tak-Tak-Tak. The machine learning process is guided by the choices made by the users in terms of the nextgame to play, as well as the efficacy of a user’s learning experience
within a game and the duration of the play sessions. We conduct a
comparative analysis between a wide variety of recommender systemapproaches to evaluate their effectiveness in this context.

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Wednesday, 8:30-10:00 - Room: 065 (building: 208)

Advancements of OR-analytics in statistics,
machine learning and data science 8
Stream: Advancements of OR-analytics in statistics, machine learning and data science
Invited session
Chair: Sona Taheri
Chair: Adil Bagirov
Chair: Gerhard-Wilhelm Weber
Chair: İsmail Özcan
1 - Solving scheduling problems in real-time through deeplearning methods

Imanol Echeverria, Roberto Santana, Maialen Murua
Recently, deep reinforcement learning (DRL) has been applied to solve
scheduling problems in real-time. These methods involve generating a
policy through interaction with thousands of random instances, leading
to enhanced solutions by maximizing a reward. However, real-world
instances often exhibit repetitive patterns and represent only a small
subset of all possible scenarios. Additionally, obtaining a large numberof examples is uncommon in real-world applications. In this paper, we
propose a methodology to learn an optimized dispatching rule tailored
to a specific set of instances. To do this, we model the scheduling problem as a Markov process, employ graph neural networks to represent
instances, and utilize behavioral cloning alongside optimal solutions tosimpler instances to determine the best policy. Given the challenge oftraining with limited instances, we suggest initially training a generalscheduler using diverse instances. Subsequently, this model undergoes
retraining to adapt to specific distributions, involving fine-tuning of its
last layers for efficient adaptation. To validate our approach, we conduct experiments on both the job-shop scheduling problem (JSSP) andEURO 2024 - Copenhagenthe flexible JSSP in several public scheduling benchmarks. Results
demonstrate that our method, trained using behavioral cloning, outperforms DRL-trained models. Additionally, we show that the retraining
strategy enables superior performance compared to a general modeltrained with random instances.

2 - A network clustering model for multiple selection questions in opinion surveys

Stefano Benati, Justo Puerto

WA-29

 WA-29
Wednesday, 8:30-10:00 - Room: 157 (building: 208)

Advances in Optimization for Industrial
Applications
Stream: Combinatorial Optimization
Invited session
Chair: Zhou Xu

Opinion surveys can contain closed questions to which respondents cangive multiple answers. We propose to model these data as networks
in which vertices are the eligible items and arcs are the respondents.
This representation opens up the possibility of using complex networks
methodologies to retrieve information and most prominently, the possibility of using clustering/community detection techniques to reduce
data complexity. We will take advantage of the implicit null hypothesisof the modularity function, namely, that items are chosen without any
preferential pairing, to show how the hypothesis can be tested throughthe usual calculation of p-values. We illustrate the methodology with
an application to Eurobarometer data. There, a question about national
concerns can receive up to two selections. We will show that community clustering groups together concerns that can be interpreted in aconsistent way and in general terms, such as Economy, or Security or
Welfare issues. Moreover, we will show how different society groups
are worried by different class of items.

3 - Refining the performance metrics and related mathematical operators for ordinal classification problems

Sajid Siraj, Edward AbelIn multilabel classification (MLC) problems, the standard confusion
matrix is generally replaced by a set of confusion matrices, with one
matrix per label. Although ordinal classification problems are slightly
different from MLC, it shares the same set of performance metrics used
for MLC. We contend that ordinal classification has nuances whichmakes it different from MLC. As in binary classification, the notionof false positive and false negative are still valid in MLC problems ina sense that any wrongly predicted label can be termed as false positive (or false negative). However, in ordinal classification, these notions are replaced by overestimates and underestimates. We propose
the use of different mathematical operators to quantify the strength ofoverestimates and underestimates. The proposed metrics tailored forordinal classifiers not only introduces a novel framework for evaluation but also signifies a crucial step forward in enhancing the performance evaluation of ordinal classification algorithms. We demonstrate
its practical usefulness with the help of a number of case studies.

4 - Outranking hypercuboid learning approach for classification problems

Nabil Belacel
Classifiers in today’s data-driven domain face myriad challenges: overfitting, computational overheads, diminished accuracy, imbalanced
datasets, and the opaque "black box" issue. Conventional classifiers
struggle with managing noisy and missing features. This study introduces classification methods leveraging hypercuboid learning andoutranking measures. Our framework autonomously discerns hypercuboid features from the training set, eliminating the need for prior domain expertise. These hypercuboids capture essential dataset patterns,
while outranking measures mitigate noise and uncertainty. Empiricalevaluations across diverse datasets from the UCI repository compare
the proposed classifiers with established models like k-NN, SVM, Random Forest, Neural Networks, and Naive Bayes. Results demonstrate
our classifiers’ robustness against imbalanced data and extraneous features, achieving comparable or superior performance to benchmarks.
Moreover, our models offer interpretability without sacrificing predictive accuracy.

1 - Integrated Regenerator Planning and RWA in WDM Optical Networks under Link Failures

Kai Pan, Chong Wang, Bin Tian, Junyan Liu, Fan Zhang
The widespread deployment of wireless telecommunication networks
and growing Internet traffic globally leads to a substantial increase insignal traffic demands in fiber optic networks. This demand surge
places significant pressure on the survivability of optical networks,
specifically when faced with fiber cable failures. To address this challenge, in this paper, we consider integrated planning of regenerator
installation, optical connection routing, and wavelength assignment(RWA) for wavelength division multiplexing (WDM) optical networks
under link failures. We minimize the number of backup regenerators
required to fulfill all connection requests under all potential link failures while accounting for the wavelength and routing constraints. We
formulate the problem as an integer program, while its size exponentially increases with the network size by nature. To solve large-scaleinstances for practical uses in the industry, we propose efficient algorithms to generate near-optimal solutions that are practically implementable. We perform extensive numerical experiments to demonstrate the effectiveness of our approach in solving real-world instances
provided by our industry partners. The results show that our approachcan efficiently obtain high-quality solutions while ensuring a minimal
increase in backup regenerators as the network grows. Our solutions
also offer valuable insights into planning a cost-effective and reliablefiber optic network for network operators.

2 - Recent Advances in Solving Large-Scale Optimizations
from an Industry Perspective

Fan Zhang, Yingying Cheng, Jiazheng Wang, Junyan Liu,
Yongfeng Li, Jie Sun, Jianshu Li, Kun Mao
In this talk, we introduce large-scale mathematical optimization problems that arise within the complex landscape of the information andcommunication industry. Our focus spans several practical scenarios, including resource allocation in large scale data networks, trafficplanning in optical networks, as well as task allocation and scheduling in deployment operations. These optimization challenges predominantly fall into the categories of LP or MILP. The scale of these problems varies significantly, ranging from millions to billions of variables
and constraints. Some medium-sized problems necessitate real-timedecision-making, with solving times in milliseconds to seconds. The
larger problems demand hours of computational effort for offline planning. The stringent requirement poses considerable challenges, especially given the constraints of single-server computational resources.
We aim to strike a balance between customized and generic design approaches across multiple dimensions, including optimality, efficiency,
robustness, and generality. Leveraging Huawei’s OptVerse optimization solver, the design solution directly translates to increased revenue
or reduced costs. Additionally, some contributions further enhance
OptVerse’s generic problem-solving performance. Lastly, we explore
the concept of co-optimization of software and hardware—a new dimension that promises innovative solution design, novel development
modes, and potential business models for future solvers.

3 - Solving LP Relaxations for Routing and Wavelength Assignment Problem with Partial Path Protection

Zhou Xu, Xianyan Yang, Junyan Liu, Fan Zhang, Fabo Sun,
Feng LiThis paper studies a routing and wavelength assignment problem foran optical-fiber telecommunication network, known as the RWAP with339

WA-30EURO 2024 - Copenhagenpartial path protection (RWAP-PPP). The existing literature lacks any
studies on its relaxations. As a result, the quality of solutions to the
RWAP-PPP cannot be properly measured, which is critical to telecommunication service providers in their bidding to customers, and intheir determination of service improvements. We derive and compare
various linear programming (LP) relaxations of the RWAP-PPP, fromwhich we propose a novel LP relaxation that can be efficiently solved
by a Benders decomposition algorithm to produce good lower bounds
for the RWAP-PPP. Computational results show that the newly proposed LP relaxation can be solved efficiently, even for some large-scale
practical networks of hundreds of nodes and thousands of requests.

4 - Bifurcation behaviors shape how continuous physicaldynamics solves discrete Ising optimization

Jie SunSimulating physical dynamics to solve hard combinatorial optimization has proven effective for medium- to large-scale problems. The
dynamics of such systems is continuous, with no guarantee of finding
optimal solutions of the original discrete problem. We investigate theopen question of when simulated physical solvers solve discrete optimizations correctly. Having established the existence of an exact mapping between CIM dynamics and discrete Ising optimization, we report
two fundamentally distinct bifurcation behaviors of the Ising dynamics at the first bifurcation point: either all nodal states simultaneously
deviate from zero (synchronized bifurcation) or undergo a cascade ofsuch deviations (retarded bifurcation). For synchronized bifurcation,
we prove that when the nodal states are uniformly bounded away fromthe origin, they contain sufficient information for exactly solving theIsing problem. When the exact mapping conditions are violated, subsequent bifurcations become necessary and often cause slow convergence. Inspired by those findings, we devise a trapping-and-correction(TAC) technique to accelerate dynamics-based Ising solvers, including
CIMs and simulated bifurcation. TAC takes advantage of early bifurcated "trapped nodes" which maintain their sign throughout the Ising
dynamics to reduce computation time effectively. Using problem instances from open benchmark and random Ising models, we validate
the superior convergence and accuracy of TAC.

 WA-30
Wednesday, 8:30-10:00 - Room: 064 (building: 208)

of[U+202F]Operations Research: code generation to increase productivity, chatbots for documentation, generation of optimization models- just[U+202F]to name a few. Thinking further, an LLM may generate models for decision support tools and execute the code to guidecomplex business decisions: a CO2-optimal delivery network, an optimal production schedule, an optimized pricing table. Large language
models (LLMs) occasionally produce incorrect or misleading results,
a phenomenon commonly called "hallucinations". We demonstrate basic examples where a slight adaptation of a question (prompt) turns the
LLM response into a false statement. Besides basic inaccuracies, there
are also considerations around data exposure, data leakage, privacy
violations, copyright violations, biased answers, dangerous or unethical usage, inappropriate language and malicious code. Best practices
need to be followed to responsibly apply GenAI in decision support
tools! Our examples demonstrate the importance of balancing innovation with risk mitigation in leveraging GenAI for critical decisionmaking tasks advocating for a holistic approach that combines technological advancements with ethical considerations and human oversight.

3 - Automatic model decomposition in Hexaly Optimizer

Julien Darlay
Hexaly Optimizer, formerly known as LocalSolver, is a model and run
solver that integrates heuristics and exact methods. A set-based modeling formalism was introduced to simplify the modeling of certain combinatorial problems like routing or packing problems. For instance, ina routing problem, list variables can be used to model the sequence ofvisits made by each truck. These decision variables are well suited fora heuristic search but are much more difficult to integrate in a mathematical programming approach to compute lower bounds. A direct
reformulation in a MILP model introduces a quadratic number of binary decisions with several big M constraints leading to poor scalability and bounds. Hexaly 13.0 automatically detects such structuresin a user model and reformulates them in an extended MILP model tocompute lower bounds. This model is solved efficiently using state of
the art branch-cut-and-price technics from the literature. This talk will
present the general approach and the algorithms used for the resolution
and some benchmarks on the CVRP library.

4 - VIVA: A web-based tool for analyzing and visualizing
optimization solver benchmarking results

Andreas Lundell, Oscar Härtull, Alireza Olama

Optimization Tools
Stream: Software for Optimization
Invited session
Chair: Andreas Lundell
1 - Gurobi OptiMods - Painless Optimization Templates

Matthias Miltenberger
One of the most important aspects of mathematical optimization andOperations Research is getting your data into a form that optimization
solvers can understand and work with. The "art of modeling", as it isoften referred to, can all too easily get in the way of actually solving
the problem at hand. Gurobi’s open-source OptiMods are data-driven
Python APIs for different common optimization use cases. They enable practitioners and learners alike to compute solutions without requiring extensive modeling experience. This session presents the goals
and design of the project and explains how to use and extend it.

2 - Generative AI in Decision Support Tools: Managing Hallucinations

When developing software for solving various classes of optimization
problems, e.g., MILP, and MINLP, it is crucial to have tools for comparing the impact of new functionality. The performance can be measured using many different metrics including solution time and qualityof the solution, so it is not always trivial to assess the added value. It isalso important for end users to be able to compare the performance ofavailable optimization solvers when selecting which one to use. So far,available tools for performing such analysis have been mainly static,i.e., there have been limited options for the users to interact, filter, andsort the results.
In this presentation, we introduce a tool called VIVA (Visualize, Interact, Verify, and Analyze Benchmarking Data for Optimization Solvers)
for comparing the performance of optimization solvers in an efficientand user-friendly way. VIVA is an open-source project implemented
in TypeScript and generally based on standardized web technologies.
This means that VIVA can be used directly in a web browser without
installing and configuring additional dependencies. Currently, VIVA
implements most of the functionality of the Python-based PAVER
tool, including its visualization capabilities, but the open frameworkof VIVA means it can be extended further in the future.

Oliver Bastert, Jens Schulz
The field of generative artificial intelligence (GenAI) has seen exponential growth over the past few years in research papers and publications. The techniques have gained traction across industries. A hypetrain is rolling around how to best[U+202F]leverage GenAI in the field340

To illustrate the capabilities of VIVA, the tool is also demonstrated onrecent benchmark results from convex and nonconvex MINLP solvers
during the presentation.

EURO 2024 - Copenhagen WA-31
Wednesday, 8:30-10:00 - Room: 046 (building: 208)

WA-32

4 - Enhancing Environmental Impact Assessment Procedures in Chile: Multi-Label Transformer Models for Predicting State Agencies Participation

Analytics for Decision Making

Alonso Leal, Carla Vairetti, Sebastian Maldonado

Stream: Analytics
Invited session

In Chile, the Environmental Impact Service (SEA) mandates assessments for industrial projects falling under specific categories, necessitating either an Environmental Impact Study (EIA) or an Environmental Impact Declaration (DIA). Central to the evaluation process is theinvitation of the correct state administration agencies for participation.
This investigation presents a novel approach leveraging pre-trainedtransformer models, tailored to predict the involvement of various statebodies in project assessments. Through the analysis of text within
the project’s primary form of declaration, we curated a dataset capturing participation patterns. Secondly, we fine-tuned the transformers
to yield multi-label predictions, this was done by the modification of
the final transformer layer, which facilitated the output of probabilistic estimates for each agency’s participation. To address class imbalance in the dataset, we integrated class weights techniques into the loss
function during model training. The experimental results showcase theeffectiveness of this approach in accurately predicting multi-label participation, thereby enhancing the decision-making process surrounding
environmental impact assessments. This research contributes to refining the SEAs procedures, ensuring comprehensive participation andevaluations of industrial projects by state agencies.

Chair: Sebastian Maldonado
1 - Profit-driven churn prevention through predict-andoptimize

Nuria Gómez-Vargas, Sebastian Maldonado, Carla Vairetti
In this work, we introduce a novel predict-and-optimize method forprofit-driven churn prevention. We frame the task of targeting customers for a retention campaign as a regret minimization problem.
The main objective is to leverage individual customer lifetime values(CLVs) to ensure that only the most valuable customers are targeted.
In contrast, many profit-driven strategies focus on churn probabilities
while considering average CLVs. This often results in significant information loss due to data aggregation. Our proposed model aligns withthe guidelines of Predict-and-Optimize (PnO) frameworks and can be
efficiently solved using stochastic gradient descent methods.

2 - Harnessing spatial granularity in boosted tree models
for house price prediction

Margot Geerts, Seppe vanden Broucke, Jochen De Weerdt

 WA-32

Spatial relations in data such as house prices are known to vary strongly
depending on their location. Several modeling techniques have been
proposed from the domain of spatial statistics such as geographicallyweighted regression. This technique addresses this problem by allowing to vary the parameters over space. Nevertheless, more recent machine learning methods such as gradient boosted trees perform betterin terms of prediction and improve scalability towards large datasets
significantly. Nevertheless, global tree-based models, trained over thecomplete spatial region of the dataset, present large local errors whichindicate their flawed ability to model spatially varying relations in thedata. A solution consisting in learning local models on spatial partitions of the dataset is not preferred due to the loss of information
shared by the partitions. In this study, we confirm the presence of thisphenomenon in a real-life house price dataset and explore pathways towards a unification of global and local models to address this problem.

Wednesday, 8:30-10:00 - Room: 41 (building: 303A)

3 - Container dwell time prediction considering feature engineering and feature selection

Mahdi Jahangard, Ying Xie
Container dwell time prediction plays a crucial role in optimizing logistics and supply chain operations at ports and container terminals.
Accurate predictions of such an indicator can enhance resource allocation, reduce congestion, and improve overall efficiency at the pointof the global trade network. Data preprocessing and feature selection
are critical steps in developing robust and efficient predictive models. This study develops feature engineering not only for structured
data but also for unstructured data where a textual feature is classified
through GloVe word embeddings and Cosine similarity to leverage thestrengths of both types of data into machine learning methods. In addition, various feature selection techniques such as Filter methods, Embedded methods, and Wrapper methods are applied to identify the mostinfluential factors affecting container dwell time, ultimately selecting
12 out of the 19 features for prediction. Instead of developing a regression model to predict dwell time, we classify dwell time based on data
distribution into two and three classes, highlighting the significanceof dwell time ranges over singular values. Random Forest and XGBoost algorithms are employed for the classification, where the former
outperforms in terms of accuracy. From an operational point of view,the results of classification can facilitate effective decision-making for
operational management such as equipment scheduling, yard stacking
planning, workload planning, etc.

Adaptive and Polyak step-size methods
Stream: Advances in large scale nonlinear optimization
Invited session
Chair: Dmitry Kamzolov
Chair: Martin Takac
1 - On the Stochastic Polyak Step Size for Machine Learning: Proximal and Momentum Versions

Fabian SchaippIn this talk, we show how to combine the stochastic Polyak step sizewith established practical techniques such as regularization and momentum. In particular, we derive a proximal version of the Polyak stepsize, and a momentum version which we call MoMo. MoMo can beseen as an adaptive learning rate for SGD with momentum; in fact we
can derive a MoMo version of any momentum method, most importantly MoMo-Adam. These derivations are possible through the connection between the Polyak step size and model-based stochastic optimization, where the model is truncated at a known lower bound. In machine learning, such lower bounds are typically known. By construction, our new adaptive learning rates reduce the amount of learning-ratetuning, which is demonstrated through deep learning experiments on
the CIFAR, Imagenet, Criteo, and IWSLT14 dataset.

2 - Unveiling the Power of Adaptive Methods Over SGD: A
Parameter-Agnostic Perspective

Xiang Li
Adaptive gradient methods are popular in optimizing modern machinelearning models, yet their theoretical benefits over vanilla Stochastic Gradient Descent (SGD) remain unclear. This presentation examines the convergence of SGD and adaptive gradient methods when
optimizing stochastic nonconvex functions without the need for setting algorithm hyper-parameters based on problem-specific knowledge. First, we explore smooth functions and compare SGD to wellknown adaptive methods like AdaGrad, Normalized SGD with Momentum (NSGD-M), and AMSGrad. Our findings reveal that while
untuned SGD can reach the optimal convergence rate, it comes at theexpense of an unavoidable catastrophic exponential dependence on the341

WA-34EURO 2024 - Copenhagensmoothness constant. Adaptive methods, on the other hand, eliminate
this reliance without needing to know the smoothness constant in advance. We then look at a broader group of functions characterizedby (L0, L1) smoothness. Here, SGD is shown to fail without proper
tuning. We present the first instance of tuning-free convergence withadaptive methods in this context, specifically with NSGD-M, achieving near-optimal rate despite an exponential dependence on the L1 constant. We also demonstrate that this dependency is unavoidable for afamily of normalized momentum methods.

3 - EXTRA-NEWTON: A First Approach to Noise-Adaptive
Accelerated Second-Order Methods

Kimon Antonakopoulos
This work proposes a universal and adaptive second-order method forminimizing second-order smooth, convex functions. Our algorithm
achieves optimal convergence rate when the oracle feedback is stochastic with variance, and improves its speed of convergence when it is run
with deterministic oracles, where $T$ is the number of iterations. Our
method also interpolates these rates without knowing the nature of theoracle apriori, which is enabled by a parameter-free adaptive step-sizethat is oblivious to the knowledge of smoothness modulus, variancebounds and the diameter of the constrained set. Building on this machinery, we show that we may be able to extract asymptotically fasterrates than the standard Nesterov’s accelerated second order methods.

4 - AdaBatchGrad: Combining Adaptive Batch Size andAdaptive Step Size

Petr Ostroukhov
This work presents a novel adaptation of the Stochastic Gradient Descent (SGD), termed AdaBatchGrad. This modification seamlessly integrates an adaptive step size with an adjustable batch size. An increasein batch size and a decrease in step size are well-known techniques to"tighten" the area of convergence of SGD and decrease its variance. A
range of studies by R. Byrd and J. Nocedal introduced various testing
techniques to assess the quality of mini-batch gradient approximations
and choose the appropriate batch sizes at every step. Methods that utilized exact tests were observed to converge sublinearly. Conversely, inexact test implementations sometimes resulted in non-convergence anderratic performance. To address these challenges, AdaBatchGrad incorporates both adaptive batch and step sizes, enhancing the method’s
robustness and stability. This makes AdaBatchGrad markedly morerobust and computationally efficient relative to prevailing methods. To
substantiate the efficacy of our method, we experimentally show, how
the introduction of adaptive step size and adaptive batch size gradually
improves the performance of regular SGD. The results imply that AdaBatchGrad surpasses alternative methods, especially when applied toinexact tests.

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Wednesday, 8:30-10:00 - Room: 43 (building: 303A)

Decision problems represented as influence
diagrams
Stream: Stochastic, Robust and Distributionally Robust
Optimization
Invited session
Chair: Milos Kopa
Chair: Ahti Salo
1 - Risk averse mixed-integer linear programming models
for influence diagrams

Topias Terho, Olli Herrala, Fabricio Oliveira
Influence diagrams are an intuitive representation of decision problems
with dependencies between uncertain events, decisions, and consequences. Recently, two frameworks that transform influence diagrams

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into mixed-integer-linear programs (MILP) have been developed. Decision Programming (Salo et al., EJOR 299/2, 2022) enforces constraints on different possible realizations of the whole problem. This
exposes the full probability distribution of consequences to be used in
the optimization model, which in turn allows risk to be explicitly constrained or optimized. Rooted junction tree approach (Parmentier et al.,Informs Journal on Optimization, 2/3, 2020) decomposes an influence
diagram into clusters, that are subsets of nodes, and finds the probability distributions of these clusters with a MILP. Compared to decision
programming, the rooted junction tree model offers better computational performance, but risk cannot be straightforwardly represented
in the optimization model. We present approaches and conditions thatcombine the computational efficiency of rooted junction trees and themodeling flexibility of decision programming. More specifically, wehighlight how risk can be directly represented in rooted junction treemodels with examples.

2 - Mixed-integer reformulations for influence diagramswith conditional information structures

Olli Herrala, Fabricio Oliveira, Tommi Ekholm
Endogenous, or decision-dependent, uncertainties pose significant
challenges for decision making under uncertainty. Various methods
have been developed for different types of endogenous uncertainty, including influence diagrams (ID) for intuitively representing decisiondependent probability distributions. If the problem instead has adecision-dependent information structure, that is, some of the information is revealed conditionally, conditional non-anticipativity constraints can be added to a stochastic programming model representing
the problem. However, combining these two types of uncertainty has
proven challenging, and no framework existed for such problems until recently. This presentation aims to shed light on two approaches
for reformulating an influence diagram as a mixed-integer linear problem (MILP), enabling us to simultaneously consider different types ofendogenous uncertainty and risk measures. A framework called Decision Programming directly reformulates the ID into a MILP, but it has
been shown that first reformulating the ID into a rooted junction tree
can greatly enhance computational performance and result in asymptotically smaller MILPs. Our discussion is focused on a Julia package
implementing the framework along with possible improvements, andan illustrative climate change mitigation case study. Additionally, we
mention some other application areas where the methods have been
applied to give the audience an overview of the possibilities.

3 - Algorithmic Advances for Influence Diagrams

Radu Marinescu, Junkyu Lee, Rina Dechter, Bobak Pezeshki,Alex Ihler
Influence diagrams provide a modeling and inference framework forsequential decision problems, representing the probabilistic knowledge
by a Bayesian network and the preferences of an agent by utility functions over the random variables and decision variables. Computing
the maximum expected utility (MEU) and the optimizing policy is exponential in the constrained induced width of the model and it has
been long recognized as one of the most difficult probabilistic inference tasks, especially for larger problem instances. We will overview asuite of exact and approximate inference algorithms for IDs developed
over the past years, starting with variable elimination (VE) schemesthat use local computations to solve IDs exactly, and continuing withvariational approaches based on message-passing over join-graph decompositions and weighted mini-buckets that yield variational bounds
on the MEU and sub-optimal decision policies. We will also showthat the latter bounding schemes can be used as heuristics generatorsthat can guide efficient search strategies such as AND/OR search forsolving IDs exactly that exploit the underlying structure of the model.
We will provide experimental results on various synthetic and realworld benchmarks for IDs to highlight the performance of the inference methods presented. Finally, we will discuss recent extensions of
IDs for decision making with imprecise information, multiple objectives and the potential of augmenting IDs with causal semantics.

4 - Optimal Testing Strategies for Information Acquisitionin Decision Programming

Ahti Salo, Topias Terho, Fabricio Oliveira

EURO 2024 - Copenhagen
In the context of influence diagrams, we formalise the notion of information decisions which are associated with decision nodes that determine what information will be acquired to support decisions at agiven decision node later; or, more specifically, what is the set of nodes
whose states are known when making this later decision. Examples ofsuch information decisions include choices among diagnostic tests because testing decisions determine what test results will be available
when making treatment decisions, for instance. For the developmentof optimal strategies, we build on Decision Programming (Salo et al.,
EJOR 299/2, 2022) in which the influence diagram is solved by converting it into an equivalent mixed-integer linear programming (MILP)
problem; this makes it possible to solve influence diagrams which do
not fulfil the usual ’no-forgetting’ assumption, or which involve logical, resource, or risk constraints that preclude the use of common solution approaches such as arc reversals and node removals. We present
optimization formulations that employ binary variables to represent information decisions. We also provide numerical examples to illustrate
the development of optimal testing strategies for information acquisition.

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Wednesday, 8:30-10:00 - Room: 44 (building: 303A)

Data Valuation from Data-driven
Optimization
Stream: Stochastic, Robust and Distributionally Robust
Optimization
Invited session
Chair: Juan Miguel Morales
Chair: Robert Mieth
1 - Wasserstein Distance Based Market for DifferentiallyPrivate Data Trading

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here a novel market-based framework, called regression markets, toprovide financial incentives for data sharing. Agents aiming to improve their forecasts can post a regression task, for which others cancontribute by sharing their data and get monetarily rewarded for it. We
introduce the market design, provide its desirable properties, and discuss some of the open challenges with practical applications of treating
data as a tradeable good.

3 - Defining and Quantifying the Marginal Value of a Data
Set via Distributionally Robust Optimization

Juan Miguel Morales, Robert Mieth, Adrián Esteban-Pérez
Data-driven stochastic optimization has empowered decision-making
by providing an effective way to exploit data to tackle complex problems in many domains. However, there is still a lack of theory andmethods to quantify the sensitivity of the decision cost to the natural
input to stochastic optimization, namely, the data set. In this talk weshow that Distributionally Robust Optimization (DRO) offers a natural framework to perform this sensitivity analysis by establishing thenotion of marginal value of the quality of a data set. We discuss theability of the Wasserstein metric to encode data quality and then introduce a Wasserstein DRO formulation to compute the marginal value ofdata sets from multiple sources or providers that may refer to the same
uncertain input parameter and/or to different ones.

4 - Data value at the grid-edge

Robert Mieth
This talk presents an approach to quantify the value of data sets available to the operator of an electric distribution system that seeks to compute optimal control setpoints for distributed energy resources. We
leverage the ability of the Wasserstein metric to encode the qualityof available data sets and present a modification of a data-driven AC
optimal power flow problem with stochastic load and renewable energy injections that can internalizes this data quality information. The
proposed approach offers an effective tool for the system operator tohandle data sets of varying quality, e.g., due to the necessary privacy
protection or aggregation of data collected at the grid edge. We showthat this not only helps the system operator to improve operations withexisting data, but also informs planning decisions for investments inadvanced data collection and communication technology.

Fei Teng, Saurab Chhachhi
Data is increasingly vital to deal with the increased intermittency andlimited controlability of renewable and distributed energy resources.
Concurrently, data sharing raises privacy concerns motivating the needfor privacy-enhancing techniques such as differential privacy. Datamarkets provide a means to enable wider access as well as determine
the appropriate privacy-utility trade-off. Existing data market frameworks either require a trusted entity to perform computationally expensive valuations or are unable to capture the combinatorial nature ofdata value and do not endogenously model the effect of differential privacy. We address these shortcomings by proposing a valuation mechanism based on the Wasserstein Distance for differentially-private data,
and two procurement mechanisms leveraging incentive mechanism design theory for task-agnostic data procurement, and task-specific jointtask and data optimisation. The latter are reformulated into tractable
mixed-integer second-order cone programs. The framework is applied
to develop a joint energy and data market. We consider the retailer energy procurement problem where consumers’ demand is uncertain andhistorical demand data is differentially-private. This is modelled as anintegrated forecasting and optimisation problem providing a means ofvaluing data directly rather than forecasts or accuracy. The value of
joint energy/data clearing are highlighted through extensive numericalstudies using real smart meter data.

2 - Regression Markets:
Forecasting

Incentivizing Data Sharing forThomas Falconer, Jalal Kazempour, Pierre Pinson
Machine learning tasks rely heavily on the quality of input data, yet
acquiring adequate datasets can often be challenging. Useful datasets
are typically distributed amongst various owners who may, in practice, be competitors in downstream markets, making them reluctant toshare information. In contrast to existing frameworks that address distributed and privacy-preserving (incentive-free) learning, we explore WA-36
Wednesday, 8:30-10:00 - Room: 32 (building: 306)

Game Theory, Solutions and Structures IX
Stream: Game Theory, Solutions and Structures
Invited session
Chair: Pamela Alejandra Bustamante Faúndez
1 - Platform Finance and Channel Strategies of Dual Channels

Nina YanIn the platform economy, e-commerce platforms serve as distribution
channels and financing providers through online loans. This study focuses on platform finance, where credit lines or interest rates are set,
offering limited or unlimited loans to capital-constrained sellers. The
optimal sellers’ channel strategy (single, online-dominant, or offlinedominant dual channels) depends on the platform’s credit line, consumers’ channel preferences, and switching behavior. With unlimited(or limited) loans, the seller’s dominated channel’s quantity decreases(or increases) with initial capital. Preferences for unlimited or limited
loans depend on the dominant channel and capital constraints. Loan
offerings lead to Pareto improvements for both sellers and platforms.
Compared to bank finance, platform finance increases sellers’ threshold quantities without stockouts in each channel, encouraging higher
quantities in both channels. The study explores the channel substitution effect on sellers’ quantity decisions, offering insights for optimal
financing and channel structures to boost profits.

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2 - Stackelberg game approaches for the adoption ofblockchain technology in competitive supply chainsconsidering traceability awareness of consumers

Zijing Yang, Songsong Liu
This work aims to investigate the blockchain introduction strategies
and conditions in a competitive supply chain considering traceability awareness of consumers and the cost of blockchain technology
base on game theory. In addition, this work explores supply chain
fair coordination issues on the key supply chain decisions for adopting
blockchain technology through a cost sharing contract. Firstly, througha proposed Stackelberg game model, a supply chain with two competitive suppliers, one manufacturer and one retailer is investigated under
two scenarios, including decision-making without blockchain technology, with blockchain technology. Next, to explore the fair coordination of blockchain technology, the Nash bargaining method is adopted.
Theoretical analysis and computational simulation are conducted toshow how prices and profits are influenced by traceability awarenessof consumers and competitive intensity of suppliers, and the impact ofblockchain costs towards blockchain introduction. The results imply
that the cost of adopting traceability technology is an important factor
to be considered when supply chains adopt the technology. Firstly, thiswork enriches the literature by extending the relevant theory to considering the traceability awareness of consumers, as well as the costof adoption the blockchain technology. Secondly, by considering cost
sharing coordination, our research contributes to cooperation and fairness among supply chain members.

3 - Branch-and-price with novel cuts, and a new Stackelberg Security Game

Pamela Alejandra Bustamante Faúndez
Anticipating the strategies of potential attackers is crucial for protecting critical infrastructure. We can represent the challenge of the defenders of such infrastructure as a Stackelberg security game. The defender must decide how to allocate limited resources to protect specifictargets, aiming to maximize their expected utility (such as minimizing
the extent of damage) and considering that attackers will respond in a
way that is most advantageous to them.
We present novel and stronger valid inequalities to find a Strong Stackelberg Equilibrium in both Stackelberg games and Stackelberg security
games. We also present a new Stackelberg security game that aims toprotect targets with a defined budget. We use branch-and-price in thisnew game to show that our approach outperforms the standard formulation in the literature, and we conduct an extensive computational
study to analyze the impact of various branch-and-price parameters on
the performance of our method in different game settings.

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Wednesday, 8:30-10:00 - Room: 33 (building: 306)

Multiobjective Combinatorial Optimization
Stream: Multiobjective Optimization
Invited session
Chair: Lavinia Amorosi
1 - An Empirical Investigation of Two Reformulation Approaches in Multiobjective Discrete Optimization

Serpil Sayin, Gökhan Kof, Tamey Eksi
We propose two alternative ways of reducing the number of objective functions of a given multiobjective discrete optimization (MODO)
problem and investigate the impact of the reduction on the nondominated set of the original problem empirically. The number of objectivefunctions is a major factor that determines the computational effort ofobtaining the nondominated set of a MODO problem. Transforming
the problem in a way to reduce the number of objective functions is344

expected to result in computational efficiencies; however, some information loss should also be exptected since the nondominated set will
no longer be computed exactly. With the goal of devising methods
that lead to minimal information loss, we investigate two alternative
approaches: one suggests eliminating objectives, the other combining
them. The elimination method uses the similarity between an objective function’s gradient and its projection onto objects defined by theremaining objectives. The combination approach relies on clustering
the objective function gradients. We assess the effectiveness of theapproaches on library problem instances. We report quality measures
such as the coverage error, additive epsilon indicator and hypervolume
to evaluate the representations obtained by solving the reduced problems. Preliminary findings indicate that proposed reductions may beeffective ways to obtain quick representations of the original problem.

2 - Parallel computation of the Pareto frontier for the biobjective minimum spanning tree problem

Mariagrazia Cairo, Lavinia Amorosi, Dell’Olmo Paolo
This talk presents a parallel algorithm for the bi-objective minimum
spanning tree (BMST) problem which takes advantage of a two-phaseprocedure for the generation of a complete set of efficient solutions.
The first phase involves computing the extreme supported efficient solutions by resorting to an algorithmic approach (Prim embedded in adichotomic search), while the second phase focuses on determining
the non-extreme supported and non-supported efficient solutions. In
this latter phase, all spanning trees of a connected graph are generated
through edge interchanges based on increasing evaluation of reduced
costs of associated weighted linear programs. The results of preliminary computational experiments will be discussed.

3 - On the accurate detection of the Pareto frontier for biobjective mixed integer linear problems

Lavinia Amorosi, Marianna De Santis
In this talk, we focus on bi-objective mixed integer linear programming problems (BOMILPs). Due to their structure, the detection of
their Pareto frontier can be hard to compute from an accuracy perspective. We propose a new criterion space algorithm able to deal with thisdifficulty, detecting the exact non dominated set of a BOMILP. The
algorithm alternates the resolution of single objective mixed integer
linear problems and bi-objective linear problems. During its execution a filtering procedure, based on a tree data-structure called BoT(bi-objective tree), stores in ordered manner all non-dominated points
and segments found. The performance of the algorithm is further improved by the use of suitably defined cuts. Experimental results on a
testbed of instances and a comparison with an existing algorithm will
be presented.

4 - Last-Mile Logistics: Comparing Alternative Formulations of a Bi-Objective Vehicle Routing Problem Basedon K-Sum Minimization

Filippo Ranza, Renata Mansini, Justo Puerto, Roberto Zanotti
In this work, we introduce a bi-objective Vehicle Routing Problem thatminimizes both the routing cost and the impact on citizens evaluatedin terms of pollution, noise, and safety (social impact). To measure thelatter we consider the number of traversals multiplied by the population
living on each street. Formulated on a multi-graph, the problem considers different paths between each pair of nodes. The two objectivefunctions are solved lexicographically. First, the routing cost is minimized, then the social impact is optimized by allowing a predefineddegradation of the routing cost. Several K-Sum compact formulations
as described in [1] and [2] in addition to a non compact one are tested
and compared. A comprehensive sensitivity analysis is also conducted
on multiple realistic instances, considering different k values. References [1] Ogryczak, Wlodzimierz, and Arie Tamir. "Minimizing thesum of the k largest functions in linear time." Information Processing
Letters 85.3 (2003): 117-122. [2] Blanco, Victor, Justo Puerto, andSafae El Haj Ben Ali. "Revisiting several problems and algorithms incontinuous location with ` τ norms." Computational Optimization andApplications 58.3 (2014): 563-595.

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Wednesday, 8:30-10:00 - Room: 34 (building: 306)

Wednesday, 8:30-10:00 - Room: 35 (building: 306)

Recent advances in LP and SDP for discrete
optimization problems

Stochastic Models in Service Operations II

Stream: Conic Optimization: Theory, Algorithms, and Applications
Invited session
Chair: Sven Polak
1 - The sphere packing problem on Tori

Andreas Spomer
How can we arrange spheres in a way such that they fill out as muchspace as possible? This age-old mathematical question has a lot of applications, not only in mathematics, but also in physics and other areasof natural science. The problem has been studied in many interesting
spaces, for example in finite fields, on the sphere and in the Euclidean
space. In recent times, a lot of progress has been made, for example
by the winner of the Fields medal Maryna Viazovska, who solved the
problem in the eight dimensional Euclidean space. In my talk, I wantto consider this problem on spaces that are given by the Cartesian product of spheres. We will look at a family of dense packings and discuss
how to verify their optimality using semidefinite programming, harmonic analysis and sum-of-squares techniques. Furthermore, we will
relate this problem to other packing problems in different spaces.

2 - From approximate to exact optimal solutions of large
semidefinite programs

David de Laat, Henry Cohn, Nando Leijenhorst
For semidefinite programs where the solution is nonunique but the dimension of the optimal face is smaller than the dimension of the affine
space, it is not immediately clear how to round the numerical output
of an interior-point solver to an exact optimal solution. Rounding eachentry of the solution matrices often does not work since the analyticalcenter of the optimal face is complicated to describe. Projecting into
the affine space also does not work, since the projection will not be
positive semidefinite. In [Dostert, de Laat, Moustrou 2021] a rounding
approach using the LLL algorithm is given. I will explain our improved method, using the LLL algorithm in different ways, which ismuch faster for large semidefinite programs. This allows us to prove
the optimality of several spherical codes using semidefinite programming bounds.

3 - Exploiting symmetries in optimal quantum circuit designFrank de Meijer, Dion Gijswijt, Renata Sotirov
A physical limitation in quantum circuit design is the fact that gates ina quantum system can only act on qubits that are physically adjacentin the architecture. To overcome this problem, SWAP gates need to beinserted to make the circuit physically realizable. The nearest neighborcompliance problem (NNCP) asks for an optimal embedding of qubits
in a given architecture such that the total number of SWAP gates to beinserted is minimized. In this paper we study the NNCP on generalquantum architectures. Building upon an existing LP formulation, weshow how the model can be reduced by exploiting the symmetries of
the graph underlying the formulation. The resulting model is equivalent to a generalized network flow problem and follows from an analysis of the automorphism group of Cayley graphs generated by transpositions. As a byproduct of our approach, we show that the NNCP
is polynomial time solvable for several classes of symmetric quantum
architectures. Numerical tests on various architectures indicate that the
reduction in the number of variables and constraints is on average atleast 90%. This opens the way to solving NNCP instances on large
quantum circuits that are far beyond the computational capacity whensolving them without the exploitation of symmetries.

Stream: Stochastic Modelling
Invited session
Chair: Roshan Mahes
Chair: Roshan Mahes
1 - Performance analysis of a cluster-based service system

Sabine Wittevrongel, Osama Salameh
Cluster-based service systems are used to provide parallel processingof jobs. A specific feature of such systems is that jobs are divided into
tasks that should run in parallel on different servers. A job does notstart execution unless the required number of idle servers is available.
This paper proposes a new analytical model based on Markov chain
theory that captures the typical dynamics of such cluster-based systemwith finite buffer size. Through our analysis, key performance measures of a cluster-based service system such as the job blocking probability, the average job delay in the queue or the average utilization of
the cluster servers can be estimated. The application of the model tocloud centers with thousands of servers and a heterogeneous workload
is shown possible.

2 - Completion Times of Jobs on Two-State Service Processes and Their Asymptotic Behavior

Melike Baykal-Gursoy
We consider a repairable service system in which a server experiences randomly occurring service interruptions during which the server
works slowly. Every service-state change preempts the task that is being processed. The server may then replace the task with a different
one, or it may repeat the same task from the beginning, under the newservice-state. We study the completion time of a task under the lasttwo cases as a function of the task size distribution, the service interruption frequency/severity, and the repair frequency. We derive closed
form expressions for the completion time distribution in Laplace domain under replace and repeat recovery disciplines and present their
asymptotic behavior. In general, the heavy tailed behavior of completion times arrises due the heavy tailness of the task time. However, inthe preempt- repeat service discipline, even in the case that the server
still serves during interruptions albeit at a slower rate, completion times
may demonstrate power tail behavior for exponential tail task time
distributions. Furthermore, our results reveal that the stationary firstorder moments, i.e., expected completion time, expected number inthe system in infinite server queues with Markov modulated service
processes are insensitive to the way the service modulation affects theservers, system-wide modulation affecting every server simultaneously
vs identical modulation affecting each server independently.

3 - Appointment Scheduling with Updates: An Exact andOptimal Approach

Roshan Mahes, Alex Kuiper
Traditionally, appointment schedules have been determined by minimizing a specific cost function consisting of clients’ waiting times andserver idling. Under stochastic service times, this intra-day problem
is predominantly studied in a static sense, assuming a fixed schedule.
However, technological advancements have opened up the possibilityof communicating with clients during the day. Using such channels
allows for updating appointment schedules on the fly, for example,
by postponing appointments in case of overcrowding. Yet, while its
promise is clear, sending updates at the wrong moments, or too many,
may be ineffective or unrealistic, potentially causing client confusionand frustration. Therefore, the static appointment scheduling problem
is extended to a dynamic setting that allows the rescheduling of futureclients.
Three rescheduling paradigms - via arrivals, the timeline, or the waiting room - are introduced that can be solved to optimality via dynamic
programming. By rescheduling, the scheduler takes control over the345

WA-40EURO 2024 - Copenhagenrunning session, and the experiments indicate that total costs are significantly decreasing, even with relatively few updates. The benefits
concentrate on increasing utilization (less idling). The magnitude ofthe decrease is moderated by the length of the grace period, which accounts for not sending updates to clients possibly underway. Also, we
argue that the third paradigm is most effective for rescheduling.

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Wednesday, 8:30-10:00 - Room: 96 (building: 306)

Equilibrium detection in applications
Stream: Interfaces Between Optimization, Hierarchical
Problems and Equilibrium Detection with Applications
Invited session

focus on the last-mile delivery segment. The research endeavors tobolster the efficiency, dependability, and cost-efficiency of the entire
delivery chain. We examine a hybrid hierarchical system that amalgamates drones with conventional delivery methods to efficiently transport parcels from their origin points to final destinations, thereby curbing costs and enhancing delivery timelines. The study presents a constrained optimization problem along with its variational formulation.
Numerical simulations underscore how the incorporation of drones
into multi-layered parcel delivery networks offers a promising avenue
to elevate the efficiency, velocity, and adaptability of parcel delivery
systems. This innovative approach empowers companies to overcome
logistical hurdles, extend their operational footprint to remote areas,
substantially diminish environmental emissions, and refine delivery
timelines, particularly for last-mile deliveries.

4 - A Multi-Leader-Follower Game for the Analysis of the Interactions in Donation-Based Live-Streaming Platforms

Laura Rosa Maria Scrimali

Chair: Giancarlo Bigi
1 - A Variational Formulation for a Trust and Reputation
SystemAttilio Marcianò, Gabriella Colajanni, Patrizia Daniele, Sofia
Giuffre’Trust and reputation systems play a pivotal role in modern decentralized environments, fostering cooperation and mitigating risks in various online interactions. The aim of our work is to introduce a variational formulation approach to model and analyze trust and reputation
systems. By formulating trust and reputation as variational problems,
this approach offers a novel perspective on understanding the underlying mechanisms governing trust establishment. The variational formulation provides a mathematical framework to determine the equilibrium weighted trust values, taking into account that each trustee tries
to maximize its gain, namely the utility minus the costs. We illustrate the applicability of this variational formulation through a pletoraof simulations, demonstrating its effectiveness in modeling trust andreputation systems. Insights gained from this approach offer valuableguidance for the design and implementation of more reliable and efficient trust and reputation mechanism in decentralized environments.

2 - From many sources to many users: water allocation
mechanisms under resource dynamics

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Petros Xepapadeas

Wednesday, 8:30-10:00 - Room: 97 (building: 306)

We study optimal water management of many sources which are partitioned into surface and groundwater, with water transportation throughnatural processes being possible among water bodies. Water stock dynamics involve natural inflows and outflows from water users’ abstractions as well as natural processes. Sustainability constraints are introduced by applying terminal conditions on the water stocks. The
net benefits of the users are quadratic with concave gross benefits
and convex costs based on the amount of water used from each water source. We focus on developing two sequential water abstraction
solutions based on serial dictatorship under explicit resource dynamic
constraints. The first is when each sequential user commits to a pathfor the whole planning horizon, and the second is when the serial dictatorship water abstraction decisions are completed in each time period.
Simultaneous water management is also studied in terms of cooperative solutions and non-cooperative open-loop and feedback Nash equilibrium solutions. The sequential and simultaneous solutions are thencompared in terms of aggregate and individual benefits, and in termsof meeting the sustainability constraints. The results of the comparison
are used to characterize water management policies.

Convex optimization algorithms

3 - Enhancing Parcel Pickup and Delivery Services withUAV Integration

Gabriella Colajanni, Patrizia Daniele, Francesco Schillaci
The optimization of parcel pickup and delivery services aims to enhance efficiency, reliability, and cost-effectiveness across the logistics process, encompassing route optimization, delivery time reduction, and cost minimization. This study delves into optimizing parcel services by integrating Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, into logistics operations, with a specific346This paper examines the interactions between content creators andviewers in donation-based live-streaming platforms. Specifically, creators produce their content, then viewers enjoy the live-streaming anddecide to donate money to creators. To capture the sequential decision
process of the model, we introduce a multi-leader-follower game, inwhich creators act as the leaders of the game and viewers as the followers. Creators first optimize their performance level and the durationof the streams to maximize their profit. Then, viewers optimize thetime spent watching a live stream to maximize their utility. Thus, thefirst stage of the game models the non-cooperative competition among
creators, while the second stage represents the behaviour of viewers
deciding on their content demands. We formulate these stages as Nashequilibrium problems, and then as variational inequalities. We analyze
the existence and uniqueness of the Stackelberg equilibrium. Then, we
derive a supervised learning algorithm to estimate the parameters of
the associated artificial neural network model, focusing on the advertisement function, to test our achievements.

Stream: Nonsmooth Optimization
Invited session
Chair: Masoud Ahookhosh
1 - Sparse convex nonparametric least squares via convex
optimization

Zhiqiang Liao
We study the problem of variable selection in convex nonparametricleast squares. Whereas the Lasso is a popular technique for simultaneous estimation and variable selection, its performance is unknown
in convex regression problems. In this work, we investigate the performance of the Lasso regularized convex nonparametric least squares
estimator in a high-dimensional setting and propose an alternative approach based on the unique structure of the subgradients. our proposed
estimators perform favorably, while generally leading to sparser models, relative to the other predictive models via the standard Lasso. Further, our estimators can be expressed as solutions to convex optimization problems and are amenable to modern optimization algorithms.

2 - A global optimization method for a quadratic reverse
convex programming problem

Syuuji Yamada

EURO 2024 - Copenhagen
In this talk, we propose a global optimization method for a quadratic
reverse convex programming problem (QRC) whose feasible set is expressed as the area excluded the interior of a convex set from anotherconvex set. It is known that many global optimization problems can betransformed into such a problem. Iterative solution methods for solving (QRC) have been proposed by many other researchers. One of the
difficulty for solving (QRC) is that all locally optimal solutions do notalways satisfy KKT conditions. In order to overcome this drawback,
we introduce a procedure by combining parametric optimization techniques and Lipschitz optimization methods for finding locally optimal
solutions of (QRC). Moreover, we propose an algorithm for finding a
globally optimal solution of (QRC) by incorporating such a procedure
into a branch and bound procedure.

3 - Non-Euclidean Gradient Methods for matrix completion

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2 - C-conjugacy for set-valued functions and first resultson duality theory

Maria Dolores Fajardo
In this talk we deal with set-valued functions with values in the power
set of a separated locally convex space where a nontrivial pointed cone
induces a partial order relation. We present a conjugation pattern whichallows a biconjugation theorem when the set-valued function is properand evenly convex, which means that its epigraph is the intersectionof an arbitrary family of open half-spaces. Duality theory is then developed via perturbational approach and the first results are also presented.

3 - On Fenchel c-conjugate dual problems for DC optimization

Susan Ghaderi, Yves Moreau, Masoud AhookhoshJose Vidal-Nunez, Maria Dolores Fajardo

This study develops and analyzes non-Euclidean gradient methods forminimizing relatively smooth nonconvex functions using Bregman distances. We explore both constant and dynamic step-size strategies, examining their convergence and complexity, particularly under relativestrong convexity. We demonstrate the methods’ effectiveness in matrix
factorization, matrix completion, and image inpainting on real datasets,
validating our theoretical findings.

In this talk we present two Fenchel-type dual problems for a DC
(differ- ence of convex functions) optimization primal problem. Thesedual problems are built by means of the c-conjugation scheme, a pattern of conjugation which has been shown to be suitable for evenly
convex functions. In particular, we study characterizations of weak,strong and stable strong duality for both pairs of primal-dual problems. Finally, we also give conditions which relate the existence ofstrong and stable strong duality for both pairs.

4 - Adaptive linesearch-free proximal gradient methods under Hölder gradient continuity

4 - A review on the radius of robust feasibility of uncertain
mathematical programs

Puya Latafat, Konstantinos Oikonomidis, Emanuel Laude,Andreas Themelis, Panagiotis Patrinos

José Vicente-Pérez, Miguel Goberna, V. Jeyakumar, Guoyin
Li

This work studies a class of adaptive methods for structured convex
optimization that use large stepsizes on par with linesearch methods
without employing any backtracking procedure. We show that the convergence of such methods extends beyond Lipschitzian setting and encompasses the case of local Hölder gradient continuity. Unlike conventional approaches that often resort to epsilon-oracles or linesearch procedures to address the absence of local Lipschitz continuity, we leverage plain Hölder inequalities without approximation or linesearch. Our
analysis establishes exact convergence results without the need forprior knowledge of local Hölder constants or the order of Hölder continuity.

The radius of robust feasibility provides a numerical value for thelargest possible uncertainty set that guarantees feasibility of a robust
counterpart of a mathematical program with uncertain constraints. The
objective of this review of the state-of-the-art in this field is to present
this useful tool of robust optimization to its potential users and to avoid
undesirable overlapping of research works on the topic as those wehave recently detected. In this paper we overview the existing literature on the radius of robust feasibility in continuous and mixed-integer
linearly constrained programs, linearly constrained semi-infinite programs, convexly constrained programs, and conic linearly constrained
programs. We also analyze the connection between the radius of robust feasibility and the distance to ill-posedness for different types of
uncertain mathematical programs.

 WA-42
Wednesday, 8:30-10:00 - Room: 98 (building: 306)

Infinite Optimization: stability and duality
Stream: Variational Analysis and Continuous Optimization
Invited session
Chair: José Vicente-Pérez
Chair: José Vicente-Pérez
1 - On the strong Slater condition of linear systems with an
evenly convex constraint set

Margarita Rodríguez Álvarez, José Vicente-Pérez
The strong Slater condition plays a significant role in the stability analysis of linear semi-infinite inequality systems. In this work, we deal
with the stability of the intersection of a given evenly convex set withthe solution set of a linear system whose coefficients can be arbitrarily
perturbed. More specifically, we analyze the set of strong Slater points
associated to a given linear inequality system with an evenly convex
constraint set X. Such sets become the solution sets of linear systems
containing weak inequalities, strict inequalities and strong Slater type
inequalities. For this type of systems we characterize the existence ofsolutions by means of dual conditions in terms of the system data, extending some results given by other authors in the field of stability ofsemi-infinite linear systems with solutions in a certain closed convex
set.

 WA-43
Wednesday, 8:30-10:00 - Room: 99 (building: 306)

Market Design 2
Stream: Market Design
Invited session
Chair: Ignacio Rios
1 - Rewarding Primary Dealer Performance

Aleksandrs Smilgins, Sasa Pekec, Jens Leth Hougaard, Peter
Bogetoft
We study the problem of allocating compensation for a collective effort
of competing market participants, such as rewarding market-makers
tasked to maintain active price quotations and low bid-ask spreads.
Several countries regularly issue government bonds, typically via an
auction, and the secondary market stability is important for success of
future auctions. In order to achieve that, participation in auctions is
restricted to selected bidders who are in turn required to be active inthe secondary market. Furthermore, some debt management offices
provide annual monetary compensation to incentivize the secondary
market performance. We develop a framework to design optimal allocation of the annual incentive amount for individual secondary market
performance over time and multiple markets. The proposed scheme iscompared to the one currently used by the Danish Debt Management
Office.

347

WA-44EURO 2024 - Copenhagen

2 - EPIC Fail: How Below-Bid Pricing Backfires in Multiunit
Auctions

Daniel Marszalec, Alexander Teytelboym, Sanna Laksá
Auctions with below-bid pricing (e.g., uniform-price, and ascending
auctions) have remarkable theoretical properties, but practitioners are
skeptical about their implementation. We present a dynamic model ofcollusion in multiunit auctions that explains this gap between theory
and practice. To sustain collusion at the reserve price, bidders submit crank-handle bids. The cost of sustaining crank-handle collusion
depends on the degree of below-bid pricing in the auction. Our model
predicts that crank-handle collusion is easier to sustain in auctions withmore below-bid pricing and when bidders are more symmetric. Evidence from auctions of fishing quota in the Faroe Islands supports our
predictions.

3 - Fair integer programming under dichotomous preferences

Tom Demeulemeester, Dries Goossens, Ben Hermans, RoelLeus
One cannot make truly fair decisions using integer linear programs unless one controls the selection probabilities of the (possibly many) optimal solutions. For this purpose, we propose a unified framework whenbinary decision variables represent agents with dichotomous preferences, who only care about whether they are selected in the final solution. We develop several general-purpose algorithms to fairly selectoptimal solutions, for example, by maximizing the Nash product or theminimum selection probability, or by using a random ordering of theagents as a selection criterion (Random Serial Dictatorship). We also
discuss in detail how to extend the proposed methods when agents havecardinal preferences. As such, we embed the "black-box" procedure ofsolving an integer linear program into a framework that is explainablefrom start to finish. Lastly, we evaluate the proposed methods on two
specific applications, namely kidney exchange (dichotomous preferences) and the scheduling problem of minimizing total tardiness on asingle machine (cardinal preferences). We find that while the methods
maximizing the Nash product or the minimum selection probability
outperform the other methods on the evaluated welfare criteria, methods such as Random Serial Dictatorship perform reasonably well incomputation times that are similar to those of finding a single optimal
solution.

4 - Stable Matching with Contingent Priorities

Ignacio Rios
We study stable matching problems under contingent priorities,
whereby the clearinghouse prioritizes some agents based on the allocation of others. Using school choice as a motivating example, we first
introduce a stylized model of a many-to-one matching market where
the clearinghouse aims to prioritize applicants with siblings assigned
to the same school and match them together. We provide a series ofguidelines to implement these contingent priorities and introduce twonovel concepts of stability that account for them. We study some properties of the corresponding mechanisms, including the existence of astable assignment under contingent priorities, its incentive properties,
and the complexity of finding one if it exists. Moreover, we provide
mathematical programming formulations to find such stable assignments whenever they exist. Finally, using data from the Chilean school
choice system, we show that our framework can significantly increase
the number of siblings assigned together while having no large effecton students without siblings.

 WA-44
Wednesday, 8:30-10:00 - Room: 20 (building: 324)

Pairwise comparisons and preference
relations 3
Stream: Multiple Criteria Decision Analysis
Invited session
Chair: Matteo Brunelli

348

Chair: Konrad Kułakowski
1 - Enhancing Sustainable Procurement in the UAE’s Public Sector: An AHP Framework for Minimizing Carbon
Emissions

Alia Alkaabi
Amidst rapid industrial growth, the UAE’s public sector plays a crucial role in national infrastructure. Traditional procurement practices
in this sector have primarily focused on commercial and technical criteria, with far less emphasis on environmental sustainability. This reliance on carbon-intensive goods and services has markedly increased
the sector’s carbon footprint. This research uses the Analytic Hierarchy
Process (AHP) to improve sustainable procurement decisions, aiming
to substantially reduce carbon emissions within the UAE’s public sector. By integrating a comprehensive set of criteria—environmental,
economic, social, and technical—the study develops a detailed AHP
framework to systematically evaluate and select procurement options
that support sustainability objectives. The findings prioritize procurement practices that significantly reduce the sector’s carbon footprint,
thus aligning procurement strategies with the UAE’s broader sustainability goals.

2 - Interval Pairwise Comparisons in the Presence of Infeasibilities: A Review and Numerical Experiments

Jiri Mazurek, Bruce Golden, Jiaqi Wang
Pairwise comparisons constitute a fundamental part of many multiplecriteria decision-making methods designed to solve complex realworld problems. One of the pervasive features associated with any
problems’ complexity is uncertainty. Experts are rarely able to consistently and accurately evaluate a set of alternatives under consideration due to time pressure, cognitive bias, the intricacy or intangible
essence of the problem, a lack of requisite knowledge or experience,or other reasons. Interval pairwise comparisons (IPCs) allow for thisuncertainty in a natural way; however, the problem of inconsistency(or infeasibility) may arise. That is, a set of interval comparisons may
not allow experts to find a solution in the form of a priority vector.
The aim of this paper is to provide a review of existing literature on
this topic and to compare existing methods via numerical examples
and simulations which indicate that the Interval Stretching Method issuperior to other published methods. In addition, the problem of solution uniqueness is investigated for selected methods. Last but notleast, a new theoretical framework for the evaluation of the extent of
the modification of an original IPC matrix is provided as well.

3 - A Desktop Tool for Multicriteria Decision Analysis

Radomir Perzina
There exists wide range of software products to support decision making. However, the main disadvantage of these products is their commercial nature, which makes them relatively expensive and inaccessible to small companies, students, and researchers. Addressing this gap,
we introduce a novel desktop tool called Multicriteria Decision Assistant (MDA). MDA emerges as an evolution of a previous software,
the FDA, enhancing user experience by eliminating the need for an internet connection, thus addressing privacy and data security concerns
by preventing sensitive data from being sent to external servers. Thisfeature significantly increases the tool’s appeal to users wary of data
privacy issues. What sets the tool apart is not just its cost-free accessibility but its educational potential. Unlike many decision support toolsthat operate as black boxes, MDA makes the decision-making processtransparent, displaying the results of intermediate calculations. Thistransparency is invaluable for educational purposes, allowing students
to grasp the underlying principles of multicriteria decision-making andencouraging a deeper understanding of the decision process itself. The
proposed software package is demonstrated on an illustrating example
of a real-life decision problem.

EURO 2024 - Copenhagen
4 - Forward Accuracy in AHP/ANP: Unraveling theConsistency-Compatibility Conundrum through Color
Brightness Comparisons

Adriana Miclea
This paper examines the existence of a linear relationship between
the consistency ratio and the compatibility index in the AHP/ANP
framework. The linear relationship is tested with respect to accuracy
principle, using an original development referred as "forward accuracy". For several years, students in Bucharest University of Economic
Studies were asked to pairwise compare the brightness of five colors.
Their consistency and compatibility indices were collected and basedon these, accuracy curves among these two measures were inferred.
While there is hope in the AHP/ANP methodology that a low value ofthe inconsistency ratio will grant compatibility and the rightness of thedecision-making structures, there is rather an insignificant number ofpractical experiments to challenge this one. This multi-annual experiment shows that in the simple situation when brightness is compared,
accuracy curves as linear relationships between consistency and compatibility remain similar, while on the same accuracy curve one could
find high consistency with low compatibility and the other way around.
Conclusions are two-folded: is need to perform lots of experiments, tofigure out what is the acceptable threshold of inconsistency ratio andalternative measures like "forward accuracy" where the impact of apair (consistency-compatibility) on the shape of the linear relationshipis assessed in terms of future impact could prove useful.

WA-45

In multi-period inventory management, decisions are made about thetiming and quantity of purchases, as well as inventory control of thegoods. To minimize costs, inventory policies are used for the procurement of goods. Most policies assume constant purchase prices andstochastic demand. However, products and services are often subject
to price fluctuations. This raises three challenges: (1) the economic
advantage of inventory policies when purchase prices fluctuate, (2) the
integration of value-oriented decision mechanisms into inventory policies, and (3) their comparison. To address these issues, a simulation
study on inventory policies was conducted taking value orientationand fluctuating purchase prices into account. The study focusses ondeveloping, integrating, and evaluating value-oriented inventory policies for goods with fluctuating prices. It compares their performancewith conventional inventory policies. The results suggest that integrating value-oriented decision mechanisms can yield benefits even withminor price fluctuations in the market. As price fluctuations increase
and solution robustness becomes more important, the need to integrate
value-oriented decision mechanisms into inventory policies remains.
The results indicate that value-oriented inventory policies can leverage
cost potential by up to 20%. Therefore, further research is needed toefficiently configure these policies for the economical procurement andstorage of goods during price fluctuations.

3 - Optimizing Job Offer Packages: How Can Organizations
Enhance Personnel Selection?

Saeed Najafi-Zangeneh, Naser Shams, Olivier Gossner, Ehsan
Abbasi WA-45
Wednesday, 8:30-10:00 - Room: 30 (building: 324)

Decision Support for Operations
Management
Stream: Decision Support Systems
Invited session
Chair: Shaofeng Liu
Chair: Femi Olan
1 - Sustainable Dynamic Routing Framework for Efficient
E-Grocery Delivery

Valentas Gruzauskas, Aurelija Burinskiene
In urban settings, e-grocery delivery significantly impacts energy use
and air pollution. Traditional routing solutions, while addressing general transportation, fall short in the specific area of fresh food delivery,
which demands a focus on sustainability. Our research introduces adynamic route planning framework that integrates graph neural networks for traffic flow forecasting with traditional optimization techniques. This framework is designed to balance environmental impact,
food quality, and operational costs, adapting to changing urban logistics factors such as food demand, fleet size, and traffic conditions. The
framework also incorporates advanced information-sharing strategies,
enhancing its adaptability and efficiency in real-time scenarios. We
emphasize the importance of sustainable and resilient solutions in thecontext of urban e-grocery logistics. The proposed solution, scalable
for large-scale agent-based modeling, aims to establish more resilientand sustainable urban delivery systems, with a future focus on a specific city case study to demonstrate its practical effectiveness. The novelty in our approach is the inclusion of real-time information-sharing
strategies and a focus on sustainability and resilience in route scheduling, which sets it apart from traditional systems. This fusion of advanced predictive analytics with practical optimization offers a newpathway to tackle the environmental and logistical challenges of urban
e-grocery delivery.

2 - Integrating and evaluating value-oriented decision making mechanisms into inventory policies

Personnel selection in competitive two-sided markets presents challenges for organizations seeking to attract high-quality candidates
while managing costs. However, existing models often overlook the
optimization of job-offer packages to address both candidate preferences and organizational objectives. This paper addresses this researchgap by proposing a novel mathematical model for determining the optimal job offer package. Our model integrates candidate preferences
and organizational costs, offering a comprehensive solution to the selection process. Utilizing a two-stage Mixed-Integer Linear Programming (MILP) approach, we employ the Gale and Shapley algorithm tofacilitate efficient matching. Through a small case study, we validate
the effectiveness of our model in providing actionable insights for organizations seeking to enhance their hiring processes and attract top
talent.

4 - Product bundling strategy for co-live streaming Ecommerce considering consumer subscription

Cai Shoujie
Livestreaming e-commerce has emerged as a highly effective channel
for manufacturers and retailers recently. When two streamers fromdifferent rooms collaborate to host a co-live streaming event and individually promote products in their respective rooms, it attracts various
types of consumers with assessments to products. In this background,
we explore the optimal decisions and the best sales strategy for threecommon ivestreaming sales strategies: pure component (PC), mixed
bundling (MB), and pure bundling (PB). The study shows that it ismore advantageous for streamers of similar or identical types to collaborate in a co-live streaming event, and MB and PB strategies consistently outperform PC strategy; however, a co-live streaming event
doesn’t offer significant advantages over a solo-live one when there isalready sufficient traffic. The extended studies explore the robustnessof the findings and uncover two additional insights. Firstly, a co-live
streaming room with a higher proportion of co-subscribers yields more
profits. Secondly, if two streamers sell similar or identical products,
PB strategy is more effective if valuation discounts are low, whereas
MB strategy shines. However, when co-live streaming sells significant
different products, MB strategy is advantageous only when streamers
with lower traffic recommend higher value products.

Philipp Erfurth, Matthias Gerhard Wichmann349

WA-46EURO 2024 - Copenhagen WA-46
Wednesday, 8:30-10:00 - Room: 40 (building: 324)

Not The Excellence In Practice Award
Stream: Making an Impact
Invited session
Chair: Ruth Kaufman
1 - Not The Excellence In Practice Award

Making An Impact, L. Alberto Franco, Karsten Kieckhäfer,Gilberto Montibeller, Louis Bouvier, Trung Hieu Tran, El
Mehdi Er Raqabi
The aim of this 90’ session is to explore the aspects of practice that
do not get included in the materials submitted for awards such as theEURO Excellence in Practice Award. Submissions naturally focus
on the criteria being judged: the originality of the methodology, thescientific excellence, the impact, and so on. But anybody who has
been engaged in a practical application will know that this only tells
part of the story. To get to the stage of an implemented application,
there will have been many hurdles along the way: getting all the data,
making sure it is correct, convincing all the stakeholders, overcoming fears and doubts of the people who have to change their way ofworking, and so forth. In this session, we will invite panellists to givea short presentation of these aspects of their projects, consider how
they dealt with the challenges and identify key lessons, to be followedby questions and discussion with the audience on the key issues andhow we address them. The latest details of panel members can befound on the Making an Impact page on the main conference website(euro2024cph.dk/programme/making-an-impact-2024)

 WA-47
Wednesday, 8:30-10:00 - Room: 50 (building: 324)

We assume fuzziness in emergency logistic planning models for thedisaster zone, such as the covering, location, transportation and routing
problems. We consider a fuzzy multi-objective emergency shelters location and victims evacuation problem (FMOESLVEP) in the disasterstricken zone. The model objectives include (1) maximizing the total selection reliability index of opened shelters; (2) minimizing total
costs, including fixed costs for opening shelters, victim transportation
costs, and service costs, (3) minimizing of monotone expectation of
total time for victims’ evacuation, and (4) minimizing the number ofopen shelters. FMOESLVEP models are developed for the different
input fuzzy information. For the evaluation of selection reliability indexes of opened shelters the models take into account the interactions
between decision making attributes by aggregating the extended Choquet integral. The realization scheme of the solution for the FMOESLVEP models is given.

3 - Facility Location Selection Problem based on Picture
Fuzzy TOPSIS

Irina Khutsishvili, Gia Sirbiladze
In the present research, a decision support methodology for facility location selection problems is developed. The methodology is based on
the TOPSIS (Technique for Order Performance by Similarity to Ideal
Solution) approach under a picture fuzzy environment. We are focusing on a special case of facility location problem, namely location planning for service centers. A set of potential center sites, or alternatives,
are typically involved in this kind of challenge, and they are assessed
based on some weighted criteria. The alternative that best meets eachcriterion will be selected for implementation. To evaluate criteria ourapproach uses experts’ assessments, that is in the proposed methodology the values of the criteria are expressed in picture fuzzy numbers,
given by the group of experts. The case when the information on thecriteria weights is completely unknown is considered. In the context
of picture fuzzy sets, the criteria weights identification based on De
Luca-Termini information entropy is presented. Hence, the proposed
approach is based on a picture fuzzy TOPSIS decision-making modelwith entropy weights. The fuzzy positive-ideal solution (FPIS) andfuzzy negative-ideal solution (FNIS) are first defined using the TOPSIS algorithm. Then the ranking of alternatives is performed following
the proximity of their distances to both FPIS and FNIS. For this purpose, the picture weighted Hamming distance is used.

Fuzzy sets and systems
Stream: Multiple Criteria Decision Analysis
Invited session
Chair: Gia Sirbiladze
1 - Associated Probabilities and Insufficient Expert Data in
Multi-Criteria Decision Making

Tinatin Davitashvili, Gia Sirbiladze
Problems of modeling of uncertainty and imprecision for the analysis of the insufficient expert data (IED) is considered in the environment of interactive group multi-criteria decision making (MCDM).
Uncertainty index is presented by the fuzzy measure on alternatives
set. Based on the informational entropy maximum principle, moments’
method for the IED is developed for the evaluation of associated probabilities class (APC) of Choquet’s second order capacity. The second
pole of the IED - the data imprecision in the form of a fuzzy subset(image) on alternatives set is constructed. Based on the Sugeno finite
integral most typical value (MTV) as a prediction on possible alternatives set for the IED is constructed. In MTV the both poles of the
IED (APC and image) are accumulated. The numerical example illustrates the possibilities of the approach, in the optimal selection of recommendation elements from MCDM alternatives. The sensitive andcomparative analysis for this example is provided.

2 - Humanitarian Relief Logistics Fuzzy Planning Model for
the Shelters’ Location and Victims’ Evacuation in the
Disaster Zone

Gia Sirbiladze

350

4 - Evidence Theory and Fuzzy Discriminations in MCDM.
Application in Emergency Psychiatric Diagnostics

Bezhan Ghvaberidze, Gia Sirbiladze
Today, much attention is paid to the engineering of dependable andresilient intelligent systems and models. This direction includes multicriteria decision making (MCDM) models and systems in emergency
situations. A new interactive MCDM approach based on evidence theory and fuzzy discrimination analysis is developed. MCDM’s utilities
in the discrimination q-rung orthopair fuzzy values in the DempsterShaper’s believe structure environment are transformed. New aggregation operators - extensions of Dempster’s extremal expectations under discrimination q-rung orthopair fuzzy information are constructed.
MCDM’s alternatives by new constructed operators are ranked. For
the illustration of obtained results a numerical example is created from
Emergency Psychiatric Diagnostics.

 WA-48
Wednesday, 8:30-10:00 - Room: 60 (building: 324)

DEA applications in Policy Making and
Planning II
Stream: Data Envelopment Analysis and its Application
Invited session
Chair: Thyago Nepomuceno

EURO 2024 - Copenhagen
1 - Measuring efficiency in tourism: a problem of sharedfactors and multiple attributes in DEA

Sonia Avilés-Sacoto, Estefania Avilés-Sacoto, Wade Cook,David Güemes-Castorena
This paper examines the tourism industry in Mexico, focusing specifically on the performance of each of its 32 states. To facilitate the development of an appropriate performance measurement tool, we begin
with the data envelopment analysis (DEA) methodology, an optimization tool used for evaluating the efficiencies of a set of decision-making
units (DMUs). While it is usually assumed that each DMU functions
independently of other units, having its own set of inputs and outputs,
there are situations where the DMUs can be interdependent due to thepresence of shared factors. Thus, a DMU can share an input or output
with other DMUs, thereby generating sub-groups of DMUs. In sucha setting, like the one considered herein, one must view a projection
to the frontier as involving all sub-group members simultaneously. As
well, in some cases the shared factor cannot be split up and assigned
to each sub-group member separately. This situation can become evenmore complex in a two-stage setting where a DMU can share a factor with some other DMUs and the DMU groupings are different in
one stage than in another. Finally, in the study herein we consider thepresence of different attributes, i.e. different types of tourism.

2 - Incorporating the Benefit of the Doubt Indicator for Improved Electric Power Management

Lucio Camara e Silva, Cristiane Perla Castro e Sant’Anna,
Josenildo Ferreira, Aldsmtyhys Cruz, Thyago Nepomuceno
This study introduces a composite indicator based on the Benefit of the
Doubt (BoD) to support System Operation Centers in adjusting realtime schedules to meet demands of Electric Power Systems (EPS). It
addresses the challenge of selecting power plants for increased electricity generation in northeastern Brazil, undertaken by a company within
the Brazilian electricity sector established in 1998. This entity is tasked
with coordinating and overseeing the operation of generation and transmission facilities within the National Interconnected System. The evaluation focuses on the Northeast Requirement Load (NRL), with output
indicators including operating time, response time, energy potential,
and variable costs. Decision-makers analyze the results and concurwith the prioritization of alternatives, employing the model to facilitate the selection of viable Thermoelectric Power Plants to alleviate
the generation shortfall in northeast Brazil over a specified timeframe.

3 - Improving the Efficiency of Criminal Investigations witha Multi-needs Approach for Case Reassignments

Thyago Nepomuceno, Akram Dehnokhalaji, Erik Alda,
Miguel Gustavo Gomes da Silva
By assuming a proportional alteration in the output/input ratio, DEA
can estimate the additional resources necessary for police units inanticipation of criminal activities. However, reallocating human resources and capital poses significant challenges within public administrations, often influenced more by political considerations than technical merits.
This study proposes a novel approach that integrates non-parametricfrontier estimations with a prioritization reasoning to guide the redistribution of cases, rather than personnel, within law enforcement agencies. We assess the production capacity of police units utilizing Data
Envelopment Analysis. A Multicriteria framework is incorporated tofacilitate reassigning criminal cases from police stations working atfull capacity to units with available resources. This approach provides
an efficient and fair allocation of cases, prioritizing factors such as casecomplexity, severity, and costs across different levels.
As a result, our methodology identifies police units operating at full
capacity, allowing the transfer of less complex, low-severity, and lowcost cases to neighboring units with spare production capacity. This
surplus capacity enables fully operational units to concentrate on critical cases that otherwise might be overlooked.

WA-49

 WA-49
Wednesday, 8:30-10:00 - Room: M1 (building: 101)

Stochastic inventory systems
Stream: Lot Sizing, Lot Scheduling and Production Planning
Invited session
Chair: Roberto Rossi
1 - On the Stochastic Inventory Problem Under Order Capacity Constraints

Roberto Rossi, Zhen Chen, Armagan Tarim
We consider the single-item single-stocking location stochastic inventory system under a fixed ordering cost component. A long-standing
problem is that of determining the structure of the optimal control policy when this system is subject to order quantity capacity constraints;
to date, only partial characterisations of the optimal policy have been
discussed.
An open question is whether a policy with a single continuous interval
over which ordering is prescribed is optimal for this problem. Under
the so-called "continuous order property" conjecture, we show that the
optimal policy takes the modified multi-(s, S) form. Moreover, we provide a numerical counterexample in which the continuous order property is violated, and hence show that a modified multi-(s, S) policy is
not optimal in general.
However, in an extensive computational study, we show that instancesviolating the continuous order property do not surface, and that theplans generated by a modified multi-(s, S) policy can therefore be considered, from a practical standpoint, near-optimal. Finally, we showthat a modified (s, S) policy also performs well in this empirical setting.

2 - Optimal Ordering Policies for Non-instantaneous Deterioration Items with Price and Stock Sensitive Demand
under Inflation and Time Discounting

Kuo-Lung Hou, Li-Chiao Lin
In real life, such as fashionable commodities and high-tech productswith short product life cycle, the length of the waiting time for the
next replenishment is the main factor for deciding whether the backlogging with be accepted or not. On the other hand, such as seasonal
items, first-hand vegetables, and fruits during the expiration period,
there was no deterioration occurring immediately. In addition, several
studies have shown that the impact of inflation on lot-sizing decisionmaking is often not negligible. We also noted that sales at the retaillevel tend to be proportional to the amount of inventory displayed. To
reflect these phenomenon, this paper presents an inventory model for
non-instantaneous deteriorating items with price- and stock-dependent
demand under inflation and time value of money over a finite planning horizon. In the model, shortages are allowed and the unsatisfied
demand is partially backlogged at the exponential rate with respect tothe waiting time. We establish the theoretical results and provide an
efficient solution procedure to find the optimal number of replenishment, the cycle time and selling price. Then the optimal order quantity
and the total present value of profits are obtained. Finally, numericalexamples are used to illustrate the proposed model. In addition, sensitivity analysis of the model parameters and some managerial insights
are also discussed.

3 - A heuristic for an inventory control policy in a closedloop distribution multi-echelon supply-chain with returns

Rodrigue FokouopThis study tackles an inventory control challenge in a complex multiechelon distribution system, drawing insights from Air Liquide’s
closed-loop supply chain with product returns. The inherent complexity arises from the distribution structure and the handling of returned
cylinders. The goal is to frame this as a multi-echelon periodic review
inventory control problem and identify the optimal control parameter.

351

WA-50EURO 2024 - Copenhagen

Conventional stochastic programming approaches prove impracticaldue to the curse of dimensionality resulting from unmet customer demand and potential lost sales. In response, we introduce a heuristic approach, validated through simulation-optimization. This heuristic involves breaking down the multi-echelon distribution system into
serial systems, optimizing target stock levels for each echelon, andaligning cumulative backorder quantities by aggregating shared echelon stock levels. The heuristic achieves a 0.74% average reduction in
total inventory cost, demonstrating comparable results to simulationoptimization, with the notable advantage of rapid computation in seconds for practical real-world applications.
Applying the heuristic to real-world data enhances inventory control inthe multi-echelon distribution system, leading to a 12.63% reduction instock costs for regularly demanded products and a 21.89% reduction
for sporadic demand. These findings highlight the benefits of a global
inventory control approach in a multi-echelon distribution system.

4 - Dimensional Analysis and Solution Estimates of Economic Lot Sizing Problem

Joao Miranda, Mariana Nagy, Miguel Casquilho
The Economic Lot Size (ELS) problem allows a very efficient resolution, and it is often used as sub-problem of more complex Optimization
problems. Namely, the ELS substructure can be found through suitabledecomposition in diverse real world problems, such as batch scheduling, capacity expansion, facility location, production-distribution networks, or in the long range planning problem. An extensive study ofsingle product uncapacitated ELS instances is carried out, and optimal
estimates for the ELS problem are treated through dimensionless ratios and quotients: in one hand, by correlating the objective function ’s
minimum value and the instance parameters; in the other hand, througha solution procedure that indicates the optimal binary solution and, insequence, the production and stocking quantities. The related results
are presented and analyzed, namely in what regards the applicationof dimensional analysis in the ELS instances at hand. Finally, future
developments are outlined and discussed, including the potential extension to other Optimization problems of both dimensional analysis
and associated approximation procedures.

 WA-50
Wednesday, 8:30-10:00 - Room: M2 (building: 101)

Retail Distribution I
Stream: Retail Operations
Invited session
Chair: Pedro Amorim1 - Order Allocation Optimization in a Fashion Marketplace

Sérgio Castro, Gonçalo Figueira, Bernardo Almada-Lobo
Order allocation is the dynamic problem of distributing incoming orders to fulfillment nodes. This problem appears in different sectors
and contexts, where the nodes may belong to the same or independent
vendors. We consider an Online Fashion Marketplace selling products
owned by multiple vendors. In this setting, customers expect immediate feedback on their product fulfillment, such that order allocation
decisions must be taken instantly. This particular Marketplace diverges
significantly from prior studies in order allocation literature due to theconsistent low sales volume per item across the majority of the productportfolio, and the daily fluctuations in inventory by vendors that introduce a notable level of uncertainty. Given the distinctive characteristicsof this problem compared to existing literature, we propose an innovative methodology to generate heuristic fulfillment policies. Leveraging
either Genetic Programming or Genetic Algorithms, we incorporate
critical policy information, including shipping costs, basket coverageand stock depth into a machine learning pipeline to generate policies
that are fast and effective. To assess the efficacy of the proposed policies, a comprehensive comparative analysis is conducted against theretailer’s existing policy and a perfect hindsight policy, from which
managerial insights are extracted.

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2 - Designing Multi-Period Two-Echelon Last-Mile Delivery
Networks with Multiple Capacity Levels under Uncertainty

Juan Carlos Pina Pardo, Pitehr Hurtado-Cayo, Matthias
Winkenbach
We study the design of two-echelon distribution networks with facility
sizing decisions over a multi-period planning horizon amidst demand
uncertainty. This research is based on a real-world problem faced by alarge consumer-packaged goods manufacturing and distribution company seeking to implement a multi-tiered distribution strategy via satellite facilities strategically located across the service area. The problem
is formulated as a two-stage stochastic program with discrete recourse,
where the first stage defines both the location and size of satellites, andthe second stage defines which satellites to operate and how to serve
customers. To solve the two-stage stochastic program, we develop
an exact solution approach combining the integer L-shaped method
and sample average approximation. Through extensive numerical experiments, we show the effectiveness of the proposed exact approachand present several managerial insights regarding facility location, thevalue of accounting for stochasticity, and the effects of explicitly incorporating facility sizing decisions when designing two-echelon distribution networks.

3 - Evolutionary Approaches to Solve the Dynamic andStochastic Inventory-Routing Problem

Francisco Maia, Gonçalo Figueira, Fábio Moreira
Sequential-decision making is an active area of research in Operations
Research (OR), where real-time decision-making, machine learningand data-driven approaches are stepping forward. The Dynamic andStochastic Inventory-Routing Problem (DSIRP) is one of the most fundamental problems companies seek to optimize, given its meaningfulness at strategic and operational levels. The challenge at hand involves the integration of inventory management and vehicle routing
problems while at the same time effectively handling the dynamic andstochastic nature of customer demands unveiled over time. A promising research path lies in combining Reinforcement Learning and OR toachieve novel inventory and routing policies with significant cost savings. Accordingly, Policy Function Approximation (PFA) emerges as
an encouraging strategy. This research intends to give new insights into
solving the single-item, single-vehicle DSIRP with a one-to-many endpoint structure, where decisions must be released periodically. Therefore, we propose novel delivery policies based on PFA, exploring two
evolutionary algorithms, i.e. Genetic Programming and Genetic Algorithms. The proposed methodology enabled us to derive novel rulesthat present competitive results compared to several literature benchmarks, optimizing the balance between holding and stockout costs, reducing waste and decreasing travel distances.

4 - Logistical Support for the shipment of food products

Cexcilia Montt, Astrid Oddershede, Luis Quezada
This paper explores the application of simulation in the redesign ofdispatch processes to address delivery fulfillment and sustainable issues. The study focuses on a company that processes and supplies
food to several employee cafeterias in a metropolitan city. The main
objective of this company’s logistics dispatch is the efficient deliveryof final products using a fleet of refrigerated electric vans to ensure thesustainability. The challenge identified centers on intermittent delivery discrepancies, which lead to customer losses and product waste.
By employing simulation and business process management (BPM),
the administrative processing time has been reduced by 10 to 15 minutes. This time saving is crucial to food durability and to avoid delays
associated with the unloading of incorrect or modified shipments. In
addition, real-time fleet tracking has been established using specialized
software. This tracking capability allows the company to analyze data
continuously, with the goal of minimizing losses, reducing costs andpromptly addressing delivery delays with up-to-date information.

EURO 2024 - Copenhagen WA-51
Wednesday, 8:30-10:00 - Room: M5 (building: 101)

Timetabling 2
Stream: Public Transport Optimization
Invited session
Chair: Pieter Vansteenwegen
1 - Analyzing Timetable Fragility

Marta Leonina Tessitore, Marcella Samà, Giorgio Sartor,
Carlo Mannino, Dario Pacciarelli
When looking at preventive approaches for disturbance management,
the focus is on designing robust timetables that are able to absorb secondary delays. Following the recovery robustness approach, we use
the concept of timetable fragility to analyze the resilience of a nominal
timetable, i.e., its ability to recover from primary delays with optimalcorrective actions. Given a timetable and suitable objective functions,the fragility defines a special class of scenarios associated with eachpair train/section and use them to identify the pairs where a primary
delay is more likely to generate large knock-on delays. In particular,
the objective function should reflect real-time decision-making criteria
adopted by dispatchers. Since no single, universally accepted measureexists, we explore some of the most common objective functions, e.g.,
the minimization of the max delay, or the minimization of the sumof consecutive train delays at the last station of their routes. Building on the concept of fragility maps, we propose additional analyses,
including exploring the impact of precedence constraints in optimal
dispatching and sensitivity analysis of the optimal solution. Considering real-life scenarios from a busy railway line in Norway, we discuss
several potential uses of the fragility to improve decisions at different
levels of the railway planning process, including dispatching, timetable
design, and network design.

2 - Heuristics for recovery robust periodic timetabling

Vera Grafe, Anita Schöbel
An important aspect of optimising public transport is finding a good
timetable. On the one hand, short travel times are important from thepassengers’ point of view. On the other hand, tight timetables without buffer times are prone to delays, which are inevitable in practiceand highly dissatisfactory for the passengers. Hence, a good timetableshould also have some degree of delay resistance. Often a periodic
timetable is desirable, i.e. a timetable which repeats in a regular pattern (e.g. every hour). However, delays do in general not occur periodically, so many robust timetable models only consider aperiodic timetables. In our work we apply the concept of recoverable robustness toperiodic timetabling with aperiodic delays, resulting in the integrationof two well-known problems: the periodic event scheduling problem(PESP) and delay management (DM). We show how we can bridge thegap between the periodic PESP and the aperidoic DM, yielding MIP
formulations. However, solving the problem to optimality is unrealistic for large instance due to its high complexity. Therefore, we pursue
heuristic approaches. We present iterative heuristics computing periodic timetables with increased robustness at the cost of only a small
increase in the nominal travel time.

3 - Integrated optimization of demand management andtimetabling of an urban transit system: A Benders decomposition approach

Yahan Lu, Lixing Yang, Jiateng Yin, Shadi Sharif Azadeh
The intelligent upgrading of metropolitan rail transit systems has madeit feasible to implement delicate demand management policies thatsynergize multiple strategies in practical operations. The interdependence of supply and demand motivates the following question: How
to scientifically and rationally develop demand-side management policies and supply-side capacity allocations to enhance the whole urban
rail transit ecosystem? To answer this question, we propose a mathematical and computational framework that optimizes train timetables, passenger flow control, and trip-shifting strategies, incorporating

WA-52

emerging trip reservations and service fairness. The problem is formulated as a nonlinear programming model and solved using a Benders decomposition-based solution algorithm within the branch-andcut method. To further improve the computational efficiency, a novel
decomposition method, and valid inequalities are developed, which decomposes the model into a train timetabling problem combined withpartial passenger assignment and a passenger flow control problem.
The computational results of real-world case studies based on the Beijing metro show that our solution method outperforms commercial
solvers in terms of computational efficiency. Our proposed integrated
optimization method consistently leads to a reduction of 16.3% in thesystem-wide waiting time of passengers while simultaneously reducing the number of operated trains.

4 - A MILP model to improve the robustness of a railway
timetable in a bottleneck area: a case study in Belgium

Inneke Van Hoeck, Pieter Vansteenwegen
Robustness is a crucial concept in the railway timetabling problem. It
is often related to the ability of a timetable to absorb small delays andavoid the propagation of these delays. We propose a MILP model toimprove the robustness of an existing microscopic timetable in a complex and heavily used part of the network. The objective is to maximize
the buffer times between each pair of trains, leading to a good spreading in time of the trains. The timetable can be improved by adapting thetiming and the routing of the trains. Since only a part of a larger network is considered, the allowed deviation from the initial timetable islimited such that the newly proposed timetable does not cause conflicts
outside of the considered area. Depending on which properties the resulting timetable should have, certain constraints can easily be addedby the user. Due to the flexibility of the proposed model, it can also be
used to analyze and optimize alternative scenarios and provide moreinsight into the robustness of the timetable. The model is applied to acase study of a bottleneck area of the Belgian railway network, locatedjust outside of Brussels. The traffic in this area is very heterogeneous,with InterCity, local, peak-hour and high-speed trains. The spreading
objective is improved with 18% compared to the current situation, thus
leading to a more robust timetable. Additionally, alternative scenarios, related to extending a line, adding trains and cancelling trains, arestudied.

 WA-52
Wednesday, 8:30-10:00 - Room: 8003 (building: 202)

Models and algorithms for real-life
combinatorial optimization problems
Stream: Combinatorial Optimization
Invited session
Chair: Maria Battarra
Chair: Anand Subramanian
1 - A mathematical formulation for a real-life single-track
train timetabling problem

Renata Mendes, Teobaldo Bulhões, Bruno Bruck, Anand
SubramanianThis study focuses on developing a mathematical model that addresses
a train timetabling problem arising at Companhia Brasileira de Trens
Urbanos (CBTU), a railway company in Brazil. The objective of the
problem involves determining arrival and departure times at multiple
stops along the network.
The operation of a railway system heavily depends on a well-organized
timetable. Adherence to safety protocols regarding vehicle operations
on tracks is also crucial. Therefore, the objective of this work is the allocation of routes and timetables for trains, while enhancing customer
satisfaction and considering operational constraints.

353

WA-53EURO 2024 - Copenhagen

The CBTU train line covers a 30-kilometer route from Santa Rita to
Cabedelo, both cities located in the state of Paraíba, Brazil. Approximately 8 thousand passengers make use of the railway daily. Hence,
determining a timetable impacts the lives of passengers needing safe,
convenient travel in sync with their routines. Furthermore, trains musttravel in opposite directions using a single track simultaneously, andcollisions pose a significant risk if schedules are not carefully planned.
Preliminary results suggest that the developed formulation can handle small-scale instances. However, the current model could perform
more effectively when dealing with real-life scenarios. The next phaseof the research involves devising a stronger formulation and exploring(math)heuristic approaches capable of tackling larger instances.

2 - The Wildfire Suppression Problem: coordination of thefire fighting resources

Maria Battarra, Mualla Gonca Avci, Mustafa Avci, Gunes
ErdoganWildfires are endangering forests, dwellings and the population. This
study was motivated by informative discussions with members of thewildfire directorate in a province of Turkey, Natural Resources Wales,
the UK Forestry Commission, and Forestry England. We introduce,
model, and solve a wildfire suppression problem that involves the coordination of various types of fire-suppression resources, as we realised
the literature models all types of fire fighting resources (i.e., fire engines, helicopters and razing teams) as generic resources, ignoring
their different operational constraints and effects in extinguishing orcontrolling the spread of a wildfire. Two integer programming formulations for the problem are presented, the performances of which areevaluated on a set of randomly generated problem instances. Realistic operational constraints about the different firefighting resources areembedded, and the fire spread is simulated depending on the wind conditions. Dwellings, infrastructures and areas of naturalistic beauty areprioritised for protection. The results indicate that the proposed formulations are able to solve instances of realistic size. The importanceof the right mix of resources employed in tackling a wildfire as well as
their coordination are highlighted. A case study inspired by the Yatagan district of the Mugla province of Turkiye has been developed, andmaps simulating the fire spread over time will be showcased.

3 - The maximum length car sequencing problem

Lara Pontes, Carlos Neves, Anand Subramanian, Maria
Battarra
This study introduces a new variant of the car sequencing problem tosupport the operations of a Brazilian automotive assembly plant. We
propose an integer linear programming (ILP) formulation to schedulethe maximum number of cars without violating the spacing constraints
associated with car options, such as air-conditioning or sunroof. In thestudied plant, these violations lead to a complete stop of the assembly
line, which is the least desirable outcome for the company. We alsopresent valid combinatorial lower and upper bounds, as well as binary
and iterative search algorithms to solve the problem when good primal
bounds are not readily available. In addition, we develop an effective
iterated local search algorithm to quickly obtain high-quality solutions,then used as a warm start to the exact methods. Computational results
demonstrate that relatively low gaps are achieved for benchmark instances within a time limit of ten minutes, and we conduct an instancespace analysis to identify the features that make the problem more difficult to solve. Moreover, the instances reflecting the company’s needs
are solved to optimality in less than a second. Finally, simulations withreal-world demands, divided into shifts, are performed for a period offour months. In this case, we use the proposed ILP model in all shifts
except the last one of each month, for which we employ an alternative model to sequence the unscheduled cars, adjusting the pace of theassembly line.

4 - Optimizing the usage of cut and fill materials in a largescale railway construction project

Felipe Lemos, Bruno Bruck, Maria Battarra, Anand
SubramanianIn this work, we investigate the problem of reusing dug materials ina large-scale railway building construction project. The project is inspired by a real-world scenario from HS2, the company responsible354

for building a 225-kilometer fast train line in the UK. The company
estimates that the dug material will amount to around 132 million tons,
and material imported for construction will be around 20 million tons.
The dug material can often be repurposed for fill operations to minimize environmental impact. In this research, we model the problem of
allocating dug materials to fill operations, considering operational andfinancial constraints. We propose two approaches to solve the problem. The first one consists of an integrated MILP model, while thesecond one is a heuristic algorithm that initially solves a simplified
version of the integrated model for each month of the time horizon,
and then calls another MILP that tries to find a feasible solution. Bothapproaches were tested on instances generated based on real data provided by the company. Preliminary results show that the first approachis only capable of finding optimal solutions for small and medium sized
instances. On the other hand, the second one is significantly more efficient in terms of runtime required to find feasible solutions, especiallyfor larger instances, but it struggled to find the optimal solutions obtained by the first approach.

 WA-53
Wednesday, 8:30-10:00 - Room: 8007 (building: 202)

Pathways to Climate Resilience
Stream: Sustainable and Resilient Systems
Invited session
Chair: Francesco De Marco
Chair: Elise Miller-Hooks
1 - Computing transformation pathways towards a climate
neutral European energy system with high spatial andtemporal resolution

Manuel Wetzel, Karl-Kiên Cao, Shima Sasanpour
The transition of energy infrastructure towards climate neutrality posessignificant challenges for infrastructure planners and policymakers.
Large-scale energy system models play a fundamental role in evaluating alternative scenarios for future development and enabling informed
decision making. However, modeling renewable energy sources andflexibility options requires high spatial and temporal resolution, resulting in some of the largest linear optimization problems tackled.
Various approaches have been developed to deal with this complexity,
including heuristics, mathematical decomposition, and complexity reduction. This study combines the REMix framework with the parallel
solver PIPS-IPM++ to compute transformation paths for the European
energy system. This combination can provide insights into the ideal
timing for converting natural gas infrastructure to hydrogen and explore the trade-off between investment rates and the remaining carbon
budget.
First, this talk addresses the computational challenges of solving large
linear optimization problems with parallel solvers and discusses thetuning of the decomposition approach to exploit the block structure.
Second, it presents insights and key findings on the cost-optimal transformation path of the European energy infrastructure towards climate
neutrality. These findings include spatial allocation of capacity andstorage, as well as robust network topologies for both power grids andgas pipelines.

2 - Climate policy uncertainty, electricity production andcredit risk: do banks enhance green investments?

Lorenzo Calò, Valeria Stefanelli, Pierluigi Toma
Credit risk in the banking sector is crucial in the current discussions
on climate change and environmental sustainability. The transition
from non-renewable to renewable energy sources, supported by theParis Agreement, is the main strategy to mitigate the effects of climatechange through the reduction of greenhouse gas emissions. Governments’ tailored climate policies are key in this context to promote agreener economic pathway. Climate policy uncertainty places banks
and their credit allocation decisions in a significant position, directlyEURO 2024 - Copenhageninfluencing investment choices in the energy sector, including the development and adoption of renewable energy technologies. In this context, credit risk emerges as a crucial factor, as banks, the main lenders,guide the directions of energy investments by granting or withdrawing
credit for various energy projects. This study aims to assess the impact of climate policy uncertainty on electricity generation in the U.S.
from 1988 to 2015, focusing on non-renewable and renewable sources.
The main goal is to investigate the effects that the CPU have on suchenergy sources. We employ the Fourier-ARDL model and Granger’s
causality test to explore the complex relationship between CPU andelectricity production. The findings have significant implications forpolicymakers, managers, and banks, with regard to energy infrastructure investment decisions and lending decisions for the acquisition ofsuch infrastructure.

3 - Identification of extreme weather events for the designof a climate resilient European energy system

Francesco De Marco, Giovanni Sansavini
Climate change impacts energy systems reliability. Extreme weather
events, such as droughts and heat waves, can undermine the abilityof technologies to convert energy and can exacerbate the demand forelectricity, heating, and cooling. This causes unexpectedly high operative costs and, in the worst cases, inability to supply demand. Hence, it
becomes crucial to consider the impact of more frequent and severe extreme weather events during the planning of a cost-optimal low-carbon
energy system to ensure climate resilience. This work introduces amethod to convert the output of the global climate model CESM2 into
hourly-resolved energy-related metrics, e.g., capacity factor, by capturing the impact of weather on conventional, renewable technologies,
and on energy demand. Such metrics are used to parametrize a multiyear transition pathway optimization problem of the European energy
system until 2050. The resilience of the system with respect to extreme
events is investigated through a scenario analysis. First, the system is
optimized in a base climatic case; then, alternative extreme scenarios
are generated via a boosting method that aggravates those short-term
weather events that are most critical to the operation of the energy system. The new climatic time series are converted again as input of the
optimization problem. The resulting climate-resilient systems trade-off
investment and operative cost to guarantee reliable and clean energy ina climatically unstable future.

 WA-54
Wednesday, 8:30-10:00 - Room: S01 (building: 101)

Network Design and Line Planning for
Public Transportation 2
Stream: Public Transport Optimization
Invited session
Chair: Rowan Hoogervorst
1 - Integrated timetabling, vehicle scheduling, and dynamic
capacity allocation under demand uncertainty

Dongyang Xia, Jihui Ma, Shadi Sharif Azadeh
The Integrated Timetabling and Vehicle Scheduling (TTVS) problem
has extensive applications in all sorts of transit networks. Recently,
the emerging modular autonomous vehicles composed of modular
units have made it possible to dynamically adjust on board capacity
to further match space-time imbalanced passenger flows. In this paper, we introduce an integrated framework for the TTVS problem fora fixed line dynamically capacitated modularized bus network, taking the time-varying and uncertain passenger demand patterns into
account. The modularized bus network comprises units that can be(de)coupled and rerouted to other lines through the network at different times and locations to respond to time-varying demand. We formulate a stochastic programming model to jointly determine the optimal robust timetable, dynamic formations of vehicles, and cross-line

WA-54

circulations of these units, aiming to minimize the weighted sum ofoperational and passengers’ costs. To obtain high-quality solutions ofrealistic instances, we propose a tailored integer L-shaped method coupled with valid inequalities to solve the stochastic mixed-integer programming model dynamically through a rolling horizon approach. An
extensive computational study based on the real-world operational dataof the Beijing bus network shows the effectiveness of the proposed approaches.

2 - Designing public transport networks via symmetric
generalized optimum requirement graphs

Piyalee Pattanaik, Irene Heinrich, Philine Schiewe
Network design is a fundamental problem in public transport planning,
fostering sustainability and efficiency. Here, we model the problem of
reducing a given street network to a public transport network as a Generalized Optimum Requirement Graph (GORG) Problem. Our aim isto uncover a sub-graph within a fixed edge budget that minimizes routing costs associated with demand, where routing costs encapsulate thesummation of all shortest path costs weighted by demand. When considering orb-webs, as an abstraction of circular cities, we can exploit
their symmetry and a reduced solution space. We show that finding optimal symmetric generalized optimum requirement graphs is nevertheless NP-hard in general and identify polynomially solvable cases. By
comparing integer-programming based solution approaches for both
the general and the symmetric GORG problem, we demonstrate thecomputational efficiencies of the reduced problem space. Allowing
only symmetric solutions, however, also influences the solution quality and introduces the so-called "price of symmetry." We analyze theinfluence of the budget and the underlying cost structure on the priceof symmetry and show that especially for a Euclidean cost structure,generic and symmetric solutions have almost identical routing costs.
Moreover, beyond a global perspective on routing costs, our evaluation
extends to assessing the fairness of solutions across passenger groups.

3 - Integrated Vehicle Circulation and Periodic Timetabling
Optimization with SMT

Florian Fuchs, Thomas Spanninger, Francesco Corman
Our study presents a novel approach to optimizing the Periodic
Timetabling Problem (PESP) and Vehicle Circulation in the Rhätische Bahn Network (RhB-Network) using Satisfiability Modulo Theories (SMT). Like traditional Mixed Integer Programming (MIP), ourmethodology utilizes the Event Activity Network (EAN) to model railway timetable events and activities. However, our approach encodes
the PESP and vehicle circulation constraints within an SMT framework, extending SAT with difference logic. This extension allows
for high-resolution time calculations, addressing the scalability limitations of existing SAT methods. Furthermore, we use selectable activities, enhancing the model’s capability to incorporate vehicle circulation effectively during train scheduling and routing. Empirical analysis on a large scale, Swiss Railway Network reveals that although
MIP and SMT solvers show comparable performance on smaller instances, the SMT solver significantly outperforms on larger scales,consistently finding optimal solutions. In particularly challenging instances, we achieve a reduction in computation time by a factor of 100
or more, illustrating the value of SMT-based methods in solving complex scheduling problems and advancing public transport optimization.

4 - Integrated route selection and redesign of the surrounding bus network for a new BRT line

Rowan Hoogervorst, Evelien van der Hurk
In this talk, we consider the development of a new BRT line and theredesign of the surrounding bus network to best match the new line.
Here, we consider the integrated problem of selecting a route for the
BRT line as well as line planning and frequency setting for the surrounding bus lines. The route for the BRT line can be chosen from aselected set of edges within a graph, while the surrounding bus lines
are chosen based on a given line pool. Moreover, travel time and operational cost benefits can be obtained for surrounding bus lines dueto the possibility of using BRT segments, which allow for increased
speed and reliability. The objective of our problem is to minimize thepassenger travel time and operational costs, where we integrate therouting of the passengers as well as line capacities into the model. We

355

WA-55EURO 2024 - Copenhagenpropose a column-generation approach to solve this problem, wherecolumns correspond to possible travel paths for passengers. Our approach is tested both for artificial instances as well as a case study fora new BRT line in Greater Copenhagen.

 WA-55
Wednesday, 8:30-10:00 - Room: S02 (building: 101)

MOST - MaaS & Innovative Services for
Sustainable Mobility
Stream: Transportation
Invited session
Chair: Michele Ottomanelli
Chair: Aleksandra Colovic
1 - Planning and managing MaaS services

Marilisa Botte, Alessia De Falco, Ilaria Henke, Fiore Tinessa,
Luigi Pariota
Mobility as a Service represents a topical theme in the current researchand innovation ecosystem thanks to its potential disruptive impacts ontransportation systems. The current literature shows a lack of tools
and examples which could provide public authorities (or other relevant
stakeholders) with useful decision-making support to quantitatively
evaluate the expected effects of the introduction of MaaS packages
on the transportation system, thus conceiving and planning them mostproperly. Many works in the literature evaluate MaaS effects by meansof agent-based simulation tools which miss the systemic and strategic perspective typical of the planning stage. Other contributions fully
neglect the planning task, by evaluating the effects of MaaS services
as empirical evidence from pilot programs and field trials. Therefore,
this contribution proposes a decision support system for estimating theeffects of the introduction of MaaS services on different stakeholders(i.e., MaaS users, other transport systems customers, transport operators, etc.) based on the aggregated representation of demand (in afour-step approach) and supply systems in the presence of MaaS. As afirst attempt, the proposed approach has been tested on a toy network toshow an example of the quantitative evaluation of the adherence of different MaaS packages to the territorial contexts and governance goals
pursued. The proposed contribution appears to be unique in the current
research literature.

2 - Urban multi-vehicle safety-critical environment for Connected and Automated Vehicles: MASA living lab in thecontext of Spoke 6

Andrea Castellano, Luca Bedogni, Giulia Berardinelli, Marko
Bertogna, Carlo Augusto Grazia, Martin Klapez, Elisa
Landini, Marco Mamei, Alessio Masola, Marco Picone,
Enrico Rossini
Nowadays, vehicles are able to drive autonomously in specific contexts. Spoke 6 project, part of the Italian National Centre for Sustainable Mobility (MOST), aims to contribute to developing an ecosystemof research and testing labs to promote the deployment of Connected
and Autonomous Vehicles (CAV). This development will result in a
network of permanent facilities for collaborative research, promoting
technology transfer, increasing technology readiness in real-world contexts, and facilitating large-scale adoption of automated vehicles. In
this context, Spoke 6 is extending the Modena Automotive Smart Area
(MASA) to create an Urban Living Lab for CAV testing and data collection in a real urban multi-vehicle setting. The MASA facilities havebeen outfitted with traffic monitoring systems (based on smart cameras,
RSUs, and OBUs) to enable the implementation of safety-critical scenarios. In this case, communication between infrastructure and road
actors can broaden the electronic horizon and raise awareness of thetraffic situation. In line with the project’s pillars, this living lab is producing outcomes on: (i) Simulation, with the development of a Digital

356

Twin of the MASA to reconstruct the environment and visualize real
traffic data; (ii) Vehicle Platforms, by equipping an automated vehicle
with communication modules and an innovative HMI; (iii) Datasets,with the collection of environmental and driving data to design andtest automated driving modules.

3 - Unleashing Mixed Integer Linear Programming: Optimal
Shuttle Routing Solutions for Enhanced Urban Mobility

Marco Fabris
Efficient urban mobility is paramount in the evolving landscape of Mobility as a Service (MaaS). This study tackles a shuttle routing problem within this framework, akin to the orienteering problem, aimed
at optimizing existing transportation networks. The problem involves
optimizing shuttle routes through a directed graph representing an urban network, where nodes correspond to shuttle stops and edges depict
road connections with associated travel times. Users at stops need toreach a common destination under specific time constraints, including
arrival time and a tolerance window. The primary objective is to maximize passenger count while adhering to these constraints and operating
within a predefined service time window. A mixed integer linear program (MILP) has been developed to obtain exact optimal solutions,so far demonstrating efficacy for networks of up to 15 nodes withinreasonable computational time. The MILP discretizes shuttle time resources, thus ensuring solutions to be characterized by loop-free paths
and optimal time schedules for the picked-up users. Further refinements to this result consider practical limitations, such as the shuttle
capacity, and fairness-based incentives for people in order to strategically foster this kind of mobility (e.g., by favoring remote nodes).
This approach lays the groundwork for scalable solutions in terms ofboth urban networks and number of deployed shuttles, as well as forreal-time data integration for dynamic scheduling.

4 - Optimal placement of electric vehicle charging stationsin urban areas

Aleksandra Colovic, Mario Marinelli, Michele Ottomanelli,Samira Stivan, Vitti Federico, Federico Devalle, Andrea
Demartini
Sustainable travel modes are considered as one of the main directions
for moving towards more sustainable living in urban areas. The technology advancements of electric vehicles (EVs) have been receiving
growing attention on global market. Still, the support of public authorities in pushing towards sustainable mobility paradigms and designing solutions for the deployment of charging infrastructures is needed.
There are several papers in the literature that have dealt with the estimation of EVs charging demand, both on macro and micro level,
the placement of EVs charging facilities and management of real-time
operations of EVs. In this context, we propose a novel optimization
model for the efficient placement of EVs charging stations by considering the influence of existing points of interest (such as restaurants,
supermarkets, touristic places, schools, hospitals, etc.) and service radius of potential EVs facilities. Generally, the coverage of a charging
point, expressed in terms of average time or distance to reach a charging station, is correlated with the willingness-to-use of EVs drivers.
Also, the presence of a particular category of points of interest, within
the predefined service radius, shows the attractiveness of charging location. The proposed model has been applied to a real network of theLombardy region. Preliminary results are encouraging and push towards further developments.

 WA-56
Wednesday, 8:30-10:00 - Room: S04 (building: 101)

Advancing mobility towards sustainable
solutions III
Stream: Transportation
Invited session
Chair: Atefeh Hemmati Golsefidi

EURO 2024 - Copenhagen
1 - An AGV scheduling problem based on partial charging
policyWei Wang, Hui Sun
This paper studies a scheduling problem for automated guided vehicles(AGVs), in which a fleet of homogeneous AGVs are used to deliverparts to workstations in a shop floor environment. During delivery thebatteries of AGVs can be recharged, in order to ensure all parts aretransferred from a central warehouse to their respective destinations
without stops. A partial charging policy, which allows to flexibly manage the charging operations for AGVs, is employed. The transportation
tasks undertaken by each AGV and the charging operations on each
AGV need to be scheduled. The objective is to minimize the makespanto complete all transportation tasks. A mixed integer programming
model is presented to describe the AGV scheduling problem. A twostage heuristic approach is proposed for problem-solving. For stage
one, two metaheuristics, i.e., a genetic algorithm (GA) and a hybrid
GA and whale optimization algorithm (WOA), are developed to assign
transportation tasks to each AGV. For stage two, a rule-based heuristic
is proposed to schedule the assigned transportation tasks and charging
operations for each AGV. Computation experiments show that the proposed solution procedures can solve the scheduling problem effectively
and efficiently.

2 - Effects of time-of-use electricity prices on electric vehicle schedules

Fabian Brockmann
In recent years, the share of electric vehicles (EVs) for freight transport increased. Transportation companies must create daily routes forthese vehicles by solving the electric vehicle routing problem (EVRP).
This is mostly done in the same manner as for traditional internal combustion vehicles but aspects regarding range and recharging needs areadded to the problem. However, dealing with EVs brings up a new
problem: The Charging Scheduling Problem (CSP). EVs have not only
to recharge along the route but also at their depot after operating hours.
This can be done either by simple plug-and-charge, manual timing, oran optimized schedule. An important aspect of flexible recharging solutions is the connection time of the EV to the charging device. Therefore, the schedule of an EV over the day (timeline) is important for
the CSP. However, most EVRP consider electricity prices as a constantfuel cost and do not consider the volatility of electricity prices. We
address this problem and show how to combine the EVRP with the
CSP.

3 - Dynamic and incremental expansion of large scale fixed
and mobile charging infrastructure in stochastic environment: A Benders decomposition based approach

Atefeh Hemmati Golsefidi, Francisco Pereira, Samitha
Samaranayake
As electric vehicle adoption increases worldwide, the growing charging demand necessitates a well-thought-out expansion of public charging infrastructure; insufficient or improperly deployed infrastructure
poses the real risk of slowing down the adoption of electric vehicles.
Public charging networks are likely to develop into very heterogeneous
systems with, for example, fixed and mobile chargers. This paper proposes a multi-period mixed-integer programming formulation for optimally placing both fixed and mobile chargers in stochastic environments with the goal of meeting time-varying charging demands at aminimum cost. To discover EVs’ uncertain spatial and temporal behavior, the energy demand scenarios are generated from an existing agentbased simulation of EVs in Frederiksberg and Copenhagen municipalities. Large-scale demand scenarios for Frederiksberg municipality are
extracted from the simulation and then fed to a two-stage stochastic optimization to find the optimal expansion of fixed and mobile charging
stations. As this formulation leads to an NP-hard problem, we develop
a Benders decomposition for solving large-scale examples. In addition, a new algorithm is proposed to get exact sub-problem solutions
and their corresponding dual variables without using linear programming solvers. Detailed experimental results confirm the effectivenessof the approach, which can provide exact integer solutions in a shortcomputational time for even large problem instances.

WA-57

 WA-57
Wednesday, 8:30-10:00 - Room: S06 (building: 101)

Large-scale Speed-sensitive Network
Optimization
Stream: Optimization at Amazon
Invited session
Chair: Daniel Guimarans
Chair: George Iosifidis
1 - Stochastic Glance-View estimator

Jean-Charles Croix, Marc Bataillou Almagro, Augustin
Lombard, Mehdi Bennaceur
The promised delivery speed enjoyed by Amazon’s customers whenglancing over the website’s items (ASIN) is the result of a cascade ofplanning algorithms. At the core of this cascade is the modelling of
the expected speed given a network design and inventory allocation.
In this work, we present a scalable probabilistic approach to estimate
the future glance-view speed, enabling us to query inventory aware
speed variations in micro seconds. The close formulae derived in thiswork decouples inventory coverage from glance-view count forecasting, allowing us to answer valuable what-if scenarios regarding inventory placement, or new site launches. This in turn can guide Amazon’s
multi-million network design initiatives from short to long term horizons.

2 - Speed-aware network design: a parametric optimization
approach

Ugo Rosolia, Marc Bataillou Almagro, George Iosifidis, Amit
Kumar, Georgios Paschos
Network design problems have been studied from the 1950s, as they
have a wide range of real-world applications. In classical network design problems the objective is to minimize the cost of routing goods
through a graph. We introduce a generalised version of such a problem, where the objective is to trade-off the speed at which goods canbe sent to their destination nodes and the cost of routing them. Compared to standard time-aware network design problems, where speed
is taken into account by adding deadline constraints - i.e., goods haveto reach destinations before a target date - we introduce a coveragefunction mapping the set of goods served by origin-destination pairs
for which we offer expedite delivery to the number of unique items
for which fast delivery services can be offered. This coverage function
models that sets of goods for different origin-destination pairs may not
be disjointed, and it allows us to compute the share of unique items
for which we can offer expedite delivery services, which we refer to as
speed-coverage. To minimize routing costs while jointly maximizing
speed-coverage, we leverage parametric optimization to reformulate
the coverage function as a convex program with an exponential number of constraints. Then, we propose a sampling strategy to reduce thedimensionality of such a reformulation. We show that when considering the overall costs given by routing costs minus speed revenues, ourapproach outperforms the baseline by 8.36% on average

3 - Local Solver for Joint Connectivity and Timing Optimization for Amazon’s Outbound Network

Philipp Loick, Jian Shen, Jean-Charles Croix, Tiphaine
Rougerie, Mattia Neroni, Amit Kumar, Georgios Paschos
The goal of Amazon’s outbound network planning is to offer customers
fast delivery at minimum costs. Key decisions are the legs and sorts toconnect sites (connectivity) and Critical Pull Times (CPTs). This problem is computationally hard to solve and was in the past decomposed
into the connectivity and timing subproblems that were solved separately. This paper presents a local-search engine to jointly optimize
connectivity and timing for Amazon’s outbound network. It bundles avariety of scientific and engineering ideas to build optimized solutions
efficiently.

357

WA-58EURO 2024 - Copenhagen WA-58
Wednesday, 8:30-10:00 - Room: S07 (building: 101)

MILPs for Vehicle Routing 2
Stream: VeRoLog - Vehicle Routing and Logistics
Invited session
Chair: Teobaldo Bulhões
1 - Two-Commodity Opposite Direction Network Flows for
Vehicle Routing Problems

Konstantin Pavlikov, Niels Christian Petersen
This paper revises the two-commodity network flow modelling approach for the Capacitated Vehicle Routing Problem (CVRP) withidentical vehicles. The flows of equally sized two commodities considered in this paper are organized in the opposite directions along thecollection of routes assigned to vehicles. The resulting mathematicalprogramming formulations allow to formulate a general, asymmetric
CVRP (even with presence of Time Windows constraints) using n(n 1)/2 binary variables, where n is the number of customers involved in
the definition of CVRP, which is the number of binary variables typically required for the symmetric CVRP only. The commodity flow
formulations of the problem is a very flexible tool for modeling the
problem that allows for various enhancements. For example, the family of Generalized Large Multistar valid inequalities can be incorporated in a commodity flow-based formulation of the problem without
using a single extra constraint. In addition, the compact mathematicalformulations of the problem are further improved using Rounded Capacity Inequalities at the root node. The computational study demonstrates that a wide range of problem instances from the literature canbe solved to optimality (or suboptimality with relatively small optimality gaps) using a simple solution methodology that solely employsa standard mixed integer linear programming solver.

2 - On a Generalization of the Information Collection Problem

Esther Jose, Nastaran Oladzad, John Becker, Rajan Batta
In today’s data-driven world, efficient collection of information is crucial. We generalize this on a graph and propose a model that can beapplied to a wide range of information collection problems. To solve
an information collection problem on a graph where nodes contain retrievable information within a specific range, we introduce a two-phaseapproach. In the first phase, we pre-process the graph to determine
the information that can be collected by moving through edges of thegraph. In the second phase, we use a mixed-integer linear programming (MILP) model to maximize the amount of valuable informationcollected by traversing the edges of the graph within a given time limit.
We provide extensions that can accommodate additional assumptions
such as varying speed, information degradation, and threat or detection avoidance. Our model has practical applications for any problem
where an agent, sensor, or decision maker is collecting informationfrom a space.

3 - Comparison of Formulations for the Family Capacitated
Vehicle Routing Problem

Raquel Bernardino, Ana Paias
The family capacitated vehicle routing problem (F-CVRP) is an NPhard problem that generalizes both the FTSP and the capacitated vehicle routing problem. The F-CVRP has practical applications in warehouse management in warehouses with scattered storage. We propose
several compact and non-compact formulations to address the problem. These formulations are compared both theoretically and computationally. Additionally, sets of valid inequalities are also derived
and tested computationally. A branch-and-cut algorithm is used as asolution method for the non-compact formulations. Computational results show that both the developed branch-and-cut algorithm and thebest-performing compact model are suited to address small to mediumsized instances of the F-CVRP.

358

4 - A Unified Exact Approach for a Broad Class of Vehicle
Routing Problems With Simultaneous Pickup and Delivery

Teobaldo Bulhões, Rafael Praxedes, Anand Subramanian,
Eduardo Uchoa
The vehicle routing problem with simultaneous pickup and delivery(VRPSPD) is a well-known combinatorial optimization problem. It involves finding the least-cost set of routes, starting and ending at the
depot, while simultaneously satisfying pickup and delivery demands
from multiple locations within a single trip without exceeding the capacity of the vehicle. There are many variants of this problem, each
one incorporating additional attributes such as a heterogeneous fleet ofvehicles, time constraints, route duration, multiple depots, and decisions regarding depot location. In this work, we propose a unified formulation for ten of these variants, which were considered special casesof a generic problem denoted as heterogeneous location routing problem with simultaneous pickup and delivery and time windows (HLRPSPDTW). To solve it, we developed a branch-cut-and-price (BCP) algorithm and validated it through extensive computational experiments
on more than 530 benchmark instances. The proposed BCP algorithm
successfully found optimal solutions for 44.3% of unsolved problems,
attained 92.6% of known optima, and improved lower bounds for
69.9% of the instances that remained unsolved.

 WA-59
Wednesday, 8:30-10:00 - Room: S08 (building: 101)

Pricing and applications 2
Stream: Pricing and Revenue Management
Invited session
Chair: Maher Agi
1 - Customized airline offer management: inductive research as proof-of-concept of high-dimensional segmentation

Daniel Schubert
Airlines serve different customers. Developing customized offer andpricing strategies have become a strategic priority to airlines and haveattracted operations research accordingly. The present paper tests thefundamental hypothesis of the offer management system architectureof Schubert et al. (2021) that aims to combine the simplicity of branded
fares with the flexibility of unbundled ancillaries. The hypothesis isthat the wealth of data available to airlines enables highly granular andhigh-dimensional segmentation based on multiple features of a particular customer search.
The hypothesis is tested through inductive research based on 202 million flight coupons of a major network airline between 2018 and 2023.
The research observes customer choice probabilities differ depending
on weekday of the booking. Next, it identifies a pattern. Customer
choice probabilities also differ based on sales channel, flight characteristics, and customer loyalty status. Generalizing these findings, thisresearch concludes with a new hypothesis that airlines can improveprediction accuracy of customer choices and hence display more relevant offers when considering these search features. If true, it presents
a low-cost process to increase prediction accuracy with data readily
available to airlines. The research is relevant to both airline practitioners and researchers for follow-up studies alike.

2 - Customer-Centric Dynamic Pricing for Free-Floating Vehicle Sharing Systems

Matthias Soppert, Christian Müller, Jochen Gönsch, Claudius
Steinhardt
Free-floating vehicle sharing systems such as car sharing systems offer customers the flexibility to pick up and drop off vehicles at any

EURO 2024 - Copenhagen
location within the business area. However, this flexibility comes withthe drawback that vehicles tend to accumulate at locations with low
demand. To counter these imbalances, pricing has proven to be an effective and cost-efficient means. The fact that modern systems rely
on mobile applications for their communication with customers, combined with the fact that providers know the exact location of each vehicle in real-time, offers new opportunities for pricing. We develop
a profit-maximizing dynamic pricing approach which is customercentric, meaning that, whenever a customer opens the mobile application, the price optimization incorporates the customer’s location as
well as the customer’s choice behavior. In particular, it considers theeffects of prices and walking distances to available vehicles on thecustomer’s rental decision. Further, the approach anticipates future
vehicle locations, rentals, and profits. More specifically, we propose
an approximate dynamic programming-based solution approach with
nonparametric value function approximation. It allows direct application in practice, because historical data can readily be used and key
parameters can be precomputed such that the online pricing problem
becomes tractable.

3 - Impact of blockchain on product pricing and vertical differentiation

Maher Agi, Erfan Asgari
We consider a manufacturer who sells two vertically differentiatedproducts: a high-quality product and a low-quality product. Using
blockchain is assumed to have a multiplicative increase of consumer
perception of the product’s quality and a loss of consumer utility due
to privacy concerns. It is also associated with a fixed cost implementation and a per unit marginal cost. Given this, we build a stylized
model based on customer utility and self-selection to study the impactof using blockchain on the conditions of co-existence of the two typesof product on the market, the products’ optimal prices, their respective
market shares, the manufacturer’s profit and his differentiation strategy.
Preliminary results show that the two products will continue to coexist while blockchain is being used only if the loss of consumer utility
due to privacy concerns is contained within a certain interval (neither
too high nor too low). The results also demonstrate a cannibalization
effect of blockchain, while the manufacturer’s overall market coverageremains the same. Moreover, the results establish the sensitivity of thebenefit from using blockchain to the level of differentiation between
the two products, and the impact of blockchain usage on the productdifferentiation level.

 WA-60
Wednesday, 8:30-10:00 - Room: S09 (building: 101)

Manufacturing scheduling withsustainability considerations
Stream: Project Management and Scheduling
Invited session
Chair: Federico Perea
1 - Scheduling Automated Stacking Cranes in Container
Terminals

Sameh Haneyah
Container terminals are crucial links in the global supply chain. Thesefacilities operate in a highly dynamic environment. In this context,
a main logistic challenge is scheduling the container handling equipment to execute the container transport and handling operations efficiently and effectively. We research the objectives and optimization
strategies to deploy in scheduling and dispatching container handlingequipment, in automated container terminals. In this effort, we review
literature on scheduling and dispatching in container terminals, model
the container terminal scheduling problem and address different solution methods. We decompose the problem and focus on the Automated
Stacking Cranes (ASC) scheduling problem. This problem addresses

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trade-offs such as delivering outbound containers to the transfer zones
just-in-time versus delivering the containers early to avoid extra handling in the yard. The choices made on when to schedule container
moves have a direct impact on the space utilization in transfer zones
and on the minimization of unnecessary travel by the ASCs. In thisstudy, we formulate the ASC Scheduling problem and discuss an online scheduling method.

2 - Scheduling manual operations on tire manufacturingmachines

Mario Flores Gomez, Stéphane Dauzere-peres, Nabil Absi
Michelin, founded in 1889 in France, has evolved into one of theworld’s leading tire manufacturers. The company’s commitment toresearch and development has propelled it to the forefront of tire technology, ensuring that its products meet the evolving needs of its various clients. This presentation is motivated by a real tire production
line which, even though it is highly automated, still requires more than
one operator to run. Operators play a crucial role in verification processes and are instrumental in resolving unforeseen issues. Our goal is
to schedule the operations on the production line more efficiently, butalso to guide the operators when random event occur. Actually, someof these random events should be considered when determining theschedules. The problem at hand can be defined as a flexible job-shop
scheduling problem, where the routing of the operations of each job includes automated and manual operations. As all the automated operations are performed by the same machine, we are interested in scheduling the manual operations that should be assigned to and scheduled on
one operator among the eligible operators. The objective to minimize
is the flow time of the tires for a given team of operators. The presentation will detail how the problem is modeled and introduce the solution
approaches for the deterministic case and for the stochastic case where
random events are taken into account. Computational experiments on
industrial data will be discussed.

3 - Scheduling on parallel metrology tools for risk minimization

Mathis Martin, Claude Yugma, Stéphane Dauzere-peres
In semiconductor manufacturing, product reliability and quality are
crucial, especially for critical applications such as automotive or
robotics. Hence, the outputs of production machines are measured
through control operations performed by metrology (measurement)
tools. As these tools become increasingly expensive, optimizing their
use to minimize the risk is essential . Risk indicators can be used toprioritize lots in metrology. This work relies on the "Wafers at Risk"(W@R), which is the number of lots processed on a production machine between two lots that are measured. Each lot selected to bemeasured induces a gain (risk reduction) on the W@R of each of the
production machines on which the lot was processed, and the earlier
the lot is measured, the earlier the gains are obtained. In this presentation, we introduce a scheduling problem on parallel machines where
the total risk reduction over time is maximized. Compared to classical parallel machine scheduling problems, computing the criterion
requires to consider how the W@R of all production machines evolve
when lots are measured on all metrology tools. To ensure the synchronization between metrology tools, time-indexed binary variables are
required in the mathematical model of the problem. Various solution
methods have been proposed, including a column generation approach,a greedy heuristic, and a Greedy Randomized Adaptive Search Procedure (GRASP). Numerical results will be presented in the conference.

4 - Acceptance Ordering Scheduling Problem: The impactof an order-portfolio on a make-to-order firm’s profitability

Federico Perea, Juan Camilo Yepes Borrero, Mozart Menezes
Firms’ growth, the darling measure of investors, comes from higher
revenues. Thus, sales and marketing departments make extreme efforts
to accept as many customer orders as possible. Unfortunately, not allorders contribute equally to profits, and some orders may even reduce
net profits. Thus, saying no (i.e., not accepting an order) may be a necessary condition for net profits growth. For understanding the impactof rejecting orders on profitability, we propose an order acceptance andscheduling problem (OAS). Although the OAS has extensively beenstudied in the literature, there is still some gap between these papers
and real-life problems in industry. In an attempt to close that gap, the359

WA-61EURO 2024 - Copenhagen

OAS we propose considers orders revenues, machines costs, holding
costs and tardiness costs. We develop a mixed integer linear programming (MILP) model for solving this problem. Since the complexityof the problem makes it impossible for the MILP to solver large-scaleinstances, we also propose a metaheuristic algorithm. Numerical experiments show that the metaheuristic finds good quality solutions inshort computational times. In the last part of the paper we confirm
some managerial insights: higher holding and tardiness costs imply alower acceptance of orders, forcing production has a concave negative
impact on net profits, and accurately estimating costs is essential forgood planning.

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employed and outlining potential outcomes, this research contributes
to the advancement of location analysis methodologies in the context
of biomass supply chain optimization.

3 - Extending service locations optimally

Ashutosh Mahajan
We consider a firm that provides offline services through manybranches to a population spread over a large geographic area. The firm
is interested in opening additional satellite facilities in addition to its
branches to improve its reach and attract new customers. Such facilities are also intended to relieve existing branches of low-value services
so that they can focus on activities more valuable to the firm. Basedon data available from a large bank in India, we create a model for deciding which locations are suitable for adding more satellite facilities.
The model considers the footfall data of the existing branches, theirgeographical information and transactional data.

Wednesday, 8:30-10:00 - Room: S10 (building: 101)

Applications of Location Methods
Stream: Locational Analysis
Invited session
Chair: Daniel Serra
1 - Deterministic Model for Optimal Industrial Parks Location: The Case of Indonesia’s Nickel Industry

Hasan Nur, Aadhaar Chaturvedi, Valery Pavlov
Industrial parks play a crucial role in facilitating the rapid industrialization of many emerging economies countries. However, planning theseparks is complex, and their success hinges on strategic location decisions. This study proposes a mixed-integer linear programming formulation to determine the optimal locations of industrial parks to minimize total costs. It integrates large-scale actual distance measurements
and multi-mode transportation considerations, capturing geographicalnuances more realistically. Recognizing the energy-intensive nature ofindustries, the model incorporates transportation costs associated withenergy sources. Real-world data sourced from the Indonesian nickel
industry lends practical relevance to the proposed model within limited facility location studies with real-world applications. The casestudy examines 81 potential park locations, 139 nickel mining, and20 coal mining sites, resulting in over 13,000 variables. Leveraging
OpenSolver, optimal solutions are obtained within 15 seconds. The
analysis unveils two optimal industrial park locations alongside capacity, raw materials, and energy supply allocation to selected industrial
parks. Insights from sensitivity analysis include the pronounced influence of land acquisition, sea transport costs, and the elevated total
costs associated with single-location industrial parks. Future research
will explore uncertainty parameters relevant to the context of emerging
economies.

2 - Combining MADM and MODM for allocating biomass
conversion plants in Southwest Germany

Andreas Rudi, Frank Schultmann
Optimizing biomass supply chains necessitates the allocation of feedstock supply and biomass conversion facilities. This presentation introduces an innovative approach that combines Multi-Attribute Decision
Making (MADM) with Multi-Objective Decision Making (MODM)
to harmonize diseconomies of supply with technological economies
of scale, thereby evaluating various biomass valorization pathways.
Leveraging MADM and GIS-based location analysis, we establish acomprehensive location suitability criterion integrated into a two-foldlinearized Mixed Integer Linear Programming (MILP) model, incorporating economic and ecological objectives. The multi-objective formulation is efficiently solved using the augmented epsilon constraint
method. Finally, we employ the Technique for Order Preference by
Similarity to an Ideal Solution (TOPSIS) method to assess the location
decision of biomass conversion facilities and determine optimal designs for biomass supply chains. This approach not only estimates thepotential for energetic or chemical biomass valorization in Southwest
Germany but also facilitates strategic decision-making by integrating
multi-criteria location analysis. By elucidating the specific methods360

 WA-62
Wednesday, 8:30-10:00 - Room: S12 (building: 101)

Machine Learning and Optimization in Ports
I
Stream: OR in Port Operations
Invited session
Chair: Frederik Schulte
1 - Container Terminal Energy Management and Operations Planning Under Uncertainty with Learning-based
Onshore Power Scenarios

Xinyu Tang, Frederik SchulteContainer terminal operators reduce the energy costs in container terminals by implementing a demand response system, which can shiftenergy consumption typically through energy prices. We investigate
operations planning and energy management through demand responseto reduce energy-related costs. We propose a two-stage stochastic
mixed integer programming model with energy-related costs as partof the objective function. The considered operations include vessel
scheduling at berths, temperature control of refrigerated containers,
and allocation of handling capacity of quay cranes, yard cranes, andautomated guided vehicles to serve each vessel. Various scenarios forvessel arrival times and electricity prices are explored to represent theuncertainty of energy demand and supply, respectively. Moreover, onshore power supply (OPS) will become an increasingly important partof energy consumption in ports. We develop a machine-learning-based
approach to determine realistic OPS demand scenarios in stochastic
optimization. The suggested model is decomposed and solved using a
progressive hedging algorithm. A case study of the Alterwerder container terminal in Hamburg is conducted to test the model. Results
show a significant improvement when applying a varying real-time energy pricing scheme compared to a single energy pricing scheme.

2 - Pick-up and Drop-off Prediction for Tugboat Operations- An Inverse Optimization Approach

Frederik Schulte, Yuanfeng Xu, Mahnam Saeednia, Pedro
Zattoni Scroccaro
Historical analysis of data related to the pick-up (i.e. the connectionof the tugboat to the vessel) and drop-off (i.e. consequent disconnection) locations at ports such as the Port of Rotterdam shows a divergence from the scheduled plans, hypothetically related to factors suchas weather, other scheduled activities for the tugboat, or other unknown factors. This poses challenges for the scheduling of tugboats
and vessel-related activities, calling for an effective approach to predict the locations where vessels are picked up and dropped off. In
this work, we propose an inverse optimization model to emulate thedecisions of pilots in selecting the actual drop-off locations, aiming
at learning the implicit decision models used by them. To this end,
the proposed inverse optimization approach takes the actual decisions

EURO 2024 - Copenhagenas inputs and determines an objective and/or constraints that replicate
these decisions. In the model, ’signals’ are defined as the importantfactors that impact the decision of pilots such as vessel-, towage-, andweather-specific information. The model is constructed based on anorienteering problem, in which the objective function aims at maximizing the total score of a path calculated as the sum of the visited
node scores. The output of the model minimizes a loss function defined as the difference between the predicted and actual locations. The
results enable advanced anticipatory tugboat scheduling that outperforms conventional myopic approaches.

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We study a reinforcement learning approach to portfolio selection
when the underlying asset price process follows a regime-switching
diffusion model. An analytical solution to the HJB equation for the exploratory mean-variance model under regime switching is derived andimplementation in practice employing a deep neural network will be
discussed. Empirical results investigating robustness with respect tomodel mis-specification will also be presented.

3 - Sparse spanning portfolios and under-diversification
with second-order stochastic dominanceNikolas Topaloglou, Stelios Arvanitis, Olivier Scaillet
3 - RORO Stowage Planning with Reinforcement Learning

Alastair Main, Filipe Rodrigues, Dario Pacino
Reduction of Greenhouse Gas (GHG) emissions in the shipping industry has been of great focus over the past years. Within Roll-on/Rolloff (RORO) shipping, GHG emissions can be reduced by decreasing
the turnaround time at port calls. Decreasing the turnaround time for
RORO vessels results in more time being available for sailing between
port calls. As more time is allotted for sailing, the vessel can significantly reduce its speed and, thereby, fuel consumption. If RORO vessels have several port calls, the cargo is handled following an approximate First-in-last-out queue. Therefore, in some cases, cargo to be discharged at the port can become stuck behind cargo not to be unloaded.
This results in a necessary re-handling of cargo, increasing time spentat the port. Optimizing the loading sequence, positioning of cargo, andcargo routing within the vessel reduces the number of cargo that needs
to be re-handled. Prior research has utilized mathematical models andheuristics to solve similar problems. The authors present preliminary
results for using a reinforcement learning framework to optimize the
RORO stowage planning problem. Leveraging reinforcement learning
allows for the development of an algorithm that can provide executableactions at the port based on current information in real time.

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Wednesday, 8:30-10:00 - Room: S14 (building: 101)

Novel Optimization Models in FinanceStream: OR in Banking, Finance and Insurance: New
Tools for Risk Management
Invited session
Chair: Stavros A. Zenios

We develop and implement methods for determining whether relaxing
sparsity con- straints on portfolios improves the investment opportunity set for risk-averse investors. We formulate a new estimation procedure for sparse second-order stochastic spanning based on a greedy
algorithm and Linear Programming. We show the optimal recoveryof the sparse solution asymptotically whether spanning holds or not.
From large equity datasets, we estimate the expected utility loss due topossible under-diversification, and find that there is no benefit from expanding a sparse opportunity set beyond 45 assets. The optimal sparse
portfolio invests in 10 industry sectors and cuts tail risk when compared to a sparse mean-variance portfolio. On a rolling-window basis, the number of assets shrinks to 25 assets in crisis periods, while
standard factor models cannot explain the performance of the sparse
portfolios.

4 - Rational reaction to irrational behavior

Jorgen-Vitting Andersen
We study the problem where a population has to determine the truthfulness to certain commonly shared information. Problematics like thisseems particular relevant in the current often tense political climate
where spreading of false news repeatedly is at the center of accusation
across the political aisle. To this aim we introduce a prototype modelby considering "an index-of-truth" (IoT) and two types of agents: i)a percentage rho of rational agents, and ii) 1-rho of irrational agents.
i) the rational agents use rational expectation in their decision making
whether to believe in the given level of an IoT. By X we denote theuncertainty in the information used by the agents. If through rational
expectations such an agent believe the IoT is right no action is taken.
On the other hand, if the agent disagrees, a decision is taking to signalthat the level of IoT is wrong influencing the value of the IoT index
in proportion to the number of agents that reacts. ii) agents that are
irrational on the other hand don’t try to gauge the proper level of the
IoT index, but rather try to position themselves in alignment of what
they believe will be the future value of the IoT. The decision makingof the irrational agents is also publically signaled and influence the IoT
index in proportion to the number of agents that react We present ananalytical expression showing how the range of the IoT index depends
on rho and X.

1 - Tran2SP: Transformer-based Two-Stage Stochastic Programming

Woo Chang Kim

 WA-64

This talk introduces Tran2SP, a novel framework for solving two-stage
stochastic programming (2SP) problems, which are characterized bymaking decisions under uncertainty. Traditional methods, while effective, often suffer from computational inefficiencies and the necessity to
re-solve for new or altered instances. Tran2SP overcomes these challenges by employing the Transformer architecture to approximate thevalue function with piecewise linear functions, enhancing scalability
and reducing computational demands. Unlike previous approaches that
require extensive data or re-learning for modifications in the problem,
Tran2SP adapts to changes efficiently, offering high-quality, robust solutions across various problem instances without the need to start fromscratch. This advancement represents a significant improvement insolving 2SP problems, highlighting Tran2SP’s potential to address the
dynamic nature of uncertainties in optimization tasks. We further validate this approach through comprehensive experiments, confirming its
effectiveness and robustness in practical applications.

Wednesday, 8:30-10:00 - Room: S16 (building: 101)

2 - Reinforcement Learning for Portfolio Optimization in a
Regime-Switching ModelDavid Saunders

Last-Mile Delivery
Stream: VeRoLog - Vehicle Routing and Logistics
Invited session
Chair: Emanuele Manni
1 - Last-mile Delivery With Heterogeneous Crowdshippingand Service Levels

Tiago Monteiro, João Pedro Pedroso, Ana Viana
Innovative strategies are emerging to handle the growing challenges oflast-mile delivery, such as high operational costs and increasing customer expectations. Crowdshipping is a novel solution for last-miledelivery that offers a quick and cheaper way to provide same-day delivery and the flexibility to scale up or down the delivery capacity as
needed: retailers, in addition to professionals, employ ordinary citizens (a.k.a. occasional couriers - OCs) to perform deliveries (this is
the case of companies such as Amazon Flex and Roadie). The OCs

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WA-64EURO 2024 - Copenhagencan be categorized into two classes depending on their commitment tothe company: dedicated OCs, who actively register, provide a schedule with their availability, and can accept some proposed deliveries
for compensation; and en-route OCs, who are in-store customers whomake deliveries on their commute, also for a compensation. This work
proposes a decision-support tool to empower decision-makers with insights into the trade-off between customer service levels and operational costs across different workforce compositions and compensation values. To achieve this, we conduct simulations where different
compositions handle deliveries: exclusively the retailer’s fleet, or the
en-route OCs, or the dedicated OCs, or a hybrid combination of those
workforces. As a collateral result, the traveled distance of different
workforce compositions and compensation strategies is measured, offering insights into their environmental impact.

tour of a traditional vehicle, which may stop at one or more parking
slots, from which one or more subtours are carried out by the driverand the ADRs on the pedestrian network. The goal is to minimize theoverall distance traveled by vehicles for traditional deliveries, as wellas the walking distance covered by drivers and ADRs. For this problem, we propose an Adaptive Large Neighborhood Search (ALNS), in
which the destroy and repair operators are parameterized, and the mostappropriate parameter setting is constantly updated. The update phaseis carried out by a reasoner, which analyzes the solutions and, according to some performance measures, identifies the most suitable routes
to be destroyed and the parameterization of the repair operator. Computational results on a set of instances of varying size, derived fromreal data of several major cities, certify the quality of the approach,compared to an ALNS with classic destroy and repair operators.

2 - Optimising Crowd-shipping With Parcel Lockers for
Last-Mile Delivery

Acknowledgement The work is supported by the research program"Sustainable Mobility Center" (Centro Nazionale per la Mobilità
Sostenibile - CN MOST), project code CN00000023, Spoke 7

Mina Akbarpour, Stefan Ropke, Yu Jiang, Otto Anker Nielsen
The importance of increasing efficiency and capacity for last-mile delivery prompts e-commerce companies to explore alternative solutions,
including the adoption of parcel lockers and leveraging crowd-shipping
platforms. This study develops a holistic framework for optimising aparcel locker-based crowd-shipping system assisting the courier company’s last-mile delivery. Our approach covers various aspects for operating such a system, including identifying the optimal locker locations, considering customers’ stochastic choices in the delivery locations, and accounting for uncertainties in demand and crowd-shippers’
availability. The proposed two-phase approach integrates generated
locations improved by simulated annealing with a simulation-based
mixed integer linear programming model for matching delivery tasks
and optimizing vehicle routing. Specifically, once the locker locations
are identified, a simulation-based approach is employed to determine
the stochastic choices made by customers and their corresponding delivery tasks. The model then matches these tasks with either available
crowd-shippers or the courier’s own delivery vehicles, subsequently
devising the most efficient routes for these delivery vehicles. Finally,
the model is evaluated using last-mile delivery data from a region in
Copenhagen. Our results highlight how the use of a parcel lockerbased crowd-shipping system can enhance the efficiency of delivery
operations for the courier company.

3 - Extending the Parcel Lockers’ Solution to the Logistics
Last Mile Problem

Boaz Golany, Yael Deutsch
We extend the model of designing a parcel lockers network as a solution to the logistics last mile problem, by choosing how many parcel lockers facilities to open, where to locate them, how many lockers
to install in each facility, how to serve demand that exceeds the facilities’ finite capacities, and what discounts to offer to customers tomotivate them to prefer the lockers’ option over the more convenient
option of receiving the parcel at their doorstep. The objective is tomaximize the total profit, consisting of the revenue from customers
who use the parcel lockers solution, minus the facilities’ fixed and operational setup costs and the different discounts offered to customers,
depending on their travel distance and on their mode of pick-ups (ontime, with minor or major delays, or by-passing the lockers and picking up directly at designated counters inside stores that were contracted
for this task). The problem is expressed as a mixed integer linear program. We show that the model is tractable and can easily be solved byappropriate solvers available today. We present a few numerical examples to show how the model can be used in practice and demonstrate
its sensitivity to additional constraints that might be imposed in certain
circumstances.

4 - Park-and-Multi-Loop Routing for Last-Mile Delivery

Emanuele Manni, Tommaso Adamo, Gianpaolo Ghiani,
Emanuela Guerriero, Gianluca Solazzo
This paper deals with the park-and-multi-loop routing problem, in thecontext of same-day delivery, in which a fleet of traditional vehicles
equipped with several autonomous delivery robots (ADRs) leave froma depot to service a set of customer requests characterized by a delivery location and a time window. Each delivery route involves a main362

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Wednesday, 10:30-12:00
 WB-01
Wednesday, 10:30-12:00 - Room: Sportshallen (building: 101)

WB-03

techniques in portfolio choice under uncertainty and ambiguity is crucial.

2 - A deep learning approach to forecasting commodity
pricesHayette Gatfaoui

Javier Alonso-Mora
Stream: Keynotes
Keynote session
Chair: Nikolaos Matsatsinis
1 - Dynamic vehicle routing and multi-objective task assignment for ride-pooling, last-mile logistics, androbotics

Javier Alonso-Mora
We move towards an era of smart cities and factories, where autonomous vehicles will provide on-demand transportation while making our streets safer, and mobile robots will automate processes incoexistence with humans. These applications require novel methods
for real-time large-scale routing of thousands of vehicles and multiobjective task assignment. In this talk and building upon our seminal work on on-demand high-capacity ridesharing via dynamic tripvehicle assignment, I will give an overview of our recent work in thisfield. I will first analyze some of the sources of uncertainty present
in ride pooling systems, followed by a discussion of methods for predictive routing that utilize a model of future demand, how these systems could be combined with public transit, and how an equilibrium
can be achieved. Secondly, I will discuss an extension of these methods for flash deliveries and a multi-objective optimization that enables
us to design the required vehicle fleet accounting for the trade-off between quality of service and operation cost. Finally, and looking intoa multi-robot task pickup and delivery problem, I will discuss how we
can compute statistically distinct plans for multi-objective task assignment, which could later be employed by a user to select the desired
operational point of the system.

 WB-02
Wednesday, 10:30-12:00 - Room: Glassalen (building: 101)

Optimal Portfolio Strategies
Stream: OR in Banking, Finance and Insurance: New
Tools for Risk Management
Invited session
Chair: Mattia Margonari
1 - Risk-Sensitive Control in Portfolio Choice: Incorporating Ambiguity Aversion and Stochastic Factors

Chi Chung Siu, Guiyuan Ma, Dantong Chu, Wai Leong Ng
This paper explores risk-sensitive control in discrete-time portfolio
choice. The investor considers stochastic factors and price impacts,
including permanent and temporary effects. Ambiguity aversion towards model estimation errors of asset returns and stochastic factors isincorporated. The objective is to maximize the investor’s preference
for local mean-variance on investment returns, while accounting formark-to-market profits and losses, execution costs, and penalties related to model estimation errors. Our study reveals that the investor’s
trading strategy may differ from the aim portfolio. Ambiguity-averse
investors tend to trade more conservatively compared to non-robust
cases. Trading decisions are influenced by factors like higher permanent price impacts and lower market resilience rates, with trading
speed linked to ambiguity aversion. Simulation studies validate the effectiveness of the robust trading strategy, outperforming the non-robust
approach. Mark-to-market profits and losses from permanent price impacts enhance the net Sharpe ratio. Incorporating risk-sensitive control

Commodity prices are a strategic concern to countries and governments (e.g., energy or food security). Producers and end-users are
sensitive to commodity price swings and volatility, which may imply
lower profits and higher energy bills respectively. Commodity prices
depend on supply and demand fundamentals but also specific drivers
such as weather-linked seasonality and events, regional/countryspecific infrastructures (e.g., bottlenecks) and regulatory/complianceconstraints. Commodity price forecasting is important to allow market participants to hedge their risk exposures (i.e., losses due to large
price swings). We apply a deep learning approach to few commodities
for forecasting prospects. In the area of big data and artificial intelligence, deep learning allows for detecting key patterns in commodity
prices and exploit them for forecasting purposes. Such an approachis data driven and relies on neural network analysis without requiring
explicit fundamental factors. Moreover, the dynamic complex behavior of commodity prices is captured. A study of several forecasting
windows is also performed. Besides, we also perform an improved
deep learning approach by including very few key fundamental factors
to check if these ones improve the forecasting degree of the method
and/or increase the forecast horizon. Hence, we test for the usefulness of an augmented information space on the forecasting power andaccuracy of deep learning in the context of few commodity markets.

3 - Venture Capital: Drivers of Growth

Mattia Margonari
The venture capital market, a private alternative investments form, hasgained increasing attention and importance in recent years. Key industry metrics such as assets under management, average funding valuation, and the number of funds and startups have risen sharply, making
2021 a record year for the sector.
Several studies have shown that this industry contributes greatly to thedevelopment of countries’ economies (LERNER; TÅG, 2013). The
United States, a market where venture capital originated and which
has been instrumental in its development, has long been able to benefit
from the positive effects of a well-organized venture capital market:
increased technological spillover effects, companies that are among
the most highly capitalized on the stock market, and more employment (through VC backed companies) are just some of the benefits that
the U.S. market has enjoyed (LERNER; NANDA, 2020; TOM, 2019).
However, in the post COVID 19 era, capital raising and performance
metrics are no longer in line with the industry’s former standards andshow a somewhat different landscape.
Against a backdrop of complex and difficult global context, this paper first examines what the growth drivers of VC are in relation toregional and national macroeconomic variables in Europe and whatchanges the venture capital market drivers have experienced from external shocks (e.g.: due to the COVID-19) (GOMPERS; GORNALL;
KAPLAN; STREBULAEV, 2020).

 WB-03
Wednesday, 10:30-12:00 - Room: 1005 (building: 202)

Interpretable Optimization Methods andApplications
Stream: Data Science Meets Optimization
Invited session
Chair: Patrick De Causmaecker363

WB-04EURO 2024 - Copenhagen

1 - Feature-Based Interpretable Optimization

Sebastian Merten, Marc Goerigk, Michael Hartisch, Kartikey
SharmaFor optimization models to be used in practice, it’s crucial that users
trust the results. This is influenced among other factors by the interpretability of the solution process. A previously introduced framework
for inherently interpretable optimization models proposes to use decision trees to map instances to solutions of the underlying optimization
model. Based on this work, we investigate how we can use more general decision rules to further increase interpretability and at the sametime give more freedom to the decision maker. These decision rules
do not map to a concrete solution, but to a solution set instead, which
is characterized by certain features. A framework for MIP formulations as well as heuristics for generating such decision rules are presented. We outline the challenges and opportunities that these methods
can present. In particular, we show the gain in solution quality that
our approach can provide by using the conventional framework for interpretable optimization models as a benchmark and discuss the relationship between interpretability and performance. All methods areevaluated by using both artificial and real-world data.

2 - Explainability and Interpretability in Mathematical Optimization

Michael Hartisch WB-04
Wednesday, 10:30-12:00 - Room: 1001 (building: 202)

Mixed Integer Nonlinear Programming andNonconvex Optimization
Stream: MINLP
Invited session
Chair: Jan Rolfes
1 - Convexification of optimization problems involving the
Euclidean norm

Nikolaos Sahinidis, Anatoliy Kuznetsov
Several important open problems in chemical physics, mathematics,
and operations research can be posed as nonconvex global optimization problems involving the Euclidean norm. For example, pairwise
potential energy minimization, object packing and cutting, the kissing
number problem, and planar facility location problems admit a representation of this form. We present new formulations and convexification strategies for problems involving the Euclidean norm and use
them to solve several instances to global optimality for the first time.

2 - Random projections for quadratically constrained
quadratic programming

Benedetto Manca, Leo Liberti, Pierre-Louis Poirion
The evolution of mathematical programming has revolutionized ourability to address once-deemed intractable real-world problems on alarge scale. Despite the efficiency of modern optimization techniques,
the reluctance to accept provably optimal solutions persists, largely
attributed to the perception of optimization software as a black box
by many stakeholders. While well-understood by the scientific community, this lack of transparency poses a barrier to practitioners. We
advocate for a paradigm shift by emphasizing the importance of incorporating aspects of interpretability and explainability in mathematicaloptimization. By clarifying the concepts of explainability and interpretability, we aim to bridge the gap between the advanced techniques
understood by scientists and the accessibility required by practitioners.
We will showcase initial steps taken in this direction and engage in a
discussion on potential future directions.

3 - Leveraging Contextual Information for Robustness in
Vehicle Routing Problems

Irfan Mahmutogullari, Tias Guns
We investigate the benefits of using contextual information in datadriven demand prediction to solve the robust capacitated vehicle routing problem with time windows. Instead of estimating the demand distribution or its mean, we introduce contextual machine learning models
that predict demand quantiles even when the number of historical observations for some or all customers is limited. We investigate the useof such predicted quantiles to make routing decisions. We also evaluate
the efficiency and robustness of the decisions obtained by both exact
and heuristic methods. Our extensive computational experiments showthat using a robust optimization model and predicting multiple quantiles is promising when extensive historical data is available. In scenarios with limited demand history, using a deterministic model with onlya single quantile shows greater potential. Interestingly, our results also
indicate that using appropriate quantile demand values within a deterministic model leads to solutions with robustness levels comparable tothose of robust models. This is important because, in most applications, practitioners use deterministic models as the industry standard,even in an uncertain environment. In addition, because they are lesscomputationally demanding and require only a single demand value
prediction, deterministic models coupled with an appropriate machinelearning model hold the potential for robust decision making.

We consider quadratically constrained quadratic programs (QCQP) ina general form with both linear and quadratic constraints. We willshow that, if the number of quadratic constraints m and the number ofdecision variables n are large enough, there exist two random matrices
T (with k rows and m columns) and R (with r rows and n columns)
such that it is possible to apply them to the parameters of the original
problem in order to obtain another QCQP with fewer decision variables and quadratic constraints which is easier and faster to solve. We
will provide theoretical results showing that the projected program isrelated to the original one in terms of both feasibility and optimality
and a procedure that exploits a solution of the projected program toobtain a solution of the original one. Numerical experiments on somedifferent classes of QCQPs will be presented.

3 - Perspective Benders Cuts for Resource Allocation
Problems with Separable Convex Costs

Kang Yang, Guopeng SongWe investigate two broad categories of resource allocation problems
in which resources can be activated to fulfill the demand of one or
multiple customers, respectively, with the objective of minimizing thetotal cost. Each resource incurs a fixed activation cost, and for eachresource-customer pair, there is a variable allocation cost represented
by separable convex functions. The traditional mixed-integer programming models for these problems are enhanced using the technique ofperspective reformulation, which involves substituting each term in theseparable objective function with its perspective term. In this work, wehave developed a branch-and-Benders-cut algorithm to address the perspective reformulation models of these two general problem classes.
Our approach incorporates two novel features. Firstly, a family of generalized Benders optimality cuts expressions suitable for perspective
reformulation models, referred to as perspective Benders cuts, is derived, which are tighter than classic generalized Benders cuts. Secondly, we have developed a unified ad-hoc procedure, named reduceand recover, to efficiently separate perspective Benders cuts by solving the reduced quadratic subproblems. These features have led to ahighly efficient solution method on these two general problem classes,
which is verified through extensive computational experiments basedon benchmark instances with four different classes of nonlinear allocation functions.

4 - A Safe Approximation of Distributionally Robust Problems Depending on Univariate Indicator Functions

Jan Rolfes, Jana Dienstbier, Frauke Liers

364

EURO 2024 - Copenhagen
Driven by an application from chromatography, we model an optimization problem with linear objective subject to robust constraints that depend on uncertain particle size distributions (psd). The ambiguity setof our model can exploit information on moments as well as confidence
sets. Moreover, we present a duality-based reformulation approach for
distributionally robust problems, where the objective of the adverserial
is allowed to depend on univariate indicator functions. This renders
the problem nonlinear and nonconvex. In order to be able to reformulate the resulting semiinfinite constraints nevertheless, a safe approximation is presented that is realized by a discretized counterpart. Its
reformulation leads to a mixed-integer linear problem that yields sufficient conditions for distributional robustness of the original problem.
Furthermore, it is proven that with increasingly fine discretizations,the discretized reformulation converges to the original distributionallyrobust problem. Computational results for the chromatographic setting show that the safe approximation yields robust solutions of highquality within short time.

 WB-06
Wednesday, 10:30-12:00 - Room: 1013 (building: 202)

Advancements of OR-analytics in statistics,
machine learning and data science 17
Stream: Advancements of OR-analytics in statistics, machine learning and data science
Invited session

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proposed algorithm is assessed, with an extension to a model-free approach inspired by advancements in artificial intelligence aimed at mitigating computational burdens associated with executing the dynamic
program for evaluating capacity plans. Execution times and optimality gaps between exhaustive and approximate dynamic programming
methods are compared on small instances of the problem. Furthermore, the scalability of the approximate method to larger instances isdemonstrated. Additionally, a parameterization of carrier capacities
is proposed to further reduce the complexity of capacity optimization
programming with minimal losses in optimality.

3 - Resource-constrainedscheduling problem

transmission

maintenance

Amira Dems, Alain Côté, Mohamed Gaha, Quentin Cappart,Franklin Nguewouo
The Transmission Maintenance Scheduling (TMS) is a strategic problem in asset management for electrical utilities around the worlds. It
consists in generating an annual plan for the preventive maintenanceof electric power transmission equipment. The resulting maintenanceplan must ensure a continuous power flow during maintenance operations. It must also satisfy various constraints such as the stability of
the network and the availability of skilled teams to fulfill the planned
maintenance tasks. The proposed TMS problem is part of an important project for asset management at Hydro-Quebec (HQ). It is a variant addressing the human resource allocation constraints to ensure thefeasibility of a maintenance plan (respecting the available number oftechnicians in each period). The problem is solved using a constraintsatisfaction approach and experiments are carried out on a HQ power
subnetwork, with limits on the capacity of available electricians.

Chair: Nahid Jafari
Chair: Gerhard-Wilhelm Weber
1 - ESG factors driven asset management: a multi-layered
approach for enhanced returns and risk mitigation

Salah Ayari, Hayette Gatfaoui
Using machine learning, we implement a novel investing strategy that
integrates environmental, social, and governance (ESG) factors, coupled with economic and risk considerations to enhance portfolio performance. The strategy involves a monthly screening of the stocks
within the Russell 3000 index based on three filters. The first filter
considers the economic cycle and uses leading indicators to identify
future outperforming sectors. Then, using a clustering algorithm, thesecond filter ranks the sector-winning stocks by their ESG scores andselects the top-rated stocks from each ESG group. Finally, the remaining stocks are grouped via a clustering algorithm that focuses onrisk metrics. Within each cluster, we select the stocks which exhibit
the lowest loss potential as measured by the conditional value at risk
(CVaR).
The resulting stock portfolio is constructed with the selected stocks andrelated asset allocation is achieved by using a convenient optimization
method. The proposed investing strategy demonstrates the potential
for achieving a desirable balance between risk, return, and ESG considerations. It proposes an active portfolio management architecture
based on three layers, the main drivers being the sector rotation and
ESG-factor investing policy.

2 - Integrated optimization of operations and capacity
strategy for drayage procurement in first-mile & LCL
container logistics

George Vassos
Integrated container logistics rely on efficient management of first-mileand less-than-container-load (LCL) processes, particularly in drayage,
which involves the short-distance movement of full container loads toconsolidation centers or for modality changes. Given that shipping
companies often engage external carriers for drayage operations, effective drayage procurement becomes crucial. This study introduces
a dynamic programming approach to tackle the drayage procurement
problem, integrating capacity planning optimization to devise a reservation plan that maximizes operational value. The scalability of the WB-07
Wednesday, 10:30-12:00 - Room: 1019 (building: 202)

Behavioural operations and games
Stream: Behavioural OR
Invited session
Chair: Ilkka Leppanen
1 - Prosocial demand information sharing in a triadic supply chain with social preferences

Junlin Chen
We study a triadic supply chain with a retailer and two complementary
suppliers. The retailer is close to the market and can privately acquire
the market segmentation information, i.e., the proportion of prosocial
consumers in the market, while the suppliers only know its distribution.
The retailer reports the demand information of prosocial customers
to the suppliers publicly, and the suppliers independently decide theirown CSR investments to expand prosocial demand in the market under
the positive externality of CSR investments. We consider supply chain
members’ social preferences, including the retailer’s lying aversion behavior for the possible manipulation of information shared with suppliers, the suppliers’ trust preferences towards the retailer’s information
sharing, as well as the suppliers’ peer-induced fairness concerns causedby the possible free-riding given CSR investments’ positive externality
characteristics. The results show that: (1) Under full rationality, suppliers’ CSR investments are independent of the retailer’s report and theequilibrium is uninformative. (2) However, the existence of social preferences can facilitate the effective sharing of prosocial demand information in the supply chain, and enhance suppliers’ CSR investments,
leading to a win-win-win condition for supply chain members as wellas prosocial customers. (3) The critical factors in achieving this condition are the matching between the retailer’s lying aversion degree andthe suppliers’365

WB-08EURO 2024 - Copenhagen

2 - Dynamics of equity and reciprocity in wholesale-retailprice coordination equilibrium

Catarina Bessa, Tânia Pinto Varela, Ana Barbosa-Povoa,
Cristóvão Silva, Samuel MonizSupply chains are portrayals of human behavior. Yet, traditional
decision-support tools focus on economic goals, neglecting behavioral
preferences such as fairness. Although fairness models are mainly
grounded in equity or reciprocity concerns, recent efforts to model
stakeholders’ preferences within supply chains only consider the former. Given the inherent unfairness in supply chain systems, a move towards the accuracy of decision-support tools would be the modeling ofreciprocity. However, based on two renowned models, a steppingstone
was made by finding a coordination mechanism between a supplier anda retailer for a wholesale-retail price contract, in which two main concerns are raised: (1) the equity-based model tends to invariably set thedisadvantage unto the supplier; (2) the reciprocity-based model fails toprovide fully reciprocal outcomes. While these insights remain valid
when profit requirements are jointly unattainable, we argue that these
dynamics must be explored across a wider spectrum of parameters. To
bridge this gap, a numerical analysis of these models is conducted andits implications for supply chains and its members are discussed. It is
crucial to highlight that (1) the equity-based coordination ensures stability in the channel’s profit when the retailer increases his demand forprofit while; (2) the reciprocity-based model assures equal profits forboth members; and (3) the behavior of both models moves closer as
the production costs increase.

3 - Holding diverse market beliefs by firms: Information
flow, profit performances, and channel structure

Zhongyuan Hao, Li Jiang
In presentative settings, suppliers sell substitutable products throughretailers to the market with uncertain demand and market competition
is in quantity. Structure B comprises a monopolistic supplier and amonopolistic retailer. With respect to structure B, structure U includes
competition between suppliers, structure D includes competition between retailers, while structure SC includes competition between two
supply chains. A retailer has access to a demand signal useful for updating the forecast of market uncertainty. A supplier has no signal
access but can offer a payment to a retailer, who decides whether toaccept the payment and disclose signal to the supplier, termed information sharing. Suppliers and retailers hold diverse beliefs about market conditions. Knowing each other’s market beliefs influences firms’
ex-post operations policies and ex-ante profit perception. We demonstrate that holding diverse beliefs about market uncertainty by firms
facilitates information sharing except in the presence of only retailer
competition, in which case it deters information sharing. Exchanging
market beliefs about market uncertainty among firms strengthens their
incentive to engage in information sharing, which exerts mixed effects
on their actual profits. By comparison, suppliers and retailers are more
likely to benefit from exchanging market beliefs about market size,
though it is inconsequential to information sharing.

4 - Newsvendor Overconfidence and Advertising

Meng Wu
Overconfidence, as the most robust behavioral bias, has been confirmed to be a leading cause of human newsvendors’ pull-to-center(PTC) ordering bias. Most existing studies have focused solely on inventory decisions, overlooking the complexities that arise from the interface between operations management (OM) and marketing. As marketing and OM become increasingly intertwined and make decisionmaking more challenging, this paper examines the impacts of overconfidence on newsvendor’s joint ordering and advertising decisions.
Our study uncovered new decision biases that arise from overconfidence and have not been previously identified. Specifically, we foundthat overconfidence leads to over-advertising and a novel ordering behavior called the leapfrogging effect, where inventory can jump fromunderordering to overordering for medium-high margin products. Furthermore, our study shows that overconfidence can still result in a
PTC-like effect for newsvendors who advertise. We also found that
overconfidence has a more detrimental effect on profits for newsvendors with advertising. These findings highlight the need for overconfident newsvendors to be aware of choosing advertising because advertising can harm profits for overconfident newsvendors, while consistently improving profits for well-calibrated newsvendors. Overall,366

our study emphasizes the need to consider the effects of behavioral biases on decision-making in more complex contexts, particularly in theoperations-marketing WB-08
Wednesday, 10:30-12:00 - Room: 1020 (building: 202)

Applications of Knowledge and Technology
Stream: Knowledge, Technology, and Innovation
Invited session
Chair: A. D. Amar
1 - Transformation in the Maritime Sector via Autonomous
Vessels and Eco-Friendly Fuels

Seyed Parsa Parvasi, Harilaos Psaraftis, Jasmine Siu Lee Lam
Mitigating climate impacts from greenhouse gases (GHG) is a complicated issue given the increasing energy demand. Utilizing environmentally sustainable vehicles and eco-friendly transport modes appears to be a highly effective strategy to tackle this problem. The European Union recognizes the crucial role of short-sea shipping (SSS)in the decarbonization efforts across the transport sector. The policy
strategically aims to improve the share of SSS by encouraging a transition from road transportation to maritime alternatives. As part of aEuropean project named AEGIS, this study seeks to use greener fuels in vessels by implementing a hybrid propulsion system that integrates methanol and batteries. The study explores the synergy of this
propulsion system with autonomous vessels, intending to improve thecompetitive standing of SSS. The research undertakes a comprehensive examination of this innovative transportation on sustainability, implementing a cost-benefit analysis (CBA) that includes economic andenvironmental. In this research, a case study focused on the transportation route between the port of Hitra in Norway and the port of Rotterdam in the Netherlands, utilizing the concept of mother and daughter
vessels. The results suggest that autonomous ships could potentiallyenhance the maritime sector’s competitive edge. This could result in
an increase in cargo transportation via this mode, thereby improving
environmental sustainability.

2 - Selecting and Switching Knowledge Assets for the Firm
Growth and Performance

A. D. Amar
Due to the emergence of robotics and autonomics, there has been aburst of interest in understanding the relationship between knowledge
assets and firm performance. It is also observed that firms are keen
to enhance performance by managing knowledge assets. On the research side, while much effort is going into this problem, clearcut answers have yet not come out. This research works on optimal answer
to this problem by manipulating knowledge assets in their four prevalent forms, i.e., research and development, capital expenditure, selling,
general, and administrative expenses, and property, plant, and equipment. We take performance as given by the rates of growth in sevenmost common metrics, i.e., revenue growth, cost of revenue, earnings from interest and taxes, profit, gross profit margin, cash flow, andtimes interest earned ratio. We formulate four hypotheses and conduct
twenty-eight tests linking four knowledge assets to seven performance
metrics. Using data from IT industries belonging to the SIC Code 737,
we study the relationships and find that for different metrics, there aredifferent knowledge assets. There is no one knowledge asset that will
work for all performance metrics. It is true even for R&D. Therefore,
to find the right knowledge asset for the desired performance metric,
we design search methods employing selection maps to apply our findings to show how to strategically assign resources to the four knowledge assets for getting any targeted performance.

EURO 2024 - Copenhagen
3 - Unravelling the Role of Perceived Value Dimensions in
Shaping Millennials’ Revisit Intentions: An Empirical
Analysis of Homestays in the State of Uttarakhand, India

Santanu Roy, Amit Tariyal, Abhay Chamoli
A multitude of studies within the domain of tourism and hospitality
sector have delved into the influence of perceived value on diverse aspects of consumer behaviour. Perceived value is regarded as a crucial
element in comprehending a customers’ tangible actions. The present
study encompasses functional value, FV, social value, SV, and emotional value, EV, as the three distinct dimensions contributing to perceived value and explores their impact on millennial tourist satisfaction, MTS, and their revisit intention, MTRI, in the context of homestay experience in the Indian Hill State of Uttarakhand. Responses
from 329 millennial tourist respondents were analysed. The results ofmultiple regression analysis reveal that an amalgamation of FV, SV
and EV effectively explains 71.2% of the observed variability in MTS.
Additionally, the calculated Durbin-Watson statistic is 1.913 indicating the absence of autocorrelation within the residuals, reinforcing themodel’s reliability. Digressing further, it is observed that FV and EV
significantly impact MTS (p < 0.001; β = 0.451 p = 0.002; β = 0.157,
respectively), but the same effect is not observed in the case of SV (p= 0.347; β = 0.057). Further, MTS is found to significantly impact
MTRI (p < 0.001; β = 0.868). Homestays occupy a substantial sectionof the hospitality industry in this hill state. The findings have important
implications for designing a strategy for augmenting millennial tourist
revisit in the present context.

 WB-09
Wednesday, 10:30-12:00 - Room: 10 (building: 116)

Production Optimization and Supply Chain
Management of Green Hydrogen under
Uncertainties
Stream: Energy Markets
Invited session
Chair: Evgeny Gurevsky
Chair: Victor Spitzer
Chair: Céline Gicquel
Chair: François Sanson
1 - Multistage stochastic optimization of an elementary hydrogen infrastructure

Raian Noufel Lefgoum, Jean-Philippe Chancelier, Michel De
Lara, Pierre Carpentier, Sezin Afsar
The desired transition towards a hydrogen economy requires hydrogencosts to come down, through optimal choices of infrastructure designand operation. In this talk, we present an approach based on multistage stochastic optimization mixing design choices with operational
decisions taken on an hourly basis. We consider an elementary hydrogen infrastructure which consists of an electrolyzer, a compressor
and a storage to serve a transportation demand. This infrastructure
is powered by three sources of energy (on site photovoltaics, renewable electricity through power purchase agreement, power grid). The
modelling of the electrolyser covers its different functioning modes
and the nonlinear relation between the production of hydrogen and theelectricity consumption. The optimization problem is to minimize theoperational costs over a week (while emphasizing the use of renewable sources), by making hourly decisions in an uncertain context. We
consider uncertainties affecting on site photovoltaics production andhydrogen demand. We formulate a multistage stochastic optimization
problem, and we develop suitable algorithms based on dynamic programming. We present numerical results for a given infrastructure design. Then, we consider various combinations of infrastructure design

WB-09

and their subsequent optimal operation. With this, we discuss the optimal sizing of equipment, especially the sensitivity of electrolyzer andstorage designs to the uncertainties.

2 - Feature-driven strategies for trading wind power andhydrogen

Lesia Mitridati
This talk presents a feature-driven model for hybrid power plants, enabling them to exploit available contextual information such as historical forecasts of day-ahead prices and wind power, and make optimal
wind power and hydrogen trading decisions in the day-ahead stage.
For that, we define and compare different linear and piece-wise linear
decision rules. In addition, we propose a real-time adjustment strategy for hydrogen production. Our numerical results show that the final
profit obtained from our proposed feature-driven trading mechanism inthe day-ahead stage together with the real-time adjustment strategy isvery close to that in an ideal benchmark with perfect information.

3 - Day-ahead lot-sizing under uncertainty: An application
to green hydrogen production

Victor Spitzer, Céline Gicquel, Evgeny Gurevsky, François
SansonGreen hydrogen production via water electrolysis powered by renewable energy is essential to decarbonize key industries. However, it
poses challenges such as ensuring hydrogen demand despite the uncertain availability of renewable energy sources. This work focuseson short-term electrolytic hydrogen production planning, consideringa real case with a production site linked to a wind farm and the electricity grid. The wind farm provides fluctuating renewable electricity
at negligible cost, while grid electricity incurs higher cost. The purchase of electricity from the grid has to be planned and declared a day
ahead of production, i.e. before the exact availability of wind power isknown. On-site hydrogen storage offers some flexibility for production
planning, as it allows to produce hydrogen in advance.
The future availability of the wind power source is informed by forecasts subject to error, leading to a production overcost. A cohesiveframework is introduced to handle this case study. First, a two-stage
stochastic programming approach is presented to model this problem.
Then, a probabilistic neural network is used to estimate the conditional
wind power uncertainty and generate scenarios from the knowledge ofpast forecast errors. Finally, a time-efficient Benders decomposition
approach is proposed, in which special features of our problem are exploited to speed up the resolution. A realistic simulation demonstrates
the benefits of the presented approach.

4 - Dealing with stochasticity in the renewable hydrogenproduction

Patricio Reyes, Kelly Wagemann, Javier Hernanz Zajara,
Santiago Fernández, Artur Garcia-Saez, Ricardo Enríquez
Energy arbitrage is a common practice in the European energy sector,
which consists of taking advantage of differences in energy prices in
the market. Companies use arbitrage to buy energy at a certain price,
store it in batteries and later sell it at a higher price. Based on the sameidea, hydrogen production can be considered renewable if the electricity supply mix contains a minimum amount of renewables. This
work models the costs of an energy supply chain, identifying the best
moments of energy purchase and sale over a time period, to control
an industrial plan producing green hydrogen, taking into account a renewable share. Optimal conditions to operate the plant are unknown,
as demand and production are uncertain variables at the moment toparticipate in the market. Our developed solution evolves from a deterministic model to an improvement with the introduction of stochasticity. The model is tested in realistic conditions with a custom package
capable of solving the optimization problem for all the study cases.
For a time horizon of 24 hours, the model is solved in less than halfa second using state of the art solvers in an HPC cluster. To incorporate uncertainty, the variability of the energy price of the electrical gridis modeled by generating random scenarios. This is achieved by predicting time series using both traditional statistical methods and neuralnetworks. It allows to reliably represent the variability of the operation
over the energy market.

367

WB-10EURO 2024 - Copenhagen WB-10
Wednesday, 10:30-12:00 - Room: 11 (building: 116)

Integrated planning in healthcare
Stream: OR in Health Services (ORAHS)
Invited session
Chair: Sean Manzi
Chair: Melanie Reuter-Oppermann
1 - An Integrative Approach to Scheduling the PreTreatment and Treatment Radiotherapy

Maxim Frankish, Broos Maenhout
Scheduling patients for radiotherapy within the oncology department isvital for ensuring timely and effective treatment. The patient’s therapy
consists of a sequence of pre-treatment consultations, i.e., preliminary
investigations to define the required treatment, followed by the radiotherapy treatment sessions on linear accelerators. Timely schedulingof pre-treatment consultations is crucial to prevent unnecessary delays
in radiotherapy treatment, thereby impacting patient health. Therefore,
we suggest integrating both scheduling problems into one mathematical model formulation. We employ an online stochastic schedulingtechnique relying on simulation-based optimisation to solve the model.
Additionally, we compare our proposed model with benchmark models utilizing a batch scheduling algorithm and ASAP heuristic. A comparison is made between our model and a sequential optimisation approach that subsequently solved the pre-treatment and treatment phase.
We use information on the future arrivals of patients to provide a precise overview of anticipated patient flow. The input data was collectedfrom a real-life case study. Our findings highlight the importance ofintegrating both phases in the scheduling process.

2 - Integrating case-mix planning and master surgery
scheduling using branch-and-pricedisordered eating across Devon. This analysis was based on an integrated dataset containing data from the majority of NHS care providers
in the geography. The most likely treatment pathways were simulated
from the model to determine what the current provision of care looks
like. We saw a high reliance on primary care services, community
mental health teams and general internal medicine. The models andfindings from this study are being further interrogated to inform thedevelopment of new care pathways to improve integrated care provision for this patient group.

4 - A metaheuristic approach for an integrated nurse routing and re-rostering problem

Anisha Maharani, Yasemin Arda, Véronique François
Hospital-at-home (HaH) provides short-term acute care to patients athome for a condition that otherwise would require inpatient admission.
Having acute illnesses, the patient mix fluctuates. Furthermore, moreaccurate information about resource availability becomes known withtime. Thus, operational plans have to be established taking into account this updated information. Given a baseline roster and patient requirements, simultaneous operational decisions on patient admission,nurse assignment, visit scheduling, and potential roster updates aretaken for a weekly horizon, yielding a combined nurse routing and rerostering problem. The integrated problem incorporates complex realworld characteristics related to patient admission, routing, rostering,and re-rostering constraints while ensuring continuity across consecutive planning horizons. A lexicographic objective function first maximizes the number of patients admitted to HaH and second minimizes
the total working duration of the nurses. This is achieved by employing destroy and repair strategies based on a large neighborhood search.
A guided local search is embedded to modify nurse rosters to further
improve the solution. Infeasible solutions are allowed to be exploredby relaxing time windows, rostering, and re-rostering constraints. The
first results obtained by this algorithm will be presented, followed bya discussion about several challenges in balancing the different objectives of the studied problem.

Sam Bakker, Broos Maenhout
In this research, we study the case mix planning problem (CMPP) accounting not only for the resources in the operating room department
but also the most important resources in other departments such as forexample, ward beds, ICU beds, and medical staff by considering themaster surgery schedule (MSS) and its implications towards the demand for care in downstream ward units. The MSS allocates operating
room time (OR time) among different departments, whereas the CMPP
searches for the most profitable mix of patient types. Integrating thestrategic and tactical patient planning decisions will reveal the impact
of capacity decisions on bed utilization on the operational level andimproves decision-making. Current literature treats these two as different, yet related problems, that are solved in a sequential manner.
Most works on this topic first identify the optimal mix of patient types(case mix), and then construct an MSS based on this case mix. In thisstudy, however, we try to solve the CMPP and the MSS in an integrated
manner. The integrated problem is solved using branch-and-price andis applied to a real case study in a large Belgian hospital. Computational experiments show the effectivity of the presented approach.

3 - Examining inter-organisational care provision for people with disordered eating

Sean Manzi
Providing care and treatment for people with disordered eating is acomplex undertaking requiring clinical input from multiple disciplines.
The psychological underpinning of the disorder involves mental healthservices, the physiological impact brings in multiple inpatient and outpatient care services and support is required from General Practitioners
in the community. The Integrated Care Board in Devon, UK have initiated an integrated care pathway for people with Anorexia Nervosa.
However, diagnoses of Anorexia Nervosa account for only 14% of disordered eating cases. This study sought to understand the current careprovision for people with other eating disorders to inform the development of defined care pathways for people with eating disorders otherthan Anorexia Nervosa. A network-based operational modelling approach was employed to analyse, the care provision for people with368

 WB-11
Wednesday, 10:30-12:00 - Room: 12 (building: 116)

Scenarios and foresight practices:
Behavioural issues II
Stream: Behavioural OR
Invited session
Chair: Martin Kunc
1 - Using judgmental forecasting and scenario thinking for
anticipating the future: what are the differences, thesimilarities, and the advantages of each?

George Wright
Judgmental forecasting is the domain of psychologists interested inforecasting and covers the areas of judgmental probability forecasting and the judgmental adjustment of time-series models, mostly at
an individual-participant level. Closely linked is the Delphi method
which is a group-based method. By contrast, scenario thinking was,until recently, the domain of practitioners interested in helping organisations make better decisions in the face of uncertainty. My paper
discusses the role of subjective probability and outcome verification,and the focus on single point estimates as opposed to creating multiple, broad-brush anticipations of the future. Until recently, very little
academic research has used experimental techniques to evaluate thequality of developed scenarios, but this position is changing. Additionally, recent work within the judgmental forecasting tradition has
combined judgmental prediction with scenario storylines, a focus thathas also become part of practice - for example, within the UK National
Grid energy scenarios that are used for national policymaking. Clearly,
scenario thinking is now becoming strongly established in practice -EURO 2024 - Copenhagen
perhaps as a response to the World’s lack of preparedness for Covid-19
pandemic. I discuss the emerging need to develop and apply yardsticksof the quality of both the scenario development process and of the resultant scenario content. I outline my thoughts on guidelines for suchstandard setting.

2 - Navigating the path to future: Insights in fostering exploration in critical infrastructure systems

Simone Lombard, Heiko von der Gracht
Scenario and foresight practices enable organisations to explore newopportunities and innovate. However, the value contribution of thesemethods depends on whether managers are able to implement the findings. Behavioural issues such as risk avoidance, conflicting organisational demands, and inappropriate decision-making logics hinder
this transfer. Organisations often tend to prioritise protecting their
dominant value creation over exploring new opportunities. The research aims to understand how behavioral issues and subtle factors influence the initiation of exploration. The ongoing research examines
exploratory foresight methods from an ambidexterity perspective. A
bibliometric analysis was conducted to understand the variety of concepts and current discussion on ambidexterity. Our findings show that
antecedents and capabilities, as well as the outcomes of ambidexterity
are considered, while the actual management practices remain largely
unexplored. Due to the complexity and idiosyncratic management approaches in companies, causality and management mechanism are difficult to research. For companies that provide critical infrastructure,
innovation management must explicitly consider the impact on existing value creation, making it possible to explore factors and causal
relationships. Further, we aim to understand multiple case studies incritical infrastructure systems and how management practices are reconfiguring structures and resources to seize future opportunities.

3 - Incorporating stakeholders’ behaviours in foresightpractices. Insights from corporate engagement in climate change adaptation action-based scenarios

Fabrice Roubelat, Anne Marchais-Roubelat
Exploring scenario design as an anticipation of future actions, the purpose of this research is to examine the issues raised by the incorporation of stakeholders’ behaviours in the process as well as in the content
of scenario and foresight practices. Scenario literature may either refer to the engagement of multiple stakeholders in the scenario process(Bryson et al., 2016, Crawford, 2019, Mukherjeea et al., 2020) or to
the incorporation of stakeholder analysis in scenario design (Bradfield
et al., 2015, Marchais-Roubelat and Roubelat, 2016). We discuss thelessons from an action-research on the engagement of industries in climate change adaptation (Roubelat and Marchais-Roubelat, 2022). The
scenario design process implied the making of groups of stakeholders with members of various industries to explore their futures fromfour perspectives: crises, value chains, networks, temporalities. The
content of scenarios was designed from prospective stakeholders’ acts
engaging industries in climate change adaptation, and to be ruled andchallenged over time together with questioning stakeholders’ capacities to act. Results stress on the process perspective the interest for a
foresight collaborative process between different industries rather thanfocusing on a specific one to share experiences. On the content one,this participatory work beyond business silos produced communitybased engagement scenarios empowering disempowered or supportingrole stakeholders.

4 - Exploring the concept of plausibility in the developmentof future scenarios

WB-12

Covid19 pandemic. We end by drawing together our reflections andcomment on their relevance for the practice of scenario development.

 WB-12
Wednesday, 10:30-12:00 - Room: 13 (building: 116)

YW4OR_2
Stream: WISDOM - Women in OR
Invited session
Chair: Heletje van Staden
1 - On the interplay between data and decisions in discrete
location problems

Hannah Bakker
The capacitated facility location problem (CFLP) is a core problem inlocation science. While the influence of the spatial patterns underlying
a CFLP problem instance is widely acknowledged, their relationshipwith the optimal decisions is insufficiently explored. I will give an
overview of my research on how spatial relationships between candidates and customers can be accessed through pattern recognition.
With the help of an illustrative example, I demonstrate that these relationships affect the stability of solutions, the performance of solution
algorithms, and even indicate the potential added value of moving toa more comprehensive problem formulation including further aspects
like time or uncertainty. I outline how explicitly acknowledging these
relationships bears significant potential for algorithm selection, algorithm development, and data-driven modeling.

2 - Managing Manufacturer Encroachment and Product
Conflicts in a Closed-Loop Supply Chain: The Case ofInformation Asymmetry

Qin Zhou
Implementation of direct channels allows manufacturers to distributenew and remanufactured products through separate channels. This
prompts careful consideration of which products to channel directly.
In the production of remanufactured products, manufacturers oftendelegate the collection of used products to retailers. However, theefficiency of this collection process is typically private information,
known only to manufacturers through its probability distribution. To
explore the impact of information asymmetry on manufacturers’ decisions, we constructed a dual-channel closed-loop supply chain model
involving a manufacturer and a retailer where the manufacturer can design non-linear contracts, incentivizing the retailer to choose contracts
that align with its capabilities, ultimately maximizing the manufacturer’s profit. The study findings indicate that when the retailer exhibitshigh collection efficiency and remanufacturing costs are low, information asymmetry can distort the production quantities of new and remanufactured products. Manufacturer encroachment consistently provesadvantageous, mitigating the negative effects of information asymmetry on product output and consumer surplus. It also raises the entry barrier for manufacturers contemplating remanufacturing. Additionally, a
cost threshold for remanufacturing is identified, creating a divergencein how information asymmetry influences manufacturers’ profits undervarious contract types.

3 - Complexities in high-tech manufacturing

Frances O’Brien, Eleanor Reynolds, Maureen Meadows

Mirjam Meijer

Scenarios are stories about alternative possible futures which capture
key uncertainties. A key concept in the development and presentationof scenarios is that of plausibility. This paper is organised into three
sections. First, we offer reflections on how plausibility has been defined and operationalised within the literature. Second, we reflect on
experiences of students working with scenarios and how they have explored the plausibility of scenarios developed as part of an assessed
participative exercise. Third, we reflect on how the concept was operationalised in a recent application of scenario planning that supported strategic conversations amongst a group of museums during theHigh-tech manufacturers produce and assemble state-of-the art products that consist of many complex components sourced from dozens
of suppliers. Examples are the production of lithography machines by
ASML or the production of aircraft by Boeing or Airbus. To assemble
these high-tech end-products and deliver them to the customers, it isimportant that all required components are available as shortage of asingle component may lead to costly delivery delays of the end-productand large inventories of other components. This requires sufficient capacity of the suppliers, who face their own trade-offs and may be hesitant to invest in too much capacity, or high inventory buffers, which369

WB-13EURO 2024 - Copenhagenis also costly and increases the risk of obsolescence. In this talk, wewill discuss such challenges, resulting trade-offs and their effects on
optimizing the decisions.

4 - Leveraging Operations Management to Improve Accessto Justice: A Research Agenda on Court Congestion

Shany Azaria
Court congestion is a pressing issue worldwide, affecting social welfare, economic development, and access to justice. This research
agenda aims to address the challenges posed by court congestion
through the lens of operations management. By employing empiricalresearch, the application of theoretical frameworks, and data-driven
methodologies, we seek to unravel the operational complexities andidentify solutions to improve judicial system efficiency. Utilizing comprehensive datasets from the US Federal District Courts and the Israeli
court system, this project employs a variety of techniques including
natural language processing, artificial intelligence, and econometrics
to analyze the effects of judicial congestion. Our approach leverages
operations management tools, process-mining, and queue-mining topropose and evaluate interventions designed to reduce congestion andenhance system performance. The significance of this research lies
in its potential to contribute to both the operations management fieldand societal welfare by offering insights and interventions that facilitate access to justice. By bridging theoretical innovation and practicalsolutions, this research aims to forge new paths in operations management and social impact.

 WB-13
Wednesday, 10:30-12:00 - Room: 15 (building: 116)

Humanitarian Aid
Stream: Secure & Sustainable Food Supply
Invited session
Chair: Joris Wagenaar
1 - A matheuristic for the foodbank routing problem

İstenç Tarhan, Ceyda Oguz
Zero Hunger, a UN Sustainable Development Goal, aims for a hungerfree world while also targeting the reduction of food waste. Supermarkets are major contributors to food waste due to surplus from wholesale
operations. Food banks play a vital role in salvaging and distributing
surplus food to the needy, but they often face budget constraints andtherefore lack of transportation means. This study proposes collaboration among food banks to address these challenges, where thosewith transport capabilities assist smaller ones. This collaborative effort
introduces a new logistical challenge, characterized by the perishable
nature of foods and the selective nature of the problem—determining
which supermarkets to visit, which foods to collect from visited supermarkets, which food banks to visit to deliver collected foods. This
multi-objective problem aims to optimize the amount of food delivered
to foodbanks as well as their fair allocation, and to minimize costs
and carbon emissions associated with transportation operations. To
solve this problem, we introduce a matheuristic algorithm. This algorithm iteratively solves single-vehicle subproblems and employs amaster model to produce a solution for the original multi-vehicle problem, leveraging single-vehicle solutions. Computational experiments
conducted with both real-life and synthetic data demonstrate the efficacy of this proposed methodology. The work described in this study
is supported in part by TUBITAK 1001 grant 121M585.

2 - Preventing malnutrition: quantitative tools for decisionmaking in optimizing diets and interventions for regionswith limited economic access to adequate diets

Marc Juanpera, Pol Gil-Figuerola, Melissa Koenen, Sabrina
Kuri, Claudia Damu, Laia Ferrer-Martí370

More than 3100 million people (42%) could not afford a healthy dietin 2021, resulting in malnutrition and immediate and long-term healthissues. Regional and national governments can promote nutrition policies (e.g., subsidizing nutrient-rich products), often targeting vulnerable population groups (e.g., children under 5, pregnant or lactating
women). Due to the different nutritional composition of the food items
and the varying nutritional requirements of the population (with age
and sex), selecting the overall best policy is not straightforward. This
study combines bi-objective optimization and multi-criteria assessments, providing comprehensive quantitative tools to assist in defining
diets and assessing the impact of interventions on the nutrition intakeof the population. It defines three stages: 1) a bi-objective optimization
model to compute Pareto-optimal solutions for daily diets concerning
cost and nutritional adequacy; 2) a multi-criteria procedure to determine the best diet for each population group, considering economic,
social (equity), and environmental factors (water and CO2 footprint);
3) a multi-criteria procedure to evaluate interventions and select themost effective based on economic, social, and institutional factors (promoting local economy). Regions of Cambodia experiencing barriers toadequate dietary access are selected as a case study. Results can aid
decision-makers in identifying diets and interventions to improve thenutrition of the population.

3 - Mapping post-disaster infrastructure using GPS datafrom relief vehicles

Polle Dankers, Joris Wagenaar, Hein Fleuren
After a disaster strikes, relief goods need to be delivered to the beneficiaries as efficient as possible. However, a disaster may destroy a partof the infrastructure. Knowledge about the state of infrastructure is essential to aid organizations in efficient routing. To map the state of theinfrastructure, we estimate a distribution of the maximum speed one
can drive on edges using GPS trajectories from relief vehicles, which
are currently simulated.
This distribution is estimated in three ways: • For frequently driven
edges, we only use their own GPS data. • For seldomly driven edges,
we also use their own GPS data. Additionally, we use a measure on
the likelihood of the edge being deliberately avoided after the disaster. We check how often it should have been part of optimized routes
based on the pre-disaster infrastructure, while it is not in the driven trajectories. If the edge is likely avoided, we estimate the proportion ofdelay incurred on the edge, assuming that the pre-disaster route would
be chosen if not damaged. • Undriven edges in pre-disaster optimized
routes are also predicted with avoidance estimation. Undriven edges
which are not in pre-disaster routes are labeled unpredictable.
The approach can be used in a static setting, predicting the state of infrastructure only once. It can be extended to a dynamic version where
the predictions from the prior time period are used as infrastructureduring the next period’s trajectory generation.

4 - A robust approach to food aid supply chains

Joris Wagenaar
One of the great challenges in reaching zero hunger is to secure theavailability of sufficient nourishment in the worst of times such as humanitarian emergencies. Food aid operations during a humanitarian
emergency are typically subject to a high level of uncertainty. We develop a novel robust optimization model for food aid operations during
a humanitarian emergency, where we include uncertainty in the procurement prices, which is one of the primary sources of uncertainty inpractice. Due to the multi-period and dynamic nature of food aid operations, we extend this robust optimization model to an adaptive robust
optimization model. Moreover, we analyse a folding horizon approachfor the nominal, robust, and adaptive robust optimization models inwhich decisions can be altered in later time periods. We compare thedifferent approaches based on a food operations case in Syria. We
show that the (adaptive) robust optimization approach outperforms thenominal approach in the non-folding horizon case, while the nominal
approach performs best in the folding horizon case. Consequently, incase decisions have to be made early on, we show that applying robust
optimization to food aid operations can make a difference. However,in case small adaptations can be made to the decisions taken in latertime periods, then food aid operations can use a relatively simple approach in practice and apply a folding horizon approach each month to
optimize decisions.

EURO 2024 - Copenhagen WB-15
Wednesday, 10:30-12:00 - Room: 18 (building: 116)

COVID-19
Stream: OR in Health Services (ORAHS)
Invited session
Chair: Stefan Kempter
1 - Admission and early discharge policies for ICU patientsduring times of intense demand in a pandemic

Dong Li, Li Ding, Navid Izady, Richard Wood, Christos
Vasilakis
This study addresses the limitations of existing intensive care unit
(ICU) triage policies by developing an algorithmic model for admissions/discharges during times of intense demand in a pandemic. Embracing a multi-value ethical framework, our model aims to maximize
aggregated benefits while considering the principles of fairness. The
proposed model accounts for the illness pathways of individual patients, the overall mixture of patient profiles, and the uncertainty in thenumber of future patients. We introduce a cost for early discharges
to account for psychological and ethical complexity and build a discrete time Markov Chain from actual patient data to capture diseaseprogression of ICU patients. We conduct a survey among ICU physicians to estimate short-term benefits of intensive care. The problem
is formulated as a discrete Markov Decision Process. To address thecomputational complexity, we relax the capacity constraint and formulate an alternative problem for each single patient. We characterise theoptimal admission and discharge policy for individual patients, whichallow us to develop a heuristic policy for the original problem. The
performance of the proposed policy and alternative benchmarks areevaluated in a comprehensive simulation study. Our results reveal thescale of impact possible through appropriate clinical decision-making
regarding patient admission and discharges, demonstrating potential
benefits in both lives and life-years saved.

2 - Contagion-aware patient choice: managing multichannel healthcare delivery during a pandemic

Elif Karul
We consider the multichannel healthcare delivery problem during apandemic and explore capacity allocation and pricing decisions forthe in-person and telemedicine channels. We propose a model that
explicitly considers patient choices between the channels, including
the fear of contracting the disease in an in-person visit. The effectof contagion on the patient’s channel choice is endogenous. Choices
drive in-person volume which in turn drives the contagion effect. We
show that in equilibrium when the potential demand increases linearly,
the in-person demand increases logarithmically due to the endogenous contagion effect. This explains the documented revenue lossesof healthcare systems during the COVID-19 pandemic and underlines
the tele-channel’s importance as a leading revenue source. Under optimal pricing, we find a positive price gap between the channels that
increases in contagion awareness of the patients, a behavioral parameter in the model. In comparison with a revenue-maximizing healthcare
provider that prefers the higher-priced in-person channel, when physician infection is also considered, a social planner is shown to allocate
more capacity to telemedicine thereby protecting physicians. Modulating channel choice by increasing patients’ contagion awareness canbe beneficial for both revenue and welfare. Even partial awareness cango a long way in compensating the revenue losses, despite the negative
impact of contagion awareness on total demand in equilibrium.

3 - What is the price for flow-sizing critical care resources
with fairness?

WB-16

The term "flow-sizing" in health systems refers to the strategic operations management of critical care resources according to the dynamic
patients’ flow, adapting the resources’ availability to the fluctuatingneeds. The necessity of a comprehensive, quantitative approach toflow-sizing has become particularly relevant after the latest COVID-19
pandemic and planning for adequate capacity to confront the next critical care crisis is urgent. This research work attempts to develop a utility
function that incorporates the major critical care capacity metrics. The
objective is to match capacity and critical care demand to ensure thatall patients receive appropriate and fair care, as well as capturing thisfairness’ price. The vertical and horizontal integration capability ofcritical care delivery (mainly ICUs) for healthcare organizations, andits flow-sizing process in a fast and cost-effective manner during normal, strain and surge conditions, are taken into consideration. As acase study, the performance of the Hellenic healthcare system in terms
of providing critical care during COVID-19 pandemic is examined.
Acknowledgement: This work has been partly supported by the University of Piraeus Research Center.

4 - Enhancing Pandemic Evolution: A Simulation Modeling
Study Utilizing German Multicenter Data to Unveil the
Value of Federated Machine Learning

Stefan Kempter
The basic idea of Federated Machine Learning is to collect models
rather than data centrally. We study the feasibility and the potential of
the rather young research area, which has been proposed by Google in2016, for digital Covid-19 diagnosis based on German multicenter dataof 3,670 patients. Therefore, we compare Federated Machine Learning to traditional testing methods such as antigen or PCR (Polymerase
chain reaction) tests on economical and operational dimensions. We
aim to inform essential decisions regarding the choice of diagnostic
methodology during the progression of a pandemic. Accordingly, we
run a time dependent simulation for Federated Machine Learning todigitally diagnose Covid-19 and find a significant potential. The federated deep learning model with six clients and full access to all datapoints achieves an F1-score of 89.9 percent. For comparison, thecentrally trained model reaches up to 92.5 percent. Our results highlight the potential of applying Federated Machine Learning to Covid19 diagnosis. The study might therefore function as a benchmark for
hospital managers to contribute to future research while maintaining
governance of their data.

 WB-16
Wednesday, 10:30-12:00 - Room: 19 (building: 116)

Sports analytics
Stream: OR in Sports
Invited session
Chair: Stephanie Kovalchik
1 - Momentum Effects in Team Sports: Analysing the Interaction between Offence and Defence in the NBA

David Winkelmann, Rouven Michels
The phenomenon of momentum in sports has been explored in various studies; however, there is little evidence regarding the interplay
between offence and defence. Using play-by-play data from NBA seasons 2015/16 to 2018/19, we investigate potential team-level momentum effects. Within state-space models, we first analyse offensive anddefensive performances independently and, afterwards, integrate theminto a joint framework. Our findings reveal the absence of significant
momentum effects in the separated state processes. In contrast, we obtain a positive and significant momentum effect in the combined model.
These results indicate that a successful defence performance affects theoffence and vice versa. As such, it underlines the necessity of including characteristics such as the interaction between offence and defence
for an accurate analysis of momentum in team sports.

Nikolaos Rachaniotis, Thomas Dasaklis, Platon Tinios371

WB-18EURO 2024 - Copenhagen

2 - Optimizing IPL cricket 2023: selecting top batsmen andbowlers with the multi-MOORA method

Uttam Kumar Bera, Dipayan Bhowmik, Azharuddin Shaikh,
Anirban Tarafdar, Pinki Majumder

Jinal Parikh, Gerhard-Wilhelm WeberIn the context of procuring cricket players during an IPL auction, acomprehensive assessment of various elements becomes imperative,
encompassing metrics such as batting average, strike rate, and total
runs amassed for batsmen, alongside economy rates and wicket tallies
for bowlers. The process of player selection inherently mirrors a MultiCriteria Decision Making (MCDM) problem, wherein the challenge
lies in reconciling conflicting objectives to identify the most suitable
candidates. This paper undertakes an empirical investigation utilizing
the multi-MOORA method, leveraging real-life data from the recent
IPL 2023 season. By meticulously considering all pertinent attributesof players, we establish a prioritized ranking scheme. Moreover, we
conduct a comparative analysis with established methodologies such as
MOORA and WASPAS to discern their efficacy in this context, thereby
shedding light on their respective strengths and weaknesses.

The International Energy Agency (IEA) recently revealed that "Strong GDP growth and a weak monsoon drove up India’s energyrelated carbon emissions by around 190 million tonnes in 2023, though
the country’s per capita emissions remain far below the global average". Given its target growth rate of 9% of reaching an economy of $5
trillion by 2024-25, it is an immense challenge for India to meet thegrowth target while keeping its GHG emissions under control. While
SDGs 7 & 13 call for affordable and clean energy & climate action
respectively by 2030, improving energy efficiency while keeping the
GHG emissions under control could be one of the key paths to address
the issues of energy security, climate change and economic stability
for India. Further, India is characterized by considerable differences inenergy efficiency in different regions resulting from differences in theireconomic and demographic trends, resource availability and industrial
profiles. Given this background, this paper aims to: 1) identify thepotential sources which influence India’s GHG emissions at regional
level 2) explore various methods of decomposition analysis viz. IDA,
SDA and PDA and apply the most appropriate amongst them to investigate potential sources on GHG emission changes 3) to extend theauthors’ research presented at IFORS-2023 by performing decomposition analysis with GDP as the desirable output and GHG emissions as
the undesirable output respectively.

3 - Fast Laps and Fast Stats: A Scalable Spatial Model of
Circuit Topographies and their Performance Impact forConstructor and Drivers in F1

Stephanie Kovalchik
Understanding how a car and driver will perform on a given circuit isa central concern of constructors in motorsport. Statistical modelling
can be a powerful tool for analysing the interplay between car, driverand circuit features through partial pooling across fine-grained circuit
topographies. Telemetry data provides a rich set of inputs for spatialmodels, yet, the size of the data requires highly scalable methods. We
have developed a framework to approximate a Bayesian hierarchicalGaussian process that allows the estimation of latent circuit topographies and their interaction with car and driver at scale. Our fast approach to modelling the spatial characteristics of circuits is applied tothe performance outcomes for all races of the 2023 Formula 1 season. Our results demonstrate the predictive and descriptive utility of
the framework as it can predict results for new circuits within season
and find topographical features where car and/or driver are expected tohave the greatest and least advantage over the field.

 WB-18
Wednesday, 10:30-12:00 - Room: 42 (building: 116)

Sustainable Economics in Developing
Countries
Stream: OR for Development and Developing Countries
Invited session
Chair: Olabode Adewoye
Chair: Jinal Parikh
Chair: Sadiye Eylul Sadanoglu
1 - Recent development on the Markov decision and semiMarkov decision processes

Olabode Adewoye
Our world is bedeviled with uncertainty and complexity. Markov models originated by A.A. Markov and further extended R.A. Howard andlater, many other contributors, formulate, analyze, and evaluate simple
and advanced models to solve the world’s dynamic and complex problems ranging from genetics to robotic engineering and maintenanceproblems and sustainable developments. This work surveys recent developments in Markov decision processes (MDP) as well as provides
insight into how MDP and Semi-Markov Decision Processes can beapplied to a variety of problems. The finding shows that any practical problem on complexity and uncertainty can at least be modeled
as a Markov model, provided we can break the problem into states,
establish the transition probabilities, the rewards structure, and the optimization criteria are known.

372

2 - Energy consumption decomposition analysis in developing countries with specific reference to India

3 - Non-linear Task Valuation in resource allocation problems

Wen-Chih Chen
Allocating resources to various tasks is a critical decision in practices.
One of the difficulties arises from the intangible nature of task values orimportance, making it challenging for decision makers to express them
explicitly. Additionally, the value is nonlinearly linked to the level ofallocated resources. This work proposes a method to estimate the nonlinear value function by the historical decisions. Simulated studies areused to demonstrate the effectiveness of the proposed method.

 WB-19
Wednesday, 10:30-12:00 - Room: 44 (building: 116)

OR in Energy
Stream: OR in Energy
Invited session
Chair: Benjamin Ivorra
1 - Unit Commitment problem with uncertain demand andrenewable energy availability

Cristian Aguayo, Bernard Fortz
Energy generation is an area where four of the many problems facing
humanity today - the energy crisis, climate change, scarcity of natural resources and global warming - interact in a vicious circle. In
this scenario, it is not surprising that governments around the world
are reviewing their energy policies. Agents such as Renewable Energy Sources (RES) and Local Energy Communities (LEC) can play afundamental role in the energy transition. However, the optimizationof operational costs continues to be a relevant factor. The Unit Commitment (UC) problem is one of the classical approaches to optimize
these costs. This problem involves decisions related to the scheduleof generating units as well as the power they must produce in order tomeet the total power demand, where the last one can be deterministic
or uncertain. By integrating RES, a new source of uncertainty is added
to the problem. In this work, various formulations for the UC problem
with uncertainty are presented and solved using benchmark data.

EURO 2024 - Copenhagen
2 - Mixed-integer non-linear programming approach for
identifying parameters inconsistencies in load flow calculations

Martin Debouté, Pierre Arvy
For several decades, electrical networks have experienced continuous
expansion in size and scale, facilitating the transmission of electrical
power from sources to end-users. The growing complexity of these
networks necessitates the use of tools like Load Flow calculations,which are crucial for power systems engineering, playing a critical rolein ensuring grid reliability and efficiency. This numerical analysis ofelectric power within interconnected systems determines steady-statevoltages, currents, and power flows across network components. Given
the significance of LF calculations, the power systems community has
invested substantial efforts in enhancing the convergence of the methods used to solve them. Despite these efforts, real-world scenarios
often involve imperfect or erroneous network parameters, leading tochallenging convergence issues for existing methods. The physicalproblem studied may be mathematically infeasible due to complex inconsistencies in the parameters. This research project explores a novel
approach centered around Mixed-Integer Non-Linear Programming todetect inconsistencies or errors in the parameters of a power network.
The study extends with the implementation of an open-source tool using the AMPL modeling language, and conducts a solution to the problem using the commercial solver Artelys Knitro. Finally, the tool has
been tested to ensure that it successfully identifies inconsistencies in areal-world use case within an acceptable time.

3 - Modeling the interaction between local energy management and a global electric system

Kallia Charron, Frederic Lantz
Local energy management (LEM) addresses local stakes and thus a
LEM strategy does not necessarily align with the aim of a centralized
electric system. Yet, if spread widely, it can affect the global load
curve and therefore the global electric system (GES) and the price associated. In addition, the investment decision into a LEM solution andoftentimes the management strategy itself are highly dependent on the
global electricity price and the accuracy of its prediction. Hence, what
would be the impact of a LEM solution adoption - by a significant
number of consumers, referred to as "local consumers" - on its own
profitability through the effect it can have on the global price? This paper aims at modeling the interaction between LEM and a GES. To doso, we propose a two-level equilibrium optimization model. One level
models the impact of implementing a LEM solution for a "local consumers" on their aggregated load curve. The second level models thevariation in the global electricity price resulting from the shift of the
global load curve induced by those local consumers. The price being
both a decision parameter for the LEM in the first level and the outputof the second, the optimization purpose is to find the equilibrium. This
paper then presents the results of the model for several LEM solutions
such as demand response, residential photovoltaic self-consumption
and management of electric vehicle charging, using aggregated simulated data for the consumption.

4 - Enhancing N-PERT Solar Cells under the Atacama
Desert Solar Spectrum

Benjamin Ivorra, Pablo Ferrada, Gabriel López Rodríguez,
Angel Manuel Ramos
In the Atacama Desert, solar radiation deviates from the global standard, boasting high irradiation levels with intense ultraviolet content.
Given the spectral dependence of photovoltaic (PV) technologies, optimizing PV devices mandates consideration of local conditions andtechnology type. Solar cells are typically optimized for standard conditions, but our study focused on adapting them for Atacama’s uniquespectrum. We optimized an n-type bifacial passivated emitter and rear
totally diffused solar cell (n-PERT) by adjusting geometrical and doping parameters using a hybrid genetic algorithm. Six parameters—celland emitter thicknesses, back surface field thickness, and doping concentrations—were optimized for both Atacama spectrum (AM 1.08)
and standard conditions (AM 1.5). Validating our model, we found thecomputed and experimental efficiencies to differ by less than 1% understandard conditions, affirming accuracy. Our optimization revealed the

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necessity of tailored configurations and doping for Atacama deployment. Reducing layer thicknesses and increasing doping led to a 5.4%
efficiency boost under AM 1.08. Finally, we emphasize the potential
impact of metallization and the feasibility of thinning the emitter andback surface field, opening avenues for enhanced PV technology inextreme environments.

 WB-20
Wednesday, 10:30-12:00 - Room: 45 (building: 116)

Moments in the history of OR 1
Stream: Moments in the history of OR
Invited session
Chair: Roberto Rossi
Chair: Gerhard-Wilhelm Weber
1 - Lessons learned on the OR way

Elise del RosarioThis is a personal sharing of lessons learned from someone who wasdeeply involved in Operations Research - in a corporate setting, as anational society organizer, an international OR group leader, a consultant in supply chain and a pro-bono government consultant. There
were many lessons learned along the way that the speaker hopes will
benefit others.

2 - The origin of the Branch-and-Bound Method

Janny Leung
In 1960, under the simple title "An automatic method of solving discrete programming problem", the method for solving mixed-integerprogramming problems was introduced. This Branch-and-Boundframework, with enhancements, remains at the core of discrete optimization software to this day, more than 60 years later. We will discuss
the many pioneering ideas of this paper, and also the two women pioneers of operational research, Ailsa Land and Alison Doig Harcourt,
who co-authored this landmark paper.

3 - The "Subject to" (s.t.) podcast: sharing experiences,gaining Insights

Anand SubramanianThe "Subject to" (s.t.) podcast offers a series of informal conversations with relevant figures in the fields of Operational Research,
Combinatorial Optimization and Logistics, and they are hosted byAnand Subramanian, an Associate Professor at Universidade Federal
da Paraíba (UFPB), Brazil. The episodes can be accessed on YouTube(https://www.youtube.com/@ Subjectto_), as well as various podcast
platforms such as Spotify, Google, Apple, etc. The goal of the s.t.
podcast is to inspire the next generation of operation researchers bymeans of informal yet in-depth conversations with great names in thefield of OR in the form of oral history. Moreover, in addition to providing technical discussions, the podcast attempts to portray the humanside of the guests, otherwise generally known only for their work, by
walking through their professional and personal life stories, and presenting a snapshot of their career within the relevant historical OR context. The episodes often touch on societal elements such as gender anddiversity issues, and other pertinent nontechnical topics like the best
strategies to better promote the field. So far, there have been more than85 episodes with many important names from 28 different nationalities
spread across 22 countries all over the world. This contribution aims
to share some insights gained from experiences of distinguished personalities, predominantly from academia, but also from the industry.

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WB-21EURO 2024 - Copenhagen

4 - The sustainability of operations - past, present, future

Joel Joris Van de Klundert
Human operations, whether in business, at home, or otherwise, arecausing a transgression of the boundaries of a safe and just operating
space for planet Earth and humankind. Developments in operations
that have steadily grown over the long course of history, and whichhave especially gained momentum since the uptake of fossil fuel powered machines in the recent and on-going industrial revolutions, now
threaten to cause irreversible damage to ecosystems and society. The
present situation calls for new perspectives and understanding of operations and operations research that enable to change the course ofdevelopment and for operations to provide sustainable solutions forthe planet and humankind.
In this talk we firstly reflect on the history of operations while explicitly
reflecting on its environmental and social sustainability and on the corresponding development of operations research, roughly from the Big
Bang onwards. Next we reflect on the (un)sustainability of today’s operations and reflect on operations research contributions (being) made.
The on going 4th industrial revolution, which depends so extensively
on analytics and data science, promises to change the way humankind
lives and works, the way we operate, once again. Hence it provides awindow of opportunity for operations researchers to learn from the past
and aim to contribute to creating sustainable operations for humankind
and planet Earth. We review the state of the art on contributions of
the fourth industrial revolution for sustainable operations and highlightpriorities for future sustainable operations research.

 WB-21
Wednesday, 10:30-12:00 - Room: 49 (building: 116)

Infectious diseases and pandemics
Stream: OR in Humanitarian Operations (HOpe)
Invited session
Chair: Laura Heuser
1 - Operationalising Policy: Distributing Vaccines in a Pandemic/Epidemic

Foteini Stavropoulou, Sarah Schiffling, Liz BreenIn the response to the COVID-19 pandemic, vaccine distribution was
initially characterised by scarcity with limited production capacity
and high demand. While policy decisions to prioritise certain patientgroups over others have been widely discussed, this paper focuses onproviding a user-friendly and adaptable approach to align vaccine allocation decisions and distribution plans with the government-imposed
prioritisation policies. We introduce the Vaccine Distribution Problem, and we propose four mathematical formulations relating to the
Vehicle Routing Problem with profits, a concept that has not been
adopted within humanitarian or healthcare supply chain applications.
This problem aims to allocate the available vaccine supplies to a setof predefined vaccination sites fairly, following a given prioritization
policy, while designing the corresponding set of vehicle routes thatmaximize the total collected profit. We incorporate split deliveries andincomplete service as well as a profit adjustment mechanism to ensure
fairness in the vaccine allocation decisions and maximise resources
utilisation. To validate and evaluate our approach, we created a realworld case study based on Liverpool, an English city that was affected
severely by COVID-19, using only publicly available data.

2 - Analysing the Potential of Mobile Laboratories To Prevent Outbreaks of Infectious Diseases

Mette Wagenvoort, Iman Parsa, Luk Van Wassenhove
Infectious diseases are a significant contributor to death rates. Furthermore, they have a significant effect on the economy due to increased public health costs and loss of income. Earlier identification of374

pathogens through testing can help achieve outbreak prevention. However, poor rural areas have limited access to health facilities, whichcauses a large delay between the emergence and the identification of adisease. Mobile laboratories can help identify outbreaks in such areas
as they allow for fast preventive testing at different locations. However,the benefits of a mobile laboratory might not outweigh the benefits obtained from directly investing the money in the local health facilities.
Therefore, we analyse the trade-off between sending mobile laboratories and investing in local health facilities such that outbreaks areprevented.

3 - Manufacturing and Procurement Strategies of Relief
Supplies Considering Disruption Risk in Response to
COVID-19

Yanfang Ai, Afshin Mansouri, Jun Tian

Confronted with the urgent demand for medical supplies in COVID-19,
governments encouraged manufacturers to invest efforts in expanding
and transitioning their production to bolster the supply capacity of relief supplies. However, the limited supply capacity and the risk of interruption in the upstream supply chain of critical raw materials hinder
the expansion of manufacturers’ capacity. We address this challenge
within a relief supply chain consisting of a supplier and two competing manufacturers under various government subsidy strategies. In thisstudy, we develop a game-theoretic model where an incumbent and an
entrant manufacturer engage in two-dimensional competition involving production investment efforts and sales volume. We examine three
scenarios: (1) no subsidy, (2) per-unit sales subsidy, and (3) per-unit
price subsidy to understand the effectiveness of subsidy strategies indifferent contexts. Furthermore, we evaluate the stability performanceof the subsidies from an evolutionary game theoretical perspective. We
find that when the entrant manufacturer has a high level of investment
efficiency, they prefer the per-unit sales subsidy over the price subsidy
strategy. Additionally, government subsidies can promote production
quantities, investment efforts, and social benefits for all stakeholders.
To demonstrate the practicality of our results, we apply them to thecase of China’s response to COVID-19 and compare it with strategies
adopted by the UK government.

4 - Influence of Inventory Policies on Epidemic Progression in Networked Metapopulations

Laura Heuser, Stephan Wagner

Infectious diseases cause more than 25% of global deaths, but appropriate treatment can reduce the impact of epidemics. To account for treatment availability, we develop a networked metapopulation Susceptible-Exposed-Infected-Treatment-Recovered (SEITR)
model that depends on supply chain inventory policies. The focus of
this paper is on inventory policies during preparation (Prepositioning)
and response (Reorder Point, Ordering, Inventory Rationing, and Delivery Scheduling) to the epidemic. Our approach involves Pythonbased simulations with customized Latin Hypercube sampling to sample parameter-policy combinations and Monte Carlo techniques to account for randomness in the model. Using the case of Uganda’s
three-tier medical supply chain, we analyze the impact of different
parameter-policy combinations on epidemic outbreaks. The analysis
quantifies this impact through key performance indicators (KPIs) fromepidemics and supply chains, including final epidemic size, treatment
ratio, outbreak duration, stockouts, service levels, and inventory left
over. We will identify optimal policy-parameter combinations that
improve responses to future epidemic outbreaks within specific categories of epidemics. We aim to provide actionable insights to enhance Uganda’s national inventory policy for public health emergencies while contributing to the literature by providing insights about themost influential policies and parameters.

EURO 2024 - Copenhagen WB-22
Wednesday, 10:30-12:00 - Room: 81 (building: 116)

Capacity expansion planning for energy
systems
Stream: Energy Management
Invited session
Chair: Beste Akbas
1 - Models for electricity capacity expansion with demand
uncertainty and a carbon budget

Beste Akbas, Eddie Anderson, Heikki Peura
Energy capacity planning faces numerous uncertainties, especially regarding future demand and changes in generation costs. The development of new technologies has challenged previous assumptions about
the affordability of renewable energy sources. As a result, the changing cost dynamics disrupt the traditional patterns, leading to significant
impacts on expansion decisions and the future composition of the generation portfolio, influenced by the shifting dynamics between renewables and conventional sources. Additionally, the construction of newelectricity generation facilities powered by gas or nuclear typically entails lengthy construction periods, with operational lifespans expected
to extend beyond 15 years. Nevertheless, capacity expansion extends
beyond long-term projects. Short-term strategies, including extending
the lifespan of existing generation capacity that would otherwise be retired, and implementing demand response initiatives, have significant
influence over the overall capacity landscape. In this study, we examine the socially optimal capacity expansion decisions, taking into
account future carbon targets, demand uncertainty, and the integrationof long-term and short-term generators.

2 - Multiperiod Planning Model for Long-Term Expansionof Energy Distribution Systems Considering Advanced
Reliability Topics

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electric, heat, gas, and long duration energy storage including all thepossible interactions between them through multi year periods, yields
an increasing model complexity. It is modeled as a mixed integer linear problem and aims to include all the key features of state-of-the-art
models obtainable from a linear formulation. The objective function is
the minimization of the overall system cost, and it includes investment,
maintenance, network expansion, transmission hourly prices, energy
regulations, and fuel prices.
The model has been implemented in Python language using Gurobi
solver with an hourly time step. It can tackle in few minutes morethan a year depending on the problem size: a yearly instance of 200
nodes yielded about 320 million continuous and tens of binary variables. The model flexibility enabled to run various sensitivity analyses
to tackle uncertainty considering fuel availability, natural resources,
different technologies, regulations, load profiles and time evolution.

4 - Renewable Hydrogen Supply –A Novel Global Dataset
on Resource Potential and Hydrogen Production Optimization.

Age van der Mei, Audun Botterud
While data for policy-making and planning is available for solar andwind resources, this is lacking for renewable hydrogen. Even as hydrogen is seen as an important part of the energy transition away fromfossil fuels, it is unclear where, and at what costs hydrogen could be
produced at scale and supplied into the global energy system. The lackof available data creates challenges for planning and investment decisions. We present a database that provides techno-economic data onhydrogen production from solar and wind and describes global technological and economic potential. The database starts from 10 years
of ERA5 reanalysis data and adds technology performance data to enable optimization of renewable hydrogen supply at the global level.
We illustrate potential use of the database by optimising the installedcapacity for solar-PV, wind turbines, lithium-ion batteries, and electrolysers to meet future hydrogen demand. The data provides a timely
and valuable view of the supply side of renewable hydrogen aiming tounderpin renewable energy and hydrogen policy considerations.

Alejandra Tabares, Julian Andrés Infante, Ana Maria Montes
In energy distribution networks, adequate investment planning is crucial not only for meeting the demands of established load points within
the network but also for maintaining specific reliability standards.
These two objectives, which involve meeting the demand economically and ensuring adequate service reliability levels, are highly dependent on the investments made in the electrical network. Therefore,
this paper presents a mathematical model that incorporates both criteria to determine investment decisions in distribution networks. The
developed model incorporates various types of investments, commonly
found in long-term projects, such as investments in substations and circuits, which are evaluated over multiple investment periods. Moreover,a set of constraints is utilized to account for potential outage scenariosin the networks and assess the robustness of the investments against
them; this includes considering circuit breakers as essential network
structures for conducting network reconfigurations that minimize thenumber of users affected by outages. As a result, cost benefits wereobserved when incorporating decisions regarding the investment in circuit breakers, circuits, substations, and necessary upgrades due to theincrease in network demand. The final study was conducted using a
14-node and a 27-node model.

3 - Multi energy mixed integer linear programming modelfor generation and network expansion planning withcoupled electricity and heat sectors considering renewable sources

Anibal Baradei, Matteo Pozzi, Angelo Gordini, Aldo Bischi
Multi energy systems optimal planning main challenge is the equilibrium between model accuracy and computational feasibility. This isdue to the need of large-scale, nationwide, generation and transmission
expansion model that incorporates aspects such as optimal units andstorage scheduling, space constraints, and renewable energy sources
availability. Furthermore, the deep decarbonization needs of the future energy systems implies to have coupled energy infrastructures, e.g.

 WB-23
Wednesday, 10:30-12:00 - Room: 82 (building: 116)

Recycling
Stream: Circular Economy, Remanufacturing and Recycling
Invited session
Chair: Müge Çakan
1 - An agent-based approach to exploring consumer behavior and the effectiveness of persuasion strategies inwaste separation and recycling in China

Yan Xu, Xinzhu Wu
Consumer behavior in terms of waste separation and recycling (WSR)
is extremely important to achieve the goal of "zero-waste city" and circular economy. However, there is a discrepancy between consumers’
willingness to participate in waste separation and their actual behavior
in China. The government’s persuasion strategies have limited impact
on changing WSR behavior. We argue that the strategies need to collectively change WSR behavior through their influence on the motivation and ability (M/A) of all consumers to achieve extensive public
participation. Therefore, we propose a novel agent-based approach that
integrates a psychological behavioral model and a simulation model toanalyze WSR behavior and the effectiveness of persuasion strategies.
A behavioral model based on the Fogg behavioral model is developed
to investigate the M/A elements that influence WSR behavior. A multiagent model is developed to analyze WSR behavior by considering theinteractions between individual consumers and the environmental and375

WB-24EURO 2024 - Copenhagen

peer pressures. By simulating the changes in WSR behavior under
different strategy implementation scenarios, we find that consumers’
M/A are constantly changing due to their interactivity, and the public
participation rate is significantly higher when diverse strategies are implemented that encompass all M/A elements. Our proposed approachand empirical results contribute to the research and implementation ofwaste management and low-carbon consumer behavior.

2 - Life Cycle Assessment of Different options for Mixed Industrial Textile Recycling

Lorena Espinoza, Andrea Espinoza, Óscar C. Vásquez
The $882 billion textile trade in 2021 raises environmental concerns,
emphasizing the importance of promoting a circular economy for sustainable textiles. As a result, policies must prioritize textile recycling, especially in developing countries, as well as information sharing across the value chain. Thus, the purpose of this study was to investigate the potential environmental benefits of two industrial recycling
processes for mixed textile residues. We conducted a life cycle assessment using the ReCiPe method at the midpoint and endpoint levels,with a focus on generating significant data availability and a broader
assessment than existing literature to support decision-making related
to textile residue recycling. The results of the textile residue recycling
process to obtain stripes (R1) and replace sawdust to fill pushing balls
demonstrate that it would produce environmental benefits regardless oflocation in several midpoint categories. In terms of endpoint results,
the DALY savings are primarily due to avoiding final disposal, while
the savings in ecosystem impacts are generated by avoiding landfill
and sawdust production. In the recycling process to obtain recycled
yarn and fill (R2), endpoint results show that the DALYs of all avoided
processes are 1.5 times those of all R2 recycling processes, owing primarily to the avoidance of virgin yarn production. Thus, both recycling
methods are recommended.

3 - Optimal recycling strategies of closed-loop power battery supply chain considering informal recycling channels

Songsong Liu, Feiyu Jin
The early electric vehicle power batteries have entered the retirement
outbreak period in China. Although vehicle manufacturer is responsible for power battery recycling as required by policy, informal recycling channel is disrupting the normal order of recycling market. This
work establishes three real situations based recycling modes in closedloop power battery supply chain, consisting of manufacturer, retailer,
cascade utilization enterprise, and informal recycling channel. Using
Stackelberg game model, the optimal recycling strategies of closedloop power battery supply chain under decentralized and centralized
decision-making are obtained based on backward induction. Then,
fair profit distribution schemes are designed for the three models under centralized decision-making. By validation through computational
simulation, the results show that: (1) Under the decentralized decisionmaking, the profits of retailers and cascade utilization enterprises areseverely squeezed by the informal recycling channel; (2) Under thecentralized decision-making, informal recycling channel erodes retailers’ profit by eroding theirs recycling volume, while the manufacturers’ profit is not affected by the change in supply chain structure. This
work enriches the theoretical literature by considering the informal recycling channel in the closed-loop power battery supply chain, andprovides guidance to the formal recycling enterprises to compete with
the informal recycling channels.

4 - Economic and Environmental Implications of Transferring Green Feedstock Credits

Müge Çakan, Moritz Fleischmann
Chemical recycling represents a new option for addressing challenges
related to mixed and contaminated plastic waste. The chemical composition of the resulting feedstock is equivalent to that of virgin feedstock.
Consequently, a product created from a blend of recycled and virgin
feedstock exhibits the same quality as a product exclusively derived
from virgin feedstock. Thus, the sole distinction between a "green"
and a "brown" product lies in the claimed origin of the feedstock.

376

This opens the option to detach recycling credits from the physicalproduct, allowing for credit transfer without the necessity for physicaltransport. However, this practice, known as multi-site attribution, is
controversial. While industry claims attribution flexibility to be necessary for the economic viability of plastics recycling non-governmentalorganizations (NGOs) strongly oppose it and position it as greenwashing. Our research addresses this controversy and seeks to unravel the intricate dynamics of chemical recycling, attribution policies,
and the evolving landscape of the recycling industry. Our aim is toassess the effects of various attribution policies on the environmental
and economic performance of manufacturers through the application ofan analytical model. Additionally, we aspire to provide policymakers
with valuable insights into the ramifications of the transfer of recycling
credits.

 WB-24
Wednesday, 10:30-12:00 - Room: 83 (building: 116)

Carbon Regulation
Stream: Sustainable Supply Chains
Invited session
Chair: Fengfeng Xie
1 - Competitive retailers’ sourcing strategies under Carbon
Border Adjustment Mechanism

Fengfeng Xie
We investigate the influence of the newly implemented Carbon Border
Adjustment Mechanism (CBAM) by the European Union on sustainable supply chain management. CBAM addresses the global disparity
in carbon leakage due to divergent carbon taxation policies that varyacross nations and regions. Given that CBAM is designed to prevent
double taxation on carbon emissions, understanding its interaction with
non-EU carbon regulation mechanisms, like cap and trade policies, iscrucial. Our study primarily focuses on how the operational decisionsof both EU and non-EU manufacturers are influenced by the interplay
between the CBAM and cap and trade policy. Additionally, we aim
to analyze how retailers within the EU determine their sourcing strategies, specifically, whether to source globally from non-EU manufacturers or opt for local sourcing within the EU. Our findings reveal thatan increase in carbon emission permits allocated to non-EU manufacturers doesn’t necessarily lead to a surge in profit. In fact, as additional
permits are issued, the supply chain associated with non-EU manufacturers may encounter profitability dilemmas, impacting both the retailer and the non-EU manufacturer. We also find that when the CBAM
fee is substantial, and the non-EU manufacturers are granted an large
number of permits, retailers are more inclined towards local sourcing.

2 - Asymmetric Emission Regulations and Interventions of
The Local Government

Taha Yasin Gürlesin, Özgen Karaer, Z. Pelin Bayindir
Carbon border adjustment can be described as a taxing mechanism
where a carbon-regulated market taxes materials imported from unregulated regions. In this study, we analyze how a manufacturer exporting to two regions—one developed (and regulated) and one developing(and unregulated)— responds to CBAM imposed by the developed region. We study two models. The first is a base model in which the
manufacturer sets prices for both regions. In the CBAM model, themanufacturer considers investing in green technology to reduce its unit
emission and then sets prices for both regions, taking into account the
carbon border tax. We also consider a local government that may offer incentives to the manufacturer to invest in green technology at theinitial stage. We find that under some market conditions, neither thegovernment’s subsidy nor the carbon border tax causes a reduction inunit emission level.

EURO 2024 - Copenhagen
3 - Who leads the way? buyer vs. supplier initiatives insupply chain carbon footprint reduction

Elif Kuscu, Özgen Karaer, Tarkan Tan
We study two supply chain models: (i) Buyer-Led Supplier Development and (ii) Supplier-Led Supplier Development, which are consisting of a buyer and a supplier. We analyze which player should
initiate the carbon footprint reduction efforts in the supply chain under a scope 3 emission tax by using different levers. The supplier, as
the producer, may invest to reduce the product carbon footprint. The
buyer, as the downstream partner, may incentivize the supplier throughsupplier development initiatives. In the buyer-led model, the buyer offers a wholesale price premium rate contingent on improvement, andthe supplier decides her carbon footprint reduction efforts accordingly.
In the supplier-led model, the supplier offers sharing the carbon footprint reduction investment cost and the buyer decides the improvementlevel. We compare these two models and gain insights into their effectiveness under varying market conditions. Our results show thatcarbon footprint reduction is guaranteed if the supplier takes the lead,
however, a wider range of improvement opportunities occur when thebuyer initiates the reduction effort.

 WB-25
Wednesday, 10:30-12:00 - Room: 011 (building: 208)

Topics in Integer Programming II
Stream: Combinatorial Optimization
Invited session
Chair: Mehmet Erol Kara
1 - Construction of value functions of integer programswith finite domain

Junlong Zhang
Value functions play a central role in integer programming duality, andthey are also used to develop solution methods for stochastic integer
programs, bilevel integer programs and robust optimization problems.
We propose a column-by-column algorithm for constructing the valuefunctions of integer programs with finite domain over the set of socalled level-set minimal vectors. The proposed algorithm starts withthe first column and sequentially adds the rest columns one by one.
The advantage is that no integer program needs to be solved in the algorithm and no dominated vectors are generated (only level-set minimal
vectors are generated). Computational results on benchmark instances
show that the proposed algorithm can reduce the computation time by
up to three orders of magnitude compared with a state-of-the-art algorithm. We also extend the proposed algorithm to build value functionsof integer programs with negative elements in the constraint matrix.

2 - An Exact Algorithm for the Euclidean k-Steiner Tree
ProblemJae Lee, Charl Ras, Marcus Brazil, Doreen Thomas
The classical minimum Steiner tree problem is an NP-hard problem
that involves finding the shortest network spanning a set of terminals
in the plane using additional points called Steiner points. In the minimum k-Steiner tree problem, the number of Steiner points is limited
to some positive integer k. In a minimum Steiner tree, every Steiner
point has degree 3, but the restriction on the number of Steiner points
also allows degree-4 Steiner points to occur. This increases the numberof topologies that are potentially optimal, which further increases thecomplexity of the problem.
To address the large number of topologies in the classical minimum
Steiner tree problem, algorithms such as the GeoSteiner Algorithm
implement various pruning tests. These pruning tests are based on
the structural and geometric properties of a minimum Steiner tree andare used to discard the majority of sub-optimal topologies early on todeal with the large number of topologies. We take this approach andWB-26

create novel pruning tests that tackle topologies involving a degree-4
Steiner point. Hence, we create an algorithm that solves the minimum
k-Steiner tree problem exactly, incorporating these new pruning tests
in order to make it more efficient.

3 - On the box-total dual integrality of the perfect matching
polytope

Francesco Pisanu, Roland Grappe, Mathieu Lacroix
A rational linear system is totally dual integral (TDI) if for every integer linear function, the associated dual problem has an integer optimal
solution whenever the optimum is finite. Following the definition, TDI
systems with integer right-hand sides characterize integer polyhedra.
A TDI system is box-TDI if adding any rational bounds on the variables (i.e., boxes) preserves its TDIness. Classical examples of boxTDI systems are those involved in the Max Flow-Min Cut Theoremof Ford and Fulkerson. In general, box-TDI systems are systems thatyield strong min-max relations.
In the early 80s, W.J. Cook proved that any TDI system describinga polyhedron allowing a box-TDI description is also box-TDI. Thus,
box-TDIness is a geometrical property, and any polyhedron that canbe described by a box-TDI system is called a box-TDI polyhedron.
Recently, a graph characterization of the box-TDIness of the matching polytope has been released. However, this characterization is too
restrictive for the perfect matching polytope (PMP). In this talk, wehighlight the differences between the two cases and provide insights
for tackling the problem of characterizing the box-TDIness of the PMP.
We also show that deciding whether the PMP of a graph is box-TDI isequivalent to testing the box-TDIness of its affine hull, which implies
that "Recognizing whether the perfect matching polytope of a given
graph is box-TDI can be done in polynomial time."

4 - A Mathematical model for routing of Industrial Inspection Robots

Mehmet Erol Kara, Aydin Sipahioglu, Ahmet Yazici
This study focuses on solving routing problem of industrial inspection
robots by using mathematical model. Inspection robots are generally
used to control. Especially in the production of bus, inspection of bodyin white of a bus has approximately 3000 subparts and all of them must
be controlled. Since inspection time is important, routing of robot(s)is crucial. Robot arm starts from a starting point and moves anothercheck point. When it finishes the control, traverses another check point
until ensures all sub parts are controlled. However, in this process, determining the control points and calculating the shortest path distance
between all pairs of nodes is important. Distance between check points
should not be greater than sensor observation capabilities. Additionally, robot’s movements are limited because of obstacles of body inwhite. Finally, a good route should be determined by visiting somecheck points. In this study the shortest path distances was calculated
by using Floyd-Warshall algorithm and an integer linear mathematicalmodel so as to minimize total traversing and inspection time is developed based on TSP. However, in this model, robot does not need tovisit all check points in the network. The developed model was successfully validated on various test problems and the optimal solution
could be obtained for small size problems. It was shown that a goodsolution can be obtained within 5 minutes for the problem having morethan 1000 sub-parts with 170 nodes.

 WB-26
Wednesday, 10:30-12:00 - Room: 012 (building: 208)

Combinatorial Optimization in Scheduling
Stream: Combinatorial Optimization
Invited session
Chair: Maciej Drozdowski

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WB-27EURO 2024 - Copenhagen

1 - Stochastic Quay Partitioning Problem

Maciej Drozdowski, Jakub Wawrzyniak, Eric Sanlaville,
Frédéric Guinand, Yoann Pigné
In this presentation we consider dividing a quay of container terminal
into berth segments so that quality of service for future ship arrivals is
as good as possible. This problem will be referred to as a stochastic
quay partitioning problem (SQPP). SQPP is defined by a ship trafficmodel (STM), arrival intensity, quay length and a set of admissible
berth lengths. Alternative solutions are evaluated on various arrival
scenarios generated for certain arrival intensity from a stochastic traffic model (the STM). Evaluation of an SQPP solution on one scenario
is a problem of scheduling the arriving vessels on the berths, which is aclassic berth allocation problem (BAP). In SQPP the sizes of BAP instances which must be solved by far exceed capabilities of the methods
presented in the existing literature. Therefore, tailored portfolios of algorithms capable of solving very large BAP instances under limited
runtime are used. Features of SQPP solutions are studied experimentally. We demonstrate, that partitioning a quay into equal length berths
is not always the best approach. A set of algorithms to partition a quay
is proposed and evaluated.

2 - Optimization of vehicle traffic in parking lots and accessroads around the car plant

Grzegorz Pawlak
The practical traffic optimization model and the development of scenarios are considered to choose the most advantageous variant for theparking lot areas organization in the car factory plant. Traffic should
run smoothly, i.e. without stoppages at the entrance/exit to the plant
and without traffic jams in the plant zone, taking into account the effective flow of vehicles including the scheduling model in the multi-storey
car parking. The micro logistic simulation model was developed, what
if analysis and computational experiments performed.

3 - Affinity Aware Scheduling in Distributed Computing via
Vector Bin Packing

Natalia Shakhlevich, Thomas Erlebach, Clément Mommessin,
Renyu Yang, Xiaoyang Sun, Satish Kumar, Junqing Xiao, Jie
Xu
The problem of allocating long-running applications (LRAs) to cloud
resources can be modelled as vector bin packing (VBP) with additional
compatibility constraints. In that problem, cloud compute nodes correspond to bins and LRAs to items. The multidimensional nature of
the problem is associated with characteristics of bins and items, such
as CPU cores, RAM, GPUs, etc. Planning resource allocation can be
performed before LRA deployment and it is aimed at minimizing thenumber of nodes to which LRAs are allocated. In our study, we model
affinity restrictions on LRA deployment. They are defined for pairs of
LRAs which replicas can be jointly co-located to the same node, butwith some limits, or for pairs of incompatible LRAs, which cannot be
co-located. In our work, we explore a broad range of VBP heuristics
capable of producing high quality solutions to the affinity-aware version of the problem and find a solution within acceptable time. The
findings are supported by extensive computational experiments performed on the Alibaba Tianchi dataset.

4 - Integrated scheduling of work and training activities fora multi-skilled workforce

Azin Rashidipour, Broos Maenhout, Matthias Bogaert
This research proposes a comprehensive approach to skill-based staff
scheduling and training, aiming to optimize workforce efficiency andminimize costs. The focus lies on efficiently assigning skilled workers to shifts within a defined time horizon and scheduling intermediate
training sessions increasing the productivity of workers. The training of workers may encompass on-the-job or off-the-job training sessions. Importantly, both staff scheduling and training decisions are
made concurrently during this phase, with training costs integrated
with other expenses. The proposed algorithm encompasses a metaheuristic procedure, which is initialised by creating baseline schedules
without training, prioritizing task assignments and workforce availability. These initial schedules can be efficiently resolved using exact Mixed Integer Programming mathematical methods in a reasonable

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computational time. The further optimisation aims to improve the costs
via introducing training and is performed via dedicated local search
operators and the combination of high-quality schedules following theprinciples of evolutionary processes.

 WB-27
Wednesday, 10:30-12:00 - Room: 047 (building: 208)

Unraveling the Black Box: Advances inModel Explainability
Stream: Mathematical Optimization for XAI
Invited session
Chair: Sandra Benítez-Peña
1 - An in-processing and optimization-based method for afair geographical regression

Pepa Ramirez Cobo
Fairness in Machine Learning is a prominent research area that, however has received little attention in the context of spatial or geographical data. This work is motivated by a real urban planning database thatshows lack of equity in terms of access to green areas. On the basisof the geographically weighted regression (GWR), this work presents
a regression model for predicting spatial responses in fair way. The
parameters of the model, namely, the bandwidth parameter and theregression coefficients are estimated in two stages. First, the bandwidth is obtained from a jack-knife cross-validation approach. Second,
the regression coefficients are the solutions of a quadratic optimization
problem with linear inequality constraints modeling the unfairness of
predicted responses. In order to deal with large datasets, an alternating
block coordinate descent algorithm is suggested. The performance ofthe method is illustrated using an assortment of real datasets.

2 - Enhancing interpretability in Twin Support Vector Machines via variable selection

Sandra Benítez-Peña
Support Vector Machines (SVM) constitute a highly effective tool forsolving binary clasification problems. On the other hand, Twin Support Vector Machines (TWSVM) are based on the idea of constructing
SVMs in pairs through generalized eigenvalues (GEPSVM). These determine two hyperplanes not necessarily parallel by solving two problems similar to SVM but of smaller size. Consequently, the computational cost during the training phase is significantly reduced compared
to traditional SVM. This requires solving two smaller quadratic programming problems (QPPs) that the one solved by the standard SVM.
Moreover, many real-life problems, such as those related to fraud prediction and credit scoring, involve costs of misclassification that canbe different for both classes. Although providing accurate values forsuch misclassification costs can be challenging for the user, identifying
acceptable misclassification rates is relatively straightforward. In thiswork, we propose a new TWSVM in which misclassification costs are
considered by incorporating performance constraints into the problem
formulation. This model is further enriched by performing variableselection (FS), a fundamental task that makes the method more interpretable and efficient. Numerical results demonstrate the effectivenessof the proposed method.

3 - Visualizing outliers in functional data with new versionsof the epigraph and the hypograph indices

Belén Pulido Bravo, Alba M. Franco-Pereira, Rosa Elvira
Lillo Rodríguez
Outlier detection in functional data presents a critical challenge in contemporary data science research. The inherent complexity of thesedatasets implies working with an infinite dimensional space. Addressing this challenge demands innovative methodologies that yield robust
and interpretable results.

EURO 2024 - Copenhagen
In this study, we introduce novel iterations of the epigraph and hypograph indexes, different from those available in the existent literature.
These indices allow to transform the original functional dataset into a
multivariate one, enabling the application of available outlier detection
methods for multivariate datasets. The inherent visual nature of theepigraph and the hypograph indices facilitates intuitive interpretationof outcomes, enabling users to extract insights into identified outliers.
This methodology is validated through comprehensive experimentation on both simulated and real datasets, demonstrating competitive
results compared to existing methodologies available in the literature.

4 - Explaining hidden variable interactions inside a model:
a comparison study between different methods.

Pablo Morala, Jenny Alexandra Cifuentes Quintero, Rosa
Elvira Lillo Rodríguez, Iñaki Ucar
Being able to explain feature importance when explaining model predictions has been the main focus of Explainable Artificial Intelligence(XAI) methods. However, many of them assign importance values
to single variables instead of taking interactions between variables
into account, an effect that usually appears in real life problems. In
this work we present a comparison study between some extensions of
SHAP values (one of the most widely used interpretability methods)
to include interactions, and a novel interpretability approach for neuralnetworks named NN2Poly, which in this study is also used in a surrogate manner to explain other kind of models. Extensive simulations
are carried out under different settings, both local and global explanations are compared and ways of computing comparable importanceorder metrics are presented.

WB-28

2 - Natural gas demand forecasting in Mexico with machinelearning algorithms to support making decision process during energy transition

Sergio Ibarra, Aida Huerta-Barrientos
Mexican energy pool has a big dependence on natural gas, most ofwhich is imported from the United States of America (USA). In thiscontext a reliable, updated and adaptable tool to forecast natural gas
demand in the medium term is required to support the Mexican government making decision process. There are now few studies related tonatural gas demand forecast in Mexico, but they are either not updated
in terms of historical data and variables used as inputs or they do not
use the latest known methods such as Neural Networks (NN) losing
the opportunity to leverage most recent forecasting techniques. This
study explores natural gas demand forecasting in Mexico using machine learning algorithms to support the energy transition. We compare
time series models ARMA & ARIMA and Long Short Term Memory
NN based on historical demand data from January 2005 to Nov 2023.
Mean Absolute Percentage Error (MAPE), Root Mean Square Error(RMSE), and Akaike Criteria (AIK) reveal each method’s strengths
and weaknesses depending on data characteristics, like the presence ofoutliers. Our contribution lies in applying advanced machine learning
to Mexican gas demand forecasting, expanding on existing researchpredominantly focused on countries like China, India, Turkey, and theUSA. This work provides valuable insights for stakeholders navigating
the energy transition in Mexico, helping optimize infrastructure planning, resource allocation, and energy security.

3 - Solving the two dimensional strip packing problem using reinforcement learning framework

Fatih Burak Akcay, Maxence Delorme WB-28
Wednesday, 10:30-12:00 - Room: 065 (building: 208)

Advancements of OR-analytics in statistics,
machine learning and data science 9
Stream: Advancements of OR-analytics in statistics, machine learning and data science
Invited session
Chair: Milagros Baldemor
Chair: Gerhard-Wilhelm Weber
1 - Stochastic reoptimisation model for inventory management

Zahra Namazian, John Betts, Peter Stuckey
Traditional inventory control approaches typically begin by estimating the demand distribution within a predefined family of distributions
based on historical observations. The traditional inventory models use
these estimated distributions to find the optimal order policy. However, these approaches often rely on insufficient information, for example, only the mean and standard deviation of demand, which may
not adequately capture demand changes over time. By contrast withthese approaches, we utilize a Light GBM model to predict the daily
demand. By rebuilding the LightGBM model at each time period
we track changes over time. Consequently, we propose an innovative approach that integrates the LightGBM model within a two-stage
stochastic optimisation framework executed on a daily basis. In particular, by employing this integrated predictive model, we achieve a more
responsive tracking of changes in demand and make the replenishment
decision through continuous reoptimisation of stochastic programmingon a daily basis. The effectiveness of our approach is demonstrated
in our case study, a retail company dataset, Where the new approach
results in reducing inventory management costs without significantly
affecting customer service levels, distinguishing it from traditional approaches.

In the 2D Strip Packing Problem (SPP), the objective is to pack items
into a fixed-width strip, minimizing the height of the packing. A common approach to solving the SPP efficiently is through decomposition
algorithms, which split the problem into primary and secondary subproblems. The primary problem involves solving a relaxed version of
the SPP, like the one-dimensional contiguous bin packing problem orparallel processor scheduling problem with contiguity constraints. Using a feasible solution from the primary problem, the secondary problem constructs a valid SPP solution. If none is found, techniques suchas adding a cut or banning the current solution are applied. Various
formulations, including integer linear programming models or constraint programming models, exist for the primary problem. Empirical evidence suggests that different solution approaches perform betterfor different instances, with no single dominating formulation. Our
research focuses on developing automated hyper-algorithms capableof selecting the most appropriate method or sequence of methods forsolving a given instance, following the trend of integrating machinelearning techniques into optimization algorithms. We utilize reinforcement learning to determine which mathematical model to employ inthe primary problem, along with other features for incorporation into
the decomposition scheme. The proposed framework’s performance isevaluated using benchmark datasets.

4 - Prediction of exchange rate changes with monetary policy indexation of FOMC meeting minutes

Jia-Hau Guo, Guan-Yu Lai
We derive four monetary policy indices from FOMC (Federal Open
Market Committee) minutes to analyze exchange rate changes in several foreign currencies. These monetary policy indices are found tohave significant impacts on foreign currency changes in emerging markets while exchange rate changes in some developed or industrial countries could depend on monetary policy indices from meeting recordsof their own central banks or central banks of their competitors. Additionally, a monetary policy uncertainty indicator based on entropy is
introduced to further improve the prediction of exchange rate changes
in certain currencies. We find that the uncertainty of meeting minutes
could reduce the market belief in monetary policy stance, and then reduce the impact of monetary policy sentiments on exchange rate variations.

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WB-29EURO 2024 - Copenhagen WB-29
Wednesday, 10:30-12:00 - Room: 157 (building: 208)

Large Scale Optimization in Air
Transportation
Stream: Combinatorial Optimization
Invited session
Chair: Francois Soumis
1 - Integral Simplex for the Set Partitioning Problem

Francois Soumis, Alpha Saliou Barry, Frédéric Quesnel,
Issmail El Hallaoui
For the set partitioning problem, the existence of a sequence of integer
solutions with non-increasing costs produced by simplex pivots, permitting to reach optimality was proved by Balas, Padberg 1972. Finding the following term in the sequence can be very time-consuming due
to degeneracy. When there is no entering variable that leads to a better integer solution, we use a linear programming sub-problem to finda group of variables to enter the basis to obtain an improved solution
which is integer most of the time. When this solution is not integer, we
solve a small integer programming problem to obtain a better integer
solution. We first, present results for large-scale problems (with up to
500 000 variables) for which optimal integer solutions are obtained,
most of the time, without any branching. We also, present the integration of this new approach with column generation and parallelism tosolve large-scale bus driver and airline pilot problems. Problems withmany thousand tasks are solved 5 to 10 faster than with branch andprice based on CPLEX

2 - Integral simplex for generalized set partitioning problems

Alpha Saliou Barry, Frédéric Quesnel, Issmail El Hallaoui,Francois Soumis
The set partitioning problem can be generalized. One generalization is
to consider that certain tasks must be covered several times. So it’s nolonger a partition that’s sought, but a set of parts that verify the rightnumber of coverages.
Unlike the set partitioning problem, the graph of integer solutions is nolonger connected when the possible arcs are simplex pivots. Howerver,
in order to restore the connectedness of the graph, it is possible to add
artificial variables locally, which allow new pivots to be made while
preserving the integrity of the solution.
We give priority to pivots that keep the solution integer. When this is nolonger possible, we solve a linear program to obtain a non-degeneratesequence of simplex pivots to perform in order to improve the solution. When this improved solution is still not integer, in some cases
the pivot sequence allows to add an artificial column associated withan integer artificial point. The linear program is solved again from thispoint to find the remaining pivots to be performed in order to obtain abetter integer solution. If integrity is still not reached, a branching is
performed.
We present results for large pairing problems (around 2,000 constraints
and 300,000 variables) and show that the method is around 2 to 5 times
faster than traditional methods (CPLEX) and that in many cases, theaddition of the artificial variable effectively allows moves on integer
points not directly accessible by pivot.

3 - The crew split problem: team creation for the cabincrew pairing problem with several qualification classes
and varying requirements.

Frédéric Quesnel, Mohand Ryad Chelbani, Francois Soumis
It is usually beneficial for airlines that cabin crews travel in teams for
entire pairings (sequence of flights, connections, and rest, forming one
or several days of work). In addition to improving morale and teamefficiency, travelling in teams increases the robustness of the schedule.
However, different flights may have different crew requirements, so it

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is not always possible to have a whole team work on two consecutive
flights. In those cases, it may be beneficial to use a core team to covermost of the requirements and use complementary crews to cover the
missing assignments. Finding the optimal way to form teams has been
identified as an issue but has never been tackled.
In this talk, we formulate the crew split problem, whose role is to determine the core team composition of each flight as well as the complementary crews that will join it. It is a multi-objective optimization
problem that, among others, maximizes the size of each core team,minimizes the estimated connexion times, and minimizes the estimateddeadhead costs. In addition, our model allows for downranking, whichis the possibility of a higher-ranked crew to work a lower-ranked position. We show the benefits of using the crew split problem on a large
real-world instance.

 WB-30
Wednesday, 10:30-12:00 - Room: 064 (building: 208)

Specialized Optimization Algorithms
Stream: Software for Optimization
Invited session
Chair: Jordi Castro
1 - Detecting and Handling Reflection Symmetries in
MINLP

Christopher Hojny
A symmetry of a mixed-integer nonlinear program (MINLP) is a bijective map that transforms feasible solutions into feasible solutions
while preserving the objective value. Already for linear problems it
is well known that symmetries can negatively impact the performanceof branch-and-bound methods, since symmetric regions of the search
space will be repeatedly explored without providing new information.
Most of the existing approaches for detecting symmetries focus on permutation symmetries that exchange the order of entries in a solution
vector. For many problems classes, however, permutation symmetries
do not capture all symmetries. For example, when packing objects into
rectangular containers, also reflection symmetries of the container canbe taken into account.
In this talk, we present a novel mechanism for computing, next to permutation symmetries, also reflection symmetries of MINLPs. As existing approaches for permutation symmetries, we introduce a suitablegraph whose automorphisms correspond to symmetries of a MINLP.
Since reflection symmetries may nontrivially interact with nonlinear
functions of a MINLP, however, our graph is much more involved thanthe previously known symmetry detection graphs. We also briefly discuss generalizations of state-of-the-art symmetry handling methods toreflection symmetries. The talk is concluded by numerical experiments
showing the effect of handling reflection symmetries in the MINLP
solver SCIP.

2 - A generalization of the Weight Reduction algorithm

Aurelio Oliveira, Carla Ghidini, Jonathan Solis Hidalgo
We propose a new linear programming algorithm that arises from thegeneralization of the weight reduction algorithm, which, in its turn, isbased on the von Neumann algorithm. The algorithm developed by
von Neumann has important features, such as fast initial convergenceand low computational cost to obtain the search direction, which are
inherited by both: the weight reduction algorithm and by the present
generalization. However, its usefulness is limited due to considerably
slow convergence. Thus, with the objective of improving the performance of the weight reduction algorithm, the idea is to consider morethan two columns to compute the direction. It is computed consideringp coordinates, choosing p/2 columns that form the smallest angles withthe residue and p/2 columns for the largest angles. The advantage ofthis new algorithm is that by using more columns it is possible to finda better direction. Computational experiments are performed and theEURO 2024 - Copenhagen
results show that with the new algorithm there is a faster decrease in
the residual value as the p value increases. Also, the number of iterations and processing time necessary to determine a good solution forthe problem, reduces with the value of p. The generalized algorithm
can also be used together with other methods, such as interior point
methods. It can be used, for instance, to obtain an advanced starting
point and, thus, improving its efficiency.

3 - Certifying Symmetry Handling Reductions in Integer
Programming

Jasper van Doornmalen, Leon Eifler, Ambros Gleixner,
Christopher Hojny
Modern solvers for mathematical optimization problems are complex
pieces of software. They usually make use of a combination of solution methods. Though the correctness of these methods is often provenon paper, with today’s tools it seems prohibitively difficult to verify
that they are implemented correctly in complex software. Even thoroughly tested state-of-the-art solvers used by many are not immune tobugs and numerical issues. We present a proof system allowing for establishing correctness of results produced by optimization algorithms.
Particular focus is placed on mixed-integer programming (MIP), extending on work for binary programs by Bogaerts, Gocht, McCreesh,
and Nordström (2022). Our proof sytsem covers a broad range of MIP
solving techniques, including cutting planes, presolving, and symmetry handling.

4 - Specialized interior point method for support vector
machines based on variable splitting

WB-31

Nicolas Nunez
Our research addresses the limited use of Learning Analytics tools andtechniques in providing personalized learning experiences for MBA
students. We propose a novel framework that incorporates Bayesian
Knowledge Tracing (BKT) and other predictive modeling techniques
to create customized learning tracks for MBA programs. Our approachleverages data from standardized admission tests and student performance to generate tailored recommendations, addressing a significant
gap in the application of learning analytics in graduate business education.
Preliminary results, based on a small sample, indicate that the use of
BKT can improve the learning experience for MBA students. By developing a clear framework for implementing BKT and other predictive modeling techniques, this research aims to provide academic directors, deans, and key decision-makers in graduate business schools
with a practical tool for enhancing personalized learning in their programs.
The proposed framework has the potential to be adapted for a broader
audience beyond MBA programs, contributing to the advancement of
personalized learning in various educational contexts. This researchaligns with the growing need for data-driven approaches to optimize
learning outcomes and student success in higher education.

2 - Guiding labeling efforts in question difficulty estimation
using active learning

Arthur Thuy, Dries Benoit

Jordi Castro
Variable splitting has traditionally been employed in (first order) Lagrangian decomposition approaches and, more recently, in the Alternating Direction Method of Multipliers (ADMM). In this presentation
we will show how variable splitting can be efficiently used within (second order) interior point methods for solving large support vector machine (SVM) problems, a binary classification technique extensively
used in machine learning. Briefly, by replicating variables, the SVM
problem can be decomposed into smaller SVMs with additional linking
constraints that equate the values of the different copies. The resulting
problem’s structure can be exploited by specialized interior point methods that compute the Newton direction with a combination of direct
and iterative solvers (i.e., Cholesky factorizations and preconditioned
conjugate gradients). This new approach is compared with state-ofthe-art solvers for SVMs, which are based on either interior point algorithms (such as SVM-OOPS) or specific algorithms developed by
the machine learning community (such as LIBSVM).
Reference:
J. Castro, New interior-point approach for one- and two-classlinear support vector machines using multiple variable splitting, Journal of Optimization Theory and Applications, (2022),
https://doi.org/10.1007/s10957-022-02103-1.

 WB-31
Wednesday, 10:30-12:00 - Room: 046 (building: 208)

Learning Analytics and other Text Analytics
tasks
Stream: Analytics
Invited session
Chair: Joaquín Roa
Chair: Matthias Bogaert
1 - Enhancing Business Education through Bayesian
Knowledge Tracing: A Framework for Personalized
Learning

Estimating the difficulty of exam questions is crucial for effectively
evaluating students’ knowledge and facilitating personalized exercise
recommendations. Obtaining labels for the training dataset typicallyinvolves time-consuming and expensive pretesting and manual calibration. Despite recent advances in fine-tuned Transformer-based models outperforming traditional machine learning approaches, the labeling expenses remain considerable. Our study addresses this labelingchallenge by leveraging active learning to drive the annotation process, directing human expert attention toward the most informative
data points. Given the lack of uncertainty in standard regression neuralnetworks, we employ Monte Carlo Dropout to capture model uncertainty in predictions on the unlabeled set. Model uncertainty tends tobe high on data points in underrepresented areas of the input space,precisely the observations we aim to label. Fine-tuning a DistilBERT
model with Monte Carlo Dropout on a dataset comprising science andmath multiple-choice questions yields promising results.

3 - Cross-Lingual Debiasing of Large Language Models

Manon Reusens, Philipp Borchert, Margot Mieskes, Jochen
De Weerdt, Bart Baesens
Bias detection and mitigation have received more and more attention
over the last few years in Natural Language Processing (NLP). This ismainly due to the societal implications. While often research into thesedebiasing techniques focuses on English and mostly monolingual models, our research aims to provide insights into the cross-lingual transferability of these debiasing techniques. More specifically, we look
into what happens to the other languages when debaising a multilingual
model in one language. We examine several languages such as English,
French, German, and Dutch. The CrowS-Pairs dataset includes stereotypes associated with historically disadvantaged groups in the UnitedStates encompassing multiple types of bias, among which gender, race,and religion. From this dataset also a French version exists, addressing stereotypes against specific demographic groups in France. For theother languages, we used translations of the dataset that were checkedby native speakers. Next, we analyzed the effects of cross-lingual debaising on multilingual BERT (mBERT). Using these translations of
the CrowS-Pairs dataset, we find that cross-lingual debiasing is possible. Moreover, we identify SentenceDebias as the best-performing
debiasing technique from our set. Finally, we find that the debiasing
techniques that add an additional pretraining step are best employed on
the lowest resource languages.

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4 - Predictive Politics: Understanding Drivers of Engagement in Belgian Politics

Dylan Van Mulders, Matthias Bogaert, Dirk Van den PoelSocial media platforms have offered politicians and political parties
new opportunities to communicate and interact with citizens. Especially in Belgium, political parties and politicians are spending a lotof money to social network sites to attract voters. Backed by Belgian
party financing, Belgian parties have been among the biggest spenders
in Europe, even outside of campaign periods. The main goal of thisresearch is to gain a deeper understanding on the online engagementwith political posts within the Belgian multi-party system. We focus onwhat politicians are talking about and how people engage with this on
X, formerly known as Twitter, since it remains one of the most political social network sites to date. To achieve this, we propose a two-step
approach to predict the level of engagement based on a unique set offeatures. This approach first classifies tweets into several like-mindedcategories, and then applies regression on each category. Supported by
the Academic Twitter API we analyzed the posts of 72 Belgian politicians as well as posts from all the major political party Twitter profiles
for a period of 2 years. The main contribution of this study is that it is
the first to combine the added value of multiple distinct characteristics
in the context of online engagement and disclose differences between
posts originating from personal and party profiles within a multi-party
political system.

 WB-32
Wednesday, 10:30-12:00 - Room: 41 (building: 303A)

Beyond First-Order Optimization Methods
Stream: Advances in large scale nonlinear optimization
Invited session
Chair: Dmitry Kamzolov
Chair: Martin Takac
1 - Adaptive Quasi-Newton and Anderson Acceleration
Framework with Explicit Global (Accelerated) Convergence Rates

Damien Scieur
Despite the impressive numerical performance of the quasi-Newton
and Anderson/nonlinear acceleration methods, their global convergence rates have remained elusive for over 50 years. This study addresses this long-standing issue by introducing a framework that derives novel, adaptive quasi-Newton and nonlinear/Anderson acceleration schemes. Under mild assumptions, the proposed iterative methods
exhibit explicit, non-asymptotic convergence rates that blend those of
the gradient descent and Cubic Regularized Newton’s methods. The
proposed approach also includes an accelerated version for convex
functions. Notably, these rates are achieved adaptively without priorknowledge of the function’s parameters. The framework presented inthis study is generic, and its special cases include algorithms such as
Newton’s method with random subspaces, finite differences, or lazy
Hessian. Numerical experiments demonstrated the efficiency of the
proposed framework, even compared to the l-BFGS algorithm with
Wolfe line-search.

2 - Accelerated Adaptive Cubic Regularized Quasi-Newton
Methods

Dmitry Kamzolov
In this paper, we propose the first Quasi-Newton method with a global
convergence rate of $O(fracL_1R2k)$ for general convex functions.
Quasi-Newton methods, such as BFGS, SR-1, are well-known for theirimpressive practical performance. However, they are theoreticallyslower than gradient descent for general convex functions. This gap
between impressive practical performance and poor theoretical guarantees was an open question for a long period of time. In this paper,382

we make a significant step to close this gap. We improve upon the existing rate and propose the Cubic Regularized Quasi-Newton Methodwith a convergence rate of $O(fracL_1R2k)$. The key to achieving
this improvement is to use the Cubic Regularized Newton Method overthe Damped Newton Method as an outer method, where the QuasiNewton update is an inexact Hessian approximation. Using this approach, we propose the first Accelerated Quasi-Newton method witha global convergence rate of $O(fracL_1R2k2)$ for general convex
functions. In special cases where we have access to additional computations, for example Hessian-vector products, we can improve the inexact Hessian approximation and achieve a global convergence rate of$O(fracL_2R3k3)$, which make it intermediate second-order method.
To make these methods practical, we introduce the Adaptive Inexact
Cubic Regularized Newton Method and its accelerated version, which
provide real-time control of the approximation error. We show that thep

3 - On the behavior of limited-memory quasi-Newton methods for quadratic problems

Aban Ansari-Önnestam, Anders Forsgren
Quasi-Newton methods form an important class of methods for solving nonlinear optimization problems. In such methods, first order information is used to approximate the second derivative. The aim is to
mimic the fast convergence that can be guaranteed by Newton-based
methods. In the best case, quasi-Newton methods will far outperform
steepest descent and other first order methods, without the computational cost of calculating the exact second derivative. These convergence guarantees hold locally, which follows closely from the fact that
if the objective function is strongly convex it can be approximated wellby a quadratic function close to the solution. Understanding the performance of quasi-Newton methods on quadratic problems with a symmetric positive definite Hessian is therefore of vital importance. In theclassic case, an approximation of the Hessian is updated at every iteration and exact line search is used. It is well known that the algorithm
terminates finitely, even when the Hessian approximation is memoryless, i.e. requires only the most recent information. This talk will address ways in which the reliance on exact line search and dependence
on conjugate search directions can be relaxed, and how these changes
affect the behavior of quasi-Newton methods.

4 - Advancing the lower bounds: An accelerated, stochastic, second-order method with optimal adaptation to inexactness

Artem Agafonov, Dmitry Kamzolov, Alexander Gasnikov, Ali
Kavis, Kimon Antonakopoulos, Volkan Cevher, Martin Takac
We present a new accelerated stochastic second-order method that isrobust to both gradient and Hessian inexactness, which occurs typically in machine learning. We establish theoretical lower bounds andprove that our algorithm achieves optimal convergence in both gradient and Hessian inexactness in this key setting. We further introduce
a tensor generalization for stochastic higher-order derivatives. Whenthe oracles are non-stochastic, the proposed tensor algorithm matches
the global convergence of Nesterov Accelerated Tensor method. Bothalgorithms allow for approximate solutions of their auxiliary subproblems with verifiable conditions on the accuracy of the solution.

 WB-34
Wednesday, 10:30-12:00 - Room: 43 (building: 303A)

Stochastic Optimization: Advanced
Applications
Stream: Stochastic, Robust and Distributionally Robust
Optimization
Invited session
Chair: Giovanni Pantuso

EURO 2024 - Copenhagen
1 - Trust Dynamics and Collaboration Costs: Navigating
Complexity in Collaborative Distribution Networks

WB-35

4 - Optimal carsharing service pricing under decisiondependent demand uncertainty

Reza Alikhani, Mahnaz Hosseinzadeh

Jiali Deng, Giovanni Pantuso

In collaborative distribution networks, building partnerships hinges on
intricate trust dynamics. The exchange of critical information is crucial, but when a host carrier holds sensitive data, like customer demands, challenges arise. The host’s comprehensive data is pivotal,
yet asymmetry may deter reciprocation, impacting collaboration. To
surmount this, we use bi-objective two-stage robust optimization, employing an extended algorithm for effective resolution. Our framework
navigates collaboration costs, offering insights into the intricate interplay with network design. Our study delves into trust’s profound impact, enhancing understanding of collaboration dynamics in distribution networks.

In carsharing pricing problems, stochastic demand is typically viewed
as an exogenous random variable, where the uncertainty is governed by
known probability distributions. This assumption neglects the inherent
correlation between up-to-date user’s adoption decisions and the released carsharing prices, which makes the stochastic demand decisiondependent. To this end, we come up with an optimal carsharing service pricing problem that incorporates endogenous uncertainty of carsharing demand, specifically by accounting for the decision-dependent
distributions of stochastic demand. The problem is formulated as a
two-stage mixed-integer stochastic program with decision-dependent
uncertainty. We show that the problem can be written as an equivalent
mixed-integer bilinear program whose size is exponential in the input
parameters. To tackle the computational complexity we adopt a versionof the L-shaped method which accounts for decision-dependent distributions. The computational experiments demonstrate empirically thatthe algorithm converges finitely. In addition, the method outperforms
by far a commercial solver used to solve the monolithic formulation,
hereby extending the domain of practically tractable problems.

2 - Optimal Participation of Energy Communities in Electricity Markets under Uncertainty.
A Multi-Stage
Stochastic Programming Approach.

Albert Solà Vilalta, Marlyn Dayana Cuadrado Guevara, Ignasi
Mañé, F.-Javier Heredia
An energy community is a legal figure, recently coined by the European Union, that creates a framework to encourage active participationof citizens and local entities in the energy transition to net-zero. In
this work, we study the optimal participation of energy communities
in day-ahead, reserve, and intraday electricity markets.
The motivation to do so is that there are time periods where energy
communities cannot meet their internal demand, and periods where
they generate excess electricity. This is because most of the electricity
they generate comes from variable renewable resources like solar andwind. Electricity market participation is a natural way to ensure theymeet their internal demand at all times, and, simultaneously, make themost of the excess electricity.
We propose a multi-stage stochastic programming model that captures
variable renewable and electricity price uncertainty. The multi-stage
aspect models the different times at which variable renewable generation is considered to be known and electricity prices from different
markets are revealed. This results in a very large scenario tree with 34
stages, and hence a very large optimization problem. Scenario reduction techniques are applied to make the problem tractable. Case studies
with real data are discussed, considering different energy community
configurations, to analyse proposed regulatory frameworks in Europe.
The added value of considering stochasticity in this problem is also
analysed.

3 - A bivariate two-moments fit and its application in appointment scheduling

Bharti Bharti, Michel Mandjes, René Bekker
The optimization of appointment scheduling within parallel server systems is important for enhancing operational efficiency and customer
satisfaction in diverse service industries, ranging from healthcare andtelecommunications to transportation and manufacturing. In this context, we tackle the challenge of appointment scheduling in systems
where clients are served by two correlated servers operating in parallel, with given distribution. We devise a stochastic optimization framework to minimize a comprehensive loss function that balances clients’
waiting times, server idle times, and the effects of individual client’s
sojourn times. To capture the diverse operational scenarios encountered in practice, we introduce novel metrics, including maximum andminimum sojourn times, reflecting real-world constraints and priorities and redefine the scheduling objective solely in terms of sojourntimes, facilitating the application of standard optimization methodologies. To accurately model the distributions of sojourn times, we propose a bivariate extension of the conventional two-moments fit using
Weibull distribution and leverage the Frechet bounds for bivariate distributions with fixed marginals to get bounds on correlation between
the two queues. Finally we conduct a series of numerical experiments
to illustrate the efficiency of our method. We compare our method withsimulated results and examine the effect for both negative and positive
correlations with different distributions.

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Wednesday, 10:30-12:00 - Room: 44 (building: 303A)

Stochastic optimization: theory andapplications
Stream: Stochastic, Robust and Distributionally Robust
Optimization
Invited session
Chair: Milos Kopa
Chair: Ari-Pekka Perkkiö
1 - Stability in games with random payoffs

Lukáš Račko, Milos Kopa, Petr Lachout
The paper deals with non-cooperative games in which the payoff function of its players is influenced by exogenous randomness. The main
goal is to provide a general concept of stability in those games because
the standard notion of Nash equilibrium is no longer satisfactory. One
could find a deterministic equivalent to the game with a random payoffby considering a risk measure and defining a new game with a payoff
function adjusted by the risk measure. This, however, causes several
problems as the Fundamental Theorem of non-cooperative game theory no longer holds for most of such defined equivalents. Our idea is
to loosen the standard concept of the best response to an alpha-best
response which requires the strategy to be the best response only witha certain high probability. Based on this idea we define the alphaNash equilibria and we prove that for every finite game with random
payoff non-trivial alpha-Nash equilibria exist. Moreover, those equilibria characterize equilibria in a broad class of deterministic equivalent games. Finally, we extend the idea of a static game with a random payoff to a game with multiple stages and we show that every
finite stochastic game may be represented as a sequential game witha random payoff. In the numerical study, this theory is applied to a
management problem of competition of hospitals for vaccines during
a pandemic.

2 - Epidemic model of a misinformation spread?

Martin Smid
We introduce a mathematical framework for modeling the dissemination of an undesirable agent, encompassing both biological pathogens
and misinformation. This framework is articulated as a heterogeneous,partially observable vector auto-regression (VAR) model. In epidemiological terminology, it can be described as a stochastic SEIR-likemodel.

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The utility of a closely related model has been previously validated
through its application in analyzing and formulating policy recommendations during the COVID-19 pandemic. In this study, we elaborateon the modifications necessary for adapting this model to accurately
represent the dynamics of undesirable content propagation, with empirical validation provided through analysis of data derived from social
networks.

3 - Dual solutions in convex stochastic optimization

Ari-Pekka Perkkiö
We study duality and optimality conditions for general convex stochastic optimization problems. The main result gives sufficient conditions
for the absence of a duality gap and the existence of dual solutions ina locally convex space of random variables. It implies, in particular,
the necessity of scenario-wise optimality conditions that are behind
many fundamental results in operations research, stochastic optimal
control and financial mathematics. Our analysis builds on the theory
of Frechet spaces of random variables whose topological dual can be
identified with the direct sum of another space of random variables anda space of singular functionals. The results are illustrated by deriving
sufficient and necessary optimality conditions for several more specificproblem classes. We obtain significant extensions to earlier models e.g.
on stochastic optimal control, portfolio optimization and mathematicalprogramming.

4 - Risk-Averse Multiarmed Bandits with Switching Penalties

Milad MalekipirbazariThis work explores the intricate dynamics of the multiarmed bandit(MAB) problem, augmented with a critical realworld consideration:
the cost implications of switching decisions. This study is at the forefront of integrating risk considerations into MAB scenarios, where
decision-makers face penalties each time they transition between options. Such scenarios are not just theoretical constructs but are reflective of numerous practical applications. Our work distinguishes itself by addressing the largely unexplored domain of risk-averse MAB
problems compounded by switching penalties. Our contribution isthreefold: firstly, we explore the qualitative aspects of optimal policies.
Secondly, we present novel theoretical results, including the development of the Risk-Averse Switching Index (RASI), which addresses thedual challenges of risk aversion and switching costs, demonstrating its
near-optimal efficacy. Lastly, through rigorous numerical experiments,
we validate our algorithm’s effectiveness and practical applicability.

In this paper we design cost allocation rules for inventory games withprivate information. First, we design incentive-compatible cost allocation rules for inventory games with private information via VickreyClarke-Groves (VCG) rules. Then, we propose incentive-compatible
and approximate budget-balanced cost allocations via polynomial approximations such as the Chebyshev approximation and the Taylor approximation. In addition, we propose an incentive-compatible costallocation with individual rationality. Finally, we conduct numericalexperiments to compare the performance of the proposed cost allocations.

3 - A Variable Horizon Stability Concept for Partially Known
Preferences in Bilateral Conflicts

Leandro Rêgo, Emerson R Sabino
Conflict analysis is a research area that using mathematical models represents and studies the evolution of conflicts in a systematic way. The
Graph Model for Conflict Resolution (GMCR) is a successful model inthis area due to its flexibility and relative ease of use. In GMCR, manystability concepts try to capture different behavioral characteristics ofdecision makers (DMs) in conflicts. In this paper, a stability concept
is proposed for the GMCR that is useful in applications where DMs
do not have full knowledge about their opponents’ preferences. As in
real conflicts there are factors that may make it difficult to fully obtain
information about others’ preferences, to understand DMs’ behavior
in such a scenario is valuable. With our proposed stability concept,
it is possible to carry out a stability analysis in the GMCR even if
only part of the preference information is known. The Variable Horizon with Partially Known Preferences Stability works by anticipating
what a given DM believes that final conflict state would be after any
chosen number of moves and countermoves following its initial movein a bilateral conflict. The proposed solution concept generalizes two
important stability notions, limited-move stability and maximin stability, which are particular cases when DMs have full knowledge andno knowledge at all, respectively. Finally, an application of a dispute
among two Brazilian states about a certain geographical area is madewith the proposed stability.

 WB-37
Wednesday, 10:30-12:00 - Room: 33 (building: 306)

Discrete Multiobjective Optimization
Stream: Multiobjective Optimization
Invited session
Chair: Clemens Thielen WB-36
Wednesday, 10:30-12:00 - Room: 32 (building: 306)

Game Theory, Solutions and Structures X
Stream: Game Theory, Solutions and Structures
Invited session
Chair: Leandro Rêgo1 - Priority queues with competitive servers

Binyamin Oz, Elisheva Zur
We consider a queueing system with competitive revenue maximizing servers who charge for priority. Customers are strategic and decide which server to seek service from and whether to pay for priority.
Through an analysis of the equilibrium in this sequential two-stage
game, we demonstrate that the system exhibits self-regulation. Specifically, contrary to systems lacking priority mechanisms, our findings
reveal that socially optimal joining rates are attained.

2 - Incentive-Compatible Cost Allocations for Inventory
Games with Private Information

Yinlian Zeng

384

1 - A Combined Framework of MCDA and MOP Methods forPlanning High School Student Distribution in the Capital Region of Denmark.

Andreas Sølling Brendstrup, Valdemar Buch, Michael Bruhn
Barfod, Thomas Stidsen
Many real-life problems frequently encompass multiple, sometimes
conflicting, objectives that require simultaneous consideration. For thispurpose, there is a need for a comprehensive assessment methodology.
Multi-criteria decision analysis (MCDA) and multi-objective optimization problems (MOP) are methodologies attempting to overcome suchproblems. MCDA focuses on deriving a satisfactory solution whileaccounting for a stakeholder preference structure. MOP methodologies focus on the optimization tasks of identifying Pareto optimal solutions, where optimal solutions must be taken in the presence of a
trade-off between two or more conflicting objectives. Over the past
decade, extensive research has been conducted, resulting in substantial
bodies of literature on both subjects, revealing potential opportunities
and synergies between them. This presentation investigates the possible combination of MOP and MCDA methods in a novel frameworkto address large, complex decision problems involving conflicting objectives and several stakeholders. The framework will be tested on the
combinatorial problem of distributing high school students within thecapital region of Denmark.

EURO 2024 - Copenhagen
2 - StudyPlanner: Helping students to plan university
courses with integer programming

Andreas Wiese, Michael Ritter, Philipp Wiedmann
At the beginning of each semester, all university students need to plan
the courses they want to take this semester. In the process, they need totake into account the rules of their degree program. Those might, forexample, regulate that some courses are compulsory, that one needs
to complete a certain number of credits from a group of courses, orthat one needs to choose a study focus from some given options. Also,
there are additional considerations, such as the preferences of the student for certain subjects, the times when the courses are scheduled, orthat some courses need to be taken in a certain order. In particular, it isa good idea to not just plan the courses of the upcoming semester, but
all courses in all future semesters.
This leads to an optimization problem with multiple objectives that
include, for example, minimizing the time until graduation and maximizing the satisfaction of the student with the selected courses. We
propose an integer programming (IP) formulation for this problem
and apply it to the Bachelor and Master degree in Mathematics at
the Technical University of Munich (TUM). In our computational experiments, we could solve most of the tested instances in about one
second using the non-commercial IP solver SCIP, and even fasterwith Gurobi. We made our planning tool available to the students of
the Department of Mathematics at TUM. It is publicly accessible athttps://studyplanner.co.cit.tum.de.

3 - A Lagrangian Bounding and Heuristic Principle for BiObjective Discrete Optimization

Nils-Hassan Quttineh, Torbjörn Larsson, Ida Åkerholm
Lagrangian relaxation is a common and often successful way toapproach computationally challenging single-objective discrete optimization problems with complicating side constraints. Its aim is often two-fold; first, it provides bounds for the optimal value, and, second, it can be used to heuristically find near-optimal feasible solutions, the quality of which can be assessed by the bounds. We consider bi-objective discrete optimization problems with complicatingside constraints and extend this Lagrangian bounding and heuristic
principle to such problems. The Lagrangian heuristic here produces
non-dominated candidates for points on the Pareto frontier, while thebounding forms a polyhedral outer approximation of the Pareto frontier, which can be used to assess the quality of the candidate points.
As an illustration example we consider a facility location problem inwhich both CO2 emission and cost should be minimized. The computational results are very encouraging, both with respect to bounding
and the heuristically found non-dominated solutions. In particular, theLagrangian bounding is much stronger than the outer approximation
given by the Pareto frontier of the problem’s linear programming relaxation.

4 - A General Label Setting Algorithm for the Multiobjective
Temporal Shortest Path Problem

Clemens Thielen, Cristina Bazgan, Johannes R. Kager, Daniel
Vanderpooten
Graphs in which the arc set changes over time (in discrete steps), arecalled temporal graphs. Given a directed temporal graph, a start node s,
and a constant number of objectives, the task in the single-source multiobjective temporal shortest path problem consists of computing theset of nondominated images of temporal paths from s to every othernode as well as one corresponding efficient path for each of these images. This problem generalizes both the multiobjective shortest path
problem in static graphs and the singleobjective temporal shortest path
problem.
This talk presents a general label setting algorithm for the single-source
multiobjective temporal shortest path problem that can handle a large
variety of different objectives. The only condition imposed on the objectives is a monotonicity property that generalizes the nonnegativityof the arc costs required for the well-known label setting algorithm forsolving the static single-source shortest path problem in both the singleobjective and the multiobjective case. The worst-case running timeWB-39

of our algorithm is polynomial in the sum of the input size of the problem instance and the number of nondominated images, which meansthat it runs in polynomial time for all tractable versions of the problem.
To complement this result, we provide a complete classification into
tractable and intractable problems for all single-source multiobjective
temporal shortest path problems involving a large variety of objectives.

 WB-38
Wednesday, 10:30-12:00 - Room: 34 (building: 306)

Convex and conic optimization
Stream: Conic Optimization: Theory, Algorithms, and Applications
Invited session
Chair: Olga Kuryatnikova
1 - Lagrangian duality for Mixed-Integer Semidefinite Programming

Renata Sotirov, Frank de Meijer
Mixed-integer semidefinite programming can be viewed as a generalization of mixed-integer programming where the vector of variables
is replaced by mixed-integer positive semidefinite matrix variables.
The combination of positive semidefiniteness and integrality allows toformulate various nonlinear optimization problems as mixed-integer
semidefinite programs (MISDPs). In this talk we show that MISDPs
induce bounds based on Lagrangian duality theory. By introducing
MISDP-based projected bundle algorithm, we show that the resulting
Lagrangian dual bounds are stronger than the standard SDP bounds forvarious optimization problems.

2 - Linear Convergence of Fixed-Point Iteration of Averaged Operators via a Generic Error-Bound Condition

Kira van Treek, Javier Peña, Juan Vera, Luis Zuluaga
Several optimization algorithms, including the ADMM and DouglasRachford algorithm (DR), can be cast as the fixed-point iteration (FPI)of an averaged operator. However, the convergence of these algorithms
is slow in general (within sublinear regime). We show linear convergence of the FPI of averaged operators by leveraging an error-boundcondition on the operator. Our work captures several existing results on
the linear convergence of the ADMM and DR under stronger assumptions. As a byproduct of our method, we bound the rate of convergenceof the DR applied to linear and quadratic optimization problems.

3 - Relaxations for the elementary shortest path problem

Mirjam Duer, Regina Schmidt
Given a directed graph with negative edge costs and possibly negative
cycles, the elementary shortst path problem consists in finding a shortest elementary path, i.e., a path from a source node to a target nodethat visits each node at most once. In this talk, we discuss various
formulations and relaxations for this NP-hard problem.

 WB-39
Wednesday, 10:30-12:00 - Room: 35 (building: 306)

Reliability Models
Stream: Stochastic Modelling
Invited session
Chair: Bram de Jonge385

WB-40EURO 2024 - Copenhagen

1 - Modelling lifetimes of systems operating under loadconditions

Juana-Maria Vivo-Molina, Manuel Franco
Systems with components operating under load conditions are vulnerable to failure, becoming more difficult the maintenance decisionmaking. Such fragility increases under structural dependency, which iscommonly used for configuring systems as a cost-effective techniquethat optimize the reliability and availability. In this study, we develop astochastic distribution to model lifetimes of systems with structural dependence under preventive maintenance policies. Further, a real-worldcase study is provided to illustrate the effectiveness of the proposedfamily.

 WB-40
Wednesday, 10:30-12:00 - Room: 96 (building: 306)

Experimental economics and game theory 1
Stream: Experimental economics and game theory
Invited session
Chair: Gerhard-Wilhelm Weber
Chair: Stefan Wolfgang Pickl
Chair: Ulrike Leopold-Wildburger
1 - Cooperative solution to noncooperative game2 - Condition-based Maintenance for a Degrading System
under Dynamic Working Conditions

Guo Shi, Bin Liu, Lesley Walls
This study proposes a maintenance strategy for a degrading system under dynamic working conditions. Dynamic working conditions including environmental factors such as temperature, and humidity, alongside operating factors like production rate and operating speed, present
challenges in accurately capturing the degradation process. In addition, the heterogeneity among observed components adds complexity
to utilising the health monitoring data. To address these issues, we
utilise Bayesian Linear regression and Bayesian Poisson regression toaccount for shock occurrence and magnitude. During system operation, regression parameters are updated upon the arrival of shocks ateach decision epoch. We formulate the optimal maintenance problem
as a Markov decision process, with decisions triggered by parameter
updates converging towards underlying values. This paper not only
establishes an analytically tractable degradation model incorporating
component heterogeneity and dynamic working conditions but also
theoretically explores the structure of optimal preventive maintenancethresholds. Additionally, to ease the computational burden due to thehigh state space, we introduce a heuristic algorithm that leverages themost likely distribution. Numerical experiments and a case study validate the effectiveness of the proposed maintenance policy, demonstrating its practical applicability and efficacy in real-world scenarios.

3 - Data-Driven Condition-Based Maintenance Optimization Given Limited Data

Yue Cai, Bram de Jonge, Ruud Teunter
Unexpected failures of operating systems can result in severe consequences and huge economic losses. To prevent them, preventive maintenance based on condition data can be performed. Existing studies
either rely on the assumption of a known deterioration process or anabundance of data. However, in practice, it is unlikely that the deterioration process is known, and data is often limited (to a few runs-tofailure), especially for new systems. This paper presents a fully datadriven approach for condition-based maintenance (CBM) optimizationthat is especially useful in situations with limited data. The approach
uses penalized logistic regression to estimate the failure probability as
a function of the deterioration level and allows any deterioration level
to be selected as the preventive maintenance threshold, also those thathave not been observed in the past. Numerical results indicate thatthe preventive maintenance thresholds resulting from our proposed approach closely approach the optimal values.

4 - Data-Driven Condition-Based Maintenance Optimization

Bram de Jonge
Most studies on condition-based maintenance optimization consider
the deterioration process of an industrial system to be known. In contrast, we make the more realistic assumption of an unknown deterioration process and develop approaches for determining when to carry out
maintenance based on limited obtained condition data. We also assume
that the parametric form of the deterioration process is unknown; ourapproaches are therefore fully data-driven. We analyze how the qualityof the decisions depends on the amount of data that is available.

386

Zvi Winer, Avishay Aiche
Content analysis reveals that in many markets in the State of Israel
where regular payment is required for continuous service, usuallymonthly, a situation can be seen where the price of the service creeps
up consistently and even though the consumer has an option to switch
to another company, at the starting price he paid, he remains loyal tothe company that increases its service consistently and continuously.
The purpose of this paper is to provide a theoretical model according
to which the company that raises its’ price capitalize on the consumer
loyalty. The subgame perfect equilibrium resulting from the model
supports the empirical evident. According to the perfect equilibrium
the firm initially offers loss prices, the consumer remains loyal to thefirm and the prices in subsequent periods compensate for the loss inthe first periods.

2 - Personalized dynamic pricing: pearls or perils?

Baile Lu, Hongyan Dai, Weihua Zhou
Leveraging large-scale data sources to employ personalized dynamic
pricing has been a prominent practice in the retail industry. However,
the outcome of this pricing strategy could be mixed. Therefore, thisstudy aims to empirically investigate the impact of personalized dynamic pricing on store performance and individual consumer behavior
under different conditions. We obtain unique access to a large data
set from a leading on-demand delivery platform that has launched apersonalized dynamic pricing strategy through price discounts in someof its grocery stores. Employing a quasi-experimental setting, we utilize the difference-in-differences method to estimate the causal impactof personalized dynamic pricing. First, we conduct store-level analyses and find that personalized dynamic pricing decreases store revenue. Then, to understand the underlying mechanism, we proceedwith individual-level analyses and find that personalized dynamic pricing increases the individual transaction amount and frequency while
increasing consumer churn. Finally, we investigate the moderating
effects of an essential feature of personalized dynamic pricing: theprice fluctuation over time. We find that a bit more fluctuation can
mitigate the negative consequences of personalized dynamic pricing
by reducing consumer churn and increasing consumer transaction activities. However, too much price fluctuation intensifies the negative
consequences of personalized dynamic pricing.

3 - A game theoretical analysis on penalty of online copyright infringement with an empirical study

I-Hsuan Hong, Jack C.P. Su, Ching-Kuan Tsai
The increasing societal emphasis on intellectual property rights andthe rampant proliferation of pirated products have led to the gradualsignificance of corresponding legal frameworks and penalties. This
study aims to explore the optimal determination of punitive damages
for intellectual property infringement on pirated video platforms undervarious circumstances, with an empirical study serving as validation.
Initially, a utility function is established to determine consumer behavior. The primary objective of this study is to investigate the Stackelberg
game model with publishers as leaders and legal platform operators, aswell as pirated platform operators, as followers. Subsequently, an analysis of infringement behavior across different scenarios is conducted,
assessing how the magnitude of punitive damages impacts publisher
revenue. An empirical study is then conducted to validate the relationship between the theoretical model and case study. The overall goalof this research is to provide more concrete and effective methods andEURO 2024 - Copenhagenstrategies for addressing intellectual property infringement issues on
online video platforms.

4 - Forcasting behaviour in instable environments

Ulrike Leopold-Wildburger
Time series models face structural instability when applied to realdata. Usually, studies yield to applications without the considerationof breaks leads to unreliable results. Progress has been achieved in thetheory of identifying, estimation and testing of structural instabilities.
Our experimental study shows who efficient the so-called bounds &likelihood heuristic can be. The subjects quickly identify breaks andare able to adapt their forecasts.
Time series models face structural instability when applied to realdata. Usually, studies yield to applications without the considerationof breaks leads to unreliable results. Progress has been achieved in thetheory of identifying, estimation and testing of structural instabilities
by our new procedure. The experimental studies show how efficient theso-called bounds & likelihood heuristic can be. The subjects quickly
identify breaks and are able to adapt their forecasts in a surprisingly
good manner. The subjects can be well explained by the b&l model
despite the occurrence of structural breaks. The bounds & likelihood
heuristic manages to model average (not individual) forecasts.

 WB-41
Wednesday, 10:30-12:00 - Room: 97 (building: 306)

Structured nonconvex optimization
Stream: Nonsmooth Optimization
Invited session
Chair: Susan Ghaderi
1 - Regularization Methods for Sparse Optimization with
Applications

Carisa K.W. Yu, Siu Kai ChoySparse optimization is a practical approach for tackling numerous big
data challenges. Extensive empirical studies in the field have demonstrated that nonconvex regularization methods exhibit a significantly
stronger sparsity capability to promote sparsity and a notably morerobust stability in sparse recovery compared to convex penalty methods. In this paper, we explore various nonconvex regularization models to address sparse optimization problems. Theoretical analysis isconducted to establish recovery bounds for these regularization models. Additionally, we propose the widely recognized proximal gradientmethod so as to solve the regularization problems and establish convergence rates for the numerical algorithms. Finally, we apply ourtheoretical findings and numerical algorithms to tackle the problem ofinferring the master gene regulator in cell fate conversion, presenting
compelling numerical results.

2 - High-order proximal point method in the nonconvex setting

WB-42

3 - First- and second-order models for nonsmooth functions based on derivative sampling

Bennet Gebken
In optimization, many solvers are based on iteratively building a local model of the objective function and then minimizing the model
instead of the original function. In the smooth case, such models canbe derived from the Taylor expansion based on derivatives of different orders. In the nonsmooth case on the other hand, models are morechallenging to construct: The first issue is the lack of a Taylor expansion. While replacing the gradient in smooth first-order methods
by the Clarke subdifferential from nonsmooth analysis yields a way tocharacterize descent directions, there is no simple way to derive higherorder models. The second issue is that generalized derivatives like the
Clarke subdiff. are difficult to work with in practice, since they can be
unstable and impossible to evaluate in the general case. In this talk, I
will present two models for (unstructured) locally Lipschitz continuousfunctions. The first model is a simple first-order model, which is basedon approximating the Clarke subdiff. by the Goldstein eps-subdiff.
which, in turn, can be approximated in practice by a deterministic gradient sampling approach. The second model is based on the idea ofsampling Hessian matrices in addition to the gradient. More precisely,
it is defined as the maximum of (existing) second-order Taylor expansions in a neighborhood of a point. After introducing each model, I
will present ways to generate them in practice and discuss the behaviorof the resulting descent methods.

4 - Radial Epiderivative based Method in nonconvex Lipschitz Optimization

Gulcin Dinc Yalcin, Refail Kasimbeyli, Erdener Özçetin, Gazi
Bilal Yildiz
This study aims to develop a method based on the radial epiderivative,
a notion defined for non-convex functions. The method begins with an
initial solution. Then, at each iteration, a direction is chosen and evaluated by using the value of the radial epiderivative at the current point. If
the direction is decent, a new point is generated in that direction. If not,a new direction is determined, and the process is repeated. Determining directions is a crucial aspect to consider. The study utilizes particle
swarm optimization (PSO) and cyclic coordinate (CC) to determine directions at each iteration. Two algorithms, radial epiderivative-based
PSO (RPSO) and radial epiderivative-based CC (RCC), are then analyzed. The algorithms’ performance is demonstrated on test problems
from the literature, and the results are promising.

 WB-42
Wednesday, 10:30-12:00 - Room: 98 (building: 306)

Iterative Methods for Feasibility and
Optimization Problems
Stream: Variational Analysis and Continuous Optimization
Invited session
Chair: Francisco Javier Aragón Artacho
1 - Local linear convergence of parallel projection algorithms with reduced lifting

Alireza Kabgani, Masoud AhookhoshRubén Campoy

The primary objective of this talk is to study exact and inexact versions of the high-order proximal-point methods in the nonconvex setting. The subsequential convergence is investigated by showing themonotonicity of the sequence Lyapunov function values, where our
Lyapunov function is the high-order Moreau envelope. We establish
the global convergence of the method under Kurdyka-Lojasiewicz inequality. As a specific case study, we will discuss the effectiveness ofthese algorithms in addressing weakly convex optimization problems
through theoretical analyses and numerical results.

Different notions on regularity of sets and of collection of sets play
an important role in the analysis of the convergence of projection algorithms in nonconvex scenarios. While some projection algorithms
can be applied to feasibility problems defined by finitely many sets,some other require the use of a product space reformulation to construct equivalent problems with two sets. In this work we analyze
how some regularity properties are preserved under a reformulation
in a product space of reduced dimension. This allows us to establish
local linear convergence of parallel projection methods which are constructed through this reformulation.

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2 - The Boosted Difference of Convex Functions Algorithm
for Value-at-Risk Constrained Portfolio Optimization

Marah-Lisanne Thormann, Vuong Phan, Alain Zemkoho

Marcel Bogdan

A highly relevant problem of modern finance is the design of Valueat-Risk (VaR) optimal portfolios. Due to contemporary financial regulations, banks and other financial institutions are tied to use the risk
measure to control their credit, market, and operational risks. For aportfolio with a discrete return distribution and finitely many scenarios, a Difference of Convex (DC) functions representation of the VaR
can be derived. Wozabal (2012) showed that this yields a solution to a
VaR constrained Markowitz style portfolio selection problem using theDifference of Convex Functions Algorithm (DCA). A recent algorithmic extension is the so-called Boosted Difference of Convex Functions
Algorithm (BDCA) which accelerates the convergence due to an additional line search step. It has been shown that the BDCA converges linearly for solving non-smooth quadratic problems with linear inequality
constraints. In this paper, we prove that the linear rate of convergenceis also guaranteed for a piecewise linear objective function with linear equality and inequality constraints using the Kurdyka-Łojasiewicz
property. An extended case study under consideration of best practices
for comparing optimization algorithms demonstrates the superiority of
the BDCA over the DCA for real-world financial market data. We areable to show that the results of the BDCA are significantly closer to theefficient frontier compared to the DCA.

The present study approaches variational inequalities governed by the
perturbed operator generated by the energy of the mathematical pendulum. It was proved recently Fan-hemicontinuity for the gradient of
the norm defined on its scalarly-positive, closed convex subdomain ofa Hilbert space. The aim of the present study is to establish whether
or not the positive result obtained for Fan-hemicontinuity can be extended to an arbitrary reflexive Banach space. In this matter it is natural to consider the duality map and its topological properties. Based on
weak-weak sequential continuity of the generalized duality map, theproperty above holds on the discrete spaces of sequences with power psummable and does not hold on the Lebsegue spaces of functions withpower p integrable, (p not equal to 2), thus restricting the space setting
where weak compactness results are applicable.

3 - Semi-Newton algorithms in nonsmooth difference programming

Francisco Javier Aragón Artacho
In this talk we present a new coderivative-based Newton algorithm fortackling unconstrained optimization problems whose objective function can be expressed as the difference of two generally nonconvex
functions, namely, a continuously differentiable function with locally
Lipschitzian derivatives and a locally Lipschitz and prox-regular function. Focusing our attention in the requirement of extended positivedefinite properties of the generalized Hessian of the first function,
we prove the well-posedness and various convergence results of the
proposed algorithm. We conclude with some numerical experiments
demonstrating the performance of the proposed algorithm in nonconvex settings.
This is a joint work with Boris S. Mordukhovich and Pedro Pérez-Aros.

 WB-43
Wednesday, 10:30-12:00 - Room: 99 (building: 306)

Recent advances on Variational Inequalities
and Equilibrium Problems I
Stream: Variational Inequalities and Equilibrium Problems: From Theoretical Advances to Real World Applications
Invited session
Chair: Mauro Passacantando
1 - A random elastic traffic equilibrium problem: stochastic
quasi-variational approach

Annamaria Barbagallo
The aim of the talk is to introduce a random elastic traffic equilibrium
problem in a Hilbert space setting (see [1]). The equilibrium condition is expressed by a random extension of the elastic Wardrop principle. Its characterization with a stochastic quasi-variational inequality
is proved. Under suitable assumptions, the existence of a random equilibrium distribution is established. Furthermore, a numerical scheme
to compute the random elastic traffic equilibrium distribution is presented. At last, a numerical example is provided.
References [1] A. Barbagallo, S. Guarino Lo Bianco, A random elastic
traffic equilibrium problem via stochastic quasi-variational inequalities, Commun. Nonlinear Sci. Numer. Simulat. 131 (2024) 107798.

388

2 - Fan-hemicontinuity for the gradient of the norm in several reflexive Banach spaces

3 - Dynamic distributed gradient method for strongly
monotone variational inequality problem over the common fixed-point constraints

Narin Petrot
In this talk, we introduce a dynamic distributed conjugate gradientmethod designed to solve the strongly monotone variational inequality
problem across the fixed-point sets intersection of firmly nonexpansive operators. Our method enables the independent computation of a
firmly nonexpansive operator alongside a dynamically updated weightat each iteration. This approach aims to enhance the convergence rateof the algorithm by adjusting control factors for each iterative step. By
imposing suitable control conditions on relevant parameters, we establish the strong convergence of the iterates towards the unique solutionof the variational inequality problem under consideration. Additionally, we conduct numerical experiments and analyze key observations
by applying this model to address image classification challenges using
support vector machine learning techniques.

 WB-44
Wednesday, 10:30-12:00 - Room: 20 (building: 324)

Pairwise comparisons and preference
relations 4
Stream: Multiple Criteria Decision Analysis
Invited session
Chair: Matteo Brunelli
Chair: Konrad Kułakowski
1 - My Part is Bigger than Yours! A Proposal for a Consensus Finding Model in a Team of Peers

Konrad Kułakowski
Quite often, scientific papers are the result of collaboration among
many researchers. Each researcher focuses primarily on their part of
the work, having different ideas about the parts provided by others. It
leads to overestimating one’s contribution to the research and underestimating the contribution and importance of others. In addition, those
whose contribution to the study is relatively significant have a better
understanding of the value of the work of the other team members.
Therefore, participation is often presented as a verbal description of
the work. Unfortunately, this is insufficient when the need is to distribute the reward fairly (e.g., a monetary bonus) for the achievement.
In the work, we propose a group decision-making method that establishes some compromise numerical ranking based on pairwise comparisons of alternatives. The model will, on the one hand, consider all
stakeholders’ opinions and, on the other hand, will give these opinions
the right weight. The presented model can be used in many situations
where there is a need for proper reward distribution, and the rewarded
team wants to avoid entrusting the reward distribution to one individual.

EURO 2024 - Copenhagen
2 - Comparative Insights: Untangling Blockchain Enablers
in Traditional vs. Electric Vehicle Manufacturer

Avinash Chauhan, M Vimala Rani
This study conducts a comprehensive comparative analysis ofblockchain technology’s impact on traditional automotive and electric vehicle (EV) manufacturing sectors, focusing on transparency,
security, efficiency, trust, compliance, sustainability, innovation, andcustomer satisfaction. It utilizes Multiple Criteria Decision Making(MCDM) methods such as AHP and TOPSIS, along with TISM andMICMAC methodologies to develop the conceptual framework andrelationship. In traditional automotive manufacturing, blockchain enables enhanced supply chain transparency, ensuring traceability ofcomponents and materials, thus improving compliance and fostering
trust among stakeholders. This transparency enhances collaborationand streamlines operations. For EV manufacturing, blockchain facilitates lifecycle management of critical components like batteries, optimizing usage, monitoring performance, and ensuring responsible disposal. It also integrates renewable energy sources, promoting sustainability and clean energy innovation. The analysis provides guidancefor decision-makers in both sectors to tailor blockchain strategies, unlocking benefits such as transparency, security, efficiency, stakeholder
collaboration, and sustainability throughout the supply chain. Embracing blockchain technology empowers manufacturers to navigate challenges and capitalize on opportunities, fostering a more resilient, efficient, and sustainable automotive industry.

3 - Multicriteria Product Prioritization and Classification
Methods to Support Production and Inventory Decisions

Murat KayaIn this study, we propose novel multicriteria product prioritization andclassification methods that use criteria from different functions of amanufacturing firm. We transform the scores for each criterion using
a quantile transformer. In the prioritization study, the goal is to choose
products for pre-build inventory and cycle-stock inventory. Criteria
weights are determined with the Analytic Hierarchy Process (AHP)
approach. In the classification study, the goal is to develop customized
production and inventory policies for different groups of products. To
this end, we use Principal Component Analysis (PCA) and Fuzzy CMeans (FCM) clustering. We illustrate the use of the methods with twocase studies from a leading tire manufacturer. The methods are transparent and easily understood by company managers. Being structured,
data-based as well as flexible, these methods can ease the decision burden on managers, and contribute to quick and well-tailored operational
decisions.

4 - A Multicriteria Framework for Assessing Digital Merchandising on Web and Mobile Equipment

Astrid Oddershede, Martin Montalva, Victor Olivares, Luis
QuezadaThis paper presents a framework for assessing digital merchandisingon web and mobile equipment. Making a digital strategy to sell products online a success, is a great challenge. It involves evaluating various aspects related to how products are presented and sold online.
Depending on the product, one can develop personalized actions foreach type of consumer who visits the site, based on their personal interests and demographic data, to give greater relevance to the mostrecent articles that have been published on the website, or adapt yourecommerce to new trends and customer behavior. An empirical work
has been done based on expert judgements for identifying dimensions’
aspects related to products presentation online sales in order to obtaincost-effectiveness. The study provides a basis for setting priorities formulating a Multicriteria decision making model.

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 WB-45
Wednesday, 10:30-12:00 - Room: 30 (building: 324)

Decision Support in the Public Sector andPolicy Making
Stream: Decision Support Systems
Invited session
Chair: Nikos Tsotsolas
1 - The comparison of social protection: evaluating collective agreements in public procurement using AHP.

Pietro Bonifaci, Matteo Attanasio, Sergio Copiello, Edda
DonatiIn the awarding of public contracts, Italian contracting authorities are
required not only to evaluate offers but also collective agreements applied to the workers of the participating undertakings. Until the innovations prompted by NGEU, the evaluation of contracts took into consideration the salary only. If it differed significantly from the values
reported in tables published by the Ministry of Labour, the prospective
contractor was excluded from the tender. A recent reform of the Italian
Public Procurement Code introduces an assessment of the overall protection system guaranteed by the specific collective agreement applied
to employees engaged in the execution of the contract. The contracting authority must therefore evaluate which of the contracts applied bycompetitors are equivalent by considering social aspects that are sometimes not directly quantifiable (maternity protection, sick leave, etc.).
We propose the use of AHP to support the decision-making processof contracting authorities in the field of social protection, to increasetransparency of the process, to guarantee effective protection of workers, and to reduce litigation risk. AHP is chosen for its ease of use andfor being already widely applied by contracting authorities in the selection of the most economically advantageous tender. AHP is tested
on a case study in which stakeholders directly taking part in collective bargaining (trade unionists, associations, etc.) are involved in theassessment process.

2 - Decision support system for district magistrate

Rajat Goyal, Sanjeev Govindrao Deshmukh, Nomesh Bolia
We can only function as a society with effective public administration. Without public administrators, a government would be unable tofunction. The responsibilities of public administrators are challenging.
India, a nation of immense diversity and cultural richness, stands as atestament to the resilience of democracy and the challenges it faces inmanaging its vast and complex administrative system. With twentyeight states and eight union territories catering to almost 1/6th of theworld’s population, India’s public administration system is an intricate
network of structures, processes, and institutions. Each Indian stateis divided into smaller administrative entities called districts. Each
district is managed by an Indian Administrative Service (IAS) officer
known as a District Magistrate (DM) or District Collector (DC). DM
is one of the key authorities ensuring that the district’s administration
runs smoothly and efficiently. With different duties and tasks, the DMs
are overburdened and overworked. As a result, the Decision Support
System (DSS) is essential for facilitating daily operations and significantly enhancing the productivity of DMs. This study aims to comprehensively analyze the challenges faced by the district administration inIndia and to answer how a decision support system helps DMs makedecisions effectively and improve quality and productivity.

3 - Multicriteria Decision Making for Health and Life Insurance Selection in India: A Comprehensive Analysis

Prashant Barsing
This research investigates the complex landscape of health and life insurance selection in India through the lens of multicriteria decisionmaking (MCDM). With the burgeoning population and diverse healthcare needs, the insurance sector plays a pivotal role in safeguarding
individuals against unforeseen health crises and securing the financial future of their dependents. The study aims to develop a robust389

WB-46EURO 2024 - Copenhagendecision-making framework that integrates multiple criteria to guideindividuals in making informed choices regarding health and life insurance policies. The research methodology involves a thorough analysis
of various factors influencing insurance decisions. A comprehensive
literature review is conducted to understand consumers’ preferences
and challenges in the Indian insurance market. The MCDM approachis employed to synthesize the collected data and derive a hierarchical model of decision criteria. The weights assigned to each bar are
determined through pairwise comparisons, reflecting the relative importance of factors influencing insurance choices. The research aims
to provide a decision support system to guide individuals in selecting
insurance plans. The findings of this study are expected to contribute to
the existing body of knowledge on insurance decision-making in India,
offering valuable insights for both consumers and insurance providers.
The research aims to enhance the overall efficiency and effectivenessof India’s health and life insurance industry.

 WB-46
Wednesday, 10:30-12:00 - Room: 40 (building: 324)

Practical Skills: Knowledge-Sharing
Tutorials - 2
Stream: Making an Impact
Invited session
Chair: Michele Quattrone
Chair: Andreas Schmitt
1 - Practical Skills: Knowledge Sharing Tutorial -2-

Making An Impact, Michele Quattrone, Andreas Schmitt
Expand your skills with these two 45’ practical sessions: Make Easy
Changes, Make Changes Easy: A Tutorial On OR Coding Quality;
followed by Tricks From The Trade For Large Scale Optimisation.
(i) Make Easy Changes, Make Changes Easy: A Tutorial On OR Coding Quality (Michele Quattrone, Air Liquide) The implementation ofwell-thought-through quality strategies on the software lifecycle is becoming a requirement for many software developments, even for the
early mock-up/prototyping phases. In this 45-minute tutorial, backed
with a practical example, we will discuss together how our coding canbenefit from this strong push, and what we can cherry-pick from hyping buzzwords such as DevOps, Continuous Integration, Continuous
Deployment, MLOps...in practice!
(ii) Tricks from the Trade for Large-Scale Optimization in a practical context (Andreas Schmitt, Zalando SE) Have you had to face up
to optimization challenges, where you have had to develop and deploy
heuristics in order to implement models into production environments?
This session will use the speaker’s experience of a successful application as a starting point for discussion.
Practical optimization problems often quickly increase in scale andcomplexity, necessitating tailored solution approaches. Our talk describes heuristics designed to work along-side a Lagrangian decomposition method, leading to almost-optimal solutions. These havebeen developed in the context of the frequently applied ’predict-thenoptimize’ paradigm, more specifically for markdown pricing strategies
in the online fashion industry. We present empirical evidence for theheuristics’ effectiveness, drawing on pricing applications at ZalandoSE.
This exposition will serve as a starting point for discussions: Participants will be encouraged to explore questions such as: What are yourexperiences with similar optimization challenges? How do you approach heuristic development in their work? What are effective approaches for deploying such models into production environments andmeasuring their impact?

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 WB-47
Wednesday, 10:30-12:00 - Room: 50 (building: 324)

MCDA in energy
Stream: Multiple Criteria Decision Analysis
Invited session
Chair: Janusz Granat
1 - Integrating multicriteria analysis with advanced data
analysis methods

Janusz Granat
Data-driven multicriteria analysis is a challenging research area integrating data analysis from two sources: first, raw data used for development of a model or a set of alternatives, and second, the data provided
by the model simulation or its interactive analysis. The presentation
focuses on the second issue.
First stage of interactive multicriteria decision analysis generates several sets of solutions. The corresponding data can be then analyzed
for supporting selection of a final decision, as well as providing information guiding specification of parameters of the user preferencesin further interactions. Applying data analysis in the process of multicriteria analysis significantly improves the decision-making process
and reduces the required computation time. Use of the cluster-basedsurrogate in discrete decision-space helps in reducing the discrete decision space dimension, and thus the computational resources needed
for solving the underlying optimization tasks.
The effectiveness of the approach is illustrated by analysis of an energy
model supporting decisions of a system operator of an onshore windfarm equipped with a hydrogen energy storage. The objective is tomaximize the cumulative profit by deciding on: (1) a constant hourly
amount of electricity to be delivered, (2) configuration of the storage
system components, and (3) control of flows to/from the storage to deal
with the variable renewable energy generation.

2 - Evaluating Sustainability in Renewable Energy Systems: Introducing a Novel Fuzzy Multi-Criteria Distanceto Ideal Solution Approach

Sepehr Hendiani, Grit Walther
Sustainable development strongly requires a shift away from fossil
fuel-based energy systems and toward ones that rely on renewable
resources. It is well recognized that the first step in improving sustainability is to evaluate the current degree of sustainability. This research proposes a unique approach for assessing the existing level ofsustainability through renewable energy systems (RESs) using multiexpert multi-criteria decision-making based on a distance to ideal solution method under interval type-2 fuzzy information. The proposed
method highlights underperforming sustainability components according to the linguistic data collected from experts, which helps decisionmakers identify the obstacles to sustainable growth. The proposed approach may make it easier to create global sustainable development
policies and plans that are more successful. This may be achieved via
improving the quality of experts’ linguistic assessments and by offering a thorough grasp of the sustainability of RES around the globe.

3 - Renewable Fuels in Transport: An Analysis of Market
Development Barriers

Ali Ebadi Torkayesh, Sepehr Hendiani, Grit Walther, Sandra
Venghaus
Aligned with the EU, Germany aims to reduce its GHG emissions by
65% compared to the 1990 levels, with the objective of reaching climate neutrality by 2045. While most sectors have achieved a notabledecline in GHG emissions, measures to reduce emissions in the transport sector have proven largely insufficient. A key challenge for decarbonizing the transport sector remains the dominating role of fossil
fuels in the current GHG emissions level. Thus, the adoption of renewable fuels is of high significance for meeting the climate targets.
Renewable fuels, e.g., power-to-x, advanced biofuels, hydrogen aresupported by the Renewable Energy Directive III, where the objective
is to meet a minimum share of 5.5% in European energy consumption

EURO 2024 - Copenhagenin the transport sector by 2030. Despite the advantages of renewablefuels, various economic, environmental, technical, regulatory, and social challenges exist to their adoption and diffusion. To investigate theimportance of the barriers and their interactions from a system perspective, an analytical approach is developed based on the decision
making trial and evaluation laboratory, K-means algorithm, the maximum mean de-entropy algorithm, interpretive structural modeling, anda ranking multi-criteria decision analysis method with Type-2 Neutrosophic Numbers. Insufficient renewable energy policies and lack of
coordination in the supply chain are identified as the main system barriers.

 WB-48
Wednesday, 10:30-12:00 - Room: 60 (building: 324)

DEA applications in transportation
Stream: Data Envelopment Analysis and its Application
Invited session
Chair: Chixiao Lu
1 - A novel two-stage network DEA approach to estimate
production efficiency of the bus transportation system.

Shivam Kushwaha, Anand Venkatesh
In this paper, we utilize the internal structure of the bus transportation
system’s to assess its production inefficiencies. Specifically, we exploit information about linkages of resources across departments andidentify inefficient sub-activities within the production system. Our
proposed framework employs a two-stage network DEA framework toassess the overall production efficiency of the bus transportation system. Since departments of the bus transportation system share limited
resources, game-theoretical approach has been employed for decomposing production efficiency to estimate optimal efficiency estimatesfor each division. To demonstrate the practical application of our approach, we implement it in the context of the Indian bus transportation
system. Finally, we conclude by discussing the policy implications of
our research and offering recommendations for managers to enhanceoverall production performance.

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a crucial catalyst for improving public transit. These organizations are
driven by a strong sense of social responsibility. As an illustration, theyfunction in locations with poor income generation to cater to the public.
Hence, it is necessary to regularly assess their performance and consistently make attempts to make necessary adjustments. Road transport
has a significant role in the economy, but it also contributes significantly to energy consumption and environmental damage, particularly
through air pollution. STUs, which run on high-speed diesel, emit air
pollution as a byproduct of fuel combustion. This has sparked worries
over its adverse effects on climate change and the long-term viability of
ecosystems. The current body of research on performance evaluationof passenger transportation systems fails to consider the fundamental
structures of the transportation process and instead approaches it as an
opaque entity. As a result, it is unable to accurately determine the exact reason for inefficiencies, which makes it challenging to adjust in
the complex transportation process. Prior research on STUs has notconsidered the possible negative impacts on the environment.

4 - The pitfall of virtual exterior projection points in unified
Non-Radial Two-Stage Network DEA and its duality: An
Empirical Study on Railway industry in Europe

Chixiao Lu, Ali Emrouznejad
As a natural monopoly transportation approach, the railway service
has gained increasing attention from the European Union. Hence, acomprehensive performance analysis system is needed to urge infrastructure managers to reduce costs. Two-stage network (2SN) Data Envelopment Analysis (DEA) is widely used as a productive efficiency
analysis technique in railway industries’ managerial decision-making
processes. However, the conventional 2SN DEA models are vulnerable to biased outcomes when the generated virtual projection pointis an exterior point. Specifically, given the inputs-outputs Production
Possibility Set (PPS) in a 2SN system is unknown, the conventional
approach identifies the virtual exterior projection point by integrating
the inputs-intermediates and intermediates-output PPSs. In this paper, unified non-radial graph models in both the slack-based schemeand the Russell-efficiency scheme are proposed to solve the issue of
exterior projection points. We also apply the dual transformation to
discuss the shadow price of inputs, outputs, and intermediates throughthe multiplier model. We apply the proposed novel approach to thedataset regarding European Railway industries. Our approach provides
a comprehensive efficiency analysis from both Investment and Operations perspective. The adjusted projection points in our DEA model
also provide an applicable benchmark to support the decision-making
process.

2 - Input congestion assessment in the North Adriatic
ports

Mozhgan Mansouri Kaleibar, Evelin Krmac
Recognizing the critical role that ports play in promoting trade andeconomic expansion worldwide, port authorities are actively seeking
strategies to reduce inefficiencies and improve port operations. The
seaports along the northern Adriatic are an essential part of the European transport network. An important approach to reduce inefficiencies in the ports is to assess and eliminate port congestion in order tominimize operational bottlenecks. Data Envelopment Analysis (DEA),
a widely used technique for assessing decision making units (DMUs)
in different sectors, is also suitable for measuring congestion in individual units. In this study, a one-step DEA model is used to determine
the efficiency scores and congestion of ports in the northern Adriatic in
the years 2020-2022, allowing for a simultaneous assessment of inefficiency and congestion. The results show which ports are confronted
with the problem of inefficiency and congestion during the period under investigation. The analysis provides these ports with insights formore precise and effective resource allocation and coordination.

3 - Evaluating eco-efficiency under dynamic network setting for passenger road transportation: A case study ofState Road Transport Undertakings in India

Sanjeet Singh, Saransh Tiwari
State Road Transport Undertakings (STUs) in India are state
government-operated transport services that connect various landscapes and isolated mountainous regions across the nation, serving as WB-49
Wednesday, 10:30-12:00 - Room: M1 (building: 101)

Flow shop scheduling problems
Stream: Lot Sizing, Lot Scheduling and Production Planning
Invited session
Chair: Yiannis Mourtos
1 - A bi-objective distributed permutation flowshop
scheduling problem with factory eligibility and qualification options

Martin Schönheit
The competitiveness of manufacturing companies, especially thosewith multiple plants, is increasingly determined by their operational
efficiency and agility. A challenging issue when considering multiple
factories is the optimal allocation and scheduling of orders, which isfurther complicated by the lack of tools and personnel that limit theability to process an order in each factory. Thus, this paper contributes
by solving a bi-objective distributed permutation flow shop scheduling problem with eligibility constraints focusing on minimizing total
cost and total weighted tardiness. For the first time, factory-dependent
due dates and qualification options through tool transfer to an initially391

WB-50EURO 2024 - Copenhagen

ineligible factory are investigated. In addition, emission costs are considered alongside transfer, delivery, and production costs to promote a
holistic problem view. Extensive computational experiments are used
to analyze the effects of varying the strictness of factory eligibility
constraints, changes in product and tool weight factor levels, and anincrease in the emission price. The results illustrate the critical roleof transportation in distributed scheduling and highlight the benefits offactory qualification in terms of on-time order delivery and cost minimization. Finally, the integration of factory eligibility constraints andfactory-dependent due dates strengthens the robustness of the management implications by better reflecting industry constraints.

2 - Multiobjective flowshop with additional resources during setups

Juan Camilo Yepes Borrero, Federico Perea, Justo Puerto
This work investigates the flow shop scheduling problem with setup requirements and additional resources for setups from a multi-objective
perspective. The setups required on machines after processing a job
depend on the sequence, as well as the resources needed to performthem. The two objectives studied are minimizing the makespan andminimizing the additional resources used. Exact algorithms and metaheuristics are proposed to solve the problem. To evaluate the resolution
methods, sets of non-dominated solutions obtained by each method arecompared.

3 - Exact methods and (meta-)heuristic solution methods
for the Hybrid Flexible Flowshop problem

Stavros Vatikiotis, Ilias Mpourdakos, Dimitrios
Papathanasiou, Yiannis Mourtos
Modern manufacturing keeps on introducing research challenges interms of both problem structure and instance size. The Hybrid Flexible Flowshop (HFFS) represents a setting where a set of jobs needs tobe processed on a set of sequential stages, each containing a different
number of parallel (and typically identical) machines; each job will beprocessed at one machine at a time and it is possible to skip somestages. Additional structure is introduced by i) machine-dependenttransportation times between the consecutive stages, ii) limited capacity buffers on the entry and on the exit of either each machine or eachstage and, iii) two different types of renewable resources - the first responsible for the transportation of jobs between stages and the second
affecting job processing in specific machines. To handle the above
on instances of several hundred of jobs, while sustaining the ability
to calculate optimality gaps, we examine both exact methods (MILP
and CP) and (meta-)heuristic algorithms. Since exact methods commonly struggle with large instances, we examine also whether MILPbased and CP-based formulations can solve parts of the problem orsequentially handle parts of the instance. These components are combined with constructive heuristics and evolutionary schemes to offer amethod versatile in the sense of offering multiple solutions for diversetime horizons. The results of the methods are compared in computational terms on both benchmark and real-life instances.

4 - Balancing and scheduling human-robot collaborated
assembly lines considering cycle time, energy costs,
and ergonomic risks

Amir Nourmohammadi, Masood Fathi
The recent Industry 4.0 trend, followed by the technological advancement of collaborative robots, has convinced many industries to shift
towards semi-automated assembly lines with human-robot collaboration (HRC). Moreover, moving towards the human-centric and resilientindustries proposed by the Industry 5.0 vision, the increasing use of
HRC-based collaborative assembly systems is anticipated in the near
future. In the HRC environment, robot agility can support humanskill upon efficiently balancing and scheduling tasks among the stations and operators. However, in this attempt, the cycle time (CT),the total energy cost (TEC) of robots, and the total ergonomic risks(TER) of humans are among three conflicting objectives. Thus, thisstudy deals with the balancing and scheduling of HRC lines where a
trade-off between CT, TEC of robots, and TER of humans is sought. A
mixed-integer linear programming model is proposed to formulate theproblem. In addition, a multi-objective optimization approach based392

on -constraint is developed to address a case study from the automotive industry and a set of generated test problems. The computational
results show that promising Pareto solutions in terms of CT, TEC, andTER can be obtained using the proposed approach.

 WB-50
Wednesday, 10:30-12:00 - Room: M2 (building: 101)

Retail Distribution II
Stream: Retail Operations
Invited session
Chair: Rob Broekmeulen
1 - Optimal Design of Walmart’s Grocery Import Network

Foaad Iravani, Vivek Tomar, Sai Rajesh Mahabhashyam
Walmart’s mission is to be the number one food destination in America. A critical driver of this mission involves creating an efficient andflexible grocery supply chain. We present an optimization model forWalmart’s grocery import network, designed to determine the optimal
locations of import facilities, inbound flows of products from overseas
suppliers, and outbound flows to Walmart’s distribution centers. The
model enables us to design the imports network for both fresh andfreezer items. We describe how the solutions from this optimization
model have shaped Walmart’s strategy for direct importation of groceries, saving billions of dollars, and exploring new opportunities forcreating efficiency in Walmart’s overall grocery network.

2 - Last-mile fulfillment in Omnichannel Retail: A combined
allocation and routing problem

Rakesh Venkitasubramony
Integrating multiple channels and delivering a seamless experience tocustomers in an Omnichannel setting presents a set of interesting andchallenging problems in fulfillment. In particular, when an order needs
to be delivered to a customer’s home, a delivery executive can pick up
the items from any of a set of stores which are in the vicinity (shipfrom store), or from any other node in the distribution network like aregional warehouse (order allocation). We develop a combined order
allocation and routing problem for a retailer who has a multi-tier distribution center network and a set of stores serving customers in a city.
We consider real-world requirements like minimum inventory requirements at stores and service time for customers. Solutions are presented
for generated test data. Some real-world policies (like batching windows) are discussed, and future research opportunities are identified.

3 - Simulation-based Multi-objective Optimization for Fleet
Resource Planning in a Convenience Store Supply
Chain

Thai-Young Kim, Benjawan Sopun
Fleet planning is a key success factor of retail management in the convenience store industry where a wide range of goods are replenished ona daily basis. The operational challenge in this industry is to decide thesize of transport equipment - reefer truck and dry truck in an optimized
manner. The idea to get some flexibility in the fleet pool decision is thatthe reefer truck can be used as a dry truck if the temperature control
is disabled with limited cost increases. In this study, a simulation approach categorized as Digital SC twins is selected to demonstrate thebenefits of increasing compatibility by opting for a mixed-truck policy including the optimal fleet size and timeline. The study optimizes
the mixed vehicle in terms of fleet maximizing the service level while
minimizing costs by applying the proposed policy. To set up a real-life
simulation baseline, a convenience store supply chain in South Korea
was modeled with some operation parameters. Initial simulation revealed the impact of time windows on reducing the number of vehicles
used in standard operation. The second experiment investigated theeffect of reefer trucks ratio to dry trucks under each time window standard operation. The finding is that the optimized fleet planning enables

EURO 2024 - Copenhagenthe supply chain to maintain the same level of on-time delivery evenwith fewer vehicles. It can effectively resolve the issue of delayed delivery due to insufficient capacity while reducing total costs and travel
distances.

4 - Lateral Trans-Shipments within an Apparel Retail Chain

Waldemar Kaczmarczyk
In the middle of the season, apparel retail chains can increase sales bytrans-shipment of unsold commodities between different stores. Planning of such an operation may be described with the help of a many-tomany transportation model. However, the product and its size variety
are tremendous, while the amount of any product in a specific size thatany store may send to another one is small. In the considered company,
all shipments should go directly from one store to another (drop-ship)rather than through the central warehouse. A forecast of the demandis unavailable, first, because the product collection changes very often, and second, because the sales of the same product differ significantly among different stores. Therefore, decisions must be based on
rules proposed by managers and verified over several seasons by experimentation. Moreover, the trans-shipment plan must keep employees’
workload acceptable. This paper presents the general concept of thetrans-shipment approach, mixed integer programming models of theconsidered problem, and the proposed hierarchical planning approach.

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propagation. This can be optimised by conflict detection and resolution(CDR) models. Usually based on alternative graph or mixed integer
linear programming (MILP) formulations, existing models mostly refer to conventional signalling systems in which the track is discretised
into blocks. Only at block entries, trains can receive a brake indication.
Hence, train separation distances are based on a number of blocks.
With the implementation of continuous braking curve supervision in
distance-to-go (DTG) signalling systems such as the European Train
Control System (ETCS), train separation is based on absolute braking
distances. Consequently, an explicit relation between speed and train
separation is required in CDR models for DTG signalling. Recently,
we enhanced the CDR model RECIFE-MILP to DTG operations. The
enhancements relate to track discretisation and speed-dependent train
separation. In this research, we investigate the effect of track discretisation granularity on the performance of the enhanced model fornext-generation DTG signalling systems: ETCS Level 3 Fixed Virtual
Block and Moving Block. The performance is assessed regarding total
delay and rescheduling decisions. The results indicate that, dependingon the track and traffic scenario, a finer granularity can lead to different
rescheduling decisions due to shorter train separation.

3 - Integrating Reinforcement Learning methodologies intoa generic CDCR Heuristic to effectively resolve occupancy conflicts at railway nodes in real-time.

Arturo Crespo Materna, Cedric Steinbach, Shanqing Chai,
Keren Zhou, Andreas Oetting, Hendrik Speh WB-51
Wednesday, 10:30-12:00 - Room: M5 (building: 101)

Railway Traffic Management
Stream: Public Transport Optimization
Invited session
Chair: Dennis Huisman
1 - Realistic assessment of rail traffic management optimization: a demand-supply simulation framework

Johan Victor Flensburg, Bianca Pascariu, Gregory Marlière,
G. Sfeir, Federico Naldini, Filipe Rodrigues, Paola Pellegrini,
Carlos M. Lima Azevedo
Our research addresses the need for a realistic assessment of rail traffic
management algorithms. We introduce a modular framework integrating demand and supply dimensions for algorithm evaluation. Our approach incorporates passenger simulation via an application programming interface (API) in the commercial rail traffic simulator OpenTrack, enabling realistic route choice modeling and passenger rerouting based on observed choices and traffic conditions. Trains are realistically simulated in OpenTrack, and historical distributions of traffic perturbations are applied via the API. When train delays increase,
the API triggers passenger rerouting: depending on the current andplanned traffic evolution, passengers decide whether to stick to their
original plan or choose a different option. This mimics realistic decisions, where passengers react to updated traffic information. The proposed framework allows for the closed-loop assessment with a traffic
control optimization algorithm, ensuring practical feasibility. We validate the framework using diverse scenarios in a Copenhagen suburban
rail network, exploring variants of optimization algorithms with objectives including train delay minimization and passenger delay considerations. Our proposal represents the first comprehensive framework
integrating passenger behavior and realistic rail traffic simulation, enhancing the evaluation of traffic management algorithms.

2 - Track discretisation in railway traffic rescheduling models for next-generation distance-to-go signalling

Nina Versluis, Paola Pellegrini, Egidio Quaglietta, Rob
Goverde, Joaquin Rodriguez
In case of disturbed railway operations, traffic management can apply rescheduling measures to resolve conflicts while minimising delayThis contribution presents a novel approach to enhance the real-time
resolution of occupancy conflicts at railway nodes by integrating Reinforcement Learning (RL) methodologies into a generic Conflict Detection and Conflict Resolution (CDCR) heuristic. Given the intricate nature of railway systems, the occurrence of stochastic events cannot be
avoided. In this context, conflicting operations between train journeys
must be resolved. These conflicts are harder to resolve within nodes
than in lines due to the larger number of possible resolution combinations. The proposed approach foresees the integration of RL into an
existing CDCR heuristic approach for resolving occupancy conflicts innodes. The generic CDCR Heuristic provides a baseline for identifying and resolving conflicts, while RL enriches the system’s intelligenceby providing real-time feedback learned from historical data. The resulting hybrid approach facilitates adaptive decision-making, optimizing train movements, and minimizing the overall delay in the system.
The integration of RL within the existing CDCR heuristic aims to enhance the system’s efficiency and effectiveness, ultimately improving
the overall efficiency of railway operations. The proposed approach isevaluated in a real-world scenario, demonstrating its potential to enhance real-time schedule adjustments by providing robust, optimized
solutions to occupancy conflicts.

 WB-52
Wednesday, 10:30-12:00 - Room: 8003 (building: 202)

Parallel Optimization and Scalability
Stream: Combinatorial Optimization
Invited session
Chair: Guido Schryen
1 - Performance Analysis of MIP Solver Software on Various Hardware Architectures

Willi Leinen, Andreas Fink, Philipp Neumann
There is still much room for exploiting high-performance computing
hardware to solve hard combinatorial optimization problems, such as
those in logistics and scheduling. However, the difficulty of partitioning and organizing the work of typical MIP tree-search algorithms limits the impact of approaches aimed at massive parallelization, eventhough the use of large computing clusters or GPUs has considerable potential in theory. As a result, MIP solvers are frequently usedon single CPUs with limited shared-memory parallelization via multithreading; however, even in these situations it is often not worthwhile
to use more than a few cores. This usually involves procedures that393

WB-53EURO 2024 - Copenhagentake advantage of performance variability by concurrently running different configurations in parallel. To gain a deeper understanding of theperformance of available MIP solver software in these multi-threading
scenarios, we conduct strong scaling experiments to analyze the influence of hardware characteristics (such as processor type, numberof cores, clock speed, cache, RAM) and performance variability. We
provide insights into performance optimization in terms of runtime,
speedup, cost, and power consumption.

2 - Minimizing service levels in the stochastic parallel machine scheduling problem

Hiba Yahyaoui, Stéphane Dauzere-peres
In this work, we address the parallel machine scheduling problem
where processing times are stochastic. The objectives to maximize
are the makespan service level, i.e. the probability that the makespanis smaller than a given threshold, and the due date service level, the(weighted or not) sum of the probabilities that the jobs are completed
on time. Mathematical models for both objectives, that rely on the generation of scenarios and on positional binary variables, are presented.
Heuristics are also proposed, and numerical results are discussed

3 - Metaheuristics for Optimization on Graphs

Tatjana Davidović, Dragan Stevanović, Predrag Djordjevic,
Robert Doža, Luka Radanovic, Marko Milenkovic, Milica
Andjelic
Optimization problems on graphs involve the search for graphs ofa given size that minimize or maximize a certain invariant or a
combination of invariants. These problems are usually NP-hardand the exhaustive enumeration of all possibilities is not acceptable. Efficient dealing with these problems can help theoreticians
to get some useful hints and make analytical proof out of theseresults. Computer-assisted proofs are common in the graph theory for a while now. Several dedicated software packages havebeen developed, such as AutoGraphiX (https://www.autographix.ca),
newGraph(https://www.mi.sanu.ac.rs/newgraph/),PHOEG(https://phoeg.umons.ac.be/phoeg), to mention a few. They are, however, too general and in some cases could require unacceptably long
CPU time to provide useful results. Another popular approach is to develop metaheuristic tailored for each particular optimization problem.
We illustrate the application of Variable Neighborhood Search (VNS)
metaheuristic to Spectral Reconstruction of Graphs and Maximizationof Spectral Radius of Threshold Graphs. The proposed algorithms aretested on a representative set of test graphs. Conducted experimental
evaluation shows the superiority of the proposed approach over the application of dedicated software. The work has been partially supportedby the Scientific Fund of the Republic of Serbia, Grant LZWK.

4 - Deriving Upper and Lower Bounds with Stochastic
Dynamic Programming for Request Acceptance and
Scheduling Decisions under Uncertainty

Marvin Caspar, Konstantin Kloster, Oliver Wendt
Sophisticated decision-making becomes necessary when weighing thevalue of accepting and simultaneously scheduling or rejecting a random incoming request now versus the value of waiting for another request in the future. This work proposes the Dynamic Request Acceptance and Unsplittable Scheduling Problem (DRAUSP), which considers a single resource with a given capacity over multiple homogeneoustime slots. During each decision period within a fixed time horizon, asingle random request arrives that demands capacity for some consecutive time slots and yields positive revenue if accepted. The objective
becomes to accept or reject the incoming requests and, if accepted, toschedule them into available time slots to optimize the revenue collected over the time horizon. We use a Stochastic Dynamic Programming (SDP) algorithm to optimize the DRAUSP, which we model asa Markov decision process. In addition, we present a dimension compression technique to compute upper and lower bounds using SDP. Our
computational study shows that we can derive appropriate bounds forsome DRAUSP instances in reasonable computational time. Moreover, we conclude that basic heuristics, such as First Come First Serve,
are insufficient to handle the complexity of DRAUSP and that moresophisticated heuristics are necessary to obtain adequate results.

394

 WB-53
Wednesday, 10:30-12:00 - Room: 8007 (building: 202)

Emissions and Heating Sector
Stream: Sustainable and Resilient Systems
Invited session
Chair: Michaela Chocholatá
1 - Transition of the heating sector in a large city: simulation of scenarios with a digital twin

Maik GüntherIn the ’Model Munich’, Stadtwerke München has collected spatial dataon buildings, parcels of land and building blocks. This digital twinof the City of Munich is primarily used to answer questions related
to the transition of the heating sector. Therefore, building-specificheat demand, utilized energy sources and technical building properties,
but also regionally resolved supply potentials for groundwater heatpumps, air heat pumps, heating networks, etc. are included. Moreover,the ’Model Munich’ is connected to the bottom-up simulation model
Invert/EE-Lab, which employs a nested logit approach for buildingspecific calculations of long-term scenarios regarding the change inheating systems and renovation activities. A specific application of
the ’Model Munich’ is municipal heat planning. In 2021, Stadtwerke
München agreed with the City of Munich to utilize this model formunicipal heat planning. Within this collaboration, numerous simulation runs were conducted using Invert/EE-Lab, from which valuable
insights could be gained. This contribution provides an overview of
the architecture of the ’Model Munich’. In addition, results of the simulation runs are presented, aiming to achieve climate neutrality by nolater than 2045 in accordance with the German Heat Planning Act.

2 - Enhancing User Comfort in Smart Buildings throughCO2 Forecasting and Operational Optimization

Albert Farriol Salas, Lucia Igualada, Tomas Montes, Manel
Serrano Borja
This study introduces an optimal operational model for a smart building, showcasing the integration of indoor Carbon Dioxide (CO2) forecasting. The study presents an hourly operational model that encompasses the Heating, Ventilation, and Air conditioning (HVAC) system,
electrical storage (ES), and Light-Emitting Diode (LED) lights, alongwith the utilization of photovoltaics (PV). The first objective is to employ a Long Short-Term Memory (LSTM) neural network to predict
indoor CO2 concentrations and identify hours when these concentrations exceed the healthy threshold. Subsequently, the operationalmodel is presented to ensure optimal energy usage, flexibility to selectively activate or deactivate the entire HVAC system during hours
when forecasted CO2 levels surpass unhealthy thresholds. This proactive measure aims to prevent the escalation of indoor CO2 levels. By
combining CO2 forecasting with operational optimization, this study
contributes to enhancing the sustainability and performance of smart
buildings while prioritizing the well-being of occupants. Additionally,
the model ensures that optimal lighting levels are maintained at all
times without compromising the visual comfort of building occupants.
Theses algorithms and models have been developed in the context of
the Netbuild project (CPP2021-009031), where the developments will
be implemented in a real smart building located in Viladecans, Spain.

3 - Low carbon and greener Europe priority as a challenge
for heating in Slovakia

Michaela Chocholatá
Low carbon and greener Europe priority has also been a great challenge for heating of households. In Slovakia, the recent results of the2021 Housing Census showed e.g., that up to 66.22% of households
are heated with gas, up to 21.32% with solid fuel and 4.48% with electricity. Various subsidy mechanisms were introduced by government
to support the transition towards low-emission energy sources used forheating. Among others, a web application "Heating in households a

EURO 2024 - Copenhagen
little differently" was developed in order to make the people in Slovakia more aware of air pollution from domestic heating as well as toshow the ways how to improve the quality of air. This paper presents
the regional data (for 79 Slovak districts) to show the huge regional
differences concerning the heating (solid fuel, gas), but also analyses
other regionally unevenly distributed variables with potential impact
on the source used for heating. Instruments of exploratory spatial data
analysis and spatial econometrics were used for analysis. The results
showed that both the spatial autocorrelation and spatial instability werestrongly confirmed in case of Slovak districts.

 WB-54
Wednesday, 10:30-12:00 - Room: S01 (building: 101)

Demand-responsive public transport 1
Stream: Public Transport Optimization
Invited session
Chair: Dilay Aktas
Chair: Pieter Vansteenwegen
1 - Multi-period line planning for varying railway demandconsidering stops, frequencies and asymmetric lines

Renate van der Knaap, Niels van Oort, Menno de Bruyn, Rob
GoverdeThe purpose of the line planning problem in railways is to determine a
set of lines with their route, stops and frequency. This line plan is an
important aspect of the quality of the service that a railway undertaking (RU) provides to its passengers. For example, it determines which
origin-destination pairs are connected by a direct trip or need a transfer
between lines. Although the railway demand is varying throughout the
day in volumes and directions, the line plan is often constant throughout the day. To better match the supply with this varying demand, we
present a mixed-integer linear programming model for multi-periodline planning. With this model, we intent to determine a line planfor each period with different demand, that minimizes the generalised
journey time (GJT) of the passengers. To meet the varying demand, weallow the model to make changes to the selected routes in the network,
the stopping pattern, and the frequency in each period. Furthermore,
we include the possibility of having asymmetric lines to deal with spatially unbalanced (peak hour) demand. Although changing the line
plan during the day has a benefit for the passengers in terms of reduced GJT, it also comes with costs: RUs must create multiple plansand passengers need to adapt to multiple plans per day. Therefore, the-constraint method is used to create Pareto optimal solutions. We use
Gurobi to solve the proposed model for a case study based on real dataof part of the Dutch railway network.

WB-55

matheuristics to enhance the effectiveness and efficiency of the proposed routes and schedules across the fleet. We would also discuss
how this work fits into a larger project ADROIT, which involves thedevelopment of analytical methods for pricing and operating demand
responsive transport using Leeds as a case study.

3 - An LNS-based heuristic for optimizing on-demandtransportation services in a hybrid mobility system

Soukaina Bayri, Kris Braekers, Yves Molenbruch
Mobility-as-a-Service (MaaS) transforms mobility systems into more
flexible and efficient ones by integrating different transportation
modes. In this research, the focus lies on the integration of timetabledpublic transport (PT), dial-a-ride (DAR) services, and walking, allowing passengers to travel by a combination of these three modes. Fromthe perspective of the provider, the exploitation of such a hybrid mobility system leads, among others, to challenging routing and schedulingproblems on the operational level. The ultimate aim is to generate efficient real-time solutions in response to all user requests by optimallycombining and aligning the available transport modes with each other,while minimizing the operational costs and the total trip times of theusers.
Therefore, in this talk, an optimization problem is introduced which
involves decisions on the combination of modes for each request andthe routing and scheduling of the DAR vehicles. The focus lies specifically on a network in which users commute between a rural and an urban area. A heuristic algorithm based on Large Neighborhood Search(LNS) is proposed. To incorporate the trade-off between the operational costs and service level, a tailored scheduling sub-procedure ispresented which minimizes the sum of the users’ trip durations for agiven route. Lastly, a sensitivity analysis is carried out on small-scale
artificial instances gaining more insight into the problem-specific parameters.

4 - Analysis of an on-demand public bus line with demand
imbalance during peak hours

Dilay Aktas, Kenneth Sörensen, Pieter Vansteenwegen
In this study, we present an on-demand system in order to improve theefficiency of a single bus line facing a clear demand imbalance during
peak hours. The on-demand system aims to increase the efficiency ofservice by allowing vehicles to take shortcuts between the city centerand the terminal given the set of passenger requests known at the time.
Passengers indicate their origin, destination, and desired pick-up timethrough an app or an online platform. Since the passenger requests are
explicitly taken into account, each passenger is assigned to a service
that serves their origin and destination. This assignment is also communicated to the passengers some time before their desired departure
time. Given the practical importance of efficiently optimizing the decisions of this system in a short time, a VNS algorithm is developed.
The algorithm decides which shortcuts to take by each bus and in eachdirection, when to depart from the city center and the terminal, andthe corresponding passenger assignments in a matter of minutes. The
performance of the system is analyzed under different circumstances.

2 - A fast insertion heuristic for demand responsive transport

Richlove Frimpong, Julia Bennell, Rym M’Hallah, Christine
CurrieDemand Responsive Transport involves the provision of door-to-door
services to customers which allows them to choose their pickup or
drop-off times and locations, while sharing the vehicle with other riders
travelling along the same route. This is similar to dial-a-ride services,
however the aim of this work is to extend this service to the entire population rather than limiting it to customers with mobility issues. To
operate such services, the service provider needs to check the feasibility of accepting or rejecting a customer’s request and also estimate thecost of offering the journey in real time.
As such, we propose an insertion heuristic which uses iterative searchmethods and an exact method that uses constraint programming toinstantly determine the feasibility of inserting the customer request
at a given price whilst anticipating future demand. This will subsequently be combined with a background optimisation algorithm using WB-55
Wednesday, 10:30-12:00 - Room: S02 (building: 101)

Public transportation
Stream: Transportation
Invited session
Chair: Antonio Mauttone
1 - New mathematical modelling for setting frequency ofbus transportation

Glaydston Ribeiro, Joyce Caetano, Laura Bahiense, Glaubos
Climaco395

WB-56EURO 2024 - Copenhagen

The Frequency Setting Problem (FSP) represents a key element in transit design, specifically in public transportation operations. It consistsof determining the number of departures to meet passengers’ demands
during a specific period, ensuring the profitability of the companies.
However, the FSP also involves different interests that commonly conflict, making the planning of the transportation system complex. When
this plurality is not correctly captured, we have an imbalance between
demand and supply. So, we are interested in addressing these distinct objectives through a sustainable perspective, with an emphasison buses.
This research introduces a mathematical model and optimization techniques to improve bus route frequency, minimizing social, operational,
and environmental costs. The model comprises a mixed-integer nonlinear formulation, and the optimization techniques involve linearization and a heuristic approach, which is based on sequentially solving relaxed mixed integer subproblems and fixing integer variables
throughout the process. To validate our proposal, we consider the bus
routes connecting the central and oceanic regions of Niterói, Brazil.
The findings suggest that the combined approach is effective in realworld scenarios, enhancing sustainable mobility through optimized
fleet allocation according to current demand. The next research steps
include creating a Multi-Criteria Decision Method (MCDA) to support
operator decision-making.

2 - Dispatch and route control of an urban public transportation system composed of buses with a mixed fleet

José Edgardo Niño Báez
Abstract: In the operation of high-frequency buses, it is important tomaintain regularity in the service, reduce user waiting times, and maintain a low cost of operation. A key aspect to achieve this is to dispatchbuses at regular intervals and control the interval during the routes,
through strategies such as holding buses at stops. In systems where
different lines operate that begin their operation at a common terminal,
it is usual for buses to remain on the same line each time they arriveat the terminal after finishing a route, which limits the flexibility of thesystem. In this work, we seek to minimize user waiting times and theoperating costs of a bus system by implementing bus spacing strategies at the terminal and a route control strategy to maintain regularity
in the service. Our preliminary results show that when it is possible tointerline buses in the terminal, waiting times are reduced between 10%
and 20% compared to the case without interlining.

3 - Frequency optimization in public transportation withstrict capacity constraints

Antonio Mauttone, Agustín Arizti, María E. Urquhart
The problem of frequency optimization consists in defining the number of vehicles per time unit, to operate each line of the system. The
inverse of the line frequency is the time interval between consecutive
vehicles in the line, which determines the waiting time. Given an available fleet and origin-destination demand data, we seek to minimize thetotal travel time of passengers, including in-vehicle and waiting components. To evaluate a frequency setting we apply the optimal strategies assignment model, which represents passenger behavior and outputs demand flows over the lines and passenger travel time from origin
to destination. The capacity of a line is determined by its frequency andthe size of the vehicles which operate the service. In systems operatingover capacity, the flows produced by the assignment model exceed line
capacity, causing failure to board the vehicles that passengers choose.
To avoid this endogenous effect which invalidates the whole model,
we propose a frequency optimization model that guarantees sufficient
capacity in all lines. This is achieved by including a strict line capacity
constraint in a bilevel programming model. After applying a reformulation, we obtain a mixed integer linear programming model which canbe solved to optimality for small to medium size cases. We apply themodel to a simple toy instance and to a well-known one, taken fromliterature. Numerical results allow for investigating the usefulness andthe limitations of the model.

4 - A Fixed-route Autonomous Modular Public Transit Service

Jane Lin

396

In this work, we present a proof-of-concept investigation of Autonomous Modular Public Transit (AMPT) at a network scale and compare it with the traditional fixed-route, fixed-vehicle size transit service
in terms of total cost, which consists of both agency’s capital and operational cost (including energy cost) and passenger time cost. We formulate and solve stylized design models for AMPT on a grid network
in a range of demand density scenarios with both homogenous andheterogeneous distributions. The AMPT models explicitly account for
pod joining and disjoining (and therefore en-route transfers of passengers) and potential energy savings due to pod train formation (pod platooning), which represent major departures from the traditional transit
models in the literature. Numerical results find that AMPT, if designed
properly, may save the total cost compared to traditional transit systems
thanks to demand responsive pod train capacity, particularly in the low
demand scenarios. The cost savings of AMPT are largely attributed topassenger time saving by en-route transfer; the agency cost of AMPT
has a mixed picture. The load factor of AMPT generally improves over
the traditional transit service. We also show how key parameter values
may affect the AMPT costs through sensitivity analysis.

 WB-56
Wednesday, 10:30-12:00 - Room: S04 (building: 101)

Advancing mobility towards sustainable
solutions IV
Stream: Transportation
Invited session
Chair: Danny Cho
1 - Scheduling AGVs based on partial charging policy tominimize the total tardinessMengxin Zhang, Hui Sun
This study addresses a problem of scheduling automated guided vehicles (AGVs), in which a fleet of heterogenous AGVs are used todeliver materials in a flexible manufacturing environment. During thetransportation process, the batteries of AGVs can be recharged so thatmaterials are delivered timely to various workstations without interruptions. A partial charging policy which enables flexible managementof charging operations is applied. By effectively scheduling transfer
jobs and potential charging operations on the AGVs, the objective is
to minimize the total weighted tardiness. A mixed integer programming formulation is presented to describe the scheduling problem. A
hybrid approach combining variable neighborhood search (VNS) andlinear programming is developed to solve this NP-hard problem. VNS
is firstly used to schedule transfer jobs on the AGV fleet without considering charging operations, based on which a linear programming
model is formulated to determine the best schedule of charging operations on each AGV. Numerical experiments show that the hybrid
solution procedure can solve the AGV scheduling problem effectively
and efficiently.

2 - The Multi-objective Transit and Car Network Design
ProblemTygo Nijsten, Jan-Pieter Dorsman, Michel Mandjes, Maaike
Snelder, Erwin Walraven
The car is currently the most popular transport mode, due to, among
other things, its convenience and comfort. In many urban areas, theresulting high car usage leads to considerable road congestion. On topof this, it goes paired with several negative externalities like noise pollution and greenhouse gas emissions. Therefore, measures to shift towards more sustainable modes of transport, like public transport, may
be beneficial. However, several trade-offs may occur, for example,
shifting towards public transport might cause higher travel times.
In the literature, it has been proven difficult to take into account all
trade-offs in one single objective function. While some research has

EURO 2024 - Copenhagen
been conducted on the multi-objective optimisation of measures to improve the transit system, hardly any research efforts have incorporated
the simultaneous optimisation of interventions to discourage car usage.
In this presentation, we study this gap in the current literature. In doing
so, we demonstrate the potential of combining the transit and car network design when trying to shift towards a more sustainable mobility
system.

3 - Sustainability in Island Waste Transportation

Javier Maturana-Ross, Lorena Bearzotti, Franco Basso
Island waste transportation involves moving waste material generated
within these remote regions, often characterized by limited infrastructure and unique geographic constraints. These conditions frequently
create substantial barriers to sustainable waste management, resulting
in inefficient collection and disposal methods, elevated transportation
expenses, and detrimental environmental impacts. This research is motivated by previous studies on island transportation planning, which
focused on optimizing transportation routes in these regions. Despite
existing literature on island waste transportation, there is a notable gap
in addressing the sustainability aspects of waste transportation. This
study aims to contribute to the literature by incorporating sustainability criteria into the transportation planning model for waste collection
in island and remote regions, and also other issues that might arise
in waste transportation, such as community engagement, ecologicalpreservation, and public health safety, among others.

4 - The International Logistics Performance Index as A
Useful Predictor of Trade openness: A Classification
and Regression Tree (CART) Analysis

Danny Cho, Tomson Ogwang
An important attribute of globalization is the escalation of the movement of goods across international borders, heightening the need for
trade facilitation. In 2007, the World Bank released the maiden version of the Logistics Performance Index (LPI) as a numerical yardstick for gauging progress towards trade facilitation. The LPI is anequally weighted six-component composite index that lies between 1(the worst-case scenario) and 5 (the best-case scenario). The LPI provides valuable information to many stakeholders around the world, including supply chain and logistics professionals, academics, and tradeagencies, among others. The purpose of this paper is to examine theability of the individual components of the LPI to predict trade openness (where trade openness is defined as the sum of exports and imports, expressed as a percentage of GDP). The empirical analysis of
the latest LPI data is conducted using Classification and Regression
Trees (CART), which does not only have the advantage of capturingpotentially complex relationship between the individual LPI components and trade but also lead to the identification of the most importantcomponents based on the trimming algorithm. Our empirical results
indicate a strong potential of CART in enhancing the usefulness of the
LPI.

 WB-57

WB-58

from an origin up to a final destination node, while the cost is proportional to the number of trucks that we schedule in order to move all
the required packages. The problem has many operational and practical constraints that make it challenging. For example, all sites haveinbound and outbound restrictions and capacity constraints, the packages are consolidated in intermediate nodes (sort centers) in various
ways, there are driving bans that the schedule needs to comply with,etc. The problem is formulated as an MILP, to which we apply a setof techniques to reduce complexity such as smart pruning of decisionvariables and solving sequentially the problem to incrementally finer
granularities. Experimental results on large-scale networks illustrate
the effectiveness and scalability of our method.

2 - Capacitated Network Design for Multicommodity Flow
problems with Sparse Commodities

Allan Sapucaia, Nikos Liakopoulos
Finding a timely and minimum cost truck schedule to transport packages closer to the customer through the Amazon Middle Mile Network is a big challenge. We breakdown it down into steps, where here
we will focus in the combinatorial problem of deciding the equipment(truck and trailer) type, their departure time and the commodity demand mix carried, while the fulfilment paths and commodity demand
are predetermined. We model the middle mile network as a time expanded graph, where nodes describe facilities, as well as their inboundand outbound, and arcs are either fixed and capacitated according to
the availability of a shared resource, or schedulable connecting different facilities, with different options of trailer capacities and costs.
Since a commodity will flow through a predetermined sequence ofsites, each commodity traverses a very limited fraction of the network.
We call this problem variation Capacitate Fixed-Charge Network Design for Multicommodity Flow Problem with fixed arcs and sparsecommodities. We present a scalable integer linear-programming based
matheuristic for the problem and show how many existing results fromthe broad network design literature can be adapted to the problem weare solving.

3 - Optimized demand-based charging networks for longhaul trucking in Europe

Jan-Hendrik Lange, Daniel Speth, Patrick Plotz
The transition from diesel vehicles to tailpipe emission free technology such as battery electric trucks (BETs) in surface transportation is
crucial to meet the ambitious decarbonization goal set by the European
Union. While the first models of electric heavy goods vehicles have become available, the charging infrastructure necessary to operate thesevehicles is still largely absent. At the same time, current BET models exhibit limited range and increased refueling times, which createsa number of challenges in both the operational and strategic planningdomain. We present work on mathematical models and algorithms to
support decision making for the placement of BETs and corresponding
charging infrastructure. Our efforts are directed towards internal strategic planning as well as collaborations with industry, academic partners
and public policy. The goal of our external collaborations is to foster
the coordination of charging infrastructure investments across multiple parties, which will help to accelerate the transition to carbon freetransportation.

Wednesday, 10:30-12:00 - Room: S06 (building: 101)

Scheduling and sustainability
Stream: Optimization at Amazon
Invited session
Chair: George Iosifidis
Chair: Daniel Guimarans
1 - Joint schedule and volume optimization at scale

Marc Bataillou Almagro, Konstantinos Benidis, Martin Gross
The joint schedule and volume optimization aims at creating an optimal truck schedule with a corresponding volume plan that can satisfy
the demand of the network in a speedy manner and with minimum
cost. The speed of delivery is related to the path that a package follows

 WB-58
Wednesday, 10:30-12:00 - Room: S07 (building: 101)

Logistics 1
Stream: VeRoLog - Vehicle Routing and Logistics
Invited session
Chair: Saba Siadati
1 - Optimizing Bundling Choices in Online Food DeliveryÇağrı Doğuş İyican, Ahmet Çınar, Yahya Ertuğrul Geçkil,Muhammed Enes Oral, Batuhan Bayrakçı, Umut Toparlak397

WB-59EURO 2024 - Copenhagen

The digital revolution has brought about profound changes in the foodservice industry, particularly with the emergence of online food delivery. Our study aims to delve into the intricacies of order bundling to not
only enhance courier satisfaction but also to maximize operational efficiency. Leveraging a combination of a Clarke and Wright heuristic and
MIP model with a careful consideration of factors such as route lengthand operational constraints, we developed robust strategies geared towards improving bundled order ratios. A significant hurdle we encountered was the challenge posed by courier complaints regarding route
structure between buyer locations. To effectively address this issue,
we introduced a variety of angle constraint structures into our model.
The outcome was striking - a substantial 7% increase in bundled orders
was achieved without any compromise on route lengths, underscoring the pivotal role of prioritizing courier satisfaction in operational
decision-making processes. In summary, our research provides invaluable insights into the optimization of online food delivery operations.
By placing a premium on courier satisfaction and proactively tackling
operational challenges, we present actionable recommendations for enhancing overall efficiency and service quality within the industry.

2 - Assessment of Sustainable Third-Party Reverse Logistics Providers Using Interval Rough DOBAS Approach

Nila B, Jagannath Roy
In the contemporary business landscape, reverse logistics (RL) activities play a pivotal role for companies aiming to achieve cost savings, improved customer service, and sustainability. Owing to resource constraints and a shortage of technology and expertise, manufacturing firms frequently form partnerships with specialized RL service providers. Yet, assessing the optimal third-party reverse logistics
providers (3PRLPs) poses a challenging and uncertain multi-criteria
decision-making problem. This paper introduces an innovative framework integrating interval rough numbers with the Dombi Bonferroni
Weighted Assessment (DOBAS) model. The objective is to offer systematic decision support for organizations in selecting the most suitable partner. The proposed methodology was verified and implemented
in evaluating 3PRLPs providers for an Indian food manufacturing company (FMC). Fourteen criteria from sustainable aspects - environmental, economic, social, and risk factors- were used to evaluate and ranksix 3PRLPs. The evaluation results provided valuable insights into thestrengths and weaknesses of each provider, allowing the FMC to makean informed decision on which 3PRLP to partner with for their needs.
The outcomes demonstrated that the proposed approach is a valuable
tool for supporting decision-making, and it is not only applicable tothe case discussed in this work but also to other situations of a similar
nature.

3 - Optimizing Material Logistics in Dynamic Production
Settings

Masood Fathi, Amir Nourmohammadi
In today’s competitive market, characterized by demanding levels of
product customization and variant production, the significance of a reliable and agile logistics system cannot be overstated in promptly meeting material demands at production lines. However, the uncertainties
inherent in the production process and unplanned changes to the production plan pose significant challenges to achieving optimal in-plantmaterial delivery for logistics planners. This study proposes a solution
comprising a mathematical optimization model and a simulation-based
optimization approach to assist material logistics planners in manufacturing companies in swiftly adjusting material delivery to accommodate changes in the production plan. The effectiveness of the proposed solution is validated through the resolution of a real case problem, demonstrating its practical applicability and benefits in modern
manufacturing environments.

4 - A Robust Time-dependent Green Location-Routing
Problem With Time Windows

Saba Siadati, Virginie Lurkin, Mehrdad Mohammadi, Tomvan Woensel
This study addresses the dynamic challenges of urban transportation
in logistics caused by time-varying traffic congestion, introducing the
Robust Time-Dependent Green Lo-cation-Routing Problem with Time

398

Windows (R-TDGLRP-TW). A key innovation is incorporating a timedependent speed function, strategically capturing the nuanced dynamics of urban transportation. Additionally, the inherent uncertainty
in travel speed (time), which significantly impacts the system’s performance, is considered in a robust optimization context. The RTDGLRP-TW optimizes hubs, vehicle routes, and delivery schedules,
minimizing economic and environmental costs. To tackle this, wedevelop a mixed-integer linear programming model for R-TDGLRPTW, incorporating the linearization of dynamic programming recursive
equations proposed in the literature into a deterministic formulation.
A matheuristic solution algorithm is then used to solve large-scale instances efficiently within a reasonable computation time. Our focus on
the dynamic aspects of ur-ban transportation aims to efficiently provide practical insights, helping service providers navigate the complex
trade-offs encountered in logistics planning.

 WB-59
Wednesday, 10:30-12:00 - Room: S08 (building: 101)

OR in Accounting: Planning, Taxation, andReportingStream: OR in Financial and Management Accounting
Invited session
Chair: Matthias Amen
1 - Defining reportable segments - a complex assignment
problem in international accounting

Matthias Amen
IFRS 8 "Operating Segments" requires the grouping of operating segments into aggregated segments. A differentiated, aggregated presentation of assets and liabilities, income and expenses has to be reported
separately for these reportable segments. A reportable segment should
consist of one or more operating segments that are similar. Not all operating segments need to be aggregated into one reportable segment.
The problem can therefore be characterised as a binary assignment
similar to the Knapsack problem.
IFRS 8 defines sets of different requirements for the definition of reportable segments. The overall objective is up to the reporting company itself. To this end, we use a technical objective function that aims
to minimise the dissimilarity inside reportable segments. By choosing
different values for a weighting parameter in the formal objective function, we can generate solutions with a different number of reportablesegments.
We formulate and solve the problem using AMPL and standard solvers.
This also enables a discussion of IFRS 8 to improve future standard
setting.

2 - Bandwidth planning for public authorities using the example of german municipalities

Christian Fritze
Public authorities are obliged to prepare annual budgets. The planning
process regularly takes place in a two-way process between the administrative board and specialist departments and is predominantly basedon a qualitative forecasting methodology and the updating of the previous year’s plans. However, subsequent plan/actual comparisons regularly show that the actual values realized deviate significantly fromthe planned values in some cases. The uncertainty of developmentsin the next financial year appears to be too great for the predominantly
applied qualitative methods for determining budget estimates to be sufficient to generate budgets that are stable in the sense of low deviations
between planned and actual figures. This is taken as an opportunity todevelop a quantitative planning methodology with the aid of Monte
Carlo simulation in order to take better account of distribution andevent risks. A multi-stage procedure is presented, which takes into
account the special features of public administration operations andincludes suggestions for practical implementation.

EURO 2024 - Copenhagen
3 - Tax incentives to facilitate sustainable investments

Carolin FamullaWith the so-called EU-taxonomy regulation in 2020, the European
Union established a uniform classification system to determine environmentally sustainable economic activities. According to the EU
regulation, economic activities are environmentally sustainable if they
contribute substantially the achievement of certain environmental objectives, e.g. climate change mitigation or pollution prevention andcontrol. Insofar as they do not explicitly contribute the environmental
objectives, economic activities should not significantly harm the environmental objectives. The EU regulation includes a range of measuresto achieve the defined environmental objectives. Incentives should be
provided to invest in sustainable and climate friendly economic activities. In this context, the question arises which tax incentives exist to
facilitate sustainable investments and how these incentives affect theinvestment decision. I implemented different tax incentive measures,
like levying an environmental tax or tax exemptions for sustainable
investments, and investigated their effect on the investment decision
made on basis of the net present value criterion. This is compared withthe case that the decision is only made on basis of the tax burden.

4 - The Government Contracting Supply Chain and Financial Reporting

Tracie Frost
This study explores the impact of government contractors’ supply
chain characteristics on contractors’ financial reporting decisions related to risk, cost, and compliance. Supply chain contracts are economically important and subject to substantial risks, as shown by reportingof supply chain risks in firms’ Item 1A disclosures; however the exact influence of supply chain risks on financial reporting outcomes remains unclear due to the lack of publicly available contract details forcommercial supply chains. To overcome this issue, I use detailed datafrom US government contracts. Government contractors an institutionally interesting setting for studying supply chain contract characteristics because they operate within complex bureaucratic frameworks
and accounting guidelines, which require robust financial operations toremain in compliance with regulations. Government contracts also account for approximately 13% of public firms’ major customers. Given
the importance of government contracts and the detailed contract datathat is available, this study investigates how supply chain contract characteristics influence financial reporting decisions. Specifically, leveraging data from the Federal Procurement Data System, I analyse theimpact of these characteristics on financial reporting outcomes, including how managers identify, assess, and mitigate risks associated withgovernment contracts; track and control contract costs; and manage
audit risk and control weaknesses.

 WB-60
Wednesday, 10:30-12:00 - Room: S09 (building: 101)

Job shop scheduling
Stream: Project Management and Scheduling
Invited session
Chair: Andrea Corsini
1 - Periodic and Real-Time Dispatching Rule Selection for
the Dynamic Job Shop Scheduling Problem

Nuno Marques, Gonçalo Figueira, Luis Guimarães
Aggressive competition and wider production mixes are profoundly
impacting manufacturing settings, which, in turn, have become more
technological, flexible and dynamic. With growing uncertainty in the
shop floor, dynamic scheduling emerged as a relevant topic to explore.
In highly dynamic environments, dispatching rules (DRs) became apopular solution method for scheduling problems due to their reactive
nature, ease of implementation and interpretability. However, DRs do
not cope well with varying conditions in two main dimensions: shop

WB-60

load and job urgency. Therefore, researchers have proposed machinelearning-based systems that select DRs as conditions change over time.
Two main approaches emerged in the literature: the periodic rule selection (PRS) and the real-time rule selection (RTRS). The goals of
this work are twofold. First, propose PRS and RTRS systems that canoutperform state-of-the-art DRs by relying on effective state and action
sets. Secondly, contrast PRS and RTRS in the same dynamic job shop
instances for stationary and non-stationary conditions, filling a gap in
the literature. Results show that the best rule selection system reduces
tardiness, on average, 14.8% in stationary instances, and 3.5% in nonstationary instances. Even in the hardest instances, the system comes
within 1% of the best DR. Moreover, PRS reveals to be the most effective approach for stationary instances. In non-stationary conditions,
RTRS was better.

2 - Evaluation of VaR and CVaR for the Makespan in Interval Valued Blocking Job Shops

Marcella Samà, Marco Pranzo, Carlo Meloni
We take into account the blocking job shop scheduling problem (BJSS)
where processing, release and sequence-dependent setup times have
uncertain interval durations. Scheduling problems which take uncertainties into account are computationally complex and the literature on
stochastic and uncertain scheduling is relatively limited compared tothe that on deterministic scheduling. As such, analysts often use deterministic models that incorporate simplified representations of the most
relevant stochastic aspects in order to determine or select the schedule to implement. We propose a method for the evaluation of the riskthat the makespan of deterministically computed solutions worsens under the worst-case realizations of the uncertainty. An interval-valued
network approach is proposed to model the feasible solutions characterized by uncertain values on the given constraints’ durations. The
study assumes the Value-at-Risk and the Conditional Value-at-Risk asmeasures, and addresses both modeling and computational issues. The
impact of different sources of uncertainty on the overall performanceof the proposed approach is analyzed. The results of the experimental
campaign show that the method, for both the computational time andthe quality of the evaluations, has broad applicability and can support
the decision-makers for taking into account their risk sensibility.

3 - A generic and efficient insertion-based heuristic forshop scheduling problems

Karim Tamssaouet
Due to the complexity of shop scheduling problems, research overthe past three decades has mainly focused on the development of
improvement heuristics, while construction heuristics are often neglected. Sometimes low-performing construction heuristics are deliberately used to demonstrate the robustness of the improvement heuristics. Yet, effective and efficient construction heuristics play a crucial role in solving large practical problems. Most of the constructive
heuristics in the literature are based on priority rules. Another typeof heuristic is based on successive insertions of operations in partial
schedules. Only a few, powerful insertion-based heuristics are known
in the literature, especially for the flow shop and job shop schedulingwith makespan minimization. However, in addition to being tailored toa specific problem, compared to priority-based heuristics, these methods suffer from a high computational cost if not carefully designed.
In this work, we show that it is possible to have a generic insertionbased construction heuristic that can solve any shop scheduling problem with regular objective function and for which an acyclic directed
graph with nonnegative weights can represent a solution. The efficiency is achieved through novel procedures quickly evaluating thefeasibility and quality of the insertion moves. Results on the flexible
job and flow shop instances and their extensions (e.g., multiple modes
and nonlinear routes) will be presented.

4 - Self-Labeling the Job Shop Scheduling Problem

Andrea Corsini, Mauro Dell’Amico
The Job Shop Problem (JSP) involves scheduling sequences of operations (jobs) on machines by minimizing the makespan [1]. Our novel
approach tackles the JSP as a sequence of decisions, nicely represented
by a branch-decision tree. At each decision node, we explore all thejob selections for scheduling the next operation. We use an encoderdecoder architecture [2], where the Graph encoder creates embeddings399

WB-61EURO 2024 - Copenhagenfor operations from the disjunctive graph of JSP instances, and the decoder generates solutions by producing at each decision node a probability of picking each job from these embeddings. Given the highcost and expertise needed for applying Supervised and Reinforcement
Learning, we propose a simpler Self-Labeling training strategy, whichconstructs multiple solutions and uses the best one as a self-generated
label to refine the model. Our model outperforms Constructive Heuristics [1] and Reinforcement Learning approaches [3], with up to 20%
reduction in optimality gaps, and also surpasses a disjunctive Mathematical Programming model on benchmark instances.

limited bargaining power and low consumers’ willingness to pay cangain an advantage by adopting fresh-priority pricing, which entails setting the retail price of the fresh food first. Under partial NFC, the supplier generally profits from clearance-priority pricing. However, givenhigh disposal costs, even the seemingly strong supplier may choose
fresh-priority pricing to avoid potentially significant losses from negotiation failure. We further analyze extended models to confirm therobustness of our findings. Our study not only contributes to the literature but also guides practice.

[1] M. Pinedo, Scheduling: Theory, Algorithms, and Systems, NewYork Springer, 2016. [2] O. Vinyals et al. Pointer Networks, Advancesin neural information processing systems, 2015. [3] P. Tassel et al An
End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based

3 - Mismatch Risk, Alliance Stability and Production Efficiency in a Coproduct Supply Chain

 WB-61
Wednesday, 10:30-12:00 - Room: S10 (building: 101)

Retail Cooperation and Competition
Stream: Retail Operations
Invited session
Chair: Mehmet Sencer Zengin
1 - Pricing and procurement policies of two substitutableproducts under supply disruption risk

Mehmet Sencer Zengin, Özgen Karaer, İsmail Serdar Bakal
We study a supply chain that is composed of a manufacturer and a retailer. The retailer sells two vertically differentiated and substitutableproducts to the market and the products are supplied by the manufacturer. The lower-quality product has a stable supply, and thus, its
wholesale and retail prices are assumed to be exogenous. The innovative (i.e., ’high-quality’) product’s price is yet to be determined andits procurement/production is prone to supply disruption risk. We explore the optimal procurement quantities of both products and pricing
decisions of the higher-quality product. As for the sequence of events,
first, the manufacturer decides the wholesale price of the innovative
product. Then, after observing this price, the retailer determines theorder quantities of both products. Afterward, the innovative productis supplied or not with a certain probability. Finally, the retailer determines the retail price of the innovative product if no disruption andcustomers make purchase decisions.
We find that the market potential of the lower-quality product, which isdetermined by the quality perception and the retail price of that product, plays a crucial role in the optimal policies. When this potential
is low, the innovative product is more inclined to be abandoned as its
quality gets closer to the lower-quality one. Also, a precautionary stockof the lower-quality product should be kept to make up for a potential
disruption of the innovative product.

2 - When should fresh-food suppliers embrace nearexpired food commercialization?

Yangyang Peng, Xin Wang, Zhengfeng Liu, Mingyuan Zhu
Products (e.g., cattle and pig) can be processed into several types of
products (parts) targeting different segments of customers, which belong to the so called coproducts. Mismatch risk, alliance stability andproduction efficiency are three significant issues in such coproduct logistics and supply chain. First, we use the unbalanced ratio to reflect
the degree of mismatch between supply and demand among different
parts of the coproduct and study how the tradeoff between the bargaining power and the mismatch cost, by different mismatch risk allocations, influences the optimal decisions and the performances of thetwo parties as well as the whole logistics and supply chain. Then, weinvestigate how the coproduct alliances are formed considering tradeoff between processing cost and mismatch ratio among heterogeneous
retailers. We establish a general collaborative game model and study
the existence of the core of the grand coalition and extend the modelto the multi-retailer settings, and the general distribution with the costleast consideration. Finally, in view of the product line design of the
coproduct supply chain, considering the uncertainty of the market size,
the size and length of the product line of the coproduct supply chain
are analyzed and we expand to multiple products and general distribution of customer types and market size. We endeavor to provide some
new insights into coproduct logistics and supply chain management at
the theoretical level.

4 - Information Sharing and Manufacturer Rebate Competition

Albert Ha
We investigate the incentive for a retailer to share private demand information with two rebate-offering manufacturers who sell substitutableproducts through the retailer. We characterize how the retailer’s information sharing decision depends on key factors such as the proportionof rebate-sensitive consumers and competition intensity. Without side
payment, the retailer will not share information with a monopolistic
manufacturer, but he may do so with none, one or both of the manufacturers when there is competition. With side payment, the retailer
always prefers to sell information concurrently instead of sequentiallyto the manufacturers.

 WB-62
Wednesday, 10:30-12:00 - Room: S12 (building: 101)

Zhengwei Lyu
Despite widespread discussion that near-expired food commercialization (NFC) may assist fresh-food suppliers to recover value from unsold inventory and enhance revenues, most suppliers remain skeptical
about NFC, hesitant to abandon the potential of and concerned about
eroding the higher-margins of fresh food sales. We develop in this paper a game-theoretic model to investigate the optimal strategy and optimal price-setting timing for the supplier that decides whether and how
to commercialize its near-expired food. We find that increasing fresh
food production shifts the supplier from non-commercialization to partial and ultimately full NFC. Under full NFC, the supplier with highbargaining power and high consumers’ willingness to pay may benefit
from adopting clearance-priority pricing, which prioritizes setting of
the retail price of the near-expired food. Conversely, the supplier with400

Machine Learning and Optimization in PortsII
Stream: OR in Port Operations
Invited session
Chair: Frederik Schulte
1 - Machine Learning Approaches for improved ETA Predictions and Turnaround Times of Inland Vessels at Sea
Ports

Peter Wenzel, Zhong Chu, Frederik SchulteEURO 2024 - Copenhagen
Predicting accurate Estimated Times of Arrival (ETA) for Inland Waterway Transportation (IWT) remains a challenge to coordinate port
operations and reduce waiting. Captains report ETAs on inland vessels based on the remaining distance to the destination and the captain’s experience. A comprehensive literature review highlights thecurrent methods for ocean-going vessel trajectory-based ETA prediction, ocean-going feature engineering for direct prediction, and predictions of ETA times in IWT. The experiments use historical position
data (AIS) reported by vessels heading to the port of Rotterdam, as
well as dimensions of the vessels, weather conditions, and water levels.
Several machine learning methods using Neural Networks, GradientBoosted Decision Trees, and Light Gradient-Boosting Machines areproposed to predict ETA at the Port of Rotterdam. Predictions are thencompared with the captain’s ETA and the Actual Arrival Times (ATA).
This paper shows that the predictions of ETA times are 98% more accurate than the captain’s ETAs. Compared to the ATA, our predictions are
99% accurate. These learning-based approaches can be further used in
IWT research to create learning-informed optimization and simulation
to reduce vessel turnaround times. In practice, this improved accuracy of arrival time prediction will enhance the reliability of IWT and
enable the port to develop an accurate terminal visit schedule for theinland barges.

2 - Explainable Decision-Making for Stochastic and Dynamic RoRo Ship Operations

Arne Heinold, Teresa Marquardt, Frank Meisel, Catherine
Cleophas
Our research addresses the loading and unloading problem for Rollon/Roll-off (RoRo) ships, a critical aspect of maritime logistics. Embracing the complexity of real-world uncertainties, this study explores
the impact of varying levels of data availability, facilitated by new
telecommunications technologies. Realistic real-world problems face
the challenges of stochastic and dynamic aspects, such as uncertainties
regarding the type of cargo or process times.
To solve this high-dimensional decision problem, we model it as a sequential decision process and use a hierarchical solution method similar to classification and regression trees (CART). The proposed solution approach yields solutions of short turn-around times but also provides explainability in decision-making. We test our approach against
myopic decision policies using real-world scenarios from the Port ofKiel. Subsequently, we apply our methodology to illustrate the influence of different data availability levels, providing specific recommendations regarding the benefits of additional data.

 WB-63
Wednesday, 10:30-12:00 - Room: S14 (building: 101)

Risk Management in Private and Public
Finance
Stream: OR in Banking, Finance and Insurance: New
Tools for Risk Management
Invited session
Chair: Loretta Mastroeni
1 - Security Breach Probability Models

Maurizio Naldi
Cybersecurity breach probability functions describe how cybersecurity
investments impact the actual vulnerability to cyberattacks through theprobability of the attack’s success. They essentially use mathematicalmodels to make cyber-risk management choices. This talk provides
an overview of the breach probability models that appear in the literature. For each of them, the form of the mathematical functions andtheir properties are described. The models exhibit a wide variety offunctional relationships between breach probability and investments,
including linear, concave, convex, and a mixture of the latter two. Each

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model describes a parametric family, with some models having a single parameter and others having two. A sensitivity analysis completes
the overview to identify the impact of the model parameters: the estimation of the parameters which have a larger influence on the breachprobability is more critical and deserves greater attention.

2 - LHP approximation for green-loan credit portfolios under skewed and heavy-tails returns

Alessandro Ramponi
In recent years, efforts to address climate change and promote thecreation of an environmentally sustainable economy have intensified.
This commitment has spurred the emergence of financial instruments
designed to support activities commonly referred to as "green". Financial institutions, as well as non-financial corporations or local governments, engage in lending activities to fund challenging low-carbon
transition objectives. This includes financing rooftop solar power,
wind farms, environmentally friendly farming practices, water conservation, the leasing of hybrid and electric vehicles, and initiatives aimed
at enhancing the energy efficiency of industrial, commercial and residential properties. One prevalent approach to model portfolio credit
risk involves employing the LHP (Large Homogeneous Portfolio) approximation within the one-factor Merton-type framework, commonlyreferred to as the ASRF (Asymptotic Single Risk Factor) model. In thiswork we consider the classical Vasicek Gaussian model under moregeneral distributional assumptions for the systematic risk factors. We
specifically examine how the quantiles of the loss portfolio are affectedby mixing distributions characterized by skewed and heavy-tail properties. This analysis is conducted in the context of loans, some of which
are designated as "green", indicating their allocation towards financing
environmentally sustainable assets.

3 - Risk management issues in current energy context

Ivan De Crescenzo
The current macroeconomic context of high interest rates and economic slowdown, both ignited by the gas crisis following the pandemic
outbreak, is profoundly impacting the energy sector as well as others.
Over the last year, renewables, most likely favoured by a reduced overall demand of power across the continent, have taken over a large shareof the market, fostering decarbonization. Gas storage at maximum capacity, almost complete fuel-switch out of coal and decreasing price ofcarbon allowances seem not to imply profitable margins, with Clean
Spark Spread and Clean Dark Spread both looking gloomy. Hence, bycontrast, this arrangement is shaping up in nothing but the next trap for
utilities. Particularly, carbon allowances experienced a drastic bearish
movement far from being forecast. Over-hedging must be deemed asa possible common scenario many operators are currently navigating.
Revenues reductions and variation margins are a threatening combination in this context of lack of liquidity. Whereas volatility and skyrocketing prices were yesterday’s enemies, stagnation and industrial crisis
appear to be the new challenges to tackle in power generation market.

4 - Systemic resilience of networked commodities

Roy CerquetiThis paper aims at providing insights on the ability of systems ofcommodities to absorb external microscopic shocks. Such ability islabelled as systemic resilience. Commodities are suitably evaluatedalong with their interconnections, on the basis of the similarities of
their financial performances. The resilience is measured through thecomparison of the shocked networks with the original unshocked ones.
The employed device is the clustering coefficient, which is a nodal
centrality measure associated with the community structure of the network. The proposed methodology is empirically tested over a large setof commodities of different nature.

401

WB-64EURO 2024 - Copenhagen WB-64
Wednesday, 10:30-12:00 - Room: S16 (building: 101)

Vehicle Routing Problems With Time
Windows
Stream: VeRoLog - Vehicle Routing and Logistics
Invited session
Chair: Pedro Munari
1 - Territory Design Strategy for the Multiperiod Vehicle
Routing Problem With Simultaneous Pickup and Delivery and Time Windows

Matías Cortés, Hernán Lespay, Carlos ObrequeThe present study proposes a Territory Design Strategy for the Multiperiod Vehicle Routing Problem with Simultaneous Pickup and Delivery and Time Windows (TD-MPVRPSPDTW). This problem deals
with the design of compact and contiguous territories to simultaneously collect and deliver orders from a warehouse to customers who
have time windows requirements in a multi-period approach. This
problem is motivated by the circularity of the supply chain because
it helps to integrate the delivery and collection of products. Besides,
the proposed territory design strategy lets generate consistent routing
plans due to each driver being assigned to one territory. We proposeda mixed-integer linear programming model (MILP) for the problem,
which is solved using a metaheuristic algorithm. We evaluate the performance of the proposed algorithm through benchmark instances.

2 - A Framework for Cluster-Based Heuristics for Vehicle
Routing With Time Windows

Xinyi Ye, Dimitrios Letsios, Haoxiang Wang
We develop a novel framework for designing cluster-based heuristics to the Vehicle Routing Problem with Time Windows (VRPTW).
Similarly to state-of-the-art cluster-based VRPTW approaches, ourframework consists of a clustering and a routing phase. However, we
also incorporate an intermediate pre-routing phase that allows using
any existing VRPTW solution method (e.g. exact or metaheuristic)in the routing phase and scaling the method’s performance. In thisway, we can achieve different desired trade-offs between solution quality and running time efficiency. Further, our framework consists ofconfigurable components in the clustering phase - namely, the number of clusters, clustering objective, and cluster feasibility - that canbe customized to enhance the performance of obtained cluster-based
VRPTW heuristics. Also, we embed an approach based on time discretization and incompatibility constraints in the clustering phase toensure that time windows are respected. We computationally validate the effectiveness of our framework and its components using theSolomon benchmarks.

3 - Flowty’s Resource Constrained Shortest Path Algorithm

Bjørn Petersen, Simon Spoorendonk
Flowty is a network optimization solver that exploits the network structure in a column generation algorithm. This talk is about the resource
constrained shortest path (RCSPP) algorithm at the core of this algorithm.
The RCSPP algorithm is a bidirectional labeling algorithm parallelized
on a bucket level, that is pulling (as opposed to pushing) labels into
buckets. The size of the buckets is small enough to avoid cycles within
the bucket.
Pulling into buckets is done in parallel for independent buckets. The
dependency graph between buckets is handled implicitly, and the bidirectional midpoint/stopping criteria is dynamic, meaning that buckets
are extended until the opposite direction is encountered.
As is always the case for pull-based labeling algorithms; carefully
choosing the order of which labels are created, it is possible to guarantee that only labels not dominated are ever stored. This not only limits
the memory footprint, it also allows labels to be stored as immutable402’object of arrays’ (as opposed to ’array of objects’) thereby enabling
the use of vectorized instructions for dominance checks and thus exploiting a second level of parallelization.
The Vehicle Routing Problem with Time Windows (VPRTW) is used
as an illustrative case.

4 - The Vehicle Routing Problem With Remote Drivers

Pedro Munari, Nils Boysen
Remote driving is a type of teleoperation that allows a human operator
to control a vehicle without direct physical access, typically throughwireless communication technologies. Recent advances in 5G wireless cellular networks, sensor technology, high-quality cameras, andmachine learning have made remote vehicle operation more feasible
and successful for a wider range of applications, both on public roads
and inside facilities such as warehouses and factories. The main advantage is that remote drivers do not have to be idle during service
operations at customers. Instead, they can switch to other vehicles,
which opens up the possibility of operating a fleet more efficiently. In
this talk, we introduce the Vehicle Routing Problem with Time Windows and Remote Drivers (VRPTWRD), which extends the traditional
vehicle routing problem to include the specificities of remote driving.
We model different versions of the VRPTWRD as mixed-integer programming problems, and use Benders decomposition to derive effective branch-and-Benders-cut approaches. Our results indicate that remote driving can effectively reduce the workforce needed to operate afleet of vehicles.

EURO 2024 - Copenhagen

Wednesday, 12:30-14:00
 WC-01
Wednesday, 12:30-14:00 - Room: Sportshallen (building: 101)

Bahar Yetis Kara
Stream: Keynotes
Keynote session
Chair: Ceyda Oguz
1 - Disaster Resilient Cities: An OR Approach to Disaster
Management

Bahar Yetis Kara
There have been decades of research in the field of humanitarian logistics, and academics in the field of logistics are becoming more andmore interested in it. That being said, we still acquire insight and identify new problems with each disaster. We have also observed various
humanitarian logistics applications during COVID-19, e.g. for PCR
test sites and vaccination centers. Unfortunately, the recent earthquake
in Turkey has led us to re-evaluate the response cycle of disaster management. Close inspection reveals that this response phase actuallyleads to a variety of new applications of distribution logistics problems.
We have conducted many meetings and workshops with municipalities
that were very active during the response of the Maraş Earthquakes.
Many municipalities aim to have "earthquake-resistant cities" with correct action plans and being ready for potential disasters. Based on our
discussions with these municipalities, we have developed an "ideal action plan". We also investigated the potential decision problems andlinked these problems with OR literature.

 WC-02
Wednesday, 12:30-14:00 - Room: Glassalen (building: 101)

Portfolio Optimization: Models and Methods
Stream: OR in Banking, Finance and Insurance: New
Tools for Risk Management
Invited session
Chair: Roy Cerqueti
1 - Optimal portfolios’ weights and rank-size shapes

Alessio Di Paolo, Roy Cerqueti, Francesco Cesarone, Valerio
Ficcadenti
In this work, we study the relationship between the mean-variancebased portfolio optimization algorithms’ inputs parameters mu andSigma and the rank-size function’s law’s estimated parameters fromthe best fit on the resulting sorted weights distribution. The objective is
to explore the way weights describe a unified framework once they are
ranked according to their sizes and, at the same time, the implications
on the optimal allocation outcomes. Indeed, characterizing portfolio
weights through rank-size curve parameters allows us to understandhow portfolio risk and return vary as these parameters change.

2 - Tracking-based green portfolio optimization

Diana Barro, Marco Corazza, Gianni Filograsso
In this contribution, we discuss how to handle financial and sustainable
investment goals, focusing on greenness and ESG features. Amongthematic investments, green and energy-related ones have emerged,
capturing investors’ attention. This contribution introduces a novel
ESG-focused tracking error model to provide ESG-tailored-made allocations. We consider two reference benchmarks, accounting for afinancial target and an ESG one, respectively. The objective function

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results in a convex linear combination of the two goals where the combining parameter accounts for the investor’s financial and ESG preferences. A symmetric tracking error measure is proposed to replicate thefinancial benchmark passively, while an asymmetric measure is used
to track and possibly outperform the thematic ESG benchmark. Identifying the benchmarks for the two components represents a crucial stepand, jointly with the choice of the combining parameter, accounts forthe portfolio’s overall risk-return and ESG profiles. In the model, thesustainability feature is handled not only with the presence of the ESG
benchmark but also with the introduction of dedicated constraints. An
application to the EUROSTOXX 600 equity market is presented anddiscussed for different choices of the combining parameter, representing different sustainability preferences and risk-return profiles.

3 - Managing ESG Ratings Disagreement in Sustainable
Portfolio Selection

Manuel Luis Martino
The inclusion of sustainable goals in the portfolio selection process
may have an actual impact on financial portfolio performance. Environmental, Social, and Governance (ESG) indices provided by therating agencies are generally considered good proxies for the performance in sustainability of an investment, as well as, appropriate
measures for Socially Responsible Investments in the market. In thisframework, the lack of alignment between ratings provided by different agencies is a crucial issue that inevitably undermines the robustness
and reliability of these measures. Indeed, the ESG rating disagreement
may produce conflicting information, implying a difficulty for the investor in the portfolio ESG evaluation. This may cause underestimation or overestimation of the market opportunities for a sustainable investment. In this paper, we deal with a multicriteria portfolio selection
problem taking into account risk, return, and ESG criteria. We present
a new approach to manage the ESG ratings disagreement between different agencies. We propose a nonlinear optimization model for our
three-criteria portfolio selection problem. We show that it can be reformulated as an equivalent convex quadratic program by exploiting
the k-sum optimization strategy. An extensive empirical analysis of
the performance of this model is provided on real-world financial datasets.

4 - Stabilizing financial networks via mergers and acquisitions

Markku Kallio, Aien Khabazian
A bi-level model is proposed to explore efficient policies for supportingnegotiations on financial crisis resolution. In a principal-agent framework, this model minimizes a welfare loss function of a central authority (social planner, SP) by simultaneous choice of subsidy levels andpotential pairs of banks to merge. The SP’s choice of mergers needs to
be incentive compatible with autonomous choices of banks and theevaluation of the financial network must obey standard accounting
principles. Incentive compatibility is enforced by two options of conditions based on stable matching or competitive bidding. For the evaluation of a financial network, we employ an extended Eisenberg-Noe
clearing payment equilibrium by considering bankruptcy costs and seniority levels of liabilities. Additionally, liabilities are not cleared
among solvent banks and corporate bonds may be used for clearing
payments. The bi-level model states conditions for the clearing equilibrium. For demonstration we use major European banks and a scenario which is linked to the adverse economic scenario used in 2016
EU-wide stress testing.

 WC-04
Wednesday, 12:30-14:00 - Room: 1001 (building: 202)

Applications of Mixed-Integer andNonconvex Optimization 1
Stream: MINLP
Invited session
Chair: Alireza Olama403

WC-06EURO 2024 - Copenhagen

1 - Bilinear Branch and Check Algorithm

Ponpot Jartnillaphand, Hoa Bui
In this presentation, we introduce the bilinear branch and check algorithm, an exact method for solving nonlinear optimization problems.
This approach extends the classical branch and check algorithm by integrating bilinear constraints into the search process. The enhancementof the bilinear constraint refines the problem formulation, expediting
the convergence of the search. The algorithm decomposes problems
into manageable subproblems, utilizing the branch and cuts to assess
feasibility and generate feasibility cuts (no-good cuts), while also enhancing master problem solutions. A key innovation lies in the algorithm’s methodology for improving master problem solutions and constructing high-quality no-good cuts among decision variables. This
allows for further decomposition of the master problem, enhancing
its effectiveness. Our numerical results demonstrate promising outcomes and exhibit greater efficiency compared to standard branch andcut methods like IBM CPLEX and the classical branch and check.

2 - Distributed Interpretable Machine Learning on GPUs

Alireza Olama, Andreas Lundell, Jan Kronqvist
The research introduces the Bi-linear consensus Alternating Direction
Method of Multipliers (Bi-cADMM) to address large-scale regularizedSparse Machine Learning (SML) problems over a network of computational nodes. These problems involve minimizing local convex loss
functions over a global decision vector with an explicit $ell_0$ norm
constraint to ensure sparsity. This approach generalizes various sparse
regression and classification models, including sparse linear and logistic regression, sparse softmax regression, and sparse support vector machines. Bi-cADMM reformulates the original non-convex SML
problem using bi-linear consensus and employs a hierarchical decomposition strategy. This strategy splits the problem into smaller, parallelcomputable sub-problems through a two-phase approach: initial sample decomposition and distribution of local datasets across nodes, followed by a delayed feature decomposition on GPUs available to eachnode. GPUs handle data-intensive computations, while CPUs manage less demanding tasks. The algorithm is implemented in an opensource Python package called Parallel Sparse Fitting Toolbox (PsFiT).
Computational experiments validate the efficiency and scalability of
Bi-cADMM through numerical benchmarks on various SML problems
with distributed datasets.

3 - Multiscale optimal mass transport

The most economical measure for tackling public health emergencies
is mass immunization. During crises, policymakers heavily rely on
global human vaccine product trade as it profoundly shapes the development of additional intervention measures. Drawing on global humanvaccine product trade data from 2019 to 2022, this study employs social network analysis methods to explore the structure of the global
human vaccine product trade network from various network statistical features such as community structure and evolution. In addition,
through a comparative examination of global monthly trade networks
for human vaccines, this study reveals the evolving structural dynamics amidst the global public health crisis (COVID-19), offering insights
into how it affects national intervention strategies and vaccine market
stability during pandemics. Overall, the global human vaccine product trade network has experienced an increase in its scale with an extended multipolar structure. Additionally, the alterations in network
structure revealed by trade volume and frequency can also demonstratecompetitive dynamics and behaviors between countries, and highlightthe shifts in vaccine strategies among countries during public healthemergencies. High-income countries with rapid vaccine rollout andintroduction (such as the United States) exhibit stronger resilience by
actively adopting comprehensive intervention measures.

2 - From docket to data: unpacking judicial congestion using process- and queue-mining

Shany Azaria
The court system, pivotal for social justice, is overflowed by congestion affecting social welfare, economic development, and access to justice. Despite its significance and operational complexity, being characterized by constrained resources, increasing demand, long in-process
waiting time, impatient customers, and LOS measured in years, empirical research on court operations remains limited. This scarcity islargely attributable to challenges in accessing reliable and comprehensive data. This study employs NLP and AI tools to transform two
decades of US Federal District Court case dockets into a detailed event
log, leveraging process- and queue-mining to explore judicial congestion. By scraping, labeling and analyzing millions of docket entries
through operations management lenses, we are able to observe the caseflow in the system and assess congestion impacts on case processing.
Our findings illuminate the judicial workflow, offering insights for reducing congestion and enhancing system efficiency. This pioneering
approach underscores the potential of data-driven analysis in court systems operations.

Martin Ryner
We present a simple to implement multiscale entropy regularized Optimal mass transport solver using wavelet decomposition. The framework iteratively gives the user approximations to the optimal solution
at a coarse to fine resolution and is particularly of interest for large
scale problems with continuous costs and marginals. We discuss convergence, computational cost and show some examples.

 WC-06
Wednesday, 12:30-14:00 - Room: 1013 (building: 202)

Advancements of OR-analytics in statistics,
machine learning and data science 18
Stream: Advancements of OR-analytics in statistics, machine learning and data science
Invited session
Chair: Anand Subramanian
Chair: Kathryn E. Stecke
1 - Understanding the structure and dynamics of the global
vaccine product trade network.

Rui Zheng, Luis Ospina-Forero, Yuwang Chen404

3 - Integrating machine learning in measuring multidimensional energy poverty: new insights from a survey analysis in Europe

Rahil Dejkam, Reinhard Madlener
Energy poverty, a multidimensional socio-economic challenge, significantly affects the welfare of many people across Europe. This paper aims to alleviate energy poverty by exploring sustainable energy
practices and policy interventions, using household survey data from
Portugal and Denmark. A Multidimensional energy poverty index(MEPI) is developed to assess energy poverty through different dimensions such as heating and cooling comfort, financial strain, access toenergy-efficient appliances, and overall health and well-being. In anext step, for selecting features, machine learning techniques are employed including recursive feature elimination and random forest analysis. These methods help to reduce the number of irrelevant and mutually correlated predictors. Subsequently, a logistic regression modelis used to predict energy-poor households based on selected socioeconomic, and policy-related factors. The logistic regression results
indicate that sustainable energy-saving behaviors and supportive government policies can mitigate energy poverty. Furthermore, for analyzing the impact of determined features the Shapley additive explanations (SHAP) method is being utilized. Finally, the main findings areevaluated further via scenario simulation analysis. The result showsthat fully adopting waste-compositing and energy-efficient microwave
ovens can decrease the proportion of energy-poor households by 93%
and 79%, respectively.

EURO 2024 - Copenhagen
4 - An artificial neural networks model to production rateestimation of short and medium sized serial production
lines

Mehmet Ulaş KoyuncuoĞlu
Accurate analysis of production lines is of critical importance at strategic and tactical level for the sustainable economy of companies. Production rate, which is defined as the amount of production per unit
time. The processing times, failure times and repair times (reliability parameters) of the machines and the number of buffers used between machines to reduce idle times greatly affect the production rate,
which is one of the main performance indicators. Unreliable lines,
where machines are subject to random failures, and balanced production lines, where the processing time of all machines is equal or expressed by independent identically distributed random variables. Exact
analytical methods, approximate analytical methods and simulation are
widely used for production rate calculation. Estimation of the production rates with artificial intelligence-based methods are very limited interms of production line configuration and belong to recent years. In
this study, an artificial neural network model is proposed to accurately
and quickly estimate the production rate of small and medium-sized
serial production lines with all machines in the line balanced, unreliable, finite buffer size. Parameter tuning is performed for the proposed
artificial neural network model and additional tests are conducted toincrease the prediction efficiency of the model. The results obtained
encourage the proposed model to be applied to large size lines.

 WC-07
Wednesday, 12:30-14:00 - Room: 1019 (building: 202)

Behavior in supply chain collaboration
Stream: Behavioural OR
Invited session
Chair: Yingshuai Zhao
Chair: Jose Benedicto Duhaylongsod
1 - Learning organization and product quality performance: A moderated mediation model

Jose Benedicto Duhaylongsod, Sheldon Carvalho, Irene De
PaterThis paper examines how a learning organization affects product quality performance from a behavioral operations perspective. Drawing onsocial information processing theory and social capital theory, we develop a theoretical model proposing (1) how a learning organization
positively affects product quality performance through internal integration and (2) how employees’ supply chain management competencies amplify this relationship. We test our model using a longitudinalsurvey with three waves of data collection from employees in leadership positions in operations, manufacturing, and production functions
located in the United Kingdom. Our findings support the proposed
relationships. These results highlight the importance of aligning organizational processes and employees’ competency development in driving cooperative work across functions and enhancing product quality
performance.

2 - Humans respond to machines’ response times inhuman-machine interaction

WC-08

costs. The retailer proposed wholesale prices to the supplier, and decisions to accept or reject these offers were made by a computerized
agent acting on behalf of the supplier. We find that individuals respondto the response time of agent partners similarly to how they respond tohuman partners. Compared to human-human bargaining, the retailer’s
profit is lower and the supplier’s profit is higher in human-machinebargaining. These findings hold significant implications for the designof online bargaining tools in e-business, particularly in enhancing theuse of human-like agents in commercial negotiations.

3 - Does audit backfire on corporate social responsibility?
An experimental study on supplier’s behaviours andmotivations

Luyao Li, Xiaobo Zhao
We consider a setting of corporate social responsibility (CSR) that suppliers choose responsibility effort and hiding effort, given buyers’ audit
levels. Theoretical research indicates a potential "backfiring condition"
where increased audits drive suppliers to prioritize hiding evidence of
CSR violations over investing in social responsibility. To validate thisprediction, we conduct a behavioural experiment with two treatments
to examine suppliers’ decisions in responsibility and hiding efforts under low and high audit levels. Different from the standard model’s
prediction of significantly higher responsibility efforts in the low-audit
treatment compared to the high-audit treatment, experimental results
show no significant difference. Additionally, actual hiding efforts bysuppliers significantly exceed (fall below) the theoretical value in the
low-audit (high-audit) treatment. We build a behavioural model consisting of probability weighting, mental accounting, and bounded rationality. Structural estimation of behavioural parameters shows thatprobability weighting leads suppliers to increase hiding efforts and reduce responsibility efforts. Conversely, mental accounting causes suppliers to overvalue the additional loss from CSR violations exposure,prompting higher responsibility efforts and lower hiding efforts. Thesefindings highlight the necessity of comprehensively considering the decision biases in supply chain social responsibility management.

4 - Shining light and harnessing shadows: The dual role ofauthentication platforms in online luxury retailing

Baolong Liu, Yanlu Zhao
This study examines an emerging type of online authentication platform for luxury products retailing, which has not been fully investigated due to its novel business models and innovative characteristics.
While regular online retailing business delivers products directly fromthe sellers to consumers, such platforms first provide authentication
services and then fulfill the orders. On the one hand, authenticationbuilds trust between sellers and consumers to encourage authenticproduct-selling behavior and expand demand (shine light), it also helps
consumers avoid counterfeits by reducing counterfeit-selling behavior(harness shadows). On the other hand, it is costly, time-consuming,and sometimes even misidentifies items. To understand whether thislengthy and expensive process benefits both sellers and consumers, wedevelop queueing models to characterize sellers-authenticator collaboration and its impacts on consumer welfare. The analysis reveals that acollaboration, which increases both the authenticator’s and the sellers’
profit, may hurt consumer welfare due to the authentication fee andwaiting, especially at intermediate levels of honesty and market belief.
The results are shown to be robust through extensive experiments. As apractical remedy, we recommend that authenticators implement seller
management systems to increase the level of honesty, which benefits
all stakeholders.

 WC-08

Zhi Zhu, Yingshuai Zhao, Fadong Chen

Wednesday, 12:30-14:00 - Room: 1020 (building: 202)

The field of human-machine interaction is increasingly focusing on theanthropomorphism of computerized agents, especially their design tomimic human behavioral patterns. This research investigates a pivotal
question: Do humans respond to human-like agents in the same way
as they do to human counterparts in interactive scenarios? We explore
this question using lab experiments within a supply chain bargaining
context, where the supplier has private information about production

Digitization in Knolwedge, Technology, andInnovation
Stream: Knowledge, Technology, and Innovation
Invited session
Chair: A. D. Amar405

WC-09EURO 2024 - Copenhagen

1 - The effect of multiple cues on the quality perception of
crowdfunding products: A cue diagnosticity framework

Yao ZhangThis paper utilizes a cue diagnosticity framework to differentiate between high-scope cues (the founder’s social capital and online comments) and low-scope cues (the number of pictures, sentences andvideos), and empirically investigates how these two types of cues are
diagnostic in the process of assessing the product quality. Based on thecue diagnosticity framework, under different high-scope cues, this paper puts forward hypotheses about the influence of low-scope cues onbackers’ quality perception of crowdfunding products. The findings
show that regardless of the social capital and comments of founders,
the number of pictures and videos in the introduction of reward-based
crowdfunding will have a positive impact on the quality perceptionof crowdfunding products. When at least one of the founder’s social
capital or comments is good, the number of sentences will have a positive effect. However, when the founder’s social capital and comments
are poor, the number of sentences will have a negative impact on thequality perception of products. Furthermore, we replace the dependent variable and change the classification method of backer’s social
capital to ensure the robustness of the results. This paper enriches theresearch of reward-based crowdfunding, and provides important implications for founders to raise more funds from backers by publishing
more effective information.

2 - Quality 4.0: Recent Trends and Future Research Directions

Kwang-Jae Kim, SeungHyun Choi, Young-Mok Bae,
YoungGwan Kim, Ye-won Park, Chanyoung Jang
Quality 4.0 represents a transformative paradigm in industrial quality management, integrating advanced technologies like the Industrial
Internet of Things, artificial intelligence, and big data analytics. It enhances product quality and operational efficiency, extending beyond
compliance and defect detection to focus on proactive measures, predictive analytics, and continuous improvement.
Despite ongoing debates about the appropriateness of the term Quality 4.0, embracing it implies a shift from reactive problem-solving to aproactive and preventive approach. Hence, research on Quality 4.0 isessential for advancing knowledge, preparing future professionals, andcritically evaluating the implications of integrating advanced technologies into quality management practices.
The talk comprises two parts. First, recent trends in Quality 4.0 will
be presented, covering the features and benefits of Quality 4.0. Application cases in Korean industries like steelmaking and consumer
electronics will also be discussed. Second, potential research directions in Quality 4.0 for the operations research community will be
explored, such as developing dynamic quality management models,simulating quality processes in autonomous manufacturing environments, and measuring and optimizing supply chain quality. These directions leverage operations research expertise to address complexities
and challenges in implementing Quality 4.0 in manufacturing and supply chain contexts.

3 - Optimizing energy consumption of robotic arm movements based on an enhanced Digital Twin approach

Pavlos Eirinakis, Dimitrios Tsakoumis, Gregory Koronakos
Robotic arms are extensively used in production environments to undertake different tasks such as welding, hemming, etc. Minimizing
energy consumption and the corresponding carbon emissions of suchrobotic systems poses a growing concern for manufacturing. We consider an enhanced Digital Twin approach that facilitates modelling energy consumption and carbon emissions for each specific task the robot
performs. Based on this, we present how Data Envelopment Analysis
and Pareto optimality can be utilized to evaluate alternative settings ofspeed and acceleration for point-to-point movements under given trajectories with respect to energy consumption and the time required toperform each task. Then, we utilize Integer Programming to optimize
its operation for the whole production cycle accordingly, and discuss
how this approach can be applied to operational environments withmultiple robots that need to coordinate.

406

 WC-09
Wednesday, 12:30-14:00 - Room: 10 (building: 116)

Hydrogen and Electricity Modeling andRegulation I
Stream: Energy Markets
Invited session
Chair: Jens Weibezahn
Chair: Lissy Langer
1 - Impact of imperfect foresight in long-term energy planning: A focus on hydrogen production in Europe andthe Nordics

Rasmus Bramstoft, David Garcia Marin, Marie Münster
Energy system models provide insight into future energy trends, withreal-life applications for supporting strategic decision-making. In thisstudy, we assess the impact of a rolling planning horizon methodology(i.e. myopic, limited foresight, and perfect foresight over the modelling horizon) in the comprehensive fully sector-coupled European
energy system model, Balmorel. We compare imperfect foresight i.e.
myopic and limited foresight with full decision-making foresight, witha particular focus on the role of hydrogen production in the Nordic
region. We apply policy targets and visions, and assess the impact
on the design of future energy systems as well as how the role of theNordic region as a hydrogen exporter is impacted. In addition to analyzing the potential impacts on the design of the energy system, wefind considerably longer computation time for solving the comprehensive large-scale energy system with limited or full foresight compared
to a myopic approach. Finally, we discuss the implications for energy
modellers and decision-makers when assessing future energy systems,
which are optimized using different planning foresight horizons.

2 - The cost-competitiveness of offshore hydrogen electrolysis in the North Sea

Kristina Haaskjold, Kari Espegren
Hydrogen has gained significant attention as a potential fuel for the future. It is a versatile energy carrier that can be produced from a varietyof low carbon sources and used in different sectors. Norway is uniquely
positioned to take advantage of the growing interest, having significant natural resources, including hydropower, new renewable energy,
as well as abundant natural gas resources and necessary infrastructure
for carbon capture and storage. Furthermore, Norway is strategicallylocated to key markets such as Germany and the Netherlands, which
are looking to import hydrogen to meet their energy needs.
Large plans for offshore wind deployment are planned, both in Norway and in Northern Europe, enabling business opportunities for producing hydrogen offshore. Benefits relate to reduced grid expansion,shorter transport distances to Europe and mitigated land use for onshore facilities. Hydrogen produced from offshore wind can also fulfill
the requirements as an RFNBO. This study analysis under which conditions offshore electrolysis in the North Sea can be competitive toonshore hydrogen production, from electrolysis and natural gas with
CCS, in Norway and in Germany. A mixed integer optimization model
is developed using the TIMES framework, with a detailed modellingof the production, distribution and storage of electricity and hydrogen.
In addition, the cost-competitiveness of different transport concepts is
assessed, including pipelines and transport by ship.

3 - Method comparison for optimal power-to-X plant sizing
and operation under wind profile uncertainties

Nicolas Campion, Ioannis Kountouris, David Garcia Marin,
Marie Münster

EURO 2024 - Copenhagen
This study centers on off-grid PtX plants powered by wind, whose optimal sizing is challenged by the variability and uncertainty of wind
power profiles. This investigation spans various sizing methods, employing deterministic and 2 stage stochastic approaches, and also emphasizes the importance of accurate fuel production cost estimates toderive optimal plant sizing and investment decisions. Results indicatethat using a deterministic least-cost investment and operation modelwith an average power profile year is most likely to lead to accurate fuelproduction cost estimates. Operational details’ influence on cost estimates is minimal, advocating for a linear plant operation model for efficient solving time. In analyzing the optimal sizing and profitability performances, this study shows that the most suitable approach depends
on the expected ammonia selling price and the fuel production cost estimates. For scenarios with significantly higher selling prices than costestimates, a deterministic sizing method with the worst weather year
and modeling the flexible ammonia plant as fully flexible proves to leadto the most profitable operation. For scenarios with ammonia selling
prices in the same range as cost estimates, using a 2-stage stochastic
method with a set of average years leads to the highest profits during
operation. While the study focuses on Bornholm, in Denmark, thereis acknowledgment of the need for further research to generalize findings.

4 - Fueling the Future: Optimizing Power-to-X Production
in Renewable Energy Hubs through Flexible Operating
Units

Johannes Giehl, Dana Josephine Hentschel, Jens Weibezahn
The European ambitions for a hydrogen economy require the rapid development and scaling of technology and business models. As frontrunners, the Nordics have the potential to contribute to and benefit from the establishment of hydrogen valleys with the developmentof energy hubs. Analyzing specific locations is essential to gain insight into the characteristics of optimal production, cost structures,and investments of hydrogen and its derivatives, like methanol. The
application of an energy hub model for a case study of producing emethanol in Southern Jutland helps to obtain specifics on the operation and structure of these business models. The model uses the Spine
environment using Mixed Integer Linear Programming to investigate
cost-optimal production and investments. Thus, the model helps to
obtain specifics of the hub by a detailed representation with variable
efficiency rates to improve the representation of flexibility by electrolysis and by-products such as the use of excess heat. An integrated
sensitivity analysis enables the evaluation of the impact of regulatory
measures and costs on the feasibility and viability of energy hubs. Results show the optimal costs alongside the energy flow of each technology. The evaluation of the energy hub focuses on the levelized cost of
the depicted energy carriers. Further development will concentrate onrefining investment implementation and integrating technological, andsocio-economic aspects identified by the Nord_H2ub project.

 WC-10
Wednesday, 12:30-14:00 - Room: 11 (building: 116)

Capacity and treatment planning in
healthcare
Stream: OR in Health Services (ORAHS)
Invited session
Chair: Erik Demeulemeester
1 - Optimizing dynamic reserved resource capacity in appointment scheduling with elective and semi-urgent patients

Jedidja Lok - Visser, Gina van Vemde, Heleen den Hertog,
Jan Gerard Maring, Gréanne Leeftink
In appointment scheduling, it is a common practice to reserve a numberof slots for (semi-)urgent demand arrivals, that require service quickly.

WC-10

The other slots are then given to clients that request an appointment
upfront. To determine the number of reserved slots, the (semi-)urgent
demand arrivals are often modelled as a distribution with static or seasonal distribution parameters. However, in many appointment scheduling processes, more information becomes available about the urgent
demand arrivals over time. An example of these processes is a radiology department, where the number of patients present in the hospital could forecast the required number of emergency scans. In thisstudy, we propose near-optimal scheduling policies that reserve slotsfor (semi-)urgent clients, using updated information on the arrival distribution of (semi-)urgent clients in the near future. We formulate thesequential decision making problem as a Markov decision process. We
test this model on a Dutch real-life case study in the neurology department of Isala Clinics, Zwolle. This neurology department implemented a brain rehabilitation program in combination with an e-coachfor stroke patients, where we can use the number of active patients inmonitoring to forecast the number of semi-urgent requests for outpatient appointments. We discuss first results on this practical case study
and theoretical instances, and present managerial implications of ournear-optimal policies.

2 - Optimising Resource Allocation for a Cervical Cancer
Screening Program

Francisco Ballestin, M. Angeles Pérez, Sacramento
Quintanilla, Javier Villena
Cervical cancer, responsible for over 300,000 deaths in 2020, ranks as
the fourth most common cancer. In 2018, the World Health Organization introduced a strategy to partially eradicate it, emphasizing national
screening programs to identify the human papillomavirus (HPV), theprimary risk factor. A region in Spain have planned to implement comprehensive population-wide screening. This study examines optimization challenges within this program, including HPV detection tests andcytology screenings. While HPV tests are conducted in hospitals, samples and cytologies are collected at community health centers (CHCs)
or homes. Midwives at CHCs may need additional hours to attend topatients. Critical resources in this program are the machines in hospitals to process the tests and the midwives. The optimization problem
can be modeled in three phases. The first phase selects the hospitals
to process the tests, assigns each CHC to a hospital, and calculates theextra cost of midwives in each CHC. The second phase determines thenumber of tests each CHC sends weekly to their hospital and establishes the program’s start and end dates in each CHC. Finally, the third
phase calculates how many patients to contact weekly in each CHC tomeet the conditions of the second phase. Computational tests using
real data validate the models.

3 - Cluster analysis of treatment plans of rehabilitation patients

Maik Overmars
We perform a cluster analysis on treatment plans of rehabilitation patients. Rehabilitation patients receive multidisciplinary care during
treatment, which allows treatment plans to be customised to each patient. We cluster patients with similar treatment plans with the goalof standardising these plans and to make predictions for new patients.
We aim to use the clustering model as an input for capacity and planning models. We partition each treatment plan into patterns based on
decision moments where the doctor decides how to continue the patient’s care. The patterns are multivariate count data, as we count the
number of appointments per discipline over the duration of the pattern.
We use finite mixture methods to cluster the patterns. Each cluster
is modelled as a multivariate Poisson distribution. Given a numberof clusters, we use Expectation-Maximization to find the optimal parameters for the model. We use the Bayesian Information Criterion
to optimise the number of clusters. We analyse the treatment plans,
now sequences of patterns, using a non-stationary Markov chain. The
transition probabilities are estimated from the clustered data. Subsequently, we calculate the probability of a treatment plan to identify
common treatment groups. Finally, we show the validity of the clustering model by simulating new patients and comparing to the actual
data. Our method is used by Reade rehabilitation centre in Amsterdam
to support the development of standard treatment plans.

407

WC-11EURO 2024 - Copenhagen

4 - Emergency surgery allocation: Simulation-optimization
approach incorporating scheduled elective surgeries
under a hybrid emergency strategy

Chao Pan, Erik Demeulemeester
A sudden surge in emergency surgeries strains the critical and limited resources of the operating room (OR). Relying solely on humanjudgment for emergency surgery decisions may worsen congestion,
leading to potential delays or cancellations of both emergency andelective surgeries. In the worst case, if the initially allocated hospital lacks capacity, the patient may face a second transfer, causing
additional treatment delays. To address these risks, we develop astochastic mixed-integer programming model for emergency patient
allocation with a hybrid emergency surgery strategy. We then design
a simulation-optimization approach to assess the expected maximum
capacity for accommodating emergency surgeries. Our approach expedites decision-making in urgent cases, allowing decision-makers tomake more rational choices swiftly. Specifically, we propose an improved sample average approximation method with a stopping rule forsingle-hospital cases, integrating it into the optimal computing budget
allocation algorithm for multi-hospital settings. To demonstrate the efficiency of the proposed algorithms, we conduct experiments with real
data from a 3A hospital in China. Our results illustrate the robustnessof our methods through a sensitivity analysis of internal factors (overtime length), external factors (release time of emergency patients), andexpected elective surgery durations. Additionally, we highlight thebenefits of collaborative decision-making in multi-hospital setting.

 WC-11
Wednesday, 12:30-14:00 - Room: 12 (building: 116)

Scenarios and foresight practices:
Behavioural issues III
Stream: Behavioural OR
Invited session
Chair: Martin Kunc
1 - Biased Foresight: A Facilitator’s Playbook

Megan Crawford
A series of lab and field studies were conducted with managers, executives, and postgraduates to test the hypothesis that priming effects
exist within any given scenario planning workshop (Crawford, 2021).
The studies show that priming scenario practitioners with real-world,
yet biased views of the external business environment can significantly
alter how they view the future, scenario development, and implications
for action. Early indicators also suggest some of these biases may beinternally consistent. If true, biases based on priming effects suggests
potentialities for both facilitated nudging (Bryson, Grime, Murthy &Wright, 2016; Crawford & Wright, 2024) and predictive modelling(i.e. machine learning and AI; Spaniol & Rowland, 2023). These empirical studies are part of a larger grounded theory investigation. Results support three key claims. First, all scenario planning workshops
are susceptible to implicit influences. Second, there may be as muchconsistency as inconsistency in biased scenario thinking and foresightacross teams. Third, facilitators need to understand these variabilities
in order to help advance meaningful strategies.

2 - Using ChatGPT in Foresight Fieldwork Pedagogy to Address the Preparedness Paradox by Mitigating the Overoptimism and Normalcy biases

Rui Goncalves, Matthew Spaniol, Nicholas Rowland, Niels
RytterThis pedagogy-methodology paper addresses the challenges of preparing students for foresight fieldwork using AI. Foresight students engage with innovation forecasting, scenario planning, and technology
roadmapping, applying them practically. Yet, universities hesitate to408

involve early career students with real-world stakeholders in fear of
reputational risk. Teachers of these tools will often underestimate therole that their own experience has in their teaching, leaving students
vulnerable to mistakes.
This experience bias influences preparedness. Effectively mitigatinga risk, diminishes its perceived severity, and individuals who overprepare may be disappointed when nothing goes wrong because theyoverinvested in preparing, reducing the perceived value of preparation.
Moreover, overconfident individuals underestimate the likelihood of
failure, assuming normalcy as the norm, possibly dismissing threat
warnings.
Using foresight pedagogy as a case study, the paper proposes an approach to fieldwork preparation, that incorporates ChatGPT in a "roleplay" as stakeholder, for lowering risks, addressing cognitive biases,
and emphasizing the importance of foresight in undergraduate education. We argue these tools can be used to develop futures literacy skills
in controlled research settings, scalable to large classes and diverse
case studies. There is potential for this approach to extend beyond future studies, aiding preparation for general community engagements.

3 - Strategic foresight: Combining human creativity withanalytical rigour

Carolina van Weerd
Within the defence and security domain, the need for foresight (in order to be able to anticipate trends that will impact the future security
environment) is increasing due to the complexity of the changing world
around us. Although there are forecasts supported by quantitative analyses, foresight is a discipline in which expert judgement remains central. Therefore we advocate a symbiosed approach of human creativity
combined with analytical support. TNO’s VISTA (View on STrategic
Anticipation) toolkit allows to combine more analytical rigour with human creativity in foresight. The toolkit provides tools to policymakers
and analysts with regards to foresight in the defence domain, basedon seven steps. These steps do not need to be taken sequentially, nor
are they always needed depending on the scope of the foresight case.
The presentation of this paper will address the individual steps of our
VISTA toolkit, including the way we combined input from analyticalmethods, scientists and military experts. Special attention will be given
to elements that distinguish our toolkit from other, frequently used approaches: (1) a technology focus as starting point, (2) an integral approach, by not only looking at trends in isolation, but to explicitly relate trends with one another throughout the entire DESTEP spectrumand (3) a more detailed insight in possible future security challenges
by adding a regional perspective to global future world scenarios.

4 - Assessing responsible anticipation over time. The factual and symbolic sides of future-oriented practices

Anne Marchais-Roubelat, Fabrice Roubelat
Assessing responsibility and irresponsibility in anticipation practices,
this research questions future-oriented organizational and personal
behaviours over time from both factual and symbolic perspectives(Marchais-Roubelat, 2012). Together with the concepts of responsible futures (Arnaldi et al, 2020, Fuller and Roubelat, 2021) and of
responsible foresight (Tonn, 2018, Van der Duin, 2019), the one of responsible anticipation introduces the stance of responsibility in futuremaking activities. As a commitment to action (Fuller, 2017, MarchaisRoubelat, 2021), anticipation offers critical viewpoints to assess the
responsibility of future-oriented practices in a principle of action forsituating acts, their effects and their assessments from multiple moving
rules over time. Supplementing axiological and deontological dimensions of anticipation (Danaher, 2021), the principle of action stresses
the factual and the symbolic sides of commitments, of their effects, aswell as of their assessment. From the longitudinal study of the sustainability of nuclear energy in anticipation practices from 1950s topresent, we suggest that this principle of action be part of a critical
approach to assess the responsibility of anticipation practices. Sucha responsible anticipation faces the gaps between future and past or
present assessments and invite to introduce the risk of anachronism toquestion responsible or irresponsible behaviours.

EURO 2024 - Copenhagen WC-12
Wednesday, 12:30-14:00 - Room: 13 (building: 116)

YW4OR_3
Stream: WISDOM - Women in OR
Invited session
Chair: Marcella Samà1 - Novel Mathematical Optimization Models for Explainable and Fair Machine Learning

Kseniia Kurishchenko
In this presentation, I give an overview of my Ph.D. dissertation aboutenhancing explainability and fairness in Machine Learning (ML) via
the Mathematical Optimization approach. The use of ML to aid DataDriven Decision-making is increasing dramatically. The wide availability of ML algorithms brings important advantages, such as the improved accuracy of decisions and the reduction in the resources required to make them. Despite excellent accuracy, state-of-the-art ML
tools are effectively black boxes that complicate model trustworthinessand may provide unfair outcomes. In my Ph.D. dissertation, I address
this issue and model the trade-off between accuracy and transparency.

2 - Machine learning at the service of meta-heuristics forsolving combinatorial optimization problems

Maryam Karimi Mamaghan
In this talk, I am going to present my academic profile and introduce
myself to the EURO community in order to initiate possible futurecollaborations. I will then talk about my research interest and mycurrent research work which is on the integration of machine learning into meta-heuristic algorithms to solve combinatorial optimization
problems. I will give an overview of the different ways where we canintegrate machine learning into meta-heuristic algorithms.

3 - Artificial Intelligence to Improve Decision-Making
Frameworks

Fatima Ezzahra Achamrah
My research operates at the cutting edge of integrating operations
research (OR) with artificial intelligence (AI), tackling real-worldcombinatorial optimization problems across both deterministic andstochastic settings. With a special focus on vehicle routing problems, I leverage state-of-the-art AI methodologies to improve decisionmaking frameworks. My ambition is to make a meaningful contribution toward enhancing the sustainability and resilience of businesses ina world that is ever VUCA, rapidly evolving, and increasingly interconnected.
Currently, I am engaged in several research that seek to harness thesynergy between AI and OR. These projects are dedicated to the designand development of intelligent decision support systems that streamline operations and adeptly manage disruptions within cyber-physicalsystems.
Looking ahead, my research trajectory is set to explore broader andmore complex systems. I am particularly enthused about investigating how OR can intersect with ground-breaking technologies, such as
Generative AI. This exploration is driven by the vision of advancing
intelligent systems that are not only capable of self-optimization andinnovation but are also sustainable and resilient against the backdropof global challenges.

 WC-13
Wednesday, 12:30-14:00 - Room: 15 (building: 116)

Sustainable Food Supply
Stream: Secure & Sustainable Food Supply
Invited session
Chair: Sonja Rohmer

WC-13

Chair: Alfaima L. Solano-Blanco
1 - Including Sustainability into the Menu Planning Problem using a Wierzbicky Achievement Function

Francisco Martos-Barrachina, Laura Delgado Antequera,
Monica Hernandez
The Menu Planning Problem (MPP) has gained increasing attention inrecent years, driven by the need to address the complex interplay between nutrition, cost, palatability and environmental sustainability inour diets. This work presents an approach to fully incorporate these
considerations using a Wierzbicky Achievement Function (WAF). The
MPP is a multi-objective optimization challenge that aims to generate
daily menus that are not only nutritious and affordable but also palatable and environmentally sustainable. Traditional approaches have focused primarily on cost and nutritional aspects, often neglecting theenvironmental impact of food choices. However, there is a growing urgency to integrate sustainability into the MPP. This holistic approach
results in a tri-objective problem that is optimized by using a WAF,
a versatile tool that enables the simultaneous optimization of multiple
conflicting objectives, to balance the trade-offs between cost, palatability, and environmental impact effectively. By assigning a wide range of
weights to each objective, we can find menus that represent a diverselevel of compromise between the objectives. Our research shows theeffectiveness of this approach by generating an extensive Pareto Frontof solutions, offering a spectrum of menu choices that cater to different
sets of preferences. This study provides a valuable framework for incorporating multiple considerations into the MPP, paving the way formore responsible dietary choices.

2 - Quadratic Optimization for Sustainable Agriculture - A
Study of Mixed Cropping Systems

Edhem Sakarya, Sonja Rohmer, Rolf Van Lieshout, Tom van
WoenselMixed cropping systems present a promising solution to conventional
agriculture by combining multiple crops within one field, thus increasing biodiversity and soil health, yet pose new planning challenges interms of plant assignment and crop scheduling. To address this issue,
we propose a novel decision problem in which crops need to be assigned to different farms and fields over time, taking into considerationa range of diverse factors, including plant interactions, field characteristics, and growth conditions, as well as the demand and geographicallocation of the customers. In this research, we formulate this problem
as a mixed-integer quadratically constrained optimization model aimed
at maximizing the total yield and propose a column generation-based
approach to solve larger instances. Preliminary results show that ourapproach outperforms commercial solvers in terms of speed and solution quality. In addition, we present a managerial analysis on the effects of companion planting and the broader implications for farmers,
enriching the discussion on mixed cropping systems and sustainableagriculture.

3 - Sustainable crop planning for mixed cropping systems- An optimisation approach

Alfaima L. Solano-Blanco, Sonja Rohmer, Tom van Woensel
Playing a vital role for global food supply, agriculture contributes significantly to current challenges such as biodiversity loss and greenhouse gas emissions. The pursuit of sustainable agriculture aims to finda balance between food production, environmental conservation, andfinancial viability. Strip cropping presents a promising avenue, in thiscontext, by organizing farmland into smaller plots of different crops
to enhance their resistance to pests, increase biodiversity and improveoverall soil health. However, at the same time, simultaneous crop management poses operational challenges that prevent the implementationof strip cropping at a large scale. The use of broader strips can decrease
these implementation barriers for farmers yet may dilute the ecological benefits by reducing interactions between adjacent crops. These
trade-offs complicate the process of devising efficient strip cropping
strategies. The aim of this research is to support farmers in this process
through the development of a decision support tool. For this purpose,
we propose a crop planning model which integrates crop selection and409

WC-15EURO 2024 - Copenhagenland allocation decisions under consideration of both spatial and temporal diversity. By determining ideal strip widths and cropping schedules, the model steers farmers towards strip cropping practices that areboth economically viable as well as ecologically favorable, thereby
promoting a sustainable future for agriculture and global food supply.

 WC-15
Wednesday, 12:30-14:00 - Room: 18 (building: 116)

COVID-19 (2)Stream: OR in Health Services (ORAHS)
Invited session
Chair: Banafsheh Behzad
1 - Identify the most influential employees in Covid-19 Pandemic by A stochastic MILP influence maximization

Mohadese Basirati, Saeed Najafi-Zangeneh, Mireille
Batton-hubert
Managers seek ways to survive pandemic situations to avoid hugefinancial losses. Preventing staff from infecting each other plays aprominent role in survival. To do so, the infectious among employees should be modeled at first. Secondly, the members who directly or
indirectly infect the maximum number of other employees are identified. This problem is considered as an instance of Influence Maximization (IM) general problems. We use a Mixed Integer Linear Program(MILP) analytical optimization for IM problem. The MILP optimization guarantees the global optimal solution. However, due to the uncertain nature of influence, the IM problem is formulated as a stochastic
optimization based on a limited number of scenarios. Therefore, thewhole stochastic nature of the influence process may not be captured.
It is of high importance to check whether the number of scenarios usedin stochastic MILP is adequate. To do so, the result of the optimization
is simulated with numerous scenarios to evaluate the gap between theobjective function and the exact expected value. The proposed stochastic MILP methodology is examined on a company with twelve employees. The efficiency of the method and adequacy of scenarios are thendiscussed.

2 - Decentralized vs centralized optimal strategies for
Covid-19 control with compliance issues

Laurent Alfandari, Debajyoti Biswas
We design a Mixed-Integer Linear Programming model for minimizing Covid-19 infections and deaths over a given horizon of several
weeks. We allow two different strategies: centralized decisions applying homogeneously to all citizens at the national level, or decentralized decisions that can vary across regions or cities having different initial situations and risks. The decisions to make are the levelof Non-Pharmaceutical Interventions (NPIs) implemented each week,
like lockdowns, travel bans, school closures, or combinations of them.
Shortages in doctors and hospital beds are considered in the model.
Beyond the epidemic model that estimates the number of infections
each week, one key aspect of this study is, in the decentralized scenario, the degree of compliance of the population facing more severe
restrictions. Our results on real data show from which level of compliance the decentralized strategy starts to outperform the centralized
strategy, under the same severity budget over the whole horizon. Compliance has been shown in previous studies to be decreasing in time,hence several kinds of compliance functions are considered to get robust results. Our results are also analyzed with or without the presenceof vaccines.

3 - Fair and Effective Vaccine Allocation During a Pandemic

Eda Yücel, Gunes Erdogan, Sibel Salman, Parinaz KiavashThis study introduces a novel model addressing the Vaccine Allocation Problem (VAP), focusing on distributing vaccines across different population locations over multiple pandemic periods. It incorporates disease progression and vaccination effects to minimize total expected mortality and location-based mortality inequalities while

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considering constraints like vaccine supply and healthcare capacities.
Utilizing an extended Susceptible-Infected-Recovered (SIR) epidemiological model, the VAP is formulated as a nonlinear mixed-integer
programming problem and solved using the Gurobi solver. Througha series of scenarios spanning 12 weeks in the UK, the study highlights the significant impact of vaccine availability and disease spreadparameters on optimizing vaccination strategies.

4 - Multi-feature SEIR model for epidemic analysis and vaccine prioritization

Hoda Bidkhori
The SEIR model has become a valuable tool for studying infectious
disease dynamics and predicting the spread of diseases, particularly
concerning the COVID pandemic. However, existing models often
oversimplify population characteristics and fail to account for differences in disease sensitivity and social contact rates that can vary significantly among individuals.
To address these limitations, we have developed a new multi-feature
SEIR model that considers the heterogeneity of health conditions (disease sensitivity) and social activity levels (contact rates) among populations affected by infectious diseases. Our model has been validated
using data from the Centers for Disease Control and Prevention on
COVID cases in Allegheny County.
In addition, we have used our multi-feature SEIR model to propose
and evaluate different vaccine prioritization strategies tailored to thecharacteristics of heterogeneous populations. We have formulated optimization problems to determine effective vaccine distribution strategies. We have designed extensive numerical simulations to compare
vaccine distribution strategies in different scenarios.

 WC-18
Wednesday, 12:30-14:00 - Room: 42 (building: 116)

Facilities Routing and Planning in
Developing Countries
Stream: OR for Development and Developing Countries
Invited session
Chair: Helper Zhou
Chair: Bruno Kamdem
Chair: Mabutho Sibanda
1 - A method for huge scale maximum covering facility location problems with an application to water well placement in West Darfur

Britt van Veggel
In the analytical context of addressing humanitarian issues, huge-scale
facility location problems play an important role. Whether it comesto finding locations for schools, hospitals, or water wells, these problems often involve many demand locations and many potential facility
locations. Solving such problems using traditional methods quickly
becomes intractable, or even impossible due to large memory requirements. We propose a decomposition approach to efficiently solve hugescale problems in an exact way by clustering the demand points, solving subproblems for each cluster independently, and combining solutions of subproblems by solving a integer programming problem. Clustering takes advantage of the spatial separation of demand locations
to address subproblems independently, guaranteeing optimality. Optionally, heuristic subclustering can be used to further divide clusters.
When subclustering is used, bounds on optimality gaps are provided,offering quality guarantees about obtained solutions. We demonstrate
the effectiveness of the proposed approach with a case study, which
is done in collaboration with the 510 team of The Dutch Red Cross,involving the placement of water wells in West Darfur (Sudan). Solutions for capacitated and uncapacitated problems are obtained withinreasonable computation times while providing optimality guarantees
where appropriate.

EURO 2024 - Copenhagen
2 - Optimal floor price for e-auction of a seasonal crop

Sundaravalli Narayanaswami
A Miniratna company of the Government of India, under the Ministry of Textiles (ministry, henceforth) has been serving in the fieldof marketing of cotton for last five decades. As a sovereign function ofGovernment of India, the organization undertakes Minimum Support
Price (MSP) operations to safeguard the cotton farmers from any distress sale and offers its cotton stocks through e-auction on regular basis
to meet out the demand of quality cotton to domestic textile Industry
at competitive rates. In this paper, an objective model to decide thevariety-wise optimal floor price of daily e-auction of cotton is developed and presented. Before the model was proposed and developed,
a team of subject matter experts and top management of of the organization considered the (i) variety-wise prevailing prices of cotton inIndian market, (ii) general price behaviour of international market and(iii) previous days’ variety-wise sale rates over & above the floor priceand decided the floor price for daily auction. The organization currently uses the optimal floor pricing model to scientifically determine
the floor price for the 15 odd cotton varieties grown in India and tradedglobally in the e-auction mode.

 WC-19
Wednesday, 12:30-14:00 - Room: 44 (building: 116)

OR in Energy II
Stream: OR in Energy
Invited session
Chair: Victor Ruela
1 - Large-Scale Binary Matrix Optimization for MultiMicrogrids Network Structure Design

Rui Wang, Gang Zhou, Bo Jiang
The Multi-Microgrid Network Structure Design Problem (MNSDP) isa binary matrix optimization problem that aims to minimize the cumulative length of preset power lines in a multi-microgrid system, whilesatisfying specific constraints. This problem is of great significancefor improving the utilization rate of renewable energy sources and thestability and resilience of power systems in remote areas from the central grid. Due to the large-scale, sparse and nonlinear characteristicsof this problem, traditional optimization methods are difficult to obtainsatisfactory solutions within a reasonable time. This paper proposesa mathematical model of the MNSDP, incorporating three types ofnodes with different reliability requirements. The paper also introduces
a benchmark test suite, MNSDP-LIB, based on real-world scenarios,
to evaluate the performance of algorithms. To solve the MNSDP, thepaper develops a new matrix-based constrained differential evolution
algorithm, LBMDE, which employs a binary-matrix-centric DE operator and an enhanced feasibility rule-based environmental selection
strategy. The paper conducts extensive experiments to compare LBMDE with several state-of-the-art evolutionary algorithms and a commercial solver on MNSDP-LIB and other binary matrix optimization
problems. The results demonstrate the superiority of LBMDE in termsof solution quality and computational efficiency, as well as its contribution to advancing renewable energy and microgrid technologies.

2 - A Binary Expansion Approach for the Optimal Demand
Response in Large and High Altitude Water Transportation System

Denise Cariaga, Miguel Anjos, Alvaro Lorca
Demand response for water networks is an optimisation model that
determines which water pumps will be turned on or off at each time
period according to a dynamic electrical tariff. This problem is important in mining due to the high power consumption of water pumps, aswell as the complicated dynamics of water flows and the power market.
The problem faces difficulties from: i) the nonlinearities of the equations for the frictional losses along the pipes and pumps, which makeWC-19

the problem a nonlinear mixed-integer model, ii) the many possiblecombinations of pressure head and flow rates, which quickly lead tohigh computational costs, and iii) the uncertainty from energy prices
and water demand. These limitations prevent the problem from being solved to optimality in a reasonable computational time in water
systems with more than two pumps and reservoirs. Therefore, we develop new optimisation models for the demand response in large andhigh-altitude water transportation systems that use a binary expansion
approach to efficiently account for the existing nonlinearities by reducing the computational difficulties while maintaining an excellent representation of the physical phenomena involved. We also tested thisapproach in different network topologies and case studies, and we conclude that the method significantly reduces the computational time forsolving the problem with high precision, which can be relevant for the
daily operation of real-world water transportation systems.

3 - Exact and metaheuristic methods for multi-objective energy aware hybrid flow shop scheduling problem withblocking and sequence-dependent setup times

Ahmed Missaoui, Cemalettin Ozturk, Barry O’Sullivan
The manufacturing sector is considered one of the most significant
consumers of energy. Given the energy crisis, manufacturing industry is paying increasing attention to energy-efficient scheduling as ashort/medium-term strategy for reducing energy consumption without
requiring significant investments. In this paper, the Hybrid Flowshop
Scheduling Problem with Blocking Constraints and Sequence Dependent Setup Times (BHFS-SDST) is addressed for the minimizationof makespan and total energy consumption (TEC). A novel constraint
programming (CP) model as an exact method is developed and its performance is compared with another exact method, mixed-integer linear programming formulation (MILP) and a multi-objective iterated
greedy (MOIG) metaheuristic. The effectiveness of the proposed approaches is tested with randomly generated small, medium, and large
benchmarks. Computational experiments demonstrate the efficiency ofthe developed methods in solving the BHFS-SDST problem.

4 - A heuristic for fitting piecewise-linear models of bivariate functions with simplices and its application to anindustrial use caseVictor Ruela, Paul van Beurden, René Hofmann, Felix
Birkelbach
A common approach to solving MINLP problems is to convert them
to a MILP using piecewise-linear (PWL) models to approximate thenonlinearities. However, finding a PWL approximation that respects
an error tolerance with the minimum number of linear pieces is a challenging task. Moreover, available methods are complicated and difficult to implement. This paper addresses finding error-bounded PWL
approximations for continuous and smooth bivariate functions. The
method proposed is an easy-to-implement heuristic applied for the caseof a triangulation generated over rectangular grids. On each iteration,a nonlinear programming problem is solved to adjust the placementof the linear pieces to minimize the approximation error. Then, we
increase the number of breakpoints for the variable with maximum
error reduction potential. For an industrial use case from the steelmaking industry, the ladle dispatching problem with refractory temperature
control, we show how applying this heuristic can reduce the complexity of the resulting MILP while respecting a strict approximation error
requirement. The heuristic achieved an average reduction of 67% in
the solution time of the MILP with an average 34% reduction in theapproximation error.

 WC-20
Wednesday, 12:30-14:00 - Room: 45 (building: 116)

Moments in the history of OR 2
Stream: Moments in the history of OR
Invited session
Chair: Gerhard-Wilhelm Weber411

WC-20EURO 2024 - Copenhagen

Chair: Roberto Rossi
1 - Tackling hard problems: on the evolution of operations
research

Laura Albert
As we think about what impact we would like operations research (OR)to have on the world, it can be helpful to look to the past for guidance
and inspiration. This talk overviews the early stages of operations research becoming a discipline and academic field of study following
World War II. In this talk, I will introduce "fun facts" about OR history, including the piece of OR history that inspired a scene in thefilm Good Will Hunting. I will also discuss early attempts to define
the field of operations research, drawing upon the writings of Philip
McCord Morse. The young field of OR experienced some growing
pains, when some leaders in the field expressed their concerns about
the demise and possible death of OR. Ultimately, OR flourished in thefollowing decades. A theme of the talk is that various efforts taken to
tackle hard problems defined the field of OR, opened up fruitful areasfor exploration, and guided the evolution of OR.

2 - 75 years of the world’s first OR journal

Graham Rand, Martin Kunc
In 1950, the world’s first OR journal, the Operational Research Quarterly, was published. Hence, 2004 sees the 75th volume of, what isnow, the Journal of the Operational Research Society. This talk will
discuss the origin of the journal, and changes in editor, frequency, publisher and content during these 75 years. A current editor-in-chief will
discuss JORS’ 75th volume and future plans.

3 - Exploring the innovative nature of Forrester’s ’Industrial
Dynamics’

David Lane
This talk reflects on the 1961 publication of Jay Forrester’s ’Industrial Dynamics’, the founding text of the field of System Dynamics.
It considers the circumstances that catalysed Forrester and explores
the influences and published antecedents of the book. It presents a
range of contemporary reactions, drawn from perspectives inside andbeyond OR. It explores the ways in which the ideas in ’Industrial Dynamics’ - ID - relate to other fields. From the perspectives of Systems
Science, OR and Problem Structuring Methods/Soft OR, it explores:
commonalities in intellectual provenance; similarities in central ideas;
influences that the book had on the field. Via a biographical sketch,the talk argues that the provenance of the ideas in ID can be seen to be
grounded in, and exemplified by, the life of Forrester himself. It closes
by drawing three conclusions. (1) The book continues to offer methodological challenges to OR, to contribute to debates on the nature andidentity of OR. (2) It vivified an important thread of feedback thinking,providing a vehicle by which abstract ideas can be put to practical use.
(3) ID anticipated developments in PSMs/Soft OR - meaning that the
book’s ideas add weight to the case made by those in this area. In otherwords, whilst ID continues to be source and origin for the core ideas of
the System Dynamics field, it offers bold and innovative ideas for all
those interested in using practical techniques to solve messy, real-workproblems.

4 - The life and work of Harry M Markowitz

Josh Fogg
Harry M Markowitz (1927-2023) was a man who lived many lifetimes.
An American Jew born in Chicago, his childhood ran through the turmoil of both the Great Depression and World War II. He went on to domuch pioneering research, for which he won both the Nobel and John
von Neumann prizes.
Within OR, he’s recognised as the father of Modern Portfolio Theory
for his work on mean-variance analysis. Not content to define just one
field, he’s also known as the grandfather of Behaviour Economics for
his ideas on behaviour under risk. Including and beyond these, he wasa prolific researcher across economics, mathematics, and computer science.
Markowitz’ collaborators in that are a "who’s who" of 20th century
scientists: Dantzig, Friedman, and Sharpe among many others. His

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academic career took him to institutions the world over, with positions
at City University of New York, London School of Economics, and the
University of Tokyo. This intertwined with a robust career in industry,including stints at RAND, IBM, and CACI, where he had a significant
impact. His life spanned a century of tremendous development, filledwith incredible (and at times unbelievable) history and many valued
contributions to the sciences.
In this talk we look over Markowitz’ life, reviewing the discoveries hemade along the way and the influence he had on the direction of somany fields.

 WC-21
Wednesday, 12:30-14:00 - Room: 49 (building: 116)

Infectious diseases and pandemics 2
Stream: OR in Humanitarian Operations (HOpe)
Invited session
Chair: Bela Vizvari
1 - An Approximate Dynamic Programming Approach forManaging Vaccine Distribution with Uncertain Supply
and Limited Resources

Peiyan He, Junlong Zhang
We study the vaccine distribution problem over a three-echelon network during a sudden pandemic outbreak. We consider uncertainties
in vaccine supply due to delays in production, disruption in global supply chain or lockdown policies in the early phases of the pandemic
outbreak. We also consider limited resources including cold trucks for
vaccine transportation and healthcare workers for vaccine administration. The problem is to dynamically allocate these limited resources
over the network such that vaccine can be transported and administrated in a timely and cost-effective manner. We formulate a dynamic
programming model for this problem and propose solution algorithms
based on approximate dynamic programming. We conduct a case study
on a vaccine distribution network in Iran and report computational results that demonstrate the effectiveness of our solution approach overtwo benchmark approaches.

2 - COVID-19 quantitative processes and vaccine inventory
policies

Bela Vizvari
COVID-19 is the first epidemic in the history of mankind such thatdaily data are available in all countries. It makes possible to get anunderstanding of the quantitative processes of pandemics.
A pandemic is a saturation process. This fact makes it possible to findgood approximations of the quantitative processes of the pandemic including the total number of cases, and the number of vaccinations. The
method is applied to 18 countries. The approximation has good quality
even in the case of multiple waves.
The procurement of vaccines is made on the level of countries. Basedon the obtained approximation, inventory policies are elaborated forcountries. The first model minimizes the total inventory cost in thecase of deterministic consumption and delivery.
The second model keeps the probability of shortage under a prescribed
level in case of stochastic delivery. This model is a generalization of
the so-called Hungarian inventory model.
Rich countries purchased huge quantities from vaccines. They could
not use completely all these vaccines. On the other hand, poor countries could not get enough vaccines. Therefore, they repeatedly infected the population of rich countries. It was a reason of the multiple
waves. It is assumed in the third model that one producer delivers toseveral countries. A fair distribution is the aim of the model with minimal total shortage.

EURO 2024 - Copenhagen WC-22
Wednesday, 12:30-14:00 - Room: 81 (building: 116)

Environment and climate change
Stream: Energy Management
Invited session
Chair: Ebru Voyvoda
1 - A New Integrated Assessment Framework for EnergyEconomy-Environment Modeling: Insights from Coal
Phase-out in the Turkish Power Sector towards Net-zero
Emission Targets

Ebru Voyvoda, Bora Kat, Umit Sahin, Saeed Teimourzadeh,
Osman Bulent Tor, Alp Erinç Yeldan
Power sector plays a crucial role towards decarbonization for many
economies, especially in line with the net-zero targets to limit global
warming by 1.5 Celsius degrees. Technical constraints intrinsic to thesector, penetration of new technologies, investment and operationalcosts, as well as its links with the rest of the economy make the power
sector a complex system to analyze. Although there are numerous
studies to integrate bottom-up power sector technology models with
top-down macroeconomic models, this study is the first attempt to link
the three separate and interrelated models within a single framework:
an electricity market simulation model, a generation expansion planning model, and an applied general equilibrium model. The proposed
framework is implemented to analyze alternative scenarios aiming at
successful phasing-out of coal-fired power plants in Turkey. The results suggest that, given the existing capacity and potential of renewables, Turkey can achieve her coal-phase out by early 2030s. The results also show, while installed capacity and generation of coal-fired
power plants are reduced, real GDP and electricity demand could bemaintained; and the CO2 emissions from power sector could be reduced by as much as 50% in 2030 compared to their 2018 level.

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2017 and 2018. The role of technology is measured through the availability of fixed broadband and fiber-to-the-home for each individual.
We find positive elasticities of substitution suggesting that as the costof commuting increases, the demand for broadband services increases.
This implies that individuals are substituting commuting with activities
that require broadband such as working from home and therefore digital technology is an emission reducer and produces productivity gains
in travelling for work and leisure. More commuting time for work willcause an increase in emissions volatility across counties, and the total
effect on expected emissions will be positive as individuals do suboptimal consumption of the two activities.

4 - Location- and Volume-Based Subsidies for CCS: A
Game-Theoretical Approach

Adrien Nicolle, David Lowing
Despite being an essential technology in industrial decarbonization
scenarios, Carbon Capture and Storage (CCS) struggles to reach largescale deployment. To tackle this issue, public entities have set up
numerous funding mechanisms to kickstart the adoption of this technology. However, these mechanisms generally support only the capture stage of the CCS value chain, thus overlooking support for dedicated transport and storage providers. In contrast, this paper develops a distribution rule to allocate a subsidy budget to the entire CCS
value chain. First, we derive a cooperative game based on the costminimizing pipeline network connecting emitters to storage sites. We
find that the Shapley value, typically computationally challenging, isattainable in polynomial time due to the specific structure of our problem. We then define each emitter’s subsidy claim from public entities
by combining the emitters’ network cost shares (i.e., location) with
their cost of capturing CO2 (i.e., volumes). Because public entities
may lack sufficient resources to cover all emitters’ claims, we turn tobankruptcy theory and apply a division rule known as the Constrained
Equal Awards rule, which we normatively justify by axioms relevant toour context. We prove that the spatial dimension substantially impacts
the results of the distribution rule. From a policy perspective, our paper’s findings suggest that current approaches should not overlook thelocation of industrial emitters.

2 - Coordinated Optimization of Water-Energy System Operation

Patrick Martin, Jannik Haas, Wolfgang Nowak WC-23

Water supply systems are critical infrastructure and will be forced toadapt to threats posed by climate change. In Literature optimizationof these systems is often done for either steady state, or single source
one year operation. Most studies focus on the water side of the operation while incorporating mostly limited energy aspects. Therefore,
fully joint planning of water-energy systems is still incipient, as is operation under a fully renewable regime. In this work, we use multiobjective optimization methods to model water-energy systems witha high temporary resolution. We focus on the operation of existingwater supply systems and expansion planning to optimally incorporate
renewable energies by striking the balance of less efficient operation
against cheaper and cleaner energy sources and storage. This is done
for different operation regimes, including a wide range of prediction
horizons, from greedy short-term decision making comparable to current operation up to global (in time) optimization as a reference bound.
We validate the model of the water supply system using data provided
by our German project partner, Landeswasserversorgung. In conclusion, we show how the operation of existing water supply networks
should change and how to adapt the network to integrate renewables in
a combined water-energy system in preparation for our future work on
long-term predictions using simulations of future climate scenarios.

Wednesday, 12:30-14:00 - Room: 82 (building: 116)

3 - Emissions Uncertainty, Technology and Optimal Commuting and Leisure Demands

Ioannis Paraskevopoulos
In this paper, we offer a theoretical framework to rationalize choicesof commuting and traveling for leisure conducted by individuals under
uncertainty about the future level of emissions. The main focus is analyzing technology’s role in this process. The model is estimated fora sample of surveyed individuals across US counties during the years

New technology for sustainable supply
chainsStream: Circular Economy, Remanufacturing and Recycling
Invited session
Chair: Marcus Brandenburg
1 - A multi-dimensional assessment of circular technologies in agriculture waste management

Patrizia Lombardi, Elena Todella, Timothée D’Amico
Agriculture emerges as a major contributor to global anthropogenic
emissions, such as waste production and greenhouse gases, and to resource depletion, such as water and land, requiring a transition froma linear to a circular approach. According to the WWF, the agricultural sector consumes about 69% of the planet’s fresh water andthe FAO estimates that the livestock sector alone is producing about
14,5% of all greenhouse gas production. Agri-food is among the most
relevant manufacturing sectors in Italy. In this context, a large-scale
and multi-disciplinary research program has been funded by the National Recovery and Resilience Plan, named AGRITECH, The National Centre for Agricultural Technologies (https://agritechcenter.it),with the aim to promote innovation and technology transfer. A large
research area of this program focuses on circular waste management
and multi-dimensional sustainability assessment. This study is part ofthis research area and focuses on orange peel waste (OPW) management processes. It proposes a critical review and selection of the KPIs

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WC-24EURO 2024 - Copenhagento assess alternative technologies of OPW management, and a preliminary application of the Analytic Hierarchy Process (AHP) to rank suchalternatives.

2 - Carbon Reduction, Blockchain Technology, and Business Model in Online Retail Platforms

Qi Sun, Yumin Fu, Cheng Ma
Online platforms are leveraging its power to foster environmentallyfriendly supply chains. This study explores the blockchain application and business model decisions in a supply chain with a manufacturer selling through an online retail platform. The manufacturer decides whether to adopt blockchain technology to provide evidence ofcarbon reduction, aiming to secure an eco-friendly label from a platform, which in turn determines the business model, choosing between
agency selling and reselling. We fully characterize the equilibrium
decisions and show how they depend on the carbon reduction efficiency, blockchain efficiency, and commission rate. The presence ofa carbon reduction manufacturer reduces the effectiveness of using thewholesale price as a lever by itself. Therefore, employing blockchain
upon a reselling platform can hurt the manufacturer. We also showthat blockchain technology and business mode are complementary, andtherefore managers should not ignore the impact of one decision on theother decision even when the latter is not a primary motivation of theformer. This study represents a significant stride towards the integration of blockchain in production and underscores collaboration withdominant platforms to offer guidelines for effectively reducing carbon
emissions.

3 - Exploring Blockchain Implications in Closed-Loop Supply Chains

Hamed Jalali, Behzad Maleki Vishkaei, Pietro De GiovanniIn the realm of supply chain dynamics, the influence of Blockchain
technology has been extensively explored within the forward channel,
yet its repercussions on reverse logistics and closed-loop supply chains(CLSC) remain understudied. For instance, Wipro, a prominent multibillion-dollar technology company, actively employs Blockchain technology to combat counterfeiting challenges within the realm of productreturns. This paper addresses this gap by delving into the implicationsof Blockchain adoption in CLSC.
Focusing on the interplay between a manufacturer and a retailer, ourstudy formulates a Stackelberg game wherein the manufacturer seeks
to deploy Blockchain technology to enhance the accuracy of valuing
end-of-life returned products. However, this adoption comes with a nuanced challenge—potential customer concerns about privacy and datacollection within Blockchain, which can adversely affect the retailer
by dampening customer demand.
Moreover, the manufacturer faces a choice between internally managing the collection of used products or outsourcing this task to theretailer. Through analysis of equilibrium strategies, we offer novel insights that contribute to a more comprehensive understanding of themultifaceted consequences of Blockchain integration in closed-loop
supply chain dynamics.

4 - Additive manufacturing in the circular economy: a simulation approach

Marcus Brandenburg, Biman Darshana Hettiarachchi, Stefan
Seuring
To study the impacts of additive manufacturing (AM) in the circular
economy (CE), we develop a causal loop diagram (CLD) that consistsof contingent factors which are detected in a structured literature review by systematic analyses of content and contingencies. The CLD isdeveloped further to a simulation model to analyze the system behavior
and to investigate core factors of AM and their roles and interrelationships in CE contexts. The findings inform about the (in-)stability ofthe system and how it can be influenced.

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Wednesday, 12:30-14:00 - Room: 83 (building: 116)

Game Theory in Sustainable Supply Chains
Stream: Sustainable Supply Chains
Invited session
Chair: Dmitry Krass
1 - Strategic analysis of e-platform supply chain decisions

Fatemeh Zand
Our study delves into a platform supply chain that comprises a
manufacturer and an e-platform, alongside a secondary marketplace.
Drawing inspiration from real-world industrial scenarios, we explore
the roles of the marketplace and the retailing channel within the eplatform. We outline the optimal operational strategies for this platform supply chain, considering various supplier market encroachment
strategies. Furthermore, we conduct a comparison of supplier encroachment strategies, developing an analytical framework to show
how to choose the optimal strategy and the critical factors influencing this decision within the platform supply chain. Additionally, we
analyze the dynamics of optimal collect and resell strategies for thee-platform, along with the corresponding operational decisions, indicating how the e-platform should adapt its strategies based on the supplier’s chosen encroachment strategy.

2 - Optimal Manufacturer Strategies and Government Intervention for AFV Transition under a Distributive Justice
Perspective

Wissam EL Hachem
Rising environmental awareness among consumers known as Consumer Environmental Awareness, led to the entry of green products
into the car market. This led to the issue of competition between thesegreen and non-green products which has been the subject of researchlately. We focus on the pricing and the degree of greenness of a productin competition with an established non-green product while considering government intervention. We build a model with two players, Government and Manufacturer, to focus on government intervention (taxes
and subsidies) in the context of transition to Alternative Fuel Vehicles(AFV) under two scenarios. In one there is no Distributive Justice(DJ), and in the other DJ enters by modifying the social pillar in thegovernment’s utility to maximize consumer’s access to vehicles. The
government’s tax and subsidy override the manufacturer’s prices when
determining the dynamics highlighting the decisiveness of government
intervention in such a setting. The dynamics of the environmental andeconomic pillars show tradeoffs which are partially alleviated when we
consider DJ. We show that when introducing DJ into the model, there
is no Pareto front where all three pillars improve simultaneously andthe government’s utility remains more or less the same. The manufacturer’s profits and consumer surplus exhibit a harmonious relationshipwhereby they increase together. Finally, the demand for AFV is always
cannibalizing the demand for ICE.

3 - Influences of Blockchain Technology on Restricting theMisuse of Green Loans in a Supply Chain

Bo Li, Ruidong Zhao
Nowadays green loans have been widely promoted to support thecapital-constrained supply chain under carbon regulation. But, the borrowers in the supply chain may use the preferential green loans forother purposes which are not related to green development. That phenomenon is mainly attributed to the lack of valid information investigation under traditional information technology. Blockchain, as an innovative technology, can provide valid financial information and identify
liability in the use of green loans, which is an effective tool to restrict
the misuse behavior. But will the high operating cost weaken the advantage of blockchain? To address this question, we consider a supply chain with a supplier and a capital-constrained manufacturer. The
manufacturer may misuse part of the green loans to purchase primary
components from the supplier. Two operational modes corresponding
to no-blockchain adoption and blockchain adoption are designed, inEURO 2024 - Copenhagen
which the supplier determines the wholesale price, and the manufacturer determines the order quantity under green loans. By comparing
the optimal decisions of supply chain members, we put forward anddifferentiate three types of effects: the carbon regulation effect, theinformation verification effect, and the blockchain cost effect, which
influence the adoption of blockchain technology in the supply chain.
In addition, we show that blockchain can restrict the misused amountof green loans and improve the environmental performance.

4 - Technological and Market Response to Environmental
Taxation by a Competitive Industry

Dmitry Krass, Anton Ovchinnikov
We consider response to environmental taxation by competing heterogenous firms producing a commodity good with a polluting byproduct (e.g., CO2). Firms are heterogeneous with respect to production efficiency and have a choice of finite types of pollution control technologies. Firms are faced with fixed plus linear production
costs. Cournot competition is assumed. The key research question isthe extent to which environmental taxes can be used to induce “green
equilibrium ’ i.e., an equilibrium where all firms select the most environmentally efficient technology. We show that, in general, neither
existence nor uniqueness of equilibria are guaranteed and characterizemarket demand functions under which existence does hold. However,even in this case, the equilibrium may include firms using less environmentally efficient technology choice at any level of environmental
taxes. Thus, environmental taxation alone cannot reliably induce green
technology choice. However, when coupled with fixed-cost subsidies,both existence and uniqueness of the green equilibrium are shown to
hold.

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WC-26

interval uncertainty, which extends OWA to handle uncertainty represented by continous intervals. We explore its properties, including its
relationship with OWA for discrete scenarios and sample convergence,and present both theoretical results and solution methods.

3 - Some particular combinatorial problem solved using integer partition theory techniques."

Zahra Yahi, Sadek Bouroubi
There is an abundant literature on integer partitions, with diverse applications, notably in computer science and more specifically in the fieldof image processing. In this paper, we present some new results. In thefirst part we present the initial result, concrenig the enumeration of triangles with fixed premeter and distincts side lengths and in the seceod
part, we give closed formula of the number of quadrilatrals with different side lengths and a fixed perimeter, achieved through the utilizationof integer partitions theory. Intermediate results are provided."Manyopen problems will be posed for further exploration."

4 - Structured (sub-)symmetry breaking in MILP by variable
aggregation

Cecile Rottner
MILP resolution is strongly impacted by symmetries, namely the existence of solutions identical up to a permutation. In this presentation,
we focus on MILP with structured symmetries, ie any column permutation of the solution matrix is a symmetry. Various types of symmetrybreaking techniques exist to handle such symmetries, from specialized
branching disjunctions, lexicographic ordering or symmetric variables
aggregation. We focus on the latter technique which has shown to bevery efficient on problems with specific structure, namely featuring thedisaggregation property. We study in which case the aggregation technique can be used and how to benefit from its efficiency in more generalcases. Case studies on variants of the Unit Commitment problems areproposed.

Wednesday, 12:30-14:00 - Room: 011 (building: 208)

Topics in Combinatorial Optimization I(Contributed)
Stream: Combinatorial Optimization
Invited session
Chair: Cecile Rottner
1 - Characterization and NP-Completeness of ATC Tool Indexing Problem without Tool Duplication

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Wednesday, 12:30-14:00 - Room: 012 (building: 208)

Topics in scheduling (Contributed)
Stream: Combinatorial Optimization
Invited session
Chair: Reshma Chandrasekharan

Deepti Mohan, Diptesh Ghosh
The evolution of automated machining centers, especially in sectors
such as aerospace and automotive manufacturing, has been significantly driven by the adoption of Automatic Tool Changers (ATCs) incomputerized numerical control (CNC) machines. These ATCs arepivotal for the efficient execution of diverse machining tasks, facilitating rapid tool changes without manual intervention. Central to enhancing the operational efficiency of these centers is the challenge of optimizing the ATC indexing process. This problem, known as the ATC
Tool Indexing Problem, is a challenging combinatorial optimization
problem. The ATC Tool Indexing Problem without Tool Duplication
is a variant of this problem, which can be formulated as a specialized
instance of the Quadratic Assignment Problem, with a distinctive structure in its distance matrix. We present a characterization of optimal
solutions to the ATC Tool Indexing Problem without tool duplication.
We also show that this problem is NP-Complete.

2 - OWA for Combinatorial Problems with Interval Uncertainty

Werner Baak, Marc Goerigk
The Ordered Weighted Averaging (OWA) method is a popular generalization of robust optimization, which is most commonly applied to
combinatorial problems if they have an uncertainty set modeled by adiscrete list of scenarios. We introduce a novel approach, OWA for

1 - An adaptive VNS optimization algorithm for order acceptance with scheduling problem in additive manufacturing

Teng Cao
We studied the order acceptance and scheduling problem on a single
machine in additive manufacturing(AM) environment, considering agroup of part orders with different sizes, profits, arrival date, due date,and delivery deadlines to be decided,manufactured and scheduled onan single AM machine. In our research, the AM machine is modeled as a batch processing Machine, processing part orders in batch
sequences.Part orders are either accepted and assigned to a batch or rejected, the accepted orders are subject to dual constraints of time window and processing space.The goal is to maximize total net revenue
considering orders revenue,processing costs, and penalties for delayeddelivery and order rejection.
In this talk, we : 1.develop an Adaptive Variable Neighborhood Search
Algorithm (AVNSA) to obtain a high-quality order acceptance list
and part processing sequence which satisfies all technical constraints.
2.present a heuristics methods for initialization. 3.propose 9 kinds ofmove operators for neighborhood search and 2 kinds of backtracking
mechanism to prevent the algorithm from getting stuck in local optima
4.make the comparisons between the proposed AVNSA and several
methods in previous research in 135 randomly generated instances.

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WC-27EURO 2024 - Copenhagen

The preliminary experimental results illustrates that,within shorter
CPU time,the AVNSA is capable to obtain better solutions with higher
net revenue,fill rate and less delay.

1 - Conic Functions: Theory and Applications in Machine
Learning

2 - Solving flexible job shop scheduling problems with multiple resource constraints using a unified CP-based solution framework

Polyhedral conic functions, introduced by Gasimov and Öztürk, are defined with graphs depicting convex polyhedra as their level sets. They
have become vital in machine learning, particularly for addressingclassification problems. Derived by augmenting a hyperplane equation with the l1 norm, a polyhedral conic function relocates within
the space through the vertex point. The PCF (Polyhedral Conic Function) algorithm efficiently separates two sets of points in multidimensional spaces by solving a linear programming subproblem. It computes parameters and yields a function partitioning the space into two
regions, thereby separating points effectively. PCFs find utility in classification by formulating error functions to minimize discrepancies between sets. They have applications in image processing and psychiatry,aiding tasks like object recognition and disease identification. Various PCF-based algorithms, like clustering-based PCF and Incremental Conic Functions, use clustering approaches to find vertex points.
These extend traditional PCF capabilities by incorporating clustering
techniques. Mathematical programming and optimization play a crucial role in PCF algorithms, providing a framework for fine-tuning
performance. Their termination in finite steps underscores their efficiency, making them appealing in real-world applications. In conclusion, PCFs offer a versatile approach to classification problems, supported by mathematical principles.

Grigorios Kasapidis, Dimitris Paraskevopoulos, Yiannis
Mourtos, Panagiotis Repoussis
This study focuses on flexible job shop scheduling problems with multiple resource constraints. It introduces a comprehensive approach formodeling various resource types like limited capacity machine buffers,
tools, utilities, and work in progress buffers. The proposed methodology involves a Constraint Programming (CP) model alongside a CPbased Adaptive Large Neighbourhood Search (ALNS-CP) algorithm.
The ALNS-CP algorithm incorporates long-term memory structures toretain valuable information about individual and paired operations assigned to machines, gathered from high-quality and diverse solutions
during the search process. These memory structures aid in formulating additional constraints for the CP solver, and therefore guiding
the search towards promising areas of the solution space. To evaluate its efficacy, extensive experiments were conducted on establishedbenchmark sets, comparing the performance of ALNS-CP with existing state-of-the-art methods. Furthermore, new instances of varying
sizes were utilized to explore how different resource types impact the
makespan. The computational findings highlight the competitivenessof the proposed framework, notably yielding 39 new best solutions
across well-known problem instances of the literature.

3 - Instance space analysis of the shift minimization personnel task scheduling problem

Reshma Chandrasekharan, Kimmo Nurmi, Nico Kyngäs, Jari
Kyngäs
The shift minimization personnel task scheduling problem concerns
the optimal assignment of a multi-skilled workforce to skill-specifictasks and the objective is to minimize the number of shifts utilized.
Due to its widespread applications in personnel scheduling, various efficient algorithms have been proposed for this problem which solves
most of the benchmark instances to optimality. However, some ofthese instances remain extremely hard to solve. Various statistical experiments have been conducted to analyze what makes some of these
instances hard and as a result, a new set of benchmark instances havebeen generated which offer a significant challenge to the state-of-theart algorithms.
In addition, this shift minimization problem translates as the list coloring problem on interval graphs, a classic coloring problem on graphs.
Through this study, we also investigate what are those graph properties that significantly affect the difficulty in coloring interval graphs.
We observe that various scheduling problems in the literature can bepolynomially reduced to the list coloring on interval graphs and therefore, our observations and insights can shed light on what makes certain interval scheduling problems and specifically the subsets of theirbenchmark instances hard.

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Wednesday, 12:30-14:00 - Room: 047 (building: 208)

Machine Learning and Ensemble Learningwith optimization methods
Stream: Mathematical Optimization for XAI
Invited session
Chair: Emilio Carrizosa
Chair: Sureyya Ozogur-Akyuz

416

Gurkan Ozturk, Emre Çimen, Zehra Kamisli Ozturk

2 - Machine-learning-based policy for the dynamic pricing
of substitutable itineraries

Yue Su, Axel Parmentier, Antoine Désir
In the airline revenue management, dynamic pricing for substitutable
itineraries presents significant challenges due to the stochastic natureof customer choices. The dynamic pricing problem can be conceptualized as a Markov decision process with the objective of maximizing
the total expected revenue over a finite selling horizon. However, thecomplexity of this model, characterized by its multi-dimensional state
and action spaces, makes it computationally prohibitive to solve exactly, even for for small-to-medium-sized instances. As a result, classic methods such as dynamic programming prove inefficient to address
the dynamic pricing problem for larger instances. In this work, we introduce learning-based policies tailored to dynamic pricing problem ofsubstitute itineraries with the same origin and destination. These policies are encoded by hybrid machine learning pipelines, while taking
into account the heterogeneous customer choices. We proposed two
different configurations of pipelines, where we have designed tailored
training process for each of them. To demonstrate the performance of
our learning-based policies, we first benchmark it against the optimal
policy obtained from dynamic programming on small-sized instances.
Then, we benchmark it against the state-of-the-art approximated policy heuristics on larger-sized instances. In addition, our method can beeasily adapted to airline network scenarios, where itineraries of different origins and destinations are included.

3 - Interpretable Ensemble Learners for Explainable Business Analytics

Eren Berk Aytac, Koen W. De Bock, Sureyya Ozogur-Akyuz
Customer churn is a common problem for companies. The problem isthat traditional business analytics models, particularly when it comesto predicting customer churn. The core of the issue is that these traditional models don’t necessarily align with business objectives. For
instance, a typical customer churn model might tell us whether a customer is likely to discontinue service, but it stops short of considering
the financial implications of churn. Additionally, algorithms’ decisionmaking processes and outcomes should be understandable and explainable to humans. Rule-based models (e.g.rule ensembles) are among themethods used to provide this level of explainability. In this study,Profit
Driven Spline Rule Ensembles Customer Churn Prediction Model has
been developed for this problem. The unique aspect here is the profitdriven approach, which ensures that the predictions are aligned withthe company’s financial goals. Part of this approach involves adapting
model performance metrics EMPC to be understandable to humans.
By utilizing spline rule ensembles, the model captures complex relationships in customer behavior that standard models might miss. The

EURO 2024 - Copenhagen
key to the model’s success lies in its interpretability. In today’s complex decision-making landscape, clarity is very important. The model
doesn’t just churn out predictions; it provides a window into the ’why’
and ’how’ of customer behavior, empowering decision-makers withthe knowledge to act decisively and effectively.

4 - Incremental Accelerated Hyperbolic Smoothing Clustering Method: Computational Results

Adilson Elias Xavier, Vinicius Layter Xavier
This work considers the minimum sum-of-squares clustering (MSSC)
problem, which has an intrinsic bi-level nature and the significant characteristic of being nondifferentiable. To overcome these difficulties,
the proposed method adopts a smoothing strategy, which engenders an
unconstrained completely differentiable single-level alternative. The
proposed algorithm also applies a partition of the set of observations
into two non-overlapping groups: “data in frontier" and “data in gravitational regions". The article introduces a novel incremental procedure
to produce starting points in an iterative way, which begins with only
one centroid, and which adds one cluster centroid at a time. To show
the distinct performance of the new algorithm, we perform numericalexperiments with a set of thirteen large test problems from the literature. In short, except for a few solutions, numerical results show ahigh level of performance of the IncAHSCM algorithm according todifferent criteria of accuracy and speed, in compassion with the bestestablished clustering algorithms. . The accuracy performance canbe attributed to the complete differentiability offered by the hyperbolic
smoothing approach, as well as, by the strategy to get good initial starting point. The high speed of the algorithm can be attributed to the partition of the set of observations into two non-overlapping parts, which
drastically simplify the computational tasks.

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Wednesday, 12:30-14:00 - Room: 065 (building: 208)

Advancements of OR-analytics in statistics,
machine learning and data science 10
Stream: Advancements of OR-analytics in statistics, machine learning and data science
Invited session
Chair: Jinal Parikh
Chair: Gerhard-Wilhelm Weber
1 - Greenhouse gases levels prediction using artificial neural networks

Hugo Valadares Siqueira, Thiago Antonini Alves, Tiago
Emmanuel Nunes Braga, Ricardo Godoi, Yara Tadano
The increase of greenhouse gas (GEE) levels alters the energy balance
between the atmosphere and the Earth surface, leading to temperaturechanges that modifies the atmosphere chemical composition. Then,the air pollution represents one important index to be analyzed, as it
is directly related to climate changes mitigation goals. The development of a public tool that allows future predictions of potential GEE
environmental impacts are essential to the economy, environment andsocial and health aspects. In this sense, the development of systems formonitoring, forecasting, and controlling emissions plays an importantrole. The main objective of this research is to apply trainable and nontrainable combination methods for air pollution forecasting in Brazil.
Trainable ensembles based on Artificial Neural Networks (ANN) andlinear regression are compared with non-trainable combinations, single ANN, and linear statistical approaches. Different models are considered so far, including Autoregressive Model, Autoregressive andMoving Average Model, Infinite Impulse Response Filters, Multilayer
Perceptron, Radial Basis Function Networks, Extreme Learning Machines, and Echo State Networks. The use of trainable ensembles led toa better performance. The use of robust tools is paramount to help governments in managing air pollution issues like hospital collapse during
adverse air quality situations.

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2 - Forecasting mobile sources risks using artificial neuralnetworks

Thiago Antonini Alves, Hugo Valadares Siqueira, Tiago
Emmanuel Nunes Braga, Deivdy Silva, Yago Honda, Yara
TadanoTo implement public policies to reduce continuous emissions of green
house gases (GEE) and other air pollutants are paramount. One of themain sources of GEE emissions are the use of fossil fuel. In this way,research related to emissions of pollutants from vehicles is paramount.
Thus, seeking public policies encouraging the use and the development of more sustainable are essential to preserve populations’ health.
The World Health Organization estimates that 6.5 million premature
deaths are related to air pollution. To better understand the health risks
caused by emissions from mobile sources, is important to select themost important input variables. Therefore, this research aims to analyze and select the input variables that most affect the air pollution
health risks. To do so, we applied three Artificial Neural Networks(Multilayer Perceptron, Extreme Learning Machines, and Echo State
Neural Networks) to estimate the impacts of air pollution on outcomesfor respiratory diseases (hospital admissions and mortality) using roadvehicles fleet, distributed and sold fuels amount, and vehicle average
mileage. The results showed that the best performance was achieved
considering all the input variables. The ELM reached the best overall performance for hospital admissions, and ESN for mortality, bothusing deseasonalization methods.

3 - Simulator-based surrogate optimisation: an enhanced
framework for surrogate modelling and optimisation

Yu Liu, Fabricio OliveiraThis work introduces a methodology for optimising complex functions
common in high-fidelity simulations across engineering fields, emphasising the strategic bridging of simulators and optimisation processes.
Initially, we employ low-discrepancy sequence sampling to select simulation points, followed by training a surrogate model using a piecewise linear neural network with Rectified Linear Unit (ReLU) activation. Using Mixed Integer Programming (MIP), we reformulate the
ReLU neural network as a MIP optimisation problem and solve it tofind an optimal parameterisation of the simulator’s input. Based on
the best solutions obtained, we then enter an iterative domain-refinedphase of resampling the simulator, retraining the surrogate model, andrebuilding and resolving the MIP problem. For resampling, we use
an infill strategy that incorporates error assessment to balance exploration and exploitation. This process continues until the accuracy ofthe objective function reaches the desired tolerance, ensuring high surrogate accuracy near the optimum, with techniques like memory structure reuse and warm starts for efficiency. Validation against standard
test functions indicates that refining the surrogate model via an errorinformed resampling strategy significantly enhances optimisation efficiency. This framework advanced in surrogate-based optimisation
by synergistically combining adaptive sampling, neural networks, and
MIP for enhanced performance in complex simulations.

4 - Optimization with a multi-objective IRP model of theperishable supply chain for fast food restaurants, caseof 3PL in Bogota D.C.

Javier Enrique Mantilla Rosas, Javier Arturo Orjuela Castro,
Juan Pablo Orejuela Cabrera
The problem of perishable goods logistics in the fast food sector in
the city of Bogota is the shortage and logistics costs of transportationroutes for the supply chain carried out by logistics operators, there isa need to analyze the current situation to identify the variables that
affect the current results and how to measure the IRP models (Programming, Multi-Objective) for the scheduling and routing of vehicles transporting perishables, in order to satisfy fast food customers(QSR - Quick Service Restaurant), Thus, the delivery of perishable
food products (perishables) must have an adequate supply chain to
avoid food waste, improve the performance of the agri-food supply
chain logistics (AFSC) sector through different optimization models(LIRP, PIRP, IRP), In this research, a mathematical model IRP is defined where G=(N,A), N is the set of vertices and A is the set of arcs,
vertex 0 is the supplier, applying the optimization defined by Archetti

417

WC-29EURO 2024 - Copenhagen(2022) IRP (Inventory Routing Problem) whose objective function is tominimize the total inventory cost at the supplier, customers and transportation, where this problem is applied to the QSR sector in order tominimize nonconformities for the FFR (Fast Food Restaurant). The
factual problem has 72 restaurants, with 3 multi-temperature products(Frozen, refrigerated and dry), the SLOAD+MCF model was chosen,
which incorporates additional multi-product flow constraints with theability to model the respective demands and capacities.

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Wednesday, 12:30-14:00 - Room: 157 (building: 208)

Optimization issues on graphs I(Contributed)
Stream: Combinatorial Optimization
Invited session
Chair: Robert Manger
1 - An Efficient O(n2) Algorithm for Solving the Dynamic
Matching Problem in TFT-LCD Cell Assembly Process

Shine-Der Lee, Peggy Lee
A polynomial time O(n2) algorithm for finding the optimum matchingof panels to assemble TFT-LCD (Thin Film Transistor-Liquid Crystal
Display) is presented in this talk. In the TFT-LCD cell assembly process, both TFT array and color filter (CF) substrate panels are pre-cut
into several equal sized sub-panels. After the precut, two sub-panels
are matched to produce the final product, i.e., the LCD. We explore thespecial structure of a novel linear programming formulation, where
the sub-panels are sorted by the number of good displays via lexicographic order, based on the location of good/bad displays on each subpanel. Enhanced by these new properties, efficient solution procedures
to find the optimum matching are developed for real time operationand sequencing in the assembly process. Computational experimentswith several common pre-cut types and different batch sizes of panels
have indicated that the solution procedures are efficient and effective.

2 - The change of Seidel energy of Turán graph due to edge
deletion

Ayse Dilek Maden, Şeyhmus Tarhan, Masood Ur Rehman
We consider any simple graph with finite vertex set and edge set. Turángraph is defined as the complete r-partite graph formed by partitioning
its vertex set into r subsets, with vertex number as equal as possible,
and joining two vertices by an edge if and only if they are located indifferent subsets.In his paper, Haemers defined the Seidel energy of agraph as the sum of the absolute values of all Seidel eigenvalues ofthis graph. Heamers noticed that the Seidel energy of any graph is invariant when the Seidel switching and complement of that graph aretaken. Tian showed complete bipartite graph and the empty graph areswitching equivalent. Thus Seidel energies of empty graph, completegraph and complete graph with same order are the minimum among
all graph of same order. Tian proved that the Seidel energy increases
when an edge is deleted in the tripartite Turán graph, and then gave theproblem of whether this property would be hold for all Turán graphs.
Liu and Chen, proved the above problem for quinate Turán graphs.
We will show that the Seidel energy always increases when an edge isdeleted for almost every Turán graphs. W.H. Haemers, Seidel switching and graph energy, MATCH Commun. Math. Comput. Chem..
G.X. Tian, Y. Li, S.Y. Cui, The change of Seidel energy of tripartite
Turán graph due to edge deletion, Linear Multilinear Algebra. Y.Y.
Liu and X. Chen, The change of Seidel energy of quinate Turán graphdue to edge deletion. Discrete Applied Mathematics.

3 - Resource-constrained network interdiction on nodes tomitigate threats

Orhan Karasakal, Ahmet Kabarcık

418

Network interdiction models have been utilized to analyze a wide variety of real-life problems such as military operations, illegal drugtrafficking, increasing the preparedness of infrastructure against natural disasters or terrorist attacks, social networking, and preventing the
spread of epidemics. This study explores a novel shortest-path interdiction problem variant with a single defense node against multiple
adversarial nodes. The defense has limited resources to mitigate thethreat posed by the adversary. By interdicting nodes and all connectedarcs from those nodes, the defense aims to minimize the threat. The
length of arcs connected to the interdicted node will increase inverselywith the change in weight of the interdicted node. In this talk, the proposed mathematical model, the solution approach, and computational
results will be presented.

4 - On a minimum-cost version of double Roman domination

Robert Manger, Ana Klobučar Barišić
Double Roman domination (DRD) is an optimization problem posedon an undirected graph. It is formally described as assigning weights tograph vertices according to certain rules. Informally, the problem canbe interpreted as allocating some service providers (e.g., military formations) to chosen locations within a geographical area. Thereby thewhole service (i.e., response to enemy attacks) should be establishedin a least expensive way, while still assuring a required "double Roman" level of robustness and reliability. The standard version of DRD
treated in the literature identifies the total expense of service with thetotal number of needed providers.
In this work a new "minimum-cost" version of DRD is considered,
which is more general and more realistic than the standard version. It
assumes that the actual expenses of providing service can differ fromone location to another. Thus, formally speaking, each vertex in ourgraph is given a cost that is interpreted as the cost of keeping one service provider at the corresponding location.
First, it is noted that the considered minimum-cost DRD problem is
NP-hard, not only for general graphs but also for many special graphclasses. Next, a dynamic programming algorithm is constructed, whichshows that the problem can still be solved in linear time if the involved
graph is a tree. Finally, as a solution for general graphs, a heuristic is
proposed based on greedy approach and local search.

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Wednesday, 12:30-14:00 - Room: 064 (building: 208)

Optimization Frameworks
Stream: Software for Optimization
Invited session
Chair: Alejandro Astudillo
1 - Nonsmooth Optimization on Riemannian Manifolds in
Manopt.jl

Ronny Bergmann
In many applications nonlinear data is measured, for example when
considering rotations, bases of sub spaces or even sub spaces themselves. While this can be modelled as linear data with constraints, thisinduce more complicated algorithms. If the constraints on the other
hand form a Riemannian manifpold, for example the Special Orthogonal group, the Stiefel or the Grassmann manifold, one can employ
classical unconstraint algorithms, while staying inherently feasible. In
the recent years these algorithms were extended to non-smooth objectives as well as constrained optimization on manifolds.
In this talk, we present a framework and several algorithms that areavailable in Manopt.jl, which together with Manifolds.jl forms a familyof Julia packages to efficiently solve optimization problems on Manifolds in Julia.

EURO 2024 - Copenhagen2 - goalp: Weighted and Lexicographic Goal Programming
Interface for R

David Palma, Richard Hodgett
Goal programming (GP) is a special kind of linear programming (LP)technique where users seek to find a solution that deviates as little aspossible from a set of goals, expressed as a series of linear equalities or inequalities. Applications include: planning manufacturing tomatch demand as closely as possible, given a limited number of raw
materials; scheduling workers’ shifts to match their stated availability
as closely as possible; etc. Solving GP problems requires expanding
the original goals into a larger LP problem. While the expansion isrelatively straightforward, it is easy to make mistakes when utilising
it manually in large problems. Furthermore, there are few software
implementations of GP, with users mostly relying on general-purpose
LP software. This makes solving large GP problems tedious and prone
to errors. We fill this gap by introducing goalp, an R package for GP.
It allows solving basic, weighted, and lexicographic GP problems, aswell as a mixture of them. The package can take as input a humanreadable representation of the goals, or a set of matrices. Then it automatically sets up the corresponding LP problem, which it later solves
using the powerful lp_solve solver. goalp also allows mixing goals
with traditional linear optimisation constraints, i.e. those not allowing
for deviations. Furthermore, it allows for continuous, discrete, and dichotomous decision variables. goalp is currently available via CRAN,
including a user guide (vignette).

3 - CasADi as the "bread and butter" in optimization forproblem-solving engineers

Alejandro Astudillo, Joris Gillis
As an open-source software framework available in C++, Python and
MATLAB since 2010, CasADi has been empowering academic andindustrial researchers to rapidly prototype and efficiently solve challenging (dynamic) optimization problems.
Demonstrated through 3700+ citing papers, such optimization problems serve as an answer to many design, planning, estimation and control needs arising in engineering fields such as heat & energy, automotive, chemistry & pharmacy, or robotics.
The focus of this talk is on how CasADi facilitates vertical integration. The key facilitator is CasADi’s versatile expression graph, capable of efficiently representing engineering specifications, dynamic
models and transcription methods. A plugin system maps these expression graphs to the needs of a range of open-source and commercial optimization solvers, often including sparsity detection, (repeated)algorithmic differentiation, and linearity detection. By manipulating,communicating and code-generating the expression graphs, one canintroduce solid software abstractions that pair readability and maintainability with excellent runtime performance. In particular, the talkpresents the case of robot motion skill tool and follows the journey ofthe involved expression graphs all the way down to the CasADi solver
plugins.

 WC-31
Wednesday, 12:30-14:00 - Room: 046 (building: 208)

Innovations in Digital Assets - IDA
Stream: Analytics
Invited session
Chair: Stefan Lessmann
Chair: Daniel Traian Pele
1 - The Chronicles of Ethereum: An Event Study on EIPs

Stefan Gaman, Min-Bin Lin, Ruting Wang, Daniel Traian Pele
The evolution of tokenomics has been greatly influenced by Ethereum(Ether), one of the most prevalent cryptocurrencies (cryptos). With
the release of the Merge upgrade (EIP-3675) on September 15, 2022,

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Ether achieved a major turning point by transitioning from the PoW(proof-of-work) consensus mechanism to PoS (proof-of-stake). This
EIP (Ethereum Improvement Proposal) marked the dawn of a newepoch in blockchain innovation, enabling the emergence of creative
applications with reduced transaction costs. Meanwhile, Ether experienced a price surge prior to deployment, followed by a subsequent deflationary trend. This paper investigates the impact of EIPs such as the
Merge on crypto supply and demand, discussing whether these proposals function similarly to traditional monetary policies and analyzing thecharacteristics of EIPs that influence substantially. Furthermore, we
discuss the broader implications of technology adoption and upgrades
for Ether, investigating how technological uncertainty influences pricing and functional performance. Through an event study examining
Ether’s EIPs, we identify event-driven opportunities and risks, providing valuable insights for investors and protocol designers (i.e. policy
makers) alike.

2 - Financial Risk Meter for The Romanian Stock Market

Alexandra Conda
This article aims to estimate the systemic risk of the Romanian stock
market, using the FRM (Financial Risk Meter) methodology. This research contribution is about applying a novel systemic risk index to the
Romanian financial system (FRM@RO), to identify potential sourcesof systemic risk, and to understand network interconnections, thus increasing risk awareness of both managers and regulators. By using
data for companies listed at the Bucharest Stock Exchange (BSE),
our article highlights several aspects of the systemic risk of the Romanian stock market. First, our study reveals that the main driver ofsystemic risk, especially during financial crises, is the volatility index,
VIX. However, local factors, such as ROBOR interest rate and sectorial
indices for financial investment companies, in general, and energy sector companies, in particular, are extremely important in triggering systemic risk. Second, the system risk indicator for the Romanian stock
market, FRM@RO, may capture both investor sentiment, measured via
the Google Trends Search Volume Index, and stock market volatility.
Third, FRM@RO can act as an early warning indicator for economic
turmoil, being able to predict periods of technical recession one quarter in advance. Fourth, by using network analysis, we can identify,
daily, the level of market interconnectedness and highlight the main
companies triggering tail co-movements.

3 - BitMood: Analyzing Bitcoin Trends through Facebook
Emotions with AI

Daniel Traian Pele, Miruna Mazurencu Marinescu Pele,
Stefan Gaman, Alexandra Conda, Raul Cristian Bag, Bogdan
Paul Saftiuc
In the rapidly evolving landscape of cryptocurrency markets, the predictive power of social media sentiment has emerged as a pivotal areaof exploration. This study presents a comprehensive analysis aimed
at uncovering the intricate relationship between public sentiment expressed on Facebook and the volatility of Bitcoin trading volumes.
Leveraging an extensive dataset of 120,000 Facebook posts taggedwith Bitcoin-related keywords, combined with granular financial datafrom CoinGecko, we employ a methodologically advanced approach
integrating Long Short-Term Memory (LSTM) networks, sentiment
analysis via FinBERT, and topic modeling techniques. The LSTM
model’s ability to capture temporal dependencies in data makes it particularly suited for the volatility inherent in cryptocurrency markets,providing a more accurate forecasting tool compared to traditional
time-series models. The findings from our analysis reveal significant
correlations between the sentiment derived from social media and thefluctuations in Bitcoin market trends. These correlations not only validate the hypothesis that social media sentiment can serve as an effective predictor of market movements but also underscore the efficacy of
LSTM networks in enhancing the precision of volume predictions.

4 - Predicting Realized Volatility: Insights from EU Energy
Listed Firms During Crises

Cosmin Octavian Cepoi, Daniel Traian Pele
In this study, we examine price volatility among energy companies
listed on European capital markets during an energy crisis. Our objective is to explore how these firms navigate turbulent market conditions, offering insights into price fluctuation dynamics within the challenging energy sector landscape. We analyze 57 companies listed on419

WC-32EURO 2024 - Copenhagenstock exchanges across Austria, Belgium, Czech Republic, Finland,
France, Germany, Hungary, Italy, Norway, Poland, Portugal, Romania,
Spain, and Switzerland from June 2021 to December 2021. Our findings indicate that incorporating informed trading activity into volatility
forecasts can be advantageous, particularly in high-volatility scenarios
within the energy market.

 WC-32
Wednesday, 12:30-14:00 - Room: 41 (building: 303A)

Computer-Assisted Proofs in Optimization
Stream: Advances in large scale nonlinear optimization
Invited session
Chair: Eduard Gorbunov
1 - Constructive approaches to the analysis and construction of optimization algorithms

4 - Provable non-accelerations of the heavy-ball method

Baptiste Goujaud, Adrien Taylor, Aymeric Dieuleveut
In this work, we show that the heavy-ball (HB) method provably does
not reach an accelerated convergence rate on smooth strongly convex
problems. More specifically, we show that for any condition number
and any choice of algorithmic parameters, either the worst-case convergence rate of HB on the class of L-smooth and µ-strongly convex
quadratic functions is not accelerated (that is, slower than 1 − O(κ)),
or there exists an L-smooth µ-strongly convex function and an initialization such that the method does not converge. To the best of ourknowledge, this result closes a simple yet open question on one of
the most used and iconic first-order optimization technique. Our approach builds on finding functions for which HB fails to converge andinstead cycles over finitely many iterates. We analytically describe
all parametrizations of HB that exhibit this cycling behavior on a particular cycle shape, whose choice is supported by a systematic andconstructive approach to the study of cycling behaviors of first-ordermethods. We show the robustness of our results to perturbations of thecycle, and extend them to classes of functions that also satisfy higherorder regularity conditions.

Adrien Taylor
In this talk, I will provide a high-level overview of recent principledapproaches for constructively analyzing and designing numerical optimization algorithms. The presentation will be example-based, as themain ingredients necessary for understanding the methodologies are
already present in the analysis of base optimization schemes, such as
gradient descent. Based on those examples, I will discuss how thosetechniques can be leveraged for constructing Lyapunov-based analyses
and optimal convex optimization algorithms. The methodology can beaccessed through easy-to-use open source packages (including PEPit:
https://github.com/PerformanceEstimation/PEPit), allowing to use theframework without the modelling pain. This talk is based on joint
works with great colleagues that I will introduce during the presentation.

2 - Automated tight Lyapunov analysis for first-order methods

Manu Upadhyaya, Sebastian Banert, Adrien Taylor, Pontus
Giselsson
We present a methodology for establishing the existence of quadratic
Lyapunov inequalities for a wide range of first-order methods used
to solve convex optimization problems. In particular, we consider(i) classes of optimization problems of finite-sum form with (possibly strongly) convex and possibly smooth functional components, (ii)
first-order methods that can be written as a linear system on state-space
form in feedback interconnection with the subdifferentials of the functional components of the objective function, and (iii) quadratic Lyapunov inequalities that can be used to draw convergence conclusions.
We present a necessary and sufficient condition for the existence of aquadratic Lyapunov inequality within a predefined class of Lyapunov
inequalities, which amounts to solving a small-sized semidefinite program. We showcase our methodology on several first-order methods
that fit the framework. Most notably, our methodology allows us tosignificantly extend the region of parameter choices that allow for duality gap convergence in the Chambolle-Pock method.

3 - Counter-examples in first-order optimization: a constructive approach

Aymeric Dieuleveut, Adrien Taylor, Baptiste Goujaud
While many approaches were developed for obtaining worst-case complexity bounds for first-order optimization methods in the last years,
there remain theoretical gaps in cases where no such bound can befound. In such cases, it is often unclear whether no such bound exists (e.g., because the algorithm might fail to systematically converge)or simply if the current techniques do not allow finding them. In thiswork, we propose an approach to automate the search for cyclic trajectories generated by first-order methods. This provides a constructive
approach to show that no appropriate complexity bound exists, thereby
complementing approaches providing sufficient conditions for convergence.

 WC-34
Wednesday, 12:30-14:00 - Room: 43 (building: 303A)

Transportation and Logistics under
Uncertainty
Stream: Stochastic, Robust and Distributionally Robust
Optimization
Invited session
Chair: Robert Hlavatý
1 - A robust satisficing multi-objective optimization approach for bike-sharing systems with heterogeneoususer types

Qingxin Chen, Shoufeng Ma, Ning Zhu
Recently bike-sharing systems begin to employ diverse pricing andsubscription policies to cater to the commuting needs of heterogeneoususers. Frequent riders choose to purchase subscriptions to reduce theircommuting costs if the system is easily accessible. To maintain a highmarket share, operators should improve their service level to retain alarge number of subscribers. In addition, there is also a small proportion of general customers who use the system occasionally. These
customers contribute to the operational profits, and providers need tofulfill as much customer demand as possible to ensure high profitability. Thus, maintaining the service level and obtaining profits are both
important operational objectives in bike-sharing systems. However,jointly optimizing these objectives for the heterogeneous users in bikesharing systems is challenging, especially when demand is highly uncertain. To address these issues, this study presents a robust satisficing
optimization model that jointly optimizes the service level and profits in bike-sharing systems under demand uncertainty. To avoid overconservation of solutions, side information like weather and weekend
is integrated into the model to describe the relationship between demand and these exogenous factors. Extensive numerical experiments
show that our model achieves higher robustness, lower violation probability and degree, and higher average-case out-of-sample performance
than other benchmarks.

2 - A Robust Stochastic Approach to Edge Computing Service Network Design with Network Attacks Uncertainties

Jie Chen420

EURO 2024 - Copenhagen
The Internet of Vehicles (IoV) serves as a foundational infrastructure
for intelligent transportation systems. For service providers in the IoV
and edge computing sectors, the meticulous design of edge computing networks is pivotal for securing a competitive advantage throughenhanced cost efficiency and superior service quality. We introduce arobust stochastic model designed to tackle the network design challenge for edge servers effectively. This model incorporates criticalconsiderations, such as the strategic placement of edge servers, capacity planning, congestion mitigation, accounting for stochastic demand,and guarding against unpredictable cyberattacks.Our primary objectiveis optimizing the network configuration to minimize total costs, including setup, capacity acquisition, congestion management, and datatransmission. To represent this multifaceted issue accurately, we employ an M/M/1 queuing framework for assessing congestion in steadystate scenarios and refine our approach with a mixed-integer secondorder cone programming framework.To manage uncertainties associated with network attacks, we incorporate an uncertainty set based on
Renyi divergence, allowing us to adjust the model’s conservativenessby modifying the divergence order. Furthermore, we propose a novel
two-layer Benders decomposition approach to efficiently navigate thecomputational complexities inherent in solving this problem.

3 - Towards efficient transportation systems - designing atransportation system under uncertainty

Robert Hlavatý, Helena Brozova
We intend to provide a different perspective on the class of optimisation
problems that are related to transportation systems. As a transportation system, we understand a situation that involves locations, meansof transport, subject of transportation and the question at hand is finding an optimal way of distribution over this system, and we specificallyfocus on such systems that can be represented and solved using mixedinteger linear programming (MILP). Instead of following the usual
solution approach, we focus on optimising the very system structure
rather than finding an optimal solution over a system with fixed parameters. For demonstration purposes, we assume - for its simplicity- the traditional transportation problem of Hitchcock and Koopmans
with possibly multiple objectives. In addition, it is considered thatthe capacity levels of suppliers (or customers) are bounded by given
maintenance costs that increase with the increasing capacity. Given
the constrained budget, we propose an optimal (in the cost-minimisingsense) transportation system design that respects all objectives. We
realise that these costs may be subject to uncertainty and therefore
propose the robust-optimal transportation system design. We combine
the De Novo optimisation approach with Gamma-robust optimisation
to demonstrate our methodology which can be extended to any othertransportation problem representable by MILP.

 WC-35
Wednesday, 12:30-14:00 - Room: 44 (building: 303A)

Robust Optimization: Theory andApplications
Stream: Stochastic, Robust and Distributionally Robust
Optimization
Invited session
Chair: Christopher Kirkbride
1 - Robust approaches for the Kidney Exchange Problem

Matteo Petris, Claudia Archetti, Ivana Ljubic
In the Kidney Exchange Problem (KEP), kidney exchanges can bemodelled in a directed weighted graph as circuits starting and endingin an incompatible pair or as paths starting at an altruistic donor. The
weights on the arcs represent the medical benefit which is a measureof the quality of the transplantation. For medical reasons, circuits andpaths are of limited length. The aim of the KEP is to determine a set of
disjoint kidney exchanges of maximal medical benefit or maximal cardinality (all weights equal to one). In this work, we consider two types

WC-35

of uncertainty in the KEP which stem from the estimation of the medical benefit (weights of the arcs) and from the failure of a transplantation(existence of an arc). Both uncertainties are modelled via uncertainty
sets with constant budget. The robust approach entails finding the best
KEP solution in the worst-case scenario within the uncertainty set. We
modelled the robust counterpart by means of a max-min formulation
which is defined on exponentially-many variables associated with thecircuits and paths. We propose two exact approaches to solve it: one
based on the result of Bertsimas and Sim and the other on a reformulation to a single-level problem. In both cases, the core algorithm isbased on a Branch-Price-and-Cut approach where the exponentiallymany variables are dynamically generated. The computational experiments prove the efficiency of our approach.

2 - Optimistic versus Pessimistic Discrete Robust Optimisation Approaches: Opportunities and Challenges

Nader Azizi
The objective of a discrete robust optimisation approach e.g., budget
uncertainty (Bertsimas and Sim, 2003&2004) is to find a solution that
optimises against all scenarios in which up "a" number of coefficientsthat maximally influence the objective vary. Therefore, a solution produced by such method is optimal when exactly "a" coefficients are subject to change and it is feasible for other cases. This view of robustness
could be regarded as "pessimistic" robust optimisation approach as thevalue of the objective reflects the worst-case scenario for a given budget of uncertainty. Building upon the concept of pessimistic robust
optimisation, in this research we present an approach that we call "optimistic" robust optimisation. For a given problem with uncertain datain the objective function, the optimistic robust optimisation approachaims to find a solution in which exactly "b" number of coefficients with"minimum" impact on the objective are subject to change. We present
examples and discuss opportunities and challenges pertain to the useof such (optimistic)robust optimisation approach.

3 - Distributionally robust fixed interval scheduling withheterogenous machines and random delays

Monika Matoušková
We deal with operational fixed interval scheduling problem where start
times are given and the actual finishing times can be influenced by random delays. We further consider heterogeneous case, i.e., multiple job
and machine types. And we assume that the multivariate distributionof delays follows an Archimedean copula. We consider the highest
worst-case probability that the schedule remains feasible, where given
proportion of marginal distributions of delays are stressed. This problem has an interesting reformulation containing a commonly used risk
measure. We implement a decomposition algorithm. A possible application is gate assignment problem where incoming flights have to be
assigned to available gates of an airport. In this problem, heterogeneity
is caused by the fact that different aircrafts might need to be assigned
to different gates. Moreover, there can be proportion of flights where
worse delays occur.

4 - Robust Markov decision processes under parametrictransition distributions

Christopher Kirkbride, Ben Black, Trivikram Dokka
We consider a robust Markov decision process for which we assume
that the true transition distribution can be uniquely specified by a parametric distribution. We enforce this by the construction of a parametric ambiguity set that ensures the worst-case distribution is from thesame parametric family of distributions. To conduct robust value iteration, we formulate the Bellman update as a linear program by discretising the ambiguity set. This scales poorly with problem size andrequires significant pre-computation. We develop two algorithms forsolving the update. Firstly, a cutting surface algorithm that reduces thetime to solve the linear program but still requires the pre-computation.
Secondly, a projection-based bisection search algorithm that removes
the need for discretisation and pre-computation. Using a multi-period
Newsvendor problem as an example, we compare these methods with
non-parametric phi-divergence based methods from the literature. We
show that the projection-based algorithm completes robust value iteration significantly faster than the other parametric algorithms, and isfaster than the phi-divergence equivalent.

421

WC-37EURO 2024 - Copenhagen WC-37
Wednesday, 12:30-14:00 - Room: 33 (building: 306)

Theory of Multiobjective Optimization
Stream: Multiobjective Optimization
Invited session
Chair: Clemens Thielen
1 - Characterizations of supported efficiency

Fabian Chlumsky-Harttmann, Anita Schöbel
When given a multi-objective optimization problem, one usually aims
to find efficient solutions. Yet, often this is hard. Solutions that areoften easier to identify than efficient solutions are supported efficientsolutions. Intuitively, the set of supported efficient solutions can be
described as the set that contains all solutions to the weighted-sum
scalarization for "reasonable" choices of weights. However, different
authors work with different definitions of supported efficiency (and—ifin the image space–supported nondominatedness, respectively).
We examine definitions found in the literature and show that there arein fact differences. We are able to categorize them into four layers with
an implicit hierarchy: The characterization at the highest level being
the most restrictive. Moreover, we show that these layers only coincidein special cases, such as discrete, linear or bi-objective problems, while
counterexamples show that these characterizations are not equivalent
for more general problems. We argue which characterizations are best
suited to be used for different kinds of multi-objective optimization
problems.
Last, we extend our results to concepts of extreme supported efficiency
and extreme supported nondominatedness, respectively.

algorithms. In this research, we explore the application of QC to discrete MOPs, capitalizing on its inherent capabilities to process vastand complex search spaces. We investigate how existing quantum optimization algorithms can be adapted and extended to the multiobjective
domain. Based on previous work, we focus on variational algorithms
and Grover Adaptive search and augment these with scalarization techniques and bound sets, among others. To demonstrate the efficacy of
our approach, we apply these QC algorithms to an unconstrained multiobjective combinatorial optimization problem. Our results showcase
that, while the computational advantage over classical computing iscurrently tested, QC adds value when tackling conceptually hard problems. Furthermore, we are paving the way for the practical applicability of quantum computing in solving real-world optimization problems.

4 - Exact solution methods for the bi-objective p-medianand p-dispersion problem

Andreas Klose
Given an undirected graph, the bi-objective p-median and p-dispersion
problem is to select p vertices of the graph such that the weighted average distance of the graph’s vertices to their nearest of the selectedp vertices is minimized and the distance between any two of the pselected vertices is maximized. For computing the bi-objective problem’s non-dominated set the "-constraint method" as well as the "hybrid weighted sum and -constraint method" seem to be suitable approaches. -constraints put on the dispersion objective may, however,be enforced in different ways. Additionally, also the remaining constraint system can be formulated quite differently. In this talk, we compare the performance of different implementations of the -constraint
and hybrid method based on different model formulations and means
to enforce the -constraint. It thereby seems that in particular the application of Benders’ decomposition for solving the scalarized problems
gives promising results.

2 - What is the meaning of pessimistic Pareto front in multiobjective bilevel problems?

Carlos Henggeler Antunes, Maria João Alves
Bilevel optimization deals with decision problems in which two
decision-makers, the leader and the follower, control different sets ofvariables and have their own objective functions subject to interdependent constraints. A lower-level optimization problem is embedded in
an upper-level problem. When the lower-level problem has multiple
objective functions, the leader should cope with the uncertainty related
to the follower’s reaction. The leader can adopt a more optimistic ormore pessimistic stance regarding the follower’s choice within his efficient region, which is restricted by the leader’s choices. The leader
may also have multiple objective functions. The concepts of optimism
and pessimism are well established when the leader has a single objective function. However, this is not the case when there are multiple
objective functions at both levels. Most approaches aim to calculate
the optimistic Pareto frontier (efficient solutions for the leader, considering that the follower’s choices are always the best for the leader).
However, this approach is seldom realistic, and the definition of thepessimistic Pareto frontier may not be consensual. We propose a definition of pessimistic Pareto front for bilevel optimization problems
with multiple objective functions at both levels, which is comparedwith the optimistic Pareto front. These new concepts are illustratedusing examples, emphasizing the difficulties associated with the computation of those solutions.

3 - Harnessing the Power of Quantum Computing for Multiobjective Optimization Algorithms

Pascal Halffmann, Ivica Turkalj
Multiobjective optimization problems (MOPs) present unique challenges, characterized by optimizing several conflicting objectives simultaneously and the, often exponential, number of Pareto-optimal solutions. Classical approaches typically involve scalarization to single
objective problems obtaining at most one Pareto-optimal solution per
single objective problem. The advent of quantum computing (QC),
with its properties like superposition and entanglement, opens new
frontiers in addressing the complexity of MOPs. So far, applicationoriented problems, like MOPs, have barely been touched by quantum422

 WC-38
Wednesday, 12:30-14:00 - Room: 34 (building: 306)

Advances in algorithms and applications forlinear and convex optimization
Stream: Conic Optimization: Theory, Algorithms, and Applications
Invited session
Chair: Christoph Funke
1 - Parametric upper and lower bounds of linear variationsof a linear problem’s LHS

Bardhyl Miftari, Guillaume Derval, Quentin Louveaux,Damien Ernst
Assessing the impact of various hypotheses is a common task whenworking on models. In this paper, we focus on the linear parametricproblem where we assess the impact of unknown constraints’ coefficients on the final objective. These multiple coefficients can varylinearly within a known range. Traditionally, this task is tackled using heavy computation, i.e., recomputing the optimum several times
for every value of the coefficients, using approximations or using nongeneric approaches. For large linear problems, we argue that computing every value of the coefficients in the interval is too computationallyheavy and does not provide any information about the overall behavior
between the points.
As a new approach to the problem, we derive several upper and lower
bounds that allow us to avoid recomputing the problem for numerous
coefficient values, provide guarantees between the computed points, as
it does not allow for outliers, and require a smaller number of optimizations. We discuss these bounds and provide an iterative algorithm thatuses them to compute an approximation to the original function within

EURO 2024 - Copenhagena given error threshold. We analyze the performance of the bounds on
toy and real-world problems and demonstrate the effectiveness of theapproach.

2 - A Combined Convex Formulation Replacing Hierarchical Extended Logit Model for Travel Demand Forecasting

Youngseo Kim, Damon Wischik, Soroosh Shafieezadeh
Abadeh, Gioele Zardini, Samitha Samaranayake
The travel demand forecasting model plays a crucial role in evaluating large-scale infrastructure projects, such as the construction of
new roads or transit lines. While combined modeling approaches havebeen explored as a solution to overcome the problem of input/output
discrepancies in a sequential four-step modeling process, previous attempts at combined models have encountered challenges in real-world
applications, primarily due to their limited behavioral richness or computational tractability. In this study, we propose a novel convex programming approach and present a key theorem demonstrating that the
produced optimal solution coincides with the one arising from the hierarchical extended logit model. This model is specifically designed
to capture correlations existing in travelers’ choices, including similarities among transport modes and route overlaps. The convex property of our model ensures the existence of solutions and naturally offers computational efficiency. The advantages of our proposed model
are twofold. First, it provides a single unifying rationale (i.e., utility/entropy maximization) that is valid across all steps. Second, its
combined nature allows one to systematically handle observed data,
enabling a better representation of reality. Our convex programming
approach shows promise in enhancing the accuracy and applicabilityof travel demand forecasting, thereby aiding in the decision-making
processes for infrastructure projects.

3 - On the convergence rate of the difference-of-convex algorithm (DCA)

Hadi Abbaszadehpeivasti
In this talk, I present the non-asymptotic convergence rate of the DCA(difference-of-convex algorithm), also known as the convex-concave
procedure, with two different termination criteria that are suitable forsmooth and non-smooth decompositions, respectively. The DCA is a
popular algorithm for difference-of-convex (DC) problems and known
to converge to a stationary point of the objective under some assumptions. I derive a worst-case convergence rate of O(1/sqrt(N)) after N
iterations of the objective gradient norm for certain classes of DC problems, without assuming strong convexity in the DC decomposition andgive an example which shows the convergence rate is exact. I also
provide a new convergence rate of O(1/N) for the DCA with the second termination criterion. Moreover, I present a new linear convergence rate result for the DCA under the assumption of the PolyakŁojasiewicz inequality. The novel aspect of this analysis is that it employs semidefinite programming performance estimation.

4 - Modified Simplex Algorithm to Efficiently Explore NearOptimal Spaces

Christoph Funke, Linda Brodnicke, Francesco Lombardi,
Giovanni Sansavini
Modeling to generate alternatives (MGA) is an increasingly popular method in energy system optimization. MGA explores the nearoptimal space (NOS), namely, system alternatives whose costs arewithin a certain fraction of the globally optimal cost. These alternatives
may be preferred by real-world stakeholders due to intangible factors.
Widespread MGA adoption is hampered by its additional computational burden. Current MGA methods identify boundary points of the
NOS through repeated, independent optimization problems. Hundredsof model runs are usually required, and such individual runs are often
inefficient because they repeat calculations or retrace previous trajectories. In this presentation, we introduce a novel algorithm, called Funplex, which uses elements from multi-objective Simplex to optimize
many MGA objectives with minimal computational redundancy . Ona simple linear program energy hub case study, Funplex is ten times
faster than existing methods and yields higher-quality NOSs. Sensitivity analyses suggest that Funplex scales well with the number ofWC-39

investment variables, making it promising for capacity planning models. The current implementation uses a full multi-objective tableau andtherefore requires high memory and has poor stability on large models.
Nonetheless, Funplex is a proof-of-concept that demonstrates the untapped potential of new solver methods. Further research on the topic
may make MGA more standard and accessible among modeling teams.

 WC-39
Wednesday, 12:30-14:00 - Room: 35 (building: 306)

Financial Modelling
Stream: Stochastic Modelling
Invited session
Chair: Antonio Luciano Martire
1 - Efficient Approximations for American Options in Markets with Daily Price Limits

Jia-Hau Guo, Yi-He Chang
This paper proposes solutions for pricing American options on stocks
in markets with daily price limits. We first extend the intraday density function of Guo and Chang (2020) to a multi-day one. Next, we
adopt the fast Fourier transform (FFT) to derive accurate and efficient
formulae for American options in the framework of Kim (1990) andChang et al. (2016) and, further, employ the three-point Richardson
extrapolation to accelerate the computation. Finally, the accuracy ofour proposed methods is verified by simulations. We also note that
more restrictive daily price limits could force put options to be exercised earlier.

2 - A new method to solve Volterra integral equations forvariable annuities evaluation with stochastic volatility

Immacolata Oliva, Roberto De Marchis, Antonio LucianoMartireA Variable Annuity (VA) differs from traditional life insurance products because, when the insurer invests the policy premium in a risky
portfolio, the policyholder bears prots or losses according to the investment performances. In general, the dynamics of such a portfolio
are defined via a geometric Brownian motion with constant interest rate
and volatility, see e.g. Shen et al. (2016) and De Angelis et al. (2022).
However, such a choice might be unrealistic, since it fails to incorporate well-known stylized facts observed in the financial market, such as
the volatility smile/skew, see e.g.Heston (1993). Moreover, the presence of an American option embedded in the structure of a VA makes
it unfeasible to determine a closed-form formula for such a contract,
regardless of the market model. Therefore, we have to resort to ad-hoc
numerical techniques, such as finite difference methods for the associated PDE, see e.g. Shen et al. (2016). Another possible route is torefer to the integral equation characterizing the early exercise boundary, as shown in Kim (1990). Within the latter setting, and along thelines of Adolfsson et al. (2013), in this paper, we propose a novel algorithm to solve integral non-linear two-dimensional Volterra equations
in a stochastic volatility framework by exploiting specific features of
the involved Fourier-type integrals.

3 - Stochastic Ito-Volterra integral equations arising in finance: a streamlined numerical approach

Antonio Luciano Martire, Immacolata Oliva
In recent years, stochastic integral equations of Volterra type have been
employed as modelling tools for financial, economic, and demographic
problems. In such a perspective, it is well known that a nonlinear
stochastic Ito-Volterra integral equation is well-known model widelyused simulating stock and electricity prices. Unfortunately, these kindsof equations do not allow closed-form solutions, but require the use ofnumerical methods to obtain suitable approximations. In this paper,
we present a feasible, fast, and accurate numerical algorithm to evaluate the unknown solution of such equations. We mainly exploit the423

WC-40EURO 2024 - Copenhagen

Lipschitz condition, which needs to be assumed in order to ensure theexistence and uniqueness of the solution of this kind of equations, anda particular version of the mean value theorem for stochastic integrals.
We also provide an extensive convergence analysis for our proposal.

 WC-40
Wednesday, 12:30-14:00 - Room: 96 (building: 306)

Experimental economics and game theory 2
Stream: Experimental economics and game theory
Invited session
Chair: Gerhard-Wilhelm Weber
Chair: Ulrike Leopold-Wildburger
Chair: Stefan Wolfgang Pickl
1 - Accounting for heuristics in reputation systems. An interdisciplinary approach in aggregation processes

René Fahr, Dirk van Straaten, Vitalik Melnikov, Eyke
Hüllermeier, Behnud Mir Djawadi
Product ratings help customers to evaluate the quality of a product prior
to their purchasing decision in online markets. To overcome information overload, the vast amount of reviews is aggregated to a single scalar value, typically the arithmetic mean. However, customers
may not base their purchasing decision on the arithmetic mean but onother aggregation functions such as the amount of 1-star and/or 5-starratings. Our research question therefore asks to what extent the arithmetic mean really reflects how customers aggregate product ratings. In
our incentivized laboratory experiment, participants are shown bundlesof different products, and subsequently, asked to rank them according to their preferences. Analyzing the rank data with a Placket-Lucemodel, we find that the arithmetic mean indeed best explains the obtained data. However, our analysis on an individual level reveals moreheterogeneous aggregation patterns. In particular, a significant shareof participants exhibits a binary bias. That is, moderate 2-star and 4star categories get over- and the extreme 1-star and 5-star categories
under-weighted. We also observe minor clusters who prefer products
which have the lowest amount of 1-star ratings. The way our participants aggregate the ratings is neither affected by variation of provided
information nor by individual characteristics such as experience, risk
attitudes, or demographics.

2 - Data-scarce identification of game dynamics via sumof-squares optimization

Antonios Varvitsiotis
Understanding how players adjust their strategies in games, based on
past experience, is a crucial tool for policymakers. It enables them toforecast the system’s eventual behavior, exert control over the system,and evaluate counterfactual scenarios. The task becomes increasingly
difficult when only a limited number of observations are available ordifficult to acquire. In this work, we introduce the Side-Information
Assisted Regression (SIAR) framework, designed to identify game
dynamics in multi-player normal-form games only using data from ashort run of a single system trajectory. To enhance system recovery in
the face of scarce data, we integrate side-information constraints into
SIAR, which restrict the set of feasible solutions to those satisfying
game-theoretic properties and common assumptions around strategicinteractions. SIAR is solved using sum-of-squares optimization, resulting in a hierarchy of approximations that provably converge to thetrue dynamics of the system. We showcase that the SIAR framework
accurately predicts player behavior across a spectrum of normal-form
games, widely-known families of game dynamics, and strong benchmarks, even if the unknown system is chaotic.

3 - Sequential sponsored-products and off-amazon advertising optimization for etailers

Yina Ning, Yangyang Xie, Houmin Yan

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The Sponsored-Products (SP) advertisement is a popular way for promoting products on Amazon. Etailers can choose and pay for specifickeywords to secure ad placements for their products. These keywords
are the ones that shoppers are likely to search for when looking for
products on Amazon. To improve the efficiency of SP ads, etailers
who have a large catalog of products often create ad groups for products with similar attributes. An ad group consists of a set of products
and a set of keywords, and all the products in the ad group share thesame keywords set. Etailers may also choose to link to external websites for advertising their products and attracting consumers, which iscalled off-Amazon (OA) ads. This study focuses on the sequential SP
and OA ads optimization problem for etailers. Practically, many etailers set sales targets for products as manufacturing and logistics areplanned ahead of time. Hence, we consider that the objective of theetailer is to minimize the expected long-run average cost associatedwith advertising and the cumulative deviation between the sales target
and actual sales. When the mean of the sales number per unit time(i.e., sales rate) for each product is known, we characterize the optimal
sequential Sponsored-Products and off-Amazon advertising policy (abbreviated as SSPOA policy) for products in an ad group, which is ofa threshold type. However, in reality, etailers may not know the exact
mean of sales rates, and therefore the SSPOA problem

4 - A Solomonic Approach to Core Allocations For Big
Boss Gamesİsmail Özcan, Ana Meca
In this study, a new core allocation formula using the tau-value andShapley-value for the big boss games class has been proposed, referred
to as the weight function, and a generalization has been provided. The
tau-value consistently belongs to the core, whereas the Shapley value
may not be part of the core in the big boss game class. While the tauvalue might favor the big boss, it might not meet the expectations ofother players. Conversely, the Shapley-value could satisfy other players but might not suffice for the big boss. To resolve this dilemma,
we proposed a new core allocation family and a Solomonic solution
procedure for the big boss games class.

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Wednesday, 12:30-14:00 - Room: 97 (building: 306)

Nonsmooth optimization algorithms I
Stream: Nonsmooth Optimization
Invited session
Chair: Alireza Kabgani
1 - Extended convergence analysis of the Scholtes-type
regularization for cardinality-constrained optimization
problems

Sebastian Lämmel, Vladimir Shikhman
We extend the convergence analysis of the Scholtes-type regularization
method for cardinality-constrained optimization problems. Its behavior is clarified in the vicinity of saddle points, and not just of minimizers as it has been done in the literature before. This becomes possible by using as an intermediate step the recently introduced regularized continuous reformulation of a cardinality-constrained optimization problem. We show that the Scholtes-type regularization method
is well-defined locally around a nondegenerate T-stationary point of
this regularized continuous reformulation. Moreover, the nondegenerate Karush-Kuhn-Tucker points of the corresponding Scholtes-type
regularization converge to a T-stationary point having the same index,
i.e. its topological type persists. As consequence, we conclude thatthe global structure of the Scholtes-type regularization essentially coincides with that of CCOP.

EURO 2024 - Copenhagen
2 - Discontinuous Nash Equilibria in a Two-Stage LinearQuadratic Dynamic Game With Linear Constraints

Rajani Singh, Agnieszka Wiszniewska-Matyszkiel
In this work, we study a simple example of a two-stage linear-quadratic
dynamic game in which the presence of simple linear state dependent constraints results in nonexistence of continuous symmetric feedback Nash equilibria and the existence of continuum of discontinuous
symmetric feedback Nash equilibria. The example is not an abstract
model—it has obvious applications in economics of resource extraction.

3 - On vector optimization problems

Ariana Pitea
Vector problems in a Reimannian geometric context are studied fromthe point of view of their Pareto optimality. Necessary conditions ofefficiency are introduced. Adequate convexity structure is used to state
sufficient efficiency conditions and also to prove duality theorems.

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Wednesday, 12:30-14:00 - Room: 98 (building: 306)

Nonlinear optimization algorithms andapplications
Stream: Variational Analysis and Continuous Optimization
Invited session

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point is on the boundary of the trust region. The lack of the quadraticmodel’s uniqueness caused by the Karush-Kuhn-Tucker multiplier’s
non-determinacy leads us to propose a new model by selectively treating the last obtained under-determined quadratic model as a quadraticmodel or a linear one. We propose the theoretical motivation, computational details, and the quadratic model’s formula derived fromthe Karush-Kuhn-Tucker conditions. The formula is implementationfriendly for the existing model-based derivative-free methods. The
numerical results with released codes support the advantages of ourquadratic model in the derivative-free optimization methods. To thebest of our knowledge, this is the first work considering the propertyof trust-region iteration and the model’s optimality when constructing
the under-determined quadratic model for derivative-free trust-region
methods.

3 - Single-loop primal dual method for complex-field phase
optimization problem in wireless communication

Cong Sun
Abstract: Reconfigurable intelligent surface (RIS) is a hot candidate
technoloty for 6G. RIS aided multiple input multiple output interference channel is considered. The precoding beamforming matrices and
RIS parameters are jointly designed for sum rate maximization. The
approximation explores the upper bound of the sum rate, and the optimization problem is reformulated as a minimax saddle point problem
through the approximated Lagrangian function. The single-loop primal dual method is designed for the saddle point problem, where theprimal variable is updated through one projected gradient step and thedual variable is solved through quadratic interpolation. It turns outthat the proposed method performs well numerically, which achieves
promising sum rate with very little computational cost.

Chair: Cong Sun
Chair: Ya-xiang Yuan
1 - Seismic optimizing inversion with multi-head attention
mechanism and transformer

Yanfei Wang
The technology of reflection coefficient inversion aims to broaden the
spectrum and increase the dominant frequency based on the effective
collection of data. Reflection coefficient inversion can be achieved
through conventional physics-based optimization modeling. In recent
years, machine learning has seen tremendous development in the fieldof inverse problems. Deep learning has made breakthrough progressin fields such as image processing and natural language processing(NLP). Following the convolutional neural network (CNN) and recurrent neural network (RNN), the transformer network architecture, comprised only of self-attention mechanisms and feedforward neural networks, has also become a research hotspot in recent years. Our latest
work has realized blind seismic reflection coefficient inversion using atransformer network. By setting up appropriate encoders and decoders
and incorporating physical mechanism constraints, the transformer has
demonstrated powerful seismic inversion capabilities. Experimental
data indicates that the new method based on the transformer networkarchitecture does not require any a priori assumptions about the seismic records’ reflection coefficient series, seismic wavelets, or Q value
models; it possesses strong noise resistance and resolution; after themodel training is completed, it has relatively good generalization performance, and the model prediction process is more efficient compared
to traditional methods.

2 - A New Derivative-free Method Using an Improved
Under-determined Quadratic Interpolation Model

Ya-xiang Yuan
In this talk, we give a new derivative-free method by introducing an improved under-determined quadratic interpolation model. We analyze
the least norm type under-determined quadratic interpolation model
proposed by Conn and Toint from the perspective of the propertyof trust-region iteration. We found the Karush-Kuhn-Tucker multiplier’s non-determinacy when constructing a quadratic model considering the trust-region iteration in the case where the current iteration

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Wednesday, 12:30-14:00 - Room: 99 (building: 306)

Recent advances on Variational Inequalities
and Equilibrium Problems II
Stream: Variational Inequalities and Equilibrium Problems: From Theoretical Advances to Real World Applications
Invited session
Chair: Mauro Passacantando
1 - Robust Generalized Nash Equilibria

Sara Mattia, Mauro Passacantando
Robust optimization is an established technique for handling data uncertainty in optimization problems. However, most of the results refers
to problems with a single decision maker, whereas less results areavailable for frameworks that include multiple decision makers. Thesesettings are very common in several applied contexts, where different stakeholders may be involved in the decision making problem,with various roles and decision power. This is the case for example,
for problems arising in healthcare, urban planning or shift schedulingproblems. The present study aims at generalizing the results that are
known for robust optimization problems with a single decision maker
to non-cooperative games, where multiple decision makers (players)
are present. In particular, we focus on the Generalized Nash Equilibrium Problem (GNEP), where both the objective function and thefeasible region of each player are affected by the actions of the otherplayers. The robust version of a GNEP with uncertain parameters isdefined and its continuity, differentiability, convexity and monotonicity properties are investigated. Moreover, an existence result of robust
equilibria is given. In the case of linear or quadratic dependence of theobjective functions and constraints on the uncertain parameters, equivalent reformulations of the robust GNEP are provided.

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2 - Complementarity problem formulation of Fujita-Ogawa
model with heterogeneous players

Shunsuke Hayashi

Panagiotis Ravanos, Giulio Caperna, Giannis Karagiannis

In this study, we focus on Fujita-Ogawa (FO) model, which is known to
be one of equilibrium models to analyze the agglomeration patterns ofcities. FO model has a scheme of population game, in which the numbers of firms and households for each location serve as variables, andtheir distance-decay interactive benefits and commuting costs serve as
payoffs. In this study, we assume the heterogenous attribute for eachfirm, and formulate its equilibrium by means of complementary conditions. We also calculate some equilibrium solutions in a graphicalmanner.

In the construction of composite indicators by the Benefit-of-theDoubt (BoD) model, proportional virtual weight restrictions (pieshares) are frequently used to place bounds on the normalised indicators’ relative contribution to the composite for each assessed entity,
based either on expert views or simply to prevent the occurrence ofzero indicator weights. Two important issues associated with pie-sharerestrictions are that they cannot accommodate zero values for individual indicators and that they result in evaluating each assessed entity
based on a different best-practice frontier, which makes the resulting
BoD scores incomparable across entities. In this paper, we introduce
a novel type of weight-share restrictions in the BoD model, which involve placing bounds on the share of each nornalised indicator’s weightto the sum of indicator weights rather than the product of an indicator
and its weight, which is the case with pie-share restrictions. We showthat weight share restrictions can accommodate zero values for individual indicators and that they result in evaluating all entities based on
a common best-practice frontier, enabling the ranking of entities basedon their BoD scores. We also discuss how, due to the nature of the BoD
model, weight-share restrictions are the proper and most intuitive way
to place an absolute bound on an indicator’s weight, and we illustrate
our findings using data from the European Commission’s Cultural andCreative Cities Monitor.

3 - Recent Advances on the Variational Ineqaulity approach
to Network Games

Fabio Raciti
Recent Advances on the Variational Inequality approach to Network
Games
Network Games, (NG), are a class of games where each individual(player) is identified with a node of a graph and the players that caninteract directly are connected through links of the graph. The specificity of these games is the central role played by the graph structurein the description of the patterns of interactions, and in the final social
or economic outcome. The mathematical consequence of this description is that some interesting results depend on quantities such as the
spectral radius of the adjacency matrix, its minimum eigenvalue, andits powers. The theory of Variational Inequalities has not yet been exploited in the context of NG, with only a few exceptions. In this talkwe outline some recent results as well as some research perspectives
within this framework.

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Wednesday, 12:30-14:00 - Room: 20 (building: 324)

MCDA and Composite Indicators: Issues,
Advances and Applications 1
Stream: Multiple Criteria Decision Analysis
Invited session
Chair: Menelaos Tasiou
Chair: Panagiotis Ravanos
1 - Deck of Cards method for Hierarchical, Robust andStochastic Ordinal Regression

Silvano Zappalà, Salvatore Corrente, Salvatore GrecoIn this paper, we present how to apply the Deck of Cards Method(DCM) for ordinal regression. While the DCM is very well-known
and applied in Multiple Criteria Decision Aiding, here, we apply it toassign a value to each alternative evaluated on a set of criteria hierarchically structured. The Decision Maker can provide precise or imprecise information at different levels of the hierarchy of criteria using
the classical DCM framework. This information is therefore used toinfer a value function compatible with it. The compatible value function can be a simple weighted sum, a piecewise linear value function,a general monotonic value function, or a Choquet integral. To provide
robust recommendations to the Decision Maker, the Robust Ordinal
Regression and the Stochastic Multicriteria Acceptability Analysis areapplied. Even if in different ways, both of them take into account thewhole set of models compatible with the preference information provided by the Decision Maker. The applicability of the new proposal isshown by an example in which Italian regions are evaluated on criteria
belonging to Circular Economy, Innovation Driven and Specialization
macro-criteria.

426

2 - Restricting Weight Shares in the Benefit-of-the-Doubt
Model

3 - Assessing the Sustainability of European Countries Using Quantitative Techniques

Sandra GonzÁlez Gallardo, Ana Belen Ruiz, Francisco Ruiz,
Mariano Luque, Pedro Fernandez-Rodero
In this work, we study the sustainability performance of European
Countries by applying a methodology that combines both multiple
criteria decision-making and statistical techniques. Using individualsustainability development indicators for the European countries available in the EUROSTAT database, we first build composite indicators
to assess the economic, social, and environmental dimensions of theterritories. Based on the information obtained, secondly, a statisticalanalysis is done to regress the composite indicators only considering
those individual indicators that are somehow controllable by policy
makers. The main objective is to get some insights into the impactthat a modification of these controllable individual indicators would
have on the overall sustainable development. Finally, we focus on the
Spanish case, whose sustainability situation can be improved, as thiscountry does not reach the best possible values of the composite indicators of the three dimensions. However, to make a decision about
how to improve its situation, further information is needed in order toknow the extent of the possible improvement, the trade-offs existing
among the dimensions, and how this improvement could be attained.
Therefore, we build a multiobjetive optimization problem based on thestatistical analysis previously performed, which is aimed at identifying
the most desired compromise among the three sustainability dimensions to enhance the sustainability situation of Spain.

4 - A Composite Indicator for Analysing the Entrepreneurship Environment in Europe: A Goal Programming Approach

Rui Fragoso, António Xavier
The Global Entrepreneurship Monitor (GEM) report provides the results on entrepreneurship held every year across the world. The rising number of participating countries in GEM contribute to build thebiggest database on entrepreneurship, which includes a variety of relevant primary information on different aspects of entrepreneurship. This
database allows to obtain insights on the patterns and trends that prevail in the participating economies, from two perspectives: geographic
regions and economic development stages. Integrating the GEM, theNational Experts’ Survey (NES) provides insights from experts in eacheconomy on nine Entrepreneurial Framework Conditions (EFC) considering a set of 12 indicators. These indicators provide relevant information regarding the entrepreneurial environment of each country.
According to the best of our knowledge, in the literature, there is not
any tool that allows analysing this information, in a simplified way

EURO 2024 - Copenhagenand establishing rankings. In that scope, a composite indicator may
be a useful tool for policy making, providing information on countries
performance, conceptualizing phenomena and simplifying complex information. Also, when defining rankings, several goals must be considered and the fact each indicator doesn’t have the same importance forthe entrepreneurial environment. In this context, the European Union
countries is a relevant study area for the diversity of countries withspecific entrepreneurial policies and environment

WC-47

result expected is a codification of realities and insights gained frommultiple case analysis, and their inclusion into a versatile operational
decision framework for various displacement contexts and future integration with traditional Operations Research models.

3 - Simulation-based scenarios for optimizing resource
planning in warehouse operations: A practical approach

Dimitrios Zormpas, Vasileios Zeimpekis

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Wednesday, 12:30-14:00 - Room: 30 (building: 324)

Simulation and Modelling for Decision
SupportStream: Decision Support Systems
Invited session
Chair: Femi Olan
Chair: Shaofeng Liu
1 - Handling Issues of Uncertainty while designing DSS

Gil GreensteinThis Research deals with various principles that were examined andidentified during Decision-Making processes and integrate them to acomprehensive methodology for modeling an analytical model which
instructs how to design a DSS properly. The analysis allows explaining a decision maker’s behavior un-der the assumptions of bounded
rationality, over time and it leads to a better understanding of the performances of DSS during their life-cycle. The model deals with thefollowing principles: The resolution of Information - The analysis
demonstrates the trade-off between accuracy of information and timeand costs re-sources to gain this information. The timing of decisionmaking - the analysis shows how to sup-port decision makers (e.g.:politicians) to take their decisions in the right moment. A model will be
under the assumption that in-formation resources become more accurate over time, but opportunities sometimes vanish over time. Bounded
Rationality of decision-makers - it is assumed that decision makers
could not adopt a new set of decision rules and it takes to know how
to handle with a new DSS platform. The analytic model will clarify
how to consider this issue during a design process of DSS. Multi Criteria approach - the analysis deals with decision situation where it isassumed that a decision-maker cannot formulate a joint utility function
constituted from different criteria which are completely inestimable orestimable partly.

2 - Rethinking Durable Solutions: A System Dynamics Approach for Enhanced Decision-Making in Displacement
Contexts

Patience Saungweme, Stefan Spinler
Escalating human displacement resulting from conflict, natural disasters, and climate change, termed complex crises, represents a major global challenge today. Refugees, the second-largest displacedgroup, situated in locations experiencing protracted crises with declining living conditions, represent a major hurdle for humanitarianefforts. There exists low interest in research on Humanitarian Operations on the overlap of Humanitarian Logistics and Political Science,essential for addressing issues in conflict-induced displacement interventions. This area lacks exploration of viable operations management
strategies in conflict zones, beyond access and security, for durable solutions realization. Decisions regarding refugees are subject to the political discretion of host countries, influenced by foreign policy negotiations and minimum standards set by the United Nations High Commissioner for Refugees for basic survival needs. This often neglects
long-term welfare needs of food security and livelihood recovery. We
aim to create a framework using System Dynamics to analyze tradeoffs between temporary and permanent solutions in crises causing displacement, incorporating protractedness and conflict sensitivity. The

Resources in warehouses include both workforce and material handling equipment and still remain a persistent challenge in warehouseplanning operations. Since the productivity of any warehouse processincreases with the speed at which the products are moved within thestorage facility, optimizing resource planning is crucial for improving
productivity and minimizing operational cost. An approach for optimizing resources is via simulation modelling. Indeed, Discrete-event
simulation (DES) is one of the popular modeling techniques which has
been used for warehouse process and resource optimization. To thisend, this article, presents multiple simulation models with the aim to
identify the optimal resources (i.e. workforce, docking stations, material handling equipment) needed for receiving, put-away, picking,packing and dispatching processes in a given case study with the aim
to complete all processes in certain time horizon. For each process,various scenarios are developed that evaluate the impact of adopting
different planning strategies. The models developed identify the mix
of workforce and equipment that provide the best process completion
time. The results are encouraging, showing that the use of simulation
models may identify the selection of the best workforce-equipment
mix and subsequently improve warehouse operations.

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Wednesday, 12:30-14:00 - Room: 40 (building: 324)

Which Solver?
Stream: Making an Impact
Invited session
Chair: Johan Kellerth Fredlund
1 - MIP Solvers In Practice

Making An Impact, Tobias Achterberg, Frédéric Gardi, Timo
Berthold, Rubén Ruiz, Andreas Westerlund
Practitioners who use MIP have a great choice of possible softwares.
The huge choice can make it harder to make decisions; and that’s where
this 90’ session should be invaluable. We bring together representatives from Xpress, Gurobi and Hexaly, with two heavy users of one
or more of these we try to answer questions like: Why use a 3rd party
solver instead of in house options? What problems have companies experienced in this approach? And of course. . . which solver is the best inpractice? There will be plenty of opportunity for audience Q&A. Thisis a reprise of a session first held in 2018. Since then, the landscapeand the power of solvers has changed immeasurably, and we will be
reviewing just how users can make the most of this. For full details of
speakers, please see the ’Making an Impact’ page on the main conference website.

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Wednesday, 12:30-14:00 - Room: 50 (building: 324)

MCDA in medicine
Stream: Multiple Criteria Decision Analysis
Invited session
Chair: Yorgos Goletsis

427WC-48EURO 2024 - Copenhagen

1 - MCDA for Health Technology Assessment: Trends andChallenges

Yorgos Goletsis, Garoufalia Naka, Kostas Athanasakis,
Michalis Doumpos
Priority setting in healthcare’s financing is a multidimensional problem
as there are conflicts among different stakeholders’ groups (patients,
scientific specialists, healthcare providers, manufacturers, insurers).
Allocating the limited resources and funding to (new) medical technology requires an evaluation of the multiple types of value medical
technology (i.e. tools, tests, devices, drugs, vaccines, processes, etc.)
can occur, which extend the traditional economic evaluation which isoften limited to the evaluation of costs and a single health related outcome (such as QALYs, DALYs, etc.).
Multicriteria Decision Analysis (MCDA) can expand the current moreor less standard assessment process by introducing multiple (and often
conflicting criteria), the preferences of multiple stakeholders, including the society.
For this reason, in our paper we are reviewing the relevant literatureand through a detailed review and bibliometric analysis of 226 papers
we identify the theoretical foundations and trends in the relevant literature. Despite the relevant progress over the last years, MCDA for
Health Technology Assessment needs to address several challenges before being widespread. Such challenges could be conceptual, methodological or organizational. Overcoming these challenges can help embedding the multicriteria concepts and methods in healthcare technology evaluations and decision making.

2 - Comparison of HB Shrinkable Estimators and DCE Simulators for Demand for Care

Christine C Huttin
OBJECTIVES: this communication aims to discuss how and in which
context a random price generator for health care markets can be useful in comparison with other approaches for economic modeling andpricing of medical products and services METHODS: a comparisonof the different approaches used will be discussed with their statistical limitations: nested logit models, cumulative logit models, latentmodel of choices , shrinkable estimators with Hierarchical Bayesian
approach, combination of robust pricing optimization with multinomial logit choice models, and choice models with random price generators and current model application of the BLP model on NGS sequencing. RESULTS: random generators proved to be useful but do
not fit any kind of structural conditions to adjust supply and demandof health re markets in more or less regulated health and economic
systems. an approach by sets of alternatives and choice models allow
to adjust for heterogeneity of demand but lack of price data in somemarkets make it useful to rely on shrinkable estimators with HB approaches. In duopoly cases, the use of BLP is currently tested and may
help for investigation of antitrust policies. CONCLUSIONS: the complexity of health care markets lead to consider various combinationsof models and even participatory modeling to reach growing consensus with various layers of decision makers both on the payer side andthe economic actors switching to data driven models for both drug andvaccines.

3 - Integrating Multi-Criteria Decision-Making (MCDM) in
Traditional Chinese Medicine (TCM) Diagnosis: A Case
Study on Post COVID-19 Syndromes

Mei-Chen Lo, Fang-Pey Chen, Yen-Ying Kung
This research delves into the synergistic application of Multi-Criteria
Decision-Making (MCDM) methodologies in Traditional Chinese
Medicine (TCM) diagnosis, with a specific focus on Post COVID19 Syndrome. The global aftermath of the pandemic has heightened
the demand for nuanced diagnostic approaches, and this study aims
to bridge the gap between traditional diagnostic principles and modern decision-making tools. Through a detailed exploration of MCDM
techniques such as Analytical Hierarchy Process (AHP) and Techniquefor Order of Preference by Similarity to Ideal Solution (TOPSIS),
the research integrates these methodologies into the TCM diagnostic
framework. The case study on Post COVID-19 Syndrome serves as a428

practical application, examining how MCDM enhances diagnostic precision and effectiveness. The findings not only contribute to the evolution of TCM practices but also hold implications for a more integrated
and responsive healthcare approach in the post-pandemic era.

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Wednesday, 12:30-14:00 - Room: 60 (building: 324)

DEA methodological developments I
Stream: Data Envelopment Analysis and its Application
Invited session
Chair: Mehdi Toloo
1 - Complete Ranking and Benchmarking Methods for Improved Efficiency Assessment in Neutrosophic Environments

Deena Sunil, Kshitish Kumar Mohanta, Vishal Chaubey,
Poornima Hulipalled
The measurement of the relative efficiency of the Decision-Making
Units (DMUs) in a fuzzy environment is conducted through the employment of the fuzzy data envelopment analysis (FDEA). In FDEA,
one of the primary concerns is to identify the benchmarking for inefficient DMUs, in order to enhance the efficiency of these particularunits. Additionally, the complete ranking of the DMUs is also an important aspect to consider within the realm of FDEA. To address theseissues, this article proposes the utilization of a reference set and complete ranking technique for the DMUs in a neutrosophic environment,
which is an extension of the fuzzy environment that effectively represents uncertainty. In order to evaluate the relative efficiency of theDMUs, the Neutrosophic DEA model is solved using the possibility
mean approach. Furthermore, a reference set based on the possibility
mean function is suggested to identify peers for the inefficient DMUs.
Moreover, the Neutrosophic super efficiency model, is employed toassess the relative super efficiency score of the DMUs. This superefficiency score is then utilized to achieve a complete ranking of the
DMUs. To demonstrate the effectiveness and applicability of the proposed approach, a numerical example is considered.

2 - Adding counterfactual interpretation to DEA

Gordana Savic, Ognjen Nikolić, Sandro Radovanović
This paper investigates the integration of the Synthetic Control Method(SCM), a proven method to analyze an impact of policy interventions,and Data Envelopment Analysis (DEA), a well-known mathematicaltechnique for measuring relative efficiency of decision-making units
with multiple inputs and multiple outputs, to evaluate the effect of policy interventions on the efficiency of decision-making unit. Specifically, this paper applies these methodologies to assess the efficiency
of Serbian banks over a given period, focusing on the influence of amerger between two principal banks on their operational performance,
and answering a counterfactual question whether efficiency of a bankwould be the same if a merger did not happen. The study demonstrates
that SCM can be an effective tool for analyzing the repercussions ofpolicy measures on decision-making unit efficiency. In other words,
we suggest combining these two methods in the future, in order toconclude whether an intervention really has an impact on efficiency.

3 - Embracing fairness within a cross-efficiency hierarchical network DEA system

Marios Kremantzis, Siwei Xiao, Leonidas Sotirios Kyrgiakos,
George Vlontzos
Several scholars have utilized hierarchical network Data Envelopment
Analysis modeling techniques to assess the performance of complex
structures. However, there has been limited consideration given to theintegration of a peer-appraisal setting within a self-evaluation hierarchical context. This aims to enhance discriminatory power and mitigate the issue of unrealistic weighting scheme. To this end, our study

EURO 2024 - Copenhagenextends the single-stage hierarchical additive self-evaluation modelof Kao (2015), by integrating the well-established cross-efficiency
method. An original combination of a maxmin secondary goalmodel and the Criteria Importance Through Inter-criteria Correlation(CRITIC) method is proposed, to expand the basic hierarchical selfevaluation model. The maxmin model addresses the issue of the nonunique optimal multipliers obtained from the self-evaluation model,
ensuring a more realistic weight scheme. The CRITIC method, thattackles the aggregation problem by objectively determining weights ofcriteria, rewards the minority and is conducive to a fairer evaluation.
Results indicate that the proposed approach is more likely to obtain aunique efficiency and ranking score for the units under consideration.
This study entails a numerical experimentation aimed at evaluating theefficiency of a set of 20 universities while validating the applicabilityof our proposed approach.

4 - Exploring Productivity Growth: A Data Envelopment
Analysis in Single Input/Output Scenarios

Mehdi Toloo, Aliasghar Arabmaldar, Adel Hatamimarbini
The Malmquist Productivity Index (MPI), a major DEA advancement, evaluates productivity changes over time in decision-making
unit (DMU) with multiple inputs and outputs. This paper addresses
the underexplored scenario of a single input with multiple outputs orvice versa. Existing MPI models often overlook non-Archimedean epsilon, but our novel approach incorporates it in scenarios featuring asingle input or output. An efficient procedure is proposed for problemsolving in both input- and output-oriented cases. Theoretical validation
through theorems is complemented by a practical analysis of productivity growth across 18 OECD countries from 2005 to 2021, demonstrating the methodology’s effectiveness.

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Wednesday, 12:30-14:00 - Room: M1 (building: 101)

Machine scheduling problems
Stream: Lot Sizing, Lot Scheduling and Production Planning
Invited session
Chair: Shudong Sun
1 - Job Scheduling by Choosing and Tiling Existing
Scheduling Segments

Gavriel David Pinto, Inessa Ainbinder, Yehuda Hassin, Daniel
Lifshitz, Uriel Israeli, Gad Rabinowitz
In a flexible job shop problem, a typical job consists of a set of operations with precedence relationships among them. Each operation
may be performed in various modes, where each mode requires certain
renewable resources, each for some specific time-period within the duration of the operation. We call the resources’ timings for performinga specific operation in a specific mode a scheduling segments. We
assume that scheduling experts develop and keep for reuse a libraryof alternative scheduling segments for each operation. The reuse of alibrary of scheduling segments is of much importance for the "engineer to order" process where a manufacturer meets the specificationsof their customer by engineering and producing the product after an order has been received. Developing a scheduling segment is a complex
and time-consuming task, for which scheduling experts can benefit byreusing existing scheduling segments. The objective is to minimize themakespan by combining scheduling segments into a feasible schedule.
We propose a new efficient heuristic for addressing this challenge. Previous methods for optimally solving this problem suffered from severe
scalability issues, while previous heuristics experienced high rates of
infeasibility that hurt both efficiency and performance. The proposed
approach integrates a genetic algorithm with a greedy procedure forovercoming these difficulties while enabling, for the first time, the solution of practically sized jobs.

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2 - Multi-agent Parallel Machine Scheduling based on Auction

Shudong Sun
A dynamic iterative auction-based approach is developed to solve themulti-agent scheduling problem with parallel machines. In the proposed approach, an auction procedure is established for dynamic jobs
participating in a real-time auction. A straightforward and easy-toimplement bidding strategy without price is presented to reduce thecomplexity of the bid determination. An adaptive Hungarian algorithm
is applied to solve the winner determination problem. An analysis isconducted to show that the proposed approach is individual rationality
and that the myopic bidding strategy is a weakly dominant strategy for
consumer to submit bids. Extensive simulation results show that thedeveloped approach can get high-quality solutions and have considerable stability on large-scale problems.

3 - Scheduling of workers and robots in collaborative assembly lines

Yiting Sun
Industry 5.0 promotes human-centric industries. Human-robot collaboration is a rapidly developing production mode. By combining thestrengths of humans and robots, it not only improves production efficiency but also reduces ergonomic risks for workers. We study how
to schedule workers and robots in a collaborative assembly line. We
formulate a mixed integer programming model and propose an adaptive simulated annealing algorithm. We compare our algorithm withgenetic algorithm and artificial bee colony algorithm. The experimental results show that our algorithm outperforms the others. Furthermore, we analyze the impact of the human-machine collaboration on
the assembly line balancing plan. We find that the fairness of workload distribution among workers may deteriorate due to human-robot
collaboration, which is something to be aware of.

4 - Mixed-model assembly sequencing and operator assignment

Carlos Miguel
Mixed-model assembly lines (MMALs) manufacture several variantsof the same product base to meet the varied model-based customer demands. However, this model diversity usually causes fluctuations in
the total processing time at some workstations. When these fluctuations exceed the cycle time, work overload is generated. This occurs,for example, when several high processing time models are processed
consecutively. Therefore, a careful sequencing of these models is essential to reduce overload. The main objective of the Mixed-Model Sequencing Problem (MMSP-W) is to establish a sequence of the models
to be manufactured so that the total overload is minimized.
Manual labor is common in MMALs, where their competence levels
determine operator performance. Thus, competence deficiencies maylead to higher processing times and increased work overload. Therefore, competence-driven operator assignment should be consideredalongside model sequencing. We achieved this by extending MMSPW to include operator assignment in its formulation, and performing
the assignments to minimize the gap between task competence requirements and the competence levels of operators. Initial findings showthat this extension enables appreciation of the influence of operator
competency in the overloads generated in a sequence. Furthermore,
the extension results in a problem with additional complexity and incremental solving times, which we address by proposing a Benders
composition-like heuristic.

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Wednesday, 12:30-14:00 - Room: M2 (building: 101)

E-CommerceStream: Retail Operations
Invited session
Chair: Pedro Amorim429

WC-51EURO 2024 - Copenhagen

1 - Enhancing Retail Strategies through Electronic Uniform
Invoice Analysis: A Study on Consumer Behavior andMarket Segmentation

Ling-Jing Kao, Pei-qi Chen, Chih-Chou Chiu
As market competition escalates, retailers are compelled to understandconsumer preferences more deeply to discern needs accurately. However, limited to internal databases, they lack access to cross-channel
consumption data for a comprehensive understanding. This study utilizes electronic invoice records to analyze consumer behavior, predicting product choices in supermarkets. Employing the Self-Organizing
Map (SOM) algorithm for consumer segmentation, it groups consumers with similar behavior, enhancing predictive accuracy. Using
variables like expenditure and purchase frequency, it constructs Long
Short-Term Memory (LSTM) models for channel forecasting. Empirical results show exceptional accuracy in product category prediction.
This research aids effective segmentation, providing insights into consumer needs for inventory optimization. Cluster analysis facilitatestargeted marketing, benefiting both retailers and consumers. Further
validation and updates are vital for practical applications.

2 - From Personalization to Optimization: A Framework for
Enhanced Product Recommendations

Atakan Yilmaz, Onur Demiray, Betül Ahat, Ahmet Çınar,Berker Özbek
Pointwise ranking models diverge from conventional recommendation
systems by tailoring item rankings directly to individual user preferences, thereby offering nuanced recommendations aligned with user
tastes. However, overlooking pairwise, listwise and position based factors in ranking output can diminish the overall user experience. To obtain user-specific rankings while considering the whole ranked list, we
propose a framework that synergistically leverages machine learningand optimization techniques. Focused on personalizing product recommendations within the Trendyol app, our approach integrates xGBoost Classifier and integer programming to derive item scores and optimize rankings while addressing diversity concerns across categories
and brands. Our framework starts with using nearly 200 features tolearn user action probabilities. Then, an integer programming model
determines rankings of products by maximizing expected purchases
while ensuring diversification. This process operates in near real-time,
updating personalized rankings every 2-3 minutes for 150,000 users.
To tackle scalability challenges, we propose an alternative formulation and decomposition algorithm. Lastly, we introduce an ML-based
multi-objective optimization framework to extend our focus to otherperformance metrics such as position-based utilities and clicks, alongside purchases. Rigorously tested via our effective AB testing system,
we comprehensively analyze and discuss our results.

3 - Intertemporal Bundling with Patient Customers: A Comparison of Behavioral Response and Normative Equivalence

Burak Gokgur, Ayse Kocabiyikoglu
We consider the problem of a retailer selling products that can be offered individually or in a bundle to a market that comprises heterogeneous customers who differ in terms of their valuations for the products
and their patience levels. First, we develop a mathematical model that
captures the retailer’s expected revenue maximization problem over an
infinite horizon in a multi-segment market setting. Then, we present
results from numerical experiments that assess the benefits of adopting an intertemporal bundling policy and investigate its behavior withrespect to the market structure. Finally, we study, via behavioral experiments, whether human decision-makers are able to fully capture thebenefits of offering the bundle at random intervals. Our computational
results indicate that intertemporally randomized bundle offer attenuates the negative revenue impact of positively correlated valuations in
the presence of patient consumers, thereby elevating the bottom line.
Besides, the behavioral experiments show that decision-makers’ responses significantly deviate from those of normative counterparts as
the heterogeneity in the market structure and the complexity of thepricing mechanism increase.

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4 - Channel Switching in Omni-Channel Retailing

Bahriye Cesaret, Armagan Bayram
The evolution of technology has changed the ways customers engage
with retailers. Customers now utilize various sales channels in theirbuying processes. Consequently, retailers are challenged with customers who tend to switch between various sales channels. Omnichannel retailing allows customers to buy products from anywhere andreturn them anywhere, while allowing retailers to fulfill orders fromanywhere. This flexibility enhances the customer experience by integrating all channels, allows retailers to achieve more availability andboosts the sales and traffic of the retailers. However, it also places significant pressure on retailers to streamline their operations efficiently
and expedite product delivery to customers. In this study, we explore two omni-channel implementations, namely, ship-from-store andhome delivery. Store customers can opt for in-store fulfillment or request home delivery, whereas online orders can be shipped from thefulfillment center or any other store location that maximizes the retailer’s overall profit. We further consider that both store and online customers can switch across channels. We construct a dynamic
programming framework to examine optimal fulfillment decisions forboth online and store orders, accounting for uncertainties in both demand and the cost of shipment to individual customers. We present
our results through optimal fulfillment strategies and numerical experiments.

 WC-51
Wednesday, 12:30-14:00 - Room: M5 (building: 101)

Timetabling 1
Stream: Public Transport Optimization
Invited session
Chair: Sven Jäger
1 - An Integrated Approach to Electric Bus Timetabling and
Scheduling for Sustainable Urban Mobility

Gajendra Nagar, Hemant Kumar Suman, Lakshay Lakshay,
Nomesh Bolia
Transportation is one of the major contributors to global carbon emissions. This makes it necessary to shift from fossil fuels to renewableenergy sources for a sustainable transportation system. Electric buses(EBs) play an important role in this transition because of their lower
carbon emissions. But to integrate the EBs into the existing network,
two main problems arise: timetabling and scheduling. These problems
can be addressed either sequentially or in an integrated way. However,the sequential approach may produce a suboptimal bus schedule due tofixed timetable and operational constraints. Also, due to the fixed departure time, EBs miss their best sequence of trips, which could raise
the further requirement for new buses. So, in order to develop an effective bus schedule, both timetabling and scheduling problems should be
addressed simultaneously due to their interdependency. In response,
this paper proposes an integrated method for solving the timetablingand scheduling problems. Initially, a mathematical model is developed
to minimize overall costs, including bus procurement and charging expenses. The developed model uses service frequency as an input togenerate a timetable and assign buses. Second, a heuristic algorithm
is developed in order to produce the result for large instances. Later,
we validated the methodology in Bangalore, India, using a realistic
scenario to demonstrate its ability to assign EBs while also generating
daily timetables.

2 - Integration of timetable synchronization and vehicle circulation for public transit networks: A novel quadratic
semi-assignment problem formulation

Tianqi Li, Lei Nie
In public transit networks, timetable synchronization is crucial for offering passengers attractive transfer services, while vehicle circulation
significantly impacts operating costs. These two problems are closelyEURO 2024 - Copenhagen
related and well worth integration since the number of required vehicles is influenced by train timetables, and both focus on service connections. However, there is very limited research on integrating thesetwo problems.
In this paper, we present an integration of these two problems into anovel quadratic semi-assignment problem (QSAP) formulation, basedon a simplified time-discretized network. The objective is to simultaneously minimize passenger total transfer time and vehicle operation
costs. We include features such as multiple period lengths, flexible
transfer connections, and vehicle connections within a pure QSAP formulation. By avoiding the inclusion of additional variables and constraints, this formulation optimally leverages existing algorithms designed specifically for QSAP. Furthermore, we reformulate the problem into a Quadratic Unconstrained Binary Optimization (QUBO) formulation, solved with advanced quantum annealing approaches. Ourmodel is also linearized into an integer linear programming (ILP) format, ensuring compatibility with solvers like Gurobi. To verify theaccuracy and computational efficiency of our models, we applied themto numerous virtual case studies and real-world cases from the Beijing
Metro Network.

3 - Optimizing Public Transport Timetables: Adaptive
Large Neighbourhood Search with Heuristic and Exact
Repair Operators

Robin Gaborit, Jiří Spitzer, Yu Jiang, Otto Anker NielsenThis study introduces an adaptive large neighbourhood search approach for solving path-oriented public transport scheduling with timedependent travel time and demand data. The decision variables are
the departure times of the vehicles from each stop, while the objective function is a weighted sum of passengers’ travel time components
costs and a penalty cost for violating the desired arrival time window.
The mathematical model was developed by Lee et al. (2022). Due tothe complexity of this model, solving real-world instances on the fly
is challenging for exact solution methods. To address this problem,
this study proposes an adaptive large neighbourhood search algorithm
featuring five destroy-repair operators. Its main novelty is that two operators employ a heuristic to repair the solution, whereas the remaining
three utilize a MIP solver. The operators also differ on which part on
the solution they destroy; three of them randomly destroy parts of theincumbent solution, and the other two are more likely to target partsof the solution with a higher contribution to the objective function.
The algorithm is implemented and tested on real data from the public
transport system of the metropolitan area of Copenhagen, Denmark.
Benchmarked against a MIP solver, our solution method outperforms
it in all instances. On average, it achieves 7.5% to 17.5% better resultsin the three largest instances. Our algorithm is also much more stable.

4 - Sustainable Timetabling:
Braking into a PESP Model

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 WC-52
Wednesday, 12:30-14:00 - Room: 8003 (building: 202)

Heuristic Algorithms for Combinatorial
Optimization Problems I (Contributed)
Stream: Combinatorial Optimization
Invited session
Chair: Ali Ekici
1 - Hybrid Genetic Algorithm for Solving The Three-index
Assignment Problem

Mohamed Mehbali
The paper introduces the three-index assignment problem (3IAP) consists of assigning n tasks to n machines in n factories provided thata task is exactly allocated to a machine in a factory so that the total
cost of allocation is minimal. The 3IAP, extending the classic twodimensional assignment problem, is established as NP-hard. Due toits computational complexity, innovative heuristics are designed fornear-optimal solutions. Applying Genetic Algorithms (GA) to 3IAP
evolves a population, seeking optimal or near-optimal solutions overgenerations. This investigation advocates enhancing their quality byintegrating a local search method. The study proposes a hybrid genetic algorithm, integrating GA with a particular local search basedon permutations, yielding high-quality solutions in a notably reduced
computational timeframe compared to deterministic methods. The efficacy of this hybrid approach is demonstrated through experimental
results and comparisons with other optimisation methodologies.

2 - ILSHADE-SPACMA: Improved version of LSHADESPACMA

Abdelazim Hussien, Adrian Pop, Fatma Hashim
LSHADE-SPACMA stands out as a highly potent algorithm, amalgamating a refined version of LSHADE with a modified version of CMAES. In this study, we endeavor to augment the exploratory capability of
LSHADE by incorporating a transfer operator. Additionally, an alternative mutation strategy is introduced to enhance overall diversity. The
proposed optimizer, dubbed ILSHADE-SPACMA, undergoes a rigorous comparison with both the original LSHADE-SPACMA algorithmand LSHADE across 12 functions sourced from CEC2022. These experiments are conducted with varying numbers of evaluations—50,000
and 100,1000—while dimensions are set to 10 and 20. The results underscore the robustness of ILSHADE-SPACMA in swiftly converging
towards near-optimal solutions within acceptable timeframes.

Integrating Regenerative

Sarah Roth, Sven Jäger, Anita Schöbel

3 - Bin Packing Problem with Conflicts and Item Fragmentation

Ali Ekici
While a shift from individual transport to public transport is necessary
to reach climate goals, also the public transport system consumes a
non-negligible amount of energy. Modern electric motors are able toregenerate energy while braking. In the context of rail traffic, the mostefficient way to use the regained energy is to transfer it via the catenary
to an accelerating train close by. Therefore, it makes sense to select the
timetable so that braking and acceleration processes of trains near eachother take place simultaneously. In this talk a new MIP model is introduced that finds a feasible periodic timetable optimizing the usage ofenergy obtained from regenerative brakes. Assigning arrival and departure times, the model also decides between which pairs of trains atransfer of energy takes place and it maximizes the total brake traction
overlap. The new model based on the PESP is compared to previous
models that optimize the usage of regenerative energy and computational results are presented. While the usage of regenerative energy
profits from synchronized brake and traction phases of two trains, thisprevents the transfer of passengers from the braking to the accelerating
train. We analyze the tradeoff between energy and travel-time optimization and show first properties of Pareto-optimal timetables for the
special case of one transfer station.

In this study, we analyze a variant of the well-known Bin Packing Problem where items can be fragmented and some pairs of items cannot bepacked into the same bin due to conflicts. The goal is to pack a given
set of items into a minimum number of fixed-capacity bins while notpacking fragments of conflicting items into the same bin. This problem
is called the Bin Packing Problem with Conflicts and Item Fragmentation. We develop a lower bounding scheme based on identifying amaximal clique of the conflict graph for the problem and propose aheuristic algorithm called the Sequential Maximum Degree Packing
Heuristic which sequentially packs the items starting from the highest
degree item using a mixed-integer model. We test the proposed solution approach on the instances with random conflict graphs and compare its performance against that of the benchmark algorithms in theliterature. We observe that our approach outperforms the benchmark
algorithms especially when the conflict graph is dense. It provides
solutions with up to 13% less optimality gap compared to the algorithms in the literature. Finally, the proposed lower bounding scheme
improves the trivial continuous lower bound only if a large maximal
clique of the conflict graph can be identified.

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WC-53EURO 2024 - Copenhagen WC-53
Wednesday, 12:30-14:00 - Room: 8007 (building: 202)

Sustainability and Equity in Ecosystems,
Ecology and Food
Stream: Sustainable and Resilient Systems
Invited session
Chair: Davide Donato Russo
1 - Optimizing Earthwork Management for Sustainable Urban Development: A Mixed-Integer Linear Programming
Approach

Che-Fu Hsueh, Yunzhu Lin
This study focuses on promoting green, sustainable, and healthy urban
development via comprehensive earthwork management, integrating
both ready-for-use and over-wet soil. We enhance the latter by blending it with specifically recycled incinerated waste granules and lime,
converting unusable wet soil into a valuable resource. This not only
saves disposal costs but also notably reduces carbon emissions. Our
mixed-integer linear programming model, designed to optimize acquisition, dispatch, and utilization of earthwork from diverse sources, effectively tackles transportation and resource allocation challenges, ensuring efficient earthwork planning aligned with sustainable construction and conservation principles. Applied to an aerotropolis project in
Taiwan, our model demonstrated significant cost efficiencies in earthwork acquisition and disposal, achieving considerable savings compared to traditional methods. Sensitivity analyses on different parameters are also provided. This approach exemplifies the integration of
innovative waste management and optimization techniques in urban
construction, offering a replicable model for future sustainable development projects.

2 - Environmental Models as a Tool for Ecological and Economic Decisions-Making

Marian Reiff, Ivan Brezina, Juraj Pekár
Achieving sustainable development is one of the biggest challenges today. It is also one of the reasons why the green economy has come tothe fore in recent years. Therefore, nowadays, it is attractive to formulate new optimization goals based on established economic and ecological priorities, which are incorporated into economic and ecologicalmodels, as green initiatives provide an impetus to move away fromthe traditional understanding of economic processes. This implies theneed to modify optimization processes to link economic and ecologicalapproaches to evaluate the economic and ecological efficiency of thedecision-making process. The first works since 1960 can be registeredin problematics of environmental control via mathematical programming models. For example, individual companies can use management decision-making methods to address previously mentioned issues
and formulate ecological-economic structural models, reverse logistics
models, more ecologically friendly distribution models, air and waterpollution models, eco-efficiency evaluation models (emission regulation), and resource allocation models. This work was supported by the
Grant Agency of Slovak Republic - VEGA grant no. 1/0120/23 Environmental models as a tool for ecological and economic decisions
making.

3 - Exploring Consumer Preferences for Subscription: Promoting Sustainable Food Alternative for Synergy between Local and Global Communities

Prashant Kumar, Nomesh Bolia
The focus on sustainable health-oriented food choices has become significant on a global scale. The trend towards consuming organic andsustainable products has seen notable growth, particularly catalysedby the impact of the COVID-19 pandemic. Consumers are now willing to pay a premium for organic and sustainable foods. The promotion of these products has the potential to uplift the income of farmers
and processors relying on these products, especially in developing nations like India. This work involves a case study in India, with the432

motivation of assessing consumers’ subscription inclinations to natural sweetener (jaggery). Jaggery prepared from sugarcane juice is an
unrefined medicinal sugar with the potential to substitute white sugar
across various applications. This survey-based study utilizes structural
equation modeling to analyse the impact of utilitarian values, customer
attitude, customer awareness, hedonic motives and perceived barriers
on subscription intentions. The promotion of jaggery consumption not
only aligns with India’s commitment to endorse indigenous productswith the potential to create rural employment but also contributes to theglobal efforts towards achieving SDGs, particularly SDG1, SDG8 andSDG13. This research aims to develop a comprehensive subscription
model that considers consumer preferences, pricing strategies, economic viability and other adaptive mechanisms to effectively scale thisproduct based on local and global conditions.

4 - Evaluate the Fairness of the Distribution of Health Services for Older People in Italy: A Methodological Approach

Davide Donato Russo
The aim of this methodological proposal is to assess the fairness ofhealthcare services for various age groups, with particular attention
to older individuals and their unique requirements. The significance ofpharmacies, hospitals, and para-pharmacies as crucial providers of specialized services underscores the issue of pedestrian access to healthcare services, especially for older adults. Expanding the scope of thismethodological approach to incorporate walkability and economic indices is another objective of this study. Fairness and walkability play
pivotal roles in establishing an age-friendly environment, particularly
for individuals with specific needs such as older pedestrians. This aspect is particularly significant for gaining insights into the accessibilityof essential services. In our methodology, we acquired data regarding
the distribution of the Italian population across diverse geographicalareas and age groups. We then integrated this information with the
distribution of health services in Italy. By leveraging the concept ofconvolution between matrices, we have developed a novel, generalizable methodological approach to analyze the fairness in the distributionof services across various territorial entities, including municipalities,
provinces, and regions. We evaluate the concept of fairness, focusing
on older people, through the combination of kernel convolution andeconomic indices for service inequalities (i.e., Gini coefficient).

 WC-54
Wednesday, 12:30-14:00 - Room: S01 (building: 101)

Demand-responsive public transport 2
Stream: Public Transport Optimization
Invited session
Chair: Dilay Aktas
Chair: Pieter Vansteenwegen
1 - Designing a hybrid urban mobility system: framework
and bi-level model

Seyedehsima Madani, Kris Braekers, Imre Keseru
Although traditional public transportation, known as a fixed-route transit (FRT) system, is a cost-efficient transit mode in areas with highdemand, it is often perceived as inconvenient due to the lack of flexibility. On the other hand, demand-responsive transit (DRT) systems,
known as a flex-route transit system (e.g. dial-a-ride services), havea high per-capita operating cost due to their personalized nature. To
combine the flexibility of DRT with the cost-efficiency of FRT, the development of a hybrid transit system could be considered a solution.
This research focuses on such a hybrid systems that integrate FRT and
DRT systems, leveraging the advantages of both. In this research, first,
a unifying framework that classifies different models of FRT and DRT
integration is presented. Second, a bi-level optimization approach is
proposed to model the design of a hybrid transit system in which users
may travel through a combination of FRT and DRT. At the upper level,
decisions are made on the FRT lines to be included in the network andEURO 2024 - Copenhagen
their frequencies. For a proposed FRT network structure, the possible sequences of travel modes for all users can be identified. At thelower level, for a subset of requests corresponding to users who cannot complete their trips solely by FRT, the routing and scheduling of
DRT vehicles and the fleet size are optimized. Preliminary results of aheuristic solution strategy for this model will be presented.

2 - Dynamic capacity planning for demand-responsive multimodal transit

Bernardo Martin-Iradi, Francesco Corman
Demand-responsive multimodal transit offers opportunities to complement existing public transport systems and provide a better service
level to passengers while simultaneously making better use of the resources. This study optimizes the capacity of such a system by strategically sizing the required fleet and allocating it to the operating services.
We formulate a two-stage stochastic optimization model that plans thetransit system and the required fleet in the first stage and optimizes
the demand-responsive operations in the second stage. We develop a
decomposition-based method that exploits the network-based formulation of the second stage, allowing us to solve practical instances.
Results from a case study in Zurich show that jointly designing andoperating public transport and on-demand systems can benefit transport operators and passengers. By allocating the system capacity more
efficiently, operators reduce operational costs while maintaining or improving the travel experience for passengers.

3 - The Potential of Governmental Regulation on Shared
Mobility-on-Demand Systems

Simon Schmidbaur, Fabian Anzenhofer, Robert Klein,
Claudius Steinhardt
The growing number of Shared Mobility-On-Demand services has thepotential to contribute to a more socially and environmentally sustainable mobility provision. However, this potential may not be fully
exploited due to possible conflicts with the objectives of the service
providers. Thus, political discourses address regulatory instruments to
influence providers’ operational planning. This paper analyzes the effects of two currently debated instruments, i.e., the introduction of aminimum pooling rate and a minimum spatial acceptance rate. This
analysis is based on mathematical optimization models that we formulate as generalizations of the selective dial-a-ride problem. Moreprecisely, the problem is first captured by a single-period model formulation and then generalized to a multi-period horizon to implement different regulatory strategies. In a comprehensive computational study,
we solve the regulated model variants exactly both for artificial andreal-world instances provided by our industry partner FLEXIBUS. We
evaluate different levels of regulation for both instruments regarding
their feasibility and their impact on the Shared Mobility-on-Demandsystem, and thereby identify their contribution to a conflicting multiobjective environment.

4 - Sustainability-oriented Dynamic Pricing for Shared
Mobility-on-Demand Systems

David Fleckenstein, Fabian Anzenhofer, Robert Klein,
Claudius Steinhardt
Shared mobility-on-demand (SMOD) systems are expected to makepublic transport more sustainable, especially in rural areas. From thesocial perspective, this means that an SMOD system should contributeto providing area-wide, reliable, and non-discriminatory access to mobility. In ecological terms, the main aim is to decrease emissions perpassenger kilometer. Finally, economic efficiency must also be considered to limit the subsidy requirements. Since providers typically offer
customers multiple fulfillment options, i.e., rides with alternative pickup times, dynamic pricing can be applied to actively steer demand,and thereby, improve the system performance. Operationally, an integrated demand management and vehicle routing problem arises, whichfeatures sustainability-based objectives in addition to the usual profit
maximization. In this talk, we discuss the challenges associated withdynamic pricing in this multi-objective setting and present algorithmic
strategies to tackle them. We evaluate our approaches based on realworld data from a rural SMOD provider.

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 WC-55
Wednesday, 12:30-14:00 - Room: S02 (building: 101)

Big data analysis and AI in transportation
Stream: Transportation
Invited session
Chair: Marc Sevaux
1 - Pricing, Admission and Repositioning in Ride-Hailing
Networks: A Deep Reinforcement Learning ApproachThomas De Munck, Jean-Sébastien Tancrez, Philippe
Chevalier
We consider the problem of a ride-hailing platform (e.g., Uber, Lyft)that connects supply with demand over a network of locations. To
this aim, the platform makes pricing, customer admission, and driver
repositioning decisions. The customers are impatient and have distinct
willingness to pay. The drivers can be repositioned by the platform, or
can choose to relocate to other locations according to their own choice.
We formulate this problem as a discrete-time Markov decision process,and find a near-optimal policy by combining mathematical optimization with deep reinforcement learning. Following our approach, we
first find a heuristic policy by solving repeatedly an optimization problem based on the expected values. Then, we train two neural networks
to replicate the heuristic policy, and learn the associated value function. We finally improve the policy further through the Proximal Policy Optimization algorithm. By applying our model to real data fromNew York City, we demonstrate the effectiveness of our approach incomparison with alternative methods. In addition, we explore the interplay between pricing, customer admission, and driver repositioning,and evaluate the effectiveness of these decisions in balancing supply
and demand.

2 - A Simulation based Decision Support System for Logistics

Emre Çimen, Gurkan Ozturk, Onur KayaThe study is related to raw material procurement and product shipments of a steel mill. These materials arrive at the factory’s port andare transported from the port to the factory by trucks. The aim is to utilize cranes efficiently, reduce costs, and minimize queues. The system
reads parameters related to ports, logistics, and factories from Excel
and uses them for the simulation model for different scenarios. This
includes ship details such as arrival dates, loading/unloading information, coil/sheet numbers and weights, and assigned port numbers.
For facilities, it involves crane-ship assignments, operational details
for various processes, and compatibility between coils and trucks. The
system allows users to input data for different scenarios. The model istransformed into a functional system using Python with the SimPy library. Key performance indicators are analyzed through graphs andtables by comparing various parameters. This includes calculating
queue lengths, waiting times, ship completion durations, demurrage
rates, and costs, and exporting results to Excel. Additionally, some
artificial intelligence methods such as SVM, Random Forest, Artificial Neural Networks, or PCFs will be added to the system to makepredictions related to these key indicators.

3 - Prioritization of traffic crash hotspots for intervention

Yehezkel Resheff, Mali Sher, Nicole Adler
Traffic crashes remain a major cause of preventable loss of life and injury. Many policy and intervention efforts aim to reduce the number
and severity of collisions. Of these, the most direct is the identificationand correction of hotspots of severe crashes. We present data from the
20 largest cities in Israel, spanning over a decade, showing that locations of clusters of light and property damage crashes have a risk of asevere crash in the following year that is comparable to that of clusters of severe crashes. This has immediate policy implications, as weshould rethink the intervention strategy that currently predominantly
considers severe hotspots. Next, we apply machine learning to model
the probability of having a future severe event at a cluster, given the
known characteristics of the location and the previous accidents that433

WC-56EURO 2024 - Copenhagenoccurred. The quality of the model is shown to differ between typesof clusters, such as severe vs. minor, intersection vs. non-intersection,etc. Finally, the problem facing a decision maker is how to use a given
budget and prioritize clusters for intervention, based on the risk scoresprovided by the model and their type-dependant uncertainties, as wellas the type-dependant cost of intervention. The overall aim of the policy is assumed to be maximization of the total reduction of expected
future severe crashes, but extensions that consider fair spread of resources are also considered.

4 - Deep Learning Based Hybrid Genetic Algorithm for the
CVRP

Adrien Pichon, Alexandru Olteanu, Marc Sevaux
Over the last years, hybrid genetic search (HGS) algorithms for thevehicle routing problem (VRP) and its variants have demonstrated
encouraging results in particular with the use of a route-first-clustersecond heuristic. As an individual is represented by a "grand tour"(TSP Solution), the advantages of this method are an easiness regarding the population management and crossover operators combinedwith an optimal split method for the transition from an individual to afull solution. The goal is to improve the efficiency of this method using
a trained deep learning model to identify good individuals in the population, thus avoiding the need to switch search space and saving computational time. To this end, a reverse split method is used to switch
freely between population and solution space to train the deep learningmodel using cost evaluation and feature extraction.

 WC-56
Wednesday, 12:30-14:00 - Room: S04 (building: 101)

Railway Applications
Stream: Transportation
Invited session
Chair: Gislind Stefan
1 - Last-Minute Crew Rescheduling: Model and Heuristic
Approach

Liyun Yu, Carl Henrik Häll, Anders Peterson, Christiane
Schmidt
We consider crew scheduling on the day before operation: because ofa lack of train drivers (e.g., due to sick leave) crew members need tobe rescheduled. We are given a feasible crew schedule for a subset ofthe drivers, a set of unassigned tasks, and a set of standby drivers with
fixed start and end time of their working hours. We aim to generate a
new crew schedule with as few unassigned tasks as possible — which
will in turn decrease the number of trains to be cancelled. We build a
Mixed Integer Linear Programming (MILP), which we solve using thecommercial solver Gurobi 11.0, and we develop a heuristic approachbased on Tabu Search. We consider the same restrictions for the MILP
model and our heuristic, this includes constraints on the total working hours, on break duration, maximum working time without a breaketc. The data of a one-day schedule is provided by Mälartåg with 153
drivers in total, in which there are 16 standby drivers with fixed depot, start and end working time. For an instance with 11 drivers being on leave, we can assign all but 5 tasks (from a total of 37, whichwere unassigned after the leave), and we obtain a result in 23 seconds.
We compare the results, computational time and space between ourmodel and approach to show that our Tabu-Search-based approach canachieve a ’good-enough’ result with less time and space needed.

2 - Optimal use of in-house and outsourced vehicles

Kritika Karwasra, Narayan Rangaraj
Rail is a good mode of transport especially for bulk commodities likecement and steel, over medium and long distances. However rail services are subject to delayed deliveries, especially due to unavailability434

of wagons (set of wagons, also known as rakes). To address these issues, companies invest in railway wagons. This fleet is maintained by
the shipper, but the movement is by the railway company with a rebate
on transportation cost. Procuring such a fleet requires a significant financial investment. We consider the case where some in-house rakes
are procured that are used along with outsourced resources. With thismixed fleet composition and considering the benefits offered by inhouse rakes over outsourced rakes, an operational question is: how
should we allocate in-house and outsource rakes to various demands?
We examine several allocation policies considering 1) distance 2) cost
rebate through use in-house fleet and 3) overall transportation cost. A
simulation modeling tool is used to assess different policies under various scenarios considering demand patterns, turnaround time, waitingtime of locomotives and availability of outsourced rakes. The study
also explores how the randomness associated with these parameters
affects these policies. The general question is that of optimal use oflimited internal capacity along with outsourced resources in a cost effective way.

3 - The Green Locomotive Assignment Problem with Deadheading on Trains

Gislind Stefan, Fatih Kocatürk, Ninja Soeffker, Jan Fabian
Ehmke
Reducing energy consumption has become an important goal in all
sectors of the economy and especially in transportation. Our goal is to
examine potentials for energy reduction in railway operations. We propose the Green Locomotive Assignment Problem (GLAP) which aims
at minimizing overall energy consumption through efficiently allocating locomotives to scheduled trains. The objective covers all key aspects of locomotive scheduling, with energy consumption during lighttraveling trips standing out as the primary cost driver. Light-travelingoccurs when a locomotive needs to relocate to a different station andtherefore moves without any train attached. The Davis equation, aphysical model that factors in the resistance encountered by a moving
train, is augmented with track gradients to accurately compute energy
consumption during light-traveling trips. We directly minimize energy
consumption and compare this to the conventional approach of minimizing light-traveling distance. Additionally, we propose a model variation of the GLAP, which incorporates the practice of deadheading on
trains. Deadheading on trains refers to the attachment of locomotives
to trains for repositioning purposes without utilizing their engines andhas the potential to reduce energy consumption. We propose mixed
integer programs and test them on small and medium sized real-world
instances using historical planning data and detailed gradient information from the Austrian Railway network.

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Wednesday, 12:30-14:00 - Room: S06 (building: 101)

Forecasting for the middle mile
Stream: Optimization at Amazon
Invited session
Chair: Inaki Estella Aguerri
Chair: Daniel Guimarans
1 - Coherent forecasting for Supply Chain Optimisation

Hermina Petric Maretic, Sidharth Gupta, Juba Nait Saada,
Chris George
The precision of planning and decision making in Amazon is heavily
reliant on sophisticated forecasting methods. These forecasts, often
made independently, enable different aspects of logistic chain, such astruck scheduling or labor planning. However, in reality, they forecast
values that often interact or depend on each other, or in the least containstrong correlations. We explore various methods to take these relationships into account, such as hierarchical reconciliation, combinations offorecasting and simulation, modelling input-output system constraints,
as well as joint time series forecasting approaches based on diffusion
models.

EURO 2024 - Copenhagen
2 - Demand forecasting for Resource Planning and Dynamic Decision Making

Inaki Estella Aguerri, Filippo Fedeli, Timoté Vaucher, Giorgio
Polla, Thomas Ovestad
Every week, planning teams in Amazon need to allocate the resources(e.g., schedule trucks, plan for labor) required to deliver customer orders in a timely and cost effective manner. This planning process isbased on demand forecasts, that estimate the packages to be shipped
across the network. As one gets closer to the delivery date, real-time
signals on the progress of the plan execution become available. This
opens the door to update the forecast and refine the resource planning
continuously to improve cost and increase effectiveness. However, regular plan changes need to account for uncertainty and end-to-end impact across the network to avoid plan fluctuations and local decision
making. In this talk, we explore different methodologies to generate the demand forecast and its refreshes with focus on accuracy, coherency, uncertainty quantification and reduced variability, as well as
its combination with different resource planning strategies for regular
updating.

3 - Transit Estimation Models for Transportation Planning

Carlos Sanchez Sanchez, Mounir Boujrad, Tim Forbes, Olle
Green, Madhavan Sriram, Chris George
Decision making in Transportation, including network design, reactive adjustments during execution, or post-run analysis, requires anunderstanding of the vehicle transit time, carbon impact, and risks.
In this talk, we explore a unified approach for the estimation of suchfactors that leverages the information available from vehicle sensors
and other geospatial data at each time horizon. We propose a modelthat combines routing and predictive algorithms to provide transit estimates for both connections between Amazon locations as well as unseen routes between third-party vendor connections. We describe how
we deal with noisy telematic signals and sparse ground truth, the machine learning approaches to work with road-level data, and the mechanisms to represent the variability inherent to a prediction that depends
on the road conditions and driver behaviour.

4 - Foundation Models for Time Series Forecasting

Chris George, Claudio Coppola, Jason Omedes, Juba Nait
Saada, Sergio Alvarez Balanya
Maintaining a complex transportation network at global scale involves
a large number of planning processes, each requiring visibility on how
expected demand from customer orders will flow through local regionsin the transportation network. For example, planning labour to process warehouses requires visibility on packages arriving at each warehouse. While traditional forecasting approaches rely on statistical ormachine learning models independently optimised to forecast for asingle granularity and horizon, they can require significant historical
data for the granularity in question and cannot adapt well to new timeseries, inhibiting the expansion of the network to new countries or demand streams. In this work we explore a new approach to time series
forecasting, where a single deep neural network trained on large collections of time series datasets is used to provide robust forecasts across
many granularities and horizons. Inspired by Large Language Models and the ability of Transformer architectures to model long range
dependencies in textual data; we hypothesise that adapting the Transformer architecture to numerical data and training them on large timeseries datasets can allow a single model to reliably forecast across multiple granularities and horizons and even outperform the traditional
multiple-model approach, particularly in cases where little historical
data is available.

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Wednesday, 12:30-14:00 - Room: S07 (building: 101)

Logistics 2
Stream: VeRoLog - Vehicle Routing and Logistics
Invited session

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1 - Rethinking Cyclic Structures in Liner Shipping Networks

Daniel Wetzel, Kevin Tierney
Liner shipping networks are a central feature of modern supply chainsthat consist of cyclical, periodic services operated by container vessels.
This specialized, cyclical structure eases planning for both shipper andcarrier, but the combination of cyclical planning with the available time
windows at ports can lead to inefficient operations. We propose to relax the cyclical assumption and allow vessels to move between services to avoid inefficient connections without interruption to container
flows. From the view of a shipper, the cyclical and periodic propertiesof the services still hold, and the liner carrier can offer a more efficient
overall network. The ensuing optimization problem consists of a combined vessel routing problem and cargo allocation problem, resulting inlarge and challenging instances. We model the problem using mixedinteger linear programming and use an expanding horizon heuristic tofind starting solutions for our model. We use real-world data to showthat giving flexibility to a liner network can result in a significant cost
reduction over standard cyclical schedules.

2 - A Novel Clustering Approach for VRPs Considering
CO2 Emissions

Sara Joosten, Alexander Hübner, Fabian Schäfer, ClemensThielen
The transportation sector is a major contributor to CO2 emissions. We
consider a pollution routing problem within the scope of the capacitated vehicle routing problem by explicitly considering CO2 emissions. We propose a novel MIP-based clustering approach that assigns
customers to tours minimizing the estimated resulting CO2 emissions.
Our approach extends the method of Fisher and Jaikumar by approximating the weight of the truck (including curb and payload weight) at
each detour. That way it can be incorporated into any MIP-formulated
routing problem (e.g., location routing problems, delivery and pickup
problems or inventory routing problems) making it essentially different from other heuristics. After evaluating different seeding strategies,
we perform tests on the Solomon benchmark and the XML dataset.
Comparisons with known optimal solutions as well as a continuous
approximation method are also drawn.

3 - Shortfall Allocation and Safety Stock Optimization for
Capacitated Joint Replenishment

Birger Raa, Tarik Aouam
We consider a periodic review inventory system in which multiple customers are jointly and periodically replenished from a single supplier
by a vehicle with limited capacity. The customers have stochastic demand, and at every replenishment, their inventory is supposed to be
filled up to a so-called ’base-stock’ level. However, when their cumulative demand between replenishments exceeds the vehicle capacity,
it is impossible to replenish all the customers up to their base-stockand shortfall occurs. When the distribution of this shortfall is approximated with a mass exponential function, closed expressions for the
expected inventory holding costs and stockout costs at the customers
can be derived. This allows us to optimize the base-stock levels of thecustomers, minimizing the total holding and stockout costs. As a result, we can evaluate several rules-of-thumb for allocation the shortfall
across the customers. Moreover, we can optimize the shortfall allocation in an iterative procedure. In an extensive computational study,
we compare the cost effectiveness of various shortfall allocation rules
across a wide range of scenarios where the customers have varying cost
parameters and storage capacity limitations. Furthermore, the convergence of the iterative shortfall allocation optimization procedure is inspected, and the accuracy of the approximation is evaluated using simulation experiments.

Chair: Simona Mancini

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4 - Dynamic Order Allocation to Locker Boxes Under Uncertain Pick-Up Times

Simona Mancini, Margaretha Gansterer
Delivery to locker boxes has become very popular in e-commerce as itis beneficial for companies and also convenient for customers who canflexibly pick-up their parcels. In our study we assume that parcels canbe picked-up within three days after delivery. Afterwards, the parcel
is removed by the company. However, the uncertainty on the pickup time negatively affects order acceptance and allocation strategiesof the company, yielding to potential inefficiencies of the overall system. To deal with this issue, we study the dynamic order allocation tolocker boxes under uncertainty on parcel pick-up times, formulated asa multi-stage stochastic optimization problem, dealing with three typesof decisions: (1) whether to accept or not an order, (2) when to delivery it, and (3) to which locker to assign it. To solve this problem, we
provide an integer programming classification algorithm which, tuned
on simulated optimal decisions made by an oracle model, derives decision rules to be dynamically applied. Results are compared against
standard classification algorithms and parametric functions. Furthermore, valuable managerial insights regarding assignment decisions oflogistics providers are presented.

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Wednesday, 12:30-14:00 - Room: S08 (building: 101)

OR in Accounting: Performance and ESG
Stream: OR in Financial and Management Accounting
Invited session
Chair: Christian Fritze
1 - Analysing the optimal time of purchase in case of increasing carbon dioxide prices

Anna Uhrmeister
The awareness of climate change and the corresponding need for climate protection have become omnipresent for companies nowadays.
In 2005, the European Commission established the European Union
Emissions Trading Scheme (EU ETS), in order to mitigate the effects
of anthropogenic climate change and render them more predictable.
From 2027, as a result of the so-called Green Deal, companies will nolonger receive subsidies and will responsible for paying the full costsof greenhouse gas emissions. The ramifications for companies are still
uncertain. Starting from this, the aim of the research endeavors to delve
deeper into the examination of the impact of greenhouse gas emission
costs on German companies. It is assumed that the effects will be mostsignificant in carbon intensive productions. Taking this into consideration, this research will model and analyse an average company in thesteel industry. Along with the implementation of the EU ETS, companies are faced with the challenge of making decisions on the purchaseof European Emission Allowances (EUA’s) or the investment in loweremission technology. The study aims to provide an overview of therange of strategies a company is faced with. Subsequently, a selectionof possible scenarios are analysed e.g. the optimum time of purchase,
considering capital commitment costs. Furthermore, the uncertainty
caused by fluctuations in EUA’s and fluctuating raw material prices are
considered.

2 - The Impact of Companies’ Performance on the Implementation of the Green Deal in the regions of Europe,
North America and Asia-Oceania

Raminta Vaitiekuniene, Kristina Sutiene
The European Green Deal envisages ensuring climate neutrality by
2050. This includes a modern and competitive economy based on aclean environment and neutralized climate. Companies are playing asignificant role in this process. The Green Deal initiative encourages
other countries to move towards a green economy, so all countries can

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use the concept of the Green Deal as a symbol of sustainability. The
purpose is to evaluate the financial and innovation performance of thelargest European, North American and Asian-Oceanian companies, revealing how companies contribute to the implementation of the Green
Deal. Systematization of scientific literature, selection of input andoutput variables based on the results of scientific literature, analysis ofstatistical relationships, and panel regression models as the main research methods are applied to achieve this purpose. The results showthat companies from all three regions (European, North American andAsian-Oceanian) significantly concur with the implementation of theGreen Deal. Return on assets and leverage as financial performanceindicators significantly influence the results of environmental performance. Research and experimental development expenditure as an indicator of innovation performance has a significant impact on environmental performance as well. Companies should find more opportunities to pursue financial, innovation and environmental goals together toachieve long-term climate goals.

3 - Data Envelopment Analysis in Management Accounting: New Frontiers Exploring Industry Characteristics

Claire Cui
Since the 1980s, Data Envelopment Analysis (DEA) has been a staple
in management accounting for performance efficiency by converting
inputs into outputs, typically within production processes. However,recent access to extensive accounting databases has shifted DEA applications to financial data (FinDEA), contrasting firms across industries. This evolution prompts two inquiries: the influence of chosen
performance metrics on FinDEA’s business assessment and the impact
of industry traits on these evaluations. Our study assesses the coherence of ten FinDEA models across six sectors, revealing that FinDEA
scores are consistent within specific performance metrics. Additionally, industry nuances distinctly influence FinDEA outcomes. These
insights inform the application of FinDEA in future management accounting research using large-scale financial data.

4 - Developing knowledge-driven hybrid models for corporate towards ESG

Lai Ii wen, Huang Sun-weng., Ho Hsiao Hua
Nowadays, whether corporate can effectively implement and develop
ESG has become a basic public evaluation indicator. However, theimplementation and development of ESG has a significant impact on
business performance. How to develop ESG to enhance corporate performance has become a focal point global companies stress. Therefore,
this study develops a knowledge-driven hybrid model to explore thekey path to the development of ESG in improving corporate operating
performance. First, we define the target company and use m-VIKOR
technology to conduct a comprehensive assessment of the company’s
ESG and operating performance. Second, we use ESG and businessperformance analysis to differentiate target companies. Third, a study
is conducted based on the core and rules of data mining explorationinfluence. Finally, we develop an effective development path based on
the rules. This research will help companies implement ESG development and help with climate change.

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Wednesday, 12:30-14:00 - Room: S09 (building: 101)

Machine Learning in Machine Scheduling
Stream: Project Management and Scheduling
Invited session
Chair: Aykut Uzunoglu
1 - Integrating Simulation, Optimization and Reinforcement
Learning for a General Class of Scheduling Problems

Haitao Li

EURO 2024 - Copenhagen
We study a general class of stochastic scheduling problems that include various machine scheduling and resource-constrained projectscheduling problems under uncertainty as special cases. There are
two general strategies to solve the addressed class of scheduling problems with stochastic activity durations: open-loop and closed-loop.
Although closed-loop policy is theoretically advantageous over openloop policy, as computing an optimal closed-loop policy requires solving the Bellman equation, which suffers the well-known "curse-ofdimensionalities" for large instances. In this paper, a Markov decision
process (MDP) model built upon discrete-time Markov chain (DTMC)
is developed for the addressed class of problems. To tackle the curseof-dimensionalities of obtaining the exact closed-loop policy to the
MDP model, we present a general approximate dynamic programming(ADP) framework that integrates simulation, optimization, and reinforcement learning, called Sim-Opt-RL, to provide quality and computationally tractable closed-loop policy. We implement the Sim-OptRL framework for a well-known and well-studied stochastic resourceconstrained project scheduling (SRCPSP), with a custom designed genetic algorithm (GA), which outperforms the existing closed-loop algorithm, and is competitive with the state-of-the-art open-loop algorithms for the SRCPSP.

2 - Cooperation and competition in two-agent schedulingwith parallel batching

Renjie Yu, Daniel Oron
We study single-machine scheduling problems involving two competing agents sharing a batching machine. This machine can handle atmost one job from each agent simultaneously. Our focus is on two
due date-related scheduling criteria: the number of late jobs and thetotal late work. We investigate two variations for each problem: minimizing the combined objectives of both agents, and minimizing theobjective of one agent while imposing an upper bound on the otheragent’s objective. All four versions of the problem are demonstrated
to be strongly NP-hard. To address this complexity, we devise efficient
Mixed-Integer Linear Programming (MILP) formulations, and employ
constructive algorithms and Tabu Search for approximate solutions. A
comprehensive numerical analysis validates the effectiveness of the approximation algorithms.

3 - Minimizing the number of late jobs and total late workwith step-learning

Johnson Phosavanh, Daniel Oron
We study single-machine scheduling problems with step-learning,where an improvement in processing time is experienced if a job isstarted after a job-dependent learning-date. We consider minimizing
two functions: the number of late jobs and the total late work, and weshow that when at least a common due-date or common learning-dateis assumed, the problem is NP-hard in the ordinary sense; however,when both are arbitrary, the problem becomes strongly NP-hard. For
each of the problems where at least one of the dates is assumed to becommon, we analyze the structure of an optimal job schedule with andwithout idle time and propose pseudo-polynomial time dynamic programming algorithms. We also show that the problem of minimizing
the weighted number of late jobs with step-learning can be solved witha minor change to the algorithms for the unweighted case. In addition
to this, we show that when a common due-date is assumed and no idle
time is allowed, the problem of minimizing the total late work is equivalent to that of minimizing the makespan. Furthermore, we provide amore efficient algorithm to solve the problem of minimizing makespanunder the assumption of a common learning-date than the one in theexisting literature.

4 - Applying Machine Learning in Machine Scheduling - Improving the Decision-Making of a Serial-Batch Scheduling Problem

Aykut Uzunoglu, Christian Gahm, Axel Tuma
Heuristic algorithms are commonly used in practical applications ofcombinatorial optimization to achieve efficient problem-solving. However, these heuristics have limitations when applied to interleaved andcomputationally hard problems. To address these shortcomings, we
propose enhancing decision-making by using Machine Learning (ML)WC-61

models. We demonstrate the effectiveness of this approach on a complex example of a serial-batch scheduling problem, which involves deciding the grouping of jobs (i.e. batching) and sequencing of batches(i.e. scheduling). The maximum potential of ML models can be exploited by strategically applying them at various junctures within the
problem-solving process. We analyze three stages to integrate MLmodels to improve decision-making. Firstly, ML models are used to
anticipate solutions for base-level nesting problems posed by the batching decisions. Secondly, the models are used to tune the parameters of
the top-level problem’s solution method circumventing time-intensive
search approaches. Finally, the most suitable solution method is selected for a problem instance by creating ordinal preferences between
solution methods based on model predictions. The effectiveness of using ML models to improve decision-making at different stages of thescheduling problem is highlighted by our empirical evaluations.

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Wednesday, 12:30-14:00 - Room: S10 (building: 101)

Retail Inventory Management III
Stream: Retail Operations
Invited session
Chair: Sila Cetinkaya
1 - The Scheduled Joint Replenishment Problem

Miguel Chastre, Albert Schrotenboer, Christina Imdahl, Tomvan Woensel
We focus on a stochastic Joint Replenishment Problem setting witha single item, one warehouse and multiple retailers facing compound
Poisson demand. The novel aspect of this setting is that the retailers’
opportunities for replenishment are constrained by cyclical schedules.
We model the underlying Markov Decision Process and determine theoptimal joint replenishment policy for small instances via a value iteration approach. Our numerical analysis investigates the influence ofschedule attributes on the structure of the final solution. To address
larger instances, we propose an iterative solution method that makes
use of a reduced state space.

2 - Optimizing Inventory Strategies for Perishable Products: A Comprehensive Model Integrating Freshness
Dynamics and Economic Value

Matan Shnaiderman, Yael Lahav, Elad Harison, Jacob kobe
HarushPerishable products are characterized by a predetermined expiration
date, beyond which their economic value declines, rendering them unusable. This paper identifies two categories of perishable products:
those consumable before expiration but hazardous post-expiry (e.g.,
medical products), and those with freshness degradation over time,
becoming unusable after their shelf life (e.g., agricultural products).
Despite these distinctions, both types may be treated collectively due
to their shared limited shelf life, impacting their marketability. The
study introduces a model where a retailer manages the demand for a
perishable product, considering its freshness level and selling price asmultiplicative components. Further, the paper explores how decisions
related to quantity optimization and profit maximization influence theorder quantity. The model facilitates retailers in determining the optimal inventory level to maximize their profits.

3 - On the Impact of Risk-Attitude when Dealing with Stockouts

Sila Cetinkaya
We examine alternative approaches for dealing with excess demandthrough the lens of a retailer whose risk-attitude is modeled explicitly.
With representative risk-neutral and risk-averse newsvendor models,
we pursue a comparison between two basic stockout policies (lostsales versus backorders) in terms of financial metrics as well as metrics

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WC-62EURO 2024 - Copenhagenthat relate to inventory availability. Depending on the risk attitude andparticular cost parameter relationships, we explore settings that reveal
the superior way for handling stockout scenarios. We discover analytical takeaways that hold in general and report numerical findings thatreveal resiliency of the superior stockout policy.

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Wednesday, 12:30-14:00 - Room: S14 (building: 101)

Applications to Economics and FinanceStream: OR in Banking, Finance and Insurance: New
Tools for Risk Management
Invited session

Wednesday, 12:30-14:00 - Room: S12 (building: 101)

Chair: Stavros A. Zenios

Container Stacking and Yard Planning III

1 - A Market-based Calibration of Capital Requirements

Stream: OR in Port Operations
Invited session

Andrea Consiglio, Mahmoud Fatouh, Simone GiansanteChair: Christopher Expósito Izquierdo

This study introduces a market-based calibration approach for riskbased capital requirements under Basel III reforms. An advanced Merton Distance-to-Default model, modified for non-normal asset distributions, estimates the minimum leverage and risk-weighted capital ratios to maintain a default risk tolerance of 0.1%. This market-oriented
method externally evaluates asset uncertainty and default risk, overcoming the limitations of internal models, which often underrate systemic risk in banking. The paper also integrates a novel optimization
model to align capital requirements with actual equity levels, ensuring
global solution convergence by transforming the initial discontinuous,
non-linear model into a smooth convex programming framework. Thisapproach enhances the robustness and reliability of determining capitalrequirements in the banking sector.

1 - Decision support system for managing integrated yard
problems

Christopher Expósito Izquierdo, Israel López-Plata, Airam
Expósito Márquez
The optimization of storage spaces in a terminal is a crucial factor
for achieving high performance in its logistical operations. Temporary storage at a terminal is facilitated through a logistical structure
known as a yard, where containers are stacked in blocks. The significance of this logistical infrastructure is underscored by the substantial
volume of containers managed; for instance, nearly 2000 million tonsof containers were stored in ports in 2022. The operations involving
container movement and storage entail the coordination of various entities, including vehicles, yard cranes, and others. This complexity
gives rise to a range of intricate logistical challenges that warrant attention. This work presents a decision-support system aimed at managing, analyzing, and optimizing container storage and movement in theyard of a maritime container terminal. The system provides a detailed
overview of the yard status and enables planning for future operations
based on expected arrivals and departures of containers, thereby optimizing available space and reducing vehicle movement and associatedemissions. The proposed system integrates a set of optimization techniques such as optimization models, heuristics, or metaheuristics, withthe primary goal of addressing various yard problems such as block
relocations or vehicle movement. Lastly, this decision-support system
is integrated as a subsystem within a digital twin designed to manage
all logistic operations within a port.

2 - Stochastic Modeling and Analysis of Two-phase Operations in Container Terminal with Automated Lifting Vehicles

Vishal Bansal, Govind Kumawat, Divyansh Tripathi
Container terminals around the world are embracing automation to improve their efficiency and throughput performance for dynamic business requirements. However, to achieve the desired cost and operational efficiency of an automated container terminal, a thorough analysis of its performance under various design parameters is necessary
at the design stage. In this paper, we propose a novel queuing modelfor the performance analysis of a container terminal that uses automated lifting vehicles for container transportation. A special type of
two-phase server is used to capture complex two-phase operations ofquay cranes, automated lifting vehicles, and stack cranes, which wereignored in previous studies. Since existing methods cannot solve ourresulting queuing model, we develop a new network decompositionbased approach to solve it. First, we validate the proposed analyticalframework by developing a simulation model and find that it is quite
accurate with a less than 5% average absolute percentage error in the
average throughput time. Next, we quantify the value of accurately
modeling the two-phase operations of resources in a container terminal. In our numerical analysis, we find that ignoring two-phase operations can lead to an overestimation of the average throughput timefrom 25% to 65%. Thus, our new modeling approach assists terminal
designers in accurately estimating the performance measures.

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2 - Does Sovereign Debt Consolidation Shape Firms’
Credit Cost?

Claire Economidou
This paper studies the effect of sovereign debt on corporate borrowing
cost. We develop a theoretical model of a global financial intermediary sector which is tested using data on global syndicated loans for alarge panel of firms, banks and countries. Our evidence shows that forgovernment debt around 75 per cent of GDP, any additional percentage increase burdens firms borrowing cost by 11.73 basis points. The
effect becomes more pronounced for firms and banks located in thesame country, while the degree varies depending on the firm’s size andprofitability. Overall, results underline that the cost of loans is a relevant mechanism through which government debt affects real economic
activity.

3 - Achieving robustness by searching for the right amountof shrinkage in minimum variance portfolios

Xiang Zhao, Selin Ahipasaoglu
We study the minimum variance portfolio optimisation problem,
wherein the covariance matrix is subject to a degree of uncertainty,
possibly due to estimation errors and the presence of outliers. Classic solutions mitigate such errors through a rank-one shrinkage matrix,
constructed using dual variables from a related constrained optimisation problem. Inspired by this, we propose a model that permits varying degrees of matrix shrinkage and identifies the most effective shrinkage scale based on the in-sample performance.Moreover, we identify
sufficient conditions that guarantee that the updated (shrunk) covariance matrix remains positive semi-definite, and obtain a practical range
for the scale of the shrinkage. Our numerical experiments on out-ofsample data demonstrate that portfolios generated by our model typically exhibit lower levels of short selling and higher Sharpe ratios compared to existing methods. Additionally, they outperform in key risk
metrics, such as Value-at-Risk (VaR) and Conditional Value-at-Risk(CVaR). This highlights our approach’s effectiveness in addressing estimation uncertainties and its potential to redefine investment strategies
in volatile markets.

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4 - Gender as a tool for diversification

Ana Escribano, Antonio Díaz, Rocio Hidalgo

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3 - A Matheuristic for the Edge Set Vehicle Routing Problem With Time Windows and VariantsRossana Cavagnini
This study investigates the impact of managerial gender on portfolio
risk, based on the assumption that women are more risk averse thantheir male counterparts. The research focuses on a key question: Does
this risk aversion manifest itself in the stock market performance offirms that are majority-led by women? To answer this question, weconstructed two distinct portfolios based on the gender of top managers: one composed of predominantly female-led companies and one
composed of exclusively male-led companies. The selection of companies was based on MSCI’s annual global gender diversity reports,
and the analysis was conducted over a sample period from 2018 to2022. Portfolio risk was assessed using the value-at-risk (VaR) metric, with dynamic optimisation procedures applied through an AR(1)EGARCH(1,1) model to minimise variance. Our results reveal that
portfolios composed of predominantly female-managed companies exhibit lower risk, as measured by VaR, compared to portfolios of maleonly managed companies.

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Wednesday, 12:30-14:00 - Room: S16 (building: 101)

Heuristics for Vehicle Routing 1

To reduce emissions, traffic, and noise, municipalities and governments are imposing fees and toll schemes to logistic companies foraccessing particular roads. Because such measures increase delivery
costs, transportation companies have to consider them when planningroutes. In this talk, we address the edge set vehicle routing problem
with time windows (ESVRPTW) initially introduced by Reinhardt etal. (2016). The ESVRPTW is a generalization of the vehicle routing
problem with time windows in which a fixed cost must be paid foraccessing a set of edges.
We propose an improved formulation for the ESVRPTW, new variable fixing techniques, and new valid inequalities. We introduce a newvariant of the ESVRPTW to represent urban contexts in which waiting at customers for the opening of their time windows is not allowed.
To solve these problems, we present a matheuristic, called MS-LBM,
that consists of a multi-start local descent framework that uses a local
branching scheme.
In addition to its flexibility in solving all ESVRPTW variants addressed in this talk, MS-LBM provides high-quality solutions fasterthan a commercial optimization solver and finds new best-known solutions. With an extensive computational study, we derive insights into
how allowing waiting at customers affects the edge-set activation androuting decisions depending on the type of infrastructure for which
fixed costs must be paid.

Stream: VeRoLog - Vehicle Routing and Logistics
Invited session
Chair: Rossana Cavagnini
1 - Exact and Heuristic Methods for the Hierarchical MultiSwitch Multi-Echelon Vehicle Routing Problem

Marduch Tadaros
This presentation covers a newly introduced variant of the Vehicle
Routing Problem - the Hierarchical Multi-Switch Multi-Echelon Vehicle Routing Problem. It is a real-world problem originating from thepolicies of a Nordic distribution company and includes; a single depot,
a non-predetermined hierarchy of intermediate facilities, and two different fleets consisting of homogeneous depot and homogeneous local
vehicles pulling swap-bodies. The central depot dispatches vehicles
with attached swap bodies, which can either directly serve customers(if only one swap body is attached) or visit switch points to transfer
loaded swap bodies to local vehicles. The local vehicles then serve
customers with the transferred swap bodies, while the original vehicle continues serving customers with the remaining loaded swap body.
The presentation will cover the problem and model formulations, properties and difficulties in solving it using a commercial solver, solution approaches for realistic-sized instances, and decomposition-based
methods to generate lower bounds and evaluate the performance ofheuristic algorithms.

2 - Improving a Hybrid Genetic Search for the Capacitated
Vehicle Routing Problem

Lars Magnus Hvattum
An open-source implementation of a hybrid genetic search is currently
among the very best heuristics for the capacitated vehicle routing problem. While being highly efficient and effective, the code is also relatively simple. It is therefore an interesting challenge to improve theperformance of the heuristic while making as small changes to thecode as possible. Two such improvements can be found by analyzing, respectively, a cross-over operator and a splitting procedure. An
improved cross-over operator is shown to reduce gaps to best-known
solutions by 4.2%, and an improved splitting procedure is shown to
reduce gaps by an additional 3.9%. In both cases, changes to the implementation is in the order of tens of lines of code.

439

WD-01EURO 2024 - Copenhagen

Wednesday, 14:30-16:00
 WD-01
Wednesday, 14:30-16:00 - Room: Sportshallen (building: 101)

Maria Besiou
Stream: Keynotes
Keynote session
Chair: Tina Wakolbinger
1 - Humanitarian operations: How can research help them
become more efficient, effective and sustainable?

Maria Besiou
In the last decades, the impact of natural and manmade disasters has
been increasing and so does the research in the field of humanitarian
logistics. But what are the challenges that humanitarian logistics facein practice and where is the research going? Is there an alignment between research and practice or a gap?
By reviewing papers on humanitarian operations, problems raised bypractitioner conferences and webinars, and conducting a survey of theauthor community on the topic, we link these problems with the literature developed in our field. We also identify characteristics of papers
that close the gap between practice and research. We then go deeper
into some research projects that have affected practice helping it to become efficient, effective, and sustainable.

 WD-06

The focus of this paper is on demonstrating the creation of portfolio uncertainty bands using a bootstrapped procedure. We restrict attention
to the model presented in Mezali and Beasley [1] and use re-samplingstatistical techniques to build in-sample portfolio uncertainty bands.
Further, we propose a number of ways in which the in-sample bootstrapped portfolios, which collectively form an uncertainty band, canbe employed to improve out-of-sample portfolio performance for bothindex tracking and enhanced indexation. Our formulation includes
transaction costs, a constraint limiting the number of stocks that canbe in the portfolio and a limit on the total transaction cost that can be
incurred. Numeric results are presented for eight test problems drawn
from major world markets, where the largest of these test problems involves over 2000 stocks. Keywords: enhanced indexation; index tracking; portfolio optimisation; quantile regression; bootstrapping

3 - Classification in a vector space with the infinity norm

Seán McGarraghy, Milena Venkova
Zhou and Schoelkopf (2005) note that "Many real-world machinelearning problems are situated on finite discrete sets. Typically we may
assume that there exist pairwise relationships among data." In this talkwe consider the general setting of classification on data sets in finite
metric spaces: these include a broad range of cases, including pairwise
relationships satisfying the triangle inequality. Instead of embedding
the data in an Euclidian space and using kernel techniques, we embed it
in a real vector space endowed with the infinity norm. This embedding
is necessarily isometric which means that the data in the normed spacecan be studied without introducing any distortion to the original metricspace setting. While the target feature space is no longer an inner product space, and so the idea of kernel does not carry forward, there is still
potential for a linear class boundary using the Hahn-Banach Theorem.
We discuss the advantages and disadvantages of such an embedding in
the context of classifiers such as support vector machines. In particular,
we are interested in the problem of adjusting an already-trained binaryclassifier in response to new data. New training set data may become
available after training a support vector machine: will the same trainedmachine still work or does it need to be adjusted; and if so, how?

Wednesday, 14:30-16:00 - Room: 1013 (building: 202)

Advancements of OR-analytics in statistics,
machine learning and data science 19
Stream: Advancements of OR-analytics in statistics, machine learning and data science
Invited session
Chair: Elise del Rosario
Chair: Jinal Parikh
1 - How to compute confidence intervals for quasi-Monte
Carlo estimators

Pierre L’Ecuyer, Marvin K. Nakayama, Art B. Owen, Bruno
TuffinRandomized Quasi-Monte Carlo (RQMC) sampling provides unbiased
estimators whose variance often converges at a faster rate than the standard Monte Carlo estimators as a function of the sample size n. But
computing valid confidence intervals is not obvious because the n observations are dependent and the central limit theorem does not apply
in general as n increases. A popular heuristic is to replicate the RQMC
process r times (say, about 10) to obtain r independent realizations of
the estimator, and use these r observations to compute a confidence interval based on the normal or Student distribution. Alternatives include
various types of nonparametric bootstrap methods for which normality
assumptions are not required. We report extensive numerical experiments that compare these methods in terms of the width and coverage
probability of the confidence intervals, for a variety of examples. The
results were not exactly as we expected. Based on these results, wegive our recommendations.

2 - Bootstrap approach to quantifying uncertainty in index
tracking and enhanced indexation

Hakim Mezali440

 WD-07
Wednesday, 14:30-16:00 - Room: 1019 (building: 202)

Modelling social-behavioural phenomena increative societies
Stream: Behavioural OR
Invited session
Chair: Leonidas Sakalauskas
1 - Modeling&Simulation of Social-Behavioral Phenomena
in Creative Societies

Leonidas Sakalauskas
Recently, there have been a growing number of challenges related toprocesses that are taking place thanks to interactions between people(their communities). Society creates a set of social behavioural phenomena (e.g., the conduct of voters during elections; the behaviourof the crowd during a terrorist attack; people’s behaviour during pandemics; institutions’ social capital development processes; the behaviour of social network users; etc.), whose knowledge of mechanisms is essential for building a sustainable society. We aim to review
the operationalisation of such phenomena as the expression of logicaland verbal communication in order to develop a paradigm of rational
choice for the efficient understanding of their nature and relevant decisionmaking, thus bridging OR with social sciences and humanities.
The creation of such a paradigm on the basis of the theory of structural equation modelling (SEM), multi-agent modelling and game theory, together with data science and mathematical sociology methods,
allows the development of data-driven operationalisation for evidencebased solutions.

EURO 2024 - Copenhagen
2 - On the gap between data and models in COVID-19 analysis

Vytautas Dulskis, Leonidas Sakalauskas, Rimas Jankunas
A scrutiny analysis of the COVID-19 data is required to get insights
into effective strategies for pandemic control. However, a particular
gap between official data and methods used to assess the effectivenessof various containment measures (e.g., COVID-19 passports) hinders
sound inference-making. Seeking to escape the burden of arising obstacles, employing the principles and methods of descriptive statistics
is often tempting, but in-depth analysis demands more sensitive andreliable methods. In this regard, this paper advocates a maximum likelihood compartmental modeling approach, which provides the flexibility to raise various hypotheses about infectivity, recovery, and mortality dynamics and to find the most likely answers given the data. Our
paper is based exclusively on COVID-19 deaths in light of official data
limitations, as relatively fewer limitations characterize these data. Nevertheless, this paper does not solve the underlying problems but hints
at potential improvements in official data reporting that could benefit
COVID-19 modeling prospects.

3 - Mobs Simulation Guided by Social Science-BasedMulti-Theoretical Framework

Nitin Agarwal, Samer Al-khateeb
Social media sometimes empowers potential adversarial state actors,
paid trolls, and extremists to incite hysteria and coordinate nefarious
actions, e.g., deviant mobs. A mob is an event organized via social
media or other forms of digital communication technologies in whicha group of people gathers online and/or offline to conduct an act collectively and then disperses. To an outsider, such an event may seem
arbitrary; however, a sophisticated amount of coordination is involved.
These mob-like events like the January 6, 2021, U.S. Capitol attack,or GameStop’s stock "flashmob investing" are becoming widespread
due to the affordability of social media, ease of use, effectiveness of
individuals or groups in conducting coordinated acts, anonymity of theinternet, etc. The prevalence of these incidents underscores the inadequacy of current systems (security, financial, etc.) in addressing such
coordinated behavior. This topic is understudied due to lack of data,
theoretical underpinning, and computational resources required to analyze the complex and dynamic social processes among mobbers. In thisstudy, we develop a model that can simulate mobs guided by constructs
extracted from various social science theories. We then aim to use themodel to study the behavior of the mobbers, the motivation of the organizers, and attempt to infer the mob’s outcome. Real-world data,albeit limited, will be used to evaluate the simulation-driven model ina real-world setting.

4 - How Blockchain and Smart Contracts Transform
Human-Machine Trust and Inter-Organizational Relationships in Industry

Yan Peng, Dan Ke
This research systematically examines the impacts of blockchain andsmart contracts on human-machine trust, and thus to understand how
these cutting-edge technologies and innovative applications are poised
to revolutionize industrial inter-organizational relationships and business collaborations. The research model reveals that how blockchain
and smart contracts enhance the reliability and efficiency of transactions, thereby fostering a new level of trust between humans and machines. We hypothesize that the immutable and transparent natureof blockchain, coupled with the autonomous execution of smart contracts, streamlines processes, reduces disputes, and facilitates seamless inter-organizational collaborations; the token economy fosteredby blockchain and consensus mechanism plays a moderating role byincentivizing organizational behaviors and creating value within theecosystems by redefining business models and revenue streams. The
study adopts a mixed-methods approach. The qualitative interviews
from experts and practitioners delve into transformation in trust dynamics and collaborations models enabled by blockchain technology, and the quantitative analysis of data collected from blockchainimplemented systems in industry focuses performance metrics andtrust indicators pre & post blockchain implementation. This research

WD-08

provides a comprehensive understanding of the transformative potential of blockchain and smart contracts in reshaping industrial landscapes.

 WD-08
Wednesday, 14:30-16:00 - Room: 1020 (building: 202)

AI in Knowledge, Technology, andInnovation
Stream: Knowledge, Technology, and Innovation
Invited session
Chair: A. D. Amar
Chair: Nikos Karacapilidis
1 - Leveraging Open Innovation Practices Through Emerging Technologies

Nikos Karacapilidis, Stavros Dimopoulos, Konstantinos
Konstantinopoulos
This paper reports on a novel software platform that aims to facilitate and enhance diverse open innovation practices in contemporaryorganizations. The proposed solution builds on and advances a series of state-of-the-art artificial intelligence and natural language processing tools and technologies to meaningfully cluster and aggregate
stakeholders’ feedback. In addition, it adopts an argumentation-based
collaboration approach to augment knowledge reification and cocreation. Through dedicated services, the platform augments knowledge management and informed decision-making in the associateddata-intensive and cognitively complex settings. The proposed solution has been shaped through long collaboration among diverse typesof stakeholders, through which a series of rich application scenarios
have been designed and analyzed. Evaluation results are positive, justifying the rationale of our approach towards integrating machine reasoning features that can automatically organize, analyze and summarize the content of ideas and positions expressed in the settings underconsideration.

2 - Innovation, Technology, and Entrepreneurship Low Development: a Literature Review based Multi-level and
Network Density Analysis.

Homero Malagon, Aida Huerta-Barrientos
Mexico like other developing countries has been battling low outcomesin innovation, technology, and entrepreneurship projects and policies,
to understand that phenomenon a multi-level and network density analyses based on a literature review of innovation systems models, inorder to analyze how many levels and agents are interacting in thesemodels as well as the relationships between agents. The innovation
systems analyzed are the National Innovation System, Regional Innovation System, Triple Helix, Quintuple Helix, Innovation Ecosystem,
Entrepreneurship Ecosystem, National Innovation Ecosystem, and Regional Innovation Ecosystem. The results show that for all models
only two levels are operating, one government level and the institutional level, and therefore not all levels are articulated; the number of
agents considered by each model is relevant but not determinant of themodel’s quality; network density better reflects the conceptualizationof each model; Entrepreneurship and Innovation Ecosystems models
have higher network density, and therefore are the recommended ones
to be used by planners, strategists, and decision-makers in order to obtain better outcomes; the most likely path to follow by the innovation
systems literature and researchers is complex adaptive systems.

3 - Analysis of digital innovation configuration effect on
supply chain resilience[U+FF1A]case of logistics firms

Xinbing Gu, Hing Kai Chan
The operation and services of logistics companies can be heavily disrupted due to disasters and crises, requiring them to be more resilient.
Digitisation innovation has advanced considerably in the last decadeand motivated supply chain resilience (SCR). Given the high level of441

WD-09EURO 2024 - Copenhagensocialised services and strong conductivity and linkage roles of logistics firms, the impact of any supply chain disruptive events on the logistics industry will be transmitted and significantly affect the production
and operation of upstream and downstream enterprises such as manufacturing and retailer companies However, it is still unclear how logistics firms systematically facilitated SCR by digital innovation. This
study identifies digital innovation as exploratory and exploitative digital innovation. Combined with factors of resourcefulness and dynamic
capability, this study applied Fuzzy-set Qualitative Comparative Analysis (fsQCA) and Artificial Neural Network to analyse the digital innovation configuration effect on supply chain resilience. Data from
167 logistics firms reveals three digital innovation model designs, eachwith its performance implications. Moreover, this study found that exploratory digital innovation is the core condition for motivating SCR.
It provides researchers with a new line of inquiry and strategy suggestions on the role of digital innovation in SCR.

Denmark’s green transition and how it could be disrupted when different sectors fail to meet set targets for decarbonizing agriculture andexpanding capacity for green hydrogen electrolysis, renewable generators, and power-to-heat. We constrain CO2 emissions on a national
level, aligned with each country’s pledged CO2 reduction targets, and,in Denmark, implement frozen-policy projections for energy production and sectoral emissions. Focused on 2030, we analyze eight scenarios to assess the impact of each sector on system investments andCO2 abatement costs. In addition, we extend the time horizon to 2040
to account for the increasing decarbonization efforts of neighboring
countries. We show: (1) increased CO2 abatement costs if the agricultural sector fails to decarbonize; (2) the crucial role of heat electrification for low-cost flexibility; (3) the dependency of green hydrogen
investments on renewable capacity limits and emission constraints insurrounding countries. Our model and data are openly available.

3 - Conditions on electricity purchasing: More (emission
reduction) bang for your buck?

 WD-09
Wednesday, 14:30-16:00 - Room: 10 (building: 116)

Hydrogen and Electricity Modeling andRegulation II
Stream: Energy Markets
Invited session
Chair: Jens Weibezahn
Chair: Lissy Langer
1 - Important features when modeling hydrogen as part ofintegrated energy systems

Marie Münster, Rasmus Bramstoft, Dimitrios Eleftheriou,
Kenneth Karlsson, Mathias Kjærgård Hald, Magdalena
Komoszynska, Henrik Lund
Recently, a number of energy system analyses have emerged, whichhighlight the potential roles of hydrogen in future energy systems. The
results however differ substantially in terms of the outcomes. This ispartly due to hydrogen being an expensive energy carrier, which requires a number of conditions to become feasible in large scale someof which include e.g. high carbon taxes, the availability of low-cost
renewable electricity and low availability of biomass. To investigate inmore detail where the differences stem from, a number of studies of
the role of hydrogen in Denmark has been compared and a modelingand reporting framework is proposed. The goal of the framework is toensure transparency and understanding of results highlighting important input parameters and conditions. The 7 main features identified
can be summarized as: 1. Model type 2. Modeling of time (aggregation, pathways and foresight etc.) 3. Modeling of space (aggregation,scope, grids etc.) 4. Scenario boundary conditions (e.g. CO2 tax/cap,
import/export possibility etc.) 5. Resource assumptions (potentials,
prices, variance etc.) 6. Technology assumptions (conversion, storagesetc.) 7. Demands (energy carriers, sectors included, amounts & timing& flexibility etc.) In order to be able to compare and understand differences between different studies it is recommended to document andreflect upon these model characteristics as well as the main assumptions and modeling choices.

2 - What Could Go Wrong? Disrupting Denmark’s Energy
Transition Using Policy-Driven Scenarios

Frederik Fristed
In the run-up to 2030, the path towards the EU’s ambitious 55%
CO2 reduction goal is shaped by existing energy infrastructure andalready adopted policies. Each nation’s energy system characteristics,
reduction targets, and local challenges affect the EU’s collective decarbonization. This work utilizes the open PyPSA-Eur model for thesector-coupled European energy system, comprising 33 countries andDenmark with a higher resolution of nine nodes. The study examines

442

Lissy Langer, Anders Bjørn, Ben Hobbs, Matthew Brander,
Rasmus Bramstoft

This paper analyzes the impact of different electricity purchase conditions on the carbon emissions of hydrogen production. These conditions are also discussed in the context of the revision of the Greenhouse Gas Protocol’s carbon accounting rules for the purchase of voluntary renewable energy certificates to report companies’ scope 2 carbon emissions. The techno-economic analysis presents a case studyof hydrogen production in Denmark, a country with a relatively highshare of renewable energy sources (RES), comparing the cost-optimalenergy system for 2030 with a counterfactual scenario with no electricity purchase conditions. The study analyzes the impact of annual andhourly matching through power purchase agreements (PPAs) with local
RES versus purchasing renewable energy certificates (RECs) throughvirtual PPAs on carbon emissions, system costs, and carbon reduction
costs. We compare several European countries with different characteristics, such as varying initial RES shares, grid carbon intensity, andRES potentials. Our initial results suggest that the purchase conditions
significantly affect the distribution of RES between countries, the additionality of RES, and the total system emissions.

4 - Three Pillars of Hourly Matching: A Tour From Model
Land to Real World

Iegor Riepin, Elisabeth Zeyen

Hourly matching—the concept of matching energy demand with clean
electricity supply on an hourly basis—has gained immense attention.
The concept got into the spotlight when regulations on how to certifyhydrogen as "green" (and thus qualify for support schemes) sparked
active debates among both the European Union and the United States’
policymakers, industry stakeholders, and research communities. Hydrogen regulation is not the only application of the concept. Throughhourly Carbon-Free Energy matching (usually called 24/7 CFE), somepublic and private energy buyers have pledged to eliminate all greenhouse gas emissions associated with their electricity use. The conceptof hourly matching is founded on three pillars: temporal alignment,
geographical scope, and additionality of clean energy supply. Several
research groups have recently developed energy system models to explore the designs and implications of temporal hydrogen regulation andvoluntary 24/7 CFE commitments. The resulting studies and models
behind these studies consider the three pillars of hourly matching. Our
work discusses these core principles of hourly matching and their implementation in models. Using our own research papers and reports as
examples, we take a critical look at which modeling choices we makeand what their real-world implications are. Our talk aims to bridge thegap between the land of energy models and the real world using thenarrow yet crucial lens of hourly matching.

EURO 2024 - Copenhagen WD-10
Wednesday, 14:30-16:00 - Room: 11 (building: 116)

WD-11

4 - Optimizing Early Discharge: Trade-Offs between Capacity and Readmissions

Jingui Xie

Admission and discharge
Stream: OR in Health Services (ORAHS)
Invited session
Chair: Jingui Xie
1 - Stochastic patient admission scheduling problem withan exponential number of scenarios

Haichao Liu, Jin-Kao Hao, Yang Wang
Hospital admission management is a fundamental challenge due to theuncertain demand for inpatient beds. This paper studies a stochastic variant of the well-known patient admission scheduling problem,
which aims to assign patients to rooms during their hospitalizations
while considering the overstay risk. We consider the problem as a
two-stage stochastic programming problem where the first stage assigns patients to rooms on their planned hospitalization days, and thesecond stage evaluates the expected costs resulting from patient overstay. We propose two novel stochastic programming models to formulate the problem, including a scenario-based model and its equivalentstate-variable model. The latter has a pseudo-polynomial number ofvariables and constraints, which is significantly smaller than the former. To solve the state-variable model efficiently, we apply the sample
average approximation method to provide an initial feasible solution to
the Gurobi solver. We carried out extensive computational experiments
to evaluate the performance of the proposed models. The computational results revealed that our state-variable model finds high-quality
feasible solutions with an average optimality gap of 3% for benchmark
instances with 250 patients and 1.9E119 scenarios in 1 hour.

2 - Data-Driven Robust Scheduling of Elective Patients

In this work, we consider the ward capacity management problem withreadmissions, where the decision-maker optimizes the elective schedule and early discharge policy, so as to minimize bed shortages. Existing research has shown that early discharge can lead to higher rates
of readmission, and longer readmission length-of-stay. This sets up
the need to balance the temporal trade-off between the immediate capacity freed up by early discharges and increased readmissions down
the road. Such re-entry structure creates challenges when modelling
via traditional methods. We appeal to the Pipeline Queues (Bandi and
Loke 2018) framework, and propose an optimization model where theearly discharge policy is expressed as a state-dependent decision rule.
The model has a reformulation, which can be solved as a sequenceof convex programs with asymptotically linear constraints. In our numerical study, we identify an intermediate region of the probabilityof readmissions where time-invariant policies can lead to as much as
77% more shortages. Ignoring the effects of early discharge on readmissions can lead to at least 75% and 150% more bed shortages intime-homogeneous and non-time-homogeneous settings respectively,
even against un-optimized elective admissions. Using optimal early
discharge strategies without jointly optimizing elective admissions willlead to 20% more shortages.

 WD-11
Wednesday, 14:30-16:00 - Room: 12 (building: 116)

Nan Yang, Jingui Xie

Heuristics in BOR

Hospital beds are an important medical resource, and the dynamic admission scheduling of elective patients can help to avoid bed shortages.
However, such elective patients scheduling problem is challenging due
to the high level of uncertainty behind the bed availability and its impact on admission rates. In fact, Meng et al. (2015) proposed a robust optimization approach to determine the elective admission quotas based on budgeted uncertainty sets to minimize the expected bed
shortages. A key challenge to be addressed by this approach is the uncertainty behind the stochastic patient arrival and length of stay (LoS).
However, the budget of the uncertainty set is not easy to determine,
which in contrast can be avoided in the robust satisficing approach
proposed by Long et al. (2022). Robust satisficing favors solutions
where the risk-aware objective function best achieves an acceptable
goal even when the actual probability distribution deviates significantly
from the empirical distribution, and it can tolerate greater uncertainty
than robust optimization. In this paper, we propose a robust satisficing method for determining quotas for elective admissions, which will
best avoid bed shortages under extreme uncertainty. We do numerical experiments for the proposed robust satisficing model based on the
MIMIC-IV dataset and compare it with the results generated by robust optimization methods. The results show that the robust satisficing
model can withstand greater uncertainty.

Stream: Behavioural OR
Invited session

3 - Predicting next week’s bed census: combining medical
expertise with data.

Hayo Bos, Stef Baas, Richard Boucherie, Erwin W. Hans,
Gréanne Leeftink
Bed census predictions play a key role in hospital capacity management decisions, such as ward dimensioning, staffing decisions, patientto-bed assignment and the development surgery schedules. Currently,
predictions are typically solely based on the doctor’s estimate of the
Expected Discharge Date (EDD). In this presentation, we propose two
probabilistic models to combine the EDD with the LOS distributions
retrieved from ERP data. Using the Poisson Binomial distribution andprobabilistic convolution, we obtain the full census distribution. We
apply our method on real world hospital data, and it turns out to bevery accurate.

Chair: Konstantinos Katsikopoulos
1 - Cognitive operations: Models that open the black box
and predict our decisions

Konstantinos Katsikopoulos
Improving human behavior requires first understanding it. Can mathematical models help provide such understanding? Yes. Cognitive Operations performs a systematic examination of two main approaches
to modeling human decision making: optimization and simple heuristics. The approaches are discussed by focusing on general types ofdecisions—under risk, under uncertainty, strategic interaction and inventory control—and by drawing on economics, psychology and artificial intelligence. Cognitive Operations is committed to supporting
academics and practitioners who seek to select a modeling approachthat suits the operational decision at hand. It shows how to build models and employs clear criteria for assessing them. For a model to open
the black box it must specify the cognitive processes that lead to theobserved decisions. To predict decisions means to fix a model in one
context and to test it in another. The comparative assessment of optimization and heuristic models serves scientific pluralism and leads
to surprising insights. The studies in Cognitive Operations zoom inon bounded rationality, a field pioneered by Herb Simon, who was afounder of behavioral economics, cognitive psychology as well as artificial intelligence, and sometimes also wear the hat of an operations
researcher. They are useful complements to empirical, experiential andconceptual studies of behavioral operations. It is possible to open theblack box and predict our decisions.

2 - Not Just Small Samples: Successfully Predicting Revenues With the Ratio Heuristic

Florian Artinger443

WD-12EURO 2024 - Copenhagen

A key psychological principle underlying intuitive forecasting is theuse of ratios. A finding is that heuristics, which operationalize suchprinciples, can only predict well in comparison to more complex models when the training sample is small. Challenging this finding, webuild on the bias-variance trade-off to develop three conditions underwhich a simple heuristic can match or even outperform more complex
prediction models. To test these conditions empirically, we investigate revenue predictions using the "ratio heuristic": multiply the quantity—revenue, in our case—observed in the first t time units by a constant to predict the future quantity. On 20 prediction tasks, the results
are in line with the hypotheses showing that a smaller sample, a longer
observation period, or unpredictable changes over time provide performance advantages for the heuristic such that it can outperform more
complex, standard prediction models. Moreover, given sufficiently
strong unpredictable changes over time can result in a setting where
even a large sample and a short observation period do not provide performance advantages for the more complex forecasting methods. Thatis, managerial heuristics may hold value for business forecasting evenin data-rich settings.

3 - Executing prescriptive psychological heuristics: Compatibility effects in decision support applications

Shashwat M. Pande, K.Nadia PapamichailIn this paper, we examine the effectiveness with which individuals execute prescriptive psychological heuristics in multi-criteria decisionmaking contexts. Despite growing evidence demonstrating the predictive accuracy of simple psychology-inspired decision-rules, there has
been limited research exploring how well individuals deploy an "adaptive toolbox" of methods intended for use as prescriptive decision-aids.
Contrasting between compensatory and non-compensatory heuristics,
our study provides some of the first experimental evidence indicatingthat compatibility among individual, model and environmental features facilitates the application of heuristic-methods in multi-criteria
decision-making settings. We draw on the neuropsychological, behavioral and decision analytic literatures to derive our experimental
framework and contextualise our findings within the broader discourse
in Behavioural OR (BOR).

2 - My (short) research journey: past accomplishments,
present projects, and future aspirations

Laura Davila-PenaNot everyone follows the same path in life, and this is no different in
the case of academia. There are those who focus on one topic from the
beginning, those who explore many and eventually define themselves,or those who keep exploring. I believe I am one of the latter.
This talk intends to give an overview of my research journey, startingback in 2016 when I first faced Operations Research. I will briefly describe the research I have done so far (vehicle routing, machine learning, cooperative game theory, scheduling, etc.), along with the process
and achievements. I will also present my ongoing work and give someinsight into the direction I aim to take in the future.

3 - How to "locate" Mathematics in everyday life

Serena Fugaro
At the beginning of my academic career, I was often unable to answer
the question "What is the use of mathematics?" convincingly , and as
a mathematician, I felt rather frustrated. Then, out of pure curiosity, I
took a course in Operational Research during my Master’s degree andfound the first concrete applications of mathematics to real-world problems. Today, thanks to my engagement in technology transfer alongside research, I could go on and on about the location of mathematics
in everyday life. In particular, the word "location" is not accidental: in
fact, over the last three years, my research activity has focused on Discrete Facility Location, with the aim of improving decision making incomplex scenarios resulting from administrative, managerial and operational needs. Actually, the decisions involved in Facility Location
are inherently strategic, and this has led us to define problems characterised by many, potentially conflicting objectives, as this talk will
detail. Indeed, we have introduced a novel class of Multi-Objective
Covering Location problems and an original Multi-Objective Facility
Location problem in Waste Management, arising from a case study forthe city of Sheffield. In both cases, we derived ad hoc mathematicalformulations and obtained accurate representations or approximationsof the arising Pareto Sets. We also conducted a systematic literature
review which allowed us to highlight the research gaps in the literature
on Multi-Objective Facility Location, and to derive a research agenda
to suggest concrete guidelines for both researchers and practitioners
interested in this area.

 WD-12
Wednesday, 14:30-16:00 - Room: 13 (building: 116)

YW4OR_4

 WD-13

Stream: WISDOM - Women in OR
Invited session

Wednesday, 14:30-16:00 - Room: 15 (building: 116)

Chair: Paula Carroll
1 - A biography

Thu Huong Dang
My name is Thu Huong Dang, and I am currently a lecturer at Lancaster University.
My doctoral research, which received partial sponsorship from Routeware, Inc., was concerned with addressing Combinatorial Optimization Problems (COPs) arising in urban waste collection and recycling.
To date, I have authored several papers on Vehicle Routing Problems
and Flowshop Scheduling Problems. I am honored to have received
the YoungWomen4OR award from the WISDOM forum within EUROand the Kingsman prize from the Management Science department forbeing recognized as the best doctoral researcher in the period 2022-23.
Now, my primary focus lies in tackling various COPs, where I explore
exact methods to achieve proven optimality and heuristic methods foraddressing real-world challenges. Over the next few years, I am contemplating expanding my research to include interdisciplinary topics,
such as the use of AI/machine learning in addressing COPs.

444

Reducing Food Waste
Stream: Secure & Sustainable Food Supply
Invited session
Chair: J.c. Gerdessen
1 - Reduced food waste despite throwing out food before
expiration: online vs. offline retail

Ayse Sena Eruguz, Jorrit Barto, Remy Spliet, Sanne Wøhlk
Online retailers throw out food that has not yet expired. This gives
rise to the question whether online retailers generate more food waste
than offline retailers, who typically throw out food only after it has
expired. We focus on the food waste at the retailer which inherently
ensues from the logistical set-up. We first provide a theoretical analysis to establish whether throwing out food before expiration indeed
results in an increase in food waste, putting online retailers at a disadvantage compared to offline retailers. We show for well-behaved inventory control policies, including the optimal policy, that food waste
increases when food is thrown out before expiration. Next, we compare the food waste of the online retailer with that of an offline retailer
in numerical experiments. Note that the online retailer has some advantages over offline retailers as well. Online retailers benefit from fullEURO 2024 - Copenhagen
control of order picking, which is instead often done by the consumer
in offline retail. Moreover, the online retailer often benefits from thepooling effect, as offline retailers might use multiple stores to satisfy
the same demand volume as an online retailer from a single warehouse.
Our numerical experiments with a base-stock policy suggests that online retail actually yields less food waste for many products, despite
throwing out food before expiration.

2 - Analysis of Markdown Strategies for Perishable Products

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factors are interrelated, it is difficult to quantify the effect of individual
factors on food waste. Simulations of complex problems have provenhelpful to shape interventions. However, they have not yet been used to
study food waste. The aim of this study is to develop a meal planningmodel with a rolling horizon to simulate disruptions that might occurin everyday life with different shopping strategies. This gives insights
into best practices regarding food recovery behaviors. The results ofthis study can be used to shape new interventions to reduce food waste.

Lena Riesenegger, Alexander Hübner
Discounting perishable products before they expire, known as markdown pricing, is today’s most common and popular reactive food waste
mitigation practice among retailers. Product age and price influence
customer behavior: While some customers do not buy products that areabout to expire, discounted prices may attract new customers who initially did not plan to buy the product. The time of discounting and theapplied discount level are crucial decisions. Early discounts can leadto margin losses, while late discounts may generate too little demand.
If the price discount is set too high, the remaining stock may be soldquickly and completely, but the margin may be considerably lower.
Current literature lacks consideration of different product ages on the
shelf and the corresponding behavioral dynamics of customers. We
contribute with a simulation study that considers price elastic demand,customers’ freshness preferences, and changes in customer behavior
when a discount is applied. We conducted several experiments considering different customer preference patterns. Results show that discounts are particularly worthwhile for products where customers preferfresh products and withdraw them according to last-expired-first-out.
Furthermore, customers switching from fresh to old, discounted products contribute to food waste reduction, while the influence on profit
depends on the product’s initial waste level.

3 - Menu planning for food services: reducing food wasteby the use of leftovers under consideration of demand
uncertainty

Marjolein Buisman, Marloes Remijnse, Ahmadreza Marandi,
Sonja Rohmer
Food waste levels in the food service sector can be up to 30%, mainly
caused by overproduction, as food is produced upfront rather than on
demand. Therefore, there is a significant risk of meals or meal components to be left uneaten. Leftover meal components might be usedin other dishes; however, due to the short shelf life of these products,
time is a limiting factor. Adequate and flexible meal planning might
increase the reuse of these leftover products and, therefore, reduce theenvironmental impact of the food sector. In this study, we propose
an innovative menu planning model for the food service sector. The
model incorporates the common meal planning decisions on which ingredients to purchase and which meals to offer on a day, but also allows storing excess meal components and using them in the following
days. This model aims to provide a menu with the least environmental impact but also aligns with the requirements of the customers andthe budgetary constraints of the food service. Using Adjustable Robust
Optimization, the model incorporates the uncertainties inherent in foodservice operations, such as fluctuating customer demand.

4 - Optimizing household food planning: a rolling horizon
MILP model for reducing food waste

Maike van Rooijen, G.D.H. (Frits) Claassen, Sander De
Leeuw, J.c. Gerdessen
One-third of the global food production is lost or wasted. Of the total
food waste in Europe, about 50% is caused by households. Food waste
is the undesired result of multiple household food management behaviors. Food waste prevention behaviors, such as planning, aim to avoid asurplus of food reaching the household. However, the unpredictabilityof everyday life causes households to deviate from plans. Food recovery behaviors focus on using all the food that has entered the household. Although food prevention interventions have been thoroughly
studied, interventions targeting food recovery have not yet received
much attention in literature. Food recovery is, among other factors,
influenced by household demographics, flexible cooking skills, shopping frequency, and shelf-stable food items on stock. As many of these WD-15
Wednesday, 14:30-16:00 - Room: 18 (building: 116)

Surgery Scheduling and Operating Room
Planning (2)Stream: OR in Health Services (ORAHS)
Invited session
Chair: Jens Brunner
1 - Solving three-stage operating room scheduling problems with uncertain surgery durations

Yang Kuei Lin
Most of scheduling problems consider a deterministic environment for
which the data are known. However, in many real-world problems,
the precise values of data in scheduling models might not know in advance. For example, the precise duration of surgery is hard to predict
because it can vary widely from one case to another, even for identicalprocedures. The post-surgery duration is also hard to predict because
it is influenced by the patient’s age, gender, condition, type of surgery
and type of anesthesia, etc. We consider a three-stage operating room
surgery scheduling problem with uncertain surgery and post-surgery
durations. The three successive stages are pre-surgery, surgery, andpost-surgery. No-wait constraint is considered among the three stages.
Surgeries are known in advance (elective). Multiple resources are considered throughout the surgical process: PHU (pre-operative holdingunit) beds in the first stage; ORs (operating rooms) in the second stage;
and PACU (post-anesthesia care unit) beds in the third stage. The
objective is to minimize the makespan. We have proposed a genetic
algorithm for robust scheduling (GARS). Randomly generated problem instances are tested to evaluate the performance of the proposed
GARS. Computational results show that the robust schedules foundby the GARS are relatively insensitive to uncertain surgery durations
when compared with the non-robust schedules found by the basic GA(BGA).

2 - Operating Room Scheduling under Human Resource

Xiajie Yi, Christine Di Martinelly
Effective Operating Room (OR) scheduling holds significant financial implications for hospitals, particularly when dealing with inpatient
procedures at the operational level. However, the duration of a surgery
is often uncertain and it is rather challenging to accurately predict. Estimates can vary significantly across different departments, leading tovarious extents of underestimations. As a result, it exacerbates nurse
shortage issue in hospitals. To address this, we propose an approach tobuild robust surgery schedules in an OR theatre made of several rooms
with different specialties and sharing nurses. Our method utilizes a
two-stage stochastic optimization model. In the first stage, we determine the number of nurses working each day, while the second stage
focuses on creating the detailed schedule. Additionally, we adopt the
sampling average approximation (SAA) technique to address uncertainties in surgery duration. Based on the samples generated from reallife data, the simulation results imply that i) the surgery schedule could
be fully scheduled with fewer number of nurses; ii) the nurse shortage/absence issue can be potentially avoided as we can accommodate
uncertain duration better.

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WD-18EURO 2024 - Copenhagen

3 - A two-stage operating room scheduling method for
reducing hospital surgical waiting times for elective
surgery lists

Victor Riquelme
Chile’s public healthcare system covers nearly 80% of the population.
Despite its large coverage, it is characterized by long waiting times of
its waiting lists. Our objective is to efficiently schedule hospital operating rooms for elective surgeries, to reduce the mean and medianwaiting time of the surgical waiting list. Our case study corresponds
to a high complexity public pediatric hospital in Santiago, Chile. The
hospital works based on morning and afternoon time blocks, each to
be allocated to a single specialty for each operating room. Overtime is
only allowed for long surgeries.
For a given planning horizon, our strategy consists of two stages. First,
we assign the medical specialties to the operating rooms, days, andblocks, by solving an integer linear programming problem that balances the available time in the planning horizon with the required
surgery time demanded by each specialty. This considers variables
such as the number of hours per block and the availability of surgeons
per specialty and day. Second, we assign patients to the blocks basedon one or more efficiency metrics. For instance, if only waiting time isconsidered, priority is given to patients with longer waiting times.
The proposed scheduling model, although suboptimal, is faster thanclassical single-stage integer linear programming approaches, andshows more flexibility, as patients can be rescheduled without the needto rerun the scheduling once specialties are assigned to blocks.

4 - Integrated surgery and staff scheduling using column
generation

Jens Brunner, Stefanie Ebel
Physicians and operating rooms are two of hospitals’ most important,expensive, and scarce resources. Therefore, effective and efficientscheduling of these resources is among hospitals’ most relevant planning tasks. The decisions to be made on the daily planning level regarding sequencing patients’ surgeries and assigning appropriate staff
to surgeries. Since there are several interdependencies between surgery
schedules and physician rosters, it is a meaningful approach to consider both planning problems within one integrated optimization problem. We provide mixed-integer programming models within a column
generation algorithm that solves both scheduling problems. We create schedules for surgeries and operating room staff. We use test data
based on a real-world dataset to provide meaningful insights. The algorithm leads to optimal solutions in more than 75% of all test casesand solves the problem efficiently within a desirable amount of time.
We further evaluate our algorithm with respect to different aspects offlexibility in the context of surgery and staff scheduling and generate
key insights about their interdependencies.

 WD-18
Wednesday, 14:30-16:00 - Room: 42 (building: 116)Developing Countries and Sustainable
Humanitarianism
Stream: OR for Development and Developing Countries
Invited session
Chair: Nina Kajiji
Chair: Gordon Dash
Chair: Milagros Baldemor
1 - Exploring subscription-based online services (SOS) for
sustainable businesses of indigenous cottage products: a behavioural study on cottage textile subscription via SEM

Shivam Singh, Nomesh Bolia

446

SOS is an e-commerce format that is getting popular across many products like apparel, food, medicine, baby care, cosmetics, and more.
Many brick-and-mortar firms are adopting SOS as their revenue model.
SOS can efficiently provide sustainable revenue to industries facing
economic sustainability issues, like the cottage industry, and help contribute to SDG 8. However, it is crucial to analyse customer’s intention
to subscribe to any SOS. Thus, using the structural equation modelling
method, this study analyses factors related to consumer attitude, motives, and barriers affecting the intention to subscribe (ITS) to handcrafted textiles that face sustainable revenue issues. This study is novel
as the variables include product consumption and subscription viewpoint. Multiple theories from the literature support the model, which
improves the model’s generalizability. Results show that consumers’
attitudes and motives towards consumption and subscription positively
affect ITS. In contrast, perceived risks, market and consumption barriers negatively affect ITS. This work finds availability and cost issues
as significant obstacles to cottage textile consumption. SOS’s doorstep
availability and reasonable price attributes can shift such consumers
towards availing cottage textile subscriptions. SOS retailing thus indicates a positive market outlook. The results are checked for demographic and psychographic group differences that enhance the market
implications of this study.

2 - Understanding donation patterns in the immediate aftermath of sudden onset disasters

Can Erzi, Raha Akhavan-Tabatabaei, Nezih Altay
The question of why and how humans choose to help others without
expecting anything in return has puzzled philosophers, psychologists,and economists for centuries. This becomes even more important during Sudden Onset Disasters (SOD), where understanding donor behavior plays a critical role in efficient management of HumanitarianSupply Chains. Demographics, trust, and personal history all have an
impact on the individual’s donation decisions. Another factor is theperceived closeness to the victims. "Psychic Distance" (PD), a concept borrowed from international business literature, is adapted to describe donor-victim relationship in terms of shared personal history
and cultural closeness. We propose another dimension to the PD under
SOD conditions: donors’ temporary mental, physiological and financial states. Since decisions during SODs are taken within a short timeframe, we expect these contextual factors to play a significant role inthe donation decisions as well. Through conducting an experimental
design, we study donation decisions of the subjects while their psychology and physiology are manipulated via different priming methods. The objective is to observe changes in intention to donate, anddonation preferences, while the perceived contextual PD is modified.

3 - Role of perceived over qualification and job crafting in
mitigating cyberloafing: A moderated-mediation model

Nivedita Jha
This research aims to develop and evaluate a model that explores therelationship between perceived overqualification, job crafting and employee cyberloafing. Drawing on job-demand resource theory, themodel proposes that employee engagement serve as an explanatory
factor in this association. Additionally, it has been suggested thatthe ambidextrous leaders also positively impact employee engagement
which in turn reduce cyberloafing.

4 - The Role of Ethics and Altruism: Mapping the Determinants of Healthcare Accessibility for Medicare Beneficiaries

Nina Kajiji, Gordon Dash, Miriam Dash
It is widely acknowledged that healthcare providers’ interests may diverge from those of patients. In and of itself, healthcare accessibility
is a complex measure rooted in the ability of an individual to receive
healthcare effectively with demonstrated anxiolytic effects. Annually,
the Center for Medicare and Medicaid Services gathers survey datafrom current beneficiaries of Medicare in the United States and Puerto
Rico (excluding healthcare facilities) to assess healthcare equity. Thesesurveys delve into chronic risk factors associated with various diseases
and mental health conditions. This study innovates extant Medicare
patient care models by identifying the error-minimizing determinants
underlying the probability of delivering a correct chronic condition diagnosis. We propose a novel multiobjective and multi-target shallowlearning radial basis function artificial neural network specification forEURO 2024 - Copenhagenclassification. Using Explainable AI, we provide policy-based results,
allowing providers to understand Medicare patients’ disease pathology
better. Diagnosticians are likely to have a lesser financial incentive torecommend profitable procedures regardless of appropriateness, reducing poor quality and costly care when providers are profit-incentivized
instead of generous.

 WD-19
Wednesday, 14:30-16:00 - Room: 44 (building: 116)

OR in Energy III
Stream: OR in Energy
Invited session
Chair: Sicheng Gong
1 - Coordinated Medium- and Low-voltage Distribution
Systems Planning Under Volatile Energy Sources Deployment

Alfredo Ernesto Oneto, Blazhe Gjorgiev, Giovanni Sansavini
Efforts to mitigate emissions have led to the introduction of more distributed and volatile generation, such as photovoltaics, along with flexible and smart loads. This shift strains the reliability of power distribution systems (PDSs), which are expected to adapt to these challenges.
Volatile energy sources further affect the interaction between mediumvoltage (MV) and low-voltage (LV) levels in PDSs. Hence, distribution
system operators need novel strategies for efficient capacity enhancements.
The distribution expansion planning (DEP) problem seeks optimal
PDS design, minimizing investment and operational costs while meeting service requirements. Existing literature on DEP often overlooks
the interplay between LV and MV systems. However, isolated investment plans may not remain optimal (or even feasible) for integrated
MV/LV systems, necessitating coordinated models. This work addresses the gap by exploring coordinated DEP for MV and LV systems
with volatile energy sources.
We leverage spatial aggregation techniques for large-scale planning.
To evaluate the proposed approach, we utilize representative PDSs in
Switzerland. This research enhances the adaptability and efficiency
of modern PDSs amidst the increasing deployment of distributed andvolatile energy sources.

2 - Robust Optimization for Long-term Uncertainty in DER
Sizing and Placement in Distribution Networks

Fernando García, Sebastián Dávila, Franco Quezada, Cristian
Duran MatelunaThis study comprehensively compares stochastic programming and robust optimization approaches for handling long-term uncertainty in the
optimal sizing and placement of distributed energy resources (DERs)
in LV distribution networks. The study covers various optimization
models, ranging from a deterministic model to a two-stage stochastic programming approach, single-stage robust optimization, adaptive
robust optimization (ARO), and a hybrid variant of stochastic robust
optimization, considering the electricity load and power generated by
PV systems as random variables. A decomposition method approachhas been used to address the three levels raised by robust optimization
using primal and dual cuts. The models have been tested using a modified version of the IEEE 33 bus system, comparing mainly the ARO
with the hybrid models because the hybrid model allows the handlingof PV generation through a stochastic approach and the electricity demand by a robust method, which could be more suitable to represent
their uncertainty in long-term scenarios. Thus, the results show that the
hybrid approach i) reduces the convergences times, ii) reduces around
80% on average the number of iterations regarding the ARO for different Budget of Uncertainty (BoU) levels, and iii) delivers a notably lessconservative solution than the ARO.

WD-22

3 - Optimal Integration of Microgrids in Smart Grids During
Cold-Load Pick-Up

Levan Pavlenishvili, Miguel Anjos
Renewable energy generation in microgrids can have an important rolein the decarbonization of electricity networks. Microgrids can also
provide ancillary services to the main grid during different operational
circumstances. This paper explores the use of microgrids during coldload pickup (CLPU) phenomena, which are typical after an outage indistribution networks with high share of thermostatically controlled
load (TCL). As load loses diversity after an outage CLPU phenomena is a drastic increase after re-connection. We present a mixedinteger linear programming (MILP) formulation for scheduling service restoration (SR). The proposed model computes the optimal reconnection sequence, generation and storage levels for the microgrids
as well as power supply level from the main grid. Unlike existing literature this paper uses a network with multiple microgrids. It also allows
for different conditions at the microgrids: with some operating in islanded mode and having diversified load, while others being in outage
and having to do service restoration with CLPU. The model is tested on
different modifications of the IEEE-13 feeder network. The proposed
approach is suitable for practical SR scheduling and to test the CLPU
capabilities of a distribution network during the planning process. The
model formulation has the flexibility to allow for additional improvements in precision and complexity in estimating its key components.

4 - Geometric Programming for Optimal Hosting Capacity
Allocation in Distribution Grids

Sicheng Gong, Koen Kok, Sjef Cobben
The hosting capacity determines the operational threshold for each
integrated energy unit. During hosting capacity allocation, mixed
load/generation profiles and intrinsic non-convex network models posesignificant computational challenges. This paper introduces an alternative approach for optimal capacity allocation, drawing inspirationfrom constraint conversion and geometric programming. This approach promises to achieve superior computational speed and a highquality optimal solution. By initially assessing the multidimensional
operational feasible region for involved units, the model simplifies
by excluding voltage and current variables, thus reducing complexity.
Through model reformulation guided by geometric programming, themodel convexity will be established, thus further improving both the
speed and solution quality of this solving process. Relevant case studies reveal that the computation time could be reduced at most by 88.3%
by adopting the proposed approach. Due to model convexity, evena higher-quality solution can be achieved accordingly, which yields
a better objective function value compared to that using the Gurobi
benchmark solver. These findings underscore the penitential of thisapproach on computation performance enhancement when determining optimal hosting capacity allocation in electricity networks.

 WD-22
Wednesday, 14:30-16:00 - Room: 81 (building: 116)

Empowering Energy AccessStream: Energy Management
Invited session
Chair: Ayse Selin Kocaman
1 - A Strategic Framework for Maximizing Utilization of
Minigrids in India: Achieving Financial Sustainability
and Productive Energy Integration

Madhav Sharma, Anoop Singh
Ensuring affordable and reliable access to energy in rural communities remains a critical challenge for achieving Sustainable Development Goal 7 (SDG7). Minigrids (MGs) powered by renewable energy(RE) sources offer a potential solution, but their long-term financial
sustainability remains a hurdle. This paper proposes a novel approach
employing strategic optimization to enhance MG financial viability447

WD-23EURO 2024 - Copenhagenthrough the integration of productive use of energy (PUE). The framework meticulously sizes MGs and generation technologies (solar PV,
biomass, etc.) to align with varying seasonal consumer demands. This
optimization approach aims to match periods of low-capacity utilization with peak energy processing activities, consequently reducing thelevelized cost of electricity (LCOE) for MG users. The benefits are
twofold: increased capacity utilization and reduced operational costs,
leading to significantly lower consumer tariffs. Through a comprehensive case study, this research demonstrates the effectiveness of strategic
optimization in meeting energy needs while ensuring the long-term financial viability of MGs. Notably, the findings emphasize the criticalrole of improved capacity utilization in fostering MG financial sustainability. This research offers valuable insights for policymakers, practitioners, and stakeholders in the RE sector, demonstrating the potentialof optimally designed MGs to drive lasting positive change in ruralcommunities.

2 - Bridging the gap: multi-stage optimization for electricity access

Gözde Yazıcı, Oya Ekin-Karasan, Ayse Selin Kocaman
Approximately 10% of the global population lacks access to electricity, with a significant portion residing in rural areas, underscoring theimportance of rural electrification efforts. In this study, we focus on determining the optimal technology choice and network design for rural
electrification at macro level. To this end, we propose a series of MILP
models that minimize costs by strategically allocating settlements tooff-grid, grid, or mini-grid options. Furthermore, we introduce multistage versions of these models to guide optimal investment decisions
over time, addressing the practical challenge of roll-out electrificationplans. Through real-life and synthetic instances, we demonstrate thepotential cost advantages of our proposed enhancements.

3 - Hydrogen Refueling Station Allocation Problem: A Case
Study for İstanbul

Saliha Büşra Gündüz, Ebru Geçici, Mehmet Güray Güler
Researchers and countries are turning to alternative energy sources as
the existing energy sources are insufficient to meet the demand. Hydrogen energy is one of the alternative energy sources and one of its main
potential contributions will be through the Hydrogen Fuel Cell Vehicles (HFCVs). To ensure the widespread use of HFCVs, hydrogen fuelmust be easily accessible. The number and location of hydrogen refueling stations (HRSs), therefore, have a crucial role for the usage ofthe hydrogen energy via HFCVs. The subject of this study is to determine of the number and locations of HRSs for Istanbul, Türkiye’s most
crowded city. The adaptation to hydrogen technology for each district
is modelled using a measure for life quality called human development
index which is then used to determine the HFCVs’ demand based on
traffic flow data. We employ three different approaches for modelingthe problem in a multi period setting: p-median model, a set coveringmodel, and a hybrid model of both. It turns out that ignoring the transition of adopting hydrogen technology may result in a significant loss.
Moreover, instead of spreading from the center to the city boundary,
the stations appear at both low populated and high populated regions
from early periods of the 30 years planning horizon. Finally, very few
numbers of HRSs are sufficient to meet high demand, but the number
increases significantly if all demand should be satisfied.

4 - A facility location-allocation problem for rural electrification

Ayse Selin Kocaman, Beste Akbas
One major impediment to developing countries’ economic growth isthe lack of access to affordable, sustainable, and reliable modern energy systems. Even today, hundreds of millions of people live in rural areas and do not have access to essential electricity services. In
this study, we present a planar facility location-allocation problem forplanning decentralized energy systems in rural development. We consider nano-grid and micro-grid systems to electrify rural households.
While micro-grids serve multiple households with a common generation facility, nano-grids are small-scale systems serving individual
consumers. The households served by micro-grids are connected to
the generation facilities with low-voltage cables, for which we employ
a distance limit constraint due to technical concerns, including power

448

loss and allowable voltage levels. For this problem, we provide mixedinteger quadratically constrained problem formulations and propose
model-based and clustering-based heuristic approaches.

 WD-23
Wednesday, 14:30-16:00 - Room: 82 (building: 116)

Information sharing in sustainable supply
chainsStream: Circular Economy, Remanufacturing and Recycling
Invited session
Chair: Christos Papanagnou
1 - Impact of Information Availability in Processing Unsolicited Donations

Adhurim Imeri, Gerald Reiner
Unsolicited in-kind donations, such as textiles, pose significant challenges to humanitarian organisations (HOs) due to the uncertainty ofdonor supply in terms of quantity and quality of items. To enable flexible operations, HOs deploy donation containers in urban areas. Theseare processed and the items which they collect are sold as second-hand
textiles in retail outlets. However, labour-intensive processing, sorting and waste make it difficult for HOs to sell all donations throughretail outlets. Instead, a portion is exported in bulk without sorting to
reduce costs. This paper presents a simulation model to understandthe processes behind unsolicited textile donations. The results showthat the critical process factors are the interplay between local demand
for used clothing, the holding costs of storage, and the proportion ofwaste in donations. The challenge is to avoid shortages in emergency
situations without incurring additional storage or disposal costs. In thiscontext, the paper uses an allocation problem to prioritise the sorting ofdonation containers. This would be facilitated by a platform that processes donor information before the items are dumped in the containers. Understanding sorting processes and donor behaviour is essential
to optimise the impact of unsolicited donations on HOs. This study
derives actionable implications for such operations and the critical roleof donor education in reducing wasteful donations. It also highlights
the need for policy

2 - Mitigating the bullwhip effect on supply chains withmultiple retailers by horizontal information sharing

Martin Hrusovsky, Emel Arıkan, Lena Silbermayr
Bullwhip effect describes the increasing magnitude of fluctuations inorders in the upstream direction within supply chains that lack proper
coordination due to various reasons. One of them is incomplete information sharing about demands, orders or inventory levels, which is
caused by the reluctance of supply chain actors to share sensitive information that might increase their vulnerability in negotiations or leakto other actors, e.g. competitors. As a consequence, full information
sharing is very often not applied in practice despite its positive effects
confirmed by a vast amount of research papers.
As an alternative, we propose to use cryptographic methods, such as
secure multi-party computation or homomorphic encryption, that enable to share necessary information (e.g. a certain demand signal ortrend) without revealing the exact values of each individual partner.
We implement this approach on a 4-stage supply chain with multiple
retailers using order-up-to policy where the information about average
market demand is shared horizontally between the retailers. In our test
we consider factors such as demand variation, correlation of demand or
number of retailers sharing the information between each other and weshow the positive effect that this information sharing has on mitigating
the bullwhip effect.

EURO 2024 - Copenhagen
3 - Benefits of Vendor-Managed Inventory Strategies for
the Reverse Logistics Operations in Closed-Loop Supply Chains

Vivien Schoepf, Arne Strauss
As closed-loop supply chains (CLSCs) are gaining importance in manyindustries as key enablers of reduced waste and ecological footprint,
new challenges arise in the integration of forward and reverse logistic
flows. This research focuses on a variation of the production inventory problem with open routes and explores the benefits of vendormanaged inventory (VMI) systems for the reverse logistics operationsof reusable transport items (RTIs). A stochastic two-stage program
considering uncertainty of return flows is developed and solved under use of sample average approximation (SAA) and relaxation-based
bounds. We find that the benefits of vendor-managed inventory strategies are not only linked to lower operational cost but also lower stockout risk in long-term planning. Further, in hybrid networks of partially
VMI and non-VMI nodes, the strategic choice for which nodes to implement VMI can positively impact the network performance. Thisapplication of VMI in reverse logistics has been scarcely explored in
the field and provides new managerial insights for practitioners in theindustry.

4 - Probabilistic Control for Reducing the Bullwhip Effectin Recycling-Enhanced Supply Chains

Christos Papanagnou, Randa HerzallahThis study introduces a decision-making methodology employing
probabilistic control to reduce the bullwhip effect within recyclinginclusive closed-loop supply chains. Replenishment policies are refined to incorporate recycling operations, and probabilistic controllers
are utilised to manage inventory and information flow dynamics. We
construct a state space model that encompasses inventory levels, information flow, and recycling activities, capturing their complex interrelations. This model is analysed under stationary conditions, withstochastic customer demand characterised using conditional probability density functions, providing insights into supply chain behaviours.
We explore the characteristics of the bullwhip effect and assess theoverall stability of the supply chain. Simulations are conducted to evaluate the effectiveness of our probabilistic control strategy in managing
and stabilising the supply chain during such disturbances. The ultimate
goal is to enhance supply chain efficiency and resilience in the face of
uncertainties through this innovative control approach.

 WD-24
Wednesday, 14:30-16:00 - Room: 83 (building: 116)

Sustainable supply chains
Stream: Circular Economy, Remanufacturing and Recycling
Invited session
Chair: Xiang Zhu
1 - Circular economy application in pharmaceutical supply
chains in the UK: a holistic evolutionary game approach

Nazanin Nami, Grigory Pishchulov, Joao Quariguasi Frota
Net
The environmental hazards of improperly managed waste have gained
universal recognition among scholars and stakeholders. These hazards
are especially critical in the pharmaceutical sector as leftover medications contain active chemicals that threaten the environment and human health. Nonetheless, implementation of adequate measures to ensure proper collection and treatment of pharmaceutical leftovers remains insufficient, and tons of unwanted medications are discarded inlandfills and wastewater annually. This is due to lack of coordination
between the parties and poor incentive systems. To address this issue,

WD-24

we study coordination in pharmaceutical reverse supply chains andgovernment incentive strategies. We employ the evolutionary gamemethodology to evaluate strategic behaviour of pharmacies, a recycler,
and the government under four incentive plans. We compare the incentive plans based on return volume, participation rate, cost, and implementation time, to recommend the most effective plan. We conduct
a numerical study to gain insights into the performance of the incentive plans in different conditions. The results reveal that a plan thatprovides proper incentives to pharmacies for targeting both, rewardand awareness-driven customers, coupled with contract-based coordination, outperforms other plans, and does not necessarily require a budget allocation. Our study is based on the UK’s National Health Systembut it is generalisable to other countries as well.

2 - Demand response control of wind-based electrolyzed
oxygen for advanced activate sludge wastewater treatment

Qipeng Liu, Yi Zheng, You Shi, Krist V.Gernaey
Green hydrogen has been regarded as a promising substance for sustainable energy conversion and storage. However, the by-productpure oxygen, from water electrolysis, has not yet been widely utilized. Meanwhile, the municipal wastewater treatment system is facing great challenges to upgrade its original capacity and the advanced
tertiary treatment. This work proposes to integrate electrolysed oxygen with activated sludge aeration and effluent post-oxidation. An onsite wind/multi-electrolysers’ water electrolysis/oxygen model is simulated to satisfy the potential oxygen demands from the Benchmark
Simulation Model No.2 (BSM2). The oxygen productivity fluctuates
because of the uncertainty from intermittent wind energy; meanwhile,
the oxygen demands of aeration and effluent oxidation are also nonlinear dynamic variables. The imbalance of day-ahead supply anddemands of oxygen is solved by a virtual oxygen storage unit in themodel. The optimization goal is to: maximize the real-time oxygen
applications of aeration or effluent oxidation; minimize the volume requirements, and usage counts of oxygen storage units. To simplify thesimulation system by making the oxygen production and demands deterministic, the short-term forecasting intelligence of linear regression(LR) algorithm is employed. Then the LR is incorporated with reinforcement learning (RL) for scheduling flexible control of oxygen dispatches, so as to make wastewater treatment processes more efficientand accurate.

3 - Optimizing Carbon Capture, Utilization, and Storage
Strategy: A Cap-and-Trade Integrated Approach forPower Companies

Xiang Zhu
In pursuit of carbon peaking and carbon neutrality goals, this conference presentation introduces a comprehensive approach: the integration of the "carbon capture, utilization, and storage" (CCUS) strategy
with the "cap and trade" principle. Addressing the challenge of securing commitments for both carbon capture and investment within
the power sector, our proposed model incorporates linear CO2 storage, emissions allowance deposit, stochastic CO2 utilization demand,
mean-reverting Lévy jump allowance price process, and a stochastic
CO2 generation process during production. We present the derived
optimal commitment policy, considering constraints such as physicalcarbon storage limitations and rational allowance deposit considerations. Through impact analysis, we demonstrate that our optimal policy allows companies to dynamically adjust investment levels to mitigate risks arising from uncertainties in CO2 generation and utilization
demand. Specifically, under the cap and trade principle, companies
can flexibly adapt to cover excess emissions or capitalize on remaining
allowance deposits, thus enhancing profitability after achieving the optimal CO2 capture level under the CCUS strategy. This framework notonly contributes to meeting environmental objectives but also provides
a strategic and adaptive approach for power companies to navigate thecomplexities of carbon management.

449

WD-25EURO 2024 - Copenhagen WD-25
Wednesday, 14:30-16:00 - Room: 011 (building: 208)

Topics in Combinatorial Optimization II(Contributed)
Stream: Combinatorial Optimization
Invited session
Chair: Johan Springael
1 - Online Exploration of Rectilinear Polygons by Multiple
Searchers

Taro Abe, Yuya Higashikawa, Shuichi Miyazaki
We consider a problem faced by multiple searchers that have to explore
an unknown simple polygon P. At the beginning of the exploration, all
the searcher are located at some point inside P, called the origin, andthe exploration finishes when any point on the boundary of P is seen byat least one searcher and all the searchers return to the origin. The costof the exploration is the time needed to finish the exploration, and theobjective of the problem is to minimize it. We consider an online exploration, where the searchers create a map of P during the exploration
and use it to decide which direction to move.
In this paper, we consider a restricted class of simple rectilinear polygons, which can be created by, starting from the root rectangle, recursively attaching a base of a new rectangle to a lateral side of somerectangle constituting the current object. We present an online exploration algorithm for this class of polygons, and show an upper boundon its competitive ratio that is sublinear in the number of searchers.

2 - The Robust Diversity Difference Problem

Martin Frohn, Kerry Manson
A phylogeny is an edge-weighted binary tree with leaves labeled bya collection of species that represents the evolutionary relationships
between the species. Given a phylogeny, a popular measure for thebiodiversity of a subset of the species is the phylogenetic diversity, defined by the sum of the edge weights in a minimal spanning subtreeof the phylogeny leaf-labelled by the subset. However, if we want tomake conservation efforts for particular species, then we consider aphylogenetic diversity index, a function that apportions the biodiversity of a subset across all of its species. In general, for a fixed subset of
species, the difference between the phylogenetic diversity and the sumof phylogenetic diversity indices is a non-negative function, called thediversity difference. We study the combinatorics of phylogenetic diversity indices and show that the maximum robust diversity difference
for a fixed number of species can be calculated in polynomial time.
Furthermore, we provide computational evidence to compare the quality of the optimal robust diversity difference index with other popular
phylogenetic indices from the literature in practice.

3 - Computing the effect of multiple cost changes in combinatorial problems

Marcel Turkensteen, Gerold Jäger
In this presentation, we consider sensitivity analysis to combinatorial
optimization problems (or binary programming problems). In these
problems, there is a ground set of elements, each of them with a given
cost. The objective is to find a feasible combination of elements which,in our case, minimizes a total objective value. Sensitivity analysis
concerns finding the range of changes in cost values such that current solutions remain optimal. The supremum decrease and increasein a single element’s cost, the lower and upper tolerances, respectively,
can be computed by solving an additional instance of the problem.
When k element costs increase or decrease simultaneously, there isa k-dimensional area in which solutions remain optimal. Jäger et al.
(2018, 2023) introduce set tolerances as the infimum and supremum
sum of cost changes that lie inside and outside that area. For problems
with sum objective, it requires solving an exponential number (in k) of
instances to optimality to describe the area (Andersen et al, 2023) andto compute set tolerances. We present new results which show that set
tolerances of problems with a bottleneck objective (the objective value

450

is the largest cost of the elements in a solution) require the solution of
only one problem instance. We also show that tolerance computations
can be independent of the choice of optimal solution.

4 - On the optimal ranking maximizing a median rank correlation coefficientJohan Springael
With the abundant availability of data, the importance of proper clustering algorithms has been growing over the past decades. This is evenmore the case when the objects to be clustered can be represent by either total or partial rankings. A well-known clustering method is theso called k-medoid algorithm. Still the major question remains how todefine the median object of a cluster and what is the best one? The similarity measure to adopt in the clustering algorithm when comparing
rankings is Kendall’s rank correlation coefficient. Here we investigate
the subproblem of maximizing the median of a set off rank correlation
coefficients so as to determine the best median ranking by determining
the optimal ranking. Since the optimal median ranking has to be determined, the rank correlation coefficients will also be variable. Hence,the sorting of Kendall’s tau’s in order to determine the median in the
optimization model should be incorporated. In case tied rank values areabsent, we will show that this optimization problem can be re-written
as a linear mathematical program. If however ties are present, the linearization will only be possible if the different possible partitions of
the rank values are preprocessed.

 WD-26
Wednesday, 14:30-16:00 - Room: 012 (building: 208)

Combinatorial optimization issues intransportation (Contributed)
Stream: Combinatorial Optimization
Invited session
Chair: Dang Viet Anh Nguyen
1 - Two-stage Time Minimizing Bulk Transportation Problem

Prabhjot Kaur, Meenakshi Mittal
This paper discusses a two-stage bulk transportation problem and develops a polynomial bound iterative algorithm to find its optimal solution. In this problem, the set of destinations is divided into two disjoint
sets consisting of ’primary’ and ’secondary’ destinations, respectively.
Due to limited availability of vehicles, the company prefers to transport
the goods to the primary destinations first (in Stage-I) and once the demand of the primary destinations is met, the secondary destinations
are catered later (in Stage-II) from the left-over availability at various
sources. Like a standard bulk transportation problem, the demand of adestination must be fulfilled by one source only, however, one source
can cater more than one destinations. The transportation time corresponding to each source-destination link is considered to be known.
The objective of the problem is to find such a transportation schedule that provides the minimum sum of transportation times of both thestages. The proposed algorithm, at each iteration, solves a related cost
minimizing transportation problem and one of its restricted variants,
using a branch and bound technique and converges systematically, tothe optimal objective value of the problem. The efficiency of the proposed algorithm is validated through various numerical illustrations.
Further, the performance of the algorithm, in terms of CPU time, forvarious randomly generated instances, is also provided.

2 - Exploring the intersection of Capacitated Vehicle Routing Problem and Centroid-Based Clustering

Abdelhakim Abdellaoui, Loubna Benabbou, Issmail El
HallaouiEURO 2024 - Copenhagen
Efficiently solving a vehicle routing problem (VRP) is critical fordelivery management companies. This work explores the theoretical and experimental connection between the Capacitated Vehicle
Routing Problem (CVRP) and Constrained Centroid-Based Clustering(CCBC). Transitioning from CVRP to CCBC represents a shift fromexponential to polynomial complexity, utilizing commonly known
clustering algorithms, such as K-means.
Our analysis highlights the intrinsic relationship between these problems through illustrative examples and deduced mathematical properties, emphasizing how achieving optimal or near-optimal CVRP solutions strongly depends on starting from specific CCBC centroid regions.
We then propose a CCBC-based approach enhanced to address CVRP,
operating in three stages: a constrained centroid-based clustering algorithm generates customer clusters, integrating multi-start centroids,a refined customer assignment metric, and dynamic cluster count adjustment. Subsequently, a Traveling Salesman Problem (TSP) solver
optimizes customer sequencing within clusters.
Moreover, our study explores the role of reinforcement learning in
identifying centroid regions leading to optimal or near-optimal CVRP
solutions. Demonstrating improvements in solution quality and computational efficiency, our findings suggest a transformative approachfor delivery management challenges, offering a significant milestone
in solving CVRP.

3 - Optimizing Aircraft Line Maintenance Scheduling: A Reinforcement Learning Approach

Syed Shaukat
In the dynamic and complex realm of the airline industry, efficientand reliable scheduling and execution of aircraft maintenance are crucial. Significant challenges in the optimal scheduling of aircraft line
maintenance arise from the need to balance maintenance requirementswith the limited availability of resources and dynamic operational constraints. Traditional approaches typically employ optimization-based
methods to address these issues. However, these methods often struggle to scale up in real-world applications, along with the uncertainties
and non-linearities prevalent in such scenarios. Recent advancements
in airline scheduling signal a paradigm shift, with simulation-based,
data-driven, and AI-enhanced approaches superseding conventional
optimization models. This paper introduces an innovative approachthat capitalizes on machine learning by integrating a hybrid simulation model with a reinforcement learning (RL) algorithm and dynamic
programming search.
Utilizing a hyper-heuristic learning approach, the RL agent explores
and learns the optimal scheduling of maintenance tasks, encompassing a criterion-based action space and considering various factors suchas aircraft availability, resource allocation, and maintenance requirements. The results surpass the performance of existing optimizationbased methods and offer valuable insights that may be applicable toother industries facing similar scheduling challenges in the transportation and supply chain sectors.

4 - GRASP reinforced by Evolutionary Path-relinking for
two-echelon covering tour vehicle routing problem

Dang Viet Anh Nguyen, Rajesh Piplani, Aldy Gunawan
We introduce the two-echelon covering tour vehicle routing problem(2E-CTVRP) - a variant of the two-echelon vehicle routing problem toaddress the transportation and provision of essentials in humanitarian
logistics. In the first echelon, a fleet of trucks is responsible for transporting supplies and drones from the depot to satellites located near
the affected area. The drones then take on last-mile delivery from thesatellites to the demand cluster centroids in the affected area. A clustering algorithm is employed to group the victim location and determine
the delivery points within the clusters. This study formulates the 2ECTVRP as a Mixed Integer Linear Program (MILP) with the objectiveof minimizing the sum of arrival time of all trucks. A hybrid metaheuristic, called the Greedy Randomized Adaptive Search Procedure
with evolutionary path relinking (GRASP-EvPR), is proposed to solve
larger instances within reasonable computational time. The numericalresults show that GRASP-EvPR can effectively handle the proposed

WD-27

instances in terms of solution quality and computational time. Furthermore, this study compares the effects of different truck fleet types
on three dimensions of humanitarian logistics: efficiency, efficacy, andequity.

 WD-27
Wednesday, 14:30-16:00 - Room: 047 (building: 208)

Machine Learning for and with Mathematical
Optimization
Stream: Mathematical Optimization for XAI
Invited session
Chair: Cristina Molero-Río
1 - Contextual Stochastic Optimization using Mirror Progressive Hedging

Luca Ferrarini, Louis Bouvier, Axel Parmentier, Vincent
LeclerePrediction and optimization algorithms can be merged to address decision problems involving uncertain parameters linked to known contextual information. Within the framework of Contextual Stochastic Optimization, in this talk we explore the integration of learning models,such as neural networks, with optimization components. Our objective
is to learn from a range of policies the most effective one, each parameterized by a neural network. In the specific, our setting is based on
hybrid pipelines composed of a machine learning layer where we consider policies encoded by a neural network and concluded by a combinatorial optimization layer. Then, we aim to minimize the expected
empirical risk loss on the data training set. To tackle this challenge,
we introduce two novel algorithms: the Bregmann Progressive Hedging algorithm, based on a variant of the Progressive Hedging, and thePrimal-Dual Mirror Descent algorithm. We present preliminary computational results on the minimum two-stage stochastic spanning tree
problem and compare the performance of our learned policy with awell-tailored strategy designed for the problem, but heavily computationally. Our results show that our policy achieves good performancelevels while being significantly less computationally demanding.

2 - A data-based approach for solving the Rank Pricing
Problem

M. Asuncion Jimenez-Cordero, Salvador Pineda Morente,
Juan Miguel Morales
The Rank Pricing Problem is a challenging mixed-integer optimization
problem. It aims to determine the optimal pricing strategies of a set of
products ranked by customer preferences. Given its NP-hard nature,
existing literature offers various exact methodologies. However, theseapproaches can be intricate to formulate and computationally intensive. In contrast, in this talk, we propose a novel data-based methodology that is simple but effective. Even though our heuristic proposal
cannot guarantee to obtain the optimal solution, the numerical resultsin different instances show its capacity to deliver high-quality results,
providing a pragmatic alternative within a short computational timeframe.

3 - Hierarchical clustering with Mixed Integer Linear Optimization Models

Lorena Nácher, Mercedes Landete, Marina Leal Palazón,
Vanesa Guerrero, Martine Labbé
Dendrograms are graphical representations of hierarchical clusteringthat illustrate how individual elements can be grouped into clusters.
Depending on the chosen distance measure, different dendrograms canbe obtained. The purity measure is used to evaluate the quality of thedendrograms. Purity is defined in terms of the homogeneity in the
distribution of observations in the dendrogram with respect to theirclasses. The first part of this work focuses on single-linkage dendrograms and on purity measure. We introduce the first mathematical optimization model to calculate the purity of single-linkage dendrogramsin hierarchical clustering with labeled data. This mixed integer linear451

WD-28EURO 2024 - Copenhagenmodel is based on the ordered median problem. The second part addresses complete-linkage dendrograms. We propose a system of linear
inequalities whose solutions are complete-linkage dendrograms. We
analyze the impact of ties in the cost matrix in the system and provethat when there are no ties the solution of the system is unique and is
the complete-linkage dendrogram while when there are ties the system
has as many solutions as alternatives complete-linkage dendrograms
the matrix has. This study contributes to understanding the construction and evaluation of dendrograms in hierarchical clustering.

4 - On the optimization of risk scores for continuous predictors

Cristina Molero-Río, Claudia D’Ambrosio
In this talk, we propose a novel Mixed-Integer Non-Linear Optimization formulation to construct a risk score. A trade-off between prediction accuracy and sparsity is sought. Previous approaches are typicallydesigned to handle binary datasets, where numerical predictor variables are discretized in a preprocessing step by using arbitrary thresholds, such as quantiles. In contrast, we allow the model decide for eachcontinuous predictor variable the particular threshold that is critical for
prediction. The resulting optimization problem is tested in synthetic
and real-world datasets.

 WD-28
Wednesday, 14:30-16:00 - Room: 065 (building: 208)

Advancements of OR-analytics in statistics,
machine learning and data science 11
Stream: Advancements of OR-analytics in statistics, machine learning and data science
Invited session
Chair: Antonio Mauttone
Chair: Gerhard-Wilhelm Weberpoor waste stream management. This study aims to propose a modeling of the deconstruction sector that optimizes dismantling operations and their associated waste flows to raise the recovery rate of thewaste, thereby lowering both the financial and environmental costsof the field. This model of the deconstruction sector is based on abi-level problem. Its upper-level part encompasses the scheduling ofoperations. The latter hinges on a weighted flexible job shop problem (wFJSP) with release dates and is assessed by a non-regular criterion. We propose a new hybrid disjunctive graph genetic algorithm(HDGGA) for wFJSP, integrating into a genetic algorithm, based ona new encoding, a local search approach employing a novel extensionof the disjunctive graph representation that takes account of the idle
machine times. The lower-level problem models the allocation of thewaste induced. Experiments are carried out on two sets of instances,including one derived from data collected amongst the actors of the
field working in the Lille metropolis. The first results exhibit the interest in the genetic-based hybrid method proposed, indicating that it
outperforms the existing genetic algorithm-based approach, especiallyin medium-sized and large-sized instances.

3 - Electrical energy demand forecasting for Türkiye

M. Mücahit Denk, Ceyda Oguz
This study aims to compare the performances of different forecasting
techniques to predict electrical energy consumption of Türkiye. The
literature on energy demand forecasting generally focuses on multiple
regression analysis and time series methods such as ARMA, ARIMA,SARIMAX methods. More recently, machine learning methods havebecome popular and artificial neural networks are particularly successful in predictions made using time series data. In this study, the energy
demand for Türkiye will be predicted by using the data set producedby the local authorities such as Load Dispatch Information System andLoad Dispatch Directorates. In addition, economic and environmental factors affecting electricity use will be determined and included in
the analysis. For example, economic indicators such as the number ofcompanies, population, low income per capita, and indicators createdfrom air temperature, business days, holidays, festivals, special daysof the Hijri and Gregorian calendars will be included in the analysis.
As the forecasting method, multiple regression, time series methods,
SARIMAX and machine learning methods will be used. Then the performance of these methods will be evaluated, and the best one will beselected.

1 - Bayesian optimisation for facility location problems

Niyati Seth, Michael Fop
We study the facility location problem under uncertainty where the decisions on where to locate facilities often require estimating different
uncertain parameters associated with the problem, for example costs,
supply, demand, distances, etc. which may fluctuate with time and circumstances. The recent body of literature has been towards incorporating this uncertainty at various levels. Following and extending thisbranch of literature, our research aims at developing facility location
procedures which account for uncertainty in the model and in the data.
To do so, we extend Bayesian optimisation methods for combinatorial
structures(BOCS) to solve the facility location problems. Our researchpresents a novel approach by extending the BOCS method to accountfor constraints, uncertainty, grid structure and interaction of the allocation nodes. Incorporating constraints in combinatorial domain within
the framework of BOCS is challenging. Hence, we propose an extension to the framework by introducing probabilistic reparametrisation
for the decision variable, which allows optimisation of the acquisition
function on a continuous space. By employing Bayesian optimisation
we incorporate uncertainty in the optimisation procedure itself, whileallowing for an integrated framework in which the estimation of optimisation variables is data driven. We demonstrate the performance ofour method in a simulated study.

2 - A hybrid genetic algorithm for non-regular flexible job
shop arising from deconstruction applications

Corentin Juvigny
The field of building deconstruction is a significant contributor to waste
generation. However, despite the availability of many waste recovery
techniques to valorize this waste, much of it is currently lost due to452

 WD-29
Wednesday, 14:30-16:00 - Room: 157 (building: 208)

Optimization issues on graphs II(Contributed)
Stream: Combinatorial Optimization
Invited session
Chair: Manuel Munoz-Marquez
1 - Pareto optimality in graphs: circulations and colorings

Yiannis Mourtos, Pavlos Eirinakis, Michalis Samaris
Motivated by the growing literature on Pareto-optimal matchings, we
examine two fundamental graph problems considering that vertices ofa graph have preferences. The first is the circulation problem, in which
every vertex sends an amount of flow equal to the one it receives; hereeach vertex has strict and transitive preferences over its out-going arcs,
i.e., over the vertices it sends its flow. The second is the list coloring
problem where each vertex has preferences over its available colors.
Both problems admit an elegant characterization of the Pareto-optimal
solutions, i.e., solutions in which no vertex can receive a better stake(i.e., flow or color) without some other vertex receiving a worse one.
Well-known mechanisms, namely Top Trading Cycles or Serial Dictatorship, can find a Pareto-optimal circulation or coloring, respectively.
Interestingly, although recognizing Pareto-optimality is easy for circulations, it becomes coNP-complete for colorings. We discuss further

EURO 2024 - Copenhagen
hard optimization problems for both settings, along with special casesthat defy hardness by restricting the structure of either the graph or the
preferences of its vertices. We also present several motivating applications, review relevant literature, and propose open issues for futurework.

2 - (k,l)-anonymity of networks via their k-metric antidimension

traditional methods for solving TSP have been extensively examined,
GNNs have been recently proposed for solving various combinatorial
optimization problems. Our choice for leveraging GNNs in this context arises from their excellence in capturing spatial relationships ingraph structured data.

Manuel Munoz-Marquez, Elena Fernandez, Dorota Kuziak,
Ismael González-Yero WD-31This work focuses on the (k,l)-anonymity of some networks as a measure of their privacy against active attacks. Two different types of networks are considered. The first one consists of graphs with a predetermined structure, namely cylinders, toruses, and 2-dimensional Hamming graphs, whereas the second one is formed by randomly generated graphs. In order to evaluate the (k,l)-anonymity of the consideredgraphs, we have computed their k-metric antidimension. To this end,
we have taken a combinatorial approach for the graphs with a predetermined structure, whereas for randomly generated graphs we havedeveloped an integer programming formulation and computationallytested its implementation. The results of the combinatorial approach,as well as those from the implementations indicate that, according tothe (k,l)-anonymity measure, only the 2-dimensional Hamming graphs
and some general random dense graphs are achieving some higher privacy properties.

AI for Energy Finance

The main contributions of this research are the following: 1. Some
theoretical results concerning cylinders, toruses and Hamming graphs
are given. 2. We have developed an integer programming formulation
for finding the k-metric antidimension of a given graph G. The formulation can be used as a tool to deal with randomly generated graphs aswell as with classes of graphs for which their k-metric antidimension
is not known theoretically.
Extensions of these results to multiset distances are currently underdevelopment.

3 - Adding Relations between a Delegate and Every Other
Member of the Same Level in a Complete K-ary Linking
Pin Organization Structure

Kiyoshi Sawada
A linking pin organization is a structure in which relations between
members of the same section are added to a pyramid organization
where there exist only relations between each superior and his direct
subordinates. This study proposes a model of adding relations between
a delegate and every other member of the same level in a complete Kary linking pin organization structure where every pair of nodes whichhave the same parent in a complete K-ary tree is adjacent. When edges
between one node and every other node of depth N in a complete Kary linking pin organization structure of height H are added, the total
shortening distance which is the sum of shortening lengths of shortest
paths between every pair of all nodes by adding edges is formulated.
An optimal depth N such that the communication of information between every member in the organization becomes the most efficient isobtained by maximizing the total shortening distance.

4 - Utilizing Graph Neural Networks for autonomous picking sequence optimization

Dimitrios Tsakoumis, Konstantinos Giannakos, Stathis
Plitsos, Pavlos Eirinakis, Giulio Vivo
Warehouse picking is an essential part of assembly production systems.
Robotic systems are often utilized to automate this process to increase
efficiency and throughput. In this regard, optimizing the picking sequence of the semi-finished products and other components is significant for overall productivity improvement. In our approach, we model
the picking process as a Travelling Salesman Problem (TSP), where arobotic agent needs to select the most efficient path to pick multiple different objects from various locations. We investigate different variantsof TSP that arise in this setting based on the specificities of the robotic
system utilized (e.g., incorporating a dual gripper for picking). Further,
we explore the potential of Graph Neural Networks (GNNs) to handle
the inherent complexities of such warehouse picking tasks. Although

WD-31

Wednesday, 14:30-16:00 - Room: 046 (building: 208)

Stream: Analytics
Invited session
Chair: Stefan Lessmann
Chair: Daniel Traian Pele
1 - Multivariate Probabilistic Forecasting of Electricity
Prices With Trading Applications

Alla Petukhina
A recently introduced approach is extended to probabilistic electricity price forecasting (EPF) utilizing distributional artificial neural networks, based on a regularized distributional multilayer perceptron(DMLP). We develop this technique for a multivariate case EPF withincorporated dependence. The performance of a fully connected architecture and a LSTM architecture of neural networks are tested. The
empirical data application analyzes two day-ahead electricity auctions
for the United Kingdom market. This creates the opportunity to buy
in the first auction for lower price and sell in the second for higher
price (or vice versa). Utilizing forecasting results, we develop trading strategies with various investors’ objectives. We find that, while
DMLP shows similar performance compared to the benchmarks, thealgorithm is considerably less computationally costly.

2 - Energy Price Modelling: A Comparative Evaluation ofFour Generations of Forecasting Methods

Stefan Lessmann, Alexandru-Victor Andrei, Georg Velev,
Daniel Traian Pele
Energy is a critical input in modern economic systems. Much literature has looked into energy forecasting to support critical decisions
at various levels and entities; from operational purchasing decisions at
individual business organizations to policy-making. In pursuit of maximum accuracy, the field has considered numerous forecasting methods. In this paper, we examine the evolution of methodology acrossdifferent generations of forecasting methods, starting from classicaleconometric approaches, over machine learning methods, and earlier
sequence learners like LSTMs, to deep transformer networks, which
may be regarded as the latest trend in forecasting. We also consider
the latest ideas on adopting pretraining and transfer learning, concepts
that have revolutionized the analysis of unstructured data and enable
modern AI, for time series forecasting. We offer a systematic reviewof the related literature and categorize different forecasting methodologies. More importantly, using data from EU energy markets, we
conduct a large-scale benchmarking experiment, which contrasts theforecasting accuracy of different approaches, focusing especially on
alternative propositions for time series transformers.

3 - The impact of energy prices on European stock markets

Robert-Adrian Grecu, Daniel Traian Pele, Alexandru-Adrian
Cramer
The aim of this paper is to illustrate the connection between the energy sector and the stock market, specifically how changes in energy
prices, such as oil or gas, influence the returns of the main European
stock indices. The analysis was carried out at the level of European
Union countries, both by taking into account the specific situation ofeach state, as well as the case of EU countries groups based on different criteria such as geographical region, industrial production level, or
economic development status. Even though the EU countries have several common economic characteristics, the results of the analysis showthat the impact of energy prices on the stock market can differ significantly from one state to another. Another important aspect observed453

WD-32EURO 2024 - Copenhagenin the results is that energy price dynamics have a different type of
impact for countries with the highest level of industrial production incomparison with the rest of the EU countries. From a quantitative perspective, the paper studies, on the one hand, the co-movement between
variables, and, on the other, the impact of several types of shocks in a
VAR framework. Moreover, we focused the analysis on the dynamic
links between energy and stock markets; in this sense, models suchas Dynamic Conditional Correlation (GARCH) and Time-Varying Parameter (VAR) were applied.

4 - Energy Time Series Forecasting with Neural Architecture Search

Georg Velev, Stefan Lessmann
The importance of accurate planning and operation of energy
ressources in today’s power system has made energy forecasting a
popular topic of research. Transformer-based networks have shownpromising results on various tasks including energy time series prediction. In this regard, neural architecture search (NAS), which facilitates the automated design of complex neural-based architectures
tailored to a specific task, has been reported in the literature to outperform hand-crafted architectures. Therefore, in this research we apply NAS using Reinforcement Learning for the prediction of energy
time series. We focus on the search for novel hybrid self-attention
modules, which incorporate different functionalities presented in theencoder of Transformer-based frameworks for time series forecasting.
Furthermore, we explore the automated design of self-attention components with memory states, in order to examine if the recurrence ona sequence-level could improve the expressive power of Transformerbased models. We report the results obtained from NAS on both realworld datasets and synthetic time series. We benchmark the performance of NAS-related models with Long Short-Term Memory neuralcells, and with the Transformer-based frameworks of Informer, Autoformer and Pyraformer.

 WD-32
Wednesday, 14:30-16:00 - Room: 41 (building: 303A)

Distributed and Federated Optimization
Stream: Advances in large scale nonlinear optimization
Invited session
Chair: Eduard Gorbunov
1 - TAMUNA: Doubly-Accelerated Distributed Optimization
with Local Training, Compression, and Partial Participation

Building distributed machine learning schemes that are robust to these
events is paramount to transitioning AI from being a mere spectacle capable of momentary feats to a dependable tool with guaranteed safety.
In this talk, I will cover some effective techniques for achieving suchrobustness. Specifically, I will present distributed machine learningalgorithms that do not trust any individual data source or computingunit.

3 - Byzantine Robustness and Partial Participation Can Be
Achieved Simultaneously: Just Clip Gradient Differences

Eduard Gorbunov
Distributed learning has emerged as a leading paradigm for training
large machine learning models. However, in real-world scenarios, participants may be unreliable or malicious, posing a significant challenge
to the integrity and accuracy of the trained models. Byzantine fault
tolerance mechanisms have been proposed to address these issues, butthey often assume full participation from all clients, which is not always practical due to the unavailability of some clients or communication constraints. In our work, we propose the first distributed methodwith client sampling and provable tolerance to Byzantine workers. The
key idea behind the developed method is the use of gradient clipping
to control stochastic gradient differences in recursive variance reduction. This allows us to bound the potential harm caused by Byzantine
workers, even during iterations when all sampled clients are Byzantine. Furthermore, we incorporate communication compression into
the method to enhance communication efficiency. Under quite generalassumptions, we prove convergence rates for the proposed method thatmatch the existing state-of-the-art (SOTA) theoretical results.

4 - Beyond spectral gap: the role of the topology in decentralized learning

Laurent Condat, Peter Richtarik

Hadrien Hendrikx

In distributed optimization and machine learning, a large number ofmachines perform computations in parallel and communicate back andforth with a distant server. Communication is typically slow and costly,
and forms the main bottleneck in this setting. This is particularly truein federated learning, where a large number of users collaborate to
optimize a global model, based on their personal data that are kept
private. In addition to communication-efficiency, a robust algorithm
should allow for partial participation. To reduce the communicationload, two strategies are popular: 1) communicate less frequently; 2)compress the communicated vectors. We introduce TAMUNA, thefirst algorithm that harnesses these two strategies jointly and allows
for partial participation. TAMUNA converges linearly to an exact solution in the strongly convex setting and provably benefits from thetwo mechanisms of local training and compression: its communication complexity is doubly accelerated, with a better dependency on thecondition number of the functions and on the model dimension.

In data-parallel optimization of machine learning models, workers collaborate to improve their estimates of the model: more accurate gradients allow them to use larger learning rates and optimize faster. In
the decentralized setting, in which workers communicate over a sparsegraph, current theory fails to capture important aspects of real-world
behavior. First, the ‘spectral gap’ of the communication graph is notpredictive of its empirical performance in (deep) learning. Second,current theory does not explain that collaboration enables larger learning rates than training alone. In fact, it prescribes smaller learning
rates, which further decrease as graphs become larger, failing to explain convergence dynamics in infinite graphs. This paper aims to paintan accurate picture of sparsely-connected distributed optimization. We
quantify how the graph topology influences convergence in a quadratic
toy problem and provide theoretical results for general smooth and(strongly) convex objectives. Our theory matches empirical observations in deep learning, and accurately describes the relative merits ofdifferent graph topologies.

2 - Machine Learning in Untrusted Distributed Environment

Nirupam Gupta

454

Machine learning algorithms, where the computing system learns fromdata in order to adjust its behavior, are the key enabler of modern AIbased technologies. This dependency on data, however, is also the
Achille’s heel of AI systems. The data, which can come from a wide
variety of sources, is not always trustworthy. Some sources can provide erroneous or corrupted data. With current machine learning algorithms, a single "bad" source can lead the entire learning scheme tomake critical mistakes. Moreover, to handle the huge amounts of data,
machine learning algorithms are often deployed over a large network ofmachines. Consequently, as the network size increases, the likelihoodof machine errors also increases. Indeed, software and hardware bugs
are prevalent, and machines can sometimes be hacked by malicious
players. Some of these players attempt to corrupt the entire learning
procedure, merely for the pleasure of claiming to have destroyed an
important system. Others attempt to influence the learning procedure
for their own benefit.

EURO 2024 - Copenhagen WD-34
Wednesday, 14:30-16:00 - Room: 43 (building: 303A)

New problems in logistics under uncertainty
Stream: Stochastic, Robust and Distributionally Robust
Optimization
Invited session
Chair: Vibeke Hvidegaard Petersen
1 - Data-driven Drone Pre-positioning for Traffic Accident
Rapid Assessment

Zhu Meng, Ning Zhu
A rise in traffic accidents has led to both traffic congestion and subsequent secondary accidents. Effectively addressing this issue requires
rapid accident investigation and management. In this paper, we aim
to improve the efficiency of traffic accident assessment and investigation with the aid of drone technologies. Our approach involves
strategically pre-positioning drones, enabling traffic supervisory agencies to dispatch drones immediately upon receiving an accident report.
Methodology-wise, we present a data-driven robust stochastic optimization (RSO) model, which encapsulates the uncertainty of traffic
accidents within a scenario-wise Wasserstein ambiguity set. To thebest of our knowledge, this is the first study that incorporates covariates, i.e., weather conditions, into the Wasserstein ambiguity set with
the CVaR metric. We demonstrate that the proposed RSO model canbe reformulated into a mixed-integer programming (MIP) model, allowing an efficient solution approach. Via a real-world dataset of London traffic accidents, we validate the practical applicability of the RSO
model. Across various parameter settings, our RSO model exhibits superior out-of-sample performance compared with various benchmark
models. The numerical results yield valuable insights for traffic supervisory agencies.

2 - Navigating parametric cost function approximation: A
practitioner’s guide for sequential decision-making withinteger constraints in a production setting

Max Barkhausen
Warren Powell recently coined the term "parametric cost function approximation" (CFA) to describe a practical approach for addressing
multistage stochastic decision problems in a deterministic manner. Despite its widespread industrial use, Powell notes a lack of scientificrigor in assessing its effectiveness. In this study, we apply CFA to areal-world multistage lot sizing and inventory management challenge
encountered in the automotive sector, where stochastic programming
techniques such as SDDiP come with weak convergence guarantees
and unacceptable run times. We aim to highlight two key decision
points in crafting a CFA algorithm and propose scientific methodologies to tackle them. Firstly, we discuss methods for selecting appropriate parameterization techniques. Second, we explore strategies for
identifying optimal parameter values, utilizing Monte Carlo simulationand derivative-free optimization methods. Moreover, we emphasize
and explain the importance of evaluating not just the expected value
but also the risk associated with resulting policies. Finally, we outline
practical strategies for deploying CFA algorithms effectively in production settings marked by varying problem instances and a rolling
planning window. Through our investigation, we contribute to enhancing the methodological soundness and practical utility of CFA in realworld decision-making contexts.

WD-35

energy from offshore wind farms by moving to a predictive maintenance strategy. In such a strategy, probabilistic models of the future
conditions of each component of a wind turbine are used as a basis
for planning the maintenance. Heavy maintenance where large components such as blades, generators, etc., are replaced, is one of thecostliest types of maintenance as it, besides new components, requires
chartering of a specialized jack-up vessel. In this work we investigate
how a predictive maintenance strategy affect the cost of heavy maintenance compared to other maintenance strategies. To do this, different
strategies are evaluated through a framework simulating the 25-year
long lifetime of a wind farm. Each year in the simulation framework,
an optimization problem determining how many components to orderand the chartering length of a jack-up vessel is solved. As these decisions must be taken well in advance of the maintenance period wherecomponent failures and weather conditions governing when the vessel
can operate are unknown, this problem is formulated as a two-stage
stochastic program. The vessel and component deployment is thensimulated as the component failures and weather conditions are realized.

 WD-35
Wednesday, 14:30-16:00 - Room: 44 (building: 303A)

Optimization under Uncertainty in
Manufacturing and Supply Chain
Management
Stream: Stochastic, Robust and Distributionally Robust
Optimization
Invited session
Chair: Berend Markhorst
1 - The Value of Flexibility in Robust Supply Chain Network
DesignAmin Ahmadi Digehsara, Amir Ardestani-Jaafari
The study addresses the supply chain network design problem (SCNDP), focusing on strategic, long-term decisions that integrate flexibility and demand information to enhance cost efficiency. We examine five policies that incorporate new flexibilities at strategic and operational levels to optimize the SCNDP. Initially, we explore a model
where local production meets demand. We expand this to include scenarios where production capacity can satisfy demands across different
locations and shared among facilities with preestablished links. Unlike
traditional models that fix production capacity early, we also consider
policies allowing production decisions to be deferred until demand is
known, favoring a make-to-order strategy. Our analysis employs a twostage robust optimization framework for which we introduce a novel
computationally efficient exact (based on Column-and-Constraint Generation (C&CG)) and multiple approximation techniques (based on
Linear Decision Rules). Our findings reveal that capacity sharing significantly reduces costs and shortage risks, showcasing its superiority
over delayed production decisions, particularly under moderate uncertainty. The study concludes by applying these insights to a real-worldcase, offering practical design recommendations.

2 - Stochastic optimization with automated number of storage tank selection for crude oil schedulingHasan Sildir, Damla Yalcin, Ozgun Deliismail, Basak Tuncer
3 - Investigating condition-based and predictive strategies
for heavy maintenance at offshore wind farms

Vibeke Hvidegaard Petersen, Christian Rognes, Herman
Grytbakk Tronsaune, Magnus Stålhane
Until recently maintenance at offshore wind farms have been conducted using a preventive- or condition-based maintenance strategy.
However, as more information becomes available, and better prediction models are developed, the goal is to further reduce the cost of

In the field of process system engineering (PSE), particularly in theoil and gas industry, a novel approach is presented that significantly
enhances sustainability. This approach is characterized by integrating
effective scheduling and planning strategies that transition from traditional deterministic optimization to a robust framework. This framework is adept at managing the uncertainties inherent in the industry,a challenge often overlooked in conventional methods. Central to thisnew strategy is the development of a Mixed-Integer Nonlinear Programming (MINLP) model for crude oil tank farm management. This

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WD-37EURO 2024 - Copenhagenmodel integrates deterministic mathematical principles with stochastic
components to address the unpredictability of oil volumes and market prices, aiming to improve operational efficiency and promote oil
sector sustainability. Furthermore, the integration of the Industrial Internet of Things (IIoT) and Process Intensification (PI) optimizes value
chains, marking a substantial advancement in addressing the complex
challenges of current industrial processes and establishing a foundation for future sustainable industrial developments. This comprehensive approach balances ecological, social, and economic dimensions
and represents a significant step towards the sustainable developmentin chemical engineering and PSE, combining advanced technologies
with robust optimization techniques.

3 - Sailing through uncertainty: mathematical optimization
for ship pipe routing in the energy transition

Berend Markhorst, Joost Berkhout, Alessandro Zocca, Jeroen
Pruyn, Rob van der Mei
The maritime industry must prepare for the energy transition from fossil fuels to sustainable alternatives, which makes the design of futureproof ships even more important. In the design phase of a ship, it iscurrently uncertain which fuels it will use in the future due to many external factors. In fact, a ship typically sails for decades, increasing the
likelihood that it will have to use different fuels over its lifetime. Whenchanging fuels, pipe route design, which ensures a connection between
the fuel tanks and the engine rooms, is expensive, time-consuming, andmainly done by hand. Motivated by this, together with maritime experts, we propose a mathematical approach for modeling uncertainty
in automatic pipe routing with deterministic, stochastic, and robust optimization. All three approaches are based on state-of-the-art integer
linear optimization models for the Stochastic Steiner Forest Problem
and adjusted to the maritime domain using specific constraints for piperouting given by the maritime experts. We compare the approaches using both artificial and realistic data of a commercial ship design company and show that considering uncertainty using stochastic optimization and robust optimization leads to cost reductions. Additionally,
we extend our approaches with decomposition methods to solve largescale (industry) instances.

 WD-37
Wednesday, 14:30-16:00 - Room: 33 (building: 306)

Multiobjective Mixed-Integer Linear
Optimization
Stream: Multiobjective Optimization
Invited session
Chair: Sophie Parragh
Chair: Markus Sinnl
1 - On computing the Edgeworth-Pareto hull of multiobjective mixed-integer linear programming problems
via an outer approximation algorithm

2 - Lagrangean lower bounding and math-heuristics for approximating the set of non-dominated points of a computationally demanding bi-objective MILP

Ann-Brith Strömberg, Gabrijela Obradovic, Felix Held,
Kristian Lundberg
We consider a computationally demanding bi-objective MILP, arisingfrom an industrial maintenance and workshop scheduling problem subject to a turn-around time contract. Since, for industrial scale instances,
this problem cannot be solved in reasonable computing time, we employ (i) so-called job variables in the workshop model, and (ii) a lower
bounding procedure based on Lagrangean relaxation of the complicating constraints of an epsilon-constraint scalarized problem combinedwith math-heuristics to identify approximately non-dominated feasible
points. A Lagrangean dual of the scalarized problem is maximized using subgradient optimization, which is shown to provide lower bounding functions of the objective of the scalarazed problem over the domain of the epsilon-constrained objective. As a means to reduce computational complexity we introduce so-called job variables, however
resulting in an in-definiteness of the Lagrangean dual variables. Thisis remedied by aggregating, over jobs, the constraints defining the objective, which in turn entails a relaxation of the scalarized problems interms of lower optimal values. To mimic the turn-around time contract,
the aggregated variables’ values are split into values for the original
job variables. The lower bounds computed are, however, still valid,
albeit slightly weaker as compared to when constraint aggregation is
not employed. A topic for further research is tightening of these lower
bounds.

3 - An interactive multi-objective algorithm using preference cones

Mariana Mesquita-Cunha, José Rui Figueira, Ana
Barbosa-Povoa
Multi-objective problems are common real-world problems. However,computing the whole Pareto front and analyzing it to make a decision
is often impractical. Therefore, we propose an interactive algorithm
to help the decision-maker (DM) reach the most preferred solution.
The interactive algorithm alternates between generation stages, using
a representation method based on the epsilon-constraint algorithm, andanalysis stages, in which pairwise comparisons are required. Convex
preference cones are used to adapt the search region based on the pairwise comparison. To that end, the H-representation of the 3-pointed
convex cone is developed so that those types of preference cones canbe included as inequality constraints in the generation phase. Moreover, two boosting strategies are put forward to increase the algorithm’s convergence. The algorithm is tested on different sets of multiobjective problems, namely binary and integer problems with multiple
constraints. The DM’s preferences are simulated through linear and
Chebyshev value functions with different weight vectors. The algorithm has proven effective, converging to the most preferred point in
the Pareto front, and the boosting strategies improved the efficiencyof the algorithm. Furthermore, the advantage of increasing the size ofthe solution subset shown to the DM in each iteration is made clear in
those instances in terms of iterations and computational time.

 WD-38

Markus Sinnl, Fritz Bökler, Sophie Parragh, Fabien Tricoire

Wednesday, 14:30-16:00 - Room: 34 (building: 306)

In this talk, we present an outer approximation algorithm for computing the Edgeworth-Pareto hull of multi-objective mixed-integer linear
programming problems (MOMILPs). It produces the extreme points(i.e., the vertices) as well as the facets of the Edgeworth-Pareto hull.
The algorithm relies on a novel oracle that solves single-objective
weighted-sum problems and we show that the required number oforacle calls is polynomial in the number of facets of the convex
hull of the extreme supported non-dominated points in the case of
MOMILPs. Moreover, for the special case of multi-objective linear
programming problems, by generating the Edgeworth-Pareto hull thealgorithm solves the problem to global optimality. A computational
study on a set of benchmark instances from the literature is provided.

Advances in polynomial optimization and its
applications
Stream: Conic Optimization: Theory, Algorithms, and Applications
Invited session
Chair: Bram Bekker
1 - Term-sparse polynomial optimization for the design offrame structures

Marouan Handa

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EURO 2024 - Copenhagen
This work investigates efficient solution to two fundamental problems
in topology optimization of frame structures. The first one involves
minimizing structural compliance under linear-elastic equilibrium andweight constraint, while the second one minimizes the weight undercompliance constraints. These problems are non-convex and generally
challenging to solve globally, with the non-convexity concentrated in apolynomial matrix inequality. Recently, these problems where tackledusing the moment-sum-of-squares (mSOS) hierarchy. However, only
smaller instances can be solved globally. Here, we aim to improve thescalability of solution to these problems by using the mSOS hierarchy
supplemented with the Term Sparsity Pattern technique (TSP). Dueto the unique polynomial structure of our problems in which the objective and constraint functions are separable polynomials, we further
improve scalability by adopting a reduced monomial basis containing
non-mixed terms only. From extensive numerical testing, we conclude
that these techniques allow for a global solution to larger instances andaccelerate the solution of the problems significantly.

2 - Weight minimization of frame structures under freevibration eigenvalue constraints with polynomial optimization

Marek Tyburec
Topology optimization of frame structures that minimizes the weight
while respecting free-vibration eigenvalue constraints presents a challenging nonconvex polynomial optimization problem. We address thischallenge using a nonlinear semidefinite programming formulation,
casting it as a bilevel program. This reformulation offers a uniquestructure: a quasiconvex, univariate lower-level problem that searches
for scaling of fixed cross-section area ratios and an upper level that optimizes these ratios. We establish conditions for the lower-level problem solvability and demonstrate how to construct feasible solutions forthe original polynomial semidefinite program. Given a feasible point,
we prove that the convergence conditions of the Lasserre hierarchy aresatisfied. By solving this hierarchy of convex relaxations, we obtainlower bounds. We further show that the relaxed solutions satisfy theconditions for the feasibility of the lower-level problem and thus enable us to construct feasible upper bounds in each relaxation. With
this, we introduce a simple sufficient condition for global -optimality
and prove that the optimality gap  approaches zero in the limit when
the set of global minimizers is convex. Numerical examples illustrate
the convergence of the hierarchy in a finite number of steps.

3 - SDP hierarchies for distance-avoiding sets on compact
spacesBram Bekker
Witsenhausen’s problem asks for the maximum fraction αn of then-dimensional unit sphere that can be covered by a measurable set
containing no pairs of orthogonal points. We extended well known
optimization hierarchies based on the Lovász theta number, like theLasserre hierarchy, to Witsenhausen’s problem and similar problems.
These hierarchies are shown to converge and are used to compute thebest upper bounds known for αn in low dimensions.

4 - A novel branch and bound algorithm for solving the linear multiplicative programming problems

Jinyu Dai
In this talk, we propose a novel branch and bound algorithm for solving the linear multiplicative programming (LMP) problem. A newquadratic relaxation is proposed. We first show that the Hessian matrix of the multiplication of two linear functions has a decomposition
form, given by the difference of two rank-one matrices, resulting in a
decomposition each quadratic term of the objective function. And thenthe minus term is relaxed into a linear one. To find the global optimal
solutions, branch and bound method is adopted. Theoretical analysis
is provided that guarantees the convergence of our algorithm. Numerical experiments show that the new algorithm runs faster than several
typical algorithms for certain types of LMP.

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 WD-39
Wednesday, 14:30-16:00 - Room: 35 (building: 306)

Analysis of Stochastic Models II
Stream: Stochastic Modelling
Invited session
Chair: Bora Cekyay
Chair: Dominik Berbig
1 - Real-Time Temperature Control for Refrigerated Trucks:
A Multistage Stochastic Programming ApproachFrancesco Giliberto
In this work, we study the problem of transporting temperaturesensitive products in refrigerated trucks, where maintaining temperatures within specified ranges is crucial to prevent quality degradation. Innovative routing models are characterized by several limitations, such as the lack of a direct link between temperature and thevariables influencing its dynamics and the assumption of deterministic
scenarios. Our work introduces a novel model that treats temperature
as a dynamic variable influenced by multiple factors, including cooling unit operation, and incorporates uncertainties such as door opening
durations and initial product temperatures. Our approach involves optimizing a high-frequency control policy for cooling units by casting the
problem as a multistage stochastic program, with each client stop representing a stage. This method poses computational challenges due tothe extensive number of stages and the complexity of thermodynamic
modeling. To address this, we employ the Stochastic Dual Dynamic
Programming algorithm and validate our methodology through a casestudy with real-world data from a specialized Dutch logistics company.
The findings demonstrate that our strategy surpasses both deterministic lookahead strategy and current industry practice in maintaining
optimal product temperatures while minimizing energy consumption.
This advancement holds significant potential for enhancing the efficiency and sustainability of refrigerated transport logistics.

2 - Dynamic Capacity Management in 5G Networks

Demet Batur, Jennifer Ryan, Mehmet C Vuran
We study the decision problem faced by the operator of a private 5G
network, known as a private cell, who must allocate available capacity
to meet the resource needs of the primary user of the network. The
operator may lease excess capacity to external secondary users to generate additional revenue. Private cells are privately owned wireless networks. Industries such as manufacturing and transportation utilize private cells to prevent downtime for their automated operations. Private
cells use network slicing, a technological advancement made available
by 5G, to meet differentiated application needs. Under slicing, thenetwork’s resources can be dynamically segmented to support specificapplications. Network slicing also allows the resource capacity currently not used by the primary user to be leased to a secondary user,
such as a broker. Given this setting, we study the problem faced by
an operator whose main responsibility is to serve the slice instances ofthe private cell’s primary user but who can also lease excess resources
to a secondary user to generate revenue. Primary user slice instances
require a specific combination of network resources, such as spectrum,computation, or storage. The operator determines which instances will
be admitted to the network for service and which resources can beleased to the secondary user. We use an MDP model to formulate thisproblem and characterize the optimal admission and leasing decisions.

3 - Optimal Replacement of Mission-Based Systems with
Partially Observable Degradation Levels

Bora Cekyay, Suleyman Ozekici
We consider an optimal replacement problem for a mission-based system with partially observable degradation levels, where a mission process modulates the system’s degradation process. The system is partially observed at predetermined time points, where perfect information related to the mission process (fully observable) is obtained, andonly partial information related to the system’s degradation level (partially observable) is obtained. We formulate the optimal replacement457

WD-40EURO 2024 - Copenhagenproblem as a partially observable Markov decision process to minimize
the long-run expected total discounted cost. We provide sufficient conditions to guarantee a control-limit type optimal replacement policy.

4 - Standardized moments of waiting

Dominik Berbig
We all have to wait. Sometimes longer, sometimes shorter. Thanks
to queuing theory, we know why this is the case. However, under theusual assumption of a continuous-time domain, we do not really know
how long we have to wait. In the case where the Markov property does
not hold for the inter-arrival-time distribution, only estimations for thewaiting times exist. They are rather easy to calculate as they are solely
based on the mean and variance of the distributions, everything else
is neglected. But these estimates can be arbitrarily inaccurate and offer no guarantee of quality. One solution is to apply the discrete-time
domain. Here, the entire distribution is known, exact parameters canbe calculated. However, this approach is much more complex. Thus,
an intermediate solution is required which is less complex but more
precise. Therefore, the effect of other standardized moments must be
analyzed and taken into consideration. First corresponding analyses
especially regarding inter-arrival times have recently been carried out
and have provided initial indications. In this paper, these results arereflected on and taken as basis. In addition, experiments specificallywith regards to service-time distributions are presented and the results
are compared. Corresponding findings and possible next steps are presented in this paper serving as a basis for discussion for future work as
this is important to calculate correct buffer sizes.

 WD-40
Wednesday, 14:30-16:00 - Room: 96 (building: 306)

Experimental economics and game theory 3
Stream: Experimental economics and game theory
Invited session
Chair: Ulrike Leopold-Wildburger
Chair: Stefan Wolfgang Pickl
Chair: Gerhard-Wilhelm Weber
1 - Exploring the effects of usefulness and ease of use ofreports on transactional decisions: a case of ERPsim
game

Sung Shim
ERPsim is a set of computer-based simulation games for the SAP
enterprise resource planning (ERP) system in which participants use
the system to manage their virtual company in a competitive market.
While many students and businesspeople have come to use ERPsim
games in learning about the SAP ERP system, little is known about
how useful and how easy to use reports available in the system are during ERPsim games. This study attempts to evaluate the usefulness andease of use of reports and their effects on transactional decisions during ERPsim games, using data from a survey of college students who
used the Distribution ERPsim game in a course on ERP systems. Previous research shows that the usefulness and ease of use of information
are crucial factors that determine the effectiveness of decision-making.
The results of this study would be helpful to those who use ERPsim
games as well as those who design ERPsim games and further the SAP
ERP system in which ERPsim games run. The results are based upon
the perceptions of users in a simulated setting; however, they could
provide valuable insights into the effects of usefulness and ease of useof reports on transactional decisions.

2 - State-aware online self-supervised domain adaptation
for bearing remaining useful life prediction

Kyeonggeun Park, Jun-Geol Baek

458

Transfer learning methods for remaining useful life (RUL) predictionof rolling bearings have rarely focused on online feature drift scenario.
Bearing degradation trends are categorized into several states, as normal, slight degradation and severe degradation states, each with distinct feature distributions. During the degradation process, changes instate lead to online feature drift, which reduces the accuracy of RUL
prediction. To overcome this challenge, this study suggests aligningfeature distributions for each state using state-awareness to adapt to online domain shifts. State awareness is achieved by deriving the HealthIndex (HI) based on the Wasserstein distance, applying smoothing
techniques, and statistically detecting the First Prediction Time (FPT)
and acceleration point. This provides state label during online degradation process. The online self-supervised domain adaptation model
leverages the current degradation data as target, with the source domain comprising previous run-to-fail data. Aligning feature distributions between these domains and states enhances RUL prediction accuracy by extracting domain-invariant and domain-specific features, aswell as state-invariant and state-specific features. Experimental validation using IEEE PHM Challenge 2012 bearing data demonstrates theeffectiveness of the proposed methodology in achieving state awareness and accurate RUL prediction through online self-supervised domain adaptation in dynamic operating environments.

3 - Dimensionality reduction techniques to support insider
trading detection

Adele Ravagnani, Fabrizio Lillo, Paola Deriu, Piero
Mazzarisi, Francesca Medda, Antonio Russo
Identification of market abuse is an extremely complicated activity that
requires the analysis of large and complex datasets. We propose an unsupervised machine learning method for contextual anomaly detection,
which allows to support market surveillance aimed at identifying potential insider trading activities. This method lies in the reconstructionbased paradigm and employs principal component analysis and autoencoders as dimensionality reduction techniques. The only input of thismethod is the trading position of each investor active on the asset for
which we have a price sensitive event (PSE). After determining reconstruction errors related to the trading profiles, several conditions are
imposed in order to identify investors whose behavior could be suspicious of insider trading related to the PSE. As a case study, we apply
our method to investor resolved data of Italian stocks around takeoverbids.

 WD-41
Wednesday, 14:30-16:00 - Room: 97 (building: 306)

Nonsmooth optimization algorithms II
Stream: Nonsmooth Optimization
Invited session
Chair: Puya Latafat
1 - On best proximity points

Monica Stanciu
The aim of this contribution is to present the existence of some bestproximity points for different type of operators that respect implicit
type inequalities on generalized metric spaces. Those operators aredefined using various classes of mappings with adequate properties ofmonotony, continuity or other specific inequalities. The passing fromclassic metric spaces to generalized metric spaces is made by an additional condition that ensures the existence of a sequence that will
converge to a best proximity point. Those points are also a part of theset of zeros for two lower semi continuous functions. Consequences
related to some fixed point results are also provided.

2 - Newton-type proximal gradient method with quadratic
subproblem approximation for unconstrained optimization with L1-regularizer

Yasushi Narushima, Hiroshi Ben

EURO 2024 - Copenhagen
We consider an unconstrained optimization problem whose objectivefunction is expressed by the sum of a continuously differentiable function and an L1-regularizer. One approach for solving such problems, the Newton-type proximal gradient method, has gained attention. In Newton-type proximal gradient methods, we need to solvea sub-problem to obtain the scaled proximal mapping at each iteration. However, the computational cost of solving this subproblem issometimes very high. In our study, we address this difficulty by approximating the sub-problem in a more easily solvable form. Concretely, we approximate the L1-norm within the objective function of
the subproblem using a quadratic function. This method allows the solution to the subproblem to be found using only simple calculations.
However, the obtained solution requires a system of linear equations
to be solved. In response to this issue, we calculate an approximation
to the Hessian in the Newton-type proximal gradient method using amemoryless quasi-Newton method. In addition, we use the ShermanMorrison-Woodbury formula to obtain the closed-form solution to theinverse matrix with reduced computational costs. Thus, we easily obtain an approximate solution for the scaled proximal mapping. Basedon the proposed method for the subproblem, we give an algorithm of
Newton-type proximal gradient methods. In addition, we show the
global convergence of the proposed algorithm under some standard assumptions.

3 - A smoothing barrier augmented Lagrangian gradientbased method for constrained bilevel programs

Mengwei Xu
Gradient-based methods are known for their efficiency in solving
unconstrained bilevel programs. However, when dealing with constrained bilevel programs, the application of this method becomes challenging due to the strict complementarity condition required for thecontinuous differentiability of the lower level solution mapping within
the Karush-Kuhn-Tucker system. To tackle the lower level problem
with inequality constraints without relying on strict complementarity,
we propose a family of smoothing functions to approximate the primaldual solution mapping based on the smoothing barrier augmented Lagrangian function, which ensures the preservation of the continuously
differentiability and the convexity of the lower level problem. By approximating the bilevel program as single-level constrained problems,
we adopt a hybrid approach that combines the gradient-based methodwith the augmented Lagrangian method. This allows us to effectively
solve the constrained bilevel programs. We prove that any accumulation point generated by the algorithm is a Clarke stationary point of the
bilevel problem. Importantly, unlike conventional smoothing function
methods, the accumulation point can also be a Bouligand stationary
point under an additional verifiable condition.

 WD-43
Wednesday, 14:30-16:00 - Room: 99 (building: 306)

Recent advances on Variational Inequalities
and Equilibrium Problems III
Stream: Variational Inequalities and Equilibrium Problems: From Theoretical Advances to Real World Applications
Invited session
Chair: Fabio Raciti

WD-44(SPD) matrix, so the objective function is well-defined on the feasible set. Without loss of generality, we also assume the numerator isstrictly convex with an SPD matrix. Contrary to the case where thenumerator is a linear function, a stationary point (SP) of StFQQP is
not necessarily a global minimum because the objective function is notquasi-convex.
We introduce a new sequential algorithm for the StFQQP, exploiting areformulation as a mathematical program with complementarity constraints derived from the KKT conditions of the StFQQP, which arein the form of the so-called symmetric Eigenvalue Complementarity
Problem (EiCP). Solving an EiCP is then equivalent to computing an
SP to the StFQQP.
Preliminary computational experiments with various test problems
suggest that the sequential algorithm is a promising technique for solving the StFQQP.

2 - Existence results for coupled systems of variational inequalities

Nicusor Costea
In this talk the existence of solutions for a system consisting of two
inequalities of variational type is discussed. Each inequality is formulated in terms of a nonlinear bifunction and a coupling functional. We
consider two sets of assumptions for each of the functional involved in
the formulation of the system and show that if the constraint sets arenonempty, closed, convex and bounded then the system possesses atleast one solution regardless of the assumption imposed on each functional. If one of the constraint sets is unbounded, then a coercivity
condition is needed to ensure the existence of solutions. We provide
two such conditions. The theoretical results are then employed to establish the existence of weak solutions for a mathematical model which
describes the antiplane shear deformation of a cylinder made of a nonlinear elastic material and a rigid foundation.

3 - Approximate computation of the Shapley value in cooperative games on transportation networks via machinelearning techniques

Mauro Passacantando, Giorgio Gnecco, Yuval Hadas,
Marcello Sanguineti
To quantify the relative importance of nodes in a network, one possible
approach uses the concept of centrality. However, classical measuresof centrality do not consider how the network behaves if any subset ofnodes is removed from the network and, when applied to transportation networks, issues such as arc capacity, route choice, and congestion
are not taken into account. Alternative measures of centrality based on
game theory are more effective, such as those based on the Shapley
value. The Shapley value is a well-established concept in cooperative game theory, used as a metric for assessing the significance ofeach player in a transferable utility game. Recently, it has found application in gauging the importance of individual arcs or nodes withintransportation networks. However, the exact computation of the Shapley value is often computationally expensive, particularly in the context of extensive networks. Here, we approximate the Shapley value
in a transferable utility game defined on a network, wherein the network’s characteristics are parameterized by a variable of interest (e.g.,
the traffic demand). Smoothness properties of the Shapley value as afunction of the parameter are investigated and exploited to motivateusing machine-learning techniques for its approximate computation.

 WD-44
Wednesday, 14:30-16:00 - Room: 20 (building: 324)

1 - Solution of Fractional Quadratic Programs on the Simplex

Valentina Sessa
We deal with the Standard Fractional Quadratic Quadratic Programming Problem (StFQQP), consisting of the minimization of the ratio of two quadratic functions over the standard simplex. We assume
the denominator is strictly convex with a symmetric positive definitive

MCDA and Composite Indicators: Issues,
Advances and Applications 2
Stream: Multiple Criteria Decision Analysis
Invited session
Chair: Laura Carosi459

WD-45EURO 2024 - Copenhagen

1 - Decision-making for the International Management Excellence Award using the PSI-COCOSO Hybrid Multicriteria Method

Solange Fortuna Lucas, André Luiz Carneiro de Vasconcellos,
Marcos dos Santos, Carlos Francisco Simoes Gomes, MiguelÂngelo Lellis Moreira, Felipe Fortuna Lucas
Several countries have adopted awards as a stimulus and recognition
for companies that stand out in the management of the quality of products and services. Stakeholder involvement must be infiltrated into thevalues and mission of companies to reinforce, mainly, corporate governance, providing transparency and accountability to the developed
businesses. The evaluation of an organization’s performance should be
related to the multiple interests pursued by the corporation, including
the expectations achieved by its stakeholders. This work shows how toevaluate the companies that apply for an international quality award,
based on the management excellence model presented, using the Preference Selection Index Method (PSI) to find the weighting of the criteria and the Combined Compromise Solution (COCOSO) Method forthe ordering of the alternatives. The criteria used to evaluate companies
are Research and Development (R&D), People, Projects, Leadership,
Processes, Information and Knowledge, Operations, and Sustainability
in Business according to the stage scale created to represent the evolution of each company’s criteria from the perspective of the internal
environment. Company D is the winner of the Management ExcellenceAward. The result is quite coherent, as it is the only company that already presents 2 (two) criteria in the higher stage, that is, the R&D andLeadership criteria have already reached the stage of Criterion Implemented with Excellence (grade 5).

2 - Leveraging Generative AI for Enhanced Digital Service
Platforms: A Comprehensive Analysis of Critical Success Factors

Ruchi Mishra, Rajesh Singh, Onkar Mishra
Generative Artificial Intelligence (AI) presents a compelling opportunity for advancing digital service platforms by enabling the creationof dynamic, personalized content and experiences. This paper aims
to explore and analyze the critical success factors (CSFs) essential forthe effective integration of generative AI within digital service platforms. Through an extensive review of existing literature and expert
insights, key factors influencing the successful implementation of generative AI in digital service contexts were identified. Subsequently,
employing qualitative analysis methodologies, a robust framework of
CSFs was developed, encompassing aspects such as algorithmic sophistication, data quality, user engagement, platform scalability, andethical considerations. Moreover, the study investigates the intricate
interplay between these factors to elucidate their collective impact on
the performance and sustainability of digital service platforms leveraging generative AI capabilities. The findings underscore the significance of addressing diverse challenges inherent in the adoption of generative AI, including algorithmic biases, privacy concerns, and user
acceptance. This research contributes valuable insights into the criticalfactors shaping the effective deployment of generative AI in digital service platforms, offering actionable guidance for platform developers,
service providers, and policymakers seeking to harness the potential ofthis transformative technology.

3 - EU Waste Recycling Targets: Where are we at? A Performance Evolution Assessment

Laura Carosi, Giovanna D’Inverno, Trinidad Gomez, Maria
Molinos-Senante, Giulia Romano
Over the last decade the European legislator has set ambitious recycling targets for municipal waste and packaging waste so to achieve amore circular economy and to contribute to the European Green Deal’ssustainability goals. In this paper, we evaluate the European countries
performance evolution with respect to the constantly evolving waste
management legal framework and their response to the definition ofincreasingly demanding targets. To do so, we suggest an innovative
dynamic composite indicator to evaluate the country performance withrespect to different packaging waste categories and taking into accounttheir deviation from the set targets. In particular, the proposed synthetic indicator distinguishes targets in terms of essential thresholds

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and aspiration levels. The essential thresholds do not allow for compensations between strengths and weaknesses of the evaluated units
and grants that certain fundamental standards are met. The aspirationlevels are targets that countries should aim at to foster their sustainability, but they are not compulsory. We monitor the evolution of 27
European countries from 2015 to 2020, where the packaging legislative framework has undergone important developments. The tool provides policy-relevant findings, important to identify best practices andto define priority actions so to enhance the transition of lagging-behindcountries towards a more circular economy.

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Wednesday, 14:30-16:00 - Room: 30 (building: 324)

Planning Techniques for Decision Support
Stream: Decision Support Systems
Invited session
Chair: Laura Wolf
1 - Proposal for an ML-supported ETCS L2 Data Point Plan
Review Process

Salome Vogel, Arturo Crespo Materna, Cedric Steinbach
The timely rollout of ETCS L2 as a standardized train control system
in Europe is depending on capacities of human planning experts, which
are especially limited in the field of plan review. Central requirements
for ETCS L2 data point (DP) plan review are, to assess the completeness and correctness of the ETCS L2 DP plan, following a different
logic than the plan creation. This proposal presents an approach to review the completeness of the ETCS L2 DP plan. The rule-based natureof the underlying planning rules and regulations leads to the choice ofa supervised learning (SL) algorithm. To assess completeness, in thisapproach the ETCS L2 DP plan under review is segmented into functional and spatial areas, with specific DP types assigned to particularreference points within those areas. The SL algorithm learns in whichcases, which type(s) of DP are required within each area. Training data
are existing ETCS L2 DP plans for different scenarios by human experts. An important benefit of the SL algorithm is that it learns frompattern in the training data instead of applying the planning rules andregulations 1:1, hence allows a plan review following a different logic
than plan creation. Additionally, the plan review supported by the proposed SL algorithm provides decision support for plan reviews carriedout by human experts. Thereby it facilitates a faster rollout of ETCS
L2, contributing to interoperability, resilience and thus increasing rail
capacity in Europe.

2 - Decision Support Models for Strategic Decision Making
in Industry 4.0

Katarzyna Gdowska
Solving strategic-level decision problems necessitates integrating advanced data analytics and decision support models, which is particularly crucial in the data-driven Industry 4.0. This paper offers an
overview of strategic decision challenges across industries, exploring
various approaches, optimization problems, and solving techniques.
Rather than following the conventional path of reviewing solely peerreviewed academic publications, this study also systematically surveys
industry literature. The nuanced needs and preferences driving strategic decision-making processes are revealed with a deliberate emphasis on decision-makers perspectives over intricate tool presentations.
Through this dual review of academic and industry literature, the paper delineates the utilization landscape of decision support methods
and tools, elucidating the rationale behind the choices made. This approach illuminates prevailing practices and reveals latent requirements,
empowering decision-makers with the insights needed for informed
method and tool selection.

EURO 2024 - Copenhagen
3 - A comparison of two approaches to hardware capacity
planning for cloud infrastructure under demand uncertainty

Laura Wolf, Patrick Jahnke, Stefan Nickel
Cloud computing and especially Infrastructure-as-a-Service (IaaS) arebecoming more and more relevant in industrial applications. In IaaS,
cloud providers offer customers resources such as compute, networkand storage. From the provider’s point of view, the question arises of
how many physical resources are required to cover the uncertain demand, to achieve utilization targets and deal with fluctuating workload
demand. This decision must be made from a midterm perspective due
to increasing supply chain cycles. We compare two approaches to deal
with this hardware capacity planning problem for cloud infrastructure
under demand uncertainty (CPCI_DU) to determine the needed capacity of a datacenter. The decision problem is modeled as a two-stage
stochastic optimization problem and solved optimally. The results areevaluated using metrics from stochastic optimization such as the valueof stochastic solution and the expected value of perfect information.
This method is compared to an optimization-simulation loop. Here, thedeterministic optimization problem is solved on the first stage and theimpact of uncertainty is evaluated in the subsequent simulation. This
optimization-simulation loop is executed until a prespecified stopping
criterion is reached. The results show the strengths of the respective
approaches to enable robust hardware planning.

WD-48

1 - Path-based DEA models

Jakub Hrdina, Maria Trnovska, Margareta Halicka
Data envelopment analysis (DEA) models appear in the envelopment
and the multiplier form, which are in a primal-dual relationship. Bothforms are essential for the proper interpretation and application of themodel. New models are typically presented in the envelopment form,
yet if they are not linear, it can be more challenging to acquire theirdual counterparts. In this contribution, we focus on path-based DEA
models, which search for the benchmarks by specifying various parametric paths running from the assessed unit to the boundary of the technology set. The class of path-based models includes well-known models such as the BCC input and output models, the hyperbolic distancefunction model and the general directional distance function model.
For a general scheme of path-based models in the envelopment form
we derive the general multiplier form of path-based models. Since
path-based models in general are not linear, we study the duality relations between the primal-dual pair of envelopment and multiplier pathbased models. Finally, we focus on the multiplier form of the BCC input and output models, the general directional distance function modeland the general hyperbolic measure model using the derived generalmultiplier form of path-based models.

2 - Multiplier form of path-based DEA models and returnsto-scale measurement

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Wednesday, 14:30-16:00 - Room: 40 (building: 324)

Human Centric Analytics (HCA)
Stream: Making an Impact
Invited session
Chair: Christina Phillips
Chair: Ruth Kaufman
1 - Human Centric Analytics (HCA): How To Foster The Human Centred Development Of Analytics That Augment
Human Work Effectively."

Making An Impact, Christina Phillips
In situations of high uncertainty and high human involvement, it canbe difficult to implement analytical tools; on the other hand those tools
can serve to reduce and define the uncertainty. To achieve this levelof use the human actors involved need to be empowered to understandand utilise the data that they produce and use. This requires both learning and experiment in a safe and creative space as mathematical tools,
data and humans come together.
In this workshop we will use examples from industry interventions to
introduce the ideas and concepts around HCA. We will discuss whatworks and what doesn’t, and why that is, and develop skills aroundchoosing the right structuring methods and analytical approaches fordiffering situations.
Design is a key part of the process, and we will look at ways to keep inmind the iterative and empathetic journey that this helps to facilitate,while also looking at ways to integrate HCA into common working
practices such as lean projects.

 WD-48
Wednesday, 14:30-16:00 - Room: 60 (building: 324)

DEA methodological developments II
Stream: Data Envelopment Analysis and its Application
Invited session

Maria Trnovska
Path-based models represent a large class of DEA models in which theefficiency score and projection point are derived using a path runningfrom the assessed unit to the boundary of the technology set. The classincludes the input and output-oriented radial models, the directional
distance measure and the hyperbolic distance measure models as special cases. The particular models fit a general envelopment scheme thatallows to derive its dual (multiplier) form, and to analyze the primaldual properties for the whole class of models. Based on this primaldual relationship, we describe a two-way connection between the supporting hyper-planes of the technology set and the optimal solutionsof the multiplier model and utilize it in the returns-to-scale measurement. We analyze and compare different approaches to the returns-toscale measurement, depending on whether the supporting hyper-planes
contain the (not necessarily strongly efficient) projection point or only
strongly efficient benchmarks. The results are demonstrated in numerical examples.

3 - Revenue Functions Are Nonconcave in the Inputs When
the Technology is Nonconvex: The Unbearable Lightness of Convexification

Kristiaan Kerstens, Oleg Badunenko, Jafar Sadeghi
Revenue functions are studied under the assumption that the technology is nonconvex. More specifically, we show theoretically that theconvex revenue functions are larger or equal to the nonconvex revenue
functions. However, with one input and constant returns to scale (CRS)
technologies, both these revenue functions coincide. Including more
inputs or varying returns to scale assumption (e.g., variable returns toscale, VRS) results in differences between the convex and nonconvex
revenue functions. We use USA state-level agricultural data to showcase our theoretical result empirically. We first visualize the revenue
functions under constant and variable returns to scale assumptions anddemonstrate that the former is identical irrespective of whether technology is convex or nonconvex while the latter revenue functions aredifferent. We further show graphically that the convex or nonconvex
revenue functions are different both under CRS and VRS with 4 inputs.
Additionally, we use statistical tests (equality of densities and stochastic dominance) to show that the convex or nonconvex revenue functions
are statistically different. Finally, we perform scale efficiency analysis
to show how using the convexity assumption may result in false policy
implications.

Chair: Paul Rouse

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4 - Some problems with benefit of the doubt models

Paul Rouse, Maryam Hasannasab, Dimitris Margaritis
Benefit of the Doubt (BoD) models are increasingly popular in theliterature with flexibility around technology assumptions and ease ofunderstanding for managers and practitioners. Most of the focus has
been on the efficiency scores with little attention paid to benchmarking
in terms of target values and the appropriateness of peers. Specifically,
there are problems that arise when weight restrictions are imposed, particularly the effects on efficient frontiers, further compounded whenvirtual weight restrictions are used. These affect target values andbenchmarking peers with nonintuitive effects as the lower bounds on
the restrictions are increased. A further question concerns how size canbe incorporated to allow for scale economies. Further issues arise withrespect to units-invariance properties for output oriented models. We
illustrate these issues using sustainability data comprising GRI measures and provide some thoughts towards addressing these problems.

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Wednesday, 14:30-16:00 - Room: M1 (building: 101)

Mathematical programming for machinescheduling
Stream: Lot Sizing, Lot Scheduling and Production Planning
Invited session
Chair: Safia Kedad-Sidhoum
1 - MILP formulations with a logarithmic number of binary
variables for planning and scheduling

Alain Hait
The way to represent the position of an activity on the time axis isat the heart of planning and scheduling models. A first distinction isdone between continuous and discrete time formulations. This work
focuses on discrete-time models that consist in choosing the start timeof an activity among a discrete set of successive values. For this purpose, several time-indexed formulations have been proposed. They use
as many binary variables as time steps on the horizon for each activity.
The number of variables could be significantly reduced using binary
encoding to represent the set of time values, but the attempts to determine activity start times this way have generally leaded to models withpoor relaxations. Vielma and Nemhauser have proposed formulations
to model disjunctions with a logarithmic number of variables and constraints. They study the association with SOS2 variables in order tomodel piecewise linear functions, but their models are also suitable fortime-indexed formulations in scheduling problems.
This work presents the adaptation of Vielma and Nemhauser’s formulation to time-indexed scheduling models. The comparison isdone with classical time-indexed formulations through the ResourceConstrained Project Scheduling Problem (RCPSP). Experiments givethe relaxation values for both formulations. An alternate formulation is
proposed, with more constraints but experimentally competitive. Then,binary encoding is explored to improve the linear relaxation.

given interval, in this work, we address a variant of the PMS designed
to represent data uncertainties affecting the processing times. The resulting Robust Parallel Machine Scheduling Problem (RPMS) uses abudgeted uncertainty set, where a limit is imposed on the number ofjobs whose processing time can change from their nominal value oneach machine. Starting from the Dynamic Programming (DP) algorithm for the 0-1 Robust Knapsack Problem, we introduce an arc flow
formulation based on the DP algorithm. To assess the efficiency ofthe model, we have tested it on a set of instances from the literature,
identifying the methods that yield the best results.

3 - A branch-and-price algorithm for scheduling continuous steel annealing lines

Sebastian Wegel
Continuous annealing is a core process in steel cold-rolling facilities,
where order-specific pieces of flat steel, wound up to coils, are processed through an annealing furnace to achieve defined material properties. To enable a continuous process, the coils are welded together
before entering the line. Whenever two consecutive coils are incompatible, a special dummy coil called stringer is used to connect them,which reduces efficiency and adds costs and emissions. We consider
the scheduling of coils with specific due dates and alternative orderspecific processing modes on parallel heterogeneous lines. The problem is to simultaneously assign coils to lines and sequence them onthese lines with a defined processing mode with adherence to tardiness constraints while minimizing the number of stringers needed andthe associated energy consumption. To address this problem, we formulate a mixed-integer linear program and propose a branch-and-pricealgorithm based on a Dantzig-Wolfe decomposition by lines to solve
it. The pricing subproblems are solved using a labeling algorithm. The
approach is implemented using the SCIP framework. We present theunderlying problem decomposition, implementation, and some firstnumerical results using a heuristic and a state-of-the-art commercial
solver as a benchmark.

4 - Feature selection for new product line design witheconomies of scale

Shaghayegh Ramezanpour Shalmani, Laurent Alfandari, Sara
Rezaee Vessal
Feature selection across different versions of a product, to create asufficient level of differentiation and yet satisfy customers’ expectations, is one of the complex decisions that companies need to makefrequently. In this research, using Mixed-Integer Linear Programming,
we decide on the levels of each feature in the various versions of the
product line, in the presence of discount on the purchase price of initial components as a function of the purchased quantity. We study
the trade-off between the cost of the products, and customers’ utility
based on a First Choice model, to maximize the firms’ profit. Since theproblem can easily get intractable with an increase in its size, we propose various families of valid cuts to strengthen the model and accelerate solving. Conducting extensive numerical experiments, we provide
managerial insights to help manufacturers’ designing their products.
We also analyze the impact of varying the purchasing discount rate ofcomponents, the degree of market competitiveness, and the degree ofheterogeneity of customers’ preferences on the optimal design choices.
The impact of these variations is measured in terms of manufacturer’s
profit, intensity of economies of scale, and the level of homogeneity
vs. heterogeneity of the different versions of the product line.

2 - Arc Flow Formulation for the Robust Parallel Machine
Scheduling Problem

Heloisa Vasques da Silva, Alberto Locatelli, Silvio Alexandre
de Araujo, Manuel Iori
In the Parallel Machine Scheduling Problem (PMS) with makespanminimization, we are given a set of jobs with processing times and aset of identical parallel machines. Each machine processes at most one
job at a time, and preemption is not allowed. The problem consists ofscheduling all jobs into the machines, minimizing the maximum completion time of the jobs. Considering the fact that the processing timesof the jobs are not exactly known in advance but typically belong to a462

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Wednesday, 14:30-16:00 - Room: M2 (building: 101)

Retail Optimization
Stream: Retail Operations
Invited session
Chair: Naoko Miyashita

EURO 2024 - Copenhagen
1 - Integrated inventory-control method for multiple salespromotion types

Naoko Miyashita, Junko Hosoda, Takahiro Nishikawa, Akira
KoyamaIn retail, sales promotion plays a key role in gaining a competitive
advantage. Products are often sold by combining multiple salespromotion types, e.g., regular sales, discounts, and coupons. Therefore, inventory control for multiple sales-promotion types has been
attracting interest. Each promotion type for a product differs in its
demands’ distribution and target service level. The inventory-policy
variables for each promotion type are typically optimized individually
to replenish the inventory separately. However, this separation methodrisks shortage and excess stock since the inventory is not shared among
the promotion types. An integrated inventory-control method of the (s,S) policy for multiple sales-promotion types is proposed. The policy
variables are calculated as follows. The required inventory is first calculated for each inventory type such as safe stock, pipeline stock andcycle stock. The policy variables are then calculated by summing therequired inventory of each inventory type for all promotion types. This
enables the sharing of inventory among promotion types and satisfies
the target service level of each type, reducing shortages. By omitting the cycle stock while replenishing for promotion types with large
uncertainty in demand, the excess stock due to forecast error is reduced. The results of numerical experiments indicate that the proposed
method reduces shortage and excess stock compared with the conventional separation method.

2 - Enhancing Shopping Centre Profitability through Optimized Tenant Mix and Synergistic Placement

Grace Maureira-Alegría, F.-Javier Heredia
The strategic allocation of tenants within shopping centers, known as"tenant mix," is crucial for enhancing profitability in the retail sector.
This research delves into creating an ideal combination of retail categories and their placement to boost rental income, a primary financial
source for mall operators. Utilizing integer linear programming, we
propose a model that integrates the concept of tenant synergy at its
core—a critical yet underexplored aspect in the literature. This model
includes constraints based on the total leasable space available and thestrategic distribution of store units. Drawing upon a dataset from 27
shopping centers in Spain, we construct a regression model to estimate base rent, positioning it as the critical component of our objectivefunction to maximize rental income. Additionally, this objective function features a synergy-based scoring system as an extra component,
designed to enhance sales revenues and create a balanced retail environment through strategic tenant placement. The effectiveness of ourmodel is demonstrated through several case studies, highlighting its
potential to increase rental income and sales. Our findings offer mall
operators a practical tool to optimize vacant spaces, facilitating strategic decision-making in the retail industry. Acknowledgments: This
research was partially supported by ANID (Chile) through Ph.D. scholarship #72190065 and AGAUR through Ph.D. project #2019DI098.

3 - Predicting Electronic Invoice Consumer Product Brand
Choices Using Deep Learning Methods

Chih-Chou Chiu, Ling-Jing Kao
Recent technological advancements have significantly impacted consumer behavior and the operational models of physical retail businesses. With the rise of data science and artificial intelligence, scholars
are increasingly utilizing deep learning methods to predict consumer
behavior in traditional channels. This study addresses a gap in the literature by focusing on predicting consumer brand choices based on electronic uniform invoice data. Specifically, it examines consumers purchasing cigarette brands in physical channels. Using Long Short-Term
Memory (LSTM), the study predicts the cigarette brands consumers
with smoking habits might choose in their next purchase. Five machinelearning approaches are also employed for comparison. The results
indicate that factors such as consumption time, location, and product prices significantly influence prediction accuracy. LSTM outperforms other models, demonstrating superior performance in predicting
consumer brand choices. These findings provide valuable insights forchannel operators to optimize marketing strategies, product offerings,

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and operational efficiency, ultimately enhancing market competitiveness. This study offers practical implications for cigarette companies
and retailers in understanding and satisfying consumer needs in physical channels using electronic invoice data.

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Wednesday, 14:30-16:00 - Room: M5 (building: 101)

TransitStream: Public Transport Optimization
Invited session
Chair: Michel Bierlaire
1 - Integrating
HeterogeneousMulti-Depot
VehicleScheduling and Public Transit Assignment for Enhanced Transit Operations: A Simulation-based Framework

Prateek Agarwal, Prashanth Kokkonda, Tarun Rambha
Public transit agencies make ad-hoc supply-side changes with an aim
to improve service quality. However, the benefits of these supply-side
improvements heavily depend on the complex behaviour of users, whotypically prioritize multiple criteria, such as travel times and the number of transfers while choosing routes. Agencies, on the other hand,
decide trip-to-vehicle assignments (Multi-Depot Vehicle Scheduling,
MDVS). However, these decisions should (1) account for temporal andspatial variation in demand and (2) consider the changes in passenger
choices they may trigger. The study proposes a local search framework that assigns a heterogeneous fleet to trips (HMDVS) so that theresulting capacity on the route is commensurate with the demand fortravellers along routes. We assess user responsiveness via a simulationagent-based public transit assignment (PTA) that predicts travellers’route choices based on transit schedule and capacity constraints. Our
local search operators are designed to effectively capture the user sensitivity to supply perturbations, leveraging the interaction between PTA
and HMDVS. Analysis on real-world transit networks such as Santa
Barbara, Columbus, and Surat show that our PTA framework can simulate millions of passengers in a few hours. Additionally, we observesavings of up to 10% in operational costs on switching from a homogenous to a heterogenous fleet.

2 - Modal split on a single edge to minimize multiple objectives

Sven Jäger, Anita Schöbel
When a traffic planner plans the offer of different transport modes, sheaims at minimizing simultaneously the total travel time, the total operating cost, and the total CO2 emission. We consider the setting of twocities with a fixed travel demand that can be connected by different
transport modes such as a highway for cars, a tram line, and regional
trains. The contribution of each mode to the three objective functions
depends on the number of passengers using that mode and on the service to be set up to serve them. We are interested in Pareto-optimalmodal splits, which cannot be improved in one objective without degrading another objective.
Motivated by this application, we study general tri-criteria problems
over a simplex (representing possible modal splits of the total demand),
where we assume that all objective functions are separable over thevariables. In the case that all objectives are linear, the Pareto-optimal
set is simply the convex hull of all Pareto-optimal pure modes, which
is a face of the simplex. We study the structure of the solution set
for more general objective functions, such as convex differentiable orpiecewise constant objective functions in order to develop tailored algorithmic approaches.

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3 - Welfare maximization for a user-centric transportation
systemMeritxell Pacheco Paneque, Shadi Sharif Azadeh, Michel
Bierlaire
Despite their relevance in policy evaluation, social impacts and sociodemographic distributional effects of transportation systems have beenless addressed than economic or environmental considerations. This
lack of social indicators mainly has to do with the way the demand ismodeled for policy analysis (e.g., by assuming an aggregate representation and/or constant demand elasticities). In this research, we derivea tractable measure of social welfare that revolves around the notion of
expected maximum utility as given by discrete choice models based on
the random utility principle. In addition, their disaggregate nature allows to carry out distributional analyses for different groups with similar socioeconomic characteristics. To illustrate this methodology, we
consider an urban multi-modal system where both private and public
transport modes are managed by a single planning authority that hascontrol of the related decisions (e.g., price, frequency) and the welfare measure is to be maximized. The optimization problem governing these decisions is constructed using the choice-based optimization
framework previously introduced by the authors, in which a linear representation of discrete choice models is embedded in a mixed-integer
linear programming formulation to capture the interaction between the
expected demand and the supply-related decisions. We propose a semisynthetic case study on the introduction of a road toll in a corridor
connecting two cities in Switzerland.

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Wednesday, 14:30-16:00 - Room: 8003 (building: 202)

Heuristic Algorithms for Combinatorial
Optimization Problems II (Contributed)

a single picking route and all items in the batch are picked together.
Tackling an Order Batching Problem usually involves solving several
activities, necessary to perform the picking process, such as: batching, routing, assigning, sequencing, or waiting, among others. In thisreview we focus on manual picking systems performed in rectangularshaped warehouses, which is probably the most common warehouseconfiguration in the literature. We review and classify the problems
in the literature on a new taxonomy, examining the most outstanding
heuristic and metaheuristics algorithms proposed to tackle the different tasks of the problem. Finally, we outline future research directions
related to the studied topic.

3 - Optimizing Refinery Maintenance Planning Processes
with Three-step Algorithm Approach

Melike Kamuran Onat, Ocan Sahin, Barış Yıldız, Büşra
Aydın, Çağrı Bahadır, Berker Günay, Mustafa Oktay Samur
Petroleum refineries are intricate industrial facilities, and effective
management of maintenance and repair activities is crucial for uninterrupted production processes. The inter-dependency of maintenancetasks and their predetermined start and end dates is the focus of thisstudy, and the use case is selected from an oil refinery. An optimization model is proposed to minimize the inefficiencies in the form of idle
machinery, underutilized teams, and relocation of maintenance technicians between the given tasks. A three-step algorithmic approach issuggested. Firstly, an unconstrained binary knapsack model is employed to reduce the problem size. Secondly, a constrained knapsackproblem is solved via mathematical programming to determine maintenance schedules. Finally, a Time-Expanded Network Flow Algorithm
is used to assign maintenance teams to the scheduled maintenancetasks, minimizing the number of workers used to execute the scheduled
maintenance tasks. The proposed algorithm will not only shorten thetime required for planning experts to create a weekly plan but also enhance the utilization of technicians. By streamlining the maintenanceand repair processes, the algorithm aims to optimize resource allocation and improve overall operational efficiency in petroleum refineries.

Stream: Combinatorial Optimization
Invited session

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Chair: Eduardo G. Pardo
Chair: Melike Kamuran Onat

Carbon Taxing and Emissions Controls

1 - Optimizing workspace scheduling for shipyard manufacturing under additional resource input consideration

Hyung Joo Cha, Seokhyun Chung, Taesu Cheong
In this presentation, we present a novel scheduling algorithm tailoredspecifically for the shipbuilding industry, with a key focus on integrating additional resource input to potentially minimize makespan.
While prior research has addressed various facets of shipyard scheduling, most efforts have centered on optimizing schedules based solely
on existing resource capacities, often neglecting the critical issue ofalleviating bottlenecks caused by resource limitations. Our research
fills this gap by introducing a novel mathematical model designed toaugment existing shipbuilding scheduling systems. This model strategically incorporates the option of supplementing resources to alleviate
bottlenecks, all while accounting for the associated acquisition costs.
Complementing this mathematical framework, we developed a heuristic algorithm meticulously crafted to enhance efficiency. Through extensive computational experiments, we demonstrate the efficacy androbustness of our proposed approach.

2 - A review of order batching optimization problems

Eduardo G. Pardo, Sergio Gil-Borras
There is a family of related optimization problems, commonly denotedas Order Batching Problems, which are tackled as part of the supply
chain management. This family, groups those problems consisting in
the optimization of the picking process in a warehouse, when the picking policy follows a batching strategy. The batching strategy consistsof grouping the items into batches before starting the picking process.
The picking of the items within the same batch is then performed on

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Wednesday, 14:30-16:00 - Room: 8007 (building: 202)

Stream: Sustainable and Resilient Systems
Invited session
Chair: Jorge Weston
1 - Integrating group heterogeneity in assessing carbon
performance of water companies: a case study for england and wales

Maria Molinos-senante, Trinidad Gomez, Rafael Caballero,
Ramon Sala-Garrido, Alexandros Maziotis
One of the main challenges that water companies face is to reducecarbon footprint in their transition to carbon neutrality. Past researchassessing carbon performance of water companies have mostly ignored
heterogeneity among the water companies evaluated. To overcome thislimitation, this study employs a parametric metafrontier approach toassess and compare carbon performance of a sample of English andWelsh water companies, embracing water and sewerage companies(WaSCs) and water only companies (WoCs). This method allows for
a comparison of the carbon performance of these two types of companies and analyses the impact of environmental variables on theirperformance. It was evidenced that water treatment complexity, main
source of raw water and population density significantly influence carbon performance of water companies. WaSCs were found to be morecarbon efficient than WoCs. Moreover, on average WaSCs have improved carbon productivity across years (2011-2020) whereas WoCs
have worsened it. The insights gained from this study are highly significant for policymakers focused on transitioning the water industry
towards net-zero carbon emissions.

EURO 2024 - Copenhagen
2 - A Bi-Level Optimization Model for Congestion andEmission Control in Transportation Networks

Anna Laura Pala, Giuseppe Stecca
Congestion addressing issue is of utmost importance to ensure the sustainable development of urban conglomerates, particularly in light ofgovernmental initiatives aimed at regulating emissions. Congestion
mitigation through resource optimization, not only enhances commuterquality of life, but also alleviates delays and reduces operational costfor carriers. This research introduces a novel bi-level model aimed
at addressing congestion and emissions control in transportation networks. At the first level, acting as authority, our model aims to mitigate
congestion and reduce emissions. By analysing traffic flows and assessing congestion levels on arcs and routes, we impose a crossing cost
proportional to the degree of arc saturation, thereby discouraging congestion buildup while simultaneously penalizing emissions to promote
environmental sustainability. At the second level, coalitions make decisions based on cost minimization. Specifically, the follower’s problem is presented as a freight forwarding issue, in which the model
enables transhipments between vehicles, implicitly allowing coalition
formation and promoting a collaborative approach. This model serves
as decision support, offering a comprehensive approach to congestionand emissions management in transportation systems. The case study
is related to CNMOST PNRR spoke 7 research program.

3 - On Carbon Tax Effectiveness in Inducing a Clean Technology Transition: An Evaluation Based on Optimal
Strategic Capacity Planning

Jorge Weston
In this work we study how carbon tax induces a transition to cleaner
production under different types of technologies and different demands
over a planning horizon. To determine the effectiveness of carbon tax
we propose a model based on strategic capacity production planningunder carbon taxes. We also present different performance measuresto quantify the effectiveness of carbon taxes. The model used is formulated as a mixed integer linear problem (MILP) and consider elements that previous works have not jointly studied, such as machine
replacement, workforce planning, and maintenance; elements that are
relevant when studying the technological transition. The effectivenessmeasures proposed in this works, consider levels of clean production
and periods to reach a full technological transition. Our computational
experiments, based on a real case, have shown that in the absence ofcarbon taxes, a firm has no incentive to transition to clean technology.
Still, the effectiveness of carbon taxes depends on the characteristics ofthe technology available for the production process and the magnitudeof the demand. Finally, we present managerial insights aimed at bothcompanies and the environmental authority to help the planning for atransition to clean technology.

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Wednesday, 14:30-16:00 - Room: S01 (building: 101)

Railway Capacity Management
Stream: Public Transport Optimization
Invited session

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and costly process subject to many constraints. Consequently, infrastructure decisions must be strategically planned over time to maximize
passenger benefits.
This research introduces a novel model aimed at optimizing the deployment of infrastructure upgrades in a railway network over a given
planning horizon. We propose a Mixed Integer Linear Programing formulation based on the Periodic Event Scheduling Problem formulation
for cyclic timetabling. Furthermore, we develop heuristics to tackle
this new problem and its operational requirements. We present results
on a set of real-life instances from the Netherlands.

2 - Timetable-based network capacity assessment

Franziska Theurich, Sara Comelli, Cédric Kekes, Karl
Nachtigall, Norman Weik
In project "Network Capacity" we develop an approach allowing tocompute the residual freight capacity for a given passenger timetable,considering freight traffic demand while maintaining punctuality andreliability in operations. Since timetable-based capacity assessment
suffers from fluctuating freight traffic demand, we use pre-constructedslots on net segments which will be combined to train paths as the basis of our model. The approach consists of two parts, timetabling anddelay prognosis, which are combined to an integrated planning framework. In the timetabling part, we solve a Multi Commodity Flow Problem to predict the required number of slots on network segments. Subsequently, we construct (possible overlapping) slots for freight traffic
in the passenger timetable using mathematical optimization techniquessuch as Branch-and-Price. These slots are the basis for routing freighttraffic demand based on a MIP-based slot allocation. In detail, slots are
selected and connected to build up conflict-free trains paths. In the delay prognosis part, a stochastic (max,+)-approach is used to assess thereliability of the timetable on the network level. It allows to calculate
the delay distribution for each train and every infrastructure element
and to assess timetable stability by suitable network performance metrics. With this approach, network capacity can be assessed by scaling
the demand up to the maximum that can be reliably satisfied.

3 - A petri net based binary search algorithm to assess feasibility of operational concepts for railway networks.

Christopher Szymula, Nikola Bešinović, Karl Nachtigall
Operational concepts i. e. service networks with macroscopic timetables are an essential railway planning tool and are used for various
tasks such as capacity assessment or demand modelling in strategic andtactical planning stages. However, it is crucial to ensure the feasibilityof such concepts as they form the high-level base for the timetablesof the following planning stages, which need to ensure conflict-free
railway operations. Providing feasible concepts is particularly difficultin large railway networks, where numerous and complex interdependencies exist. We therefore propose a petri net based binary searchalgorithm to determine the feasibility of operational concepts in railway networks. There, the petri net based formulation is used to assess
the networks operational period by determining the cycle mean. The
minimal cycle mean i. e. the lowest operational period is determinedby applying the petri net formulation within a binary search algorithm.
The obtained approach determines the lowest operational period in atimely manner. It can thus be used as a lower bound method for constructive capacity assessment algorithms or for the evaluation of operational concepts in the strategic and tactical planning stages. The
approach further allows to derive additional information on the criticalprocesses and thus provides a starting point to further concept improvements.

Chair: Franziska Theurich
Chair: Cédric Kekes
1 - A Cyclic Timetabling Approach to Determine Optimal
European Railway Traffic Management System Rollout WD-55
Wednesday, 14:30-16:00 - Room: S02 (building: 101)

Pedro José Correia Duarte, Lucas Veelenturf, Dennis Huisman

Supply Chain Network Optimization

The enhancement of European Railway Traffic Management Systems(ERTMS) can lead to shorter headway requirements between trains
and faster connections for passengers. However, the deployment of
ERTMS or other digital infrastructure in a railway network is a lengthy

Stream: Transportation
Invited session
Chair: Foaad Iravani

465

WD-56EURO 2024 - Copenhagen

1 - Tactical transportation planning for a Norwegian aluminium producerPeter Schütz, Julia Rudby, Kristoffer Tufte-Johnsen
We study the tactical transportation planning problem of a Norwegianaluminium producer. Today, products are shipped from the production plants in Norway to customers in Europe using a combination ofmaritime and land-based transportation. On the first leg from the production plants to terminals on continent, products are shipped using
vessels on a fixed, weekly schedule. From the terminals, products aretransported by truck to the customers when needed. Each customer
is served through a dedicated terminal. The current planning process
has resulted in challenges as vessel capacities are not sufficiently accounted for, often resulting in the need to manually reassign shipmentsfrom dedicated routes to alternative routes. We present a mathematicalmodel for supporting decision-makers when setting up the transportation plan. The goal is to minimize the total transportation costs while
ensuring that all customers are served within the delivery deadline. We
further analyze the benefits of changing the current system with dedicated terminals. The analysis intends to provide insights regarding theinterplay between flexibility in the transportation system, transportation costs, vessel capacity utilization, and system complexity

2 - Navigating Arctic Waters: A Risk-Averse Stochastic
Shortest Path Problem

Gleb Sibul, Peter Schütz, Kjetil Fagerholt
Rapid global warming and declining ice are making Arctic routes morenavigable. These routes can reduce shipping time between Europe andAsia, offering economic and environmental benefits by lowering fuelconsumption and associated emissions. The main uncertainty in theplanning of the routes stems from dynamically changing ice conditions. In this paper, we formulate a risk-averse stochastic shortest path
problem for a vessel sailing in the Arctic Ocean. It estimates an optimal
vessel path before the departure, without knowing the exact realizationof ice conditions. The objective is to minimize total travel time under conditional value at risk, considering the performance in the worst
scenarios. The sailing speed (and hence the travel time) depends on
the ice conditions, where a ship can sail at normal service speed in
ice-free water, while the speed must be decreased in unfavorable ice
conditions. The model is tested on historical and projected ice data toestimate transit routes between Europe and Asia via the Arctic Ocean.
We are interested in obtaining estimates of travel times between Europe and Asia and evaluating possible delays to gain insights into the
future of Arctic Shipping.

3 - Traffic rerouting under uncertainty

Oskar Eikenbroek, Ricardo de la Paz Guala
Travel demand management measures providing personalized routeadvice to receptive road users are potentially powerful in redistributing traffic for the benefit of network performance, typically expressedusing indicators on efficiency, safety, emissions and equity. With
the well-known trade-off between individual and societal objectives,
system-beneficial route advice may involve a detour compared to thehabitual choice. Sustained effects however can only be achieved if
travelers (incidentally) comply with such advice. Real-world experiments show that travelers can be persuaded or nudged to do so provided that the suggested detour is bounded and acceptable. Determining the system optimal advice while meeting user constraints is a difficult task since travel times depend on the compliance and the potential
re-routing behavior of all travelers, which are subject to uncertainty
when the advice is given. In this research, we formulate an optimization problem with equilibrium constraints determining the best possible advice in terms of network performance while explicitly accounting for compliance rates, different perceptions of road users and variations in travel times. We use techniques from variational analysis todevelop a numerical method to solve the problem. Results reveal that
it is key to account for uncertainties in travel times to prevent negative
experiences with social route advice and to assure that intended effects
are achieved.

4 - Production Network Design under Supply Uncertainty

Michel Fender, Nadia Jaoui, Nizar El Hachemi, Walid Klibi

466

Our research addresses a production network design problem involving strategic decisions related to selecting production site locations andtheir capacities, choosing suppliers, and sizing an internal fleet of vehicles and/or engaging external transportation providers. The problem also considers tactical decisions regarding flows between origindestination pairs, produced quantities, and inventory levels. These
decisions are made within a dynamic environment considering uncertainty in suppliers and production capacities, as well as the availabilityof transportation vehicles. The problem’s structure allows us to model
it as a multi-stage stochastic model. However, we approximate it as a
two-stage model where strategic decisions are made at the first stage,
while tactical ones are set as second-stage decisions. To solve various
instances of our problem, we assess the efficiency of three resolution
methods: Cplex’s default branch-and-cut algorithm, Cplex’s Benders
decomposition, and partial Benders decomposition. Next, we demonstrate the relevance of integrating the three strategic decisions insteadof making them separately and study the impact of different degrees of
uncertainty on these decisions. Our findings justify that partial Benders decomposition produces high-quality solutions while achieving
computational efficiency, highlighting the suitability of the stochastic
model and the resolution approach for dealing with practical supply
chain applications.

 WD-56
Wednesday, 14:30-16:00 - Room: S04 (building: 101)

Last mile delivery with drones
Stream: Transportation
Invited session
Chair: Giovanni Campuzano
1 - Collision-free Trajectory Planning for Drones with Velocity Dependent Energy Consumption and Moving Piggyback Vehicles

Christin Münch, Alf Kimms
When drones are to be used in last-mile delivery, the limited drone
flight range poses a major challenge. To mitigate this drawback, we allow the drone to be launched and recovered from moving trucks. Further, we allow for variable drone velocities, as the energy consumption
and therefore the drone flight range is heavily dependent on its velocity. We consider one truck that launches the drone, and another one thatrecovers it. Both trucks drive along streets, and the launching and recovery can take place on any position along these streets. To synchronize the trucks with the drone, the departure times of both trucks are
determined as well. Further, we incorporate obstacles that the drone
must avoid, which may represent buildings, mountains, no-fly zones,or areas with a high probability of the drone being captured. We takeon a geometric approach where the drone operates in the Euclidean
space, and to the best of our knowledge, such a geometric viewpoint
has not yet been adopted in a MILP for truck-drone last-mile delivery. Our MILP determines a collision-free trajectory for the drone in
an environment with obstacles where the drone is launched and recovered by moving vehicles, such that the parcel is delivered as early as
possible. The results of our extensive computational study, including
the evaluation of three valid inequalities, prove the usefulness of ourmodel, as even large instances with up to 300 obstacles can be solved
within reasonable computation time.

2 - An exact method for the Single Drone-Truck Routing
ProblemJahir Llagas, Marcel Turkensteen, Sanne Wøhlk
In urban logistics, the integration of drones with traditional delivery
methods presents a promising avenue for improving efficiency andsustainability. This study addresses the optimization of last-mile deliveries by a single drone-truck system. Similar to the vehicle routing
problem (VRP), the objective is to minimize travel cost of the travel
time while serving all clients exactly once. The vehicle routing problem with drones (VRPD) presents the drone as an additional delivery
vehicle that the truck can pick up and transport. This problem involves

EURO 2024 - Copenhagena network composed of clients, a depot, and dock hubs. The drone cantake off from the truck at any vertex, visit at most as many clients as
the number of its compartments, and must land at a dock hub, where
the truck can pick it up again. Moreover, the drone’s battery capacity
must be considered on each trip. In this project, we will increase thedrone’s autonomy by allowing it to visit dock hubs as battery swapping points during a trip (increasing its driving range) and by including cross-docking. The cross-docking process allows the truck to leaveproducts at the dock hubs, where the drone will visit, pick up the products, and start a new trip. We propose an exact approach to solving the
problem and present preliminary results.

3 - Application of Genetic Network Programming to optimize logistics services in mountain areas using drones

Alessia Grosso, Caterina Caramuta, Giovanni LongoIn recent years, an increasing number of companies have been introducing drones into the transport sector, mainly to cope with last-mile
distribution issues, being these means of transport more cost effective,
sustainable and efficient than traditional modes. Notably, their use inremoted areas can increase accessibility and provide support in caseof emergency. Existing research studies mainly focus on drone components and on routing optimization problems, but there seems to bea lack of papers dealing with the design of distribution chains implementing drones for last-mile logistics services. This research proposes
an application of genetic network programming to optimize the logistics network configuration, with the aim of defining the optimal combination of transport modes serving the implementation of drones in thelast-mile distribution. In particular, a multi-criteria method is used toevaluate the fitness of the candidate solutions by combining environmental impacts, transportation performances and costs. Moreover, an
ad-hoc crossover operator is developed to preserve the most promising
structures during the evolution process. The methodology has beentested through an application to a case study in a mountain area in the
North-East of Italy.

4 - Optimizing Last-Mile Logistics: Analyzing CustomerCentric Truck-and-Drone Delivery Systems

Giovanni Campuzano, Alan Osorio-Mora, Eduardo
Lalla-Ruiz, Martijn Mes, Paolo Toth
Addressing transportation problems with customer-centric objectivefunctions has become essential for last-mile service providers seeking
to enhance service quality, cultivate customer loyalty, and strategically
position themselves in the market. These problems have been getting
more and more attention from service providers and have shown substantial benefits in markets with large volumes of deliveries per day,such as the last mile. In this paper, we study two truck-and-dronedelivery systems that minimize the total waiting time of customers.
To study realistic scenarios, we incorporate the interdependencies ofasymmetric transportation times, variable drone speeds, weather conditions, energy consumption, and time windows. We formulate thesetransportation problems as Mixed Integer Linear Programs (MILPs).
Furthermore, we propose a Simulated Annealing metaheuristic framework with Local Search procedures (SA-LS) with a cutoff mechanism
to avoid large computational times in high-temperature stages. In theexperiments, we provide insights into the benefits of addressing customer satisfaction for truck-and-drone last-mile delivery systems. Accordingly, the results show that even though the latency and makespanare time-related objective functions, minimizing the latency does notdirectly minimize the makespan, and vice versa. Moreover, enabling
the drone to fly at slower speed levels generates significant reductionsin the total customer waiting times.

 WD-57
Wednesday, 14:30-16:00 - Room: S06 (building: 101)

Real Option Analysis
Stream: Real Option Analysis
Invited session

WD-57

1 - Time-to-build, regulation, and investment

Haejun Jeon
This study investigates the effects of uncertainty in time-to-build andregulation, which hinders immediate revenue generation after investment, on a firm’s optimal investment decision. We show that in theabsence of regulation, uncertainty in time-to-build always accelerates
investment and enhances firm value. We also show that in the absenceof time-to-build, uncertainty in regulation can mitigate the distortionof investment induced by regulation. Furthermore, in the presence ofboth time-to-build and regulation, there can exist harmless regulationthat does not induce any distortion in the investment decision and does
not harm firm value. Lastly, in the presence of both time-to-build andregulation, not only uncertainty in time-to-build but also its presence
can accelerate investment.

2 - Investment, partial irreversibility, and competition

Takashi Shibata, Michi Nishihara, Yuan Tian
This study considers the optimal investment timing decision when theinvestment is not completely irreversible in a competitive market. As
shown in Section 8.2 of Dixit and Pindyck (1994), the optimal investment decision is independent of the degree of competition when theinvestment is completely irreversible. This study finds that the optimal
investment decision depends on the degree of competition when theinvestment is not completely irreversible. As an extreme case, if thecompetition is strongly intense, the firm never invests. The weaker thecompetition, the later the investment is exercised.

3 - Generalized Optimal Stopping for Decision Making inEnergy Markets

Frank Heinz, Reinhard MadlenerThis article presents a generalized method for multi-dimensional optimal stopping, tailored to problems that arise in electricity markets,
when addressing decisions under uncertainty. Electricity markets arehighly transparent, and considerable research is available that is dealing with the impact of the ongoing energy transition. However, thisclass of problems does not fit well to established methods for optimal stopping, and therefore, we propose an alternative, generalized
approach. We derive a general form of the Hamilton-Jacobi-Bellman
equation and set up an example which represents the retrofit of an electrolyzer to an offshore wind farm under the conditions of a transitioning day-ahead market for electricity. We demonstrate the functionalityof this approach by solving the optimal stopping problem numericallyand show that the method supports decision making well on such an
irreversible investment under uncertainty.

4 - Fuzzy Real Options and Game Theory Approach in M&A
Pricing Issue

Qiyuan Dai
In today’s business landscape, mergers and acquisitions (M&A) are a
crucial strategy for growth and development. However, M&A deals
often fail due to discrepancies in valuation. To address this issue, thispaper aims to present a unique approach to estimating M&A prices bycombining fuzzy real options methodology and game theory. By calculating the sum of a company’s intrinsic value and M&A incremental
value with fuzzy real options, we arrive at an M&A price. However,
we find that this price can be influenced by the negotiation process.
To determine the final target price during bargaining, the game theory
is employed. Our study applies this method to a real case and results
show a range of M&A prices that closely match the actual transaction price. This pricing methodology has practical value for corporate
decision-making and is particularly relevant in today’s competitive anduncertain market environment.

Chair: Qiyuan Dai

467

WD-58EURO 2024 - Copenhagen WD-58
Wednesday, 14:30-16:00 - Room: S07 (building: 101)

Logistics 3
Stream: VeRoLog - Vehicle Routing and Logistics
Invited session
Chair: Bilgenur Erdogan
1 - Online Restaurant Meal Delivery Problem:
Bundling and Fair Assignment

Order

Ke Fang, Ahmed Kheiri, Christopher Kirkbride
Online food ordering is well-developed nowadays, with many peoplechoosing to order their meal online and waiting at home instead ofgoing to a restaurant. We consider a dynamic online restaurant mealdelivery problem (ORMDP) where a pool of drivers deliver food frommultiple restaurants to ordering customers. The objectives are to reduce both delivery delays and unfairness for drivers to satisfy those twomain stakeholders in the meal delivery process. In practice, order delays increase significantly when the demand for orders is high relative
to the number of available drivers. To address this issue, we consider
the ORMDP as two sub-problems, order bundling and order assignment. First, we implement a policy-based order bundling algorithm
that gather orders into groups within drivers’ capacity. Second, we
propose an order assignment algorithm to allocate the groups to drivers
considering both delay reduction and fairness to drivers. In order to enhance realism, we emulate the behavior of drivers as they await assignments by employing an Iterative K-means Clustering approach (IKC).
This method entails clustering proximate restaurants into zones, fromwhich drivers opt for selections in accordance with probability distributions. Finally, we developed a discrete-time simulation model to test
our methods using public data. The experimental results show that order bundling and fair assignment can effectively improve the delivery
service in ORMDP.

2 - A Novel Instance Generator for Simulating Middle-Mile
Logistics Networks

Aymane Lotfi, Matteo Petris, Bruno De Backer, Thibaut
Cuvelier, Claudia Archetti
To tackle the challenge of optimizing middle-mile logistics, the crucial
link between warehouses and final deliveries, we introduce a novel
instance generator that aims to create a rich and adaptable dataset ofdiverse instances to empower researchers and developers. The instancedefines a logistics network with hubs, vehicles, routes, lines, and rotations. Additionally, it specifies a list of shipments that need to betransported through this network. To customize the instance, the user
can adjust various parameters, such as the number of hubs, density ofthe space graphs, distribution of shipment weights, or the maximum
number of vehicles. The generator reflects real-world complexities
through variations in network size and structure. We developed a random graph generator to mimic real-world middle mile networks, bygenerating space graphs for hubs. Subsequently, lines and routes are
randomly constructed on the generated space graphs, while adhering touser-defined constraints. The tool is in the form of an optimized C++
library that enables the generation of instances with a large numberof hubs and shipments. It offers the immense potential for advancing
middle-mile logistics optimization by providing a comprehensive andadaptable dataset for benchmarking optimization approaches, training
machine learning models, and analyzing the impact of network configurations and shipments characteristics on overall efficiency.

3 - Logistics Concept Design for a Battery Pack Production
Initiative

Bilgenur Erdogan, Quang-Vinh Dang, Mehrdad Mohammadi,
Ivo AdanThis study focuses on designing logistics concepts for an electricalbattery pack production setup, inspired by our industry partner. The

468

production line is composed of automated workstations using industrial fixed robot arms and manual workstations with human operators. Alongside these, autonomous mobile robots (AMRs) and automated guided vehicles (AGVs) are employed to provide material supply and product transportation, respectively. The complexity rises dueto the interdependencies among production, material supply, and product transportation. We propose several logistics concepts for this new
production setup in order to achieve the best throughput rate while
efficiently making use of AGVs and AMRs. Using real-world datafrom the industry partner, the efficacy of the proposed concepts is illustrated under varying numbers of AGVs, AMRs, and product types
through simulation. Numerical results highlight significant changes
in the logistics concept choice based on fleet sizes and product types,
with extendable implications for high-mix-low-volume production andautonomous manufacturing systems.

 WD-59
Wednesday, 14:30-16:00 - Room: S08 (building: 101)

OR in Accounting: Wealth and Risk
Stream: OR in Financial and Management Accounting
Invited session
Chair: Carolin Famulla
1 - Persuasive channel choices - evidence from managerinvestor interactions

Anthony Haake, Wolfgang Breuer, Bertram Steininger
This study develops and evaluates a linear optimization model for an
optimal channel mix for persuasive communication. It is based on thepremise that different disclosure channels induce different processing
costs and that a listener’s processing capacity for information is constrained. Under these conditions, a persuader benefits from distributing
positive and negative information differently to maximize the listener’s
understanding of the positive and to minimize it for the negative information. By doing so, a persuader effectively increases her persuasive
power. We show that our model predicts actual channel choices madeby real-world persuaders in the setting of earnings announcements withan out-of-sample approach. Although our model only considers theinformation’s tone and readability, it predicts up to 69% of channel
choices correctly, which is statistically significant. We provide robustness tests by assessing the sensitivity of the model’s predictive power tochanges in numerical assumptions. Additional analysis indicates that
persuasive channel choices are a selfish act, as these are predominantly
done by firms with low ESG scores and high institutional ownership.
A critical discussion of our results and an outlook for future research
is provided.

2 - Forecasting M&A shareholder wealth effects to prevent
value-destroying deals: Can it be done?

Joao Quariguasi Frota Neto, Konstantinos Bozos, Marie
Dutordoir, Konstantinos Nikolopoulos
M&A announcements can result in substantial positive or negative
ab- normal acquiring-firm stock returns and sizeable associated dollar
value gains or losses. Despite the extensive existing M&A literature,whether such loses and gains are predictable remains unknown. Similarly explored are the choices of model for such forecasting. This paper
fills this gap. We employ acquirer, target, deal and macroeconomic features commonly used in the literature and test the accuracy of parametric and non-parametric models. As expected, given the high noise-toratio inherent of financial forecasting, predictability is low. However,
non-parametric models are able to consis- tently forecast abnormal returns associated with M&As, sometimes sur- passing their parametriccounterparts. Our analyses of feature importance shows that a handful consistently outweighs the relevance of the remaining subset. We
further construct two portfolios of M&As, one containing ex- pectedvalue-generating M&As and another expected value-destroying MA,
and show that they are significantly different.

EURO 2024 - Copenhagen3 - Nonlinear Inferences on European Banking Resiliencein Operating Efficiency and Riskiness Around the Financial Crisis

Muktak Tripathi, Madjid Tavana, Tracie Frost, Violeta
Cvetkoska
We examine European banks’ financial performance and risk management and their trends over the years and across banking characteristics
around the financial crisis of 2007-2008. By testing for the linear andnonlinear variations using an exogenous quasi-experiment design, we
demonstrate the usefulness of understanding the cause of variations inbanks’ operating efficiency and riskiness. We adopt the positive theory of capital allocation decisions and risk-taking activities to assess
the relationship between banks’ efficiency and risk with value creationfrom intermediation services. First, we peer-benchmark banks’ efficiency, using data envelopment analysis, and obtain Fitch risk ratings
to test for their yearly and geographical resilience, including linear
and nonlinear variations before and after the crisis. Second, we determine bank-specific and industry-specific operational and financial
factors, including longitudinal and cross-sectional heterogeneities, that
affect European banks’ operating efficiency and riskiness. We find thatbanking efficiency can provide intuition on mitigating excessive investments and risk-taking around the financial crisis. Further, we capture
the informational relevance of banking efficiency to third-party agencies in assessing financial strengths and credit risks. Our study provides a foundation to examine variations in European banks’ performance and risk realizations, including financial decisions and operating saliency.

 WD-60
Wednesday, 14:30-16:00 - Room: S09 (building: 101)

Flow shop and single machine scheduling
Stream: Project Management and Scheduling
Invited session
Chair: Elena Rener
1 - An Iterated Greedy Search Procedure for the Distributed Flow Shop Scheduling Problem With InterFactory Transportation

Tristan Becker, Janis Sebastian Neufeld, Udo Buscher
Large industrial corporations typically operate a network of several
factories, leading to a distributed flow shop scheduling problem forflowline manufacturing processes. This requires assigning jobs to oneof multiple distributed factories, each equipped with identical flow
shops, where each job must be completed in their designated factory.
Our research builds upon the traditional distributed flow shop scheduling problem by including the transport of intermediate goods between
factories. This transport can occur after each machine operation, withthe transportation times varying based on the distance between the origin and destination factories. The objective is to minimize the maximum makespan across all factories. We propose an iterated greedy
search algorithm for the distributed flow shop scheduling problem withinter-factory transportation. Utilizing a graph representation, we havedeveloped a speed-up technique to enhance the search efficiency of thealgorithm. Through computational experiments, we demonstrate theefficacy and efficiency of our proposed algorithm. Finally, our research
quantifies the value of introducing the possibility of inter-factory transportation. While transportation can potentially delay processing at subsequent machine stages, our findings suggest that this is outweighed by
improved machine utilization across factories. Therefore, we observe
an enhanced overall efficiency and reduced makespans.

2 - Polynomial-time algorithms for solving timing problems
in single-machine rescheduling

Elena Rener, Fabio Salassa, Vincent T’kindt

WD-61

Rescheduling problems are understood as strategies for updating existing schedules to deal with unexpected changes in the available data.
When the change is given by the arrival of a new set of jobs, which areunknown at the time of scheduling, we speak of rescheduling for neworders. In these problems, we consider two sets of jobs, a set of oldjobs and a set of new jobs, that must be scheduled together on a single machine. For the set of old jobs, we know the original completion
times, that is, the times based on the schedule prior to the insertionof the new jobs. According to these times, additional activities may
have been planned and resources made available. Thus, the goal ofrescheduling is twofold: to minimize some classical objective function
over all jobs, and to minimize the disruption of the original schedule.
When the disruption is measured as the total absolute time deviationfrom the original completion times, the search for optimal solutions
raises the problem of determining the insertion of idle time. We provide timing algorithms for solving this problem when the sequence ofjobs is known. We show that the proposed algorithms devise the efficient frontier of solutions in polynomial time, and compute an optimal
solution in polynomial time, if a disruption threshold is given.

3 - Flow shops with reentry: Total completion time optimalschedules

Nicklas Klein, Michael Pinedo
Flow shops are widely studied machine environments in which all jobs
must visit all machines in the same order. While conventional flow
shops assume that each job traverses the shop only once, many industrial environments require jobs to pass through the shop multiple
times before completion, i.e., after traversing the shop and completingits processing on the last machine, a job must return to the first machine and traverse the shop again until it has completed all its required
loops. There are numerous applications, e.g., in semiconductor manufacturing, of such a setting, which is called a flow shop with reentry. The planning problem is to schedule all loops of all jobs while
minimizing the total (weighted) completion time. We consider a reentrant flow shop with unit processing times and show that the Least
Remaining Loops First (LRL) priority rule minimizes the total completion time. Furthermore, we show that the problem of minimizing
the total weighted completion time is NP-hard. For this objective, weshow that the Weighted Least Remaining Loops First (WLRL) priority
rule has a worst-case performance ratio of about 1.2.

4 - Balancing the average weighted completion times by
network design

Antonio Fuduli, Matteo Avolio
We face a single-machine NP-hard scheduling problem, whose objective is to balance the average weighted completion times of two different classes of jobs. Since both the job sets contribute to the same
objective function, this problem can be interpreted as a cooperative
two-agent scheduling problem, whereas the standard multiagent problems are of the competitive type since each class of job is involved onlyin optimizing its agent’s criterion. Balancing the average completion
times of different sets of tasks finds application in many fields, suchas in in services, for balancing the waiting times of groups of people,
and in logistics for balancing the delivery times. To solve this problem, we propose a Lagrangian relaxation approach based on a network
flow formulation. A possible bi-criterion extension is also investigated,taking into account the additional objective aimed at minimizing the
maximum between the two average weighted completion times. Preliminary numerical results are presented.

 WD-61
Wednesday, 14:30-16:00 - Room: S10 (building: 101)

Online, Omnichannel, and Pricing
Stream: Retail Operations
Invited session
Chair: Selin Ahipasaoglu

469

WD-62EURO 2024 - Copenhagen

1 - Channel Structure of Online Retail Platforms: Impact ofAsymmetric Information and Capacity Constraints

Hasan Shorakaei, Mehmet Begen, Hubert Pun
Problem definition: When selling online, manufacturers can either sell
directly to customers through the platform (agency channel), sell theirproducts to the platform (reselling channel), or engage in both channels simultaneously. Manufacturers typically operate with limited production capacity, and this information is often not disclosed to online
platforms. Methodology/results: Our study develops a screening gameframework where the manufacturer has private information about its
capacity, and the online platform presents a menu of quantity-pricepairs contracts to the manufacturer. The manufacturer then determines
the quantity allocated to the reselling channel and to the agency channel. Our finding highlights that the manufacturer is more likely to use
both (agency and reselling) channels under the asymmetric information
game. We also show that a manufacturer with high capacity benefits
from keeping its capacity information private, but a manufacturer withlow capacity does not. Furthermore, the platform and the manufacturer
may not benefit from the manufacturer having more production capacity. Lastly, we find that if the manufacturer’s capacity is sufficiently
small, then the platform may be better off not knowing the manufacturer’s capacity. Managerial implications: Our study offers insights
and guidance for manufacturers and online platforms, assisting themin making strategic decisions and navigating the dynamics of information sharing within the online retail.

2 - Assortment Optimization for Fashion Products in Omnichannel Retail

Kritika Swaminathan, Rakesh Venkitasubramony
Forecasting and assortment planning for fashion products have been achallenging area in Operations Research due to different factors, particularly the high percentage of new products that lack any historicalsales data. This problem gets more interesting when we incorporate
the possibility that products can be ordered and fulfilled in different
channels - what we commonly call as Omnichannel. Our study focuses on assortment optimization for products in fashion retail acrossmultiple channels in omnichannel retail. The demand for a combination of attributes is predicted from the existing sales data. Then, from a
universe of feasible attribute combinations available across channels both online and offline - under asymmetric integration, we identify theright mix of products for each channel. We model this decision as aprofit maximization function considering predicted sales revenue andthe fulfilment cost for different products across multiple channels.

3 - Multi-Purchase Choice Models of a Sequential Purchasing Process

Kexin Lai, Christine Currie, Selin Ahipasaoglu
We consider the situation where customers make a finite sequence ofpurchase decisions and buy at most one item at each stage, where thesets of items on offer at each stage are disjoint and could be drawn
from one or more categories. Our aim is to optimize item prices tomaximize overall expected revenue. The basis of the optimization is achoice model that describes how customers make purchase decisions
at each stage. We consider two choice models: a single-stage bundlepurchase model (known as the Multi-variate Multinomial Logit model)
and a multi-stage sequential purchase model.
Under the single-stage model, we assume that customers form a choice’bundle’ by selecting at most one item from each product set. We
model this behavior similarly to the standard multinomial logit model,
where customers choose the utility-maximizing bundle. In the multistage sequential choice model, customers make a sequence of choices,where the choice made in one stage depends on the choices made in
the previous stages. We include product interactions in both models toaccount for product compatibility.
Similar problems exist in industries such as tourism (travelers visit different destinations), food (restaurants with a rotating selection of specials), retail (stores with rotating inventory), and entertainment (cinemas with rotating films). Our empirical results on real data suggestthat models with greater purchase history and information attain higher
levels of accuracy.

470

 WD-62
Wednesday, 14:30-16:00 - Room: S12 (building: 101)

Port Performance
Stream: OR in Port Operations
Invited session
Chair: Eduardo Lalla-Ruiz
Chair: Rosa G. González-Ramírez
1 - Comparing the technical efficiency of Chilean and Mexicans ports using a stochastic frontier approach

Fabricio Moreno Baca, Ivan Derpich, Claudia Duran,
Cristobal Castaneda
In this work, a stochastic cost frontier approach is presented to compare the efficiency of Chilean ports and Mexicans ports. This approach is based on the work of several authors,the authors who presented an activity-based operational method for estimate CO2 emissions from container shipping considering repositioning of empty containers.Among other findings they demonstrate that the repositioning
Efficient disposal of empty containers can reduce emissions in maritime transport. Based on this background, the following subsection
presents an econometric estimation model of CO2 emissions from
Chilean and Mexicans ports based on in empty containers and the activity of export and import ships.The theoretical framework that supports the work corresponds to the theory of the stochastic frontier ofcosts introduced by Farrel. The most used frontier models are production and cost models. In this approach, cost frontier models will be applied. The linearized functional form of the Cobb-Douglas production
function is the origin of the development of the cost frontier stochastic,
which will be used in this work to characterize the technical efficiencyof each of the ports. A cost frontier model with panel data will be used.
We will use the standard form of the Cobbs-Douglas type cost frontier
due to its flexibility to be estimated with a linear function with paneldata.

2 - Measuring innovation performance in smart ports under
uncertainty: A neutrosophic approach

Antonios Paraskevas, Michael Madas
Smart ports offer a paradigm shift in the constantly evolving marine logistics industry, utilizing cutting-edge technology to enhance efficiency
and sustainability. However, accurately measuring innovation performance in these complex and uncertain situations requires a creative
and adaptable approach. This study presents a Neutrosophic Approach
to measuring innovation success at smart ports while acknowledging
and quantifying the inherent uncertainties. Our research incorporates
neutrosophic set theory, which is a mathematical framework capable ofhandling imprecise and indeterminate data. This methodology enables
the consideration of unpredictable factors that influence innovation insmart ports, such as technological disruptions, legislative changes, andmarket dynamics. By employing neutrosophic logic, the model captures the inherent ambiguity and vagueness associated with evaluating
innovation performance in this rapidly evolving market. The study
approach involves a comprehensive assessment of key performance indicators (KPIs) related to smart port innovation, accounting for bothstandard metrics and emerging indicators specific to the digitalization
and automation of port operations. Neutrosophic language variables
are utilized to measure the degree of truth, indeterminacy and falsehood associated with each KPI, facilitating a more nuanced evaluationby providing a comprehensive understanding of innovation success insmart ports amidst uncertainty.

3 - Port fariff considerations

Alejandra Gómez_Padilla, Rosa G. González-Ramírez
In this research we intend to review port tariff literature. In our analysis we consider strategic pricing, infrastructure cost recovery, market
conditions and external costs. We are interested in identifying the optimization models and methods used to define tariff structures, and theirrelation with the strategies, the infrastructure and the market. Strategic

EURO 2024 - Copenhagenpricing schemes are based on a different tariff between cargo owners
due to segment demand or freight traffic. Infrastructure tariffs are usually managed by the port authorities and may be influenced by policies
and regulations. The market is influenced by the global supply chain.
We expect to identify which are the main elements in literature to decide pricing strategies, infrastructure and the market characteristics aswell as the mathematical modeling problems that have been considered.

4 - Performance analysis of liquid bulk terminals

Werner Scheinhardt, Jan-Kees van Ommeren, Debjit Roy
Bulk liquid terminals play a crucial role in timely (un)loading of liquids from tankers and facilitating transport to and from the hinterland
via pipelines, trains or trucks. At an import terminal, arriving tankers
berth to be unloaded via loading arms and pipelines connected to storage facilities. Whenever the storage is full, the unloading rate essentially decreases to the drain rate of the storage, which increases the sojourn times of tankers considerably. On the other hand, when the storage is empty, the demand on the landside cannot be met and throughput will decrease. The performance of an import terminal serving asingle liquid type can be analyzed using feedback fluid queue models, assuming that tankers arrive according to some random process,carrying random amounts of liquid, and experiencing delay when theberth is in use. Importantly, such models explicitly take into accountthe interaction between tankers and storage reservoir. In this talk we
focus on the more complex case with two types of liquid that cannot bemixed, each with their own storage reservoir and tankers, while sharing
the same (single) berth. A straightforward fluid queue model is then nolonger applicable, but we apply an iterative approximation scheme that
performs remarkably well. We present graphs showing how the various parameters influence the behavior of the main performance measures, viz. the expected sojourn times of both types of tankers and thethroughputs of both types of liquid.

 WD-63
Wednesday, 14:30-16:00 - Room: S14 (building: 101)

Complexity and Financial Patterns
Stream: OR in Banking, Finance and Insurance: New
Tools for Risk Management
Invited session
Chair: Roy Cerqueti
1 - Volatility of Stock Market Aggregate Volatility (VoV)
Forecast Based on Cross-Sectional Intrinsic Entropy’s
Volatility Estimates

Claudiu Vinte, Marcel Ausloos, Bogdan Iftimie
In the context of index volatility versus the volatility of the entire market, the following question becomes pertinent: Does the market volatility track the index volatility, or is it the other way around? To estimate
the market’s overall volatility, we employ the cross-sectional intrinsic entropy (CSIE) model as the underlying methodology for tracking the aggregate volatility of all the symbols listed and traded on agiven stock market. We investigate the appropriate GARCH models
to forecast the variance of market cross-sectional aggregate volatility
estimates and the variance of market indices volatility estimates (VoV)
forecasting. The CSIE model provides estimates of the daily aggregate volatility for the entire market. Alternatively, index volatility estimates are determined on n-day rolling windows. Therefore, CSIE market volatility estimates are integrated into the research methodology ascomparable n-day moving averages. Our study uses historical end-ofday (EOD) data consisting of traded volume, opening, high, low, andclosing prices (OHLCV) for all companies listed on the NYSE and
NASDAQ and the corresponding market indices, S&P500 and NASDAQ Composite respectively. We conclude that the S&P 500 is not
fully representative of the market, and particularly in the periods preceding recessions, it does not capture overall market immanent deep

WD-63

trends, specifically those revealed through the CSIE market volatility
estimator.

2 - Balance in financial signed networks: a new systemicrisk measure

Rosanna Grassi, Paolo Bartesaghi, Fernando Diaz-Diaz,
Pierpaolo Uberti
Changes in the correlation structures between asset returns strongly
reflect the systemic events in the financial context. Commonly, correlation is used in absolute value. However, the question remains as
to what information about the systemic risk is contained in the correlation signs, especially when interpreted as the weights of the edges
in a signed network. From this perspective, balance theory can provide an interesting insight into catching signals of systemic crises anda meaningful tool to manage portfolio risk. The crucial point is that inbalanced systems it is easier to predict the behaviour of a single entity.
Hence, the balance contributes to increasing the level of "predictability" of the network behaviour. In a financial context, this is essential
in managing risk to face systemic crises. We show how the global balance index can be interpreted as a possible indicator of systemic risk.
Moreover, we compare the global balance with some systemic risk
indicators from the literature. We focus on the indicators that wellcapture the loss of linear independence in the matrix of observations
through the analysis of the minimum eigenvalues of the correlation
matrix, interpreted as the distance from an appropriate space of rankdefective matrices. Preliminary results are promising and they create a
new bridge between systemic risk measures and signed networks.

3 - Are digital entrepreneurial ecosystems resilient to external perturbations? A pre- and post-pandemic empirical analysis

Antonio Iovanella
The complexity of the innovation and entrepreneurship process callsfor ever-increasing connectivity. As a result, scholars have begun tofocus more on entrepreneurial ecosystems (EE), which emphasise theinterconnection of every player in this process. In this vein, external collaborations have been encouraged by the development of digital technology in response to COVID-19. At the same time, the pandemic has posed challenges to the resilience of EE, and the digitaldimension may foster resilience from this perspective. In this paper,
we use complexity theory and Social Network Analysis to investigate
how resilient a digital EE of European startups is to external perturbations both before and after the pandemic. By doing this, we help
to clarify the relationship between digital platforms and EE as wellas their role in enhancing resilience. Our results show a higher levelof robustness to random node removal. Nevertheless, there have beenshifts in the paths connecting members as startups diversified their interests across sectors and countries. The new configuration facilitates
the shock propagation within the network, leaving room for designing
targeted policies and managerial practices to increase the ecosystem
resilience. The network’s ability to propagate shocks is facilitated by
the new arrangement, leaving room for the development of focusedpolicies and managerial techniques that strengthen the resilience of theecosystem.

4 - FICO Decision Optimizer - Generating predictive models with Action EffectClaudio Gambella, Livio Bertacco, Sebastien Lannez, Ben
Willcocks, Brendan del Favero, Ryan Weber
The FICO Decision Optimizer (DO) application is an optimization
software package to perform optimal assignment of treatments to aportfolio of customers. DO leverages optimization algorithms with thegoal to empower non-OR professionals with a tool that creates andsolves Generalized Assignment Problems, without requiring them toformulate the models. DO considers different kinds of constraints (e.g.,budget, ratio) and allows users to generate highly interpretable decision trees. The DO models depend on structural inputs (e.g., portfolio
or customer attributes) and uncertain inputs (predictable target values,
such as the impact of treatments on customers). While the DO interface enables configuring and editing of the predictive models required
to produce the uncertain data, developing Predictive Causal Models
usually requires additional tools and knowledge for a Business Analyst. Furthermore, there is inherent historical data bias, because the471

WD-64EURO 2024 - Copenhagen

historical actions are likely to be targeted on certain segments. Consequently, there may be significant data gaps for some actions. To
simplify the user experience, we developed an Action Effect (AE) approach to predict the impact of the treatment assignment on every customer of the portfolio. We present the logic behind the quadratic programming models that create the predictive models. We conclude ourpresentation with a discussion about the advantages of this methodology over standard unconstrained linear regression.

 WD-64
Wednesday, 14:30-16:00 - Room: S16 (building: 101)

Heuristics for Vehicle Routing 2
Stream: VeRoLog - Vehicle Routing and Logistics
Invited session
Chair: Patricia Zech
1 - Matheuristic for Feeder Network Design Problem With
Optional Paired Demands and Split Delivery

Anandhu Dileep, Ahmad Hemmati
In this work, we study a complex variant of the feeder network design
problem (FNDP) arising in the field of maritime transportation. The
problem is about designing routes for ship services between several
shipping ports, to deliver cargoes between these ports based on a fixed
weekly demand. In many cases, this is done using disjoint networks ofships services, called feeder networks.
Each feeder network is connected to a main port by a long ship service
called a mother route. Some of the paired demands are delivered by themother routes themselves, and the remaining demands using daughter
routes, which are shorter ship services that branch off from some of theports in the mother routes.
In the variant of the FNDP studied, we also allow split deliveries ofthe demands and the rejection of some of the demands. There are time
constraints on the routes and on the travel time for delivery of eachcargo. We formulate a mathematical model for the problem and propose a matheuristic framework for obtaining high quality solutions.

2 - Hybrid Randomised and Greedy Technique as a Novel
Approach to Solve the Open Vehicle Routing Problem(OVRP)

Abdullah Almouhanna, Hassana Abdullahi, Angel A. Juan
The Open Vehicle Routing Problem (OVRP) is a complex combinatorial optimization challenge with numerous real-world applications intransportation, logistics, and urban planning. The OVRP differs fromthe Vehicle Routing Problem (VRP) because the vehicles either are
not required to return to the depot, or if they are to so do by revisiting
the customers in the reverse order. The OVRP is faced by a company
which either does not own a vehicle fleet at all, or its vehicle fleet is inappropriate or inadequate to satisfy the demand of its customers. Our
approach is based on combination of the Iterated Greedy (IG) withbiased randomization technique. The IG has been developed by tosolve the Permutation Flowshop Scheduling Problem (PFSP) in orderto minimise makespan. It is reasonably simple to implement and parameter free. One of our aim of choosing this method is that, the IG has
been applied successfully for some Combinatorial Problems specifically and has been proven to generate high quality solutions. Despite
the proven impressive performance of IG, it has not been applied in
the OVRP. Biased randomisation technique refers to the utilisation ofpseudo-random numbers to generate random outcomes throughout thesolution search. By randomizing some steps in a deterministic heuristic, it is transformed into a probabilistic procedure. Then, it can be
run multiple times - either in sequential or parallel mode - in order toobtain different outcomes or solutions.

472

3 - A Metaheuristic Approach for the Meal Delivery Routing
ProblemDaniel Giraldo-Herrera, David Álvarez-Martínez
With the exponential growth of e-commerce platforms, fueled by
the increasing demand for meal delivery services, delivery efficiency
emerges as a pivotal concern for businesses. This paper delves into
the Meal Delivery Routing Problem (MDRP) within the realm of ecommerce, addressing the delivery applications, couriers, restaurants,
and customers’ objectives that often conflict among them. The study
focuses on the complexities of last-mile logistics, emphasizing the imperative for a robust solution to achieve operational efficiency, enhancecustomer satisfaction, promote environmental sustainability, and minimize lost sales. This paper presents a GRASP metaheuristic solution that tackles the MDRP by optimizing courier assignment to orders
while accounting for dynamic variables like courier availability, order demands, and geographical considerations. The methodology isvalidated using real-life scenarios based on data from a delivery app
operating in South America. Comparative analyses with a simulationoptimization-based study underscore the efficacy of GRASP in enhancing order fulfillment and routing efficiency, showcasing its strengths
across diverse locales such as Sao Paulo (Brazil), Bogota, and Medellin(Colombia). As future research, we propose exploring optimization
models geared toward maximizing the welfare of all stakeholders
within the meal delivery services system. Such an endeavor aims tofoster genuine democratization of delivery services.

4 - Analyzing the Benefits of Node Aggregations in Vehicle
Routing Problems

Patricia Zech, Christian Almeder
The constant growth of e-commerce challenges last mile delivery service providers to find cost-efficient route plans that fulfill the demandof thousands of customers daily. To facilitate problem abstraction,a variety of distribution or collection problems as postal delivery or
garbage collection are modelled as arc routing problems. Compared todense urban areas, where an aggregated view on arcs instead of nodes
is straight forward for reducing the complexity of the problem, the situation in rural or suburban areas is different. Aggregation of nodes
along a long street segment might cause a depletion of the route quality. We are investigating the possibility of various node aggregation
heuristics for such situations, which transforms a large-scale vehicle
routing problem (VRP) into a smaller capacitated general routing problem (CGRP), where demand might be located on nodes, edges, or arcs.
Several mathematical problem formulations for the latter are analyzed
and evaluated regarding their solution performance compared to standard VRP models. The goal is to specify the trade-off between thelevel of aggregation, the loss of solution quality and the decrease incomputational time.

EURO 2024 - Copenhagen

WF-01

Wednesday, 16:30-17:30

Wednesday, 17:30-18:30

 WE-01

 WF-01

Wednesday, 16:30-17:30 - Room: Sportshallen (building: 101)

Wednesday, 17:30-18:30 - Room: Sportshallen (building: 101)Charles Corbett [IFORS Distinguished
Lecture]Closing Session

Stream: Plenaries
Plenary session

Stream: Opening and Closing
Plenary session

Chair: Janny Leung
1 - The operations of well-being: How operations interacts
with happiness, equity, and sustainability

Charles Corbett
What does well-being have to do with operations? Well-being encompasses a lot: Are we happy as individuals? Are groups treated fairly?
Is society sustainable? Operations management has many impacts on
well-being at each of these levels, some more obvious than others. This
talk will offer a wide-ranging exploration of linkages between operations and well-being. It organizes "operations" into five broad areas:
pace and productivity, predictability and probability, process and prevention, performance and payment, and pollution and protection. For
each of those, it explores what makes individuals (un)happy, what isfair, and what is sustainable. Operations cannot solve all societal problems but the links between quality of operations and quality of life are
more numerous and nuanced than we usually realize.

473

Streams
Advancements of OR-analytics instatistics, machine learning anddata science

Gerhard-Wilhelm Weber
Poznan University of Technology
gerhardwilhelm.weber@put.poznan.plTrack(s): 6 28
Advances in large scale nonlinear
optimization

Stefania Bellavia
Universita di Firenze
stefania.bellavia@unifi.it
Benedetta Morini
Universita di Firenze
benedetta.morini@unifi.it
Track(s): 32 34
Advances in Stochastic Modellingand Learning Methods

Odysseas Kanavetas
Leiden University
o.kanavetas@math.leidenuniv.nl
Flora Spieksma
Universiteit Leiden
spieksma@math.leidenuniv.nl
Track(s): 40
Agent-based Models inManagement, Economic andOrganisation Sciences

Stephan Leitner
University of Klagenfurt
stephan.leitner@aau.at
Friederike Wall
Alpen-Adria-Universitaet Klagenfurt
friederike.wall@uni-klu.ac.atTrack(s): 43
AI & Innovation in Sustainable
FinancePeter Schwendner
Zhaw University
scwp@zhaw.ch
Marc Weibel
ZHAW School of Management andLaw
wmar@zhaw.ch
Track(s): 8474

Analytics

Combinatorial Optimization

Dries Benoit
Ghent University
dries.benoit@ugent.be

Silvano Martello
University of Bologna
silvano.martello@unibo.it

Kristof Coussement
IESEG School of Managementk.coussement@ieseg.fr

Paolo Toth
University of Bologna
paolo.toth@unibo.it
Track(s): 25 26 29 52

Sebastian Maldonado
University of Chilesebastianm@fen.uchile.cl
Richard Weber
University of Chilerichard.weber@uchile.cl
Track(s): 31
Automated Timetabling

Sanja Petrovic
Nottingham University Business
Schoolsanja.petrovic@nottingham.ac.uk
Greet Vanden Berghe
KU Leuvengreet.vandenberghe@cs.kuleuven.be
Track(s): 58
Behavioural OR

L. Alberto Franco
University of Bristol
alberto.franco@bristol.ac.uk
Raimo P. Hämäläinen
Aalto University, School of Science
raimo.hamalainen@aalto.fi
Track(s): 7 11
Circular Economy,
Remanufacturing and Recycling

Renzo Akkerman
Wageningen University
renzo.akkerman@wur.nl
Gerald Reiner
Vienna University of Economics andBusinessgerald.reiner@wu.ac.at
Lena Silbermayr
WU Vienna University of Economics
and Businesslena.silbermayr@wu.ac.atTrack(s): 23 24

Computational Biology,
Bioinformatics and Medicine

Agnieszka Rybarczyk
Poznan University of Technology
arybarczyk@cs.put.poznan.pl
Aleksandra Swiercz
Poznan University of Technology
aswiercz@cs.put.poznan.pl
Metin Turkay
Koc University
mturkay@ku.edu.tr
Track(s): 20
Conic Optimization: Theory,
Algorithms, and Applications

Miguel Anjos
University of Edinburgh
anjos@stanfordalumni.org
Etienne De Klerk
Tilburg University
e.deklerk@uvt.nl
Track(s): 38
Cutting and Packing (ESICUP)

Tony Wauters
KU Leuven
tony.wauters@cs.kuleuven.be
Track(s): 7
Data Envelopment Analysis andits Application

Ali Emrouznejad
University of Surrey
a.emrouznejad@surrey.ac.uk
Track(s): 48

EURO 2024 - Copenhagen

Data Science Meets Optimization

Patrick De Causmaecker
KU Leuven
patrick.decausmaecker@kuleuven.be
Track(s): 3
Decision Support Systems

Pavlos Delias
Democritus University of Thrace
pdelias@af.ihu.gr
Shaofeng Liu
University of Plymouth
shaofeng.liu@plymouth.ac.uk
Track(s): 45

Experimental economics andgame theory

Ulrike Leopold-Wildburger
University of Graz
ulrike.leopold@uni-graz.at
Stefan Wolfgang Pickl
UBw München COMTESSA
stefan.pickl@unibw.deTrack(s): 40
Game Theory, Solutions andStructures

Energy Management

Encarnación Algaba
Seville University
ealgaba@us.es
Track(s): 36

Ayse Selin Kocaman
Bilkent University
selin.kocaman@bilkent.edu.tr
Track(s): 22

Interfaces Between Optimization,
Hierarchical Problems andEquilibrium Detection with
Applications

Energy Markets

Valentin Bertsch
Ruhr-Universität Bochum
valentin.bertsch@ee.rub.de

Giancarlo Bigi
Universita’ di Pisa
giancarlo.bigi@unipi.it

STREAMS

Locational Analysis

Hatice Çalık
KU Leuvenhatice.calik@kuleuven.be
Carmela Piccolo
University of Naples Federico II
carmela.piccolo@unina.it
Diego Ponce
Universidad de Sevilladponce@us.es
Track(s): 61
Lot Sizing, Lot Scheduling and
Production Planning

Safia Kedad-Sidhoum
Cnam - Cedricsafia.kedad_sidhoum@cnam.fr
Wilco van den Heuvel
Erasmus University Rotterdam
wvandenheuvel@ese.eur.nl
Track(s): 49
Making an Impact

Patrizia Daniele
University of Catania
patrizia.daniele@unict.it

Making An Impact
EURO Practitioners’ Forummai@euro-online.org

Laura Rosa Maria Scrimali
Università di Catania
scrimali@dmi.unict.it
Track(s): 40

Ruth Kaufman
The OR Society
ruth.kaufman@btinternet.com
Track(s): 46

EURO Doctoral Dissertation
Award

KeynotesMarket Design

Track(s): 2

Track(s): 1

EURO Excellence in PracticeAward

Knowledge, Technology, andInnovation

Aleksandrs Smilgins
Department of Economics
asm.eco@cbs.dk
Track(s): 43

Track(s): 2

A. D. Amar
Seton Hall University
ad.amar@shu.edu
Track(s): 8

Dogan Keles
Technical University of Denmark(DTU)
dogke@dtu.dk
Track(s): 9 14EURO Prize for OR for the
Common Good

Track(s): 2

475

STREAMS

Mathematical Optimization for
XAI

Jochen De Weerdt
KU Leuvenjochen.deweerdt@kuleuven.be
Kseniia Kurishchenko
Copenhagen Business School
kk.eco@cbs.dk
Jasone Ramírez-Ayerbe
Instituto de Matemáticas de la
Universidad de Sevillamrayerbe@us.es
Dolores Romero Morales
Copenhagen Business School
drm.eco@cbs.dk
Track(s): 27
Memorial Session for Jakob
KrarupDavid Pisinger
DTU
dapi@dtu.dk
Track(s): 65
Military, Defense, andInternational Security

Greg Parlier
Colonel, US Army Retired
gparlier@knology.net
Track(s): 20
Minlp

Jan Kronqvist
KTH Royal Institute of Technology
jankr@kth.se
Track(s): 4
Modern Decision Making in
Finance and Insurance

Bertrand TavinEMLYON Business Schooltavin@em-lyon.com
Track(s): 57

EURO 2024 - Copenhagen

Moments in the history of OR

Optimization at Amazon

Roberto Rossi
University of Edinburgh
robros@gmail.com

Daniel Guimarans
Amazonguimd@amazon.es
Track(s): 57

Gerhard-Wilhelm Weber
Poznan University of Technology
gerhardwilhelm.weber@put.poznan.plTrack(s): 20
Multiobjective Optimization

Alexandru Kristaly
Babes-Bolyai University
alexandrukristaly@yahoo.com

Gabriele Eichfelder
Technische Universität Ilmenau
gabriele.eichfelder@tu-ilmenau.de
Track(s): 37

Sandor Zoltan NemethUniversity of Birmingham
s.nemeth@bham.ac.uk
Track(s): 41

Multiple Criteria Decision
Analysis

OR and Ethics

Salvatore Corrente
University of Catania
salvatore.corrente@unict.it
Milosz Kadzinski
Poznan University of Technology
milosz.kadzinski@cs.put.poznan.plTrack(s): 44 47
Nonsmooth Optimization

Masoud Ahookhosh
University of Antwerp
masoud.ahookhosh@uantwerp.be
Puya Latafat
KU LEuvenpuya.latafat@kuleuven.be
Track(s): 41
Opening and Closing

Dorien DeTombe
Delft University of Technology
detombe@nosmo.nl
R. M. Kazakov
Heriot Watt University
r.kazakov@hw.ac.uk
Cathal MacSwiney Brugha
University College Dublin
cathal.brugha@ucd.ie
Ulrike Reisach
Neu-Ulm University of Applied
Sciences
ulrike.reisach@hnu.de
Gerhard-Wilhelm Weber
Poznan University of Technology
gerhardwilhelm.weber@put.poznan.plTrack(s): 20

Track(s): 1

OR Education

Optimal Control Theory andApplications

Jeroen Belien
KU Leuven
Jeroen.Belien@kuleuven.be

Andrea Seidl
Seeburg Castle University
andrea.seidl@uni-seeburg.at
Stefan Wrzaczek
Institute for Applied Systems
Analysis (IIASA)
wrzaczek@iiasa.ac.atTrack(s): 33

476

Optimization on Geodesic MetricSpaces: Smooth and NonsmoothMaria Antónia Carravilla
INESC TEC, Faculty of Engineering,
University of Porto
mac@fe.up.pt
Stefan Creemers
IESEG School of Management
sc@cromso.com
Track(s): 16

EURO 2024 - Copenhagen

OR for Development andDeveloping Countries

Gordon Dash
University of Rhode Island
ghdash@uri.edu
Nina Kajiji
University of Rhode Island, and The
NKD Group, Inc.
nina@nkd-group.com
Gerhard-Wilhelm Weber
Poznan University of Technology
gerhardwilhelm.weber@put.poznan.plTrack(s): 18
OR in Agriculture and Forestry

LluisM Pla
University of Lleida
lmpla@matematica.udl.es
Track(s): 12
OR in Banking, Finance andInsurance: New Tools for Risk
Management

Rita D’Ecclesia
Sapienza University of Romerita.decclesia@uniroma1.it
Track(s): 2 63
OR in Energy

Luis Baringo
Universidad de Castilla-La Mancha
luis.baringo@uclm.esÁlvaro García-Cerezo
Universidad de Castilla-La Mancha
alvaro.garciacerezo@uclm.es
Track(s): 19 21
OR in Financial and Management
Accounting

Matthias Amen
Bielefeld University
matthias.amen@uni-bielefeld.de
Track(s): 59

STREAMS

OR in Health Services (ORAHS)

OR Journals

Roberto Aringhieri
Università degli Studi di Torino
roberto.aringhieri@unito.it

Roman Slowinski
Poznan University of Technology
roman.slowinski@cs.put.poznan.plTrack(s): 8

Kris Braekers
Hasselt University
kris.braekers@uhasselt.bePlenaries

Walter Gutjahr
University of Vienna
walter.gutjahr@univie.ac.atTrack(s): 1

Patrick Hirsch
University of Natural Resources andLife Sciences, Vienna
patrick.hirsch@boku.ac.at

Luce Brotcorne
INRIA
luce.brotcorne@inria.fr
Track(s): 59

Melanie Reuter-Oppermann
University of Twente
m.n.reuter-oppermann@utwente.nl
Track(s): 10 15 17

Project Management and
Scheduling

OR in Humanitarian Operations(HOpe)

Douglas Alem
University of Edinburgh
douglas.alem@ed.ac.uk
Tina Wakolbinger
WU (Vienna University of Economics
and Business)
tina.wakolbinger@wu.ac.atTrack(s): 21
OR in Port Operations

Rosa G. González-Ramírez
Universidad de Los Andes, Chilergonzalez@uandes.cl
Eduardo Lalla-Ruiz
University of Twente
e.a.lalla@utwente.nl
Track(s): 62
OR in Sports

Dries Goossens
Ghent University
dries.goossens@ugent.be
Track(s): 16

Pricing and Revenue
Management

Erik Demeulemeester
KU Leuvenerik.demeulemeester@kuleuven.be
Joanna Józefowska
Poznan University of Technology
joanna.jozefowska@cs.put.poznan.plTrack(s): 60
Public Transport Optimization

Dennis Huisman
Erasmus University
huisman@ese.eur.nl
Marie Schmidt
University of Würzburg
marie.schmidt@uni-wuerzburg.de
Track(s): 51 54
Quantum Computing
Optimization

Brandon Augustino
Lehigh University
bra216@lehigh.edu
Tamás Terlaky
Lehigh University
terlaky@lehigh.edu
Track(s): 42

477

STREAMS

Real Option Analysis

Mikael Collan
VATT Institute for Economic
Research
mikael.collan@vatt.fi
Track(s): 57
Recent Advancements in AI

Sureyya Ozogur-Akyuz
Bahcesehir University
sureyya.akyuz@eng.bau.edu.tr
Duygu Üçüncü
Bahcesehir University
duygu.ucuncu@bau.edu.tr
Track(s): 4
Retail Operations

Pedro Amorim
Faculty of Engineering of Universityof Porto
amorim.pedro@fe.up.pt
Rob Broekmeulen
TU Eindhoven
r.a.c.m.broekmeulen@tue.nl

EURO 2024 - Copenhagen

Secure & Sustainable Food
SupplyMarleen Balvert
Tilburg University
m.balvert@tilburguniversity.edu
J.c. Gerdessen
Wageningen University
joke.vanlemmen@wur.nl
Track(s): 13
Soft OR and Problem Structuring
Methods

Irene Pluchinotta
University College London
i.pluchinotta@ucl.ac.uk
Chris Smith
University of Manchester
christopher.smith@manchester.ac.uk
Mike Yearworth
University of Exeter
m.yearworth@exeter.ac.uk
Track(s): 13
Software for Optimization

Alexander Hübner
Technical University Munich
alexander.huebner@tum.de

Timo Berthold
Fair Isaac Deutschland GmbHtimoberthold@fico.com

Heinrich Kuhn
Catholic University ofEichstaett-Ingolstadt
heinrich.kuhn@ku.de

Robert Luce
Gurobiluce@gurobi.com
Track(s): 30

Winfried Steiner
Clausthal University of Technology,
Institute of Management andEconomics
winfried.steiner@tu-clausthal.de
Track(s): 50 61

Stochastic and Deterministic
Global Optimization

Boglárka G.-Tóth
University of Szeged
boglarka@inf.szte.hu
Track(s): 41

Risk management in finance

Tomáš Tichý
Faculty of Economics,
VSB-Technical University Ostrava
tomas.tichy@vsb.cz
Track(s): 51

Stochastic Modelling

Raik Stolletz
University of Mannheim
stolletz@bwl.uni-mannheim.de
Track(s): 39

Stochastic, Robust andDistributionally Robust
Optimization

Milos Kopa
Charles University in Prague, Facultyof Mathematics and Physics
kopa@karlin.mff.cuni.cz
Francesca Maggioni
University of Bergamo
francesca.maggioni@unibg.it
Track(s): 34 35
Sustainable and Resilient
Systems

Lina Kattan
University of Calgary
lkattan@ucalgary.ca
Elise Miller-Hooks
George Mason University
miller@gmu.edu
Hulya Julie Yazici
Florida Gulf Coast University
hyazici@fgcu.edu
Track(s): 53
Sustainable Cities

Virginia Pellerey
Politecnico di Torino
virginia.pellerey@polito.it
Sara Torabi
Politecnico di Torino
sara.torabi@polito.it
Grit Walther
RWTH Aachen University
walther@om.rwth-aachen.de
Track(s): 18
Sustainable Supply Chains

Karsten Kieckhäfer
FernUniversität in Hagen
karsten.kieckhaefer@fernunihagen.de
Track(s): 24
Transportation

Riccardo Rossi
University of Padova
riccardo.rossi@unipd.it
Track(s): 55 56 60

478

EURO 2024 - Copenhagen

Variational Analysis andContinuous Optimization

Vector and Set Optimization

Fernando García Castaño
University of Alicantefernando.gc@ua.es

Lidia Huerga
Universidad Nacional de Educación aDistancia, UNED
lhuerga@ind.uned.es

Abderrahim Hantoute
Universidad de Alicantehantoute@ua.es
Track(s): 42

Vicente Novo
Universidad Nacional de Educacion a
Distancia
vnovo@ind.uned.es

Variational Inequalities andEquilibrium Problems: From
Theoretical Advances to RealWorld Applications

Miguel Sama
Universidad Nacional de Educación aDistancia, UNED
msama@ind.uned.es
Track(s): 41

Mauro Passacantando
University of Milano-Bicoccamauro.passacantando@unimib.it

STREAMSVeRoLog - Vehicle Routing and
Logistics

Rossana Cavagnini
RWTH Aachen University
cavagnini@dpo.rwth-aachen.de
Roberto Roberti
University of Padova
roberto.roberti@unipd.it
Track(s): 58 64
WISDOM - Women in OR

Paula Carroll
UCDpaula.carroll@ucd.ie
Track(s): 12

Fabio Raciti
University of Catania
fraciti@dmi.unict.it
Track(s): 43

479

Session Chair Index
Coelho, Wesley . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-42
wesley.coelho@pasqal.com
PASQAL, Paris, France
Di Gregorio, Silvia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-04
digregorio@lipn.univ-paris13.fr
University Sorbonne Paris Nord, Paris, France
Abastante, Francesca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-47
francesca.abastante@polito.it
Urban and Regional Studies and Planning, Politecnico di
Torino, Torino, Italy
Achterberg, Tobias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-02
achterberg@zib.de
Gurobi, Berlin, Germany
Adewoye, Olabode . . . . . . . . . . WB-18, MD-25, MA-28, MB-28
boka152002@yahoo.com
Mathematics, Yaba College of Technology, Yaba, Lagos
State, Nigeria
Agi, Maher. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-59
maher.agi@rennes-sb.com
Rennes School of Business, Rennes, France
Ahipasaoglu, Selin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-61
sda1u20@soton.ac.uk
University of Southampton, Winchester, Hampshire, United
Kingdom
Ahookhosh, Masoud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-41
masoud.ahookhosh@uantwerp.be
Department of Mathematics, University of Antwerp,
Antwerp, Belgium
Akbas, Beste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-22
beste916@gmail.com
Imperial College London, London, United Kingdom
Akkerman, Renzo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-23
renzo.akkerman@wur.nl
Operations Research and Logistics, Wageningen University,
Wageningen, Netherlands
Aktas, Dilay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-54, WC-54
dilay.aktas@kuleuven.be
Institute for Mobility - CIB, KU Leuven, Heverlee, Leuven,
Belgium

maleandri@luiss.it
Economics and Finance, Luiss University, Rome, Italy
Alem, Douglas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-21
douglas.alem@ed.ac.uk
Business School, University of Edinburgh, Edinburgh, United
Kingdom
Algaba, Encarnación . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-36
ealgaba@us.es
Applied Mathematics II, Seville University, Sevilla, Spain
Alguacil, Natalia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-21
natalia.alguacil@uclm.es
University of Castilla-La Mancha, Spain
Ali, Sadia Samar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-18
sadiasamarali@gmail.com
Department of Industrial Engineering, King Abdul-Aziz University, Jeddah, Saudi Arabia
Almeder, Christian. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-49
almeder@europa-uni.de
Chair for Supply Chain Management, European University
Viadrina, Frankfurt (Oder), Germany
Almeida, João . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-58
joao.carvalho.almeida@tecnico.ulisboa.pt
CEGIST, University of Lisbon - Instituto Superior Técnico,Carnaxide, Portugal
Alonso-Ayuso, Antonio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-25
antonio.alonso@urjc.es
Rey Juan Carlos University, Mostoles, Madrid, Spain
Alumur, Sibel A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-61
sibel.alumur@uwaterloo.ca
Department of Management Science and Engineering, University of Waterloo, Waterloo, ON, Canada
Amar, A. D. . . . . . . . . . . . . . . . . WA-08, WB-08, WC-08, WD-08
ad.amar@shu.edu
Management Department, Seton Hall University, South Orange, NJ, United States
Amen, Matthias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-59
matthias.amen@uni-bielefeld.de
Chair for Quantitative Accounting & Financial Reporting,
Bielefeld University, Bielefeld, Germany
Amorim, Pedro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-50, WC-50
amorim.pedro@fe.up.pt
Industrial Engineering and Management, Faculty of Engineering of University of Porto, Porto, Portugal

Alaei Jordehi, Shafagh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-55
shafagh.alaeijordehi@vub.be
Department of Business Technology and OperationsMobilise, Vrije Universiteit Brussel, Hasselt, Limburg, Belgium

Amorosi, Lavinia . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-26, WA-37
lavinia.amorosi@uniroma1.it
Statistical Sciences, Sapienza, Rome, Italy

Albert, Laura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-28, MD-28
laura@engr.wisc.edu
Industrial & Systems Engineering, University of WisconsinMadison, MADISON, WI, United States

André, Eric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-57
eandre@em-lyon.com
Quantitative Finance & Economics, Emlyon Business
School, Ecully, France

Aleandri, Michele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-36

Anita, Stefana-Lucia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-33

480

EURO 2024 - Copenhagen

SESSION CHAIR INDEX

stefi_anita@yahoo.com
Octav Mayer Institute of Mathematics of the RomanianAcademy, Iasi, Romania

Balezentis, Tomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-48
tomas@laei.lt
Lithuanian Centre for Social Sciences, Lithuania

Anjos, Miguel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-22
anjos@stanfordalumni.org
School of Mathematics, University of Edinburgh, United
Kingdom

Bandeira, Jorge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-56
jorgebandeira@ua.pt
University of Aveiro, Aveiro, Portugal

Annunziata, Arturo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-12
arturo.annunziata@unifi.it
Information engineering, University of Florence, Italy
Antón Maraña, Paula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-15
panton@ubu.es
Applied Economics, University of Burgos, Burgos, Spain,
Spain
Aragón Artacho, Francisco Javier . . . . . . . . . . . . . . . . . . WB-42
francisco.aragon@ua.es
Mathematics, University of Alicante, Alicante, Spain
Araz, Ozgur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-61
oaraz2@unl.edu
Supply Chain Management and Analytics, University of Nebraska Lincoln, NE, United States
Archetti, Claudia . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-52, MB-52
archetti@essec.edu
Department of Information Systems, Decision Sciences andStatistics, ESSEC Business school, Cergy, France
Archibald, Thomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-59
t.archibald@ed.ac.uk
Business School, University of Edinburgh, Edinburgh, United
Kingdom
Arican, Erkut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-04
erkut.arican@bau.edu.tr
Bahcesehir University, Turkey
Astudillo, Alejandro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-30
alejandro.astudillovigoya@kuleuven.be
Department of Mechanical Engineering, KU Leuven, Leuven, Vlaams-Brabant, Belgium
Avrachenkov, Konstantin . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-40
konstantin.avratchenkov@inria.fr
INRIA, Sophia Antipolis, France
Bagirov, Adil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-28, WA-28
a.bagirov@federation.edu.au
The Centre for Smart Analytics, Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, Victoria, Australia
Baldacci, Roberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-58
rbaldacci@hbku.edu.qa
College of Science and Engineering, Hamad Bin Khalifa
University, Doha, Qatar
Baldemor, Milagros. . . . . . . . . . . . . . . . . WD-18, MD-25, WB-28
milagros_baldemor@yahoo.com.phMathematics, PSAI, San Fernando City, Philippines

Barbati, Maria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-44
maria.barbati@unive.it
Department of Economics, Ca’ Foscari University of Venice,
Italy
Basso, Franco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-12
francobasso@gmail.com
Pontificia Universidad Católica de Valparaíso, Chile
Batta, Rajan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-29
batta@buffalo.edu
Industrial and Systems Engineering, University at Buffalo(SUNY), Buffalo, New York, United States
Battarra, Maria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-52
m.battarra@bath.ac.uk
Bath, United Kingdom
Bayraksan, Guzin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-35
bayraksan.1@osu.edu
Integrated Systems Engineering, Ohio State University,
Columbus, OH, United States
Bešinović, Nikola . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-55
nikola.besinovic@tu-dresden.de
Chair of Railway Operations, Faculty of Transport and Traffic Sciences, Technical University of Dresden, Dresden, Germany
Behzad, Banafsheh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-15
banafsheh.behzad@csulb.edu
California State University, Long Beach, Long Beach, California, United States
Bekius, Femke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-11
femke.bekius@ru.nl
Nijmegen School of Management, Radboud University, Nijmegen, Netherlands
Bekker, Bram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-38
b.bekker@tudelft.nl
DIAM, Delft University of Technology, Delft, Zuid-Holland,
Netherlands
Benítez-Peña, Sandra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-27
sbenitez@est-econ.uc3m.es
University Carlos III of Madrid, Spain
Bendersky, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-20
michael.bendersky@gmail.com
Holon Institute of Technology, Israel
Benfenati, Alessandro . . . . . . . . . . . . . . . MA-34, MB-34, MC-34
alessandro.benfenati@unimi.it
Environmental and Science Policy, Universita’ di Milano,
Italy

481

SESSION CHAIR INDEXEURO 2024 - Copenhagen

Benoit, Dries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-31
dries.benoit@ugent.beGhent University, Gent, Belgium
Beraldi, Patrizia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-35
patrizia.beraldi@unical.it
Department of Mechanical, Energy and Management Engineering, University of Calabria, Rende (CS), ITALY, Italy
Berbig, Dominik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-39
dominik.berbig@hs-pforzheim.de
Pforzheim University, Germany
Bergantinos, Gustavo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-36
gbergant@uvigo.es
Statisitics and O. R., University of Vigo, Vigo, Pontevedra,
Spain
Berk, Erhan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-17
eberk@hho.msu.edu.tr
National Defense University, İstanbul, Turkey
Bertsch, Valentin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-09
valentin.bertsch@ee.rub.de
Chair of Energy Systems and Energy Economics, RuhrUniversität Bochum, Bochum, Germany
Besik, Deniz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-24
dbesik@richmond.edu
Analytics & Operations, University of Richmond, Richmond,
VA, United States

Ghent, Oost-Vlaanderen, Belgium
Borgonovo, Emanuele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-11
emanuele.borgonovo@unibocconi.it
Decision Sciences and ELEUSI Research Center, Bocconi
University, Milano, Italy
Bottero, Marta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-47
marta.bottero@polito.it
Urban and Regional Studies and Planning, Politecnico di
Torino, Torino, Italy
Bovet Emanuel, Peter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-20
peter.bovet-emanuel@fhs.se
Departement of War Studies, Swedish Defence University,
stockholm, Sweden
Boyle, Laura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-10
laura.boyle@qub.ac.uk
Queen’s University Belfast, United Kingdom
Braekers, Kris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-15
kris.braekers@uhasselt.beResearch Group Logistics, Hasselt University, Hasselt, Belgium
Brailsford, Sally . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-17
s.c.brailsford@soton.ac.uk
Southampton Business School, University of Southampton,
Southampton, United Kingdom

Biancalani, Francesco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-63
francesco.biancalani@imtlucca.it
AXES, IMT School for Advanced StudiesLucca, Lucca, Italy

Brandenburg, Marcus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-23
marcus.brandenburg@hs-flensburg.de
Supply Chain Management, Flensburg University of Applied
Sciences, Germany

Bierlaire, Michel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-51
michel.bierlaire@epfl.ch
Enac Inter Transp-or, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Breder, Marco Sebastian . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-09
marco.breder@wiwinf.uni-due.de
Chair for Management Science and Energy Economics, University of Duisburg-Essen, Essen, Germany

Bigi, Giancarlo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-40
giancarlo.bigi@unipi.it
Dipartimento di Informatica, Universita’ di Pisa, Pisa, Italy

Bregar, Andrej . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-47
andrej.bregar@informatika.si
Informatika, Maribor, Slovenia

Bedowska-Sójka,˛
Barbara . . . . . . . . . . . . . . . . . . . . . . . . . . MB-08
barbara.bedowska-sojka@ue.poznan.plDepartment of Econometrics, Poznan University of Economics and Business, Poznan, Poland

Broekmeulen, Rob . . . . . . . . . . . . . . . . . . . . . . . . . . MB-50, WB-50
r.a.c.m.broekmeulen@tue.nl
OPAC, TU Eindhoven, Eindhoven, -, Netherlands

Blaise, Léa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-60
lblaise@hexaly.com
Hexaly, Paris, France
Boduroglu, Ilkay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-63
ilkay.boduroglu@riskoptima.com.tr
R&D, Riskoptima Wealth Tech Corp., Istanbul, N/A, Turkey
Boelens, Femke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-60
f.a.boelens@utwente.nl
University of Twente, Netherlands
Bogaert, Matthias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-31
matthias.bogaert@ugent.beMarketing, Innovation and Organization, Ghent University,

482

Brunelli, Matteo. . . . . . . . . . . . . . TC-44, TD-44, WA-44, WB-44
matteo.brunelli@unitn.it
University of Trento, Italy
Bruninx, Kenneth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-09
k.bruninx@tudelft.nl
TU Delft, Delft, Netherlands
Brunner, Jens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-15, MD-24
jotbr@dtu.dk
Department of Technology, Management, and Economics,
Technical University of Denmark, Kgs. Lyngby, Capital Region, Denmark
Bruno, Giuseppe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-61
giuseppe.bruno@unina.it

EURO 2024 - Copenhagen
Dipartimento di Ingegneria Industriale, Università Federico
II di Napoli, Napoli, IT, ItalyBulhões, Teobaldo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-58
tbulhoes@ci.ufpb.br
Universidade Federal da Paraíba, João Pessoa, Paraíba, Brazil
Bustamante Faúndez, Pamela Alejandra . . . . . . . . . . . . WA-36
pebustamante@uc.cl
Department of Industrial and Systems Engineering, Pontificia
Universidad Católica de Chile, Santiago, Santiago, Chile
Caballini, Claudia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-62
claudia.caballini@polito.it
Department of Environmental Engineering , Land and Infrastrucures, Politecnico di Torino, Torino, Italy
Cabrera, Javier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-40
cabrera@stat.rutgers.edu
Rutgers University, Newark, NJ, United StatesÇakan, Müge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-23
hcakan@mail.uni-mannheim.de
University of Mannheim, Mannheim - Schwetzingerstadt,
Baden-Württemberg, Germany
Calatroni, Luca . . . . . . . . . . . . . . . . . . . . . . MD-32, TC-32, TD-32
calatroni@i3s.unice.fr
I3S - CNRS/Université Côte d’Azur, Nice, France
Caliskan-Demir, Melisa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-04
melisacaliskan@aydin.edu.tr
Industrial Engineering, Istanbul Aydin University, Istanbul,
Turkey, Other / None, Turkey
Camanho, Ana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-48
acamanho@fe.up.pt
Faculty of Engineering, University of Porto, Porto, Portugal
Campuzano, Giovanni . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-56
g.f.campuzanoarroyo@utwente.nl
IEBIS, University of Twente, Enschede, - Select -, Netherlands
Canas, Francisco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-26
francisco.m.p.canas@gmail.com
Departamento de Estatística e Investigação Operacional, Centro de Matemática, Aplicações Fundamentais e Investigação
Operacional (CMAFcIO), Universidade de Lisboa, Lisbon,
Portugal

SESSION CHAIR INDEX

Carreras, Ashley . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-13
acarreras@alfaisal.edu
Business, Alfaisal University, Riyadh, Saudi Arabia
Carrizosa, Emilio . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-01, WC-27
ecarrizosa@us.es
IMUS - Instituto de Matemáticas de la Universidad de Sevilla,
Sevilla, Spain
Carroll, Paula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-12
paula.carroll@ucd.ie
Management Information Systems, UCD, Dublin, Ireland
Castro, Jordi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-30
jordi.castro@upc.edu
Dept. of Statistics and Operations Research, Universitat Politecnica de Catalunya, Barcelona, Catalonia, Spain
Cavagnini, Rossana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-64
cavagnini@dpo.rwth-aachen.de
RWTH Aachen University, Germany
Cekyay, Bora . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-39
bora.cekyay@yildiz.edu.tr
Industrial Engineering, Yildiz Technical University, Istanbul,
TurkeyCerqueti, Roy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-02, WD-63
roy.cerqueti@uniroma1.it
Department of Social and Economic Sciences, Sapienza University of Rome, Rome, Rome, Italy
Cerulli, Martina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-29, TD-29
mcerulli@unisa.it
Computer Science Department, University of Salerno, Fisciano, Italy
Cetinkaya, Sila . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-61
sila@smu.edu
Southern Methodist University, Dallas, United States
Chandrasekharan, Reshma . . . . . . . . . . . . . . . . . . . . . . . . . WC-26
reshma.chirayil@iimb.ac.in
Decision Sciences Area, Indian Institute of ManagementBangalore, India, Bangalore, Karnataka, India
Charaf, Sara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-60
s.charaf@tue.nl
School of Industrial Engineering, Eindhoven University ofTechnology, Eindhoven, Netherlands, Netherlands

Cancela, Hector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-28, TD-28
cancela@fing.edu.uy
Facultad de Ingeniería, Universidad de la República, Montevideo, Montevideo, Uruguay

Chen, Zhen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-39
zhen.chen@brunel.ac.uk
Brunel University London, United Kingdom

Carosi, Laura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-44
laura.carosi@unipi.it
Department of Economics and Management, University of
Pisa, Pisa, Italy

Cherri, Adriana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-29, TB-29
adriana.cherri@unesp.br
Mathematics Department, UNESP, Bauru - INESC TEC, Portugal, Bauru, SP, Brazil

Carravilla, Maria Antónia . . . . . . . . . . . . . . . . . . . . . . . . . . MD-01
mac@fe.up.pt
INESC TEC, Faculty of Engineering, University of Porto,
Porto, Portugal

Cho, Danny . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-56
dcho@brocku.ca
Goodman School of Business, Brock University, St.
Catharines, Ontario, Canada

483

SESSION CHAIR INDEXEURO 2024 - Copenhagen

Chocholatá, Michaela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-53
michaela.chocholata@euba.sk
Faculty of Economic Informatics, Department of Operations Research and Econometrics, University of Economics,
Bratislava, Slovakia
Chubanov, Sergei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-52
sergei.chubanov@uni-siegen.de
Bosch Center For Artificial Intelligence, Germany
Cigdem Karakoyun, Ece . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-22
karakoyun@ese.eur.nl
Econometrics, Erasmus University Rotterdam, Netherlands
Cipolla, Stefano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-38
s.cipolla@soton.ac.uk
School of Mathematical Sciences, University of Southampton, United Kingdom
Cohen Kadosh, Simona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-53
cohensim@post.bgu.ac.il
Logistics, Sapir Academic College, Gedera, Israel
Colovic, Aleksandra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
aleksandra.colovic@poliba.it
DICATECh, Polytechnic University of Bari, Bari, Italy
Contreras Rada, Lucas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-58
lcontrerasrada@correo.unicordoba.edu.co
Industrial Engineer, University of cordoba, monteria, cordoba, Colombia
Corsini, Andrea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-60
andrea.corsini@unimore.it
Department of Science and Methods for Engineering, University of Modena and Reggio Emilia, Italy
Costa, Luciano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-18, MD-59
luciano.costa@academico.ufpb.br
Production Engineering Department, Federal University of
Paraíba, Brazil
Creemers, Stefan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-42
sc@cromso.com
IESEG School of Management, Lille, France
Csato, Laszlo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-16
laszlo.csato@uni-corvinus.hu
Institute for Computer Science and Control (SZTAKI) andCorvinus University of Budapest, Budapest, Hungary
D’Acierno, Luca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-55
luca.dacierno@unina.it
Department of Civil, Architectural and Environmental Engineering, Federico II University of Naples, Naples, Italy
D’Ecclesia, Rita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-02
rita.decclesia@uniroma1.it
Statistics, Sapienza University of Rome, Rome, Roma, Italy
D’Inverno, Giovanna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-48
giovanna.dinverno@unipi.it
Department of Economics and Management, University of
Pisa, Pisa, Italy484

da Silva, Antonio Sergio . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-17
tao281168@gmail.com
PPG Engenharia de Produção, Universidade Federal Fluminense, Niteroi, Rio de Janeiro, Brazil
Dai, Qiyuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-57
qiyuan.dai@vsb.cz
Finance, Technical University of Ostrava, Ostrava, Czech
Republic, China
Dall’Aglio, Marco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-36
mdallaglio@luiss.it
Dept of Economics and Finance, LUISS University, Rome,
Italy
Dash, Gordon TB-06, TC-06, TC-18, TD-18, WD-18, MB-25,
TC-31
ghdash@uri.edu
Finance and Decision Sciences, University of Rhode Island,
Kingston, RI, United States
Datola, Giulia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-47
giulia.datola@polito.it
Interuniversity Department of Urban and Regional Studies
and Planning, Politecnico di Torino, Torino, Italy, Italy
de Araujo, Silvio Alexandre . . . . . . . . . . . . . . . . . . . . . . . . . MB-49
silvio.araujo@unesp.br
Departamento de Matemática, Universidade EstadualPaulista-UNESP, SÃo JosÉ Do Rio Preto, Sao Paulo, Brazil
De Causmaecker, Patrick . . . . . . . . . . . . . . . . . . . . WB-03, TD-31
patrick.decausmaecker@kuleuven.be
Computer Science, CODeS research group, KU Leuven, Kortrijk, Flanders, Belgium
de Jonge, Bram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-39
b.de.jonge@rug.nl
Operations, University of Groningen, Groningen, Netherlandsde Laat, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-38
mail@daviddelaat.nl
TU Delft, Delft, Netherlands
De Maio, Annarita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-58
annarita.demaio@unical.it
Department of Economics, Statistic and Finance "Giovanni
Anania", University of Calabria, Italy
De Marco, Francesco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-53
fdemarco@ethz.ch
D-MAVT, ETH Zurich, Zürich, Switzerland
De Santis, Marianna . . . . . . . . . . . . . . . . . . . . . . . . MA-32, MB-32
mdesantis@diag.uniroma1.it
DIAG, Sapienza, University of Rome, Roma, Italy
De Simone, Valentina . . . . . . . . . . . . . . . . . . . . . . . . TA-32, TB-32
valentina.desimone@unicampania.it
Mathematics and Physics, University of Campania "L. Vanvitelli", Italy
de Werra, Dominique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-25

EURO 2024 - Copenhagen
dominique.dewerra@epfl.ch
IMA, EPFL, Lausanne, Switzerland
De Wolf, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-19
daniel.dewolf@univ-littoral.fr
Tves & Core -ucl, Université du Littoral, Dunkerque Cedex
1, France
Deissenroth-Uhrig, Marc . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-14
deissenroth@izes.de
IZES, Saarbrücken, Germany
del Rosario, Elise . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-06, WA-18
elise@jgdelrosario.com
OSSFFI, Quezon City, Metro Manila, Philippines
Delias, Pavlos . . . . . . . . . . . . . . . . . . . . . . . MC-45, MD-45, TA-45
pdelias@af.ihu.gr
Business School, Democritus University of Thrace, Kavala,
GreeceDell’Amico, Mauro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-02
mauro.dellamico@unimore.it
DISMI, Università degli Studi di Modena e Reggio Emilia,
Reggio Emilia, Italy
DellOvo, Marta . . . . . . . . . . . . . . . . . . . . . . MC-47, MD-47, TA-47
marta.dellovo@polimi.it
Department of Architecture and Urban Studies, Politecnicoof Milano, Milano, Italy
Demeulemeester, Erik . . . . . . . . . . . . . . . . . . . . . . WC-10, MC-60
erik.demeulemeester@kuleuven.be
KBI, KU Leuven, Leuven, Belgium
DeTombe, Dorien. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-20
detombe@nosmo.nl
School of Systems Engineering, Delft University of Technology, Delft, Europe, Netherlands
Devine, Mel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-09
mel.devine@ucd.ie
University College Dublin, IrelandDiaz-Cachinero, Pablo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-19
pablo.diaz.cachinero@uc3m.es
Universidad Carlos III de Madrid, Leganés, Spain
Diaz-Tena, Nuria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-40
nd796@newark.rutgers.edu
MSIS, Rutgers University, Princeton, NJ, United States
Dimitriou, Ioannis . . . . . . . . . . . . . . . . . . . . . . . . . . MB-40, MC-40
idimit@uoi.gr
Mathematics, University of Ioannina, Ioannina, Greece
Ding, Yifu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-22
yifuding@mit.edu
MIT Energy Initiative, MIT, Cambridge, Massachusetts,
United States
Doerner, Karl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-64
karl.doerner@univie.ac.at
Department of Business Decisions and Analytics, Universityof Vienna, Vienna, Vienna, Austria

SESSION CHAIR INDEX

Dollevoet, Twan . . . . . . . . . . . . . . . . . . . . . . TC-10, TD-10, TB-51
dollevoet@ese.eur.nl
Econometric Institute, Erasmus University of Rotterdam,
Rotterdam, Netherlands
Dominguez, Ruth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-19
ruth142@gmail.com
Economics and Management, University of Brescia, Brescia,
Italy
Dong, Chuanwen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-24
cdong@escp.eu
ESCP Business School, Germany
Dong, Yuqun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-22
230228714@seu.edu.cn
Nanjing, China
Donmez, Zehranaz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-21
zehranaz.donmez@bilkent.edu.tr
Industrial Engineering, Bilkent University, ankara, Ankara,
TurkeyDoumpos, Michalis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-47
mdoumpos@tuc.gr
School of Production Engineering and Management, Technical University of Crete, Chania, Greece
Drozdowski, Maciej . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-26
maciej.drozdowski@cs.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poznan, Poland
Dufour, Maxime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-30
maxime.dufour@artelys.com
Artelys, Paris, France
Duhaylongsod, Jose Benedicto . . . . . . . . . . . . . . . . . . . . . . WC-07
j.duhaylongsod@ieseg.fr
IÉSEG School of Management, Paris, France
Dziuba, Daryna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-49
dziuba@europa-uni.de
European University Viadrina, Frankfurt Oder, Germany
E.-Nagy, Marianna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-38
marianna.eisenberg-nagy@uni-corvinus.hu
Corvinus University of Budapest, Hungary
Ekici, Ali . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-52
ali.ekici@ozyegin.edu.tr
Industrial Engineering, Ozyegin University, Istanbul, Turkey
El Hallaoui, Issmail . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-25
issmail.elhallaoui@gerad.ca
Math. and Ind. Eng., Polytechnique Montréal and GERAD,
Montreal, Qué., Canada
Elbracht, Theresa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-43
t.elbracht@uni-bielefeld.de
Department of Business Administration and Economics,
Bielefeld University, Germany
Engels, Tim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-39

485

SESSION CHAIR INDEXEURO 2024 - Copenhagent.p.g.engels@tue.nl
Eindhoven University of Technology, Netherlands
Erdogan, Bilgenur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-58
b.erdogan@tue.nl
Industrial Engineering & Innovation Sciences, Eindhoven
University of Technology, ’S-HERTOGENBOSCH, Netherlands
Escudero, Laureano F. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-35
laureano.escudero@urjc.es
Estadística e Investigación Operativa, Universidad Rey Juan
Carlos, Mostoles (Madrid), Spain
Esenduran, Gokce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-23
gesendur@purdue.edu
Supply Chain and Operations Management, Purdue University, West Lafayette, United States
España, Víctor Javier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-48
vespana@umh.es
Center of Operations Research (CIO), Miguel Hernández
University of Elche, Elche, Alicante, Spain
Estella Aguerri, Inaki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-57
iaguerri@amazon.es
Amazon, Spain
Euler, Reinhardt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-25
reinhardt.euler@univ-brest.fr
Lab-STICC UMR 6285 Université de Brest, Brest cedex 3,
FranceExpósito Izquierdo, Christopher . . . . . MB-62, TC-62, WC-62
cexposit@ull.edu.es
Ingeniería Informática y de Sistemas, University of La Laguna, San Cristóbal de La Laguna, Spain
Faggian, Silvia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-33
faggian@unive.it
Dip.to di Economia, Università "Ca’ Foscari" Venezia,
Venezia, Italy
Famulla, Carolin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-59
carolin.famulla@uni-bielefeld.de
Chair for Quantitative Accounting & Financial Reporting,
Bielefeld University, Bielefeld, Germany
Farina, Alessandro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-56
alessandro.farina@unipi.it
Department of Civil and Industrial Engineering, Universityof Pisa, Pisa, Italy, Italy

Figueira, José Rui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-44
figueira@tecnico.ulisboa.pt
CEG-IST, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
Figuerola-Ferretti, Isabel . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-63
ifiguerola@comillas.edu
Universidad Ponticficia Comillas, Madrid, Madrid, Spain
Finhold, Elisabeth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-49
elisabeth.finhold@itwm.fraunhofer.de
Optimization, Fraunhofer Institute for Industrial Mathematics ITWM, Kaiserslautern, Germany
Finke, Jonas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-09
jonas.finke@ee.rub.de
Chair of Energy Systems and Energy Economics, RuhrUniversität Bochum, Germany
Flores, Angela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-19
angelafq@gmail.com
Universidad de Chile, Santiago, Chile
Fokouop, Rodrigue. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-46
rodrigue.fokouop-w@airliquide.com
Computational and Data Sciences Lab, Operational Researchteam, Air Liquide R&D, Palaiseau, France/Ile de France,
FranceFonseca, Joao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-46
joao@portchain.com
Analytics, Portchain, Denmark
Fontaine, Pirmin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-64
pirmin.fontaine@ku.de
Ingolstadt School of Management, Catholic University ofEichstätt-Ingolstadt, Germany
Forsgren, Anders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-10
andersf@kth.se
Department of Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden
Foster, Joshua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-59
jfoster@ivey.ca
Ivey Business School, Western University, Canada
Fourer, Robert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-30
4er@ampl.com
AMPL Optimization Inc., Evanston, IL, United States

Feldman, Pnina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-50
feldmanp@darden.virginia.edu
United States

Fragkos, Ioannis. . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-29, MB-29
fragkos@rsm.nl
Technology & Operations Management, Rotterdam Schoolof Management, Rotterdam, Netherlands

Fiala, Petr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-23
pfiala@vse.cz
Dept. of Econometrics, Prague University of Economics andBusiness, Prague 3, Czech Republic

Franco, L. Alberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-11
alberto.franco@bristol.ac.uk
University of Bristol Business School, University of Bristol,
Bristol, United Kingdom

Figà-Talamanca, Gianna . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-63
gianna.figatalamanca@unipg.it
Economics, Università di Perugia, Perugia, Italy, Italy

Fritze, Christian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-59
christian.fritze@hspv.nrw.de
University of Applied Sciences for Police and Public Admin-

486

EURO 2024 - Copenhagenistration, Bielefeld, Germany
Funke, Christoph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-38
funkec@ethz.ch
Reliability and Risk Engineering, ETH Zürich, Switzerland
Furini, Fabio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-52
fabio.furini@uniroma1.it
DIAG, La Sapienza, Roma, Please Select, Italy
G. Pardo, Eduardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-52
eduardo.pardo@urjc.es
Computer Science, Universidad Rey Juan Carlos, Mostoles,– Please Select (only U.S. / Can / Aus), Spain
G.-Tóth, Boglárka . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-01, TC-61
boglarka@inf.szte.hu
Department of Computational Optimization, University ofSzeged, Szeged, Hungary
Gaggero, Giacomo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-63
giacomo.gaggero@edu.unige.it
Department of Economics, University of Genoa, Genova,
italy, Italy
Gajda, Mikele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-55
mikele.gajda@unil.ch
Department of Operations, UNIL | Université de Lausanne,
Lausanne, Switzerland
García Castaño, Fernando . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-42
fernando.gc@ua.es
Mathematics, University of Alicante, Alicante, Comunidad
Valenciana, Spain
Garcia-Rodenas, Ricardo . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-56
ricardo.garcia@uclm.es
Escuela Superior de Informatica, Universidad de Castilla La
Mancha, Ciudad Real, Ciudad Real, Spain

SESSION CHAIR INDEX

celine.gicquel@lri.fr
Laboratoire Interdisciplinaire des Sciences du Numérique,
Université Paris Saclay, Orsay Cedex, France
Glanzer, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-35
martin.glanzer@uni-mannheim.de
University of Mannheim Business School, Germany
Goerigk, Marc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-34
marc.goerigk@uni-passau.deBusiness Decisions and Data Science, University of Passau,
Passau, Germany
Góez, Julio C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-38
julio.goez@nhh.no
Business and Management Science, NHH Norwegian Schoolof Economics, Bergen, Norway
Goletsis, Yorgos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-47
goletsis@uoi.gr
Department of Economics, University of Ioannina, Ioannina,
GreeceGondzio, Jacek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-38, TD-38
j.gondzio@ed.ac.uk
School of Mathematics, University of Edinburgh, Edinburgh,
United Kingdom
Gong, Sicheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-19
s.gong@tue.nl
Department of Electrical Engineering, TU Eindhoven, Eindhoven, Noord-Brabant, Netherlands
González-Díaz, Julio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-30
julio.gonzalez@usc.es
Statistics, Mathematical Analysis and Optimization, University of Santiago de Compostela, Santiago de Compostela,
Spain

Gardi, Frédéric . . . . . . . . . . . . . . . . . . . . . MC-02, MD-30, TD-46
fgardi@hexaly.com
Hexaly, Brooklyn, New York, United States

González-Ramírez, Rosa G.. . . . . . . . . . . . . . . . . . . . . . . . . WD-62
rgonzalez@uandes.cl
Universidad de Los Andes, Chile, Santiago, Reg. Metropolitana de Santiago, Chile

Geismar, Neil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-23
ngeismar@mays.tamu.edu
Information and Operations Management, Texas A&M University, College Station, Texas, United States

Goossens, Dries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-16
dries.goossens@ugent.be
Business Informatics and Operations Management, Ghent
University, Gent, Belgium

Georgiou, Andreas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-48
acg@uom.edu.gr
Quantitative Methods and Decision Analytics Lab, Dpt ofBusiness Administration, University of Macedonia, Thessaloniki, Greece

Gorbunov, Eduard . . . . . . . . . . . . . . . . . . . . . . . . . WC-32, WD-32
eduard.gorbunov@mbzuai.ac.ae
Mohamed bin Zayed University of Artificial Intelligence(MBZUAI), Abu Dabi, United Arab Emirates

Gerdessen, J.c. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-13, WD-13
joke.vanlemmen@wur.nl
Operations Research and Logistics, Wageningen University,
Wageningen, Netherlands

Granat, Janusz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-47
janusz.granat@pw.edu.pl
Institute of Control and Computation Engineering, Warsaw
University of Technology, Warsaw, Poland

Ghaderi, Susan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-41
susan.ghaderi@kuleuven.be
ESAT, KU Leuven, Leuven, Flanders, Belgium

Grapiglia, Geovani . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-32
geovani.grapiglia@uclouvain.be
Applied Mathematics, Université catholique de Louvain,
LOUVAIN-LA-NEUVE, Belgium

Gicquel, Céline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-09

Grass, Emilia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-21

487

SESSION CHAIR INDEXEURO 2024 - Copenhagen

emilia.grass@kit.edu
Germany
Greco, Salvatore . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-44, MB-44
salgreco@unict.it
Department of Economics and Business, University of Catania, Catania, Italy
Gregori, Martina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-16
martina.gregori@uniroma1.it
DIAG, Sapienza University of Rome, Italy
Gribling, Sander . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-42
s.j.gribling@tilburguniversity.edu
Tilburg University, Netherlands
Gridchina, Tatiana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-28
22lv3@queensu.ca
Analytics, Queen’s University, Kingston, Ontario, Canada
Gripton, Adam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-43
a.gripton@hw.ac.uk
School of Social Sciences, Heriot-Watt University, Currie,
United Kingdom
Grunow, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-49
martin.grunow@tum.de
TUM School of Management, Technical University of Munich, München, Germany

Harper, Paul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-06
harper@cardiff.ac.uk
School of Mathematics, Cardiff University, Cardiff, Wales,
United Kingdom
Hartl, Richard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-60
richard.hartl@univie.ac.at
Business Decisions & Analytics, University of Vienna, Vienna, Austria
Hägele, Sascha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-43
s.haegele@con.uni-saarland.de
Chair of Managerial Accounting, Saarland University, Saarbrücken, Germany
Hämäläinen, Raimo P.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-11
raimo.hamalainen@aalto.fi
Systems Analysis Laboratory, Aalto University, School of
Science, Aalto, Finland
Heinold, Arne . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-62
arne.heinold@klu.org
Kühne Logistics University, Germany
Heipcke, Susanne . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-46
susanneheipcke@fico.com
Xpress Optimization, FICO, Marseille, France

Gualandi, Stefano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-26
stefano.gualandi@gmail.com
Matematica, Università di Pavia, Pavia, Italy, Italy

Hemmati Golsefidi, Atefeh . . . . . . . . . . . . . . . . . . . . . . . . . . WA-56
ahego@dtu.dk
DTU Managment, Technical University of Denmark (DTU),
Herlev, Copenhagen, Denmark

Guerrero, Vanesa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-27
vanesa.guerrero@uc3m.es
Department of Statistics, Universidad Carlos III de Madrid,
Getafe, Spain

Herrera, Hugo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-12
hugo.herrera@uib.no
Geography Department, University of Bergen, Bergen, Norway

Guillén, María D. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-48
maria.guilleng@umh.es
Center of Operation Research, University Miguel Hernandez
of Elche, Spain

Heuser, Laura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-21
lheuser@ethz.ch
ETH Zurich, Zürich, Switzerland

Guimarans, Daniel . . . . . . . . . . TD-57, WA-57, WB-57, WC-57
guimd@amazon.es
Amazon, Luxembourg, Luxembourg
Gunnec, Dilek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-12, TA-21
dilek.gunnec@ozyegin.edu.tr
Industrial Engineering, Ozyegin University, Istanbul, Turkey
Gurevsky, Evgeny . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-09
evgeny.gurevsky@univ-nantes.fr
LS2N, Université de Nantes, France
Habibi, Khakim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-24
muhammad.habibi@rennes-sb.com
Supply Chain Management & Information Systems, ESC
Rennes School of Business, Rennes, Bretagne, France
Hantoute, Abderrahim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-42
hantoute@ua.es
Mathematics, Universidad de Alicante, Alicante, Spain,
Spain

488

Hieber, Peter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-57
peter.hieber@unil.ch
Switzerland
Himstedt, Barbara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-55
barbara.himstedt@bwl.uni-kiel.de
Christian-Albrechts-University, Kiel, Germany
Hirotsu, Nobuyoshi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-16
nhirotsu@hotmail.com
Juntendo University, Inzai, Chiba, Japan
Hladik, Milan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-01
hladik@kam.mff.cuni.cz
Department of Applied Mathematics, Charles University,
Faculty of Mathematics and Physics, Prague, Czech Republic
Hlavatý, Robert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-34
hlavaty85@gmail.com
Department of Systems Engineering, CZU Prague, Czech
RepublicEURO 2024 - Copenhagen
Homaei, Shamim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-19
shamim.homaie@gmail.com
Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology,
Trondheim, Norway
Hoogervorst, Rowan . . . . . . . . . . . . . . . . . . . . . . . . . TC-51, WA-54
rowho@dtu.dk
DTU Management, DTU, Kongens Lyngby, Denmark
Hörsting, Lena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-43
lena.hoersting@bwl.uni-kiel.deService Analytics, CAU Kiel University, Germany
Hosseini, Amir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-26
amir.hosseini@uni-passau.de
University of Passau, Passau, Germany
Hu, Yaohua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-41
hyh19840428@163.com
School of Mathematical Sciences, Shenzhen University,
Shenzhen, Guangdong Province, China
Hübner, Alexander. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-50
alexander.huebner@tum.de
Supply and Value Chain Management, Technical University
Munich, Straubing, Germany
Huchzermeier, Arnd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-50
ah@whu.edu
Production Management, WHU - Otto Beisheim School ofManagement, Vallendar, Germany
Huerta-Barrientos, Aida . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-12
aida.huerta@comunidad.unam.mx
Operations Research, National Autonomous University of
Mexico, Mexico City, D.F., Mexico
Huisman, Dennis . . . . . . . . . . . . . . . . . . . . TD-46, WB-51, MD-54
huisman@ese.eur.nl
Econometric Institute, Erasmus University, Rotterdam,
Netherlands
Hvidegaard Petersen, Vibeke . . . . . . . . . . . . . . . . . . . . . . . WD-34
vibeke.h.petersen@ntnu.no
NTNU, TRONDHEIM, Norway
Iosifidis, George . . . . . . . . . . . . . . . . . . . . . TD-57, WA-57, WB-57
g.iosifidis@tudelft.nl
TU Delft, Netherlands
Iravani, Foaad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-55
foaad.iravani@walmart.com
CENTROID, Walmart, United States
Iris, Cagatay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-62
c.iris@liverpool.ac.uk
Management School, University of Liverpool, Liverpool,
United Kingdom
Islind, Anna Sigríður . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-31
annasi@ru.is
Reykjavik University, Reykjavik, Iceland
Islind, Anna Sigridur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-27

SESSION CHAIR INDEX

islind@ru.is
Reykjavik University, Reykjavik, Iceland
Ivorra, Benjamin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-19
ivorra@ucm.es
Universidad Complutense de Madrid, Spain
Jaehn, Florian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-54
florian.jaehn@hsu-hh.deManagement Science and Operations Research, HelmutSchmidt-University - University of the Federal Armed Forces
Hamburg, Hamburg, Germany
Jafari, Nahid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-06, MB-28
jnahid@hotmail.com
School of Business, SUNY Farmingdale, New York, NY,
United States
Jalali, Hamed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-50
hamed.jalali@neoma-bs.fr
Department of information systems, supply chain management, and decision making, NEOMA Business school, MontSaint-Aignan, France
Jäger, Sven . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-51
sven.jaeger@math.rptu.de
Department of Mathematics, RPTU Kaiserslautern-Landau,
Kaiserslautern, Rheinland-Pfalz, Germany
Johanndeiter, Silke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-09
silke.johanndeiter@ruhr-uni-bochum.de
Ruhr-Universität Bochum, Germany
Jorge, Diana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-56
diana.jorge@tecnico.ulisboa.pt
Instituto Superior Técnico, Universidade de Lisboa, Portugal
Joshi, Harit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-43
harit.joshi@spjimr.org
Operations, Supply Chain and Quantitative Methods,
SPJIMR, Mumbai, Maharashtra, India
Józefowska, Joanna . . . . . . . . . . . . . . . . . . . . . . . . . MC-25, TD-60
joanna.jozefowska@cs.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poznan, Poland
Juneja, Januj . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-08
jjuneja@mail.sdsu.edu
Finance, San Diego State University, San Diego, CA, UnitedStatesKabasinskas, Audrius . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-01
audrius.kabasinskas@ktu.lt
Department of Mathematical Modeling, Kaunas Universityof Technology, Kaunas, Lithuania
Kabgani, Alireza . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-41
alireza.kabgani@uantwerpen.be
Department of Mathematics, University of Antwerp,
Antwerp, BELGIUM, Belgium
Kajiji, Nina . . . TB-06, TC-06, TC-18, TD-18, WD-18, MB-25,
TA-31
nina@nkd-group.com

489

SESSION CHAIR INDEXEURO 2024 - Copenhagen

Computer Science and Statistics, University of Rhode Island,
and The NKD Group, Inc., Kingston, RI, United States
Kalczynski, Pawel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-61
pkalczynski@fullerton.edu
Information Systems Decision Sciences, California State
University, Fullerton, Fullerton, CA, United States
Kamdem, Bruno . . . . . . . . . . . . . . . . . . . . WA-06, WC-18, MB-25
bgk8384@nyu.edu
Finance and Risk Engineering, New York University, Brooklyn, NY, United States
Kamzolov, Dmitry . . . . . . . . . . . . . . . . . . . . . . . . . . WA-32, WB-32
kamzolov.dmitry@mbzuai.ac.ae
Machine Learning, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dabi, United Arab Emirates
Kanavetas, Odysseas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-40
o.kanavetas@math.leidenuniv.nl
Mathematical Institute, Leiden University, Leiden, Netherlands
Kara, Mehmet Erol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-25
mehmeterol.kara@ogu.edu.tr
Industrial Engineering, Eskisehir Osmangazi University, Eskisehir, Odunpazari, Turkey
Karabatı, Selçuk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-24
skarabati@ku.edu.tr
College of Administrative Sciences and Economics, Koç University, Sarıyer, İstanbul, Turkey
Karacapilidis, Nikos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-08
karacap@upatras.gr
MEAD - IMIS Lab, University of Patras, Rio Patras, Greece
Karasakal, Esra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-61
koktener@metu.edu.tr
Industrial Engineering Department, Middle East Technical
University, Ankara, Turkey
Katehakis, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-40
mnk@rutgers.edu
Management Science and Information Systems, Rutgers University, Piscataway, NJ, United States
Katsikopoulos, Konstantinos . . . . . . . . . . . . . . . . . . . . . . . . WD-11
k.katsikopoulos@soton.ac.uk
Southampton Business School, Southampton, United Kingdom
Kaufman, Ruth . . . . . MC-02, MA-46, MB-46, TB-46, WA-46,
WD-46
ruth.kaufman@btinternet.com
The OR Society, London, United Kingdom
Kazakov, R. M. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-20
r.kazakov@hw.ac.uk
Heriot Watt University, Edinburgh, NA, United Kingdom
Kedad-Sidhoum, Safia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-49
safia.kedad_sidhoum@cnam.fr
Cnam - Cedric, Paris, Cedex 03, France

490

Kekes, Cédric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-54
cedric.kekes@tu-dresden.de
Chair of Traffic Flow Science, TU Dresden, Germany
Keles, Dogan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-09
dogke@dtu.dk
Technical University of Denmark (DTU), Lyngby, Denmark
Kellerth Fredlund, Johan . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-46
carljohan.kellerthfredlund@boeing.com
SwedenKempter, Stefan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-15
steke@dtu.dk
Technology, Management and Economics, DTU, Germany
Kerr-Delworth, Paul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-30
pkerrdel@mathworks.com
MathWorks, Cambridge, Cambs, United Kingdom
Kieckhäfer, Karsten . . . . . . . . . . . . . . . . . . . . . . . . MB-24, MC-24
karsten.kieckhaefer@fernuni-hagen.de
Chair of Production and Logistics Management, FernUniversität in Hagen, Hagen, Germany
Kilic, Onur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-49
o.a.kilic@rug.nl
Department of Operations, University of Groningen, Netherlands
Kirkbride, Christopher . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-35
c.kirkbride@lancaster.ac.uk
The Management School, Lancaster University, Lancaster,
Lancashire, United Kingdom
Klatzer, Thomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-09
thomas.klatzer@tugraz.at
Institute of Electricity Economics and Energy Innovation,
Graz University of Technology, Graz, Austria
Klein, Vienna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-59
vienna.klein@uni-a.de
Chair of Analytics & Optimization, University of Augsburg,
Augsburg, Germany
Klingebiel, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-58
martin.klingebiel@prod.uni-hannover.deInstitut für Produktionswirtschaft, Universität Hannover,Hannover, Germany
Koßmann, Gereon. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-42
kossmann@physik.rwth-aachen.de
RWTH Aachen, Aachen, Germany
Köbis, Elisabeth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-01
elisabeth.kobis@ntnu.no
NTNU, Norway
Kocaman, Ayse Selin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-22
selin.kocaman@bilkent.edu.tr
Industrial Engineering, Bilkent University, Ankara, Turkey
Kóczy, László . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-36
koczy@krtk.mta.hu

EURO 2024 - Copenhagen
Centre for Economic and Regional Studies, Hungarian
Academy of Sciences, Budapest, Hungary
Koliechkina, Liudmyla . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-52
ludaplt1971@gmail.com
Department of Algorithms and Databases, Faculty of Mathematics and Computer Science, University of Lodz, Lodz,
PolandKopa, Milos . . . . . . . . . . . . . . . . . . . . . . . . . TC-34, WA-34, WB-35
kopa@karlin.mff.cuni.cz
Department of Probability and Mathematical Statistics,
Charles University in Prague, Faculty of Mathematics andPhysics, Prague, Czech Republic
Kort, Peter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-33, MC-33
kort@tilburguniversity.edu
Tilburg University, Netherlands
Kostyukova, Olga . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-38
kostyukova@im.bas-net.by
Mathematical System Theory, Institute of Mathematics, National Academy of Sciences of Belarus, Minsk, Belarus
Kourtzidis, Stavros . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-48
s.kourtzidis@dundee.ac.uk
School of Business, University of Dundee, Dundee, Scotland,
United Kingdom
Kovalchik, Stephanie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-16
skovalchik@zelusanalytics.com
Zelus Analytics, Germany

SESSION CHAIR INDEX

Kuiper, Alex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-15
a.kuiper@uva.nl
Business Analytics, University of Amsterdam, Amsterdam,
Netherlands
Kunc, Martin . . . . . . . . . . . . . . . . . . . . . . . WA-11, WB-11, WC-11
m.h.kunc@soton.ac.uk
Southampton Business School, University of Southampton,
United Kingdom
Kułakowski, Konrad . . . . . . . . . TC-44, TD-44, WA-44, WB-44
konrad.kulakowski@agh.edu.pl
Applied Computer Science, AGH University of Science andTechnology, Kraków, Lesser Poland, Poland
Kurishchenko, Kseniia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-27
kk.eco@cbs.dk
Economics, Copenhagen Business School, Frederiksberg,
Denmark
Kuryatnikova, Olga . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-38
kuryatnikova@ese.eur.nl
Erasmus University Rotterdam, Netherlands
Kurz, Sascha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-36
sascha.kurz@uni-bayreuth.deMathematics, Physics and Computer Science, University of
Bayreuth, Bayreuth, Bavaria, Germany
Labrecciosa, Paola . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-36
paola.labrecciosa@essca.fr
ELS, ESSCA School of Management, France

Krass, Dmitry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-24
krass@rotman.utoronto.ca
Rotman School of Mgmt, University of Toronto, Toronto,
Ontario, Canada

Lacomme, Philippe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-42
placomme@isima.fr
ISIMA, LIMOS, AUBIERE, AUVERGNE, France

Krauth, Maurice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-55
maurice.krauth@tu-dresden.de
Chair of Railway Operations, Technische Universität Dresden, Germany

Lalla-Ruiz, Eduardo . . . . . . . . . . . . . . . . . . . . . . . MD-62, WD-62
e.a.lalla@utwente.nl
Faculty of Behavioural, Management and Social Sciences,
University of Twente, Enschede, Netherlands

Kresta, Aleš . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-51, TA-51
ales.kresta@vsb.cz
Department of Finance, Faculty of Economics, VSBTechnical University of Ostrava, Ostrava, Czech RepublicLami, Isabella . . . . . . . . . . . . . . . . . . . . . . . MA-13, MC-13, TB-47
isabella.lami@polito.it
DIST, Politecnico di Torino, Torino, Torino, Italy

Kristaly, Alexandru . . . . . . . . . . . . . . . . . . . . . . . . . MD-41, TA-41
alexandrukristaly@yahoo.com
Babes-Bolyai University, Cluj-Napoca, Romania
Krogh Boomsma, Trine . . . . . . . . . . . . . . . . . . . . . MB-21, MC-52
trine@math.ku.dk
Department of Mathematical Sciences, University of Copenhagen, København Ø, Denmark
Kronqvist, Jan . . . . . . . . . . . . . . . . . . . . . . . TA-04, TB-04, WA-04
jankr@kth.se
Mathematics, KTH Royal Institute of Technology, Sweden
Kuhn, Heinrich . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-50
heinrich.kuhn@ku.de
Supply Chain Management & Operations, Catholic University of Eichstaett-Ingolstadt, Ingolstadt, Bavaria, Germany

Landini, Simone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-63
landini@ires.piemonte.it
Economics, IRES Piemonte, Torino, Italy
Langer, Lissy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-09, WD-09
lilang@dtu.dk
Technical University of Denmark (DTU), Denmark
Lapucci, Matteo . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-32, MB-32
matteo.lapucci@unifi.it
Department of Information Engineering, University of Florence, Firenze, Italy
Latafat, Puya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-41, WD-41
puya.latafat@kuleuven.beElectrical Engineering (ESAT) STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU
LEuven, Leuven, Belgium

491

SESSION CHAIR INDEXEURO 2024 - Copenhagen

Lauven, Lars-Peter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-14
lars.lauven@uni-kassel.de
Chair of Energy Management and Power System Operation,
University of Kassel, Kassel, Germany
Lawless, Connor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-27
cal379@cornell.edu
Cornell University, Ithaca, NY, United States
Le, Thi My Le . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-41
thi-my-le.le@univ-lorraine.fr
Computer Science, University of Lorraine, METZ, France
Legros, Benjamin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-39
benjamin.legros@centraliens.net
Ecole de Management de Normandie, France
Leitner, Stephan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-43
stephan.leitner@aau.at
Department of Management Control and Strategic Management, University of Klagenfurt, Klagenfurt, Austria
Leopold-Wildburger, Ulrike. . . . . . . . . WB-40, WC-40, WD-40
ulrike.leopold@uni-graz.at
Department of Operations and Information Systems, University of Graz, Graz, Austria
Leppanen, Ilkka . . . . . . . . . . . . . . . . . . . . . TD-07, WA-07, WB-07
ilkka.j.leppanen@aalto.fi
School of Business, Aalto University, Espoo, Finland
Lessmann, Stefan . . . . . . . . . . . . . . . . . . . . . . . . . . WC-31, WD-31
stefan.lessmann@hu-berlin.de
School of Business and Economics, Humboldt-University of
Berlin, Berlin, Germany
Leung, Janny . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WE-01
jannyleung@um.edu.moState Key Lav of Internet of Things for Smart City, The University of Macau, Taipa, Macao, CHINA, Macao
Levantesi, Susanna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-63
susanna.levantesi@uniroma1.it
Sapienza University of Rome, roma, Italy
Levin, Yuri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-28
ylevin@business.queensu.ca
School of Business, Queen’s University, Kingston, Ontario,
CanadaLeyer, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-07
michael.leyer@wiwi.uni-marburg.de
Chair of Digitalisation and Process Management, Universityof Marburg, Marburg, Germany
Li, Dong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-59
dong.li@lancaster.ac.uk
Management Science, Lancaster University, Lancaster,
United Kingdom
Lidbetter, Thomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-36
tlidbetter@business.rutgers.edu
Rutgers Business School, Rutgers University, Newark, NJ,
United States

492

Lienert, Judit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-11
judit.lienert@eawag.ch
Environmental Social Sciences (ESS), Eawag: Swiss Federal
Institute of Aquatic Science and Technology, Duebendorf,
Switzerland
Liers, Frauke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-34
frauke.liers@math.uni-erlangen.de
Department Mathematik, FAU Erlangen-Nuremberg, Erlangen, Germany
Lin, Yunzhu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-56
mschang@cycu.edu.tw
Department of Civil Engineering, Chung Yuan Christian University, Taoyuan, Taiwan
Lindahl, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-46
miclindahl@gmail.com
Management Engineering, Technical University of Denmark,DenmarkLiu, Chang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-03
changliu@hkaift.com
City University of Hong Kong, HK, Hong Kong
Liu, Shaofeng . . . . . . . . . . . . . . . . . . . . . . . TD-45, WA-45, WC-45
shaofeng.liu@plymouth.ac.uk
Plymouth Business School, University of Plymouth, Plymouth, United Kingdom
Liu, Songsong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-23
s.liu@hit.edu.cn
School of Management, Harbin Institute of Technology,
Harbin, China
Ljubic, Ivana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-29, TD-29
ivana.ljubic@essec.edu
IDS, ESSEC Business School of Paris, Cergy-Pontoise,FranceLlagostera Blasco, Pol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-12
pol.llagostera@udl.cat
Mathematics, University of Lleida, Lleida, Catalonia, Spain
Lleo, Sebastien . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-57
sebastien.lleo@neoma-bs.fr
Finance Department, NEOMA Business School, Reims,
FranceLoiseau, Irene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-29, MD-29
irene@dc.uba.ar
Departamento de Computación-, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires,
Argentina
Lombardi, Patrizia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-18
patrizia.lombardi@polito.it
DIST, Polytechnic of Turin, Turin, Italy
Lopes, Rui Borges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-49
rui.borges@ua.pt
Dep. of Economics, Management and Industrial Engineering, CIDMA / University of Aveiro, Aveiro, Portugal

EURO 2024 - Copenhagen
Lopez Flores, Nidia Guadalupe . . . . . . . . . . . . . . WA-27, MD-31
nidia20@ru.is
Computer Science, Reykjavík University, Reykjavik, Iceland
Lopez Redondo, Juana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-22
jlredondo@ual.es
Department of Informatics, University of Almeria, Almeria,
Spain
López-Cerdá, Marco A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-42
marco.antonio@ua.es
Statistics and Operations Research, Alicante University, Alicante, Spain
Lorentziadis, Panos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-36
paloren@otenet.gr
Business Administration, Athens University of Economics
and Business, ATHENS, Greece
Lu, Chixiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-48
c.lu@surrey.ac.uk
Surrey Business school, University of Surrey, Guildford, Surrey, United Kingdom
Luan, Xiaojie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-54
xjluan@bjtu.edu.cn
Beijing Jiaotong University, Beijing, China
Luce, Robert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-30
luce@gurobi.com
Gurobi, Monheim am Rhein, Germany
Lundell, Andreas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-30
alundell@abo.fi
Department of Information Technologies, Åbo Akademi University, Turku, Finland
Lurkin, Virginie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-01
virginie.lurkin@unil.ch
HEC, University of Lausanne, Eindhoven, Switzerland
Lysgaard, Jens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-58
lys@econ.au.dk
Department of Economics and Business Economics, Aarhus
University, Aarhus V, Denmark
Macedo, Eloisa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-56, TD-56
macedo@ua.pt
Department of Mechanical Engineering, University of
Aveiro, Aveiro, Aveiro, Portugal
MacSwiney Brugha, Cathal . . . . . . . . . MD-02, MA-20, MC-20
cathal.brugha@ucd.ie
Centre for Business Analytics, University College Dublin,
Dublin 4, Ireland
Madaan, Jitender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-53
jmadaan@iitd.ac.in
Department of Management studies, Indian Institute of Technology Delhi, New Delhi, Delhi, India
Madlener, Reinhard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-14
rmadlener@eonerc.rwth-aachen.de
School of Business and Economics / E.ON Energy ResearchCenter, RWTH Aachen University, Aachen, Germany

SESSION CHAIR INDEX

Madsen, Henrik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-14
hmad@dtu.dk
Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
Maggioni, Francesca . . MA-35, MC-35, TA-35, TB-35, TD-35
francesca.maggioni@unibg.it
Department of Management, Information and Production Engineering, University of Bergamo, Bergamo, Italy
Mahes, Roshan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-39
a.v.mahes@uva.nl
University of Amsterdam, Netherlands
Mahes, Roshan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-39
roshan-1@live.com
Amsterdam, Noord-Holland, Netherlands
Malaguti, Enrico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-25
enrico.malaguti@unibo.it
DEI, University of Bologna, Bologna, Italy
Maldonado, Sebastian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-31
sebastianm@fen.uchile.cl
Department of Management Control and Information Systems, University of Chile, Santiago, Chile
Mancini, Simona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-58
simona.mancini@unipa.it
Dipartimento di Ingegneria, Università degli Studi diPalermo, Palermo, Italy, Italy
Manerba, Daniele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-35
daniele.manerba@unibs.it
Dept. of Information Engineering, Università degli Studi diBrescia, Brescia, Italy
Manger, Robert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-29
manger@math.hr
Department of Mathematics, Faculty of Science, Universityof Zagreb, Zagreb, Croatia
Manni, Emanuele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-64
emanuele.manni@unisalento.it
Department of Engineering, University of Salento, Lecce,
Italy
Manzi, Sean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-10
sean.manzi@dartington.org.uk
Dartington Service Design Lab, Buckfastleigh, Devon,
United Kingdom
Margonari, Mattia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-02
mattia.margonari@uniroma1.it
Sapienza Universita’ di Roma, Italy
Marianov, Vladimir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-61
marianov@uc.cl
Electrical Engineering, Pontificia Universidad Catolica deChile, Santiago, Chile
Markhorst, Berend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-35
berend.markhorst@cwi.nl
Stochastics Group, CWI, Amsterdam, Noord-Holland,

493

SESSION CHAIR INDEXEURO 2024 - Copenhagen

Netherlands
Martello, Silvano . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-26, TB-26
silvano.martello@unibo.it
DEI, University of Bologna, Bologna, Italy
Martire, Antonio Luciano . . . . . . . . . . . . . . . . . . . . . . . . . . WC-39
antonioluciano.martire@uniroma1.it
MEMOTEF, Sapienza University of Rome, Italy
Masala, Giovanni . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-22
gb.masala@unica.it
Economics and Business Sciences, University of Cagliari,
Cagliari, Italy
Mastroeni, Loretta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-63
loretta.mastroeni@uniroma3.it
Dept. of Economics, University of Roma TRE, Rome, Italy
Matsatsinis, Nikolaos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-01
nikos@ergasya.tuc.gr
Department of Production Engineering and Management,
Technical University of Crete, Chania, Crete, Greece
Mauttone, Antonio . . . . . . . . . . TA-06, MA-25, WD-28, WB-55
mauttone@fing.edu.uy
Operations Research Department, Universidad de la
República, Montevideo, Uruguaym.merkert@tu-braunschweig.de
Institute for Mathematical Optimization, TU Braunschweig,
Braunschweig, Niedersachsen, Germany
Mesa, Juan A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-61
jmesa@us.es
Applied Mathematics II, University of Seville, Sevilla, Spain
Mieth, Robert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-35
robert.mieth@rutgers.edu
Industrial and Systems Engineering, Rutgers University, New
Brunswick, New Jersey, United States
Mikkelsen, Jesper Bang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-53
jbmikkelsen@econ.au.dk
Department of Economics and Business Economics, Aarhus
University, Aarhus V, Denmark
Miller-Hooks, Elise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-53
miller@gmu.edu
Civil, Environmental and Infrastructure Engineering, George
Mason University, Fairfax, VA, United States
Minner, Stefan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-45
stefan.minner@tum.de
TUM School of Management, Technische Universität
München, Munich, Germany

Mazza, Rina Mary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-50
rmazza@dimes.unical.it
DIMES, University of Calabria, Rende, CS, Italy

Miroforidis, Janusz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-45
janusz.miroforidis@ibspan.waw.pl
Department of Decision Support in the Presence of Risk, Systems Research Institute, Polish Academy of Sciences, Poland

Mazzoccoli, Alessandro. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-63
alessandro.mazzoccoli@gmail.com
Economics, Roma TRE University, Rome, Italy

Mitridati, Lesia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-14
lemitri@dtu.dk
DTU, Lyngby, Denmark

McKenna, Russell . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-09, MC-19
rmckenna@ethz.ch
ETH Zürich, Zürich, Switzerland

Miyashita, Naoko . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-50
naoko.miyashita.sq@hitachi.com
Research and Development Group, Hitachi, Ltd., Japan

Meisel, Frank . . . . . . . . . . . . . . . . TC-55, MA-62, MD-62, TA-62
meisel@bwl.uni-kiel.de
Christian-Albrechts-University, Kiel, Germany

Molero-Río, Cristina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-27
molero@lix.polytechnique.fr
École Polytechnique, France

Mencarelli, Luca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-04
luca.mencarelli@unipi.it
Dipartimento di Informatica, Università di Pisa, Italy

Molho, Elena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-41
elena.molho@unipv.it
Dipartimento di Scienze Economiche e Aziendali, Università
di Pavia, Pavia, Italy

Mencaroni, Andrea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-23
andrea.mencaroni@ugent.beIndustrial System Engineering and Product Design, Ghent
University, Zwijnaarde, Belgium
Menzietti, Massimiliano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-02
massimiliano.menzietti@unical.it
University of Calabria, Cosenza, Italy
Merino, María . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-15
maria.merino@ehu.es
Mathematics, University of the Basque Country-UPV/EHU;
Basque Center for Applied Mathematics-BCAM, Leioa; Bilbao, Vizcaya, Spain
Merkert, Maximilian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-04

494

Molinari, Cesare . . . . . . . . . . . . . . . . . . . . . MD-32, TC-32, TD-32
molinari@dima.unige.it
Università di Genova, Genova, GE, Italy
Monaci, Marta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-52
marta.monaci@uniroma1.it
Department of Computer, Control and Management Engineering "Antonio Ruberti" (DIAG), Sapienza University ofRome, Rome, Lazio, Italy
Montibeller, Gilberto . . . . . . . . MB-11, MC-11, MD-11, TA-11
g.montibeller@lboro.ac.uk
Management Science and Operations Group, Loughborough
University, Loughborough, United Kingdom

EURO 2024 - Copenhagen

SESSION CHAIR INDEX

Morales, Juan Miguel . . . . . . . . . . . . . . . . . . . . . . . MA-19, WA-35
juan.morales@uma.es
Applied Mathematics, University of Málaga, Málaga, Spains.nemeth@bham.ac.uk
School of Mathematics, University of Birmingham, Birmingham, United Kingdom

Morini, Benedetta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-34
benedetta.morini@unifi.it
Dipartimento di Ingegneria Industriale, Universita di Firenze,
Firenze, Italy

Nepomuceno, Thyago . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-48
thyago.nepomuceno@ufpe.br
Department of Statistics, Federal University of Pernambuco,
BrazilMorotti, Elena . . . . . . . . . . . . . . . . . . . . . . MA-34, MB-34, MC-34
elena.morotti4@unibo.it
University of Bologna, Bologna, Italy

Nesterov, Yurii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-32
yurii.nesterov@uclouvain.be
CORE, Université catholique de Louvain (UCL), Louvain-laneuve, Belgium

Mourtos, Yiannis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-49
mourtos@aueb.gr
Management Science & Technology, Athens University of
Economics & Business, Greece
Mouzakitis, Apostolos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-08
a.mouzakitis@outlook.com
PhD Studies, New York College Greece, Athens, Greece
Müller, Benjamin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-30
benjamin.mueller@zib.de
COPT GmbH, Berlin, Germany
Munari, Pedro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-25, WB-64
munari@dep.ufscar.br
Production Engineering Department, Federal University of
Sao Carlos, Sao Carlos, SP, Brazil
Munoz-Marquez, Manuel . . . . . . . . . . . . . . . . . . . . . . . . . . WD-29
manuel.munoz@uca.es
Statistics and Operations Research, Cadiz University, Puerto
Real, Cádiz, Spain
Murayama, Akito . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-18
murayama@up.t.u-tokyo.ac.jp
Department of Urban Engineering, The University of Tokyo,
Bunkyo-Ku, Tokyo, Japan
Murphy, Ken . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-16
murphyke@uci.edu
Merage School of Business, UC Irvine, Irvine, CA, UnitedStatesNadar, Emre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-22
emre.nadar@bilkent.edu.tr
Bilkent University, Ankara, Ankara, Turkey
Nagurney, Anna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-21
nagurney@isenberg.umass.edu
Department of Operations and Information Management,
University of Massachusetts Amherst, Amherst, Massachusetts, United States
Nediak, Mikhael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-28
mikhail.nediak@queensu.ca
Queens University, Kingston, Canada
Neděla, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-51, MD-51
ned0043@vsb.cz
Finance, VSB-TU Ostrava, Ostrava, Czech Republic
Nemeth, Sandor Zoltan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-41

Neumann, Simone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-54
simone.neumann@uni-hamburg.de
Sustainable Logistics and Mobility, Universität Hamburg,
Hamburg, Hamburg, Germany
Nguyen, Dang Viet Anh . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-26
andng@dtu.dk
Department of Technology, Management and Economics,
Technical University of Denmark, Copenhagen, Denmark
Nishihara, Michi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-59
nishihara@econ.osaka-u.ac.jp
Graduate School of Economics, Osaka University, Osaka,Japan
Obasohan, Phillips Edomwonyi . . . . . . . . . . . . . . . . . . . . . MB-15
philiobas@yahoo.com
Liberal Studies, Niger State Polytechnic, Bida, Niger, Nigeria
Oguz, Ceyda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-01
coguz@ku.edu.tr
Department of Industrial Engineering, Koc University, Istanbul, Turkey
Olama, Alireza . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-04
alireza.olama@abo.fi
Department of Information Technology, Åbo Akademi University, Vaasa, Finland
Olan, Femi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-45, WC-45
femi.olan@essex.ac.uk
Strategy, Operations & Entrepreneurship, University of Essex, Colchester, — Select One —, United Kingdom
Oliveira, José Fernando . . . . . . . . . . . . . . MB-07, TA-29, TB-29
jfo@fe.up.pt
INESC TEC, Faculty of Engineering, University of Porto,
Porto, Portugal
Oliveira, Monica . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-07
monica.oliveira@tecnico.ulisboa.pt
Centre for Management Studies of Instituto Superior Técnico, IST, Universidade de Lisboa, Lisboa, Portugal
Onat, Melike Kamuran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-52
melikekamuranonat@gmail.com
R&D, TÜPRAŞ, Turkey
Oppio, Alessandra . . . . . . . . . . . . . . . . . . . MC-47, MD-47, TA-47
alessandra.oppio@polimi.it
Department of Architecture and Urban Studies, Politecnico

495

SESSION CHAIR INDEXEURO 2024 - Copenhagenof Milano, Milano, ItalyÓskarsdóttir, María . . . . . . . . . . . . . . . . . . . . . . . . WA-27, MD-31
mariaoskars@ru.is
Department of Computer Science, Reykjavik University, Iceland
Ostermeier, Manuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-50
manuel.ostermeier@uni-a.deUniversity of Augsburg, Augsburg, Germany
Otto, Alena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-26
alena.otto@uni-passau.de
University of Passau, Passau, Germany

HEC Liège-Management School of the Université of Liège,
Liège, Liège, Belgium
Parikh, Jinal TD-06, WD-06, WB-18, MA-25, WC-28, TB-31
jinal.parikh@ahduni.edu.inTechnology, Operations & Decision Sciences, Amrut Mody
School of Management, Ahmedabad University, Ahmedabad,
Gujarat, India
Parkes, Andrew J. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-03
andrew.parkes@nottingham.ac.uk
School of Computer Science, University of Nottingham, Nottingham, United Kingdom

Ottomanelli, Michele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
michele.ottomanelli@poliba.it
DICATECh, Politecnico di Bari, Bari, Italy

Parpas, Panos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-34
panos.parpas@imperial.ac.uk
Computing, Imperial College London, London, United KingdomÖzcan, Ender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-03
ender.ozcan@nottingham.ac.uk
Computer Science, University of Nottingham, Nottingham,
United Kingdom

Parragh, Sophie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-37
sophie.parragh@jku.at
Institute of Production and Logistics Management, Johannes
Kepler University Linz, Linz, AustriaÖzcan, İsmail . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-28, WA-28
iozcan@umh.es
Operations Research Center, Universidad Miguel Hernández,
Elche, Alicante, Spain

Passacantando, Mauro . . . . . . . . . . . . . . . . . . . . . WB-43, WC-43
mauro.passacantando@unimib.it
Department of Business and Law, University of MilanoBicocca, Milano, Italy

Ozogur-Akyuz, Sureyya . . . . . . . . . . . . . . . . . . . . MB-04, WC-27
sureyya.akyuz@eng.bau.edu.tr
Department of Mathematics Engineering, Bahcesehir University, Istanbul, Turkey

Pawlak, Grzegorz . . . . . . . . . . . . . . . . . . . . . . . . . . MA-03, MB-03
grzegorz.pawlak@cs.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poznan, Poland

Pacino, Dario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ME-01
darpa@dtu.dk
Department of Management, Technology and Economics,
Technical University of Denmark, Kgs. Lyngby, Denmark

Päsler, Lars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-21
lars.pasler@student.uva.nl
Universiteit van Amsterdam, Netherlands

Padron, Silvia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-55
s.padron@tbs-education.fr
TBS Business School, France
Palagi, Laura . . . . . . . . . . . . . . . . MA-02, MB-02, TA-52, TB-52
palagi@diag.uniroma1.it
Department of Computer, Control, and Management Engineering A. Ruberti, Sapienza University of Rome, Rome,
Italy
Pantuso, Giovanni . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-34, TC-35
giopantuso@gmail.com
Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
Papadimitriou, Dimitri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-03
dpapadimitriou@3nlab.org
3nLab, Leuven, Belgium
Papanagnou, Christos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-23
chrobo@yahoo.com
Engineering Systems and Supply Chain Management, Aston
University, Birmingham, United Kingdom
Paquay, Célia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-07
cpaquay@uliege.be

496

Peeperkorn, Jari . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-27, TB-27
jari.peeperkorn@kuleuven.be
Research Center for Information Systems Engineering(LIRIS), KU Leuven, Leuven, Belgium
Pele, Daniel Traian . . . . . . . . . . . . . . . . . . . . . . . . . WC-31, WD-31
danpele@ase.ro
Bucharest University of Economic Studies. Institute for Economic Forecasting, Romanian Academy, Bucharest, Romania
Pelizzari, Cristian . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-02, TD-39
cristian.pelizzari@unibs.it
Department of Economics and Management, University of
Brescia, Brescia Bs, Italy
Pellerey, Virginia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-18
virginia.pellerey@polito.it
DIST, Politecnico di Torino, Torino, Italy
Perea, Federico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-60
perea@us.es
Departamento de Matemática Aplicada II, Universidad de
Sevilla, Sevilla, Spain
Perez Valdes, Gerardo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-23
gerardoa.perez-valdes@sintef.no
Economics and Technology Management, SINTEF Technol-

EURO 2024 - Copenhagen

SESSION CHAIR INDEX

ogy and Society, Trondheim, Norway
Perkkiö, Ari-Pekka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-35
perkkioe@math.lmu.de
Germany
Pesch, Erwin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-26
erwin.pesch@uni-siegen.de
Faculty III, University of Siegen, Siegen, Germany
Phillips, Christina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-46
c.j.phillips@ljmu.ac.uk
LBS, Liverpool John Moores University, Liverpool, Merseyside, United Kingdom
Phung-Duc, Tuan . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-40, MC-40
tuan@sk.tsukuba.ac.jp
University of Tsukuba, Tsukuba, Japan
Piccotto, Filippo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-08
filippo.piccotto@phd.units.it
Department of Economics, Business, Mathematics and Statistics, University of Trieste, Latisana, Udine, Italy
Pickl, Stefan Wolfgang . . . . . . . . . . . . . . WB-40, WC-40, WD-40
stefan.pickl@unibw.de
Department of Computer Science, UBw München
COMTESSA, Neubiberg-München, Bavaria, Germany
Pignatelli, Maurizia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-18
maurizia.pignatelli@polito.it
DIST, Politecnico di Torino, Turin, Italy
Pimentel, Rita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-57
rita.pimentel@ntnu.no
Industrial Economics and Technology Management, NTNU,
Trondheim, Trondelag, Norway
Pineda Morente, Salvador . . . . . . . . . . . . . . . . . . . . . . . . . . MA-19
spinedamorente@gmail.com
Electrical Engineering, University of Málaga, Málaga, Spain
Pinto, Maria José . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-64
mariajosemjp@fab.mil.br
EC4ISR Division, Institute for Advanced Studies (IEAv), SãoJosé dos Campos, São Paulo, Brazil
Pirnia, Mehrdad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-14
mpirnia@uwaterloo.ca
Management Sciences, University of Waterloo, Waterloo,
Ontario, Canada
Pisinger, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-26, MD-65
dapi@dtu.dk
Management Engineering, DTU, Lyngby, Denmark
Plana, Isaac . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-64
isaac.plana@uv.es
Matemáticas para la Economía y la Empresa, University of
Valencia, Valencia, Spain
Pluchinotta, Irene . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-13, TA-13
i.pluchinotta@ucl.ac.uk
The Bartlett, University College London, London, United
Kingdom

Polak, Sven . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-38
s.c.polak@tilburguniversity.edu
Tilburg University, Netherlands
Polyakovskiy, Sergey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-07
sergey.polyakovskiy@deakin.edu.au
Deakin University, Australia
Porta, Federica . . . . . . . . . . . . . . . . . . . . . MA-34, MB-34, MC-34
federica.porta@unimore.it
Universita’ di Modena e Reggio Emilia, Italy
Potgieter, Linke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-10
lpotgieter@sun.ac.za
Logistics, Stellenbosch University, Matieland, Western Cape,South Africa
Pouliasi, Aliki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-56
apouliasi@mail.ntua.gr
Rural and Surveying Engineering, National Technical University of Athens, Athens, Greece
Pourahmadi, Farzaneh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-14
farpour@dtu.dk
Technical University of Denmark, Lyngby, Denmark
Prodhon, Caroline . . . . . . . . . . . . . . . . . . . . . . . . . . TD-24, MA-42
caroline.prodhon@utt.fr
University of Technology of Troyes, Troyes, France
Qiu, Feng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-22
fqiu@anl.gov
Argonne National Laboratory, Lemont, IL, United States
Quattrone, Michele. . . . . . . . . . . . . . . . . . . . . . . . . . TB-46, WB-46
michele.quattrone@airliquide.com
Air Liquide R&d, Les loges en josas, France
Quezada, Luis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-60
luis.quezada@usach.cl
Department of Industrial Engineering, University Of Santiago Of Chile, SANTIAGO, SANTIAGO, Chile
Raciti, Fabio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-43
fraciti@dmi.unict.it
Mathematics and Computer Science, University of Catania,
Catania, Italy
Raffaele, Alice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-16, MD-64
alice.raffaele@unical.it
Department of Mechanical, Energy, and Management Engineering, University of Calabria, Rende, Italy
Rahman, Shimanto. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-28
shimanto.rahman@ugent.be
Economics and Business Administration, Ghent University,
Ghent, East-Flanders, Belgium
Rakotonirainy, Rosephine Georgina . . . . . . . . . . . . . . . . . TA-07
rosephine.rakotonirainy@uct.ac.za
Department of Statistical Sciences, University of Cape Town,
Cape Town, Western Cape, South Africa
Ramírez-Ayerbe, Jasone . . . . . . . . . . . . . . . . . . . . . MD-27, TA-27

497

SESSION CHAIR INDEXEURO 2024 - Copenhagenmrayerbe@us.es
Instituto de Matemáticas de la Universidad de Sevilla, Spain
Ramirez, Hector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-42
hramirez@dim.uchile.clCenter for Mathematical Modeling, Universidad de Chile,
Santiago, RM, Chile
Rastani, Sina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-26, TC-26
s.rastani@sheffield.ac.uk
Operations Management and Decision Sciences, Sheffield
University Management School, Sheffield, United Kingdom
Ravanos, Panagiotis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-44
panagiotis.ravanos@ec.europa.eu
Directorate S: Innovation in Science and Policymaking, Unit
S3: Science for Modelling, Monitoring and Evaluation, European Commission, Joint Research Center, ISPA, Italy
Rêgo, Leandro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-36
leandro@dema.ufc.br
Statistics and Applied Math Department, Universidade Federal do Ceará, Fortaleza, Ceará, Brazil
Rebegoldi, Simone . . . . . . . . . . . . . . . . . . MA-34, MB-34, MC-34
simone.rebegoldi@unimore.it
Dipartimento di Scienze Fisiche, Informatiche e Matematiche, Università di Modena e Reggio Emilia, Italy
Reisach, Ulrike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-20
ulrike.reisach@hnu.de
Information Management Department, Neu-Ulm Universityof Applied Sciences, Neu-Ulm, Bavaria, Germany
Rener, Elena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-60
elena.rener@unibe.ch
University of Bern, Switzerland
Reuter-Oppermann, Melanie MA-10, MD-10, WA-10, WB-10
m.n.reuter-oppermann@utwente.nl
BMS, University of Twente, Enschede, Netherlands
Ricard, Léa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-52
lea.ricard@epfl.ch
Epfl Enac Iic Transp-or, Lausanne, Switzerland
Rios, Ignacio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-43
ignacio.riosuribe@utdallas.edu
University of Texas at Dallas, Richardson, TX, United States
Rizzini, Giorgio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-02
giorgio.rizzini@sns.it
Italy
Roa, Joaquín . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-31
joaquin.roa.l@uchile.cl
Centre for Public Systems, University of Chile, Santiago,
Chile
Roberti, Roberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-64
roberto.roberti@unipd.it
Information Engineering, University of Padova, Padova, Italy
Rodrigues, Filipe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-62
frodrigues@iseg.ulisboa.pt

498

ISEG, University of Lisbon, Portugal
Rohmer, Sonja . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-13
sonja-ursula-katharina.rohmer@hec.ca
Department of Logistics and Operations Management, HEC
Montreal, Montreal, Canada
Rolfes, Jan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-04
jrolfes@kth.se
Optimization and System Theory, Royal Institute of Technology (KTH) Stockholm, Stockholm, Sweden
Römer, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-03, TC-03
michael.roemer@uni-bielefeld.de
Decision Analytics Group, Bielefeld University, Bielefeld,
Germany
Romero Morales, Dolores . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-01
drm.eco@cbs.dk
Copenhagen Business School, Frederiksberg, Denmark
Rosell, Francisca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-12
francisca.rosell@eurecat.org
Applied Artificial Intelligence Unit, Eurecat, Barcelona, Catalonia, Spain
Rossi, Roberto . . . . . . . . . . . . . . . . . . . . . . WB-20, WC-20, WA-49
robros@gmail.com
Business School, University of Edinburgh, Edinburgh, United
Kingdom
Rottner, Cecile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-25
cecile.rottner@gmail.com
Edf R&d, France
Rouse, Paul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-48
p.rouse@auckland.ac.nz
Accounting & Finance, University of Auckland, Auckland,
New ZealandRoy, Santanu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-08
rsan58@yahoo.co.uk
Icfai Business School (ibs) Dehradun, The Icfai University,
DEHRADUN, UTTARAKHAND, India
Ruela, Victor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-19
victor.ruela@tuwien.ac.at
Institute of Energy Systems and Thermodynamics, TU Wien,
Vienna, Austria
Ruiz, Rubén . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-46
rruiz@eio.upv.es
Departamento de Estadistica e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain
Russo, Davide Donato . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-53
davide.russo@unimol.it
University of Molise, Italy
Rybarczyk, Agnieszka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-20
arybarczyk@cs.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poznan, Poland

EURO 2024 - Copenhagen

SESSION CHAIR INDEX

Saberi, Sara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-23
ssaberi@wpi.edu
Business School, Worcester Polytechnic Institute, Worcester,
Massachusetts, United States

Sartor, Giorgio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-54
giorgio.sartor@sintef.noMathematics and Cybernetics, SINTEF Digital, Oslo, Norway

Sadanoglu, Sadiye Eylul . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-18
ss3395q@gre.ac.uk
Systems Management and Strategy, Business School, University of Greenwich, london, United Kingdom

Sass, Jörn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-57
joern.sass@rptu.deMathematics, RPTU Kaiserslautern-Landau, Kaiserslautern,
Germany

Saeednia, Mahnam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-62
m.saeednia@tudelft.nl
Tu delft, Delft, Netherlands

Schiffer, Maximilian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-54
schiffer@tum.de
School of Management & Munich Data Science Institute,
Technical University of Munich, Munich, Germany

Sage, Felix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-16
felix.sage@uni-due.de
Mercator School of Management, University of DuisburgEssen, Duisburg, Germany
Sakalauskas, Leonidas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-07
leonidas.sakalauskas@mif.vu.lt
Applied Informatics, Vytautas Magnus University, Kaunas,
Lithuania
Salani, Matteo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-26
matteo.salani@idsia.ch
Idsia - Usi/supsi, Lugano, Switzerland
Salazar González, Juan José . . . . . . . . . . . . . . . . . . . . . . . . MD-29
jjsalaza@ull.es
Estadística e Investigación Operativa, Universidad de La Laguna (Tenerife), La Laguna, Tenerife, Spain
Saldanha-da-Gama, Francisco . . . . . . . . . . . . . . . . TA-26, TC-26
francisco.saldanha-da-gama@sheffield.ac.uk
Sheffield University Management School, Sheffield, United
Kingdom
Salo, Ahti . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-34
ahti.salo@aalto.fi
Systems Analysis Laboratory, Aalto University School of
Science, Aalto, Finland

Schmidt, Marie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-56
marie.schmidt@uni-wuerzburg.de
Institute for Computer Science, University of Würzburg,
Würzburg, Germany
Schmitt, Andreas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-46
aschmitt@mathematik.tu-darmstadt.de
TU Darmstadt, Germany
Schneider, Lorenz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-57
schneider@em-lyon.com
Economics, Finance, Accounting, EMLYON Business
School, Ecully, France
Schöbel, Anita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TE-01, TD-51
schoebel@mathematik.uni-kl.de
Department of Mathematics, University of KaiserslauternLandau, Kaiserslautern, Germany
Schryen, Guido . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-52
guido.schryen@upb.de
Department of Management Information Systems, Paderborn
University, Germany
Schulte, Frederik . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-62, WB-62
f.schulte@tudelft.nl
Transport Engineering and Logistics, Delft University ofTechnology, Delft, Netherlands

Sama, Miguel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-41
msama@ind.uned.es
Matemática Aplicada, Universidad Nacional de Educación aDistancia, UNED, Madrid, Spain

Scrimali, Laura Rosa Maria . . . . . . . . . . . . . . . . . . . . . . . . . TD-40
scrimali@dmi.unict.it
DMI, Università di Catania, Catania, Italy

Samà, Marcella . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-12
marcella.sama@uniroma3.it
Roma Tre University, Roma, Italy

Seidl, Andrea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-33
andrea.seidl@uni-seeburg.at
Seeburg Castle University, Seekirchen, Austria

Sanson, François . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-09
francois.sanson@lhyfe.com
Lhyfe, Nantes, France

Serra, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-61
daniel.serra@upf.edu
Economics and Business, Universitat Pompeu Fabra,
Barcelona, Spain

Santos, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-64
daniel.rebelo.santos@tecnico.ulisboa.pt
Instituto Superior Técnico, Universidade de Lisboa, Lisboa,
Portugal
Sarkis, Miriam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-53
miriam.sarkis16@imperial.ac.uk
Imperial College London, United Kingdom

Sevaux, Marc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-55
marc.sevaux@univ-ubs.fr
Lab-STICC, UMR 6285, CNRS, Université Bretagne Sud,
Lorient, France
Sgarbossa, Fabio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-58
fabio.sgarbossa@ntnu.no
Department of Mechanical and Industrial Engineering,

499

SESSION CHAIR INDEXEURO 2024 - Copenhagen

NTNU, Trondheim, Norway
Shinano, Yuji . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-30
shinano@zib.de
Optimization, Zuse Institue Berlin, Berlin, Berlin, Germany
Siadati, Saba . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-58
s.siadati@tue.nl
Industrial Engineering & Innovation Sciences, Eindhoven
University of Technology, Eindhoven, Netherlands
Sibanda, Mabutho . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-18
sibandam@ukzn.ac.za
Accounting, Economics & Finance, University of KwaZuluNatal, Durban, KwaZulu-Natal, South Africa
Sinnl, Markus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-37
markus.sinnl@jku.at
Institute of Business Analytics and Technology Transformation, Johannes Kepler University Linz, Linz, Austria
Sirbiladze, Gia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-47
gia.sirbiladze@tsu.ge
Department of Computer Sciences, Iv.Javakhishvili Tbilisi
State University, Tbilisi, Georgia
Siskos, Eleftherios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-44
esiskos@tuc.gr
School of Production Engineering and Management, Technical University of Crete, Chania, Greece
Slowinski, Roman . . . . . . . . . . . . . . . . . . MD-08, MA-44, MB-44
roman.slowinski@cs.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poznan, Poland
Smilgins, Aleksandrs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-43
asm.eco@cbs.dk
Department of Economics, Frederiksberg, Denmark
Smith, Chris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-13
christopher.smith@manchester.ac.uk
Management Science and Marketing, University of Manchester, Manchester, United Kingdom
Solano-Blanco, Alfaima L. . . . . . . . . . . . . . . . . . . . . . . . . . . WC-13
a.l.solano.blanco@tue.nl
Industrial Engineering and Innovation Sciences, Eindhoven
University of Technology, Eindhoven, NL, Netherlands
Solymosi, Tamás . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-36
tamas.solymosi@uni-corvinus.hu
Operations Research and Actuarial Sciences, Corvinus University of Budapest, Budapest, Hungary
Sotiros, Dimitrios-Georgios . . . . . . . . . . . . . . . . . . . . . . . . . MD-48
dsotiros@unipi.gr
Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, Wroclaw, Poland
Sotirov, Renata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-38
r.sotirov@uvt.nl
Tilburg University, Tilburg, Outside of Canada & USA,
Netherlands

500

Soumis, Francois . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-29
francois.soumis@gerad.ca
GERAD, Montreal, Québec, Canada
Souza, Edson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-33
e.goncalvesdesouza@ieseg.fr
Operations Management, IESEG School of Management,
Lille, France
Speranza, M. Grazia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-26
speranza@eco.unibs.it
Dept. of Quantitative Methods, University of Brescia, Brescia, Italy
Spieckermann, Caroline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-39
caroline.spieckermann@tum.de
TUM School of Management, Technical University of Munich, Munich, Germany
Spieksma, Flora . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-40
spieksma@math.leidenuniv.nl
Universiteit Leiden, Leiden, Netherlands
Spitzer, Victor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-09
victor.spitzer@universite-paris-saclay.fr
LISN, Université Paris Saclay, France
Springael, Johan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-25
johan.springael@uantwerpen.be
Engineering Management, University of Antwerp, Antwerp,
Belgium
Stecke, Kathryn E. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-06
kstecke@utdallas.edu
Operations Management, The University of Texas at Dallas,
RICHARDSON, TX, United States
Steenmans, Ine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-13, TA-13
ine.steenmans@ucl.ac.uk
Science, Tehcnolog, University College London, London,
London, United Kingdom
Stefan, Gislind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-56
gislind.stefan@univie.ac.at
Business Decisions and Analytics, University of Vienna,
Wien, Wien, Austria
Steidl, Stella Nadine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-53
stellanadine.steidl@pg.canterbury.ac.nz
Civil and Natural Resources Engineering, University of Canterbury, Christchurch, Canterbury, New Zealand
Steiner, Winfried . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-50
winfried.steiner@tu-clausthal.deMarketing, Clausthal University of Technology, Institute ofManagement and Economics, Clausthal-Zellerfeld, Germany
Stiglmayr, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-54
stiglmayr@math.uni-wuppertal.deSchool of Mathematics and Natural Sciences, University ofWuppertal, Wuppertal, Germany
Subramanian, Anand . . . . . . . . . . . . . . . . . . . . . . . WC-06, WA-52
anand@ci.ufpb.brEURO 2024 - Copenhagen
Universidade Federal da Paraíba, João Pessoa, Paraíba, Brazil
Sun, Cong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-42
suncong@lsec.cc.ac.cn
School of Science, Beijing University of Posts and Telecommunications, Beijing, China
Sun, Shudong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-49
sdsun@nwpu.edu.cn
Industrial Engineering, Northwestern Polytechnical Uni.,
Xi’an, China
Suryapeta, Akshitha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-59
phd21025@iiml.ac.in
Operations Management, IIM Lucknow, Lucknow, Uttar
Pradesh, India

SESSION CHAIR INDEXMathematics Department, University of Aveiro, Aveiro, Portugal
Theurich, Franziska . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-54
franziska.theurich@tu-dresden.de
Chair of Traffic Flow Science, TU Dresden, Dresden, Germany
Thielen, Clemens . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-37, WC-37
clemens.thielen@tum.de
TUM Campus Straubing for Biotechnology and Sustainability, Technical University of Munich, Straubing, Germany
Tierney, Kevin . . . . . . . . . . . . . . . . . . . . . . . MC-03, MD-03, TA-03
kevin.tierney@uni-bielefeld.de
Decision and Operation Technologies, Bielefeld University,
Bielefeld, Germany

Swiercz, Aleksandra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-20
aswiercz@cs.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poznan, Poland

Tollenaere, Jonas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-07
jonas.tollenaere@kuleuven.be
NUMA, KU Leuven, Belgium

Sziklai, Balázs R. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-16
sziklai.balazs@krtk.mta.hu
Centre for Economic and Regional Studies, Hungarian
Academy of Sciences, Budapest, HU, Hungary

Toloo, Mehdi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-48
m.toloo@surrey.ac.uk
Department of Business Analytics & Operations, Universityof Surrey, Guildford, Surrey, United Kingdom

Taghavi, Majid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-15
majid.taghavi@smu.ca
Sobey School of Business, Saint Mary’s University, Halifax,
NS, Canada

Topaloglou, Nikolas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-63
nikolas@aueb.gr
International and European Economic Studies, Athens University of Economics and Business, Athens, Greece

Taheri, Sona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-28, WA-28
sona.taheri@rmit.edu.au
RMIT University, Victoria, Australia

Torabi, Sara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-18, MB-18
sara.torabi@polito.it
Interuniversity Department of Regional and Urban Studies
and Planning (DIST), Politecnico di Torino, Turin, Piedmont,
Italy

Takac, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-32, WB-32
martin.taki@gmail.com
Lehigh University, United States
Takahashi, Natsumi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-55
ntaka@nda.ac.jp
Department of Electrical and Electronic Engineering, National Defense Academy, Kanagawa, Japan
Tan, Nuoya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-13
n.tan2@exeter.ac.uk
University of Exeter, United Kingdom
Tariq, Syed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-21
stariq@lincoln.ac.uk
Lincoln International Business School, University of Lincoln,
Lincoln, Lincolnshire, United Kingdom
Tasiou, Menelaos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-44
menelaos.tasiou@port.ac.uk
Portsmouth Business School, University of Portsmouth,
Portsmouth, United Kingdom
Tavin, Bertrand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-57, TB-57
tavin@em-lyon.com
EMLYON Business School, Lyon, France
Tchemisova, Tatiana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-38
tatiana@ua.pt

Toraldo, Gerardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-32, TB-32
gerardo.toraldo@unicampania.it
Universita degli Studi della Campania, Caserta, Italy
Torres, Fabian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-52
fabian.torres@epfl.ch
Epfl Enac Iic Transp-or, Lausanne, Switzerland
Torri, Gabriele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-51
gabriele.torri@unibg.it
Management, Economics and Quantitative Methods, University of Bergamo, Bergamo, Italy
Toth, Paolo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-26, TB-26
paolo.toth@unibo.it
DEI, University of Bologna, Bologna, Italy
Tragler, Gernot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-33
gernot.tragler@tuwien.ac.at
Institute for Statistics and Mathematical Methods in Economics, Vienna University of Technology, Vienna, Austria
Trautmann, Norbert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-60
norbert.trautmann@unibe.ch
Department of Business Administration, University of Bern,
Bern, BE, Switzerland

501

SESSION CHAIR INDEXEURO 2024 - Copenhagen

Truden, Christian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-55
christian.truden@aau.at
Department of Operations, Energy, and Environmental Management, University of Klagenfurt, Klagenfurt, Austria
Tsotsolas, Nikos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-44, WB-45
ntsotsol@uniwa.gr
Business Administration, Univeristy of West Attica, Egaleo,
Attica, Greece
Tundulyasaree, Krissada . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-58
k.tundulyasaree@tue.nl
Operations, Planning, Accounting and Control, Eindhoven
University of Technology, Eindhoven, Netherlands
Tuni, Andrea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-23
andrea.tuni@polito.it
Department of Management and Production Engineering, Politecnico di Torino, Torino, Italy
Uberti, Mariacristina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-63
mariacristina.uberti@unito.it
Management, University of Torino, Torino, ItalyÜçüncü, Duygu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-04
duygu.ucuncu@bau.edu.tr
Bahcesehir University, Istanbul, Turkey

j.m.vandenakker@uu.nl
Information and Computing Sciences, Utrecht University,
Utrecht, Nederland, Netherlands
van den Heuvel, Wilco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-49
wvandenheuvel@ese.eur.nl
Econometric Institute, Erasmus University Rotterdam, Rotterdam, Netherlands
van der Hagen, Liana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-29
vanderhagen@rsm.nl
Technology and Operations Management, Rotterdam Schoolof Management, Erasmus University Rotterdam, Netherlands
van der Mei, Rob . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-10
mei@cwi.nl
CWI, Netherlands
van Essen, Theresia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-10
j.t.vanessen@tudelft.nl
Delft University of Technology, Netherlands
van Rossum, Bart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-54
vanrossum@ese.eur.nl
Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, Zuid-Holland, Netherlands

Ulus, Firdevs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-37, TB-37
firdevs@bilkent.edu.tr
Industrial Engineering, Bilkent University, Ankara, Turkey

van Staden, Heletje . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-12
heletje.vanstaden@ucd.ie
Michael Smurfit Graduate Business School, University College Dublin, Blackrock, Dublin, Ireland

Ulusoy, Aly-Joy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-37, TD-37
aly-joy.ulusoy15@imperial.ac.uk
Civil and Environmental Engineering, Imperial College London, London, - None -, United Kingdom

van Vuuren, Jan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-25
vuuren@sun.ac.za
Department of Industrial Engineering, Stellenbosch University, Stellenbosch, Western Cape, South Africa

Uzunoglu, Aykut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-60
aykut.uzunoglu@wiwi.uni-augsburg.de
Chair of Production & Supply Chain Management, Augsburg
University, Germany

Vansteenwegen, Pieter . . . . . . . . . . . . . . WA-51, WB-54, WC-54
pieter.vansteenwegen@kuleuven.be
Institute for Mobility - CIB, KU Leuven, Leuven, Belgium

Vairetti, Carla . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-31
cvairetti@uandes.cl
Universidad de los Andes, Santiago, Región Metropolitana,
Chile
Valente, Christian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-30
ccv@ampl.com
AMPL Optimization, United States
van Ackere, Ann . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-19
ann.vanackere@unil.ch
HEC, University of Lausanne, Switzerland
Van Aken, Sander . . . . . . . . . . . . MC-46, MD-46, TB-46, TD-46
sander.van-aken@flixbus.com
Flix SE, Germany

Vasilakis, Christos. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-17
c.vasilakis@bath.ac.uk
School of Management, University of Bath, Bath, United
Kingdom
Venghaus, Sandra . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-19, TB-53
venghaus@socecon.rwth-aachen.de
Decision Analysis and Socio-Economic Assessment, RWTH
Aachen, Aachen, Germany
Verbeke, Wouter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-31
wouter.verbeke@kuleuven.be
Faculty of Economics and Business, KU Leuven, Leuven,
Belgium
Verter, Vedat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-15
vedat.verter@queensu.ca
Smith School of Business, Queen’s University, Canada

Van de Klundert, Joel Joris . . . . . . . . . . . . . . . . . . TC-10, TD-10
joris.vandeklundert@uai.cl
School of Business, Universidad Adolfo Ibanez, Santiago,
Chile

Vicente-Pérez, José . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-42
jose.vicente@ua.es
University of Alicante, Alicante, Spainvan den Akker, Marjan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-54

Villa, Silvia . . . . . . . . . . . . . . . . . . . . . . . . . . MD-32, TC-32, TD-32

502

EURO 2024 - Copenhagensilvia.villa@unige.it
Department of Mathematics, MaLGa, università di Genova,
Genova, Italy
Vitali, Sebastiano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-34
sebastiano.vitali@unibg.it
Department of Economics, University of Bergamo, Italy
Vizvari, Bela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-21
vizvaribela@gmail.com
Industrial Engineering, Eastern Mediterranean University,
Mersin 10, Turkey
Vocaturo, Francesca . . . . . . . . . . . . . . . . . . . . . . . . MA-35, TD-35
vocaturo@unical.it
Department of Economics, Statistics and Finance, Universityof Calabria, Arcavacata di Rende (CS), Italy
Voyvoda, Ebru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-22
voyvoda@metu.edu.tr
Department of Economics, Middle East Technical University,
Ankara, Turkey
Wagenaar, Joris . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-13, MA-54
j.c.wagenaar_1@uvt.nl
Econometrics and Operations Research, Tilburg University,
Netherlands
Wakolbinger, Tina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-01
tina.wakolbinger@wu.ac.at
WU (Vienna University of Economics and Business), Vienna,
Austria
Wall, Friederike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-43
friederike.wall@uni-klu.ac.atDept. for Controlling and Strategic Management, AlpenAdria-Universitaet Klagenfurt, Klagenfurt, Austria
Wallenius, Jyrki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-11
jyrki.wallenius@aalto.fi
Information and Service Management, Aalto University
School of Business, Helsinki, Uusimaa, Finland
Walther, Grit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-24
walther@om.rwth-aachen.de
School of Business and Economics, Chair of Operations
Management, RWTH Aachen University, Aachen, Germany
Weber, Christoph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-09
christoph.weber@uni-duisburg-essen.de
University Duisburg-Essen, Essen, Germany
Weber, Gerhard-Wilhelm . . . . . MA-01, TA-06, TB-06, TC-06,
WA-06, WB-06, TC-18, TD-18, WB-20, WC-20, MA-25,
MB-25, MC-25, MD-25, MA-28, MB-28, MC-28, MD-28,
TB-28, TC-28, TD-28, WA-28, WB-28, WC-28, WD-28,
TA-31, TB-31, TC-31, WB-40, WC-40, WD-40
gerhard-wilhelm.weber@put.poznan.pl
Faculty of Engineering Management, Poznan University ofTechnology, Poznan, Poland
Weibezahn, Jens . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-09, WD-09
jew.eco@cbs.dk
Copenhagen School of Energy Infrastructure (CSEI), Copenhagen Business School, Copenhagen, Denmark

SESSION CHAIR INDEX

Weidinger, Felix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-64
felix.weidinger@tu-darmstadt.deTechnical University of Darmstadt, Germany
Weston, Jorge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-53
weston.jorge@gmail.com
Friedrich-Alexander-Universtät Erlangen-Nürnberg, Germany
White, Leroy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-13
l.white2@exeter.ac.uk
University of Exeter Business School, Exeter, United Kingdom
Witt, Jonas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-46
jonas.witt@rwth-aachen.deOperations Research, RWTH Aachen University, Germany
Wolf, Laura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-45
laura.wolf@kit.edu
Institute for Operations Research (IOR), Karlsruhe Instituteof Technology (KIT), Karlsruhe, Baden-Württemberg, Germany
Wrzaczek, Stefan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-33
wrzaczek@iiasa.ac.at
Economic Frontiers, Institute for Applied Systems Analysis(IIASA), Laxenburg, Austria
Xambre, Ana Raquel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-16
raquelx@ua.pt
DEGEIT/CIDMA, University of Aveiro, Aveiro, Aveiro, Portugal
Xie, Fengfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-24
fengfeng.xie@nottingham.edu.cn
Department of Entrepreneurship, Marketing and Management Systems, University of Nottingham Ningbo China,
Ningbo, China
Xie, Jingui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-10
jingui.xie@tum.deTechnical University of Munich, Heilbronn, BadenWuerttemberg, Germany
Xu, Zhou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-29
zhou.xu@polyu.edu.hk
Department of Logistics and Maritime Studies, The Hong
Kong Polytechnic University, Hong Kong, Hong Kong
Xue, Zhaojie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-62
zjxue@szu.edu.cn
Transportation Engineering, Shenzhen University, China
Yang, Xiaoqi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-41
mayangxq@polyu.edu.hk
Department of Applied Mathematics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Ye, Xinyi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-49
xinyi.ye@kcl.ac.uk
Department of Informatics, King’s College London, United
Kingdom

503

SESSION CHAIR INDEXEURO 2024 - Copenhagen

Yearworth, Mike . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-13, MC-13
m.yearworth@exeter.ac.uk
Business School, University of Exeter, Exeter, Devon, United
Kingdom
Yeh, Li-Ting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-48
dow0623@gmail.com
Cooperative Economics and Social Entrepreneurship, Feng
Chia University, Taichung City, Taiwan
You, Jintao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-62
youjintao@163.com
Shenzhen Research Institute of Big Data, China
Yu, Man . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-15
manyu@ust.hk
Hong Kong

Zenios, Stavros A. . . . . . . . . . . . . . . . . . . . . . . . . . . WA-63, WC-63
zenioss@ucy.ac.cy
Durham University and University of Cyprus, Cyprus
Zhang, Yingqian . . . . . . . . . . . . . . . . . . . . MC-03, MD-03, TA-03
yqzhang@tue.nl
Industrial Engineering, TU Eindhoven, Netherlands
Zhao, Yanlu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-52
yanlu.zhao@durham.ac.uk
Durham University, United Kingdom
Zhao, Yingshuai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-07
zhao@wiso.uni-koeln.de
University of Cologne, Köln, Germany

Yuan, Ya-xiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-42
yyx@lsec.cc.ac.cn
Institute of Computational Mathematics and Sci./Eng. Computing, AMSS, Chinese Academy of Sciences, Beijing, China

Zhou, Helper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-06, WC-18
helperzhou@gmail.com
School of Accounting, Economics and Finance, Universityof KwaZulu Natal, Bulawayo, Zimbabwe

Zadnik Stirn, Lidija . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-12
lidija.zadnik@bf.uni-lj.si
Biotechnical Faculty, University of Ljubljana, Ljubljana,
Slovenia, Slovenia

Zhou, Yeqing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-59
y.zhou2@tue.nl
OPAC, Eindhoven University of Technology, Eindhoven,
Netherlands

Zanetti, Filippo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-38
f.zanetti@sms.ed.ac.uk
School of Mathematics, University of Edinburgh, Edinburgh,
United Kingdom

Zhu, Xiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-24
x.zhu@rug.nl
Operations, University of Groningen, Groningen, Groningen,
Netherlands

Zech, Patricia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-64
zech@europa-uni.de
Chair for Supply Chain Management, European University
Viadrina, Frankfurt (Oder), Germany

Zhu, Yongqiu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-54
y.zhu-5@tudelft.nl
Department of Transport and Planning, Delft University ofTechnology, Delft, Netherlands

Zengin, Mehmet Sencer . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-61
mzengin@metu.edu.tr
Industrial Engineering, Middle East Technical University,
Ankara, Turkey

Zopounidis, Constantin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-47
kostas@dpem.tuc.gr
Dept. of Production Engineering and Management, Technical
University of Crete, Chania, Greece504

Author Index
Coelho, Wesley . . . . . . . . . . . . . . . . . . . . . . . MA-42, MC-42
wesley.coelho@pasqal.com
PASQAL, Paris, France
Di Gregorio, Silvia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-04
digregorio@lipn.univ-paris13.fr
University Sorbonne Paris Nord, Paris, France
El Ouardighi, Fouad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-33
elouardighi@essec.edu
ESSEC Business School, Cergy Pontoise, France
Getachew Demissie, Merkebe . . . . . . . . . . . . . . . . . . . . . . . MC-10
merkebe.demissie@ucalgary.ca
University of Calgary, Calgary, Canada
Hicks, Anne . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-10
eagreene@ualberta.ca
university of Alberta, CanadaA, Dhanya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-17
amrita.dhanya@gmail.com
Transfusion Medicine, Amrita Institute of Medical Sciences,
Kochi, Kerala, India
Aakvik, Robin Halseth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-61
robin.h.aakvik@ntnu.no
NTNU, NorwayÁlvarez, J.d. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-22
jhervas@ual.es
Department of Informatics, University of Almeria, Almeria,
Spain, SpainÁlvarez-Martínez, David . . . . . . . . . . . . . . . . . . . . TC-64, WD-64
d.alvarezm@uniandes.edu.co
Industrial Engineering, Universidad de Los Andes, Bogotá,
Colombia
Aazami, Adel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-50
adel.aazami@wu.ac.at
Institute of Transport and Logistics Management, Department of Global Business and Trade, Vienna University of
Economics and Business, Vienna, Austria
Abastante, Francesca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-18
francesca.abastante@polito.it
Urban and Regional Studies and Planning, Politecnico di
Torino, Torino, Italy
Abbasi, Ehsan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-45
ea8092@gmail.com
Amirkabir University of Technology, Iran, Islamic RepublicofAbbaszadehpeivasti, Hadi . . . . . . . . . . . . . . . . . . . . . . . . . . WC-38
h.abbaszadehpeivasti@tilburguniversity.edu
Tilburg University, Tilburg, North Brabant, Netherlands
Abdellaoui, Abdelhakim . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-26
abdelhakim.abdellaoui@polymtl.ca
Magi, Polytechnique Montreal, Canada

Abdelshafy, Ali . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-19
ali.abdelshafy@om.rwth-aachen.de
Chair of Operations Management, RWTH Aachen, Aachen,
NRW, Germany
Abdin, Adam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-28
adam.abdin@centralesupelec.fr
Laboratoire Genie Industriel, CentraleSupelec, Paris, France
Abdolhamidi, Dorsa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-59
dorsa.abdolhamidi@unil.ch
HEC-Department of Operations, University of Lausanne,
Lausanne, Vaud, Switzerland
Abdulaleem, Najeeb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-37
nabbas985@gmail.com
Mathematics, Mahrah University, Al-Mahrah, Yemen,
Yemen
Abdullahi, Hassana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-64
habdullahi@cardiffmet.ac.uk
Cardiff School of Management, Cardiff Metropolitan University, Cardiff, United Kingdom
Abe, Taro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-25
ad24m001@guh.u-hyogo.ac.jp
University of Hyogo, Japan
Abel, Edward . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-06, WA-28
edabelcs@gmail.com
SDU, Denmark
Abry, Patrice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-63
patrice.abry@ens-lyon.fr
Signaux, Système & Physique, Ecole Normale Supérieure
Lyon, France
Absi, Nabil . . . . . . . . . . . . . . . . . . . . . . . . . MD-08, MD-54, WA-60
absi@emse.fr
Ecole des Mines de Saint-Etienne - LIMOS, GARDANNE,
FranceAburto, Luis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-59
luis.aburto@uai.cl
Facultad de Ingeniería y Ciencias, Universidad Adolfo
Ibañez, Santiago, RM, Chile
Accorsi, Luca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-58
luca.accorsi4@unibo.it
DEI, University of Bologna, Bologna, Italy
Acevedo, Pablo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-12
pabloacevedo@uc.cl
Pontificia Universidad Catolica de Chile, Chile
Achamrah, Fatima Ezzahra . . . . . . . . . . . . . . . . . WC-12, TC-26
acha.f.ezzahra@gmail.com
Management School, University of Sheffield, Sheffield,
United Kingdom
Achterberg, Tobias . . . . . . . . . . . . . . . . . . . . . . . . . MB-30, WC-46
achterberg@zib.de
Gurobi, Berlin, Germany

505

AUTHOR INDEXEURO 2024 - Copenhagen

Ackermann, Christian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-54
ackermann@bwl.uni-hildesheim.deOperations Research Group, University of Hildesheim,
Hildesheim, Germany
Ackermann, Heiner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-49
heiner.ackermann@itwm.fraunhofer.de
Department of Optimization, Fraunhofer Institute for Industrial Mathematics ITWM, Kaiserslautern, Germany
Acosta, Rick . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-36
racosta@unimagdalena.edu.co
Facultad de ingeniería, Universidad del Magdalena, Santa
MARTA, Magdalena, Colombia

Logistics and Operations Management, HEC Montréal,
CanadaAdulyasak, Yossiri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-35
yossiri.adulyasak@hec.ca
Gestion des Opérations et de la Logistique, HEC Montréal,
Montréal, QC, CanadaÅkerholm, Ida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-37
akerholmida@gmail.com
Department of Mathematics, Linköping University, Sweden
Afsar, Bekir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-11
bekir.b.afsar@jyu.fi
University of Jyväskylä, Finland

Adamczyk, Bartosz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-20
bartosz.adamczyk@student.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poznan, wielkopolskie, Poland

Afsar, Sezin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-09
afsarsezin@uniovi.es
University of Oviedo, Spain

Adamo, Tommaso . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-64
tommaso.adamo@unisalento.it
Engineering for Innovation, University of Salento, Lecce,
Italy

Agafonov, Artem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-32
agafonov.artem98@gmail.com
Machine Learning, MBZUAI, Abu Dhabi, United Arab Emirates

Adams, Thomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-10
tada955@aucklanduni.ac.nz
New Zealand

Agarwal, Nitin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-07
nxagarwal@ualr.edu
COSMOS Research Center, UA Little Rock, United States

Adan, Ivo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-31, WD-58
i.adan@tue.nl
School of Industrial Engineering, Eindhoven University ofTechnology, Eindhoven, Netherlands

Agarwal, Prateek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-51
prateeka@iisc.ac.in
Civil Engineering, Indian Institute of Science, Bangalore,
Karnataka, India

Adan, Ivo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-40
iadan@tue.nl
Eindhoven, Netherlands, Netherlands

Agatz, Niels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-29
nagatz@rsm.nl
Decision and Information Science, RSM University, Rotterdam, Netherlands

Adewoye, Olabode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-18
boka152002@yahoo.com
Mathematics, Yaba College of Technology, Yaba, Lagos
State, Nigeria
Adhiana, Tigar Putri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-59
tigar.adhiana@liverpool.ac.uk
School of Management, University of Liverpool, Liverpool,
United Kingdom
Adjiman, Claire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-37
c.adjiman@imperial.ac.uk
Chemical Engineering, Imperial College London, London,
United Kingdom
Adler, Nicole . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-55
msnic@huji.ac.il
School of Business Administration, Hebrew University ofJerusalem, Jerusalem, Israel
Adriaens, Peter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-24
adriaens@umich.edu
Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, United States
Adsanver, Birce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-21
birce.adsanver@hec.ca

506

Aggoune, Riad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-42
riad.aggoune@list.lu
Luxembourg Institute of Science and Technology, Luxembourg
Agi, Maher. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-59
maher.agi@rennes-sb.com
Rennes School of Business, Rennes, France
Agra, Agostinho . . . . . . . . . . . . . . . . . . . . . . TA-31, TA-49, TA-62
aagra@ua.pt
Matemática, Universidade de Aveiro, Aveiro, Portugal
Aguayo, Cristian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-19
cristian.aguayo.quintana@ulb.be
Département d’Informatique, Université Libre Bruxelles,
Belgium
Aguirre Rodríguez, Elen Yanina . . . . . . . . . . . . . . TA-06, TD-28
elen.aguirre@unesp.br
São Paulo State University, Guaratinguetá, São Paulo, Brazil
Aguirre Rodríguez, Elias Carlos . . . . . . . . . . . . . . TA-06, TD-28
eliascar2097@gmail.com
São Paulo State University, Guaratinguetá, São Paulo, Brazil

EURO 2024 - Copenhagen

AUTHOR INDEX

Ahat, Betül . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-45, WC-50
betul.ahat@trendyol.com
Data Science, Trendyol, TurkeyAğırman, Aleyna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-15
aleyna.agirman@hotmail.com
Ankara Yıldırım Beyazıt University, Turkey

Ahipasaoglu, Selin . . . . . . . . . . . . . . . . . . . . . . . . . . MA-55, WC-63
s.d.ahipasaoglu@soton.ac.uk
School of Mathematical Sciences, University of Southampton, United Kingdom

Ajayan, Sreesankar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-18
sreesankarajayan@am.amrita.edu
Center for Research in Analytics, Technologies & Education,
Amrita Vishwa Vidyapeetham, Kollam, Kerala, India

Ahipasaoglu, Selin . . . . . . . . . . . . . . . . . . . . . . . . . . MB-59, WD-61
sda1u20@soton.ac.uk
University of Southampton, Winchester, Hampshire, United
Kingdom

Ajith, Anjali . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-17
anjaliajith868@gmail.com
Neonatology, Amrita Institute of Medical Sciences, Kochi,
Kerala, India

Ahmadi Digehsara, Amin . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-35
amin.ahmadidigehsara@ubc.ca
Faculty of Management, The University of British Columbia,
kelowna, BC, Canada

Akartunali, Kerem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-29
kerem.akartunali@strath.ac.uk
Management Science, University of Strathclyde, Glasgow,
United Kingdom

Ahn, Dohyun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-51
dohyun@se.cuhk.edu.hk
Systems Engineering and Engineering Management, The
Chinese University of Hong Kong, Hong Kong

Akbari, Karl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-11
karl.akbari@mail.ntust.edu.tw
Department of Business Administration, National Taiwan
University of Science and Technology, Taipei, Taipei, Taiwan

Ahookhosh, Masoud . . . . . . . . . . . . . . . . . . . . . . . . WA-41, WB-41
masoud.ahookhosh@uantwerp.be
Department of Mathematics, University of Antwerp,
Antwerp, Belgium

Akbarpour, Mina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-64
minak@dtu.dk
Department of Technology, Management and Economics,
Technical University of Denmark, Denmark

Ahuja, Alka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-15
Alka.ahuja@wales.nhs.uk
TEC Cymru, Newport, South Glamorgan, United Kingdom

Akbarzadeh, Babak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-15
babak.akbarzadeh@ugent.be
Economic and Business Administration, University of Ghent,
Gent, Gent, Belgium

Ahumada, Vicente . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-59
vahumada@alumnos.uai.cl
Facultad de Ingeniería y Ciencias, Universidad Adolfo
Ibáñez, Viña del Mar, Valparaíso, Chile

Akbas, Beste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-22, WD-22
beste916@gmail.com
Imperial College London, London, United Kingdom

Ai, Yanfang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-21
aiyanfang@stu.xjtu.edu.cn
School of Management, Xi’an Jiaotong University,
UXBRIDGE, London, United Kingdom

Akcay, Fatih Burak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-28
f.b.akcay@tilburguniversity.edu
Econometrics and Operations Research, Tilburg University,
Tilburg, Netherlands

Aiad, Janis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-42
janis.aiad@polytechnique.edu
Polytechnique, France

Akhavan-Tabatabaei, Raha . . . . . . . . . . . . . . . . . . . . . . . . . WD-18
akhavan@sabanciuniv.edu
Sabanci School of Management, İstanbul, Turkey

Aiche, Avishay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-40
zvishay.aiche@gmail.com
Western Galilee Academic College, Acco, Israel, Israel

Akkerman, Renzo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-23
renzo.akkerman@wur.nl
Operations Research and Logistics, Wageningen University,
Wageningen, Netherlands

Aigner, Kevin-Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-34
kevin-martin.aigner@fau.deFriedrich-Alexander-Universität Erlangen-Nürnberg, Germany
Ainbinder, Inessa . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-49, WC-49
inessaai@jce.ac.il
Industrial Engineering & Management, Azrieli Academic
College of Engineering, Ashdod, Israel
Airola, Antti . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-28
ajairo@utu.fi
University of Turku, Finland

Akpınar, Şener . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-25
sener.akpinar@deu.edu.tr
Industrial Engineering, Dokuz Eylül University, Turkey
Akse, Ruben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-11
ruben.akse@ru.nl
Business Administration, Radboud University, Netherlands
Aksin, Zeynep . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-28
zaksin@ku.edu.tr
Graduate School of Business, Koc University, Istanbul,
Turkey507

AUTHOR INDEXEURO 2024 - Copenhagen

Aksoy, Meltem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-03
meltem.aksoy@tu-dortmund.de
Research Center Trustworthy Data Science and Security,
Dortmund, Germany
Aktas, Dilay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-51, WB-54
dilay.aktas@kuleuven.be
Institute for Mobility - CIB, KU Leuven, Heverlee, Leuven,
Belgium
Aktas, Taha Huzeyfe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-10
t.h.aktas@uva.nl
Business Analytics, Amsterdam Business School, Amsterdam, Netherlands
Al Hariri, Khaled . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-04
khaled.alhariri@bahcesehir.edu.tr
Computer Engineering, Bahçeşehir University, Istanbul,
TurkeyAL-Behadili, Mohanad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-55
mohanad.al-behadili1@port.ac.uk
School of Mathematics and Physics, University Of
Portsmouth, Portsmouth, England, United Kingdom
Al-khateeb, Samer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-07
sameral-khateeb1@creighton.edu
Department of Computer Science, Design and Journalism,
Creighton University, Omaha, Nebraska, United States
Alacevich, Allegra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-18
allegra.alacevich@compagniadisanpaolo.it
Fondazione Compagnia di SanPaolo, Torino, Italy, Italy

Departamento de Industrias, Universidad Tecnica Federico
Santa Maria, Santiago, Región Metropolitana, Chile
Alcántara, Antonio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-19
antalcan@est-econ.uc3m.es
Statistics, University Carlos III, Madrid, Spain
Alcaraz, Javier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-60
jalcaraz@umh.es
Center of Operations Research, Universidad Miguel Hernández de Elche, Elche, Alicante, Spain
Alda, Erik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-48
ealda@marymount.edu
School of Behavioral and Social Sciences, Marymount University, United States
Aldasoro, Unai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-15
unai.aldasoro@ehu.eus
Applied Mathematics, University of the Basque Country(UPV/EHU), Donostia, Basque Country, Spain
Aleandri, Michele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-36
maleandri@luiss.it
Economics and Finance, Luiss University, Rome, Italy
Alegoz, Mehmet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-24
mehmetalegoz@eskisehir.edu.tr
Industrial Engineering, Eskisehir Technical University,
TurkeyAlekseeva, Ekaterina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-42
katerina.alekseeva@gmail.com
EURODECISION, VERSAILLES, France

Alaei Jordehi, Shafagh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-55
shafagh.alaeijordehi@vub.be
Department of Business Technology and OperationsMobilise, Vrije Universiteit Brussel, Hasselt, Limburg, Belgium

Alem, Douglas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-21, TC-21
douglas.alem@ed.ac.uk
Business School, University of Edinburgh, Edinburgh, United
Kingdom

Alagoz, Oguzhan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-17
alagoz@engr.wisc.edu
Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, United States

Aleotti, Stefano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-34, MB-34
saleotti@uninsubria.it
Scienza ed alta tecnologia, Università degli Studidell’Insubria, Como, Italy

Alamatsaz, Kayhan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-51
kayhan.alamatsaz@mail.concordia.ca
Concordia University, Montreal, Quebec, Canada

Alfandari, Laurent . . . . . . . . . . . . . . . . . WC-15, WD-49, MA-52
alfandari@essec.edu
ESSEC Business School, Cergy-Pontoise Cedex, France

Alarcon Ortega, Emilio Jose . . . . . . . . . . . . . . . . . . . . . . . . . TA-64
emilio.jose.alarcon.ortega@univie.ac.at
Business Decisions and Analytics, University of Vienna, Vienna, Vienna, Austria

Algaba, Encarnación . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-36
ealgaba@us.es
Applied Mathematics II, Seville University, Sevilla, Spain

Albarran, Irene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-63
ialbarra@est-econ.uc3m.es
Statistics, University Carlos III of Madrid, Spain
Albert, Laura . . . . . . . . . . . . . . . . . . . . . . . WC-20, TB-28, MA-46
laura@engr.wisc.edu
Industrial & Systems Engineering, University of WisconsinMadison, MADISON, WI, United States
Albornoz, Victor M. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-12
victor.albornoz@usm.cl

508

Algarvio, Hugo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-09
hugo.algarvio@lneg.pt
Lisbon, Lisbon, Portugal
Alguacil, Natalia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-21
natalia.alguacil@uclm.es
University of Castilla-La Mancha, Spain
Alhamwi, Alaa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-53
Alaa.Alhamwi@dlr.deInstitute of Networked Energy Systems, German Aerospace
Center, Oldenburg, Germany

EURO 2024 - Copenhagen

Ali, Sara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-07
up201700364@fe.up.pt
INESC TEC, Faculty of Engineering, University of Porto,
Porto, Porto, Portugal
Alidaee, Bahram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-25
balidaee@bus.olemiss.edu
Marketing, University of Mississippi, University, MS, UnitedStatesAUTHOR INDEX

almouhanna@gmail.com
Department of Mathematics, University of Portsmouth,
Portsmouth, Hampshire, United Kingdom
Aloise, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-61
daniel.aloise@polymtl.ca
Polytechnique Montreal, Canada
Alonso-Ayuso, Antonio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-25
antonio.alonso@urjc.es
Rey Juan Carlos University, Mostoles, Madrid, Spain

Alikhani, Reza . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-34
r.alikhani@montpellier-bs.com
Operation, Information, and Decision, Montpellier Business
School, Montpellier, Select Region, France

Alonso-González, Pablo Jesús . . . . . . . . . . . . . . . . . . . . . . . MB-63
pablo.alonsog@uah.es
Economics, Universidad de Alcalá, Spain

Alkaabi, Alia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-44
aakaabi@transco.ae
Business, ADU, Abu Dhabi, United Arab Emirates

Alonso-Mora, Javier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-01
j.alonsomora@tudelft.nl
Delft University of Technology, Deflt, Netherlands

Alkemade, Floor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-24
f.alkemade@tue.nl
Industrial Engineering and Innovation Sciences, Eindhoven
University of Technology, Eindhoven, Netherlands, Netherlands

Alp, Osman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-07
osman.alp@ucalgary.ca
Haskayne School of Business, University of Calgary, Canada

Alkoby, Shani. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-36
shani.alkoby@gmail.com
Industrial engineering and Management, Ariel University,
Ariel, Israel, Israel
Allahyari, Somayeh . . . . . . . . . . . . . . . . . . . . . . . . . MB-07, MC-29
s.allahyari@bham.ac.uk
Business School, The University of Birmingham, Birmingham, West Midland, United Kingdom
Allaoui, Hamid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-10
hamid.allaoui@univ-artois.fr
University of Artois, ARRAS, France
Allmendinger, Richard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-15
richard.allmendinger@manchester.ac.uk
Alliance Manchester Business School, The University ofManchester, Manchester, United Kingdom
Almada-Lobo, Bernardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-50
almada.lobo@fe.up.pt
INESC-TEC, Faculty of Engineering of Porto University,
Porto, Portugal
Almeder, Christian . . . . . . . . . . . . . . . . . . . . . . . . . . TD-49, WD-64
almeder@europa-uni.de
Chair for Supply Chain Management, European University
Viadrina, Frankfurt (Oder), Germany
Almeida Santos, Artur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-44
arthur.jf.mg@hotmail.com
Universidade Federal de Ouro Preto, Brazil
Almeida, João . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-58
joao.carvalho.almeida@tecnico.ulisboa.pt
CEGIST, University of Lisbon - Instituto Superior Técnico,Carnaxide, Portugal
Almouhanna, Abdullah . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-64

Alptekin, Büşra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-37
alptekinb20@itu.edu.tr
Industrial Engineering PhD program, Istanbul Technical University, İstanbul, Turkey
Alskaif, Tarek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-22
tarek.alskaif@wur.nl
Information Technology, Wageningen University, Wageningen, Netherlands
Altan, Basak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-49
basak.altan@ozyegin.edu.tr
Department of Economics, Ozyegin University, Istanbul,
TurkeyAltay, Nezih . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-18
naltay@depaul.edu
Management, DePaul University, Chicago, Illinois, UnitedStates
Altun, Mehmet Emre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-45
mehmetemre.altun@trendyol.com
Trendyol, Turkey
Alumur, Sibel A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-61
sibel.alumur@uwaterloo.ca
Department of Management Science and Engineering, University of Waterloo, Waterloo, ON, Canada
Alvarez Balanya, Sergio . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-57
sbalanya@amazon.es
Amazon, Spain
Alvarez-Vazquez, Lino J. . . . . . . . . . . . . . . . . . . . . . TA-33, TB-33
lino@dma.uvigo.es
Matematica Aplicada II, Universidade de Vigo, Vigo, Pontevedra, Spain
Alvelos, Filipe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-12, TA-31
falvelos@dps.uminho.pt
Department of Production and Systems, ALGORITMI Re-

509

AUTHOR INDEXEURO 2024 - Copenhagensearch Center / LASI, University of Minho, Braga, Portugal
Alvelos, Helena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-31
helena.alvelos@ua.pt
Degeit / Cidma, University of Aveiro, AVEIRO, Portugal
Alves Beirigo, Breno . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-24
b.alvesbeirigo@utwente.nl
Industrial Engineering and Business Information Systems,
University of Twente, Netherlands
Alves Junior, Paulo Nocera . . . . . . . . . . . . . . . . . . . . . . . . . MC-48
pjnocera@yahoo.com.br
Universidad Católica del Norte (UCN), Escuela de Ingenieríade Coquimbo (EIC), Coquimbo, Chile
Alves, Maria João . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-37
mjalves@fe.uc.pt
Faculty of Economics of University of Coimbra / INESC Coimbra, Coimbra, Portugal
Alvite Pazó, Raúl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-04
raul.alvite@rai.usc.es
University of Santiago de Compostela, Spain
Alvite Pazó, Samuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-04
samuel.alvite@rai.usc.es
University of Santiago de Compostela, Spain

Bayes Business School, City, University of London,SLOUGH, BERKSHIRE, United Kingdom
Amirmoeini, Kamyar . . . . . . . . . . . . . . . . . . . . . . . MA-14, WA-22
kamyar.amirmoeini@hec.ca
Decision Sciences, GERAD, HEC Montreal, Outremont,
Québec, Canada
Amorim, Pedro . . . . . . . . . . . . . . MD-01, MA-50, MB-50, TD-50
amorim.pedro@fe.up.pt
Industrial Engineering and Management, Faculty of Engineering of University of Porto, Porto, Portugal
Amorosi, Lavinia . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-26, WA-37
lavinia.amorosi@uniroma1.it
Statistical Sciences, Sapienza, Rome, Italy
An Impact, Making . . MB-46, MC-46, MD-46, TA-46, TC-46,
TD-46, WA-46, WB-46, WC-46, WD-46
mai@euro-online.org
EURO Practitioners’ Forum, Glasgow, United Kingdom
Anagnostopoulos, Theodoros . . . . . . . . . . . . . . . . . . . . . . . MD-45
theodoros.anagnostopoulos@uniwa.gr
University of West Attica, Greece
Anantharaman, Rahul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-19
rahul.anantharaman@sintef.noGas Technology, SINTEF Energy Research, Norway

Amakor, Augustina Chidinma . . . . . . . . . . . . . . . . . . . . . . . TB-37
augustina.chidinma.amakor@uni-paderborn.de
Computer Science, Universität Paderborn, Paderborn, Germany

Anaya-Arenas, Ana María . . . . . . . . . . . . . . . . . . . . . . . . . . MB-55
anaya-arenas.ana_maria@uqam.ca
Université du Québec à Montréal, Montreal, Quebec, Canada

Amar, A. D. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-08
ad.amar@shu.edu
Management Department, Seton Hall University, South Orange, NJ, United States

Anderluh, Alexandra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-24
alexandra.anderluh@fhstp.ac.at
Institute for Integrated Mobility Research, St. Pölten University of Applied Sciences, St. Pölten, Austria

Amar, Arti . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-08
a.amar2@hud.ac.uk
KTP office, University of Huddersfield, Huddersfield, West
Yorkshire, United Kingdom

Andersen, Anders Reenberg . . . . . . . . . . . . . . . . . . . . . . . . MD-15
arean@dtu.dk
Department of Applied Mathematics and Computer Science,
Technical University of Denmark, Kgs. Lyngby, Capital Region, Denmark

Amaral, Paula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-25
paca@fct.unl.pt
Faculdade de Ciências e Tecnologia, Universidade Nova deLisboa, Caparica, Lisbon, Portugal
Amasyali, Mehmet Fatih . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-03
amasyali@yildiz.edu.tr
Computer Engineering, Yildiz Technical University, Turkey
Amen, Matthias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-59
matthias.amen@uni-bielefeld.de
Chair for Quantitative Accounting & Financial Reporting,
Bielefeld University, Bielefeld, Germany
Amend, Sabine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-20
sabinelistens@gmail.com
University of Applied Sciences Neu-Ulm, Bridgeport, CT,
United States
Aminjarahi, Mohammad . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-15
mohammad.aminjarahi@bayes.city.ac.uk

510

Andersen, Jorgen-Vitting . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-63
jorgen-vitting.andersen@univ-paris1.fr
CNRS, Paris, France
Andersen, Kim Allan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-52
kia@asb.dk
CORAL, Department of Business Studies, Aarhus School ofBusiness, Aarhus V, Denmark
Anderson, Eddie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-22
e.anderson@imperial.ac.uk
Analytics and Operations Management, Imperial College
London, United Kingdom
Andersson Granberg, Tobias . . . . . . . . . . . . . . . . . . . . . . . . MB-10
tobias.andersson.granberg@liu.se
Science and Technology, Linköping University, Norrkoping,
SwedenAndersson, Anders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-62

EURO 2024 - Copenhagen
anders.andersson@vti.se
Vehicle Systems and Driving Simulation, Swedish National
Road and Transport Research Institute (VTI), Linköping,
SwedenAndersson, Henrik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-60
henrik.andersson@ntnu.no
Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology,
Trondheim, Norway
Andersson, Joakim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-50
joakim.andersson.3@volvo.com
SML, Volvo Group, Gothenburg, Sweden
Andjelic, Milica . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-52
andjelic.milica@gmail.com
Department of Mathematics, Kuwait University, Kuwait
André, Eric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-57
eandre@em-lyon.com
Quantitative Finance & Economics, Emlyon Business
School, Ecully, France
Andreani, Roberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-41
andreani@ime.unicamp.br
Departament of Applied Mathematics, State University ofCampinas, Campinas, Sao Paulo, Brazil
Andrei, Alexandru-Victor . . . . . . . . . . . . . . . . . . . . . . . . . . WD-31
andrei1victor23@stud.ase.ro
Bucharest University of Economic Studies, Romania
Andreoulaki, Ioanna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-06
iandreoulaki@epu.ntua.gr
Decision Support Systems Laboratory, School of Electrical& Computer Engineering, National Technical University ofAthens, Athens, Greece
Andrianesis, Panagiotis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-53
panosa@bu.edu
Division of Systems Engineering, Boston University, Brookline, MA, United States
Andris, Jenny . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-23
jenny.andris@tu-clausthal.deInstitute of Management and Economics, Clausthal University of Technology, Clausthal-Zellerfeld, Germany
Aneja, Yash . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-49
aneja@uwindsor.ca
Odette School of Business, University of Windsor, Windsor,
Ontario, Canada
Angelakis, Vangelis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-62
vangelis.angelakis@liu.se
Science and Technology, Linköping University, Norrkoping,
SwedenAngelelli, Enrico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-39
angele@eco.unibs.it
Metodi Quantitativi, University of Brescia, Brescia, Italy
Angilella, Silvia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-47
angisil@unict.it

AUTHOR INDEX

Department of Economics and Business, Univ of Catania,
Catania, Italy
Angrisano, Mariarosaria . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-47
mariarosaria.angrisano@unipegaso.it
Engineering Department, Pegaso Telematic University,
Naples, Italy, Italy
Anita, Stefana-Lucia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-33
stefi_anita@yahoo.com
Octav Mayer Institute of Mathematics of the RomanianAcademy, Iasi, Romania
Aniyappan, Ajitha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-18
ajithamanuajithamanu7688@gmail.com
Infectious Disease, Amrita Institute of Medical Sciences,
Kochi, Kerala, India
Anjos, Miguel . . . . . . . . MD-09, WC-19, WD-19, TB-22, TB-38
anjos@stanfordalumni.org
School of Mathematics, University of Edinburgh, United
Kingdom
Annunziata, Arturo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-12
arturo.annunziata@unifi.it
Information engineering, University of Florence, Italy
Ansari, Sina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-15
sina.ansari@depaul.edu
DePaul University, United States
Ansari-Önnestam, Aban . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-32
aban.ansari-onnestam@liu.se
Mathematics, Linköping University, Sweden
Antczak, Maciej . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-20
mantczak@cs.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poznan, Wielkopolska, Poland
Antczak, Tadeusz. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-41
tadeusz.antczak@wmii.uni.lodz.pl
Faculty of Mathematics and Computer Science, University ofLodz, Lodz, lodzkie, Poland
Antón Maraña, Paula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-15
panton@ubu.es
Applied Economics, University of Burgos, Burgos, Spain,
Spain
Anton-Sanchez, Laura. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-60
l.anton@umh.es
Center of Operations Research, Universidad Miguel Hernández de Elche, Elche, Spain
Antonakopoulos, Kimon . . . . . . . . . . . . . . . . . . . . WA-32, WB-32
kimon.antonakopoulos@epfl.ch
EPFL, Switzerland
Antonelli, Laura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-34
laura.antonelli@icar.cnr.it
Institute for high performance computing and networking(ICAR), Consiglio Nazionale delle Ricerche, Italy
Antonini Alves, Thiago . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-28

511

AUTHOR INDEXEURO 2024 - Copenhagenantonini@utfpr.edu.br
Department of Mechanical Engineering, Federal Universityof Technology - Parana, Ponta Grossa, PR, Brazil
Antunes, Carla . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-47
cmantunes@ualg.pt
MED and University of Algarve, Portugal
Anzenhofer, Fabian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-54
fabian.anzenhofer@wiwi.uni-augsburg.de
Chair of Analytics and Optimization, University of Augsburg, Augsburg, Bayern, Germany
Aouam, Tarik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-21
tarik.aouam@ugent.be
Department of Business Informatics and Operations Management, Ghent University, Gent, Belgium
Aouam, Tarik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-58, TA-62
Tarik.AOUAM@um6p.ma
Center for Sustainable Operations and Logistics in Africa,
Mohammed VI Polytechnic University, Morocco
Aparicio, Juan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-48, MB-48
j.aparicio@umh.es
Center of Operations Research, Miguel Hernandez University of Elche, Elche, Alicante, Spain
Aprahamian, Hrayer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-27
hrayer@tamu.edu
Industrial and Systems Engineering, Texas A&M University,
College Station, TX, United States
Arabmaldar, Aliasghar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-48
a.arabmaldar@herts.ac.uk
Business Analytics and Systems, University of Hertfordshire,
Hatfield, Hertfordshire, United Kingdom
Aragón Artacho, Francisco Javier . . . . . . . . . . . . . . . . . . WB-42
francisco.aragon@ua.es
Mathematics, University of Alicante, Alicante, Spain

Kingdom
Arcidiacono, Sally Giuseppe . . . . . . . . . . . . . . . . . . . . . . . . MA-44
s.arcidiacono@unict.it
Department of Economics and Business, University of Catania, Acireale, Italy
Arda, Yasemin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-10
yasemin.arda@uliege.be
HEC Liège - Management School of the University of Liège,
Liège, Belgium
Ardestani-Jaafari, Amir . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-35
amir.ardestani@ubc.ca
Management, UBC, Canada
Ardizzoni, Stefano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-18
stefano.ardizzoni@unipr.it
Department of Engineering and Architecture, University ofParma, Italy
Arellano, Jose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-14
josearellanoralero@gmail.com
Universidad de Castilla-La Mancha, Spain
Arias, Pol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-03
pol.arias5@gmail.com
Germany
Arias-Osorio, Javier. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-58
jearias@uis.edu.co
School of Industrial and Business Studies, Universidad Industrial de Santander, Bucaramanga, Santander, Colombia
Arican, Erkut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-04
erkut.arican@bau.edu.tr
Bahcesehir University, Turkey
Arıkan, Emel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-23
emel.arikan@wu.ac.at
Department of Information Systems and Operations, WU Vienna University of Economics and Business, Vienna, Austria

Araz, Ozgur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-61
oaraz2@unl.edu
Supply Chain Management and Analytics, University of Nebraska Lincoln, NE, United States

Arizti, Agustín . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-55
agustin.arizti@gmail.com
Universidad de la República, Uruguay

Arbi, Siwar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-60
siwar.arbi@imt-atlantique.fr
IMT Atlantique, France

Armani, Mohammed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-13
moarmani@outlook.com
KNUST, Ghana

Archankul, Arnon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-33
arnon.archankul@york.ac.uk
Department of Mathematics, University of York, York,
United Kingdom

Arneric, Josip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-57
jarneric@efzg.hr
Faculty of Economics and Business, University of Zagreb,
Zagreb, Croatia

Archetti, Claudia . . . . TC-29, WC-35, MA-52, MB-52, TD-52,
WD-58, MA-64
archetti@essec.edu
Department of Information Systems, Decision Sciences andStatistics, ESSEC Business school, Cergy, France

Arntzen, Rebekka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-10
rebekka.arntzen@cwi.nl
Centrum Wiskunde & Informatica, Amsterdam, Netherlands

Archibald, Thomas . . . . . . . . . . . . . . . . . . . . . . . . . . TB-59, MC-61
t.archibald@ed.ac.uk
Business School, University of Edinburgh, Edinburgh, United

512

Aronshtam, Lior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-52
liorar1@sce.ac.il
Software Engineering, Shamoon College of Engineering,
Ashdod, Israel

EURO 2024 - Copenhagen
Arruda, Edilson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-55
e.f.arruda@southampton.ac.uk
Department of Decision Analytics and Risk, University ofSouthampton, Southampton, Hampshire, United Kingdom
Artinger, Florian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-11
artinger@berlin-international.deBusiness Administration, Berlin International University ofApplied Sciences, Berlin, Berlin, Germany
Arvanitis, Stelios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-63
stelios@aueb.gr
Athens University of Economics and Business, Greece
Arvy, Pierre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-19
pierre.arvy@artelys.com
Artelys, France
Asadi, Amin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-25
amin.asadi@utwente.nl
Industrial Engineering & Management (IEBIS), Universityof Twente, Enschede, Netherlands
Ascencio, Luis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-62
lascencio@miuandes.cl
Facultad de Ingenieria y ciencias aplicadas, Universidad delos Andes, Las Condes, Region Metropolitana, Chile
Asgari, Erfan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-59
asgarie@excelia-group.com
Excelia Business School, France
Asgharieh Ahari, Sahand . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-55
s.asgharieh.ahari@rug.nl
Operations, University of Groningen, Groningen, Netherlands
Asheim, Leif-Jarle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-12
Leif-Jarle.Asheim@nibio.no
Norwegian Institute of Bioeconomy Research, Norway
Asmuss, Svetlana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-33
svetlana.asmuss@lu.lv
Department of Mathematics, University of Latvia, Riga,
LatviaAssis, Arineia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-29
arineiana@gmail.com
DEP/DAA, UFSCAR/IFG, Senador Canedo - GO, Goiás,
BrazilAstle, William J. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-15
wja24@cam.ac.uk
MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
Astorino, Annabella . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-06
annabella.astorino@dimes.unical.it
DIMES, University of Calabria, Rende, Italy
Astudillo, Alejandro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-30
alejandro.astudillovigoya@kuleuven.be
Department of Mechanical Engineering, KU Leuven, Leuven, Vlaams-Brabant, Belgium

AUTHOR INDEX

Atan, Zumbul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-31, MB-40
z.atan@tue.nl
Technical University of Eindhoven, Netherlands
Atashi Khoei, Arsham . . . . . . . . . . . . . . . . . . . . . . . TA-49, MA-64
aak258@bath.ac.uk
School of Management, University of Bath, Bath, United
Kingdom
Atasoy, Bilge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-24, TD-62
b.atasoy@tudelft.nl
Maritime and Transport Technology, Delft University ofTechnology, Delft, Netherlands
Atefi, Reza . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-29
atefi@mail.um.ac.ir
Department of Industrial Engineering, Ferdowsi Universityof Mashhad, Mashhad, Iran, Islamic Republic of
Athanasakis, Kostas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-47
kathanasakis@uniwa.gr
Department of Public Health Policy, University of West Attica, Athens, Greece
Attanasio, Matteo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-45
matteo.attanasio@phd.unipd.it
Department of Political Science, Law and International Studies (SPGI), University of Padua, Padua, Italy
Aubert, Alice H. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-11
aube@zhaw.ch
Institute of Natural Resource Sciences, ZHAW Zurich University of Applied Sciences, Wädenswil, Switzerland
Aujol, Jean-François . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-34
Jean-Francois.Aujol@math.u-bordeaux.fr
IMB, Université de Bordeaux, France
Ausloos, Marcel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-63
ma683@leicester.ac.uk
School of Business, Leicester, United Kingdom
Avanzini, Elbio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-12
elavanzini@uc.cl
Industrial and Systems Engineering, Pontificia Universidad
Católica de Chile, Santiago, RM, Chile
Avataneo, Federico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-63
federico.avataneo@intesasanpaolo.com
Credit Rating Models, Intesa Sanpaolo spa, Torino, Italy,
Italy
Avci, Harun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-22, TA-22
harun.avci@u.northwestern.edu
Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois, United States
Avci, Mualla Gonca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-52
gonca.yunusoglu@deu.edu.tr
Industrial Engineering, Dokuz Eylul University, Turkey
Avci, Mustafa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-52
mustafa.avci@bakircay.edu.tr
Industrial Engineering, Izmir Bakircay University, İzmir,
Turkey513

AUTHOR INDEXEURO 2024 - Copenhagen

Avgerinos, Ioannis . . . . . . . . . . . . . . . . . . . . . . . . . . WA-25, MD-49
iavgerinos@aueb.gr
Department of Management Science and Technology, Athens
University of Economics and Business, Athens, Greece
Avila, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-09
daniel.avila@uclouvain.be
UCLouvain, Belgium
Avilés-Sacoto, Estefania . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-48
eaviles@ups.edu.ec
Environmental Engineering, Universidad Politécnica Salesiana, Cuenca, Azuay, Ecuador
Avilés-Sacoto, Sonia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-48
sonia_v_a_s@hotmail.com
Industrial Engineering, Universidad San Francisco de Quito
USFQ, Quito, Pichincha, Ecuador
Avolio, Matteo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-60
matteo.avolio@unical.it
Department of Mathematics and Computer Science, University of Calabria, Rende, Italy
Avrachenkov, Konstantin . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-40
konstantin.avratchenkov@inria.fr
INRIA, Sophia Antipolis, France
Ay, Atilla . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-44
ayax@jmu.edu
James Madison University, United States
Ayari, Salah . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-06
saleh.ayari@gmail.com
Université de Lille, Aubervilliers, France
Ayatollahi, Asef . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-18
asef.ayatollahi@polimi.it
Deaprtment of Architecture and urban studies (Dastu), Politecnico di Milano, Italy
Aydın, Büşra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-37, WD-52
busra.aydin@tupras.com.tr
TUPRAS, İstanbul, Turkey
Ayfantopoulou, Georgia . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-18
gea@certh.gr
Hellenic Institute of Transport (ΗΙΤ), Centre for Research
and Technology Hellas (CERTH), Thessaloniki, Greece
Aytac, Eren Berk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-27
aytacerenberk@gmail.com
Turkeyazaria.shany@gmail.com
Rotman School of Management, University of Toronto,
Toronto, ON, Canada
Azizi, Nader . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-35, MC-61
nader.azizi@ed.ac.uk
University of Edinburgh, United Kingdom
Azizoglu, Meral . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-10
ma@metu.edu.tr
Department of Industrial Engineering, Middle East Technical
University, Ankara, Turkey
Azzamouri, Ahlam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-24
ahlam.azzamouri@emines.um6p.ma
EMINES-School of Industrial Management, Mohammed VI
Polytechnic University, Morocco
Azzamouri, Bassma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-24
bassma.azzamouri@um6p.ma
Africa Business School, Mohammed VI Polytechnic University, Morocco
B, Nila . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-58
nb720101@student.nitw.ac.in
Mathematics, NIT Warangal, Warangal, Telangana, India
Baak, Werner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-25
werner.baak@uni-passau.deBusiness Decisions and Data Science, University of Passau,
Germany
Baals, Julian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-50
julian.baals@uni-jena.de
Friedrich Schiller University Jena, Germany
Baas, Stef . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-10
s.p.r.baas@utwente.nl
University of Twente, Netherlands
Babu, Binil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-18
binilbabu92@gmail.com
Infectious Disease, Amrita Institute of Medical Sciences,
Kochi, Kerala, India
Bachler, Sebastian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-07
Sebastian.Bachler@uibk.ac.atUniversity of Innsbruck, Austria
Bachmat, Eitan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-54
ebgantenbein@gmail.com
IsraelAzadi, Majid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-48
m.azadi@aston.ac.uk
Mount Waverley, Victoria, United Kingdom

Backe, Stian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-14, WA-19
stian.backe@ntnu.no
Department of industrial economics and technology management, The Norwegian University of Science and Technology,
Trondheim, Sør-Trøndelag, Norway

Azani, Maryam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-11
m.azani@tue.nl
OPAC, Technical university of Eindhoven, Veldhoven,
Netherlands

Badiee, Aghdas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-43
aghdas.badiee@tuni.fi
Edinburgh business school, Heriot-watt University, Edinburgh, United Kingdom

Azaria, Shany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-06, WB-12

Badosa, Jordi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-22

514

EURO 2024 - Copenhagen
jordi.badosa@lmd.polytechnique.fr
LMD-IPSL, Ecole Polytechnique, Institut Polytechnique deParis, ENS, PSL Research University, Sorbonne Université,
CNRS, France
Badunenko, Oleg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-48
oleg.badunenko@port.ac.uk
University of Portsmouth, Porthsmouth, United Kingdom
Bae, Young-Mok . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-08
youngmok92@gmail.com
Korea, Republic ofBaek, Jun-Geol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-04, WD-40
jungeol@korea.ac.kr
School of Industrial Management Engineering, Korea University, Seoul, Korea, Republic of
Baesens, Bart . . . . . . . . . . . . . . . . . . . . . . . WA-27, MB-31, WB-31
bart.baesens@econ.kuleuven.ac.be
Decision
Sciences
andInformation
Mangement,
K.U.Leuven, Leuven, Leuven, Belgium
Baesens, Bart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-08
bart.baesens@kuleuven.be
Leuven, Belgium
Bag, Raul Cristian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-31
raulbag1996@gmail.com
Bucharest University of Economic Studies, Romania
Bahadır, Çağrı . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-52
cagri.bahadir@tupras.com.tr
Tüpraş, Turkey
Bahadornia, Mostafa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-58
mostafa.bahadornia@uhasselt.be
Logistics Research Group, Hasselt University, Hasselt, Limburg, Belgium
Bahiense, Laura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-55
laura@cos.ufrj.br
Federal University of Rio de Janeiro, Brazil
Bahn, Olivier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-14, WA-22
olivier.bahn@hec.ca
GERAD and Decision Sciences, HEC Montréal, Montréal,
Qc, Canada
Bahtiyar, Irem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-26
irem.bahtiyar@bilkent.edu.tr
Bilkent University, Ankara, Select Region, Turkey
Baigorri, Miguel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-07
miguel.baigorri@unavarra.es
Statistics, informatics and Mathematics, Public University ofNavarra, Pamplona, Navarra, Spain
Baione, Fabio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-63
fabio.baione@uniroma1.it
Università La Sapienza, Italy
Baiou, Mourad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-12
baiou@isima.fr
CNRS, University Clermont II, AUBIERE, France

AUTHOR INDEX

Bajovic, Dragana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-34
dbajovic@uns.ac.rs
Faculty of Technical Sciences, Univ. of Novi Sad, Serbia
Bakal, İsmail Serdar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-61
isbakal@metu.edu.tr
Industrial Engineering, Middle East Technical University,
Ankara, Turkey
Baker, Pete . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-13
pbaker@cgdev.org
Center for Global Development, London, United Kingdom
Bakker, Hannah . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-12, TD-61
hannah.bakker@kit.edu
Institute of Operations Research, Karlsruhe Institute of Technology, Germany
Bakker, Sam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-10
samuel.bakker@ugent.be
Business Informatics & Operations Managament, Ghent University, Belgium
Bakker, Steffen . . . . . . . . . . . . . . . . . . . . . . MD-14, TD-56, MA-62
steffen.bakker@ntnu.no
Industrial Economics and Technology Management, NTNU,
Trondheim, Trøndelag, Norway
Balcik, Burcu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-21
burcu.balcik@ozyegin.edu.tr
Ozyegin University, X, Turkey
Baldacci, Roberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-58
rbaldacci@hbku.edu.qa
College of Science and Engineering, Hamad Bin Khalifa
University, Doha, Qatar
Baldemor, Milagros . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-16
milagros_baldemor@yahoo.com.phMathematics, PSAI, San Fernando City, Philippines
Baldomero-Naranjo, Marta . . . . . . . . . . . . . . . . . . WA-12, TA-61
marta.baldomero@uca.es
Estadística e Investigación Operativa, Universidad de Cádiz,Puerto Real, Cádiz, Spain
Baldua, Madhusudan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-51
madhusudanb@iisc.ac.in
Civil Engineering, Indian Institute of Science, India
Balezentis, Tomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-48
tomas@laei.lt
Lithuanian Centre for Social Sciences, Lithuania
Ballestin, Francisco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-10
francisco.ballestin@uv.es
Matematicas para la Economia, Universidad de Valencia, Valencia, Spain
Baltas, Alexandros . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-26
baltasalx@aueb.gr
Athens University of Economics and Business, Greece
Bamford, Jim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-08

515

AUTHOR INDEXEURO 2024 - Copenhagenj.bamford@yorksj.ac.uk
York Business School, York St John University, York, North
Yorkshire, United Kingdom

michele.barbato@unimi.it
Dipartimento di Informatica “Giovanni degli Antoni”, Università degli Studi di Milano, Milano, Italy

Bandeira, Jorge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-56
jorgebandeira@ua.pt
University of Aveiro, Aveiro, Portugal

Barber, Josep Xavier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-48
xbarber@umh.es
Center of Operations Research (CIO), Miguel Hernández
University of Elche, Elche, Alicante, Spain

Banert, Sebastian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-32
sebastian.banert@control.lth.se
Department of Automatic Control, Lund University, Lund,
SwedenBang, Rasmus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-12
Rasmus.Bang@snf.no
SNF - Centre for Applied Research at NHH, Norway
Bangerter, Elise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-58
elise.bangerter@unifr.ch
DS&OR group, University of Fribourg, Fribourg, Fribourg,
Switzerland
Bansal, Vishal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-62
vishalb@iimcal.ac.in
Operations Management, Indian Institute of Management
Calcutta, Kolkata, West Bengal, India
Bao, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-21
s2435163@ed.ac.uk
Business School, University of Edinburgh, Edinburgh, Scotland, United Kingdom
Bao, Yunhong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-17
yunhong.bao@duke.edu
Duke University, Durham, North Carolina, United States
Bao, Yun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-12
yb10@alumnes.udl.cat
University of Lleida, Spain
Bao, Yun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-12
yunbao1108@gmail.com
Mathematics, University of Lleida, Lleida, Spain
Baradei, Anibal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-22
anibal.baradei@phd.unipi.it
Department of Energy, Systems, Territory and Construction
Engineering, University of Pisa, Bologna, Bologna, Italy
Baratto, Marie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-10, TD-29
marie.baratto@uliege.be
Operations, HEC - Management School, University of Liège,
Liège, Belgium
Barbagallo, Annamaria . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-43
annamaria.barbagallo@unina.it
Department of Mathematics and Applications "R. Caccioppoli", Universitá di Naples Federico II, Napoli, Italy

Barbieri, Sebastiano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-47
sebastiano.barbieri@polito.it
DIST, Politecnico di Torino, Turin, Italy
Barbosa, Aglaucibelly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-19
cibelly_maciel@hotmail.com
Department of Production Engineering, Federal Universityof Paraiba, João Pessoa, PB, Brazil
Barbosa, Flávia . . . . . . . . . . . . . . . . . . . . . . MC-19, TD-20, TD-53
flaflabarbosa0@gmail.com
Porto, Inesc Tec, Portugal
Barbosa-Povoa, Ana WB-07, MC-10, MB-24, MD-24, MA-35,
WD-37
apovoa@tecnico.ulisboa.pt
Departamento de Engenharia e Gestão, Instituto Superior
Técnico, Universidade de Lisboa, Lisboa, Portugal
Bardhan, Amit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-55
amit-bardhan@fms.edu
Faculty of Management Studies, University of Delhi, India
Barfod, Michael Bruhn . . . . . . . . . . . . . . . . . . . . . . TB-18, WB-37
mbba@dtu.dk
Department of Management Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark
Bargetto, Roberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-26
roberto.bargetto@polito.it
DIGEP, Politecnico di Torino, Torino, Italy
Baringo, Luis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-21
luis.baringo@uclm.es
Electrical Engineering, Universidad de Castilla-La Mancha,
Ciudad Real, Spain
Barkel, Mathijs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-10, TC-25
m.j.barkel@tilburguniversity.edu
Department of Econometrics and Operations Research,
Tilburg University, Tilburg, Netherlands
Barker, Kash . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-21
kashbarker@ou.edu
School of Industrial and Systems Engineering, University ofOklahoma, Norman, OK, United States
Barkhausen, Max . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-34
barkhausen@tulana.detulanā, Germany

Barbati, Maria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-44
maria.barbati@unive.it
Department of Economics, Ca’ Foscari University of Venice,
Italy

Barner, Lukas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-09
lukas.barner@tu-berlin.de
TU Berlin / DIW Berlin, Berlin, Berlin, Germany

Barbato, Michele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-25

Barone, Simona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-08

516

EURO 2024 - Copenhagen

AUTHOR INDEX

simona.barone@unito.it
Department of Management, University of Turin, Turin, Italy,
Italy

Industrial Engineering and Applied Mathematics Department, Ecole des Mines de Saint- Etienne, SAINT-ETIENNE,
Loire, France

Barrales-Araneda, Alex. . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-31
abarrales@ubiobio.cl
University of Bío-Bío, Chile

Basso, Franco . . . . . . . . . . . . . . . . . . . . . . . MC-12, TB-23, WB-56
francobasso@gmail.com
Pontificia Universidad Católica de Valparaíso, Chile

Barrans, Simon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-08
s.m.barrans@hud.ac.uk
Huddersfield, W Yorkshire, United Kingdom

Basso, Saverio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-26
saverio.basso@supsi.ch
Idsia - Usi/supsi, Switzerland

Barrera, Ivan Felipe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-47
ifelipebj@hotmail.com
Department of Applied Statistics and Operations Research
and Quality, Universitat Politècnica de València, Valencia,
Valencia, Spain

Basteck, Christian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-36
christian.basteck@wzb.eu
WZB Berlin, Berlin, Berlin, Germany

Barreto, Sérgio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-48
sbarreto@ua.pt
ISCA - Higher Institute for Accounting and Administration,
University of Aveiro - GOVCOPP, Aveiro, Portugal
Barro, Diana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-02
d.barro@unive.it
Dept. of Economics, University of Venice, Venezia, Italy
Barron, Yonit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-23
ybarron@ariel.ac.il
Industrial & Management, Ariel University, Haifa, Israel
Barros, Anne . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-35
anne.barros@centralesupelec.fr
CentraleSupélec, France
Barry, Alpha Saliou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-29
alpha-2-saliou.barry@polymtl.ca
GERAD, Polytechnique, Montreal, QC, Canada
Barsing, Prashant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-45
prashant.barsing@nmims.edu
School of Business Management, NMIMS University,
Raigarh, MAHARASHTRA, India
Bartesaghi, Paolo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-63
paolo.bartesaghi@unimib.it
Dipartimento di Statistica e Metodi Quantitativi, Universityof Milano Bicocca, Milano, Italy

Basten, Rob . . . . . . . . . . . . . . . . . . . . . . . . . MB-11, WA-11, TD-34
r.j.i.basten@tue.nl
School of Industrial Engineering, Eindhoven University ofTechnology, Eindhoven, Netherlands
Bastert, Oliver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-30
oliverbastert@fico.com
FICO, Darmstadt, Germany
Bastianutti, Riccardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-06
riccardo.bastianutti@unisalento.it
Economics, Unisalento, Casasrano, Italy, Italy
Bataillou Almagro, Marc . . . . . . . . . . . . . . . . . . . WA-57, WB-57
maalmagr@amazon.es
Amazon, Luxembourg
Batalha, Mario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-29
dmob@ufscar.br
Federal University of Sao Carlos, Brazil
Batenburg, Nena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-21
batenburg@math.ku.dk
Mathematical Sciences, University of Copenhagen, Denmark
Batta, Rajan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-29, WA-58
batta@buffalo.edu
Industrial and Systems Engineering, University at Buffalo(SUNY), Buffalo, New York, United States
Battarra, Maria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-52
m.battarra@bath.ac.uk
Bath, United Kingdom

Barto, Jorrit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-13
barto.jorrit@gmail.com
Econometric Institute, Erasmus University Rotterdam, Rotterdam, Netherlands

Batton-hubert, Mireille . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-15
batton@emse.fr
Ecole des mines de Saint Etienne, France

Barz, Christiane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-59
christiane.barz@business.uzh.ch
Institute of Business Administration, University of Zurich,
Zurich, Switzerland

Batur, Demet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-39
dbatur@unl.edu
Supply Chain Management and Analytics, University of
Nebraska-Lincoln, Lincoln, NE, United States

Bashiri, Mahdi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-23
Mahdi.Bashiri@coventry.ac.uk
Coventry University, United Kingdom

Baucum, Matt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-31
mbaucum@business.fsu.edu
Florida State University, United States

Basirati, Mohadese . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-15
mohadese.basirati@emse.fr

Bayindir, Z. Pelin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-24
bpelin@metu.edu.tr

517

AUTHOR INDEXEURO 2024 - Copenhagen

Department of Industrial Engineering, Middle East Technical
University, Ankara, Turkey

Department of Management, Information and Production Engineering, University of Bergamo, Italy

Baykal-Gursoy, Melike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-39
gursoy@rutgers.edu
Industrial and Systems Engineering, Rutgers University, Piscataway, NJ, United States

Beck, J. Christopher . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-30
jcb@mie.utoronto.ca
Mechanical & Industrial Engineering, University of Toronto,
Toronto, Ontario, Canada

Bélanger, Valérie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-21, TD-21
valerie.3.belanger@hec.ca
Logistics and Operations management, HEC Montréal,
CanadaBeck, Tammy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-61
tbeck8@unl.edu
Management, University of Nebraska, Lincoln, Nebraska,
United States

Bayrakçı, Batuhan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-58
batuhan.bayrakci@trendyol.com
Technology, Trendyol, Turkey

Beck, Yvonne . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-13
yvonne.beck@isi.fraunhofer.deSustainability Innovations and Policy, Fraunhofer ISI, Karlsruhe, Germany

Bayraksan, Guzin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-35
bayraksan.1@osu.edu
Integrated Systems Engineering, Ohio State University,
Columbus, OH, United States

Becker, John . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-58
becker6@buffalo.edu
University at Buffalo, United States

Bayram, Armagan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-50
armagan@umich.edu
University of Michigan Dearborn, United States

Becker, Thijs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-26
thijs.becker@vito.be
VITO, Belgium

Bayri, Soukaina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-54
soukaina.bayri@uhasselt.be
Logistics, Hasselt University, Hasselt, Limburg, Belgium

Becker, Tristan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-60, TD-64
msci@tu-dresden.de
TU Dresden, Germany

Bärmann, Andreas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-34
andreas.baermann@fau.de
FAU Erlangen-Nürnberg, Erlangen, Germany

Bedogni, Luca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
luca.bedogni@unimore.it
University of Modena and Reggio Emilia, Italy

Bazgan, Cristina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-37
cristina.bazgan@dauphine.fr
Lamsade, Université Paris Dauphine, Paris, 75, France

Begen, Mehmet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-16, WD-61
mbegen@ivey.uwo.ca
Ivey Business School, Western University, London, ON,
CanadaBazotte Corgozinho, Maria Carolina . . . . . . . . . . . . . . . . . TC-35
maria-carolina.bazotte-corgozinho@polymtl.ca
Mathematics and Industrial Engineering Department, Polytechnique Montreal, Montréal, Quebec, Canada

Behjat, Laleh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-10, TB-53
laleh@ucalgary.ca
Electrical and Computer Engineering, University of Calgary,
Calgary, Canada

Bešinović, Nikola . . . . . . . . . . . . . TA-54, WD-54, MD-55, TA-55
nikola.besinovic@tu-dresden.de
Chair of Railway Operations, Faculty of Transport and Traffic Sciences, Technical University of Dresden, Dresden, Germany

Behzad, Banafsheh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-15
banafsheh.behzad@csulb.edu
California State University, Long Beach, Long Beach, California, United States

Bea Martínez, Manuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-13
mbeamartinez@gmail.com
I-catalist S.l., Granada, Granada, Spain

Bekci, Recep . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-50
rybekci@gmail.com
Management Sciences, University of Waterloo, Canada

Bearzotti, Lorena . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-56, TB-62
lorena.bearzotti@pucv.cl
Escuela de Ingeniería de Transporte, Pontificia Universidad
Católica de Valparaíso, Valparaíso, Valparaíso, Chile

Bekius, Femke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-07, TC-11
femke.bekius@ru.nl
Nijmegen School of Management, Radboud University, Nijmegen, Netherlands

Beato-Moreno, Antonio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-41
beato@us.es
Estadística e Investigación Operativa, Universidad de Sevilla,
Sevilla, Sevilla, Spain

Bekker, Bram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-38
b.bekker@tudelft.nl
DIAM, Delft University of Technology, Delft, Zuid-Holland,
Netherlands

Beatrici, Paolo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-35
paolo.beatrici@unibg.it

Bekker, René . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-10, WB-34
r.bekker@vu.nl

518

EURO 2024 - Copenhagen
Vrije Universiteit, Amsterdam, Netherlands
Bektas, Tolga . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-25, TD-58
T.Bektas@liverpool.ac.uk
University of Liverpool Management School, University ofLiverpool, Liverpool, United Kingdom
Belacel, Nabil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-28
nabil.belacel@nrc.gc.ca
Data Science for Complex Systems |, Digital Technologies
Research CenterNational Research Council-ICT, Ottawa, Ontario, Canada

AUTHOR INDEX

sbenitez@est-econ.uc3m.es
University Carlos III of Madrid, Spain
Bendapudi, Perraju . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-17
bendapudip@gmail.com
Neonatology, Amrita Institute of Medical Sciences, Kochi,
Kerala, India
Bendersky, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-20
michael.bendersky@gmail.com
Holon Institute of Technology, Israel

Beljadid, Ahmed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-02
ahmed.beljadid@polymtl.ca
Polytechnique Montréal, Montréal, Québec, Canada

Bengtsson, Ivar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-10
ivarben@kth.se
Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden

Belkhiria, Fares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-28
fares.belkhiria@mcgill.ca
McGill University, Canada

Benidis, Konstantinos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-57
benidisk@gmail.com
Amazon, Berlin, Germany

Bellavia, Stefania . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-34, MB-34
stefania.bellavia@unifi.it
Dipartimento di Ingegneria Industriale, Universita di Firenze,
Firenze, Italy

Benjabeur, Sami . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-26
sbenjabeur@univ-catholyon.fr
ESDES, France

Belton, Ian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-43
i.belton@strath.ac.uk
Strathclyde Business School, Management Science Department, United Kingdom
Ben Ahmed, Mohamed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-62
bamohamed.enit@hotmail.fr
Industrial Economics and Technology Management, Norwegian University of Science and Technology, Trondheim,
NorwayBen Ameur, Hachmi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-51
hechba@yahoo.fr
Finance, INSEEC Business School, PARIS, aucune, France
Ben Safta, Khalil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-14
khalil.ben.safta@iee.fraunhofer.de
Network planning and operation, Fraunhofer IEE, Kassel,
Germany
Ben Yahia, Sarra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-63
benyahiasarra9@gmail.com
CES : Centre d’économie de la Sorbonne, Université Paris 1
Panthéon Sorbonne, France

Bennaceur, Mehdi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-57
mehdibnc@amazon.com
Amazon, Canada
Bennell, Julia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-07, WB-54
j.bennell@leeds.ac.uk
Leeds University Business School, University of Leeds,
Leeds, West Yorkshire, United Kingdom
Bennouna, Mohammed Ali . . . . . . . . . . . . . . . . . . . . . . . . . MC-02
BENNOUNA.Mohammed@ocpgroup.ma
OCP Group, Casablanca, Morocco
Benoist, Thierry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-30
tbenoist@localsolver.com
Hexaly, PARIS, France
Benoit, Dries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-31, WB-31
dries.benoit@ugent.beGhent University, Gent, Belgium
Bera, Uttam Kumar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-16
bera_uttam@yahoo.co.in
Mathematics, NIT Agartala, Jirania, Tripura, India

Ben, Hiroshi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-41
ben0101@keio.jp
Keio University, Japan

Beraldi, Patrizia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-35
patrizia.beraldi@unical.it
Department of Mechanical, Energy and Management Engineering, University of Calabria, Rende (CS), ITALY, Italy

Benabbou, Loubna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-26
loubna_benabbou@uqar.ca
UQAR, Canada

Berardinelli, Giulia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
gberardi@unimore.it
University of Modena and Reggio Emilia, Italy

Benati, Stefano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-28
stefano.benati@unitn.it
School of International Studies, University of Trento, Trento,
Italy

Berbeglia, Gerardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-59
gberbeglia@gmail.com
Melbourne Business School, carlton, victoria, Australia

Benítez-Peña, Sandra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-27

Berbig, Dominik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-39
dominik.berbig@hs-pforzheim.de519

AUTHOR INDEXEURO 2024 - Copenhagen

Pforzheim University, Germany
Berg Rosendal, Mathias . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-21
mberos@dtu.dk
Department of Management, Technology and Economics,
Technical University of Denmark, Kongens Lyngby, Denmark
Bergantinos, Gustavo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-36
gbergant@uvigo.es
Statisitics and O. R., University of Vigo, Vigo, Pontevedra,
Spain
Bergmann, Anton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-14
anton.bergmann@iee.fraunhofer.de
Network planning, Fraunhofer, Kassel, Germany
Bergmann, Ronny . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-30, TB-41
ronny.bergmann@ntnu.no
Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Sør-Trøndelag,
NorwayBerk, Erhan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-17
eberk@hho.msu.edu.tr
National Defense University, İstanbul, Turkey
Berkelmans, Guus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-28
guus.berkelmans@gmail.com
Stochastics group, CWI, Amsterdam, Netherlands
Berkemeier, Manuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-37
manuelbb@math.upb.de
Computer Science, Paderborn University, Paderborn, NRW,
Germany
Berkhout, Joost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-35
j2.berkhout@vu.nl
Vrije Universiteit Amsterdam, Netherlands
Berling, Peter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-50
lars.p.berling@ntnu.no
Industrial Economics and Technology Management, Norwegian University of Science and Technology, Trondheim,
NorwayBernardelli, Ambrogio Maria . . . . . . . . . . . . . . . . . . . . . . . MC-26
ambrogiomaria.bernardelli01@universitadipavia.it
Dipartimento di Matematica "Felice Casorati", Universitàdegli Studi di Pavia, Pavia, Italy
Bernardino, Raquel . . . . . . . . . . . . . . . . . . . . . . . . . WA-58, TA-64
rbernardino@iseg.ulisboa.pt
Iseg, Cemapre, Ul, Portugal
Bernstein, Fernando . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-40
fernando@duke.edu
Duke University, United States
Bertacco, Livio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-63
LivioBertacco@fico.com
FICO Optimization, a, Italy
Bertazzi, Luca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-29
luca.bertazzi@unibs.it

520

Dept. of Economics and Management, University of Brescia,
Brescia, Italy
Berthold, Timo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-30, WC-46
timoberthold@fico.com
Fair Isaac Deutschland GmbH, Frankfurt (Main), Germany
Bertogna, Marko . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
marko.bertogna@unimore.it
University of Modena and Reggio Emilia, Italy
Bertok, Botond . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-49
botondbertok@gmail.com
Széchenyi István University, Hungary
Bertrand, Yannis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-27
yannis.bertrand@kuleuven.be
Belgium
Bertsch, Valentin . . . . . . . . . . . . MA-09, MC-09, TC-09, MA-21
valentin.bertsch@ee.rub.de
Chair of Energy Systems and Energy Economics, RuhrUniversität Bochum, Bochum, Germany
Bertsch, Valentin . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-09, TD-09
valentin.bertsch@ee.ruhr-uni-bochum.de
Chair of Energy Systems & Energy Economics, RuhrUniversität Bochum, Bochum, Germany
Bertucci, Juan Pablo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-56
j.p.bertucci@tue.nl
Mechanical Engineering, Eindhoven University of Technology, Eindhoven, North Brabant, Netherlands
Besik, Deniz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-12, TC-24
dbesik@richmond.edu
Analytics & Operations, University of Richmond, Richmond,
VA, United States
Besiou, Maria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-01
maria.besiou@the-klu.org
Kuehne Logistics University, Hamburg, Germany
Besis, Apostolos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-49
abesis@elval.com
R&D, Elval, aluminium rolling division of ElvalHalcor SA,
Oinofyta, Viotia, Greece
Bessa, Catarina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-07
ana.bessa@dem.uc.pt
Department of Mechanical Engineering, University Of Coimbra, Coimbra, Coimbra, Portugal
Bessemans, Pauline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-29
pauline.bessemans@uliege.be
QuantOM - HEC, University of Liège, Liège, Belgium
Bestuzheva, Ksenia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-04
bestuzheva@zib.de
Department of Mathematical Optimization, Zuse Institute
Berlin, Germany
Bettinelli, Andrea. . . . . . . . . . . . . . . . . . . . . . . . . . . MB-29, MA-54
andrea.bettinelli@optit.net
Optit, Italy

EURO 2024 - Copenhagen

Betts, John . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-28
john.betts@monash.edu
Data Science and AI, Monash University, Australia
Beutler, Philipp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-11
philipp.beutler@eawag.ch
Environmental Social Sciences (ESS), Eawag: Swiss Federal
Institute of Aquatic Science and Technology, Duebendorf,
Switzerland
Bhargavi Katikala, Geetha . . . . . . . . . . . . . . . . . . . . . . . . . MD-50
geethabhargavi.katikala@gmail.com
Godrej Capital Ltd, India
Bharti, Bharti . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-34
b.bharti@uva.nl
Korteweg-de Vries Institute for Mathematics, University of
Amsterdam, Amsterdam, Amsterdam, Netherlands
Bhasin, Shallu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-13
shallu.bhasin@mech.iitd.ac.in
IIT Dellhi, India
Bhattacharyya, Shouvik . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-64
phd21007@iiml.ac.in
Decision Sciences, IIM Lucknow, Lucknow, Uttar Pradesh,India
Bhowmik, Dipayan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-16
dipayanbhowmik11@gmail.com
Mathematics, National Institute of Technology Agartala,
Agartala, Tripura, India
Bhulai, Sandjai . . . . . . . . . . . . . . . MB-04, TB-23, TD-28, TA-64
s.bhulai@vu.nl
Department of Mathematics, Vrije Universiteit Amsterdam,
Amsterdam, Netherlands
Biancalana, Davide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-63
davide.biancalana@uniroma1.it
Sapienza University of Rome, 00100, Italy, Italy
Biancalani, Francesco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-63
francesco.biancalani@imtlucca.it
AXES, IMT School for Advanced StudiesLucca, Lucca, Italy
Bianchi Marzoli, Pietro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-19
p.bianchimarzoli@gmail.com
ETH Zurich, Switzerland
Bianchi, Guillermo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-60
guillermo.bianchi@alumnos.ucn.cl
Ingeniería Industrial, Universidad Católica del Norte, Antofagasta, Antofagasta, Chile
Biancifiori, Sara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-18
sara.biancifiori@iusspavia.it
PhD SDC, IUSS Pavia, Pavia, Italy, Italy

AUTHOR INDEX

alexanderbiele@fico.com
FICO, Hamburg, Germany
Bienstock, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-04
dano@columbia.edu
Columbia University, New York, United States
Bier, Vicki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-11
vmbier@gmail.com
University of Wisconsin-Madison, Madison, Wisconsin,
United States
Bierlaire, Michel . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-51, MD-59
michel.bierlaire@epfl.ch
Enac Inter Transp-or, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
Bigi, Giancarlo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-40
giancarlo.bigi@unipi.it
Dipartimento di Informatica, Universita’ di Pisa, Pisa, Italy
Bilgen, Bilge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-12
bilge.bilgen@deu.edu.tr
Industrial Engineering, Dokuz Eylul University, Izmir,
TurkeyBiliciler, Gunes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-24
gbiliciler@ku.edu.tr
College of Administrative Sciences and Economics, Koç University, Turkey
Billaut, Jean-Charles . . . . . . . . . . . . . . . . . . . . . . . . . TD-24, TA-25
jean-charles.billaut@univ-tours.fr
University of Tours, Tours, France
Bilsel, Murat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-22
murat.bilsel@marmara.edu.tr
Industrial Engineering, Marmara University, Istanbul, Istanbul, Turkey
Binder, Leon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-07, MD-11
leon.binder@th-deg.de
Deggendorf Institute of Technology, Germany
Birbil, Ilker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-27
birbil@ese.eur.nl
Econometric Institute, Erasmus University Rotterdam, Rotterdam, Netherlands, Netherlands
Birdal, Tolga . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-04
tbirdal@imperial.ac.uk
Computing, Imperial College London, London, London,
United Kingdom
Birkelbach, Felix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-19
felix.birkelbach@tuwien.ac.at
Institute of Energy Systems and Thermodynamics, TU Wien,
Wien, Austria

Bidkhori, Hoda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-15
hbidkhor@gmu.edu
George Mason University, United States

Biró, Péter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-01
biro.peter@krtk.hu
Centre for Economic and Regional Studies, and Corvinus
University of Budapest, Budapest, Hungary

Biele, Alexander . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-30

Birolini, Sebastian . . . . . . . . . . . . . . . . . . . WA-10, TD-35, TD-56

521

AUTHOR INDEXEURO 2024 - Copenhagensebastian.birolini@unibg.it
Department of Management, Information, and Production
Engineering, University of Bergamo, Italy
Bischi, Aldo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-22
aldo.bischi@unipi.it
Department of Energy, Systems, Territory and Construction
Engineering, University of Pisa, Pisa, Italy
Bisello, Adriano . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-18, MD-18
adriano.bisello@eurac.edu
Senior Researcher, Institute for Renewable Energy - Eurac
Research, Bolzano, Italia, Italy
Bisgaard, Albert Skovgaard . . . . . . . . . . . . . . . . . . . . . . . . MD-40
askbi@dtu.dk
Denmark
Biswas, Debajyoti . . . . . . . . . . . . . . . . . . . . . . . . . . WC-15, MA-52
debajyoti.biswas@essec.edu
Management Information Systems, University College
Dublin, Dublin, Ireland
Bitan, Yuval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-53
ybitan@bgu.ac.il
Ben Gurion University of the Negev, Israel
Bitsis, Kyriakos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-48
kmpitsis@uom.edu.gr
Business Administration, University of Macedonia, Greece
Bedowska-Sójka,˛
Barbara . . . . . . . . . . . . . . . . . . . . . . . . . . MB-08
barbara.bedowska-sojka@ue.poznan.plDepartment of Econometrics, Poznan University of Economics and Business, Poznan, Poland
Bjørn, Anders. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-09
anbjo@dtu.dk
Technical University of Denmark, Denmark
Bjørndal, Endre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-09
endre.bjorndal@nhh.no
Department of Business and Management Science, NHH
Norwegian School of Economics, Bergen, Norway
Bjørndal, Mette . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-09
mette.bjorndal@nhh.no
Department of Business and Management Science, NHH
Norwegian School of Economics, Bergen, Norway
Bjørnerem, Erik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-09
erik.bjornerem@gmail.com
Electric Energy, NTNU, Trondheim, Norway
Black, Ben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-35
b.black1@lancaster.ac.uk
STOR-i Centre for Doctoral Training, Lancaster University,
Lancaster, United Kingdom

Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Copenhagen, Denmark
Blandamour, Nicolas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-10
nicolas.blandamour@gmail.com
Hexaly, France
Blank, Julian. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-11
blankjul@msu.edu
Michigan State University, East Lansing, Michigan, UnitedStatesBlasco, Hélène . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-25
helene.blasco@univ-tours.fr
Université de Tours, INSERM, France
Blazewicz, Jacek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-20
jblazewicz@cs.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poznan, Poland
Blazquez, Carola . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-18
cblazquez@unab.cl
Department of Engineering Science, Universidad Andres
Bello, Santiago, Chile
Blokhin, Yehor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-04
e.blokhin23@gmail.com
Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden
Blunck, Henning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-46
henning.blunck@dhl.com
DHL Data & Analytics, Germany
Boccardo, Piero . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-47
piero.boccardo@polito.it
Interuniversity Department of Regional and Urban Studies
and Planning, Politecnico di Torino, Turin, Italy
Bockel-Rickermann, Christopher . . . . . . . . . . . . . . . . . . . MC-31
christopher.rickermann@kuleuven.be
Faculty of Economics & Business, KU Leuven, Belgium
Böcker, Benjamin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-09
benjamin.boecker@uni-due.deHouse of Energy Markets and Finance, University ofDuisburg-Essen, Essen, North Rhine-Westphalia, Germany
Bodas, Shreehari . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-40
s.a.bodas@uva.nl
Korteweg-de Vries Institute for Mathematics, University of
Amsterdam, Amsterdam, Netherlands
Boduroglu, Ilkay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-63
ilkay.boduroglu@riskoptima.com.tr
R&D, Riskoptima Wealth Tech Corp., Istanbul, N/A, Turkey

Blaise, Léa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-60
lblaise@hexaly.com
Hexaly, Paris, France

Boeckmann, Jan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-02
jan.boeckmann@jku.at
Institute for Business Analytics and Technology Transformation, Johannes-Kepler University Linz, Linz, Austria

Blanco, Ignacio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-62
igbl@dtu.dk

Boelens, Femke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-60
f.a.boelens@utwente.nl

522

EURO 2024 - Copenhagen

AUTHOR INDEX

University of Twente, Netherlands
Bogaert, Matthias . . . . . . . . . . . . . . . . . . . WB-26, TA-28, WB-31
matthias.bogaert@ugent.beMarketing, Innovation and Organization, Ghent University,
Ghent, Oost-Vlaanderen, Belgium
Bogdan, Marcel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-43
marcel.bogdan@umfst.ro
Department of Industrial Engineering and Management,
George Emil Palade University of Medicine, Phaamacy, Science, and Technology of Targu Mures, Targu Mures, Romania
Bogetoft, Peter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-27, WA-43
pb.eco@cbs.dk
Copenhagen Business School, Frederiksberg, Denmark
Bogs, Stephan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-19
stephan.bogs@om.rwth-aachen.de
School of Business and Economics, Chair of Operations
Management, RWTH Aachen, Aachen, Germany
Bökler, Fritz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-37
fritz.boekler@tu-dortmund.deComputer Science, Algorithm Engineering, TU Dortmund,Dortmund, Germany
Bolia, Nomesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-18
nomesh@gmail.com
Department of Mechanical Engineering, Indian Institute ofTechnology (IIT), Delhi, New Delhi, Delhi, India
Bolia, Nomesh . . . . . . . WA-13, WB-45, WC-51, TC-53, WC-53
nomesh@mech.iitd.ac.in
Department of Mechanical Engineering, Indian Institute ofTechnology (IIT), Delhi, New Delhi, Delhi, India
Bolovaneanu, Vlad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-08
vlad.bolovaneanu@ase.ro
Statistics and Econometrics, Bucharest Academy of Economic Studies, Bucharest, Bucharest, Romania
Bolusani, Suresh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-30
bolusani@zib.de
Zuse Institute Berlin, Germany
Bomze, Immanuel . . . . . . . . . . . . MB-01, MA-32, TA-35, TB-52
immanuel.bomze@univie.ac.atDept. of Statistics and OR, University of Vienna, Vienna,
Austria
Bonaccorsi, Giovanni . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-14
giovanni.bonaccorsi@polimi.it
Management Engineering, Politecnico di Milano, Milano,
Italy
Bonasera, Lorenzo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-26
lorenzo.bonasera01@universitadipavia.it
Dipartimento di Matematica "Felice Casorati", Universitàdegli Studi di Pavia, Pavia, Italy
Bonettini, Silvia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-34
silvia.bonettini@unimore.it
Università degli studi di Modena e Reggio Emilia, Italy

Bonifaci, Pietro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-44, WB-45
pbonifaci@iuav.it
Department of Architecture, University IUAV of Venice,Venice, Italy
Bonomelli, Marco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-51
marco.bonomelli@unibg.it
Management, Economics and Quantitative Methods, University of Bergamo, Bergamo, Italy
Bonomi, Valentina . . . . . . . . . . . . . . . . . . . . . . . . . . MC-10, MC-35
valentina.bonomi@unibs.it
CEGIST, Instituto Tècnico Lisboa, Portugal
Borchert, Philipp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-31
philipp.borchert@ieseg.fr
IESEG School of Management, France
Boresta, Marco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-03, TB-35
marco.boresta@iasi.cnr.it
Istituto di analisi dei sistemi ed informatica "Antonio Ruberti" (IASI), Consiglio Nazionale delle Ricerche (CNR),
Roma, Italy, Italy
Borgonovo, Emanuele . . . . . . . . . . . . . . . . . . . . . . MD-08, MB-11
emanuele.borgonovo@unibocconi.it
Decision Sciences and ELEUSI Research Center, Bocconi
University, Milano, Italy
Borm, Peter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-36
P.E.M.Borm@uvt.nl
Tilburg University, Tilburg, Netherlands
Borndörfer, Ralf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-60
borndoerfer@zib.de
Optimization, Zuse-Institute Berlin, Berlin, Germany
Borrego-Marín, María . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-36
mbmarin@us.es
Applied Economics III, University of Seville, Seville, Spain
Bortolomiol, Stefano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-58
stefano.bortolomiol@optit.net
Optit.srl, Italy
Borzée, Joke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-17
joke.borzee@vlerick.com
Operations management, KU Leuven & Vlerick Business
School, Belgium
Bos, Hayo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-10
hayobos@gmail.com
University of Twente, Netherlands
Bosch Pons, Antoni . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-22
abosch@irec.cat
Energy Systems Analytics Research Group, Institut de Recerca en Energia de Catalunya, Spain
Bosch, Robbert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-23
r.r.bosch@utwente.nl
BMS, University of Twente, Enschede, Netherlands
Boschetto, Davide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-42

523

AUTHOR INDEXEURO 2024 - Copenhagen

davide.boschetto@ensta-paris.fr
ENSTA, Paris, France
Botte, Marilisa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-55, WA-55
marilisa.botte@unina.it
Department of Architecture, Federico II University of Naples,
Naples, Italy
Bottero, Marta . . . . . . . MB-13, MD-18, MC-47, TB-47, TC-47
marta.bottero@polito.it
Urban and Regional Studies and Planning, Politecnico di
Torino, Torino, Italy
Botterud, Audun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-22
audunb@mit.edu
Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, UnitedStatesBoucekkine, Raouf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-33
raouf.boucekkine@rennes-sb.fr
Rennes School of Business, France

Department of Mathematics and Computer Science, Eindhoven University of Technology, EINDHOVEN, Netherlands
Boyle, Laura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-10
laura.boyle@qub.ac.uk
Queen’s University Belfast, United Kingdom
Boysen, Nils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-50, WB-64
nils.boysen@uni-jena.de
Lehrstuhl für ABWL/ Operations Management, FriedrichSchiller-Universität Jena, Jena, Germany
Boz, Semih . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-64
sboz@umass.edu
Operations Management, University Of Massachusetts
Amherst, Northampton, MA, United States
Bozos, Konstantinos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-59
k.bozos@lubs.leeds.ac.uk
Accounting & Finance, University of Leeds, Leeds, United
Kingdom

Boucherie, Richard . . . . . . . . . . . . . . . . . . . . . . . . . WD-10, TB-60
r.j.boucherie@utwente.nl
Stochastich Operations Research, University of Twente, Enschede, Netherlands

Braekers, Kris . . . . . . WA-15, MC-54, WB-54, WC-54, TA-58,
TD-61, MA-64
kris.braekers@uhasselt.beResearch Group Logistics, Hasselt University, Hasselt, Belgium

Bouchery, Yann . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-24
yann.bouchery@kedgebs.com
Center of Excellence in Supply Chain (CESIT), Kedge Business School, France

Brailsford, Sally . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-17
s.c.brailsford@soton.ac.uk
Southampton Business School, University of Southampton,
Southampton, United Kingdom

Bouilhaguet, Frédéric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-55
Frederic.bouilhaguet@xops-aero.com
IRIT, INP Toulouse, France

Bramstoft, Rasmus . . . . . . . . . WC-09, WD-09, MA-19, MB-21
rabpe@dtu.dk
Technical University of Denmark, Kongens Lyngby, Denmark

Boujrad, Mounir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-57
boujrad@amazon.lu
Luxembourg
Bouman, Paul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-28, MA-46
bouman@ese.eur.nl
Econometric Institute, Erasmus University Rotterdam, Rotterdam, Zuid-Holland, Netherlands
Bouroubi, Sadek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-25
bouroubis@yahoo.fr
Université de USTHB, Algeria
Bourreau, Eric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-42
eric.bourreau@lirmm.fr
LIRMM, Montpellier University, Montpellier, France
Bouvier, Louis . . . . . . . . . . . . . . . . . . . . . . MC-02, WD-27, WA-46
louis.bouvier@enpc.fr
CERMICS, Ecole des Ponts ParisTech, France
Bovet Emanuel, Peter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-20
peter.bovet-emanuel@fhs.se
Departement of War Studies, Swedish Defence University,
stockholm, Sweden
Boxma, Onno . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-40
o.j.boxma@tue.nl

524

Branda, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-35
branda@karlin.mff.cuni.cz
Charles University, Prague 2, Czech Republic
Brandão, FIlipe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-30
fdabrandao@ampl.com
AMPL, Portugal
Brandenburg, Marcus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-23
marcus.brandenburg@hs-flensburg.de
Supply Chain Management, Flensburg University of Applied
Sciences, Germany
Brander, Matthew. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-09
matthew.brander@ed.ac.uk
University of Edinburgh Business School, United Kingdom
Brandstetter, Christof . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-61
christof.brandstetter@jku.at
Institute of Business Analytics and Technology Transformation, Johannes Kepler University Linz, Austria
Braun, Kristin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-34
kristin.braun@fau.deFriedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany

EURO 2024 - Copenhagen
Braun, Niklas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-61
niklas.braun@kit.edu
Institute for Industrial Production (IIP), Karlsruhe Institute ofTechnology (KIT), Germany
Bravo, Cristian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-31
cbravoro@uwo.ca
Department of Statistical and Actuarial Sciences, Western
University, London, Ontario, Canada
Bréal, Jean-Baptiste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-53
jean-baptiste.breal@edf.fr
SYSTEME, EDF lab’ Paris-Saclay, Palaiseau, France, France
Brazil, Marcus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-25
brazil@unimelb.edu.au
Dept. Electrical and Electronic Engineering, University of
Melbourne, Melbourne, Victoria, Australia
Breder, Marco Sebastian . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-09
marco.breder@wiwinf.uni-due.de
Chair for Management Science and Energy Economics, University of Duisburg-Essen, Essen, Germany
Brederveld, Marieke. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-23
marieke.brederveld@wur.nl
Netherlands
Breen, Liz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-21
l.breen@bradford.ac.uk
School of Pharmacy and Medical Sciences, University ofBradford, United Kingdom
Bregar, Andrej . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-47
andrej.bregar@informatika.si
Informatika, Maribor, Slovenia

AUTHOR INDEX

Brihaye, Thomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-09
thomas.brihaye@umons.ac.be
UMONS, Belgium
Brilli, Andrea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-32
brilli@diag.uniroma1.it
Department of Computer Control and Management Engineering "A. Ruberti", Sapienza University of Rome, Roma,
RM, Italy
Briones, Jamaica . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-13
jambriones@nus.edu.sg
National University of Singapore, Singapore, Singapore, Singapore
Brito Oliveira, Beatriz . . . . . . . . . . . . . . . . TB-28, TA-29, MB-59
beatriz.oliveira@fe.up.pt
INESC TEC, Faculty of Engineering, University of Porto,
Porto, Portugal, Portugal
Brockmann, Fabian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-56
fabian.brockmann@nhh.no
Business and Management Science, Norwegian School of
Economics (NHH), Bergen, Vestland, Norway
Brodnicke, Linda . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-19, WC-38
lbrodnicke@ethz.ch
Mechanical and Process Engineering, ETH Zurich, Zürich,
Switzerland
Broekmeulen, Rob . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-50
r.a.c.m.broekmeulen@tue.nl
OPAC, TU Eindhoven, Eindhoven, -, Netherlands
Broihan, Justine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-30
jbroihan@gams.com
GAMS, Germany

Brendstrup, Andreas Sølling . . . . . . . . . . . . . . . . . . . . . . . . WB-37
andreas@brendstrup.dk
Technology, Management and Economics, Technical University of Denmark, Copenhagen N, Denmark

Brosi, Prisca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-11
prisca.brosi@klu.or
Kühne Logistics University, Germany

Breuer, Wolfgang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-59
wolfgang.breuer@bfw.rwth-aachen.de
RWTH Aachen, Aachen, Deutschland, Germany

Brotcorne, Luce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-22
luce.brotcorne@inria.fr
INRIA, Villeneuve d’Ascq, France

Brezina, Ivan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-53
ivan.brezina@euba.sk
Department of Operations Research and Economics, University of Economics in Bratislava, Bratislava, Slovakia

Brouer, Berit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ME-01
berit.brouer@maersk.com
Maersk, Copenhagen, Denmark

Brice, Sarie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-18
BriceSN@cardiff.ac.uk
School of Mathematics, Cardiff University, Cardiff, Cardiff,
United Kingdom
Briceño-Arias, Luis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-42
luis.briceno@usm.cl
Matemática, Universidad Técnica Federico Santa María, Santiago, Chile
Brieditis, Ludwig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-50
ludwig.brieditis@iml.lth.se
Mechanical Engineering Sciences, Lund University, Sweden

Brozova, Helena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-34
brozova@pef.czu.cz
Dept. of Systems Engineering, Czech University of Life Sciences Prague, Prague 6 - Suchdol, Czech Republic
Bruck, Bruno . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-52
bruno.bruck@ci.ufpb.br
Federal University of Paraiba, Joao Pessoa, PB, Brazil
Bruck, Bruno . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-35, WA-52
bruno.p.bruck@gmail.com
Universidade Federal da Paraíba, Brazil
Brunelli, Matteo . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-13, MC-44
matteo.brunelli@unitn.it

525

AUTHOR INDEXEURO 2024 - Copenhagen

University of Trento, Italy
Brunetti, Diego . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-16
diego.brunetti@gmail.com
IC/UBA, Argentina
Bruni, Maria Elena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-60
mariaelena.bruni@unical.it
Department of Mechanical, Energy and Management Engineering, unical, cosenza, italy, Italy
Bruninx, Kenneth . . . . . . . . . . . . . . . . . . . . TB-03, TD-09, MD-14
k.bruninx@tudelft.nl
TU Delft, Delft, Netherlands
Brunner, Jens O. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-15
jotbr@dtu.dk
Department of Technology, Management, and Economics,
Technical University of Denmark (DTU), Denmark
Brunner, Jens . . . . . . . . . . . . . . . . . . . . . . . TC-15, WD-15, MD-24
jotbr@dtu.dk
Department of Technology, Management, and Economics,
Technical University of Denmark, Kgs. Lyngby, Capital Region, Denmark
Bruno, Giuseppe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-61
giuseppe.bruno@unina.it
Dipartimento di Ingegneria Industriale, Università Federico
II di Napoli, Napoli, IT, Italy
Bubba, Tatiana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-34
tab73@bath.ac.uk
University of Bath, United Kingdom
Buccini, Alessandro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-34
alessandro.buccini@unica.it
Università degli Studi di Cagliari, Italy
Buch, Valdemar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-37
valdemarbuch@gmail.com
Technology, Management and Economics, Technical University of Denmark, København NV, Copenhagen, Denmark
Buchheim, Christoph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-25
christoph.buchheim@tu-dortmund.deFakultät für Mathematik, Technische Universität Dortmund,Dortmund, Germany
Budak, Gercek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-15
gbudak@ybu.edu.tr
Industrial Engineeing, Ankara Yıldırım Beyazıt University,
Ankara, Outside US, Turkey
Budkina, Natalja . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-33
natalja.budkina@rtu.lv
Institute of Applied Mathematics, Riga Technical University,
Riga, Latvia
Bui, Hoa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-04
hoa.bui@curtin.edu.au
Curtin Centre for Optimisation and Decision Science, Curtin
University, East Victoria Park, WA, Australia
Buijs, Ruurd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-23

526

rjwb@cwi.nl
Stochastics, CWI, Amsterdam, Netherlands
Buisman, Marjolein . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-13
marjolein.buisman@whu.edu
Production Management, WHU - Otto Beisheim School ofManagement, Vallendar, Germany
Bukhsh, Zaharah . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-03, TA-03
z.bukhsh@tue.nl
Industrial Engineering, TU Eindhoven, Netherlands
Bula, Inese. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-36
inese.bula@lu.lv
Department of Mathematics, University of Latvia, and Institute of Mathematics and Computer Science of University ofLatvia, Riga, LatviaBulhões, Teobaldo. . . . . . . . . . . . . . . . . . . WA-52, MC-58, WA-58
tbulhoes@ci.ufpb.br
Universidade Federal da Paraíba, João Pessoa, Paraíba, Brazil
Buliga, Andrei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-27
abuliga@unibz.it
Computer Science, Free University of Bozen-Bolzano, Italy
Bullich-Massagué, Eduard . . . . . . . . . . . . . . . . . . . . . . . . . . MB-14
eduard.bullich@upc.edu
Centre d’Innovació Tecnològica en Convertidors Estàtics
i Accionaments (CITCEA), Universitat Politècnica deCatalunya - BarcelonaTech (UPC), Barcelona, Catalonia,
Spain
Bullock, Andrew . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-55
andrew.bullock@port.ac.uk
School of Mathematics and Physics, University ofPortsmouth, Portsmouth, United Kingdom
Bun, Nathan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-33
nathananthonybun@gmail.com
Econometrics and Operations Research, Tilburg University,
Tilburg, Brabant, Netherlands
Bunn, Derek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-09, MD-22
DBunn@london.edu
London Business School, London, United Kingdom
Buratto, Alessandra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-33
buratto@math.unipd.it
Department of Mathematics, University of Padova, Padova,
Italy
Burgard, Jan Pablo . . . . . . . . . . . . . . . . . . . . . . . . . . MB-28, TA-28
burgardj@uni-trier.de
Trier University, Germany
Burger, Katharina . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-13, MB-16
katharina.burger@ucl.ac.uk
The Bartlett School of Sustainable Construction, UCL, London, United Kingdom, United Kingdom
Burgherr, Peter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-44, TA-44
peter.burgherr@psi.chLaboratory for Energy Systems Analysis, Paul Scherrer Institut (PSI), Villigen, Switzerland

EURO 2024 - Copenhagen

Burinskiene, Aurelija . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-45
aurelija.burinskiene@vilniustech.lt
Business Management Faculty, Vilnius Gediminas Technical
University, Vilnius, Lithuania
Burmeister, Sascha C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-19
sascha.burmeister@upb.de
Department of Management Information Systems, Paderborn
University, Paderborn, Germany

AUTHOR INDEX

Applied Economics Mathematic, University Malaga, Malaga,
Spain
Caballini, Claudia . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-62, TD-62
claudia.caballini@polito.it
Department of Environmental Engineering , Land and Infrastrucures, Politecnico di Torino, Torino, Italy
Cabrera, Diego . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-18
diego.cabrera@usach.cl
University of Santiago of Chile, Chile

Burmeister, Yannick . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-56
burmeister@verkehr.tu-darmstadt.de
Railway Engineering, Technical University of Darmstadt,
Darmstadt, Germany

Cacchiani, Valentina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-54
valentina.cacchiani@unibo.it
DEI, University of Bologna, Bologna, Italy

Burnecki, Krzysztof . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-02
krzysztof.burnecki@pwr.edu.pl
Faculty of Pure and Applied Mathematics, Wroclaw University of Science and Technology, Wrocław, Poland

Cadarso, Luis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-29, MB-56
luis.cadarso@urjc.es
European Institute for Aviation Training and Accreditation,
Rey Juan Carlos University, Fuenlabrada, Madrid, Spain

Buscher, Udo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-54, WD-60
udo.buscher@tu-dresden.deIndustrial Management, TU Dresden, Dresden, Germany

Caelli, Terry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-20
terry.caelli@gmail.com
Electrical and Electronic Engineeering, University of Melbourne, Parkville, Victoria, Australia

Büsing, Christina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-61
buesing@combi.rwth-aachen.de
Lehr- und Forschungsgebiet Kombinatorische Optimierung,
RWTH Aachen University, Aachen, Nordrhein-Westfalen,
Germany
Bustamante Faúndez, Pamela Alejandra . . . . . WA-36, MC-61
pebustamante@uc.cl
Department of Industrial and Systems Engineering, Pontificia
Universidad Católica de Chile, Santiago, Santiago, Chile
Buter, Robin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-31
r.buter@utwente.nl
Industrial Engineering & Business Information Systems,
University of Twente, Netherlands
Butler, Andrew . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-63
ad.butler@mail.utoronto.ca
ReSolve Asset Management, Canada
Butun, Cihan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-55
c.butun@lboro.ac.uk
Loughborough Business School, Loughborough University,
Loughborough, United Kingdom
Buyukdeveci, Ozge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-58
ozge.buyukdeveci@solvoyo.com
Industrial Engineering, Dokuz Eylul, Izmir, Buca, Turkey
Byrne, Dáire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-09
daire.byrne@ucdconnect.ie
College of Business, University College Dublin, Dublin 4,
IrelandC, Rajendran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-49
craj@iitm.ac.in
IITMadras, Chennai, TN, India
Caballero, Rafael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-53
rafael.caballero@uma.es

Caetano, Joyce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-55
joyceazevedo@pet.coppe.ufrj.br
Engenharia de Transportes, Universidade Federal do Rio deJaneiro (UFRJ), Rio de Janeiro, Rio de Janeiro, Brazil
Cai, Jianfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-23
caijf@nwpu.edu.cn
Northwestern Polytechnical University, China
Cai, Yue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-39
yue.cai@rug.nl
Operations, University of Groningen, Netherlands
Cairo, Mariagrazia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-37
mariagrazia.cairo@uniroma1.it
Statistical Sciences, Sapienza University of Rome, Rome,
Italy, Italy
Cajochen, Anna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-11
anna.cajochen@eawag.ch
Environmental Social Sciences (ESS), Eawag: Swiss Federal
Institute of Aquatic Science and Technology, Duebendorf,
ZH, SwitzerlandÇakan, Müge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-23
hcakan@mail.uni-mannheim.de
University of Mannheim, Mannheim - Schwetzingerstadt,
Baden-Württemberg, GermanyÇakir, Esra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-47
esrckr_@hotmail.com
Galatasaray Univeristy, TurkeyÇakırgil, Seray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-49
seraycakirgil@gmail.com
Industrial Engineering, TOBB University of Economics andTechnology, Ankara, Turkey
Calamita, Alice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-52

527

AUTHOR INDEXEURO 2024 - Copenhagen

alice.calamita@modelite.tech
Modelite, Italy
Calatroni, Luca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-34
calatroni@i3s.unice.fr
I3S - CNRS/Université Côte d’Azur, Nice, France
Calò, Lorenzo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-53
lorenzo.calo@unisalento.it
Department of Economic Sciences, Università del Salento,
Italy
Caldeira, Ken . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-14
kcaldeira@carnegiescience.edu
Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, United States
Caliskan-Demir, Melisa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-04
melisacaliskan@aydin.edu.tr
Industrial Engineering, Istanbul Aydin University, Istanbul,
Turkey, Other / None, TurkeyÇalık, Hatice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-26
hatice.calik@kuleuven.be
Department of Electrical Engineering, KU Leuven, Leuven,
Belgium
Caljon, Daan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-31
daan.caljon@gmail.com
Faculty of Economics and Business, KU Leuven, Belgium
Callaghan, Hannah . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-1023546735@sun.ac.za
Logistics, Stellenbosch Univeristy, South Africa
Callico, Adrien . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-24
adriencallico@hotmail.fr
LIFAT, Université de Tours, Tours, France
Camacho, Jesús . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-42
j.camacho@umh.es
Center of Operations Research, Miguel Hernández University of Elche, Elche, Spain
Camacho-Vallejo, José-Fernando . . . . . . . . . . . . . . . . . . . MD-61
fernando.camacho@tec.mx
Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey,
MexicoCamanho, Ana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-53
acamanho@fe.up.pt
Faculty of Engineering, University of Porto, Porto, Portugal
Cambazard, Hadrien . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-52
hcambazard@gmail.com
Operations Research, G-SCOP, Grenoble, France
Campbell, James . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-61
campbell@umsl.edu
College of Business Administration, University of MissouriSt. Louis, St. Louis, MO, United States
Campedelli, Gian Maria . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-33
gcampedelli@fbk.eu
Fondazione Bruno Kessler, Italy

528

Campinho, Matheus . . . . . . . . . . . . . . . . . . . . . . . . . MB-07, TB-29
matheus.f.campinho@inesctec.pt
Inesc Tec, Portugal
Campion, Nicolas . . . . . . . . . . . . . . . . . . . . . . . . . . WC-09, MA-14
njbca@dtu.dk
DTU Management, DTU, Denmark
Campoy, Rubén . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-42
ruben.campoy@ua.es
Department of Mathematics, Universidad de Alicante, San
Vicente del Raspeig, Alicante, Spain
Campuzano, Giovanni . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-56
g.f.campuzanoarroyo@utwente.nl
IEBIS, University of Twente, Enschede, - Select -, Netherlands
Canales, Gabriel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-19
gabriel.canales@ug.uchile.cl
Departamento de Ingeniería Eléctrica, Universidad de Chile,
Santiago de Chile, Región Metropolitana, Chile
Canas, Francisco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-26
francisco.m.p.canas@gmail.com
Departamento de Estatística e Investigação Operacional, Centro de Matemática, Aplicações Fundamentais e Investigação
Operacional (CMAFcIO), Universidade de Lisboa, Lisbon,
PortugalCancela, Hector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-12, TC-18
cancela@fing.edu.uy
Facultad de Ingeniería, Universidad de la República, Montevideo, Montevideo, Uruguay
Canellas, Maureen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-20
Maureen.Canellas@umassmemorial.org
University of Massachusetts Memorial Medical Center,
Worcester, MA, United States
Canesi, Rubina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-47
rubina.canesi@unipd.it
ICEA, Univeristy of Padova, Padova, Veneto, Italy
Canessa, Rafaella . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-19
rafaella.canessa@pg.canterbury.ac.nz
Civil and Natural Resources Engineering, University of Canterbury, Christchurch, Canterbury, New ZealandCánovas, Maria Josefa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-42
canovas@umh.es
Operations Research Center, Miguel Hernández University,
Elche, Alicante, Spain
Cao, Karl-Kiên . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-19, WA-53
Karl-Kien.Cao@dlr.de
Energy Systems Analysis, German Aerospace Center (DLR),
Institute of Networked Energy Systems, Stuttgart, Germany
Cao, Teng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-26
caoteng_zach@foxmail.com
Management and Economics Department, Tianjin University,
Tianjin, China

EURO 2024 - Copenhagen

AUTHOR INDEX

Cao, Yiyin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-40
yiyincao2-c@my.cityu.edu.hk
Department of advanced design and systems engineering,
City University of Hong Kong, Kowloon, Hong Kong

Cardinali, Michela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-18
michela.cardinali@ccrvenaria.it
Fondazione Centro Conservazione e Restauro dei Beni Culturali La Venaria Reale, Venaria Reale, Italy, Italy

Cao, Yunzhi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-59
yunzhicao2-c@my.cityu.edu.hk
City University of Hong Kong, HongKong, Hong Kong

Cardoen, Brecht . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-17
brecht.cardoen@vlerick.com
Vlerick Business School, Ghent, Belgium

Capar, Ibrahim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-61
icapar@bgsu.edu
Bowling Green State University, Bowling Green, Ohio,
United States

Carello, Giuliana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-58
giuliana.carello@polimi.it
Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy

Caperna, Giulio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-44
giulio.caperna@gmail.com
Competence Centre on Composite Indicators and Scoreboards, Joint Research Centre of the European Commission,
Italy

Cariaga, Denise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-19
d.c.cariaga-sandoval@sms.ed.ac.uk
School of Mathematics, University of Edinburgh, Edinburgh,
United Kingdom

Cappanera, Paola . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-25
paola.cappanera@unifi.it
Dipartimento di Sistemi e Informatica, University of Florence, Florence, Italy
Cappart, Quentin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-06
quentin.cappart@polymtl.ca
Computer and Software Engineering, Polytechnique Montréal, Montréal, Quebec, Canada
Cappello, Cheren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-47
c.cappello@studenti.uniss.it
Department of Architecture Design and Urban Planning, University of Sassari, Sassari, Italy, Italy
Caprari, Elisa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-41
elisa.caprari@unipv.it
Dipartimento di Scienze Economiche e Aziendali, Università
di Pavia, PAVIA, Italy
Caprioli, Caterina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-47
caterina.caprioli@polito.it
Interuniversity Department of Regional and Urban Studies
and Planning, Politecnico di Torino, Turin, Italy
Caraballo, Maria de los Angeles . . . . . . . . . . . . . . . . . . . . . TA-36
mcaraba@us.es
University of Seville, Sevilla, Spain
Caramuta, Caterina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-56
ccaramuta@units.it
DEAMS, University of Trieste, Trieste, Italy
Carannante, Maria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-63
mcarannante@unisa.it
University of Salerno, Salerno, Italy
Carbonera, Michele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-35
michele.carbonera@unimib.it
Università Milano Bicocca, Italy
Carciaghi, Francesco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-37
francesco.carciaghi@unifi.it
University of Florence, Italy

Caris, An . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-61
an.caris@uhasselt.beResearch Group Logistics, Hasselt University, Diepenbeek,
Belgium
Carlier, Jacques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-60
carlier@utc.fr
Laboratoire Heudiasyc, Université de Technologie de Compiègne, Compiègne, France
Carling, Kenneth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-31
kca@du.seDalarna University, Sweden
Carmen, Rodriguez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-33
carmen.rodriguez@usc.es
Universidad de Santiago de Compostela, Santiago de Compostela, Spain
Carosi, Laura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-44
laura.carosi@unipi.it
Department of Economics and Management, University of
Pisa, Pisa, Italy
Carpente Rodríguez, María Luisa . . . . . . . . . . . . . . . . . . . MC-45
luisa.carpente@udc.es
Departamento de Matemáticas, Universidade da Coruña,
Spain
Carpentier, Pierre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-09
Pierre.Carpentier@ensta.fr
UMA, ENSTA, Paris, France
Carrabs, Francesco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-26
fcarrabs@unisa.it
Department of Mathematics, University of Salerno, Fisciano,
Italy
Carrasco, Rodrigo A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-22
rodrigo.carrasco@gmail.com
Institute of Mathematical Engineering and Computation,
Pontificia Universidad Católica de Chile, Santiago, RM,
Chile
Carravilla, Maria Antónia . . . . . . . . MB-07, TB-28, 29, MB-59
mac@fe.up.pt

529

AUTHOR INDEXEURO 2024 - Copenhagen

INESC TEC, Faculty of Engineering, University of Porto,
Porto, Portugal
Carreras, Ashley . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-13
acarreras@alfaisal.edu
Business, Alfaisal University, Riyadh, Saudi Arabia
Carrión, Miguel . . . . . . . . . . . . . . . . . . . . . MB-14, MB-19, TC-34
miguel.carrion@uclm.es
Electrical Engineering, Universidad de Castilla - La Mancha,
Toledo, Spain
Carrizosa, Emilio . . . . . . . . . . . . . . . . . . . . MB-27, MD-27, TA-27
ecarrizosa@us.es
IMUS - Instituto de Matemáticas de la Universidad de Sevilla,
Sevilla, Spain
Carroll, Daire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-26
daire.carroll@bioenv.gu.se
Department of Biological and Environmental Sciences, University of Gothenburg, Sweden
Carroll, Paula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-26, MB-61
paula.carroll@ucd.ie
Management Information Systems, UCD, Dublin, Ireland
Carvalho, Ana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-07, MD-24
anacarvalho@ist.utl.pt
Management, CEG-IST, Porto Salvo„ Portugal
Carvalho, Margarida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-35
carvalho@iro.umontreal.ca
Université de Montréal, Montréal, Québec, Canada
Carvalho, Sheldon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-07
s.carvalho@mdx.ac.ae
Middlesex University Dubai, United Arab Emirates
Casado, Silvia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-15
scasado@ubu.es
University of Burgos, BURGOS, Spain
Casajus, André . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-36
mail@casajus.de
Economics and Information Systems, HHL Leipzig Graduate
School of Management, Leipzig, Saxony, Germany
Casalta, Mariana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-53
up201605416@edu.fe.up.pt
Faculdade de Engenharia de Universidade do Porto, Portugal
Casas, Pablo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-31
pc2n21@soton.ac.uk
Decision analytics and risk, University of Southampton,
United Kingdom
Caselli, Giulia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-29
giulia.caselli@unimore.it
DISMI, Università degli Studi di Modena e Reggio Emilia,
Sassuolo, Modena, Italy
Casellina, Simone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-63
simoneenrico.casellina@eba.europa.eu
European Banking Authority, Paris La Défense CEDEX,France530

Caspar, Marvin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-52
marvin.caspar@wiwi.uni-kl.de
Business Information Systems & Operations Researc, RPTU
Kaiserslautern-Landau, Kaiserslautern, Germany
Casprini, Danny . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-47, TC-47
danny.casprini@polimi.it
Tiresia, Politecnico Of Milano, Miano, Milano, Italy
Casquilho, Miguel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-49
mcasquilho@tecnico.ulisboa.pt
IST, Univ. de Lisboa, Portugal
Cassimiro de Oliveira, Nicole Cristina . . . . . . . . . . . . . . . TA-10
nicole@ime.unicamp.br
Computational & Applied Mathematics, University of Campinas (UNICAMP), Campinas, São Paulo, Brazil
Castaneda, Cristobal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-62
cristobal.castanedam@utem.cl
Industria, Universidad Tecnologica Metropolitana, Santiago,
Region Metropolitana, Chile
Castellano, Andrea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
andrea.castellano@unimore.it
Physics, Informatics and Mathematics, University of Modena
e Reggio Emilia, Modena, Italy
Castelletti, Andrea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-43
andrea.castelletti@polimi.it
Electronics, Information, and Bioengineering, Politecnico di
Milano, Milan, Italy
Castellini, Marta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-33
marta.castellini@unipd.it
Department of Economics and Management, University of
Padua, Fondazione Eni Enrico Mattei, Italy
Castro e Sant’Anna, Cristiane Perla . . . . . . . . . . . . . . . . . WA-48
crisperla@yahoo.com.br
Engenharia de produção, UFPE, Olinda, Pernambuco, Brazil
Castro, Deoclecio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-36
deoclecio@gmail.com
School of Economics and Management, University of Porto,
Maia, Porto, Portugal
Castro, Jordi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-30
jordi.castro@upc.edu
Dept. of Statistics and Operations Research, Universitat Politecnica de Catalunya, Barcelona, Catalonia, Spain
Castro, Sérgio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-50
sergio.v.castro@inesctec.pt
Inesc Tec, Portugal
Catanzaro, Daniele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-20
daniele.catanzaro@uclouvain.beCenter for Operations Research and Econometrics, Université Catholique de Louvain, Louvain-la-Neuve, BelgiumÇatay, Bülent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-26
catay@sabanciuniv.edu
Faculty of Engineering and Natural Sciences, Sabanci Uni-

EURO 2024 - Copenhagenversity, Istanbul, Turkey
Catellani, Eleonora . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-47
eleonora.catellani@polimi.it
Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Milan, Italy, Italy
Cattaneo, Mattia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-10
mattia.cattaneo@unibg.it
Department of Management, Information and Production Engineering, University of Bergamo, Italy
Catusse, Nicolas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-52
nicolas.catusse@grenoble-inp.fr
Grenoble INP / G-SCOP, France
Caulkins, Jonathan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-33
caulkins@andrew.cmu.edu
H. John Heinz III School of Public Policy & Management,
Carnegie Mellon University, Pittsburgh, United States
Caunhye, Aakil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-21, TC-21
Aakil.Caunhye@ed.ac.uk
The University of Edinburgh, United Kingdom
Cavagnini, Rossana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-64
cavagnini@dpo.rwth-aachen.de
RWTH Aachen University, Germany
Cavalcante, Tássio Côrtes . . . . . . . . . . . . . . . . . . . . . . . . . . MC-64
cavalcantetcc@fab.mil.br
IEAv, Brazil
Cavaliere, Francesco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-25
cavalieref@dei.unipd.it
DEI, University of Padova, Italy
Cavaliere, Francesco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-58
francesco.cavaliere@unipd.it
University of Padova, Italy
Cavana, Giulio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-47, TC-47
giulio.cavana@polito.it
DIST, Politecnico di Torino, Modena, Modena, Italy
Cavus, Ozlem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-35
ozlem.cavus@bilkent.edu.tr
Industrial Engineering, Bilkent University, Turkey
Cawood, Mark . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-61
cawood@clemson.edu
Mathematical and Statistical Sciences, Clemson University,
Clemson, SC, United States
Côté, Alain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-06
cote.alain7@hydroquebec.com
IREQ, Hydro-Québec, Varennes, Québec, Canada
Ceccaldi, Pierre-François . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-17
pf.ceccaldi-carp@hopital-foch.com
Service de Gynécologie-Obstétrique et Médecine de la reproduction, Hôpital Foch, France
Cefis, Mattia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-16
m.cefis001@unibs.it

AUTHOR INDEX

Department of Economics and Management, University of
Brescia, Brescia, Italy
Cekyay, Bora . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-39
bora.cekyay@yildiz.edu.tr
Industrial Engineering, Yildiz Technical University, Istanbul,
TurkeyCelik, Melih. . . . . . . . . . . . . . . . . . . . . . . . . . TA-10, TA-21, MA-64
m.celik@bath.ac.uk
School of Management, University of Bath, Bath, United
Kingdom
Celik, Sifanur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-64
s.celik@tue.nl
Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven,
Netherlands
Cepeda Valero, Mauricio . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-51
omcepedav@unal.edu.co
Ingeniería industrial, Universidad Central, Bogotá, Bogotá,
Colombia
Cepoi, Cosmin Octavian . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-31
cosmin.cepoi@fin.ase.ro
Money and Banking, Bucharest University of Economic
Studies, Romania
Cerboni Baiardi, Lorenzo . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-41
lorenzo.cerboni@unibo.it
Department of Mathematics, University of Bologna,
Bologna, Bo, Italy
Cerda, Cesar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-22
cecerda@alumnos.uai.cl
Faculty of Engineering and Sciences, Universidad Adolfo
Ibáñez, Santiago, Chile
Ceria, Sebastián . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-16
sebastian.ceria@gmail.com
Racing de Santander, Spain
Cerneviciene, Jurgita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-02
jurmark@ktu.lt
Mathematical Modelling, Kaunas University of Technology,
Kaunas, Lithuania
Cerqueti, Roy . . . . . . . . . . . . . . . . . . . . . . WC-02, MA-63, WB-63
roy.cerqueti@uniroma1.it
Department of Social and Economic Sciences, Sapienza University of Rome, Rome, Rome, Italy
Cerqueus, Audrey. . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-24, TD-60
audrey.cerqueus@imt-atlantique.fr
DAPI, IMT Atlantique, Nantes, France
Cerreta, Maria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-13, TD-47
cerreta@unina.it
Department of Architecture (DiARC), University of Naples
Federico II, Naples, Italy, Italy
Cerrone, Carmine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-26
carmine.cerrone@unige.it
University of Genoa, Genoa, Italy

531

AUTHOR INDEXEURO 2024 - Copenhagen
Northeastern University, Boston, United States

Cerulli, Martina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-29, TD-52
mcerulli@unisa.it
Computer Science Department, University of Salerno, Fisciano, Italy
Cerulli, Raffaele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-29
raffaele@unisa.it
Department of Mathematics, University of Salerno, Fisciano(SA), Italy
Cerveira, Adelaide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-49
cerveira@utad.pt
Mathematics Department, Utad & Liaad/inesc-tec, Vila Real,
Portugal
Ceryan, Oben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-59
oben.ceryan@city.ac.uk
Bayes Business School, City, University of London, London,
United Kingdom
Cesaret, Bahriye . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-50
bahriye.cesaret@ozyegin.edu.tr
Faculty of Business, Ozyegin University, Istanbul, stateName, Turkey
Cesarone, Francesco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-02
francesco.cesarone@uniroma3.it
Department of Business Studies, Roma Tre University,
Rome, ItalyÇetiner, Burak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-43
burakcetiner95@hotmail.com
Industrial Engineering, Marmara Üniversitesi, İstanbul, İstanbul, Turkey
Cetinkaya, Sila . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-61
sila@smu.edu
Southern Methodist University, Dallas, United States
Cevher, Volkan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-32
volkan.cevher@epfl.ch
School of Engineering, EPFL, Lausanne, Switzerland
Ceyhan, Gökhan . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-03, MA-46
gokhanceyhan01@gmail.com
Operations Research, Just Eat Takeaway.com, Berlin, Germany
Ceylan Güngör, Zahide Özden . . . . . . . . . . . . . . . . . . . . . . . TB-18
zahideozdenceylan@gmail.com
Industrial Engineering, ITU, Istanbul, Turkey
Cha, Hyung Joo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-52
hyungjoo_cha@korea.ac.kr
Industrial Management Engineering, Korea University,
Seoul, Seongbuk-gu, Korea, Republic of
Chai, Shanqing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-51
Chai@verkehr.tu-darmstadt.deInstitut für Bahnsysteme und Bahntechnik, TU Darmstadt,
Darmstadt, Germany
Chai, Sining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-50
chai.si@northeastern.edu

532

Chakhar, Salem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-62
salem.chakhar@port.ac.uk
Operations and Systems Management, University ofPortsmouth, Portsmouth, United Kingdom
Chakraborty, Arnab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-55
ch.arnab@yahoo.com
Vinod Gupta School Of Management, IIT Kharagpur,
Kharagpur, West Bengal, India
Chambolle, Antonin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-32
antonin.chambolle@ceremade.dauphine.fr
CEREMADE, CNRS, Université Paris-Dauphine, PSL, Paris,
FranceChamoli, Abhay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-08
chefabhay04@gmail.com
School Of Hospitality Management, Ims Unison University,
DEHRADUN, UTTARAKHAND, India
Chan, Hing Kai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-08, TA-23
hingkai.chan@nottingham.edu.cn
Nottingham University Business School China, University of
Nottingham Ningbo China, Ningbo, Zhejiang, China
Chancelier, Jean-Philippe . . . . . . . . . . . . . . . . . . . . WB-09, TC-42
jpc@cermics.enpc.fr
CERMICS Ecole des Ponts et Chaussées, Université Paris
Est, Marne La vallee cedex 02, France
Chandrasekharan, Reshma . . . . . . . . . . . . . . . . . . WC-26, TD-54
reshma.chirayil@iimb.ac.in
Decision Sciences Area, Indian Institute of ManagementBangalore, India, Bangalore, Karnataka, India
Chang, Lung-fu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-57
lfchang@ntub.edu.tw
Department of Finance, National Taipei University of Business, Taipei, Taiwan
Chang, Tsung-Sheng . . . . . . . . . . . . . . . . MD-21, MB-58, TD-64
tsc@nycu.edu.tw
Department of Transportation and Logistics Management,
National Yang Ming Chiao Tung University, Taiwan
Chang, Yi-He . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-39
sai4311z.iof06g@nctu.edu.tw
Information Management and Finance, National Yang Ming
Chiao Tung University, Taiwan
Chang, Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-56
el1j6h89@gmail.com
Department of Civil Engineering, Chung Yuan Christian University, Taiwan
Chaparro, Pablo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-51
pchaparro@fen.uchile.cl
Department of Management Control and Information Systems, Universidad de Chile, Chile
Charaf, Sara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-60
s.charaf@tue.nl
School of Industrial Engineering, Eindhoven University ofEURO 2024 - Copenhagen

AUTHOR INDEXTechnology, Eindhoven, Netherlands, Netherlands
Chareyre, Hugo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-30, TD-49
hugo.chareyre@artelys.com
Artelys, PARIS, France

Chen, Chien-Wen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-44
chencw@o365.fcu.edu.tw
Business Administration, Feng Chia University, Taichung,TaiwanChargui, Kaoutar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-62
kaoutar.chargui@uir.ac.ma
BEAR Lab, Rabat Business School, Sala Al Jadida, Morocco

Chen, Dawei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-28
d.chen@tue.nl
Netherlands

Chargui, Tarik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-62
tarik.chargui@uphf.fr
Lamih Umr Cnrs 8201, Université Polytechnique Hauts-deFrance, Valenciennes, France

Chen, Du . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-03
chen1443@e.ntu.edu.sg
Nanyang Business School, Nanyang Technological University, Singapore

Charron, Kallia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-19
kallia.charron@neuf.fr
Laboratoire EconomiX, Nanterre University, Nanterre,FranceChen, Fadong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-07
fadongchen@zju.edu.cn
Zhejiang University, China

Charvillat, Vincent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-55
Vincent.Charvillat@enseeiht.fr
IRIT, INP Toulouse, France

Chen, Fang-Pey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-47
fpchen@vghtpe.gov.tw
National Yang Ming Chiao Tung University, Taiwan

Chastre, Miguel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-61
m.chastre@tue.nl
IE&IS, Eindhoven University of Technology, Netherlands

Chen, Frank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-03
youhchen@cityu.edu.hk
Management Sciences, The City Univ of Hong Kong, Hong
Kong

Chaturvedi, Aadhaar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-61
aadhaar.chaturvedi@aucklanduni.ac.nz
Information Systems and Operations Management, University of Auckland, New ZealandChen, Huilan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-45
huilan.chen@plymouth.ac.uk
Plymouth Business School, University of Plymouth, Plymouth, United Kingdom

Chaubey, Vishal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-48
chaubeyvishal01@gmail.com
Department of Mathematics, Indira Ghandhi National Tribal
University, Anuppur, Madhya Pradesh, India

Chen, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-34
jchen07@163.com
College of Management and Economics, Tianjin University,
Tianjin, Tianjin, China

Chauhan, Avinash . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-44
avinashchauhan15@kgpian.iitkgp.ac.in
Vinod gupta school of management, Indian Institute of Technology, Kharagpur, Kharagpur, West Bengal, India

Chen, Ji . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-15
jichen@dufe.edu.cn
Dongbei University of Finance and Economics, China

Chaves, Antonio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-49
antoniochaves@gmail.com
UNIFESP, Brazil

Chen, JongChen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-04
jcchen@yuntech.edu.tw
Information Management, National YunLin University of
Science and Technology, Douliu, Taiwan, Taiwan

Che, Ada . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-25, MB-62
ache@nwpu.edu.cn
School of Management, Northwestern Polytechnical University, Xi’an, Shaanxi, China

Chen, Junlin . . . . . . . . . . . . . . . . . . . . . . . . . WB-07, TA-43, TC-43
chenjunlin@cufe.edu.cn
Management Science and Engineering, Central University of
Finance and Economics, Beijing, China

Chelbani, Mohand Ryad . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-29
mohand-ryad.chelbani@polymtl.ca
GERAD, Polytechnique, montréal, QC, Canada

Chen, Ke-Fang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-44
cocoruby0305@gmail.com
Department of Information Management, National United
University, Taiwan, Miaoli, Miaoli, Taiwan

Chen, Bo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-60
b.chen@warwick.ac.uk
Warwick Business School, University of Warwick, Coventry,
United Kingdom
Chen, Chen-Tung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-44
ctchen@nuu.edu.tw
Department of Information Management, National United
University, Taiwan, Miaoli, Miaoli, Taiwan

Chen, Pei-qi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-50
peichi19971021@gmail.com
Business Management, National Taipei University of Technology, Taiwan
Chen, Qingxin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-34
cqx0103@163.com
College of Management and Economics, Tianjin University,

533

AUTHOR INDEXEURO 2024 - Copenhagen

China

University of Technology, Netherlands

Chen, Wen-Chih . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-18
wenchih.chen@nycu.edu.tw
Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

Cheng, Ying-Mei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-60
yingmei.cheng@msa.hinet.net
Department of Civil Engineering and Hazard Mitigation,
China University of Technology, Taipei, Taiwan

Chen, Wenhao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-31
wchen564@uwo.ca
Department of Statistical and Actuarial Science, Western
University, London, ON, Canada

Cheng, Yingying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-29
cheng.yingying1@huawei.com
Huawei Hong Kong Research Centre, Hong Kong

Chen, Xiangdong . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-55, MC-56
cxd@nus.edu.sg
Civil and Environment Engineering, National University ofSingapore, Singapore

Cheon, Hyungkyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-36
hkyucheon@gmail.com
Industrial and Management Engineering, POSTECH, Pohang, Gyeongbuk, Korea, Republic ofChen, Xiangsheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-62
xschen@szu.edu.cn
Shenzhen university, China

Cheong, Taesu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-52
tcheong@korea.ac.kr
School of Industrial Management Engineering, Korea University, Seoul, Korea, Republic ofChen, Xiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-28
xiao.chen@leicester.ac.uk
University of Leicester, United Kingdom

Cherchye, Laurens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-17
laurens.cherchye@kulak.ac.be
Catholic University Leuven, Belgium

Chen, Yanfei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-60
yanfei.chen@kuleuven.be
Department of Decision Sciences and Information Management, KU Leuven, Leuven, Belgium

Chernonog, Tatyana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-08
tatyana.chernonog@biu.ac.il
Management, Bar Ilan University, Ramat-Gan, Israel

Chen, Yen-Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-64
yenyuchen@nycu.edu.tw
TaiwanChen, Yuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-52
yuan.chen@univie.ac.at
Department of Statistics and Operations Research, Universityof Wien, Austria
Chen, Yuwang . . . . . . . . . . . . . . . . . . . . . . . TB-06, WC-06, WA-22
yu-wang.chen@manchester.ac.uk
Alliance Manchester Business School, The University ofManchester, Manchester, United Kingdom
Chen, Zeyi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-03
CHEN1417@e.ntu.edu.sg
School of Physical and Mathematical Sciences, Nanyang
Technological University, Singapore
Chen, Zhen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-39, WA-49
zhen.chen@brunel.ac.uk
Brunel University London, United Kingdom
Chenavaz, Régis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-33
r.chenavaz@gmail.com
KEDGE Business School, France
Cheng, Qixiu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-56
qixiu.kevin.cheng@gmail.com
University of Bristol Business School, University of Bristol,
United Kingdom
Cheng, Rong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-26
r.cheng@tue.nl
Industrial Engineering & Innovation Sciences, Eindhoven

534

Cherrett, Tom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-10, TD-24
T.J.Cherrett@soton.ac.uk
University of Southampton, United Kingdom
Cherri, Adriana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-29, TB-29
adriana.cherri@unesp.br
Mathematics Department, UNESP, Bauru - INESC TEC, Portugal, Bauru, SP, Brazil
Cherri, Luiz Henrique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-29
luizcherri@gmail.com
Instituto de Ciências Matemáticas e de Computação, University of São Paulo, São Carlos, São Paulo, Brazil
Chevalier, Philippe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-55
philippe.chevalier@uclouvain.be
Louvain Institute of Data Analytics and Modelling, UCLouvain, Louvain-la-Neuve, Belgium
Chevalier-Roignant, Benoit . . . . . . . . . . . . . . . . . . . TA-57, TA-59
benoit.chevalier-roignant@outlook.com
Emlyon Business School, France
Chevrier, Remy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-54
remy.chevrier@sncf.fr
DTIPG, SNCF, France
Chhachhi, Saurab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-35
saurab.chhachhi11@imperial.ac.uk
Imperial College London, United Kingdom
Chiani, Leonardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-11
leonardo.chiani@polimi.it
Politecnico di Milano and RFF-CMCC European Institute on
Economics and the Environment, Italy, Italy

EURO 2024 - Copenhagen

AUTHOR INDEX

Chie, Bin-Tzong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-43
bt.chie@gms.tku.edu.tw
Department of Industrial Economics, Tamkang University,
New Taipei, Taiwan

Chong, Wing Fung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-57
alfred.chong@hw.ac.uk
Heriot-Watt University, United Kingdom

Chiffi, Daniele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-47
daniele.chiffi@polimi.it
Department of architecture and Urban Studies, Politecnico di
Milano, Italy

Choucair, Yasmin Izzo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-49
yasizzochou@gmail.com
Department of Industrial and Systems Engineering, Universidade Federal de Santa Catarina, Florianopolis, SC, Brazil

Chilibroste, Pablo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-12
pchili@fagro.edu.uy
Animal production and pastures, UdelaR, Paysandu, Uruguay

Chowdhury, Nahid-Ur-Rahman . . . . . . . . . . . . . . . . . . . . . MA-21
nahidurrahman.chowdh@uclm.es
University of Castilla-La Mancha, Ciudad Real, Spain

Chini Nielsen, Clara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-26
ccni@dtu.dk
Technical University of Denmark, Denmark

Choy, Siu Kai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-41
skchoy@hsu.edu.hk
Mathematics and Statistics, The Hang Seng University ofHong Kong, Hong Kong

Chiou, Suh-Wen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-55
chiou@mail.ndhu.edu.tw
Information Management, National Dong Hwa University,
Hualien, Taiwan
Chiu, Chih-Chou . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-50, WD-50
chih3c@ntut.edu.tw
National Taipei University of Technology, Taiwan
Chlumsky-Harttmann, Fabian . . . . . . . . . . . . . . . . . . . . . . WC-37
chlumsky@mathematik.uni-kl.de
Department of Mathematics, University of KaiserslauternLandau, Kaiserslautern, Germany
Chnina, Khaoula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-47
chnina.khaoula@gmail.com
Industrial Engineering, Eastern Mediterranean University,
famagusta, Turkey
Cho, Danny . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-56
dcho@brocku.ca
Goodman School of Business, Brock University, St.
Catharines, Ontario, Canada
Chocholatá, Michaela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-53
michaela.chocholata@euba.sk
Faculty of Economic Informatics, Department of Operations Research and Econometrics, University of Economics,
Bratislava, Slovakia
Choi, Dong Gu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-36
dgchoi@postech.ac.kr
POSTECH, Korea, Republic of
Choi, SeungHyun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-08
cshyun102@postech.ac.kr
Dept.
of Industrial and Management Engineering,
POSTECH, Korea, Republic of
Choi, Tsan-Ming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-28
t.m.choi@liverpool.ac.uk
University of Liverpool, Liverpool, United Kingdom
Choicharoon, Aritad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-45
a.choicharoon@gmail.com
Leeds University Business School, University of Leeds,
Leeds, West Yorkshire, United Kingdom

Christian, Jens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-24
jens.christian@rptu.de
Chair of Production Management, RPTU KaiserslauternLandau, Kaiserslautern, Germany
Chu, Dantong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-02
dtchu@link.cuhk.edu.hk
Department of Statistics, Chinese University of Hong Kong,
Hong Kong
Chu, Zhong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-62
zhong.chu@connect.polyu.hk
The Hong Kong Polytechnic University, Hong Kong
Chua, Geoffrey A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-03
gbachua@ntu.edu.sg
Nanyang Business School, Nanyang Technological University, Singapore, Singapore
Chubanov, Sergei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-52
sergei.chubanov@uni-siegen.de
Bosch Center For Artificial Intelligence, Germany
Chung, Byung Do . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-23
bd.chung@yonsei.ac.kr
Industrial Engineering, Yonsei University, Seoul, Korea, Republic of
Chung, Moonwon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-50
m.chung74@csuohio.edu
Cleveland State University, United States
Chung, Sai-Ho . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-28
nick.sh.chung@polyu.edu.hk
The Hong Kong Polytechnic University, Hong Kong, Hong
Kong
Chung, Seokhyun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-52
schung@virginia.edu
University of Virginia, United States
Chung, William . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-22
mswchung@cityu.edu.hk
Management Sciences, City University of Hong Kong,
Kowloon, Hong Kong

535

AUTHOR INDEXEURO 2024 - Copenhagen

Chungcharoen, Ekachidd . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-53
ekachidd@tbs.tu.ac.th
Operations Management, Thammasat Business School,
Bangkok, Thailand
Ciavotta, Michele. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-35
michele.ciavotta@unimib.it
University of Milano Bicocca, Italy
Ciccullo, Federica . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-47
federica.ciccullo@polimi.it
Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Milano, Italy, Italy
Cicek, Engin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-22
engincicek@marun.edu.tr
Industrial Engineering, Marmara University, Istanbul, Turkey
Cifuentes Quintero, Jenny Alexandra . . . . . . . . . . . . . . . WB-27
jacifuentes@comillas.edu
Quantitative Methods, Universidad Pontificia Comillas,
Madrid, Spain
Cifuentes, Dagoberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-21
dagocifuentes@udec.cl
Industrial Engineering, University of Concepcion, Concepcion, Biobio, Chile
Cigdem Karakoyun, Ece . . . . . . . . . . . . . . . . . . . . . MD-22, TA-22
karakoyun@ese.eur.nl
Econometrics, Erasmus University Rotterdam, Netherlands
Cilali, Buket . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-21
buket.cilali@ou.edu
University of Oklahoma, Norman, OK, United StatesÇimen, Emre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-27, WC-55
ecimen@eskisehir.edu.tr
Industrial Engineering, Eskisehir Technical Universty, Eskişehir, Turkey
Cipolla, Stefano. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-38, TD-38
s.cipolla@soton.ac.uk
School of Mathematical Sciences, University of Southampton, United KingdomÇınar, Ahmet . . . . . . . . . . . . . . . . . . . . . . . MC-45, WC-50, WB-58
ahmet.cinar@trendyol.com
Data Science, Trendyol, Sarıyer, İstanbul, TurkeyÇınar, Alim Buğra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-52
a.b.cinar@vu.nl
Operations Analytics, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
Claassen, G.D.H. (Frits) . . . . . . . . . . . . . WA-13, WD-13, MB-50
frits.claassen@wur.nl
Operations Research and Logistics, Wageningen University,
Wageningen, Netherlands
Claeys, Dieter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-15, MD-23
dieter.claeys@ugent.beIndustrial Systems Engineering and Product Design, Ghent
University, Zwijnaarde, Belgium

536

Clark, Andrew . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-28
andrew@monitaur.ai
United States
Cleophas, Catherine . . . . . . . . . . . . . . . . . TC-43, MA-62, WB-62
cleophas@bwl.uni-kiel.deService Analytics, CAU Kiel University, Germany
Climaco, Glaubos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-55
glaubosclimaco@gmail.com
Federal University of Rio de Janeiro, Rio de Janeiro, Rio deJaneiro, Brazil
Cobben, Sjef . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-19
j.f.g.cobben@tue.nl
EES, Eindhoven University of Technology, Netherlands
Codina, Esteve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-56
esteve.codina@upc.edu
Statistics and Operational Research, UPC, Barcelona, Spain
Coelho, José . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-06
Jose.Coelho@uab.pt
Department of Sciences and Technology (DCeT), Universidade Aberta, Lisbon, Portugal
Cohen Kadosh, Simona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-53
cohensim@post.bgu.ac.il
Logistics, Sapir Academic College, Gedera, Israel
Cohn, Henry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-38
cohn@mit.edu
Microsoft Research and MIT, United States
Cojocaru, Monica-Gabriela . . . . . . . . . . . . . . . . . . . . . . . . . TD-40
mcojocar@uoguelph.ca
Mathematics & Statistics, University of Guelph, Guelph, Ontario, Canada
Colajanni, Gabriella . . . . . . . . . . . . . . . . . . . . . . . . MC-16, WA-40
colajanni@dmi.unict.it
Department of Mathematics and Computer Science, University of Catania, Catania, Catania, Italy
Colajanni, Gabriella . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-40
gabriella.colajanni@unict.it
Department of Mathematics and Computer Science, University of Catania, Italy
Colamartino, Chiara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-12
colamartino@lum.it
Management, Finance and Technology, LUM University
Giuseppe Degennaro, Casamassima, Bari, Italy
Colares, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-48
danielvcolares@gmail.com
ISCA, University of Aveiro, Portugal
Colley, Rachael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-25
rachael.colley@glasgow.ac.uk
School of Computing Science, University of Glasgow, United
Kingdom
Colombani, Yves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-30
YvesColombani@fico.com

EURO 2024 - Copenhagen

AUTHOR INDEX

Xpress Optimization, FICO, Marseille, France
Colombo, Luca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-36
luca.colombo@rennes-sb.com
Finance and Accounting, Rennes School of Business, France

Consigli, Giorgio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-57
giorgio.consigli@ku.ac.ae
Mathematics, Khalifa University of Science and Technology,
Abu Dhabi, Not in USA and Canada, United Arab Emirates

Colorni, Alberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-13
alberto.colorni@polimi.it
Department of Industrial Design, delle Arti e della Comunicazione, Politecnico di Milano, Milano, Italy

Consiglio, Andrea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-63
andrea.consiglio@unipa.it
Dept. of Economics, Business and Statistics, University ofPalermo, Palermo, Italy

Colovic, Aleksandra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
aleksandra.colovic@poliba.it
DICATECh, Polytechnic University of Bari, Bari, Italy

Consolini, Luca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-18
luca.consolini@unipr.it
Department of Engineering and Architecture, University ofParma, Italy

Colucci, Gianvito . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-09
gianvito.colucci@polito.it
Energy, Politecnico di Torino, Torino, Italy
Comelli, Sara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-54
sara.comelli@tum.deProfessorship for Design and Operation of Public Rail Transport Systems (RTS), Technical University of Munich, Garching, Germany
Comincioli, Nicola . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-33
nicola.comincioli@unibs.it
University of Brescia and Fondazione Eni Enrico Mattei, Italy

Contardo, Claudio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-59
claudio.contardo@concordia.ca
Concordia University, Canada
Conte, Daniele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-56
daniele.conte@unifi.it
Department of Civil and Industrial Engineering, Universityof Pisa, Pisa, Italy
Contreras Rada, Lucas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-58
lcontrerasrada@correo.unicordoba.edu.co
Industrial Engineer, University of cordoba, monteria, cordoba, Colombia

Comrie, Emma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-13
emma.comrie@strath.ac.uk
Management Science, University of Strathclyde, United
Kingdom

Contreras, Javier . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-19, MA-21
Javier.Contreras@uclm.es
University of Castilla - La Mancha, Ciudad Real, Spain

Conda, Alexandra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-31
alexandra.conda@csie.ase.ro
Statistics and Econometrics, The Bucharest University of
Economic Studies, Bucharest, Romania

Contreras-Bolton, Carlos . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-26
ccontreras.bolton@gmail.com
Departamento de Ingeniería Industrial, Universidad de Concepción, Concepción, Chile

Conda, Alexandra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-31
alexandraconda8@gmail.com
Bucharest University of Economic Studies, Romania

Contzen, Nadja . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-11
Nadja.Contzen@eawag.ch
ESS, Eawag, Dübendorf, Switzerland, Switzerland

Condat, Laurent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-32
laurent.condat@kaust.edu.sa
AI Initiative, KAUST, Thuwal, Saudi Arabia

Cook, Wade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-48
wcook@schulich.yorku.ca
Schulich School of Business, York University, Toronto, Ontario, Canada

Confalonieri, Carlo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-63
carlo.confalonieri@unito.it
Department of Management "Valter Cantino", University ofTurin, Turin, Italy

Copiello, Sergio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-44, WB-45
sergio.copiello@iuav.it
Department of Architecture, University IUAV of Venice,Venezia, Italy

Coniglio, Stefano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-52
stefano.coniglio@gmail.com
Mathematical Sciences, University of Southampton,
Southampton, United Kingdom

Coppola, Claudio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-57
claudcop@amazon.co.uk
Amazon, United Kingdom

Conlon, Thomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-51
conlon.thomas@ucd.ie
Michael Smurfit School of Business, University College
Dublin, Dublin, Ireland

Corazza, Marco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-02
corazza@unive.it
Department of Economics, Ca’ Foscari University of Venice,Venezia, Italy

Connor, Mark . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-16
mark.connor@ucd.ie
Dublin, Dublin, Ireland

Corberán, Teresa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-64
tecorfa@alumni.uv.es
Estadística i Investigació Operativa, Universitat de València,

537

AUTHOR INDEXEURO 2024 - Copenhagen

València, Spain

Department of Science and Methods for Engineering, University of Modena and Reggio Emilia, Italy

Corbett, Charles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WE-01
charles.corbett@anderson.ucla.edu
UCLA Anderson School of Management, Los Angeles, CA,
United States

Corsini, Niccolò . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-64
corsini@logidot.com
Logidot, United Kingdom

Cordeau, Jean-François . . . . . . . . . . . . . . . . . . . . MD-50, MD-59
jean-francois.cordeau@hec.ca
Department of Logistics and Operations Management, HEC
Montréal, Montreal, Canada

Cortés, Matías . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-64
matias.cortes06@alumnos.ucn.cl
Departamento de Ingeniería Industrial, Universidad Católica
del Norte, Antofagasta, Chile

Córdoba-Sarmiento, Edgar E. . . . . . . . . . . . . . . . . TA-58, TC-58
edgar.cordoba@correo.uis.edu.co
School of Industrial and Business Studies, Universidad Industrial de Santander, Bucaramanga, Santander, Colombia

Costa Carballo, Telmo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-58
telmocostacarballo@gmail.com
Analytics, Inditex, Spain

Corgnati, Stefano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-47
stefano.corgnati@polito.it
Department of Energy, Politecnico di Torino, Torino, Italy
Corman, Francesco . . . . . . . . . . . . . . . . . . . . . . . . . WA-54, WC-54
francesco.corman@ivt.baug.ethz.ch
ETH Zurich, Switzerland
Cornelissens, Trijntje . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-58
trijntje.cornelissens@uantwerpen.be
Engineering management, University of Antwerp, Antwerpen, Belgium
Corona-Gutiérrez, Karina . . . . . . . . . . . . . . . . . . . . . . . . . . MD-29
kcorona@up.edu.mx
Facultad de Ingeniería, Universidad Panamericana, Zapopan,
Jalisco, Mexico
Correcher, Juan F. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-52
juan.correcher@uv.es
Estadística i Investigació Operativa, Universitat de València,
Spain
Correia Duarte, Pedro José . . . . . . . . . . . . . . . . . . . . . . . . . WD-54
correiaduarte@ese.eur.nl
Econometric Institute, Erasmus University Rotterdam,
Netherlands
Correia, Carolina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-16
carolinamaximocorreia@ua.pt
DEGEIT, University of Aveiro, Aveiro, Portugal
Correia, Gonçalo . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-56, MB-59
g.correia@tudelft.nl
Transport & Planning, TUDelft, Delft, Zuid Holland, Netherlands
Corrente, Salvatore . . . . . . . . . MA-44, MB-44, MC-44, WC-44
salvatore.corrente@unict.it
Department of Economics and Business, University of Catania, Catania, Italy, Italy
Corsaro, Stefania . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-16, TB-32
stefania.corsaro@uniparthenope.it
University of Naples Parthenope, Napoli, Italy
Corsini, Andrea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-60
andrea.corsini@unimore.it

538

Costa e Silva, Eliana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-49
eos@estg.ipp.pt
CIICESI, ESTG, Instituto Politécnico do Porto, Felgueiras,
Portugal
Costa Freitas, Maria de Belém . . . . . . . . . . . . . . . . . . . . . . . TB-47
mbfreitas@ualg.pt
Science and Technology Faculty, MED and University of Algarve, Faro, Portugal
Costa Melo, Isotilia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-48
isotilia@gmail.com
ICN Business School, France
Costa Nascimento, Luiz Fernando . . . . . . . . . . . . . . . . . . . TD-28
fernando.nascimento@unesp.br
São Paulo State University, Guaratinguetá, São Paulo, Brazil
Costa, Luciano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-59
luciano.costa@academico.ufpb.br
Production Engineering Department, Federal University of
Paraíba, Brazil
Costea, Nicusor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-43
nicusor.costea2606@upb.roMathematics and Computer Science, Politehnica National
University of Science and Technology Bucharest, Bucharest,
Romania
Cotter, John . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-51
john.cotter@ucd.ie
Michael Smurfit School of Business, University College
Dublin, Dublin, Ireland
Coulier, Bryan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-26
bryan.coulier@kuleuven.be
Computer Science, KU Leuven, Belgium
Couto, António . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-09
antonio.couto@lneg.pt
LNEG, Portugal
Couto, Kelvin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-41
kelvin.couto@ifg.edu.br
Mathematics, Federal Institute of Goias, Brazil
Coutton, Baptiste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-62
bnco@dtu.dk
DTU Management, Danmarks Tekniske Universitet, Den-

EURO 2024 - Copenhagen
mark
Craddock, Ian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-15
Ian.Craddock@bristol.ac.uk
University of Bristol, United Kingdom
Crama, Yves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-10, TD-29
yves.crama@uliege.be
HEC - Management School, University of Liège, Liege, Belgium
Cramer, Alexandru-Adrian . . . . . . . . . . . . . . . . . . . . . . . . . WD-31
alexadriancramer@yahoo.com
Economic Cybernetics and Statistics, Bucharest Universityof Economic Studies, Bucharest, Romania
Cramer, Florian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-50
florian.cramer@uni-bayreuth.de
Chair of Food Supply Chain Management, University of
Bayreuth, Germany
Crawford, Megan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-11
m.crawford@napier.ac.uk
Management, Edinburgh Napier University, Edinburgh,
United Kingdom
Creemers, Stefan . . . . . . . . . . . . . . . . . . . WA-23, MA-33, MC-42
sc@cromso.com
IESEG School of Management, Lille, France
Cremers, Roby . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-10
77265rcr@eur.nl
Econometric Institute, Erasmus University of Rotterdam,
Rotterdam, Netherlands
Crespo Materna, Arturo . . . . . WD-45, WB-51, TD-54, MB-56
crespo@verkehr.tu-darmstadt.de
Department of Civil and Environmental Engineering, Technical University of Darmstadt, Institute of Railway Engineering, Darmstadt, Germany
Crisci, Serena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-32
serena.crisci@unicampania.it
Department of Mathematics and Physics, University of Campania "L. Vanvitelli", Italy
Cristofari, Andrea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-32
andrea.cristofari@uniroma2.it
Department of Civil Engineering and Computer Science Engineering, University of Rome "Tor Vergata", Rome, Italy
Croella, Anna Livia . . . . . . . . . . . . . . . . . . . . . . . . . MD-16, TB-52
annalivia.croella@uniroma1.it
Sapienza University of Rome, Rome, Lazio, Italy, Italy
Croix, Jean-Charles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-57
jaycncy@gmail.com
Amazon, Luxembourg
Cruijssen, Frans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-64
Frans.Cruijssen@tilburguniversity.edu
Tilburg University, Tilburg, Netherlands
Cruz, Aldsmtyhys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-48
aldsmythys@gmail.com

AUTHOR INDEX

Diretoria de Operação, Operador Nacinal do Sistema, Recife,
Pernambuco, Brazil
Cruz, Samuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-34
cruzas@usi.ch
Euler Institute, Università della Svizzera italiana, Viganello,
Ticino, Switzerland
Csato, Laszlo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-16, WA-16
laszlo.csato@uni-corvinus.hu
Institute for Computer Science and Control (SZTAKI) andCorvinus University of Budapest, Budapest, Hungary
Cuadrado Guevara, Marlyn Dayana . . . . . . . . . . . . . . . . WB-34
marlyn.cuadrado@upc.edu
Statistics and Operations Research, Universitat Politècnica
de Catalunya - BarcelonaTech, Barcelona, Barcelona, Spain
Cuellar-Usaquen, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-64
dh.cuellar@uniandes.edu.co
Universidad de Los Andes, Bogotá, Colombia
Cui, Claire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-59
claire.mr.cui@gmail.com
Auckland CBD, New ZealandCurrie, Christine . . . . . . . . . . . . . . . . . . . WB-54, MB-59, WD-61
christine.currie@soton.ac.uk
School of Mathematics, University of Southampton,
Southampton, United Kingdom
Cussens, James . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-26
james.cussens@bristol.ac.uk
School of Computer Science, University of Bristol, Bristol,
United Kingdom
Custodio, Ana Luisa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-32
alcustodio@fct.unl.pt
Dept. Mathematics, Universidade Nova de Lisboa, Caparica,
Portugal
Cuvelier, Thibaut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-03
cuvelier.thibaut@gmail.com
Operations Research, Google Research, Paris, France
Cuvelier, Thibaut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-58
thibaut@tcuvelier.beOperations Research, Google Research, Belgium
Cvetkoska, Violeta . . . . . . . . . . . . . . . . . . . . . . . . . MD-48, WD-59
vcvetkoska@eccf.ukim.edu.mk
Management, Ss. Cyril and Methodius University in Skopje,
Faculty of Economics-Skopje, Skopje, North Macedonia,
Macedonia, the Former Yugoslav Republic of
D Amato, Valeria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-63
vdamato@unisa.it
Economics and Statistics, University of Salerno, Fisciano(Salerno), Italy
D’Acierno, Luca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-55
luca.dacierno@unina.it
Department of Civil, Architectural and Environmental Engineering, Federico II University of Naples, Naples, Italy

539

AUTHOR INDEXEURO 2024 - Copenhagen

D’Alpaos, Chiara . . . . . . . . . . . . . MB-13, MC-33, TA-47, TB-47
chiara.dalpaos@unipd.it
Department of Civil Environmental and Architectural Engineering, University of Padova, Padova, Italy
D’Ambrosio, Claudia . . . . . . . . . . . . . . . . TB-04, TC-04, WD-27
dambrosio@lix.polytechnique.fr
LIX, CNRS - Ecole Polytechnique, Palaiseau, France
D’Amico, Timothée . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-23
timothee.damico@gmail.com
Switzerland
D’Ecclesia, Rita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-63, TA-63
rita.decclesia@uniroma1.it
Statistics, Sapienza University of Rome, Rome, Roma, Italy
D’Haen, Ruben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-64
ruben.dhaen@uhasselt.beResearch Group Logistics, Hasselt University, Belgium
D’Inverno, Giovanna . . . . . . . . . . . . . . . . . . . . . . . . WD-44, TA-48
giovanna.dinverno@unipi.it
Department of Economics and Management, University of
Pisa, Pisa, Italy
D. Palsule-Desai, Omkar . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-24
omkardpd@iimidr.ac.in
India Institute of Management Indore, India

Dagistanli, Gunsu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-10
gunsu@metu.edu.tr
Industrial Engineering, Middle East Technical University,
Ankara, Turkey
Dahanayaka, Mahekha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-58
u.d.d.m.dahanayaka@utwente.nl
Department of High-tech Business and Entrepreneurship,
University of Twente, Enschede, Netherlands
Dahimi, Aria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-52
a.dahimi@tue.nl
IE&IS, Eindhoven University of Technology, Eindhoven,
North Brabant, Netherlands
Dai, Hongyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-40
daihy@cufe.edu.cn
Central University of Finance and Economics, Beijing, China
Dai, Jinyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-38
daiyk@126.com
ChinaDai, Qiyuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-57
qiyuan.dai@vsb.cz
Finance, Technical University of Ostrava, Ostrava, Czech
Republic, China

da Silva, Aneirson Francisco . . . . . . . . . . . . . . . . . . . . . . . . TD-28
aneirson.silva@unesp.br
São Paulo State University, Guaratinguetá, São Paulo, Brazil

Dall’Aglio, Marco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-36
mdallaglio@luiss.it
Dept of Economics and Finance, LUISS University, Rome,
Italyda Silva, Antonio Sergio . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-17
tao281168@gmail.com
PPG Engenharia de Produção, Universidade Federal Fluminense, Niteroi, Rio de Janeiro, Brazil

Dalle, Guillaume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-02
guillaume.dalle@epfl.ch
IdePHICS, INDY & SPOC labs, EPFL, Lausanne, Switzerlandda Silva, João Camilo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-64
joaocsilva.js@gmail.com
IEAv, Brazil

Daltrophe, Hadassa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-52
hadasda1@sce.ac.il
SCE, Ashdod, Israel, Israelda Silva, Miguel Gustavo Gomes . . . . . . . . . . . . . . . . . . . . WA-48
miguel.gustavo@ufpe.br
Campus Agreste, Federal University of Pernambuco, Caruaru, Brazil

Damu, Claudia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-13
claudia.damu@wfp.org
World Food Programme, Italy

Dabbene, Fabrizio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-35
fabrizio.dabbene@polito.it
CNR, Torino, Italy

Daneshvar, Sahand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-47
sahand.daneshvar@emu.edu.tr
Industrial Engineering, Eastern Mediterranean University,
Famagusta, Famagusta, Turkey

Dabia, Said . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-35
s.dabia@vu.nl
Department of Information, Logistics and Innovation, VU
University Amsterdam, Netherlands

Dang, Chuangyin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-40
mecdang@cityu.edu.hk
Systems Engineering, City University of Hong Kong,
Kowloon, Hong Kong

Dabkiene, Vida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-48
vida.dabkiene@ekvi.lt
Lithuanian Centre for Social Sciences, Vilnius, Lithuania

Dang, Quang-Vinh . . . . . . . . . . . . . . . . . . . . . . . . . MA-31, WD-58
q.v.dang@tue.nl
Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Netherlands

Dadush, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-26
dadush@cwi.nl
Networks & Optimization, CWI, Amsterdam, North Holland,
Netherlands

540

Dang, Thu Huong . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-12, TD-26
t.h.dang@lancaster.ac.uk
Mathematics and Statistics, Management Science, LancasterEURO 2024 - Copenhagen
University, Fulwood, Preston, Lancashire, United Kingdom
Dangerfield, Brian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-29
dangerfiel@gmail.com
Loughborough Business School, Loughborough University,
United Kingdom
Daniele, Patrizia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-40
patrizia.daniele@unict.it
Department of Mathematics and Computer Science, University of Catania, Catania, Italy
Dankers, Polle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-13
p.w.dankers@tilburguniversity.edu
Department of Econometrics and Operations Research,
Tilburg University, Netherlands
Darlay, Julien . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-30
jdarlay@hexaly.com
Hexaly, Paris, France
Darvay, Zsolt. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-38
zsolt.darvay@ubbcluj.ro
Department of Mathematics and Computer Science of the
Hungarian Line, Babes-Bolyai University, Cluj-Napoca, Romania
Das, Soham . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-17
mez198426@mech.iitd.ac.in
Department of Mechanical Engineering, Indian Institute ofTechnology Delhi, New Delhi, Delhi, India
Dasaklis, Thomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-15
dasaklis@unipi.gr
Hellenic Open University, Greece
Dash, Gordon. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-18, WD-18
ghdash@uri.edu
Finance and Decision Sciences, University of Rhode Island,
Kingston, RI, United States
Dash, Miriam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-18
dash.miriam@gmail.com
Urologic Surgery, Mayo Clinic, United States
Datola, Giulia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-47
giulia.datola@polimi.it
DAStU - Department of Architecture and Urban Studies, Politecnico di Milano, Milan, Italy, Italy
Daultani, Yash . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-44
yash@iiml.ac.in
Operations Management, Indian Institute of Management
Lucknow, Lucknow, Uttar Pradesh, India
Dauzere-peres, Stéphane . . . . . WB-52, MD-54, TB-58, WA-60
dauzere-peres@emse.fr
Manufacturing Sciences and Logistics, Ecole des Mines deSaint-Etienne - LIMOS, Gardanne, FranceDavidović, Tatjana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-52
tanjad@mi.sanu.ac.rs
Mathematical Institute of Serbian Academy of Sciences andArts, Belgrade, Serbia, Serbia

AUTHOR INDEX

Dávila, Sebastián . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-19
sebastian.davila@usach.cl
Industrial Engineering, Universidad de Santiago de Chile,
Chile
Davila-Pena, Laura . . . . . . . . . . . . . . . . . . . . . . . . . WD-12, MD-36
l.davila-pena@kent.ac.uk
Department of Analytics, Operations and Systems, Kent
Business School, University of Kent, Canterbury, Kent,
United Kingdom
Davis, Alice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-15
alice.davis@mayden.co.uk
Mayden, Bath, United Kingdom
Davis, Robert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-31
rdavis88@uthsc.edu
University of Tennessee Health Science Center, United States
Davison, Matt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-31
mdavison@uwo.ca
Applied Math and Statistical & Actuarial Sciences, University of Western Ontairo, London, Ontario, Canada
Davitashvili, Tinatin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-47
tinatin.davitashvili@tsu.ge
The Faculty of Exact and Natural Sciences, Ivane
Javakhishvili Tbilisi State University, Tbilisi, Georgia
Daw, Andrew . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-20
dawandre@usc.edu
University of Southern California, United States
Dayarian, Iman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-56
idayarian@ua.edu
The University of Alabama, United States
Díaz Pérez, Álvaro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-39
aldiazp@inst.uc3m.es
uc3m-Santander Big Data Institute, Universidad Carlos III de
Madrid, Madrid, Spain
Díaz, Antonio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-51, WC-63
antonio.diaz@uclm.es
Universidad de Castilla-La Mancha, Albacete, Spain
Díaz-Ríos, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-29
ddiazrio@ull.edu.es
Universidad de La Laguna, La Laguna, Tenerife, Spain
Désir, Antoine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-27
antoine.desir@insead.edu
ISEAD Business School, Fontainebleau, France
De Angelis, Tiziano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-63
tiziano.deangelis@unito.it
University of Torino, Italy
de Araujo, Silvio Alexandre . . . . . . . . . . . . . . . . . MB-49, WD-49
silvio.araujo@unesp.br
Departamento de Matemática, Universidade EstadualPaulista-UNESP, SÃo JosÉ Do Rio Preto, Sao Paulo, Brazil
de Armas, Jésica . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-21
jesica.dearmas@upf.edu

541

AUTHOR INDEXEURO 2024 - Copenhagen

Universitat Pompeu Fabra, Spainde Arruda Signorini, Caroline . . . . . . . . . . . . . . . . . . . . . . MB-49
carolineasignorini@gmail.com
Departamento de Matemática, Universidade EstadualPaulista (Unesp), Brazil
De Backer, Bruno . . . . . . . . . . . . . . . . . . . . . . . . . . MC-03, WD-58
bdb@google.com
Operations Research, Google Research, Paris, France
De Blauwe, Jilles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-14
jcdbl@dtu.dk
DTU Management, DTU, Denmark
De Bock, Koen W. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-27
koen.debock@gmail.com
Department of Marketing, Audencia Business School,
Nantes, France

vincent.degooyert@ru.nl
Nijmegen School of Management, Radboud University,
Netherlands
De Greve, Zacharie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-09
zacharie.degreve@umons.ac.beElectrical Power Engineering Unit, Unkiversité de Mons,
Mons, Hainaut, Belgium
de Jonge, Bram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-39
b.de.jonge@rug.nl
Operations, University of Groningen, Groningen, Netherlands
De Kerpel, Lukas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-31
lukas.dekerpel@ugent.beMarketing, cluster Data Analytics, Ghent University, Lede,
East-Flanders, Belgium

De Boeck, Jérôme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-45
jerome.deboeck@uliege.be
HEC Liège, Université de Liège, Liège, Belgium

De Klerk, Etienne . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-38
e.deklerk@uvt.nl
Econometrics and OR, Tilburg University, Tilburg, Netherlandsde Bruin, Kelly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-22
kelly.de.bruin@econ.umu.se
cere, Umeå University, Umeå, Sweden

de Kok, Ton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-11, TD-34
a.g.d.kok@tue.nl
School of IE, TUE, Eindhoven, Netherlandsde Bruin, Philip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-54
p.debruin@uu.nl
Information and Computing Sciences, Utrecht University,
Utrecht, Netherlandsde Koster, René . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-18, MD-50
rkoster@rsm.nl
Rotterdam School of Management, Erasmus University Rotterdam, Rotterdam, Netherlandsde Bruyn, Menno . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-54
Menno.deBruyn@NS.NL
Netherlands Railways, Netherlands

de la Paz Guala, Ricardo . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-55
delapaz8866@gmail.com
Facultad de Ciencias Físicas y Matemáticas, Universidad deChile, Santiago, Región Metropolitana, Chile

De Cock, Kurt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-15
Kurt.DeCock@ugent.beIndustrial Systems Engineering and Product Design, Ghent
University, Belgium

de Laat, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-38, WA-38
mail@daviddelaat.nl
TU Delft, Delft, Netherlands

De Crescenzo, Ivan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-63
ivan.decrescenzo@uniroma1.it
Department of Economic and Social Sciences, Sapienza University of Rome, Rome, Italy
De Falco, Alessia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
alessia.defalco@unina.it
Federico II University of Naples, Italy
de Ferrante, Hans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-10
h.c.d.ferrante@tue.nl
Mathematics & Operations Research, Eindhoven Universityof Technology, Eindhoven, Noord-Brabant, Netherlands

De Laet, Tinne . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-27
tinne.delaet@kuleuven.be
KU Leuven, Belgium
De Lara, Michel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-09, TC-42
michel.delara@enpc.fr
École des Ponts ParisTech, Marne la Vallée Cedex, France
De Leeuw, Sander . . . . . . . . . . . . . . . . . . WA-13, WD-13, MC-23
sander.deleeuw@wur.nl
Wageningen University & Research, WAGENINGEN„ NL,
Netherlands

De Giovanni, Domenico . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-33
pietro.degiovanni@sdabocconi.it
Unical, Calabria, Italy

De Maio, Annarita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-58
annarita.demaio@unical.it
Department of Economics, Statistic and Finance "Giovanni
Anania", University of Calabria, Italy

De Giovanni, Pietro . . . . . . . . . . . . . . . . . . . . . . . . WC-23, MD-33
ddegiovanni@unical.it
Bocconi, Milan, Italy

De Marchi, Alberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-32
alberto.demarchi@unibw.de
University of the Bundeswehr Munich, Neubiberg, Germany

De Gooyert, Vincent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-11

De Marchi, Monica Maria . . . . . . . . . . . . . . . . . . . . . . . . . . MC-64

542

EURO 2024 - Copenhagenmonicammm@fab.mil.br
EC4ISR Division, Institute for Advanced Studies - IEAv, SãoJosé dos Campos, São Paulo, Brazil
De Marchis, Roberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-39
roberto.demarchis@uniroma1.it
Sapienza, Italy
De Marco, Francesco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-53
fdemarco@ethz.ch
D-MAVT, ETH Zurich, Zürich, Switzerlandde Matta, Renato . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-17
renato-dematta@uiowa.edu
Business Analytics and Information Systems, University of
Iowa, Iowa City, Iowa, United States
De Matteis, Luca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-55
luca.dematteis@unina.it
Department of Civil, Architectural and Environmental Engineering, Federico II University of Naples, Naples, Italy, Italy
de Meijer, Frank . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-38, WB-38
f.j.j.demeijer@tudelft.nl
Delft Institute of Applied Mathematics, Delft University ofTechnology, Delft, Zuid-Holland, Netherlands
De Moor, Jakob . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-27
jakob.demoor@kuleuven.be
FEB, KU Leuven, Leuven, Vlaams-Brabant, Belgium
de Muinck Keizer, Willem . . . . . . . . . . . . . . . . . . . . . . . . . . MC-38
willemdmk@gmail.com
Delft Institute of Applied Mathematics, TU Delft, Delft,
Netherlands
De Munck, Thomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-55
thomas.demunck@uclouvain.beCenter for Operations Research and Econometrics, UCLouvain, Louvain-la-Neuve, Belgium
De Pater, Irene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-07
i.depater@ecu.edu.au
School of Business and Law, Edith Cowan University, Australia
de Queiroz, Anderson Rodrigo . . . . . . . . . . . . . . . . . . . . . . TD-28
ardequei@ncsu.edu
North Carolina State University, North Carolina, UnitedStatesDe Rock, Bram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-17
bderock@ulb.ac.beEcares, Université Libré de Bruxelles, Brussels, Belgium
De Santis, Alberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-03
desantis@diag.uniroma1.it
Sapienza University of Rome, Rome, Italy
De Santis, Marianna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-37
marianna.desantis@unifi.it
University of Florence, Italy
De Santis, Marianna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-37
mdesantis@diag.uniroma1.it

AUTHOR INDEXDIAG, Sapienza, University of Rome, Roma, Italy
De Simone, Valentina . . . . . . . . . . . . . . . . . . . . . . . . TB-32, MA-34
valentina.desimone@unicampania.it
Mathematics and Physics, University of Campania "L. Vanvitelli", Italy
de Souza, Marcelo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-15
marcelo.desouza@udesc.br
Department of Software Engineering, Universidade do Estado de Santa Catarina, Brazil
de Vicente, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-35
a12032143@unet.univie.ac.atUniversity of Vienna, Austria
De Vincentiis, Paola . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-63
paola.devincentiis@unito.it
Management, University of Torino, Italy
De Vos, Simon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-31
simon.devos@kuleuven.be
Faculty of Economics and Business, KU Leuven, Leuven,
Belgiumde Vries, Harwin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-21
harwin.devries@rsm.nl
Rotterdam School of Management, Rotterdam, Netherlands
De Vuyst, Stijn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-15, MD-23
stijn.devuyst@ugent.beIndustrial Systems Engineering and Product Design, Ghent
University, Zwijnaarde, Belgium
De Weerdt, Jochen . . . . . . . . . . . . . . . . . . TB-27, WA-31, WB-31
jochen.deweerdt@kuleuven.be
Decision Sciences and Information Management, KU Leuven, Belgium
de Werra, Dominique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-25
dominique.dewerra@epfl.ch
IMA, EPFL, Lausanne, Switzerland
De Wolf, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-19
daniel.dewolf@univ-littoral.fr
Tves & Core -ucl, Université du Littoral, Dunkerque Cedex
1, Francede Wolff, Timo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-42
dewolff@math.tu-berlin.de
TU Braunschweig, Germany
Dearing, Lin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-61
pmdrn@clemson.edu
Mathematical and Statistical Sciences, Clemson University,
Clemson, South Carolina, United States
Deb, Kalyanmoy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-11
kdeb@egr.msu.edu
Computer Science and Engineering, Michigan State University, Lansing, MI, United States
Debo, Laurens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-15
laurens.g.debo@tuck.dartmouth.edu
Dartmouth College, United States

543

AUTHOR INDEXEURO 2024 - Copenhagen

Debouté, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-19
martin.deboute@artelys.com
Artelys, Paris, France
Debry, Edouard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-42
edouard.debry@mbda-systems.com
MBDA, Le Plessis-Robinson, France
Dechter, Rina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-34
dechter@ics.uci.edu
University of California Irvine, United States
Dehaybe, Henri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-20
henri.dehaybe@hotmail.be
Center for Operations Research and Econometrics, Université Catholique de Louvain, Gembloux, Belgiumarne.delaet@uhasselt.beResearch Group Logistics, Hasselt University, Diepenbeek,
Limburg, Belgium
Delahoz-Domínguez, Enrique . . . . . . . . . . . . . . . . . . . . . . . . TA-48
enriquedelahoz@unimagdalena.edu.co
Universidad del Magdalena, Colombia
Delarue, Erik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-03
erik.delarue@kuleuven.be
Applied Mechanics and Energy Conversion Section, KU Leuven, Leuven, Belgium
Deleplanque, Samuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-42
deleplanque.samuel@gmail.com
Univ. Lille, CNRS, Centrale Lille, Junia, Univ. Polytechnique Hauts-de-France, UMR 8520 - IEMN - Lille, France,
Lille, France

Dehghan, Milad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-07
m.dehghan@deakin.edu.au
Information Technology, Deakin University, Melbourne, Victoria, Australia

Delgado Antequera, Laura . . . . . . . . . . . . . . . . . . . . . . . . . WC-13
lauda1g10@uma.es
University of Malaga, Malaga, Spain

DehghanChenary, Maryam . . . . . . . . . . . . . . . . . . . . . . . . . MB-60
maryam.dehghan.chenary@univie.ac.at
Vienna University, Wien, Austria

Delgado, Alberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-46, TA-62
alberto.delgado@optivation.dk
Optivation, Copenhagen, Denmark

Dehnokhalaji, Akram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-48
akramdehnokhalaji@gmail.com
Operations and Information Management Department, AstonBusiness School, United Kingdom

Delias, Pavlos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-45
pdelias@af.ihu.gr
Business School, Democritus University of Thrace, Kavala,
GreeceDejkam, Rahil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-06, TA-14
rahil.dejkam@eonerc.rwth-aachen.de
School of Business and economics, Chair for Energy Economics and Management (FCN-ECO), Online, Aachen, Germany

Deliismail, Ozgun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-35
ozgun.deliismail@socar.com.tr
SOCAR Turkey R&D and Innovation Co., Turkey

Dekker, Rommert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-62
rdekker@ese.eur.nl
Erasmus University Rotterdam, Rotterdam, Netherlands

Dell’Amico, Mauro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-60
mauro.dellamico@unimore.it
DISMI, Università degli Studi di Modena e Reggio Emilia,
Reggio Emilia, Italy

del Cerro, Arturo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-27
adelcerr@pa.uc3m.es
Universidad Carlos III de Madrid, Spain

Dell’Anna, Federico . . . . . . . . . . . . . . . . . . . . . . . . . MD-18, TC-47
federico.dellanna@polito.it
Interuniversity Department of Regional and Urban Studies
and Planning, Politecnico di Torino, Torino, Torino, Italy

del Favero, Brendan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-63
bdelfavero@fico.com
FICO, Xpress Optimization, US, United States

Della Croce, Federico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-26
federico.dellacroce@polito.it
DIGEP, Politecnico di Torino, Torino, Italy

Del Nibletto, Marco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-18
marco.delnibletto@polito.it
Dipartimento interateneo di SCIENZE, PROGETTO E
POLITICHE DEL TERRITORIO (DIST), Politecnico di
Torino, Torino, Torino, Italy

Delle Donne, Diego . . . . . . . . . . . . . . . . . . . . . . . . . . TC-27, MB-52
diegodd@gmail.com
IDS department, ESSEC Bussines School, Paris, Ile deFrance, Francedel Rio Chanona, Antonio . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-45
a.del-rio-chanona@imperial.ac.uk
Imperial College London, London, United Kingdom
del Rosario, Elise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-20
elise@jgdelrosario.com
OSSFFI, Quezon City, Metro Manila, Philippines
Delaet, Arne . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-15

544

Dello Ioio, Giuseppina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-16
giuseppina.delloioio@studenti.uniparthenope.it
Department of Economic and Legal Studies, University of
Naples Parthenope, Naples, Italy
DellOvo, Marta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-47, TD-47
marta.dellovo@polimi.it
Department of Architecture and Urban Studies, Politecnicoof Milano, Milano, Italy

EURO 2024 - Copenhagen
Delorme, Maxence . . . . . . . . . . . . TC-10, TB-25, TC-25, WB-28
m.delorme@tilburguniversity.edu
Tilburg University, Netherlands
Delorme, Maxence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-10
maxence.delorme2@unibo.it
University of Bologna, Bologna, Italy
Delorme, Xavier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-29
delorme@emse.fr
Fayol-emse, Cnrs, Umr 6158, Limos, Ecole des Mines deSaint Etienne, Saint Etienne, France

AUTHOR INDEX

m.h.den.hertog@isala.nl
Isala, Netherlands
Deng, Jiali . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-34
jd@math.ku.dk
Mathematical Sciences, University of Copenhagen, København, Denmark
Deniz, Berk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-23
cihan.deniz@ozu.edu.tr
Ozyegin University, Turkey

Delplancke, Claire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-32
claire.delplancke@edf.fr
EDF Lab Paris-Saclay, France

Denk, M. Mücahit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-28
mdenk17@ku.edu.tr
Graduate School of Sciences and Engineering, Koç Üniversitesi, İstanbul, Turkey

Demartini, Andrea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
andrea.demartini@a2a.it
A2A company, Italy

Densing, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-09, MB-14
martin.densing@psi.ch
Energy Economics, PSI, Villigen, Switzerland

Demeulemeester, Erik . . . . . . . . . . . . . . . WA-06, WC-10, MC-60
erik.demeulemeester@kuleuven.be
KBI, KU Leuven, Leuven, Belgium

Deprez, Bruno . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-31
bruno.deprez@kuleuven.be
Faculty of Economics and Business, KU Leuven, Leuven,
Belgium

Demeulemeester, Tom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-43
tom.demeulemeester@kuleuven.be
ORSTAT, KU Leuven, Belgium
Demir, Ali Eren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-26
eren.demir@bilkent.edu.tr
Bilkent University, Ankara, Select Region, Turkey
Demiray, Onur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-50
onur.demiray@trendyol.com
Data Science, Trendyol, Turkey
Demirel, Fatih Duygun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-12
d.demirel@iku.edu.tr
Industrial Engineering, Istanbul Kültür University, Turkey
Demirel, Guven . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-24
guvendemirel@gmail.com
School of Business and Management, Queen Mary University of London, London, United Kingdom
Demirel, Halil ibrahim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-04
halilibrahim.demirel@bahcesehir.edu.tr
TurkeyDemirtas, Derya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-31
d.demirtas@utwente.nl
Industrial Engineering & Business Information Systems,
University of Twente, Enschede, Netherlands
Dems, Amira . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-06
amira.demes@gmail.com
HydroQuebec research institute, Hydro-Quebec, varennes,
Quebec, Canadaden Hertog, Dick . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-15, MD-27
d.denhertog@uva.nl
University of Amsterdam, Amsterdam, Netherlands
den Hertog, Heleen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-10

Deriu, Paola . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-40
p.deriu@consob.it
Commissione Nazionale per le Società e la Borsa, Italy
Derpich, Ivan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-62
ivan.derpich@usach.cl
Industrial Engineering Department, University of Santiago of
Chile, Santiago, Region metropolitana, Chile
Derval, Guillaume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-38
gderval@uliege.be
University of Liège, Liege, Liege, Belgium
Derval, Guillaume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-38
guillaumederval@gmail.com
Belgium
Desaulniers, Guy . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-51, MD-52
Guy.Desaulniers@gerad.ca
École Polytechnique de Montréal and GERAD, Montréal,
CanadaDesbordes, Antoine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-44
antoine.desbordes@psi.chLaboratory for Energy Systems Analysis, Paul Scherrer Institute, France
Deshmukh, Sanjeev Govindrao . . . . . . . . . . . . . . . . . . . . . WB-45
deshmukh@mech.iitd.ac.in
Mechanical Engineering, Indian Institute Of Technology
Delhi, New Delhi, Delhi, India
Despic, Ozren. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-31
o.despic@aston.ac.uk
Operations and Information Management, Aston University,
Birmingham, West Midlands, United Kingdom
Desport, Pierre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-24
pierre.desport@univ-tours.fr

545

AUTHOR INDEXEURO 2024 - Copenhagen

LIFAT, Université de Tours, France

Department of Economics, Aristotle University of Thessaloniki, Greece

Despotis, Dimitris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-48
despotis@unipi.gr
Department of Informatics, University of Piraeus, Piraeus,
GreeceDiaz-Cachinero, Pablo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-19
pablo.diaz.cachinero@uc3m.es
Universidad Carlos III de Madrid, Leganés, Spain

Detienne, Boris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-21
boris.detienne@math.u-bordeaux1.fr
Mathematics Institute of Bordeaux, University of Bordeaux,Talence Cedex, France

Diaz-Diaz, Fernando . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-63
fernandodiaz@ifisc.uib-csic.es
Institute of Cross Disciplinary Physics and Complex Systems, Spain

DeTombe, Dorien. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-20
detombe@nosmo.nl
School of Systems Engineering, Delft University of Technology, Delft, Europe, Netherlands

Diaz-Tena, Nuria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-40
nd796@newark.rutgers.edu
MSIS, Rutgers University, Princeton, NJ, United States

Deutsch, Yael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-64
yaeldeut@gmail.com
Bar Ilan, Haifa, Israel, Israel
Devalle, Federico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
federico.devalle@a2a.it
A2A company, Italy
Devine, Mel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-09
mel.devine@ucd.ie
University College Dublin, Ireland
Dewantara, Muhammad Satrya . . . . . . . . . . . . . . . . . . . . . TD-62
muhammadsatryadewantara@student.tudelft.nl
TU Delft, Netherlands
Deza, Antoine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-42
deza@mcmaster.ca
McMaster University, Hamilton, Ontario, Canada, Canada
Dezem, Vinícius . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-04, TD-37
vinicius.dezem@posgrad.ufsc.br
Knowledge Engineering and Management, Federal University of Santa Catarina, Florianópolis, (SC) Santa Catarina,
BrazilDi Cosmo, Valeria . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-09, WA-19
valeria.dicosmo@unito.it
Dipartimento di Economia e Statistica "Cognetti de Martiis",
Università di Torino, Italy
Di Martinelly, Christine . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-15
c.dimartinelly@ieseg.fr
Management, IESEG School of Management, Univ. Lille,
CNRS, UMR 9221 - LEM - Lille Economie Management,
Lille, Nord, France
Di Paolo, Alessio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-02
alessio.dipaolo@uniroma3.it
Business Studies, Roma Tre University, Rome, Italy, Italy
Di Teodoro, Giulia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-27
giuliaditeodoro@gmail.com
Department of Information Engineering, University of Pisa,
Pisa, Italy
Diamantidis, Alexandros . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-10
adiama@econ.auth.gr

546

Diego Martín, Francisco Javier . . . . . . . . . . . . . . . . . . . . . MD-58
javier.diego@oga.ai
oga, CAMAS-SEVILLA, Sevilla, Spain
Dienstbier, Jana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-04
jana.jd.dienstbier@fau.de
Department of Data Science, Friedrich-Alexander University
Erlangen-Nuremberg, Germany
Dieter, Peter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-64
peter.dieter@uni-paderborn.de
Management Information Systems, Paderborn University,
Germany
Dieuleveut, Aymeric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-32
aymeric.dieuleveut@gmail.com
CMAP (Applied Maths), Ecole Polytechnique, Institut Polytechnique de Paris, France
Dieuleveut, Aymeric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-32
aymeric.dieuleveut@polytechnique.edu
CMAP, Ecole Polytechnique, Paris, France
Digalakis, Vassilis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-27
digalakis@hec.fr
Information systems and operations management, HEC Paris,
FranceDiglio, Antonio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-61
antonio.diglio@unina.it
Department of Industrial Engineering, University of Naples
Federico II, Napoli, Napoli, Italy
Dileep, Anandhu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-64
anandhu.dileep@uib.noInformatics, University of Bergen, Bergen, Norway
Dimanchev, Emil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-09
emil.dimanchev@ntnu.no
Electric Power Engineering, NTNU, Trondheim, Norway
Dimant, Shai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-53
dimant@mathematik.uni-kl.de
Department Mathematics, AG OPT, RPTU Kaiserslautern,
Kaiserslautern, Rheinland-Pfalz, Germany
Dimitrakos, Theodosis . . . . . . . . . . . . . . . . . . . . . . . MA-10, TA-64
dimitheo@aegean.gr
Mathematics, University of the Aegean, Karlovassi, Samos,

EURO 2024 - Copenhagen
GreeceDimitriou, Ioannis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-40
idimit@uoi.gr
Mathematics, University of Ioannina, Ioannina, Greece
Dimopoulos, Stavros . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-08
sdim@sdim.gr
University of Patras, Greece
Dinc Yalcin, Gulcin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-41
gdinc@eskisehir.edu.tr
Industrial Engineering, Eskisehir Technical University, Eskisehir, Turkey, Turkey
Ding, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-15
li.ding@durham.ac.uk
Durham business school, Durham University, Duhram,
United Kingdom

AUTHOR INDEX

Dokka, Trivikram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-35
t.dokka@lancaster.ac.uk
Management Science Department, Lancaster University,
Lancaster, United Kingdom
Dolgui, Alexandre . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-24, TD-60
alexandre.dolgui@imt-atlantique.fr
LS2N, IMT Atlantique, NANTES Cedex 3, France
Dollevoet, Twan . . . . . . . . . . . . . . . . . . . . . TD-10, MA-54, MB-54
dollevoet@ese.eur.nl
Econometric Institute, Erasmus University of Rotterdam,
Rotterdam, Netherlands
Domínguez-Martín, Bencomo . . . . . . . . . . . . . . . . . . . . . . . MC-25
bdomingu@ull.edu.es
University of La Laguna, La Laguna, Spain

Ding, Sihan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-40
dingsihan@hotmail.com
Apple Inc, China

Domenech, Bruno . . . . . . . . . . . . . . . . . . . . . . . . . . MB-14, MB-19
bruno.domenech@upc.edu
Institute of Industrial and Control Engineering (IOC), Universitat Politècnica de Catalunya - BarcelonaTech (UPC),
Barcelona, Spain

Ding, Yifu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-22
yifuding@mit.edu
MIT Energy Initiative, MIT, Cambridge, Massachusetts,
United States

Domenig, Christoph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-19
christoph.domenig@gmail.com
Department of Mechanical and Process Engineering, Chairof Energy System Analysis, Zürich, Zürich, Switzerland

Disney, Stephen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-39
S.M.Disney@exeter.ac.uk
University of Exeter Business School, United Kingdom

Dominguez, Ruth . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-19, TC-34
ruth142@gmail.com
Economics and Management, University of Brescia, Brescia,
Italy

Djordjevic, Predrag . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-52
predrag.djordjevic@pmf.edu.rs
Mathematical Institute, Serbian Academy of Sciences andArts, Niš, Serbia, Serbia
Dkhili, Nouha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-22
nouha.dkhili@totalenergies.com
TotalEnergies, Palaiseau, Île-de-France, France
Doža, Robert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-52
dozarobert98@gmail.com
Faculty of Mathematics, University of Belgrade, Serbia
Doan, Xuan Vinh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-36
xuan.doan@wbs.ac.uk
Warwick Business School, The University of Warwick,
Coventry, West Midlands, United Kingdom
Dobre, Cristian. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-16
c.dobre@tilburguniversity.edu
Econometric and Operations Resreach, Tilburg University,
Maarssen, Netherlands
Doerner, Karl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-58, TA-64
karl.doerner@univie.ac.at
Department of Business Decisions and Analytics, Universityof Vienna, Vienna, Vienna, Austria
Doikov, Nikita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-32
nikitad101@gmail.com
EPFL, Switzerland

Donatelli, Marco . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-34, MB-34
marco.donatelli@uninsubria.it
Università degli studi dell’Insubria, Italy
Donati, Edda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-45
edonati@iuav.it
Department of Architecture and Arts, university IUAV of
Venice, Venice, Italy, Italy
Doneda, Martina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-58
martina.doneda@polimi.it
DEIB, Politecnico di Milano, Italy
Dong, Chuanwen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-24
cdong@escp.eu
ESCP Business School, Germany
Dong, Yuqun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-22
230228714@seu.edu.cn
Nanjing, China
Donmez, Zehranaz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-21
zehranaz.donmez@bilkent.edu.tr
Industrial Engineering, Bilkent University, ankara, Ankara,
TurkeyDoostmohammadi, Mahdi . . . . . . . . . . . . . . . . . . . . . . . . . . MA-29
m.doostmohammadi@strath.ac.uk
Management Science, University of Strathclyde, United
Kingdom

547

AUTHOR INDEXEURO 2024 - Copenhagen

Dora, S Yesaswi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-51
yesaswi@kgpian.iitkgp.ac.in
Vinod Gupta School Of Management, Iit Kharagpur,
KHARAGPUR, West Bengal, India

philippe.drobinski@lmd.ipsl.fr
LMD-IPSL, Ecole Polytechnique, Institut Polytechnique deParis, ENS, PSL Research University, Sorbonne Université,
CNRS, France

Dorazio, Alessandro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-63
alessandro.dorazio@uniroma1.it
Department of Statistics, Sapienza University of Rome, Italy

Drozdowski, Maciej . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-26
maciej.drozdowski@cs.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poznan, Poland

Dornai, Zsófia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-36
zsofid77@gmail.com
Budapest University of Technology and Economics, Instituteof Mathematics, Hungary
Dorsman, Jan-Pieter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-56
j.l.dorsman@uva.nl
Korteweg-de Vries Institute for Mathematics, University of
Amsterdam, Netherlands
Dose, Valerio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-52
valerio.dose@uniroma1.it
Department of Computer, Control and Management Engineering, Sapienza University of Rome, Italy
Dossal, Charles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-34
charles.dossal@math.u-bordeaux1.fr
Université Bordeaux 1, Talence Cedex, France
Dotti, Giulia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-45
giulia.dotti@unimore.it
University of Modena and Reggio Emilia, Italy
Doumpos, Michalis . . . . . . . . . . . . . . . . . MA-47, MB-47, WC-47
mdoumpos@tuc.gr
School of Production Engineering and Management, Technical University of Crete, Chania, Greece
Downward, Anthony . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-31
a.downward@auckland.ac.nz
Engineering Science, University of Auckland, Auckland,
New Zealand
Dowsey, Andrew . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-15
andrew.dowsey@bristol.ac.uk
University of Bristol, United Kingdom
Doyen, Matteo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-18
matteo.doyen@etu.univ-tours.fr
Polytech Tours, University of Tours, Tours, France
Dragomir, Alina-Gabriela . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-58
alina-gabriela.dragomir@univie.ac.at
Business Decisions and Analytics, University of Vienna, Vienna, Austria
Drezner, Netanel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-23
drezner@g.jct.ac.il
Ariel University, Israel
Drezner, Zvi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-61
zdrezner@fullerton.edu
California State University, Fullerton, Fullerton, California,
United States
Drobinski, Philippe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-22

548

Du Toit, Tiny . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-25
tiny.dutoit@nwu.ac.za
School of Computer Science and Information Systems,
North-West University, Potchefstroom, North-West, South
AfricaDuan, Keru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-29
kxd226@student.bham.ac.uk
Business School, The University of Birmingham, Birmingham, West Midland, United Kingdom
Dubé, Laurette . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-28
laurette.dube@McGill.Ca
McGill University, Canada
Duer, Mirjam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-38
mirjam.duer@math.uni-augsburg.de
Augsburg University, Augsburg, Germany
Dufour, Maxime. . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-30, TD-49
maxime.dufour@artelys.com
Artelys, Paris, France
Dugundji, Elenna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-23
E.R.Dugundji@cwi.nl
CWI, Amsterdam, Noord-Holland, Netherlands
Duhaylongsod, Jose Benedicto . . . . . . . . . . . . . . . . . . . . . . WC-07
j.duhaylongsod@ieseg.fr
IÉSEG School of Management, Paris, France
Dukkanci, Okan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-61
dukkanci@europa-uni.de
Business Informatics, European University Viadrina, Frankfurt, Oder, Germany
Dullaert, Wout . . . . . . . . . . . . . . . . . . . . . . WA-15, TB-26, MB-52
w.e.h.dullaert@vu.nl
Operation analytics, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
Dullaert, Wout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-36
wout.dullaert@vu.nl
Faculty of Economics and Business Administration, VU University Amsterdam, Amsterdam, Netherlands
Dulskis, Vytautas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-07
vytautas.dulskis@mif.vu.lt
Institute of Data Science and Digital Technologies, Vilnius
University, Lithuania
Dumas, Jonathan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-35
jonathan.dumas@rte-france.com
RTE, Saint-Cyr-l’Ecole, FranceEURO 2024 - Copenhagen

AUTHOR INDEX

Dumont, Morgane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-20
morgane.dumont@uliege.be
HECLiege, HECLiege - University of Liege, Liege, Belgium

Dutta, Pritha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-24
pdutta@willamette.edu
Willamette University, United States

Dupaquis, Alexandre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-55
alexandre.dupaquis@gmail.com
APO, Irit Toulouse, Toulouse, France

Dvalishvili, Phridon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-33
pridon.dvalishvili@tsu.ge
Computer Science, Tbilisi State University, Tbilisi, Georgia

Dupont, Alix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-53
alix.dupont@edf.fr
LIA, Avignon University, Avignon, France, France

Dvorkin, Vladimir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-14
dvorkin@umich.edu
Electrical Engineering and Computer Science, University ofMichigan, United States

Dupont, Clarisse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-22
dupont@rsm.nl
Rotterdam School of Management, Erasmus University Rotterdam, Netherlands
Duran Mateluna, Cristian . . . . . . . . . . . . . . . . . . . . . . . . . . WD-19
cristian.duran@ensta-paris.fr
UMA, ENSTA, Institut Polytechnique de Paris, MASSY,FranceDuran, Claudia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-62
c.durans@utem.cl
Industry, Universidad Tecnológica Metropolitana, Santiago,
Chile
Duran, Ege . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-49
ege.duran@outlook.com
School of Computer Science and Information Technology,
University College Cork, Cork, Ireland
Durán, Guillermo . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-16, TD-16
gduran@dm.uba.ar
Calculus Institute, University of Buenos Aires, Argentina
Duran, Manuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-16
manud2307@gmail.com
FIUBA, UBA, CABA, Ciudad Autónoma de Buenos Aires,
Argentina
Durán-Micco, Javier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-55
javierduranmicco@gmail.com
Department of Maritime and Transport Technology, Delft
University of Technology, Netherlands
Durban, Maria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-27
marialuz.durban@uc3m.es
Universidad Carlos III de Marid, Spain
Durea, Marius . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-41
durea@uaic.ro
Mathematics, University Al. I. Cuza and Octav Mayer Institute of Mathematics, Iasi, Iasi, Romania
Duro, Joao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-13
j.a.duro@sheffield.ac.uk
Automatic Control and Systems Engineering, University of
Sheffield, Sheffield, United Kingdom
Dutordoir, Marie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-59
marie.dutordoir@manchester.ac.uk
Alliance Manchester Business School, University of Manchester, Manchester, United Kingdom

Dwyer_Matzky, Keely . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-10
keely_dwyermatzky@urmc.rochester.edu
University of Rochester Medical Center, United States
Dyner, Isaac . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-19
isaac.dynerr@utadeo.edu.co
Universidad Jorge Tadeo Lozano, Bogotá, Colombia
Dzahini, Kwassi Joseph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-32
kdzahini@anl.gov
Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, Illinois, United States
Dzahini, Kwassi Joseph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-32
liljoseph92@yahoo.com
Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, Illinois, United States
Dzik, Alicja . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-20
alicja.dzik@student.put.poznan.pl
Poznan University of Technology, Poland
Dziuba, Daryna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-49
dziuba@europa-uni.de
European University Viadrina, Frankfurt Oder, Germany
e Silva, Lucio Camara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-48
lucio.silva@ufpe.br
Núcleo de Tecnologia, Federal University of Pernambuco,
Caruaru, PE, Brazil
E.-Nagy, Marianna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-38
marianna.eisenberg-nagy@uni-corvinus.hu
Corvinus University of Budapest, Hungary
Ebadi Torkayesh, Ali . . . . . . . . . . . . . . . . WA-19, WB-47, TB-53
ali.torkayesh@socecon.rwth-aachen.de
Decision Analysis and Socio-Economic Assessment, RWTH
Aachen, Aachen, Germany
Ebel, Stefanie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-15
stefanie.ebel@uni-a.de
Chair of Health Care Operations/Health Information Management, Faculty of Business and Economics, University ofAugsburg, Augsburg, Bavaria, Germany
Eberhard, Onno . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-03
onnoeberhard@gmail.com
Max Planck Institute for Intelligent Systems / University of
Tübingen, Tübingen, Germany
Eberhardt, Katharina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-21

549

AUTHOR INDEXEURO 2024 - Copenhagenkatharina.eberhardt@kit.edu
Institute for Industrial Production (IIP), Karlsruhe Institute ofTechnology, Bitte auswählen ..., Germany
Ebrahimi Bajgani, Sahar . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-23
sebrahimibajgani@wpi.edu
Worcester Polytechnic Institute, Worcester, MA, UnitedStatesEbrahimi, Ahmad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-60
ahmad.ebrahimi@srbiau.ac.ir
Department of Industrial and Technology Management, Faculty of Management and Economics, Science and Researchbranch- Islamic Azad University, Tehran, Iran, Iran, Islamic
Republic ofEcheverria, Imanol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-28
imanol.echeverria@tecnalia.com
Smart Industry, Tecnalia, Spain
Eckley, Idris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-50
i.eckley@lancaster.ac.uk
Mathematics and Statistics, Lancaster University, United
Kingdom
Economidou, Claire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-63
economidou1@gmail.com
Economics, University of Piraeus, Piraeus, Attica, Greece
Efkes, Britta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-52
schulze@math.uni-wuppertal.de
Department of Mathematics and Informatics, University ofWuppertal, Wuppertal, NRW, Germany
Ehmke, Jan Fabian. . . . . . . . . . . . . . . . . . . . . . . . . . TC-54, WC-56
jan.ehmke@univie.ac.at
Business Decisions and Analytics, University of Vienna,
Wien, Austria
Ehrhardt, Matthias J. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-32
matthias.ehrhardt@bath.ac.uk
University of Bath, United Kingdom
Eibler, Katharina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-49
katharina.eibler@mondigroup.com
Wien, Austria
Eichfelder, Gabriele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-37
gabriele.eichfelder@tu-ilmenau.deInstitute of Mathematics, Technische Universität Ilmenau,
Ilmenau, Germany
Eichman, Josh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-22
jeichman@irec.cat
Energy Systems Analytics Research Group, Institut de Recerca en Energia de Catalunya, Spain
Eifler, Leon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-30
eifler@zib.de
Zuse-Institute Berlin (ZIB), Berlin, Germany
Eigruber, Markus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-11
markus.eigruber@tuwien.ac.at
Institute of Management Science, TU Wien, Austria

550

Eikenbroek, Oskar . . . . . . . . . . . . . . . . . . . . . . . . . . WD-55, TD-56
o.a.l.eikenbroek@utwente.nl
Transport Engineering and Management, University ofTwente, Enschede, Netherlands
Eirinakis, Pavlos . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-08, WD-29
pavlose@unipi.gr
Industrial Management and Technology, University of Piraeus, Greece
Eiselt, H.a. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-61
haeiselt@unb.ca
University of New Brunswick, Fredericton, NB, Canada
Eisenhandler, Ohad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-02
ohade@afeka.ac.il
Afeka College of Engineering, Tel Aviv, Israel
Eisenhandler, Ohad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-54
ohadeis@gmail.com
Industrial Engineering, Tel Aviv University, Tel Aviv, Israel
Ekholm, Tommi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-34
tommi.ekholm@fmi.fi
P.O. Box 503, Finnish Meteorological Institute, Helsinki,
Finland
Ekici, Ali . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-52, MB-60
ali.ekici@ozyegin.edu.tr
Industrial Engineering, Ozyegin University, Istanbul, Turkey
Ekin-Karasan, Oya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-22
karasan@bilkent.edu.tr
Industrial Engineering, Bilkent University, Turkey
Eksi, Tamey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-37
TEKSI20@KU.EDU.TR
Graduate School of Sciences and Engineering, Koç University, Turkey
El Ghazi, Aboubakr Achraf . . . . . . . . . . . . . . . . . . . . . . . . . MC-43
aboubakr.elghazi@dlr.deInstitute of Networked Energy Systems, German Aerospace
Center (DLR), Stuttgart, Germany
EL Hachem, Wissam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-24
elhachemw@gmail.com
Operations Management, EMLYON Business School,
LYON, France
El Hachemi, Nizar . . . . . . . . . . . . . . . . . . . MC-02, WD-55, TA-62
Nizar.ELHACHEMI@um6p.ma
Africa Business School, Mohammed VI Polytechnic University, Ben Guerir, Morocco
El Hallaoui, Issmail . . . . . . . . . MC-02, WA-25, WD-26, WB-29
issmail.elhallaoui@gerad.ca
Math. and Ind. Eng., Polytechnique Montréal and GERAD,
Montreal, Qué., Canada
El Harrab, Mohamed Saâd . . . . . . . . . . . . . . . . . . . . . . . . . WA-03
mohamed-saad.el_harrab@minesparis.psl.eu
Center for Management Science (CGS), i’ UMR 9217 CNRS,
Mines Paris - PSL, Paris, FranceEURO 2024 - Copenhagen
El RAOUI, Hanane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-43
hanane.el-raoui@strath.ac.uk
Management Science, University of Strathclyde, Glasgow,
United Kingdom
El-Adle, Amro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-64
aeladle@odu.edu
Department of Information Technology & Decision Sciences,
Old Dominion University, Virginia, United States
Elawady, Mohamed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-43
mohamed.elawady@strath.ac.uk
Computer and Information Sciences, University of Strathclyde, United Kingdom
Elbracht, Theresa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-43
t.elbracht@uni-bielefeld.de
Department of Business Administration and Economics,
Bielefeld University, Germany
Eleftheriou, Dimitrios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-09
del@eaea.dk
EA Energy Analysis, Copenhagen, Denmark
Elhafsi, Mohsen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-39
melhafsi@ucr.edu
School of Business Administration, University of California,
Riverside, CA, United States
Ellis, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-15
dae30@bath.ac.uk
School of Management, University of Bath, United Kingdom
Elmachtoub, Adam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-59
adam@ieor.columbia.edu
Industrial Engineering and Operations Research, Columbia
University, New York, NY, United States
Elsenbroich, Corinna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-13
Corinna.Elsenbroich@glasgow.ac.uk
University of Glasgow, United Kingdom
Emde, Simon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-50
simon.emde@uni-jena.de
Friedrich-Schiller-Universität Jena, Jena, Germany
Emiris, Dimitrios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-60
emiris@unipi.gr
Industrial Management & Technology, University of Piraeus,
Piraeus, Greece
Emrouznejad, Ali. . . . . . . . . . . . . . . . . . . . . . . . . . . MC-48, WB-48
a.emrouznejad@surrey.ac.uk
Surrey Business School, University of Surrey, Guildford,
Surrey, United Kingdom
Engels, Tim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-39
t.p.g.engels@tue.nl
Eindhoven University of Technology, Netherlands
Engin, Ayşegül . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-07, TB-11
ayseguel.engin@univie.ac.at
Business Aministration, University of Vienna, Vienna, Vienna, Austria

AUTHOR INDEXEngland, Tracey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-17
t.j.england@soton.ac.uk
University of Southampton, United Kingdom
Enríquez, Ricardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-09
ricardo.enriquez@repsol.com
Quantum Advisory Team, Repsol Technology Lab, Madrid,
Spain
Epstein, Leonardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-50
lepstein@uandes.cl
School of Business and Economics, Universidad de los Andes, Santiago, Santiago, Chile
Er Raqabi, El Mehdi . . . . . . . . . . . . . . . . MC-02, WA-25, WA-46
el-mehdi.er-raqabi@polymtl.ca
Math. and Ind. Eng., Polytechnique Montréal and GERAD,
Montréal, Québec, Canada
Er-Rahmadi, Btissam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-28
btissam.errahmadi@gmail.com
Knowledge Graph Lab, Huawei Technologies R&D, Edinburgh, Lothian, United Kingdom
Eran, Ronen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-02
ronenhani2@gmail.com
Afeka College of Engineering, Tel Aviv, Israel
Erdmann, Julia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-64
julia.erdmann@tu-dresden.deTechnische Universität Dresden, Germany
Erdogan, Bilgenur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-58
b.erdogan@tue.nl
Industrial Engineering & Innovation Sciences, Eindhoven
University of Technology, ’S-HERTOGENBOSCH, Netherlands
Erdogan, Gunes . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-15, WA-52
G.Erdogan@bath.ac.uk
School of Management, University of Bath, Bath, Somerset,
United Kingdom
Erdogan, Mehmet Serdar . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-56
mehmet.erdogan@kcl.ac.uk
United Kingdom
Erfurth, Philipp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-45
philipp.erfurth@wiwi.tu-chemnitz.deProfessur BWL - Produktionsmanagement, TU Chemnitz,
Chemnitz, Saxony, Germany
Erginel, Nihal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-55
nerginel@eskisehir.edu.tr
Industrial Engineering Department, Eskisehir Technical University, Turkey
Eriş, Müjgan Bilge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-12
mujganbilge.eris@yeditepe.edu.tr
Industrial Engineering, Yeditepe University, İstanbul, Turkey
Erland, Sveinung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-54
sver@hvl.no
Department of Maritime Studies, Western Norway Universityof Applied Sciences, Haugesund, Norway

551

AUTHOR INDEXEURO 2024 - Copenhagen

Erlebach, Thomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-26
thomas.erlebach@durham.ac.uk
University of Durham, United Kingdom

Espinosa, Miguel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-25
miguel.espinosa@galp.com
IT & Digital Department, GALP, Portugal

Erlwein-Sayer, Christina . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-08
erlwein@htw-berlin.de
Department of Business Mathematics, HTW University ofApplied Sciences Berlin, Berlin, Germany

Espinoza, Andrea . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-23, WB-23
andrea.espinozap@usach.cl
Universidad de Santiago de Chile, Chile

Ernst, Damien . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-38, WC-38
dernst@uliege.be
SmartGrids, University of Liège, Liege, Liege, Belgium

Espinoza, Lorena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-23
lorena.espinozap@usach.cl
Universidad de Santiago de Chile, Chile

Ersoy, Elif . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-27
elif.ersoy@ozu.edu.tr
Industrial Engineering, Ozyegin University, Istanbul, Turkey

Esposito Amideo, Annunziata . . . . . . . . . . . . . . . . TD-26, MB-61
annunziata.espositoamideo@ucd.ie
Quinn School of Business, University College Dublin,
Dublin, Ireland

Eruguz, Ayse Sena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-13
aseruguz@gmail.com
Operations Analytics, Vrije Universiteit Amsterdam, ROTTERDAM, Netherlands

Esposito, Flavia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-13
flavia.esposito@uniba.it
Department of Mathematics, University of Bari Aldo Moro,
Bari, Bari, Italy

Erzi, Can . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-18
can.erzi@sabanciuniv.edu
Business School, Sabanci University, Istanbul, Tuzla, Turkey

Esser, Katharina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-09
katharina.esser@rub.de
Chair of Energy Systems and Energy Economics, RuhrUniversity Bochum, Bochum, NRW, Germany

Escobar-Rodríguez, Laura Y. . . . . . . . . . . . . . . . . . . . . . . . . TA-58
Lescobar718@unab.edu.co
Universidad Autónoma de Bucaramanga, Bucaramanga, Santander, Colombia
Escribano, Ana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-63
ana.escribano@uclm.es
Department of Economics and Finance, Universidad deCastilla-La Mancha, Albacete, Albacete, Spain
Escudero, Laureano F. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-35
laureano.escudero@urjc.es
Estadística e Investigación Operativa, Universidad Rey Juan
Carlos, Mostoles (Madrid), Spain

Estanqueiro, Ana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-09
ana.estanqueiro@lneg.pt
LNEG, Portugal
Estévez Fernández, Arantza . . . . . . . . . . . . . . . . . . . . . . . . MB-36
arantza.estevezfernandez@vu.nl
Tinbergen Institute and Department of Econometrics andOperations Research., Vrije Universiteim, Amsterdam., Amsterdam, Netherlands
Esteban-Pérez, Adrián . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-35
estebanperez@rsm.nl
Rotterdam School of Management, Erasmus University ofRotterdam, Rotterdam, Netherlands

Esenduran, Gokce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-23
gesendur@purdue.edu
Supply Chain and Operations Management, Purdue University, West Lafayette, United States

Estella Aguerri, Inaki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-57
iaguerri@amazon.es
Amazon, Spain

Eskandarpour, Majid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-25
majideskandarpour@gmail.com
IÉSEG School of Management (LEM-CNRS 9221), France

Estevez, Claudia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-43
estevez_cp@javeriana.edu.co
Colombia

España, Víctor Javier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-48
vespana@umh.es
Center of Operations Research (CIO), Miguel Hernández
University of Elche, Elche, Alicante, Spain

Euler, Reinhardt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-25
reinhardt.euler@univ-brest.fr
Lab-STICC UMR 6285 Université de Brest, Brest cedex 3,
FranceEsparcia Sanchís, Carlos . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-51
carlos.esparcia@uclm.es
Department of Economic Analysis and Finance, Universidad
de Castilla-La Mancha, Albacete, Spain

Euler, Ricardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-60
euler@zib.de
Network Optimization, Zuse Institute Berlin, Berlin, Berlin,
Germany

Espegren, Kari . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-09
kari.espegren@ife.no
Energy Systems Analysis, Institute for Energy Technology,
NorwayEvangelista, Davide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-34
davide.evangelista5@unibo.it
University of Bologna, Bologna, Italy

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EURO 2024 - Copenhagen
Ewald, Christian Oliver . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-63
christian.ewald@inn.no
Lillehammer, Norway
Expósito Izquierdo, Christopher . . . . . . . . . . . . . . . . . . . . WC-62
cexposit@ull.edu.es
Ingeniería Informática y de Sistemas, University of La Laguna, San Cristóbal de La Laguna, Spain

AUTHOR INDEX

Fakhimi, Masoud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-28
masoud.fakhimi@surrey.ac.uk
Surrey Business School, University of Surrey, Guildford,
Surrey, United Kingdom
Fakhredin, Fatemeh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-03
fatemeh.fakhredin@klu.org
Kühne Logistics University, Hamburg, Hamburg, Germany

Expósito Márquez, Airam . . . . . . . . . . . . . . . . . . . . . . . . . . WC-62
aexposim@ull.edu.es
Ingeniería Informática y de Sistemas. Instituto Universitario de Desarrollo Regional, Universidad de La Laguna, San
Cristóbal de La Laguna, Santa Cruz de Tenerife, Spain

Falbo, Paolo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-02, TD-39
paolo.falbo@unibs.it
Department of Economics and Management, University of
Brescia, Brescia Bs, Italy

Eziz, Aysajan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-24
aeziz@ivey.ca
Ivey Business School, Western University, London, Ontario,
CanadaFalconer, Thomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-35
falco@dtu.dk
Department of Wind and Energy Systems, Technical University of Denmark, Kgs. Lyngby, Denmark

Fabbri, Giorgio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-33
giorgio.fabbri@univ-grenoble-alpes.fr
CNRS Grenoble, Grenoble, France

Faleschini, Flora . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-47
flora.faleschini@unipd.it
ICEA, University of Padova, Padova, Italy

Fabbrocino, Francesco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-47
francesco.fabbrocino@unipegaso.it
Unipegaso, Italy

Fampa, Marcia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-04
fampa@cos.ufrj.br
Universidade Federal do Rio de Janeiro, Rio de Janeiro,
BrazilFabris, Marco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
marco.fabris.1@unipd.it
Department of Information Engineering, University of
Padova, Padua, Italy
Fagerholt, Kjetil . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-55, MA-62
kjetil.fagerholt@ntnu.no
Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology,
Trondheim, Norway
Faggian, Silvia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-33
faggian@unive.it
Dip.to di Economia, Università "Ca’ Foscari" Venezia,
Venezia, Italy
Faheem, . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-57
fahzaf@amazon.lu
SNT, Amazon, Luxembourg
Fahr, René . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-40
rene.fahr@uni-paderborn.de
Management, Paderborn University, Paderborn, Germany
Fairbrother, Jamie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-55
j.fairbrother@lancaster.ac.uk
Department of Management Science, Lancaster University,
United Kingdom

Famulla, Carolin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-59
carolin.famulla@uni-bielefeld.de
Chair for Quantitative Accounting & Financial Reporting,
Bielefeld University, Bielefeld, Germany
Fan, Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-21
fanyu@ustc.edu.cn
University of Science and Technology of China, China
Fang, Jianxin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-39
jianxin.fang@xjtlu.edu.cn
Department of Intelligent Operations and Marketing, Xi’an
Jiaotong-Liverpool University, Suzhou, Jiangsu, China
Fang, Ke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-58
k.fang1@lancaster.ac.uk
Management School, Lancaster University, China
Fang, Yiping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-28, TD-35
yiping.fang@centralesupelec.fr
CentraleSupélec, France
Fang, Yumeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-56
yumeng.fang@liser.lu
Urban Development and Mobility, Luxembourg Institute ofSocio-Economic Research (LISER), Luxembourg

Fajardo, Maria Dolores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-42
md.fajardo@ua.es
Mathematics, University Of Alicante, Alicante, Spain

Fanzeres, Bruno . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-09
bruno.santos@puc-rio.br
Industrial Engineering, Pontifical Catholic University of Riode Janeiro, Rio de Janeiro, RJ, Brazil

Fajemisin, Ade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-29
ade.fajems@gmail.com
Digitalization Academy, SKEMA Business School, Antibes,
FranceFarahmand, Zakir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-56
z.h.farahmand@utwente.nl
Civil Engineering and Management, University of Twente,
Enschede, Overijssel, Netherlands

553

AUTHOR INDEXEURO 2024 - Copenhagen

Farajiamiri, Mina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-24
mina.amiri@om.rwth-aachen.de
Chair of Operations Management, RWTH Aachen University, Aachen, NRW, Germany

Applied Mathematics and Theoretical Physics, University ofCambridge, Cambridge, United Kingdom
Fedeli, Filippo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-57
filfedel@amazon.com
Amazon, Spain

Farall, Andrés . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-16
afarall@ic.fcen.uba.ar
Instituto de Cálculo, Universidad de Buenos Aires, CABA,
Buenos Aires, Argentina

Federico, Claudio A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-64
claudiofederico@ieav.cta.br
IEAv, São José dos Campos, SP, Brazil

Farhadi, Farbod . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-15
ffarhadi@rwu.edu
Roger Williams University, United States

Federico, Vitti . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
federico.vitti@a2a.it
A2A company, Italy

Farina, Alessandro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-56
alessandro.farina@unipi.it
Department of Civil and Industrial Engineering, Universityof Pisa, Pisa, Italy, Italy

Federspiel, Florian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-11
florian.federspiel@incae.edu
Quantitative Methods and Analytics, INCAE Business
School, Costa Rica

Farina, Gianluca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-51
gf2107@hotmail.com
University of Bergamo, Bergamo, Italy

Feichtinger, Gustav . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-33
gustav.feichtinger@tuwien.ac.at
Institute of Statistics and Mathematical Methods in Economics, Vienna University of Technology, Wien, Austria

Farkas, Csaba. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-41
farkascs@ms.sapientia.ro
Department of Mathematics and Informatics, Sapientia Hungarian University of Transylvania, Târgu Mures, , Romania
Farré Rozada, Laura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-16
info@laurafarrerozada.com
Royal Birmingham Conservatoire, Birmingham City University, Birmingham, United Kingdom
Farriol Salas, Albert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-53
afarriol@irec.cat
IREC, Spain
Farzadnia, Farnaz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-58
ff.eco@cbs.dk
Economics, Copenhagen Business School, Denmark
Fathi, Masood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-49, WB-58
masood.fathi@his.seIntelligent Production Systems, University of Skövde,Skövde, Sweden
Fatouh, Mahmoud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-63
mahmoud.fatouh@bankofengland.co.uk
Economics, Bank of England, Lodon, United Kingdom
Fattahi, Mohammad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-23
mohammad.fattahi@northumbria.ac.uk
Marketing, Operations, and Systems, Northumbria University, Newcastle Business School, Newcastle upon Tyne, Tyne
and Wear, United Kingdom
Faulin, Javier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-29
javier.faulin@unavarra.es
Institute Smart Cities- Dept Statistics and Operations Research, Public University of Navarre, Pamplona, Navarra,
Spain
Fawzi, Hamza . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-38
hf323@cam.ac.uk

554

Feillet, Dominique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-08
dominique.feillet@emse.fr
Ecole des Mines de Saint Etienne, Gardanne, France
Feldman, Pnina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-50
feldmanp@darden.virginia.edu
United States
Fender, Michel . . . . . . . . . . . . . . . . . . . . . . MC-02, WD-55, TA-62
Michel.Fender@um6p.ma
Africa Business School, Mohammed VI Polytechnic University, Ben Guerir, Morocco
Feng, Jianguang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-15
feng@nwpu.edu.cn
School of Management, Northwestern Polytechnical University, China
Feng, Wenxiu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-35
100475987@alumnos.uc3m.es
Department of Statistics, Universidad Carlos III de Madrid,
Madrid, Spain
Feng, Xiaochen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-64
xf433@bath.ac.uk
School of Management, University of Bath, Bath, England,
United Kingdom
Feng, Yuanjun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-62
yuanjun.feng2@liverpool.ac.uk
Operations & Supply Chain Management, University of Liverpool, United Kingdom
Fercoq, Olivier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-32
olivier.fercoq@telecom-paris.fr
Telecom Paris University, Palaiseau, France
Fernandes da Silva, Everton . . . . . . . . . . . . . . . . . . . . . . . . . TB-29
everton.fernandesdasilva@kuleuven.be
Computer Science, KU Leuven, Belgium

EURO 2024 - Copenhagen

Fernandes, Daniela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-50
up201603215@up.pt
Inesc Tec, Portugal
Fernandes, Gilberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-48
gilbertofernandes@ua.pt
ISCA_UA, Universidade Aveiro, Aveiro, Portugal
Fernandes, Paulo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-56
paulo.fernandes@ua.pt
University of Aveiro, Aveiro, Portugal
Fernandez Pons, Diego Olivier . . . . . . . . . . . . . . . . . . . . . . . TA-60
diego.olivier.fernandez.pons@gmail.com
CoEnzyme, Paris, Others, France
Fernandez, Elena . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-29, WD-29
elena.fernandez@uca.es
Statistics and Operations Research, University of Cadiz,
Puerto Real, Spain
Fernandez, Jose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-61
josefdez@um.es
Estadistica e Investigacion Operativa, Universidad de Murcia, Espinardo - Murcia, Spain
Fernández, Santiago . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-09
sfernandezp@repsol.com
BSC - Repsol Research Center, Spain
Fernandez-Rodero, Pedro . . . . . . . . . . . . . . . . . . . . . . . . . . WC-44
pefrod@uma.es
University of Málaga, Spain
Ferone, Daniele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-29
daniele.ferone@unina.it
Department of Mathematics and Applications, University of
Naples "Federico II", Italy
Ferrada, Pablo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-19
pablo.ferrada@uantof.clCentro de Desarrollo Energético Antofagasta, Universidad deAntofagasta, Chile
Ferrari, Benedetta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-25
benedetta.ferrari@unimore.it
DISMI, University of Modena and Reggio Emilia, Reggio
Emilia, Reggio Emilia, Italy
Ferrari, Giorgio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-33
giorgio.ferrari@uni-bielefeld.deCenter for Mathematical Economics, Bielefeld University,
Bielefeld, NRW, Germany
Ferrarini, Luca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-27
luca.ferrarini@enpc.fr
CERMICS, Ecole des Ponts ParisTech, Verona, Italy, Italy
Ferraro, Laura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-18
laura.ferraro@unical.it
Business Administration, University of Calabria, Arcavacata,
CS, Italy

AUTHOR INDEX

adamferreiradacosta@fico.com
FICO Xpress Optimization, France
Ferreira, Deisemara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-29
deise@ufscar.br
Department of Physics, Chemistry and Mathematics, Federal
University of São Carlos, Sorocaba, São Paulo, Brazil
Ferreira, Josenildo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-48
josenildo.ferreira@ufpe.br
Programa de Pós-Graduação em Engenharia de Produção,Universidade Federal de Pernambuco, Caruaru, Pernambuco,
BrazilFerreira, Kamyla . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-52
kamylamaaria@gmail.com
Production Engineering Department, Federal University of
Sao Carlos, São Carlos, São Paulo, Brazil
Ferreira, Orizon P . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-41
orizon@ufg.br
IME-Instituto de Matemática e Estatística, Universidade Federal de Goiás, Goiania, GO, Brazil
Ferrer-Martí, Laia . . . . . . . . . . . . . . . . . . . . . . . . . . WB-13, MB-18
laia.ferrer@upc.edu
Institute of Industrial and Control Engineering, Technical
University of Catalonia, Spain
Fey, Maximilian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-22
maximilian.fey@ier.uni-stuttgart.deInstitute of Energy Economics and Rational Energy Use, University of Stuttgart, Stuttgart, Germany
Fiacchini, Mirko . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-20
mirko.fiacchini@gipsa-lab.fr
Control Systems Department, GIPSA-lab, Grenoble, France
Fiala, Petr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-23
pfiala@vse.cz
Dept. of Econometrics, Prague University of Economics andBusiness, Prague 3, Czech Republic
Ficcadenti, Valerio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-02
ficcadv2@lsbu.ac.uk
Business School, London South Bank University, London,
United Kingdom
Fickett, Dale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-04
dfickett@richmond.edu
Robins School of Business, United States
Fiestras-Janeiro, Maria Gloria . . . . . . . . . . . . . . . . . . . . . . MC-36
fiestras@uvigo.es
Universidade de Vigo, Vigo, Spain
Figueira, Gonçalo . . . . . . . . . . . . . . . . . . . . . . . . . . WA-50, WB-60
goncalo.figueira@fe.up.pt
INESC TEC and FEUP, Porto, Portugal
Figueira, José Rui . . . . . . . . . . . . . . . . . . . WD-37, TB-44, MA-58
figueira@tecnico.ulisboa.pt
CEG-IST, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

Ferreira Da Costa, Adam . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-30

555

AUTHOR INDEXEURO 2024 - Copenhagen

Figuerola-Ferretti, Isabel . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-63
ifiguerola@comillas.edu
Universidad Ponticficia Comillas, Madrid, Madrid, Spain
Fikar, Christian . . . . . . . . . . . . . . . . . . . . . . WA-13, TD-24, TB-50
christian.fikar@uni-bayreuth.de
Chair of Food Supply Chain Management, University of
Bayreuth, Kulmbach, Germany
Filippi, Carlo . . . . . . . . . . . . . . . . . . . . . . . . WA-02, TA-26, MB-29
carlo.filippi@unibs.it
Dept. of Economics and Management, University of Brescia,
Brescia, BS, Italy
Filograsso, Gianni . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-02
gianni.filograsso@unive.it
Department of Economics, Ca’ Foscari University, Italy
Filom, Siyavash . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-62
filoms@mcmaster.ca
McMaster University, Canada
Finhold, Elisabeth . . . . . . . . . . . . . . . . . . . . . . . . . . MA-09, MD-49
elisabeth.finhold@itwm.fraunhofer.de
Optimization, Fraunhofer Institute for Industrial Mathematics ITWM, Kaiserslautern, Germany
Fink, Andreas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-52
andreas.fink@hsu-hamburg.de
Chair of Information Systems, Helmut-Schmidt-University,
Hamburg, Germany
Finke, Jonas . . . . . . . . . . . . . . . . . . . . . . . . . MA-09, TC-09, TD-09
jonas.finke@ee.rub.de
Chair of Energy Systems and Energy Economics, RuhrUniversität Bochum, Germany
Fiori Maccioni, Alessandro . . . . . . . . . . . . . . . . . . . . . . . . . WA-02
alex.fiori.maccioni@gmail.com
Department of Economics and Business Sciences, Universityof Cagliari, Italy
Fiorica, Dario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-18
s222631@student.dtu.dk
Technical University of Denmark, Denmark
Fiorotto, Diego. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-15
diego_fiorotto@hotmail.com
Centro de Pesquisa Operacional, Universidade Estadual deCampinas - FCA/UNICAMP, Limeira, Sao Paulo, Brazil
Fischer, Vera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-45
vera.fischer@unifr.ch
DIUF, University of Fribourg, Fribourg, FR, Switzerland
Fischetti, Martina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-46
Martina.FISCHETTI@ec.europa.eu
European Commission, Joint Research Center, Seville, Spain
Fischetti, Martina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-27
mfischetti@us.es
Spain
Fischetti, Matteo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-58
fisch@dei.unipd.it

556

DEI, University of Padua, Padova, Italy, Italy
Fisher, Tim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-31
Tim.Fisher@butlerscheeses.co.uk
Dairy Technology, Butlers Farmhouse Cheeses, Preston, Lancashire, United Kingdom
Flapper, Simme Douwe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-60
s.d.p.flapper@tue.nl
Technische Universiteit Eindhoven, Netherlands
Flaten, Ola . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-12
Ola.Flaten@nibio.no
Norwegian Institute of Bioeconomy Research, Norway
Fleckenstein, David . . . . . . . . . . . . . . . . . . . . . . . . . WC-54, TD-59
david.fleckenstein@uni-a.de
Chair of Analytics & Optimization, University of Augsburg,
Germany
Fleischmann, Moritz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-23
Moritz.Fleischmann@bwl.uni-mannheim.de
Chair of Logistics and Supply Chain Management, University of Mannheim, Mannheim, Germany
Flensburg, Johan Victor . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-51
jvfl@dtu.dk
Department of Technology, Management, and Economics,
Technical University of Denmark, Kongens Lyngby, Denmark
Fleten, Stein-Erik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-09
stein-erik.fleten@iot.ntnu.no
Industrial Economics and Technology Management, NTNU
Norway, Trondheim, Norway
Fleuren, Hein . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-13
fleuren@tilburguniversity.edu
Tilburg University, Tilburg, Netherlands
Fleury, Gérard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-42
gerard.fleury@isima.fr
LIMOS, Aubiere, France
Flocco, Dominic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-21
dflocco@umd.edu
Department of Mathematics, University of Maryland, College Park, Maryland, United States
Florea, Elena-Andreea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-41
andreea_acsinte@yahoo.com
Mathematics, University Al. I. Cuza and Octav Mayer Institute of Mathematics, Iasi, Romania
Flores Gomez, Mario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-60
mario.flores@emse.fr
SFL, EMSE, France
Flores, Angela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-19, TD-19
angelafq@gmail.com
Universidad de Chile, Santiago, Chile
Flores, Jose Luis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-47
jlflores@ikerlan.es
IKERLAN, Spain

EURO 2024 - Copenhagen

AUTHOR INDEX

Barcelona, Barcelona, Spain
Florou, Giannoula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-45
gflorou@teiemt.gr
Accounting and Finance, Moustheni, Greece
Fochesato, Marta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-14
mfochesato@ethz.ch
Department of Electrical Engineering and Information Technology, ETH Zürich, Zürich, Switzerland

Forget, Nicolas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-52
nicolas.forget@jku.at
Institute of Production and Logistics Management, Johannes
Kepler University Linz, Linz, Austria
Forghani, Asefe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-62
asefef@ruc.dk
Roskilde University, Denmark

Fodor, Julian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-49
julian.fodor@uibk.ac.at
Information Systems, Production and Logistics Management,
University of Innsbruck, Innsbruck, Austria

Formanowicz, Dorota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-20
doforman@ump.edu.pl
Poznan University of Medical Sciences, Poznan, Poland

Fogg, Josh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-20
j.fogg@ed.ac.uk
School of Mathematics, University of Edinburgh, Maxwell
Institute Graduate School, United Kingdom

Formanowicz, Piotr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-20
piotr@cs.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poznan, Poland

Fokouop, Rodrigue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-49
rodrigue.fokouop-w@airliquide.com
Computational and Data Sciences Lab, Operational Researchteam, Air Liquide R&D, Palaiseau, France/Ile de France,
FranceFornara, Laura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-18
laura.fornara@compagniadisanpaolo.it
Fondazione Compagnia di SanPaolo, Torino, Italy, Italy

Fong, Cheng-Lin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-21
charlie11.mg11@nycu.edu.tw
Departement of Transportation and Logistics Management,
National Yang Ming Chiao Tung University, Taiwan
Fonseca, Joao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-46
joao@portchain.com
Analytics, Portchain, Denmark
Fontaine, Pirmin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-64
pirmin.fontaine@ku.de
Ingolstadt School of Management, Catholic University ofEichstätt-Ingolstadt, Germany
Fontalvo-Herrera, Tomás . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-48
tfontalvoh@unicartagena.edu.co
Universidad de Cartagena, Colombia

Forsgren, Anders . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-10, WB-32
andersf@kth.se
Department of Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden
Fortuna Lucas, Felipe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-44
felipefortuna@id.uff.br
TPP - Industrial Engineering, Universidade Federal Fluminense, Rio de Janeiro, Rio de Janeiro, Brazil
Fortuna Lucas, Solange . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-44
solangefortunalucas@gmail.com
Production Engineering, Fluminense Federal UNiversity, Rio
De Janeiro, RJ, Brazil
Fortuna, Francesca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-63
francesca.fortuna@uniroma3.it
Economic, Roma Tre University, Rome, Italy, Italy

Fontan, Florian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-30
florian.fontan@artelys.com
Artelys, France

Fortunato Filho, Fabio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-32
f235849@dac.unicamp.br
Department of Applied MathematicsIMECC, The State University of Campinas (UNICAMP), Campinas, SP, Brazil

Fontini, Fulvio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-33
fulvio.fontini@unipd.it
Department of Economics and Management, University of
Padua, Padua, Italy

Fortz, Bernard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-19
bernard.fortz@uliege.be
HEC Liège, Management school of the University of Liège,
Liège, Belgium

Fooladi Mahani, Sayeh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-28
up202200544@edu.fe.up.pt
Inesc Tec, Faculty of Engineering, University of Porto, Porto,
Portugal

Foster, Joshua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-59
jfoster@ivey.ca
Ivey Business School, Western University, Canada

Fop, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-28
michael.fop@ucd.ie
School of Mathematics and Statistics, University College
Dublin, Dublin, Leinster, Ireland
Forbes, Tim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-57
timforby@gmail.com

Fotova Čiković, Katerina . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-48
kcikovic@unin.hr
Economy, University North, Croatia, Koprivnica,
Koprivničko-križevačka, Croatia
Fourcade, Louis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-58
louis.fourcade@sncf.fr
emse, France

557

AUTHOR INDEXEURO 2024 - Copenhagen

Fourer, Robert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-30
4er@ampl.com
AMPL Optimization Inc., Evanston, IL, United States

Fransoo, Jan C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-39, TD-50
Jan.Fransoo@tilburguniversity.edu
Tilburg University, Tilburg, Netherlands

Fragkos, Ioannis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-29
fragkos@rsm.nl
Technology & Operations Management, Rotterdam Schoolof Management, Rotterdam, Netherlands

Franz, Moritz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-14
moritz.franz@uni-kassel.de
Chair of Energy Management and Power System Operation,
University of Kassel, Kassel, Hesse, Germany

Fragoso, Rui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-44
rfragoso@uevora.pt
CEFAGE, University of Évora, EVORA, Alentejo, Portugal

Franze, Lea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-24
lea.franze@fernuni-hagen.de
Chair of Production and Logistics Management, FernUniversität in Hagen, Germany

Fram, Benjamin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-09
Benjamin.Fram@nhh.noDept. of Business and Management Science, NHH Norwegian School of Economics, Norway

Fredriksson, Albin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-10
albin.fredriksson@raysearchlabs.com
RaySearch Laboratories AB, Stockholm, Sweden

Franchini, Giorgia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-34
giofranchini@unimore.it
UNIMORE, Modena, Emilia-Romagna, Italy

Fredriksson, Anna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-62
anna.fredriksson@liu.se
Science and Technology, Linköping University, Norrköping,
SwedenFrancisco Rodriguez, Maria Andreina . . . . . . . . . . . . . . MD-56
maria.andreina.francisco@it.uu.se
Uppsala University, Uppsala, Sweden
Franco, Carlos Jaime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-19
cjfranco@unal.edu.co
Ciencias de la Computación y la Decisión, Universidad Nacional de Colombia, Medellín, Colombia
Franco, L. Alberto . . . . . . . . . . . . . . . . . . . TB-11, TC-11, WA-46
alberto.franco@bristol.ac.uk
University of Bristol Business School, University of Bristol,
Bristol, United Kingdom
Franco, Manuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-39
mfranco@um.es
Statistics and Operations Research, University of Murcia,
Spain
Franco-Pereira, Alba M. . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-27
albfranc@ucm.es
Statistics and OR, Universidad Complutense de Madrid,
Madrid, Spain
François, Véronique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-10
veronique.francois@uliege.be
HEC Management School, University of Liège, Liege, Belgium
Frangioni, Antonio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-04
frangio@di.unipi.it
Dipartimento di Informatica, Universita’ di Pisa, Pisa, Italy
Frankish, Maxim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-10
maxim.frankish@ugent.be
Business Informatics and Operations Management, Ghent
University, Belgium
Fransoo, Jan C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-11
j.c.fransoo@tm.tue.nl
Department of Technology Management, Technische Universiteit Eindhoven, Eindhoven, Netherlands

558

Frejinger, Emma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-01
emma.frejinger@cirrelt.ca
Université de Montréal, Montréal, Canada
Freni, Giuseppe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-33
freni@uniparthenope.it
Dip.
di Studi Economici e Aziendali, Università"Parthenope" Napoli, Napoli, Italy
Frey, Ulrich . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-43
ulrich.frey@dlr.deGerman Aerospace Center, Stuttgart, Germany
Fribance, Michelle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-31
michelle.fribance@fau.deFriedrich Alexander University, Fürth, Bavaria, Germany
Friberg, Henrik A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-38
henrik.alsing.friberg@mosek.com
MOSEK ApS, Denmark
Frieß, Nathalie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-53
nathalie.friess@uni-graz.at
Department of Operations and Information Systems, University of Graz, Graz, Steiermark, Austria
Fries, Carlos Ernani . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-49
carlos.fries@ufsc.br
Department of Industrial and Systems Engineering, Universidade Federal de Santa Catarina, Florianopolis, Santa Catarina, Brazil
Frimpong, Richlove . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-54
richlove.frimpong@kcl.ac.uk
King’s College London, United Kingdom
Fristed, Frederik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-09
frefri@dtu.dk
Department of Technology, Management and Economics,
Technical University of Denmark (DTU), Kgs. Lyngby, Denmark

EURO 2024 - Copenhagen

AUTHOR INDEX

Frits, Marton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-49
frits.marton@mik.uni-pannon.hu
University of Pannonia, Hungary

Fügenschuh, Armin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-25
fuegenschuh@b-tu.de
MINT, Brandenburg Technical University, Cottbus, Germany

Fritze, Christian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-59
christian.fritze@hspv.nrw.de
University of Applied Sciences for Police and Public Administration, Bielefeld, Germany

Fujii, Koichi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-30
fujii@msi.co.jp
NTT DATA Mathematical Systems Inc., Japan

Froehle, Craig. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-20
froehlcm@ucmail.uc.edu
Operations, Business Analytics, and Information Systems,
University of Cincinnati, United States
Fröhlich, Erik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-19
erik.froehlich@ise.fraunhofer.de
Freiburg, Germany
Frohn, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-25
martin.frohn@maastrichtuniversity.nl
Netherlands
Frost, Tracie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-59, WD-59
tracie.frost@polyu.edu.hk
School of Accounting and Finance, Hong Kong Polytechnic
University, Hung Hom, Kowloon, Hong Kong
Ftiti, Zied . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-51
zftitio@gmail.com
Quantitative Methods, EDC Paris Business School,
Courbevoie, Paris, France
Fu, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-35
mfu@umd.edu
Smith School of Business, University of Maryland, College
Park, MD, United States
Fu, Yumin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-23
fufu811@163.com
Harbin Engineering University, China
Fuchß, Patricia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-21
patricia.fuchss@web.de
Institute for Industrial Production (IIP), Karlsruhe Institute ofTechnology (KIT), Karlsruhe, Germany
Fuchs, Florian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-54
flfuchs@student.ethz.ch
Institute for Transport Planning and Systems, ETH Zurich,
Zurich, Switzerland
Fuduli, Antonio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-06, WD-60
antonio.fuduli@unical.it
Department of Mathematics and Computer Science, University of Calabria, Rende, Italy

Fuller, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-14
dfuller@uwaterloo.ca
Department of Management Sciences, Waterloo University,
Waterloo, Canada
Fulzele, Vijayta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-53
vijayta.fulzele@snu.edu.in
Greater Noida, Uttar Pradesh, India
Funaki, Yukihiko . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-36
funaki@waseda.jp
School of Political Science and Economics, Waseda University, Shinjuku, Tokyo, Japan
Funke, Christoph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-38
funkec@ethz.ch
Reliability and Risk Engineering, ETH Zürich, Switzerland
Furini, Fabio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-52, TB-52
fabio.furini@uniroma1.it
DIAG, La Sapienza, Roma, Please Select, Italy
Fusco Girard, Luigi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-47
luigi.fuscogirard@gmail.com
Istituto di Ricerca su Innovazione e Servizi per lo Sviluppo,
Istituto di Ricerca su Innovazione e Servizi per lo Sviluppo CNR IRISS, Naples, Italy, Italy
G.-Tóth, Boglárka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-61
boglarka@inf.szte.hu
Department of Computational Optimization, University ofSzeged, Szeged, Hungary
Gabay, Michaël . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-09
michael.gabay@artelys.com
Artelys, France
Gaborit, Robin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-51
rpaga@dtu.dk
Technical University of Denmark, Lyngby, Denmark
Gabriel, Steven . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-09, MB-21
sgabriel@umd.edu
Mech. Engin./ Applied Math and Scientific Computation
Program, University of Maryland, College Park, MD, UnitedStates

Fuentes, Gabriel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-55
gabriel.fuentes@nhh.no
BERGEN, Norway

Gabrielli, Paolo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-14, MC-19
gapaolo@ethz.ch
Mechanical and Process Engineering, ETH Zurich, Zurich,
Zurich, Switzerland

Fugaro, Serena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-12, TC-61
s.fugaro@iac.cnr.it
Institute for Applications of Calculus - National ResearchCouncil of Italy, Rome, Italy

Gadegaard, Sune Lauth . . . . . . . . . . . . . . . . . . . . . MC-52, MC-58
sgadegaard@econ.au.dk
Department of Economics and Business Economics, Aarhus
University, Aarhus V, Denmark, Denmark

559

AUTHOR INDEXEURO 2024 - Copenhagen
Mathematics, LMU Munich, Munich, Bayern, Germany

Gaeta, Maria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-47
maria.gaeta@rse-web.it
Ricerca sul Sistema Energetico - RSE SpA, Milan, Italy
Gaete, Myriam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-12
myrgete@gmail.com
Doctorado en Sistemas de Ingeniería, Faculty of Engineering,
Universidad de Talca, Curico, Maule, Chile
Gaggero, Giacomo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-63
giacomo.gaggero@edu.unige.it
Department of Economics, University of Genoa, Genova,
italy, Italy

Gally, Tristan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-30
tristangally@fico.com
Fair Isaac Services Limited, Birmingham, West Midlands,
United Kingdom
Gaman, Stefan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-31
gamanstefan18@stud.ase.ro
Cybernetics and Economic Statistics, Bucharest Universityof Economic Studies, Bucharest, Romania
Gambella, Claudio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-63
claudiogambella@fico.com
FICO House, FICO, Xpress Optimization, Italy

Gago-Carro, Imanol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-15
igago@bcamath.org
BCAM - Basque Center for Applied Mathematics, Bilbao,
Basque Country, Spain

Gansterer, Margaretha . . . . . . . . . . . . . . . . . . . . . . WC-58, TA-64
margaretha.gansterer@aau.at
Klagenfurt University, Klagenfurt, Austria

Gaha, Mohamed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-06
mohamed.gaha@hotmail.com
Montréal, QC, Canada

Gansterer, Margaretha . . . . . . . . . . . . . . . . . . . . . . TC-49, MC-55
margaretha.gansterer@univie.ac.atUniversity of Klagenfurt, Austria

Gahm, Christian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-60
christian.gahm@wiwi.uni-augsburg.de
Chair of Production & Supply Chain Management, Augsburg
University, Augsburg, Germany

Ganter, Alissa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-14
aganter@ethz.ch
Energy & Process Systems Engineering, ETH Zürich, Zürich,
Switzerland

Gailly, Frederik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-21
frederik.gailly@ugent.be
Business informatics and operations management, Ghent
University, Gent, Gent, Belgium

Gao, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-43
j.gao-1@tudelft.nl
Transport & Planning, Delft University of Technology (TU
Delft), Delft, Netherlands

Gajanand, M. S. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-48
gajanand@iimtrichy.ac.in
Operations Management & Decision Sciences, Indian Institute of Management Tiruchirappalli, Trichy, Tamil Nadu,
India

Gao, Juhong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-28
gaojuhong@tju.edu.cn
College of Management and Economics, Tianjin University,
Tianjin, China

Gajda, Mikele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-55
mikele.gajda@unil.ch
Department of Operations, UNIL | Université de Lausanne,
Lausanne, Switzerland
Galariotis, Emilios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-47
egalariotis@hotmail.com
Technical University of Crete, Greece
Galij, Stanisław . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-48
stanislaw.galij@doctorate.put.poznan.pl
Faculty of Computing and Telecomunications, Poznan University of Technology, Bydgoszcz, Poland
Galindo, Amparo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-37
a.galindo@imperial.ac.uk
Department of Chemical Engineering, Imperial College London, London, United Kingdom
Galli, Laura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-04
laura.galli@unipi.it
Dipartimento di Informatica, University of Pisa, Pisa, Italy
Galli, Leonardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-32
galli@math.lmu.de

560

Gao, Qian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-51
qian.gao.st@vsb.cz
VSB - Technical University of Ostrava, Czech RepublicGao, Shikai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-51
shikai.gao.st@vsb.cz
Finance, VSB - Technical University of Ostrava, OSTRAVA,
Czech Republic, Czech RepublicGao, Yingying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-21
yingying.gao@student.kuleuven.beCollege of Systems Engineering, National University of Defense Technology, leuven, Belgium
Gao, Zhi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-09
z.gao1@uu.nl
Copernicus Institute of Sustainable Development, Utrecht
University, ZEIST, Netherlands
Gapeev, Pavel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-33
p.v.gapeev@lse.ac.uk
LSE, United Kingdom
Garavito, Edwin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-58
garavito@uis.edu.co
Escuela de Estudios Industriales y Empresariales, Univer-EURO 2024 - Copenhagensidad Industrial de Santander, Bucaramanga, Santander,
ColombiaGarcÍa SÁnchez, José Ángel . . . . . . . . . . . . . . . . . . . . . . . . MA-63
jagarsanc@gmail.com
CES : Centre d’économie de la Sorbonne, Université Paris 1
Panthéon Sorbonne, France
García Castaño, Fernando . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-42
fernando.gc@ua.es
Mathematics, University of Alicante, Alicante, Comunidad
Valenciana, Spain
García, Fernando . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-22
f.garcia@irec.cat
IREC, Spain
García, Fernando . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-19
fernando.garciam@usach.cl
USACH, Chile
García-Bertrand, Raquel . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-21
Raquel.Garcia@uclm.es
Electrical Engineering, University of Castilla-La Mancha,
Ciudad Real, Ciudad Real, Spain
García-Cerezo, Álvaro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-14
alvaro.garciacerezo@uclm.es
Electrical Engineering, Universidad de Castilla-La Mancha,
Toledo, Castilla-La Mancha, Spain
García-Chan, Néstor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-33
nestor.gchan@academicos.udg.mx
Depto. Fisica, Universidad de Guadalajara, Guadalajara,
Jalisco, Mexico
García-Jurado, Ignacio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-36
igjurado@udc.es
Department of Mathematics, Coruna University, Coruna,
Spain
Garcia Marin, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-09
davma@dtu.dk
Managememt, DTU, Denmark
Garcia-Rodenas, Ricardo . . . . . . . . . . . . . . . . . . . . TA-34, MB-56
ricardo.garcia@uclm.es
Escuela Superior de Informatica, Universidad de Castilla La
Mancha, Ciudad Real, Ciudad Real, Spain
Garcia-Saez, Artur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-09
artur.garcia@bsc.es
CASE, Barcelona Supercomputing Center, Barcelona, Spain
Gardeyn, Jeroen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-07
jeroen.gardeyn@kuleuven.be
Computer Science, KU Leuven, Belgium
Gardi, Frédéric . . . . . . . . . . . . . . MD-30, TC-30, TD-46, WC-46
fgardi@hexaly.com
Hexaly, Brooklyn, New York, United States
Garin, M. Araceli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-35
mariaaraceli.garin@ehu.eus
Quantitative Methods, UPV/EHU, Spain

AUTHOR INDEX

Garn, Wolfgang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-23
w.garn@surrey.ac.uk
Surrey Business School, University of Surrey, Guildford,
Surrey, United Kingdom
Gartner, Daniel . . . . . . . . . . . . . . MC-15, TB-15, MC-17, WA-18
gartnerd@cardiff.ac.uk
School of Mathematics, Cardiff University, United Kingdom
Gasnikov, Alexander . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-32
gasnikov@yandex.ru
Mathematical Foundation of Control, Moscow Institute ofPhysics and Technology, Dolgoprudny, Russian Federation
Gaspar-Cunha, Antonio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-25
agc@dep.uminho.pt
Dept. of Polymer Engineering, Institute of Polymers andComposites, Guimarães, Portugal
Gast Zepeda, Nayeli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-03
nayeli.gast@uni-bielefeld.de
Management Science & Business Analytics, Bielefeld University, Bielefeld, North Rhine-Westphalia, Germany
Gatfaoui, Hayette . . . . . . . . . . . . . . . . . . . . . . . . . . WB-02, WB-06
hgatfaoui@gmail.com
Finance, IESEG School of Management, Paris La Défense,
Ile de France, France
Gaudioso, Manlio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-41
manlio.gaudioso@unical.it
DIMES, University of Calabria, Italy
Gaugl, Robert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-09
robert.gaugl@tugraz.at
Institute of Electricity Economics and Energy Innovation,
Graz University of Technology, Graz, Austria
Gaul, Daniela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-54
gaul@math.uni-wuppertal.de
Department of Mathematics and Informatics, University ofWuppertal, Germany
Gaury, Mickael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-21
mickael.gaury@kedgebs.com
Kedge Business School - Université de Bordeaux, France
Gavanelli, Marco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-25
Marco.Gavanelli@unife.it
Dipartimento di Ingegneria, Università di Ferrara, Ferrara,
Italy
Gavirneni, Nagesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-24
nagesh@cornell.edu
Johnson School of Business, Cornell University, Ithaca, New
York, United States
Gazzani, Matteo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-09
m.gazzani@uu.nl
Copernicus Institute of Sustainable Development, Utrecht
University, Utrecht, Netherlands
Gazzotti, Paolo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-43
paolo.gazzotti@polimi.it

561

AUTHOR INDEXEURO 2024 - Copenhagen

Department of Electronics, Information and Bioengineering,
Politecnico di Milano, Milano, Italy
Gdowska, Katarzyna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-45
kgdowska@agh.edu.pl
Faculty of Management, AGH University of Science andTechnology, Krakow, Malopolska Region, Poland
Ge, Fangsheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-23
f.ge@soton.ac.uk
Decision Analytics and Risk, University of Southampton,
United Kingdom

gentile@iasi.cnr.it
IASI-CNR, Roma, Italy
George, Chris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-57
cgeamzn@amazon.co.uk
ATS, Amazon, London, United Kingdom
Georghiou, Angelos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-35
georghiou.angelos@ucy.ac.cy
University of Cyprus, Cyprus

Gebken, Bennet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-41
bgebken@math.upb.de
Department of Mathematics, Paderborn University, Germany

Georgiou, Andreas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-48
acg@uom.edu.gr
Quantitative Methods and Decision Analytics Lab, Dpt ofBusiness Administration, University of Macedonia, Thessaloniki, Greece

Geçici, Ebru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-22
geciciebru@gmail.com
Industrial Engineering, Boğaziçi University, Istanbul, Turkey,
TurkeyGerdessen, J.c. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-13
joke.vanlemmen@wur.nl
Operations Research and Logistics, Wageningen University,
Wageningen, Netherlands

Geçkil, Yahya Ertuğrul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-58
yahya.geckil@trendyol.com
Optimization / Data Science, Trendyol, Ankara, Turkey

Gernert, Andreas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-50
andreas.gernert@klu.org
Kuehne Logistics University, Germany

Geerts, Margot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-31
margot.geerts@kuleuven.be
Decision Sciences and Information Management, KU Leuven, Belgium

Gerstl, Enrique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-26
enrique.gerstl@mail.huji.ac.il
School of Business Administration, The Hebrew University,
Jerusalem, Israel

Geismar, Neil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-23
ngeismar@mays.tamu.edu
Information and Operations Management, Texas A&M University, College Station, Texas, United States

Gestrelius, Sara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-54
sara.gestrelius@ri.se
Mobility and Systems, Rise Research Institutes Of Sweden,
SwedenGel, Esma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-15
esma.gel@unl.edu
College of Business, University of Nebraska-Lincoln, Lincoln, NE, United States

Geurts, Tijmen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-07
tijmen.geurts@radboudumc.nl
Geriatrics, Radboudumc, Nijmegen, Netherlands

Geldermann, Jutta . . . . . . . . . . . . . . . . . . . MA-10, TD-11, TA-44
jutta.geldermann@uni-due.de
Chair of Business Administration and Production Management, University of Duisburg-Essen, Duisburg, Germany
Gelper, Sarah . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-07
s.gelper@tue.nl
Eindhoven University of Technology, Netherlands
Gemmel, Paul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-17
paul.gemmel@ugent.beMarketing, innovation and Organization, Ghent University,
Gent, Belgium
Gendreau, Michel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-51
michel.gendreau@polymtl.ca
Mathématique et génie industriel, École polytechnique deMontréal, Montréal, Canada
Genre-Grandpierre, Cyrille . . . . . . . . . . . . . . . . . . . . . . . . . TA-18
cyrille.genre-grandpierre@univ-avignon.fr
University ofAvignon, Avignon, France
Gentile, Claudio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-04

562

Gfrerer, Helmut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-42
helmut.gfrerer@jku.at
Institute for Computational Mathematics, Johannes Kepler
University Linz, Linz, Austria
Ghaboulian Zare, Sara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-14
sara.ghaboulian.zare@umontreal.ca
University of Montréal, Canada
Ghaddar, Bissan . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-04, MA-19
bghaddar@ivey.ca
Ivey Business School, Canada
Ghaderi, Moha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-11, TA-44
mohammad.ghaderi@upf.edu
Economics and Business, Pompeu Fabra University,
Barcelona, Spain
Ghaderi, Susan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-41
susan.ghaderi@kuleuven.be
ESAT, KU Leuven, Leuven, Flanders, Belgium
GhaemiDizaji, Manizheh . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-53
manizheh.ghaemidizaj@ucalgary.ca
University of Calgary, Calgary, Alberta, Canada

EURO 2024 - Copenhagen

Ghate, Archis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-41
archis.ghate@gmail.com
Industrial Engineering, Clemson University, CLEMSON, SC,
United States
Ghatkar, Shraddha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-13
s.ghatkar@sheffield.ac.uk
Automatic Control and Systems Engineering, University of
Sheffield, United Kingdom
Ghavmaifar, Ali . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-26
aghava@dtu.dk
Department of Technology, Management and Economics,
Technical University of Denmark, Lyngby, Copenhagen,Denmark
Ghiami, Yousef . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-36
Y.Ghiami@vu.nl
Information, Logistics and Innovation, VU University Amsterdam, Amsterdam, Netherlands
Ghiani, Gianpaolo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-64
gianpaolo.ghiani@unisalento.it
Dipartimento di Ingegneria dell’ Innovazione, University ofSalento, Lecce, Italy
Ghiassi-Farrokhfal, Yashar . . . . . . . . . . . . . . . . . . TA-09, MD-22
y.ghiassi@rsm.nl
Rotterdam School of Management, Erasmus University Rotterdam, Rotterdam, Netherlands
Ghidini, Carla . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-30
cghidini@unicamp.br
University of Campinas, Limeira, Brazil
Ghitri, Mohammed Ibrahim Abdelkayoum . . . . . . . . . . TC-42
moghitri@gmail.com
Mathematics, Universidad de Alicante, Alicante, Alicante,
Spain
Ghoniem, Ahmed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-64
aghoniem@isenberg.umass.edu
Operations & Information Management, University of Massachusetts Amherst, USA, USA, United States

AUTHOR INDEX

Giallombardo, Giovanni . . . . . . . . . . . . . MD-03, MB-35, TC-41
giovanni.giallombardo@unical.it
DIMES, University of Calabria, Rende, Italy
Gianessi, Paolo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-29
paolo.gianessi@emse.fr
Mines Saint-Etienne, Univ. Clermont Auvergne, CNRS,UMR 6158 LIMOS, Institut Henri Fayol, F - 42023 SaintEtienne France, France
Giannakos, Konstantinos . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-29
constantinosgiannakos@gmail.com
University of Piraeus Research Center, Kato Patisia, Attiki,
GreeceGiannikas, Vaggelis . . . . . . . . . . . . . . . . . . . . . . . . . TA-49, MA-64
V.Giannikas@bath.ac.uk
University of Bath, Bath, United Kingdom
Giansante, Simone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-63
simone.giansante@unipa.it
Economics, Business and Statistics, University of Palermo,
Palermo, PA, Italy
Giaretti, Matteo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-50
matteo.giaretti@glovoapp.com
Glovo, Spain
Gibson, Will . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-08
will@aus.co.uk
Operations, Associated Utility Supplies Ltd, Huddersfield,
West Yorkshire, United Kingdom
Gicquel, Céline . . . . . . . . . . . . . . . . . . . . . . . WB-09, TA-18, TB-49
celine.gicquel@lri.fr
Laboratoire Interdisciplinaire des Sciences du Numérique,
Université Paris Saclay, Orsay Cedex, France
Giehl, Johannes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-09
jfg.eco@cbs.dk
Denmark
Gieschen, Antonia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-28
antonia.gieschen@ed.ac.uk
University of Edinburgh, United Kingdom

Ghorashi Khalilabadi, Mahdi . . . . . . . . . . . . . . . . . . . . . . . MD-50
ghorashikhalilabadi@rsm.nl
Technology and Operations Management, Rotterdam Schoolof Management, Rotterdam, Zuid Holland, Netherlands

Gijswijt, Dion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-38
D.C.Gijswijt@tudelft.nl
Delft University of Technology, Netherlands

Ghosh, Diptesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-25
diptesh@iima.ac.in
Production and Quantitative Methods, Indian Institute ofManagement, Ahmedabad, Ahmedabad, Gujarat, India

Gil-Borras, Sergio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-52
sergio.gil@upm.es
Sistemas Informaticos, Universidad Politecnica de Madrid,
Madrid, Madrid, Spain

Ghvaberidze, Bezhan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-47
bezhan.ghvaberidze@tsu.ge
Department of Computer Sciences, Iv. Javakhishvili Tbilisi
State University, Tbilisi, Georgia

Gil-Figuerola, Pol . . . . . . . . . . . . . . . . . . . . . . . . . . WB-13, MB-18
pol.gil.figuerola@upc.edu
Department of Management, Universitat Politècnica deCatalunya, Barcelona, Catalunya, Spain

Giacometti, Rosella . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-51
rosella.giacometti@unibg.it
Management, Economics and Quantitative Methods, University of Bergamo, Bergamo, Italy

Giliberto, Francesco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-39
francescogili98@gmail.com
Operations Analytics, Vrije University, Amsterdam, North
Holland, Netherlands

563

AUTHOR INDEXEURO 2024 - Copenhagen

Gillis, Joris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-30
joris.gillis@kuleuven.be
Department of mechanical engineering, KU Leuven, Belgium

Gnecco, Giorgio . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-43, MA-63
giorgio.gnecco@imtlucca.it
IMT - School for Advanced Studies, Lucca, Lucca, Italy

Gils, Hans Christian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-19
hans-christian.gils@dlr.de
Energy Systems Analysis, DLR - German Aerospace Center,
Germany

Gobbi, Alessandro . . . . . . . . . . . . . . . . . . . . . . . . . . MC-16, MC-35
alessandro.gobbi@unibs.it
Dipartimento di Ingegneria dell’Informazione, Universitàdegli Studi di Brescia, Italy

Ginzo Villamayor, María José . . . . . . . . . . . . . . . . . . . . . . . MC-45
mariajose.ginzo@usc.es
Universidad de Santiago de Compostela, Spain

Goberna, Miguel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-42
mgoberna@ua.es
Departamento de Matemáticas, Universidad de Alicante, San
Vicente del Raspeig, Alicante, Spain

Giordano, Raffaele . . . . . . . . . . . . . . . . . . . . . . . . . . MB-13, TA-13
raffaele.giordano@cnr.it
CNR-IRSA, Italy
Giraldo-Herrera, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-64
ds.giraldoh@uniandes.edu.co
Industrial Enginnering, Universidad de Los Andes, Bogota,
Bogota, Colombia
Giribone, Pier Giuseppe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-63
pier.giuseppe.giribone@edu.unige.it
Department of Economics, University of Genoa, Italy
Giselsson, Pontus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-32
pontusg@control.lth.se
Dept. of Automatic Control, Lund University, Sweden
Giuffre’, Sofia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-40
sofia.giuffre@unirc.it
D.I.I.E.S., University of Reggio Calabria, Reggio Calabria,
Italy

Godinho, Maria Teresa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-52
mtgodinho@ipbeja.pt
Mathematics, ESTIG-IPBEJA &CmafCIO, Beja, Portugal
Godoi, Ricardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-28
rhmgodoi@ufpr.br
Federal University of Parana, Curitiba, PR, Brazil
Goerigk, Marc . . . . . . . . . . . . . . . . . . . . . . WB-03, WC-25, TD-34
marc.goerigk@uni-passau.deBusiness Decisions and Data Science, University of Passau,
Passau, Germany
Góez, Julio C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-55
julio.goez@nhh.no
Business and Management Science, NHH Norwegian Schoolof Economics, Bergen, Norway
Gokbayrak, Esra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-23
esra.gokbayrak@ozu.edu.tr
Industrial Engineering, Ozyegin University, Istanbul, Turkey

Giuffrida, Salvatore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-47
sgiuffrida@dica.unict.it
Department of Civil Engineering and Architecture, University of Catania, Catania, Italy

Gokgur, Burak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-50
bgokgur@sabanciuniv.edu
Sabancı University, Turkey

Gjorgiev, Blazhe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-19
gblazhe@ethz.ch
Mechanical and Process Engineering, ETH Zürich, Zürich,
Switzerland

Gokgur, Burak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-24
burak.gokgur@sabanciuniv.edu
Operations Management and Information Systems, Sabancı
University, Turkey

Gkiotsalitis, Konstantinos . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-56
gkiotsalitis@mail.ntua.gr
Civil Engineering, NTUA, Greece

Golalikhani, Masoud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-59
alikhani.masoud@yahoo.com
INESC TEC, Faculty of Engineering, University of Porto,
Porto, Porto, Portugal

Glanzer, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-35
martin.glanzer@uni-mannheim.de
University of Mannheim Business School, Germany
Gleixner, Ambros . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-30
gleixner@htw-berlin.de
HTW Berlin, Berlin, Germany
Gnam, Lukas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-19
lukas.gnam@fh-burgenland.at
Fachhochschule Burgenland GmbH, Pinkafeld, Austria
Gnägi, Mario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-60
mario.gnaegi@unibe.ch
University of Bern, Bern, Switzerland

564

Golany, Boaz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-64
golany@technion.ac.il
Data & Decision Sciences, Technion - Israel Institute of Technology, Haifa, Israel
Golden, Bruce . . . . . . . . . . . . . . . . . . . . . . . MD-26, TC-29, WA-44
bgolden@rhsmith.umd.edu
Decision & Information Technologies, University of Maryland, College Park, MD, United States
Goletsis, Yorgos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-47
goletsis@uoi.gr
Department of Economics, University of Ioannina, Ioannina,
GreeceEURO 2024 - Copenhagen

AUTHOR INDEX

Essen, Duisburg, Germany
Gomes de Pinho Zanco, Daniel . . . . . . . . . . . . . . . . . . . . . . TB-16
Daniel.Zanco@inrs.ca
INRS, Canada
Gomez de la Varga, Jose . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-19
josegv@uma.es
OASYS Group / Electrical Engineering, University ofMalaga, Malaga, Spain

Gönül Sezer, Eylül Damla . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-12
eylul.sezer@yeditepe.edu.tr
Industrial Engineering, Yeditepe University, TurkeyGonzÁlez Gallardo, Sandra. . . . . . . . . . . . . . . . . . . . . . . . . WC-44
sandragg@uma.es
Applied Economics (Mathematics), University of Malaga,
MALAGA, Spain

Gomez, Camilo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-64
gomez.ch@uniandes.edu.co
Industrial Engineering, Universidad de los Andes, Bogota,
Bogota, Colombia

González Rodríguez, Brais . . . . . . . . . . . . . . . . . . WA-04, MB-30
braisgonzalez.rodriguez@usc.es
University of Santiago de Compostela, Spain

Gomez, Trinidad . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-44, WD-53
trinidad@uma.es
Applied Economics Mathematics, University of Malaga,
MALAGA, Spain

Gonzalez, Andres . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-21, TD-51
andres.gonzalez@ou.edu
School of Industrial and Systems Engineering, University ofOklahoma, Norman, OK, United States

Gómez-Casares, Ignacio . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-30
ignaciogomez.casares@usc.es
Department of Statistics, Mathematical Analysis and Optimization, University of Santiago de Compostela, Spain

González, Graciela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-23
graciela.gonzalez.p@mail.pucv.cl
Pontificia Universidad Católica de Valparaíso, Chile

Gómez-Rocha, José Emmanuel . . . . . . . . . . . . . . . . . . . . . MD-61
emmanuel.gr@tec.mx
Tecnológico de Monterrey, Mexico
Gómez-Vargas, Nuria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-31
ngvargas@us.es
Statistics and Operations Research, Universidad de Sevilla,
Seville, Spain

Gonzalez-Araya, Marcela C. . . . . . . . . . . . . . . . . . . . . . . . . MC-12
mgonzalez@utalca.cl
Department of Industrial Engineering, Universidad de Talca,
Curicó, Región del Maule, Chile
Gonzalez-Calderon, Carlos A.. . . . . . . . . . . . . . . . . . . . . . . WA-21
cagonza0@unal.edu.co
Universidad Nacional de Colombia at Medellin, Colombia

Gómez_Padilla, Alejandra . . . . . . . . . . . . . . . . . . . . . . . . . . WD-62
alejandra.gomez@cucei.udg.mx
Industrial Engineering, Universidad de Guadalajara,
Guadalajara, Jalisco, Mexico

González-Díaz, Julio . . . . . . . . . . . . . . . . WA-04, MB-30, MD-58
julio.gonzalez@usc.es
Statistics, Mathematical Analysis and Optimization, University of Santiago de Compostela, Santiago de Compostela,
Spain

Gonçalves, Norberto Jorge . . . . . . . . . . . . . . . . . . . . . . . . . . MC-49
njg@utad.pt
Physics Department, University of Trás-os-Montes e Alto
Douro, Vila Real, Portugal

González-Ramírez, Rosa G. . . MC-62, MD-62, TC-62, WD-62
rgonzalez@uandes.cl
Universidad de Los Andes, Chile, Santiago, Reg. Metropolitana de Santiago, Chile

Goncalves, Rui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-11
rug@iti.sdu.dk
SDU, Odense, Southern Denmark, Denmark

González-Yero, Ismael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-29
ismael.gonzalez@uca.es
University of Cadiz, Spain

Gonçalves-Dosantos, Juan Carlos . . . . . . . . . . . MB-36, MA-61
juan.carlos.goncalves@udc.es
Universidade da Coruña, Spain

Goossens, Dries . . . . . . . . . . . . . . . . . . . . . . TA-16, WA-16, WA-43
dries.goossens@ugent.be
Business Informatics and Operations Management, Ghent
University, Gent, Belgium

Gondzio, Jacek . . . . . . . . . . . . . . . . . . . . . . MC-30, TC-38, TD-38
j.gondzio@ed.ac.uk
School of Mathematics, University of Edinburgh, Edinburgh,
United Kingdom
Gong, Sicheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-19
s.gong@tue.nl
Department of Electrical Engineering, TU Eindhoven, Eindhoven, Noord-Brabant, Netherlands
Gönsch, Jochen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-59, WA-59
jochen.goensch@uni-due.de
Mercator School of Management, University of Duisburg-

Gopalakrishnan, Sanjith . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-24
sanjith.gopalakrishnan@mcgill.ca
Desautels Faculty of Management, McGill University, Montreal, Quebec, Canada
Gorbunov, Eduard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-32
eduard.gorbunov@mbzuai.ac.ae
Mohamed bin Zayed University of Artificial Intelligence(MBZUAI), Abu Dabi, United Arab Emirates
Gordini, Angelo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-22
angelo.gordini@optit.net

565

AUTHOR INDEXEURO 2024 - Copenhagen

Optit srl, Imola, (BO), Italy
Gorge, Agnes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-24, TA-62
Agnes.GORGE@um6p.ma
Africa Business School, Mohammed VI Polytechnic University, BENGUERIR, Marrakech, Morocco
Górka, Joanna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-08
joanna.gorka@umk.pl
Nicolaus Copernicus University in Toruń, Poland
Gossner, Olivier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-45
olivier.gossner@polytechnique.edu
Ecole Polytechnique, France
Goswami, Mohit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-44
mohitgoswami@iimraipur.ac.in
Operations Management, Indian Institute of Management
Raipur, Raipur, Chhattisgarh, India
Gottschalg, Gideon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-64
gideon.gottschalg@whu.edu
Mercator Endowed Chair of Demand Management & Sustainable Transport, WHU - Otto Beisheim School of Management, Vallendar, Germany
Goujaud, Baptiste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-32
baptiste.goujaud@gmail.com
CMAP, Ecole Polytechnique, Palaiseau, IDF, France
Goujaud, Baptiste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-32
baptiste.goujaud@polytechnique.edu
CMAP, Ecole Polytechnique, Palaiseau, France
Gouveia, Luís . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-26
legouveia@fc.ul.pt
DEIO - Departamento de Estatística e Investigação Operacional, Universidade de Lisboa - Faculdade de Ciências,
Lisboa, Portugal
Goverde, Rob . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-51, WB-54
r.m.p.goverde@tudelft.nl
Transport and Planning, Delft University of Technology,
Delft, Netherlands
Goyal, Rajat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-45
rajatgoel03@gmail.com
Mechanical Engineering, Iit Delhi, New Delhi, Delhi, India
Gozzi, Fausto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-33
fgozzi@luiss.it
Dipartimento di Economia e Finanza, Luiss University Roma - Italy, Roma, RM, Italy
Gracia, Maria D. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-62
mgracia@docentes.uat.edu.mx
Faculty of Engineering Tampico, Universidad Autonoma de
Tamaulipas, Tampico, Tamaulipas, Mexico
Grafe, Vera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-51
vera.grafe@math.rptu.de
Department of Mathematics, RPTU Kaiserslautern-Landau,
Germany
Granados, Daniela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-58

566

dzg0046@auburn.edu
Industrial Engineering, Auburn University, Auburn, AL,
United States
Granat, Janusz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-47
janusz.granat@pw.edu.pl
Institute of Control and Computation Engineering, Warsaw
University of Technology, Warsaw, Poland
Grané, Aurea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-63
aurea.grane@uc3m.es
Statistics, University Carlos III of Madrid, Spain
Granda, Bibiana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-12
bibianag@ucm.es
Department of Statistics and Operational Research, Interdisciplinary Mathematics Institute, Universidad Complutense de
Madrid, Madrid, Spain
Grapiglia, Geovani . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-32
geovani.grapiglia@uclouvain.be
Applied Mathematics, Université catholique de Louvain,
LOUVAIN-LA-NEUVE, Belgium
Grappe, Roland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-25
roland.grappe@lamsade.dauphine.fr
Université Paris Dauphine, Paris, France
Grass, Dieter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-33
dieter.grass@tuwien.ac.at
Vienna University of Technology, Vienna, Austria
Grass, Emilia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-12, TB-21
emilia.grass@kit.edu
Germany
Grassi, Rosanna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-63
rosanna.grassi@unimib.it
Dep.t of Statistics and Quantitative Methods, Universita
Milano-Bicocca, Milano, Italy
Graulich, Tim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-19
taigr@dtu.dk
DTU Management, Technical University of Denmark, Denmark
Gravagnuolo, Antonia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-47
antonia.gravagnuolo@cnr.it
Heritage Science, Institute of Heritage Science, National Research Council (CNR-ISPC), Naples, 80134, Italy, Naples,
Italy, Italy
Grazia, Carlo Augusto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
carloaugusto.grazia@unimore.it
Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, Modena, MO, Italy
Grbic, Djordje . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-03
djgr@itu.dk
IT University of Copenhagen, Denmark
Greco, Salvatore . . . . . . . . . . . . MA-44, MB-44, MC-44, WC-44
salgreco@unict.it
Department of Economics and Business, University of Catania, Catania, Italy

EURO 2024 - Copenhagen

Grecu, Robert-Adrian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-31
robertgrecu@yahoo.com
Money and Banking, Bucharest University of Economic
Studies, Bucharest, Romania
Green, Olle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-57
greeolls@amazon.com
Machine Learning & Engineering, Amazon, London, London, United Kingdom

AUTHOR INDEX

alessia.grosso@phd.units.it
DEAMS - Department of Economics, Business, Mathematics
and Statistics, University of Trieste, Trieste, Italy
Grübel, Julia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-19
julia.gruebel@utn.de
Department Liberal Arts and Sciences, University of Technology Nuremberg, Nürnberg, Germany

Greening, Phil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-43
p.greening@hw.ac.uk
Heriott-watt, United Kingdom

Grübler, Lene Marie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-19
grueblen@b-tu.de
Strategic Grid Planning / Chair of Energy Economics,
50Hertz Transmission GmbH / BTU Cottbus, Berlin, Berlin,
Germany

Greenstein, Gil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-45
gilgr@hit.ac.il
Faculty of Industrial Engineering and Technology Management, H.I.T - Holon Institute of Technology, Holon, Israel

Grunow, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-23, TA-49
martin.grunow@tum.de
TUM School of Management, Technical University of Munich, München, Germany

Gregori, Martina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-16
martina.gregori@uniroma1.it
DIAG, Sapienza University of Rome, Italy

Gruzauskas, Valentas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-45
v.gruzauskas@gmail.com
2Business Management Faculty, Vilnius Gediminas Technical University, Lithuania

Greil, Thorsten . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-49
thorsten.greil@tum.de
TUM School of Management, Technical University of Munich, Munich, Germany

Grynia, Dariusz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-44
dariusz.grynia@doctorate.put.poznan.pl
Poznan University of Technology, Poland

Gribling, Sander . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-42
s.j.gribling@tilburguniversity.edu
Tilburg University, Netherlands

Grytbakk Tronsaune, Herman . . . . . . . . . . . . . . . . . . . . . . WD-34
herman.g.tronsaune@ntnu.no
NTNU, Norway

Grillenzoni, Carlo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-44
carlo.grillenzoni@iuav.it
Culture del Progetto, University IUAV of Venice, Venice,
Italy

Grzyb, Slawomir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-48
slawomir.grzyb@eviden.com
PolandGrimm, Veronika . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-19
veronika.grimm@fau.deFriedrich-Alexander-Universität Erlangen-Nürnberg, Nürnberg, Germany
Gripton, Adam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-43
a.gripton@hw.ac.uk
School of Social Sciences, Heriot-Watt University, Currie,
United Kingdom
Griset, Rodolphe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-42
rodolphe.griset@hotmail.fr
FranceGroen, Merel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-21
merelgroen@live.nl
Utrecht, Netherlands
Gross, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-42
david.gross@thp.uni-koeln.de
University of Cologne, Germany

Gu, Xinbing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-08
xinbing.gu@nottingham.edu.cn
China
Guajardo, Mario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-12, TA-16
mario.guajardo@nhh.no
Business and Management Science, NHH Norwegian Schoolof Economics, Bergen, Norway
Gualandi, Stefano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-26
stefano.gualandi@unipv.it
Pavia, Department of Mathematics, "Felice Casorati", Pavia,
Italy
Guan, Hao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-55, MC-56
e0546034@u.nus.edu
CEE, National University of Singapore, Singapore
Guan, Linfei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-62
939075539@qq.com
China

Gross, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-57
grosmar@amazon.lu
Amazon, Luxembourg

Guardo, Elisa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-55
e.guardo-martinez@soton.ac.uk
Business School - Decision analytics and risk, University ofSouthampton, United Kingdom

Grosso, Alessia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-56

Guastaroba, Gianfranco . . . . . . . . . . . . . TA-26, WA-26, MB-29

567

AUTHOR INDEXEURO 2024 - Copenhagen

gianfranco.guastaroba@unibs.it
Department of Economics and Management, University of
Brescia, Brescia, Italy

ggazzinelli9@gmail.com
Universidade Estadual de Campinas (UNICAMP), Campinas, SP, Brazil

Guderian, Carsten C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-57
carsten.guderian@fau.deChair of Technology Management, Friedrich-AlexanderUniversity Erlangen-Nuremberg, Fürth, Germany

Guimarães, Luis . . . . . . . . . . . . . . . . . . . . MC-19, TD-20, WB-60
guimaraes.luis@fe.up.pt
INESC TEC, Faculadade de Engenharia, Universidade do
Porto, Portugal

Gudipati, Mahesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-24
maheshreddy.g@gmail.com
Indian Institute Of Management, Kozhikode, Pune, Maharashtra, India

Guimarans, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . MD-55, TD-57
guimd@amazon.es
Amazon, Luxembourg, Luxembourg

Gudmundsson, Jens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-43
jg@ifro.ku.dk
University of Copenhagen, Frederiksberg, Denmark
Guellaf, Hicham . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-02
H.GUELLAF@ocpgroup.ma
OCP Group, Casablanca, Morocco
Güemes-Castorena, David . . . . . . . . . . . . . . . . . . . . . . . . . . WA-48
guemes@itesm.mx
School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Nuevo León, Mexico
Guericke, Daniela . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-19, TC-22
d.guericke@utwente.nl
Department of Industrial Engineering and Business Information Systems, University of Twente, Enschede, Netherlands
Guerra Pires, Arthur Leandro . . . . . . . . . . . . . . . . . . . . . . . TB-19
a.guerrapires@hotmail.com
Post-Graduate Program in Production Engineering and Systems - Federal University of Paraiba, Federal University of
Paraiba, João Pessoa, Paraíba, Brazil
Guerrero, Vanesa . . . . . . . . . . . . . . . . . . . MC-16, TD-27, WD-27
vanesa.guerrero@uc3m.es
Department of Statistics, Universidad Carlos III de Madrid,
Getafe, Spain
Guerriero, Emanuela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-64
emanuela.guerriero@unile.it
University of Lecce, Lecce, Italy
Gueye, Serigne . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-18
serigne.gueye@univ-avignon.fr
Universite D’avignon Ceri-lia, France
Guillaume, Joseph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-13
Joseph.guillaume@aalto.fi
Aalto University, Espoo, Finland
Guillén, María D. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-48
maria.guilleng@umh.es
Center of Operation Research, University Miguel Hernandez
of Elche, Spain
Guillot, Gaël . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-22
gael.guillot@inria.fr
INRIA Lille - Nord Europe, France
Guimaraes, Gabriel Gazzinelli . . . . . . . . . . . . . . . . . . . . . . . TA-07

568

Guinand, Frédéric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-26
frederic.guinand@univ-lehavre.fr
Université du Havre, Le Havre, France
Guissou, Ibtissam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-62
ibtissam.guissou@um6p.ma
Africa Business School, Mohammed VI Polytechnic University, Rabat, Morocco
Guivarch, Ronan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-55
ronan.guivarch@toulouse-inp.fr
IRIT, INP Toulouse, France
Gül, Serhat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-10
serhat.gul@tedu.edu.tr
TED University, Turkey
Güler, Mehmet Güray . . . . . . . . . . . . . . . . . . . . . . MB-09, WD-22
mgguler@yildiz.edu.tr
Industrial Engineering Dep., İstanbul Technical University,
TurkeyGullhav, Anders N. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-60
anders.gullhav@iot.ntnu.no
Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology,
Trondheim, Trondheim, Norway
Gullu, Refik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-23
refik.gullu@boun.edu.tr
Industrial Engineering Department, Bogazici University, Istanbul, Turkey
Gulum Tas, Pelin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-11
p.gulumtas@tudelft.nl
Engineering Systems and Services, Delft University of Technology, Netherlands
Gumus, Mehmet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-15
mehmet.gumus@mcgill.ca
Desautels Faculty of Management, McGill University, Montreal, Quebec, Canada
Gunawan, Aldy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-26
aldygunawan@smu.edu.sg
School of Computing and Information Systems, Singapore
Management University, Singapore, Singapore
Günay, Berker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-52
berker.gunay@tupras.com.tr
Tüpraş, Turkey

EURO 2024 - Copenhagen
Gündüz, Saliha Büşra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-22
salihabusra.gunduz@unifi.it
Information Engineering, University of Florence, Italy
Guney, Evren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-64
guneye@mef.edu.tr
Industrial Engineering, MEF University, Istanbul, Turkey
Gunnarsdóttir, Eva Ósk . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-27
evag18@ru.is
Department of Computer Science, Reykjavík University, Iceland
Gunnarsson, Björn Rafn . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-08
bjornrafn.gunnarsson@kuleuven.be
KU Leuven, Belgium
Gunnec, Dilek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-21
dilek.gunnec@ozyegin.edu.tr
Industrial Engineering, Ozyegin University, Istanbul, Turkey
Guns, Tias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-03
tias.guns@kuleuven.be
KULeuven, Leuven, Belgium
Günther, Maik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-53
m.guenther@iu.org
IU International University of Applied Sciences / Stadtwerke
München GmbH, Denklingen, Germany
Guo, Jia-Hau. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-28, WC-39
gjiahau@nycu.edu.tw
Department of Information Management and Finance, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
Guo, Mengzhuo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-03
mengzhguo@scu.edu.cn
Sichuan University, China
Guo, Min . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-23
min.guo@nottingham.edu.cn
Nottingham University Business School China, University of
Nottingham Ningbo China, China
Guo, Qi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-63
guoqi0901@gmail.com
Statistics, University Carlos III of Madrid, Madrid, SPAIN,
Spain
Guo, Shu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-59
eunice.guo@liverpool.ac.uk
Management School, University of Liverpool, Liverpool,
United Kingdom
Guo, Weikang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-06
weikg1314@outlook.com
Computer science and technology, KTH, Ghent University,
Stockholm, Sweden
Guo, Yufeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-21219w701w@stu.kobe-u.ac.jp
Graduate School of Maritime Sciences, Kobe University,
Kobe, Japan
Gupta, Nirupam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-32

AUTHOR INDEX

nirupam.gupta@epfl.chComputer Science, EPFL, Lausanne, Switzerland
Gupta, Rohit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-58
rohit.gupta@klm.com
KLM Royal Dutch Airlines, Netherlands
Gupta, Sidharth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-57
sidgupp@amazon.co.uk
Amazon, United Kingdom
Gurevsky, Evgeny . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-09, TD-60
evgeny.gurevsky@univ-nantes.fr
LS2N, Université de Nantes, France
Gurler Kandemir, Asli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-24
asligurler@sabanciuniv.edu
Sabancı University, Turkey
Gürlesin, Taha Yasin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-24
yasing@metu.edu.tr
Industrial Engineering, Middle East Technical University,
Ankara, Çankaya, Turkey
Gursoy, Kemal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-06
kgursoy@business.rutgers.edu
MSIS, Rutgers University, Piscataway, New Jersey, UnitedStatesGüsser-Fachbach, Ines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-23
ines.fachbach@gmail.com
University of Graz, Austria
Gutfraind, Alexander. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-11
sgutfraind@gmail.com
Department of Medicine, Loyola University of Chicago,
Chicago, Illinois, United States
Gutierrez, Pablo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-26
pgutierrez2018@udec.cl
Departamento De Ingeniería Industrial, Universidad De Concepción, Concepción, Bío Bío, Chile
Gutjahr, Georg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-17, WA-18
georg.gutjahr@gmail.com
Computer Science, Amrita Vishwa Vidyapeetham„ Kollam,
Kerala, India
Gutjahr, Walter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-35
walter.gutjahr@univie.ac.at
Department of Statistics and Operations Research, Universityof Vienna, Vienna, Vienna, Austria
Guyot, Alexis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-47
aguyot@audencia.com
Audencia Business School, France
Györgyi, Péter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-26
gyorgyi.peter@sztaki.hu
Institute for Computer Science and Control, Budapest, Hungary
Ha, Albert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-61
imayha@ust.hk
Information Systems, Business Statistics and Operations

569

AUTHOR INDEXEURO 2024 - Copenhagen

Management, Hong Kong University of Science and Technology, Hong Kong, Hong Kong
Ha, Minh Hoang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-26
hoang.haminh@phenikaa-uni.edu.vn
OR lab, Phenikaa University, Hanoi, Viet Nam
Haag, Fridolin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-11
fridolin.haag@leibniz-zmt.deIntegrated Modelling, Leibniz Centre for Tropical Marine
Research (ZMT), Bremen, Germany
Haake, Anthony . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-59
anthony.haake@bfw.rwth-aachen.de
Finance, RWTH Aachen, Aachen, NRW, Germany
Haalboom, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-55
jelle_daniel.haalboom@tu-dresden.de
Chair of Railway Operations, TUD Dresden University ofTechnology, Germany
Haas, Jannik . . . . . . . . . . . . . . . . TB-19, WA-19, WC-22, MC-53
jannik.haas@canterbury.ac.nz
Civil and Natural Resources Engineering, University of Canterbury, Christchurch, Canterbury, New Zealand
Haase, Knut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-55
knut.haase@uni-hamburg.de
Institut f. Verkehrswirtschaft, Lehrstuhl BWL, insb. Verkehr,
Universität Hamburg, Hamburg, Germany
Haaskjold, Kristina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-09
kristina.haaskjold@ife.no
Energy System Analysis, Institute for Energy Technology(IFE), Norway
Haberman, Steven . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-63
s.haberman@city.ac.uk
Casa Business School, London, United Kingdom
Habibi, Khakim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-24
muhammad.habibi@rennes-sb.com
Supply Chain Management & Information Systems, ESC
Rennes School of Business, Rennes, Bretagne, France
Habibi, Soodeh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-34
s.habibi@liverpool.ac.uk
University of Liverpool, United Kingdom
Hadas, Yuval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-43
yuval.hadas@biu.ac.il
Department of Management, Bar-Ilan University, Ramat Gan,
IsraelHaering, Tom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-59
tom.haering@epfl.ch
School of Architecture, Civil and Environmental Engineering, EPFL, Ecublens, Vaud, Switzerland
Haeser, Gabriel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-41
ghaeser@ime.usp.br
Department of Applied Mathematics, University of Sao
Paulo, Brazil
Haeussler, Stefan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-07

570

stefan.haeussler@uibk.ac.at
Information Systems, Production and Logistics Management,
University of Innsbruck, Innsbruck, Austria
Haferkamp, Jarmo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-64
jarmo.haferkamp@ovgu.de
Management Science, Otto von Guericke University Magdeburg, Germany
Hagen-Zanker, Alex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-28
a.hagen-zanker@surrey.ac.uk
School of Sustainability, Civil and Environmental Engineering, University of Surrey, Guildford, Please select, United
Kingdom
Hager, Florentina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-10
f.hager@utwente.nl
CHOIR, University of Twente, Enschede, Netherlands
Hagspiel, Verena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-33
verena.hagspiel@ntnu.no
NTNU, Trondheim, Norway
Haijima, Daiki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-52
aled.williams123@me.com
London School of Economics and Political Science, United
Kingdom
Hait, Alain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-49
alain.hait@isae.fr
Complex Systems Engineering, ISAE-SUPAERO Universityof Toulouse, Toulouse, France
Haiying, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-51
hyli@bjtu.edu.cn
Beijing jiaotong university, China
Hajaj, Chen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-06, MD-28
chenha@ariel.ac.il
Ariel university, Ariel, Israel
Hajizadeh, Roghayeh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-64
roghayeh.hajizadeh@liu.se
Division of Optimization, Department of Mathematics,
Linköping University, Linköping, Sweden
Hajjar, Ali . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-17
ahajjar@mbsc.edu.sa
King Abdullah Economic City, Saudi Arabia
Hajji, Ahmed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-22
ahmed.hajji.personal@gmail.com
Technical University of Munich, Germany
Hald, Mathias Kjærgård . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-09
MKC@energinet.dk
Energinet, Denmark
Halffmann, Pascal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-37
pascal.halffmann@itwm.fraunhofer.deFinancial Mathematics, Fraunhofer Institute for Industrial
Mathematics ITWM, Kaiserslautern, Germany
Halicka, Margareta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-48
halicka@fmph.uniba.sk

EURO 2024 - Copenhagen
Comenius University in Bratislava, Slovakia
Hall, Julian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-30
jajhall@ed.ac.uk
University of Edinburgh, United Kingdom
Halskov, Thomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-27
tha.eco@cbs.dk
Copenhagen Business School, Denmark
Hambauer, Nico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-31
nico.hambauer@ur.de
Faculty of Informatics and Data Science, University of Regensburg, Regensburg, Bavaria, Germany
Hamouda, Essia . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-39, TD-53
ehamouda@csusb.edu
IDS, CSUSB, Riverside, CA, United States
Han, Peiran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-54
peiranhan@bjtu.edu.cn
School of Traffic and Transportation, Beijing Jiaotong University, Beijing, Beijing, China
Han, Wenting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-23
hanwt@mail.nwpu.edu.cn
School Of Management, Northwestern Polytechnical University, Xi’an, China
Handa, Marouan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-38
handa@utia.cas.cz
MTR, Institute of Information Theory and Automation of theCAS, Prague, Czech Republic
Haneyah, Sameh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-60
samehhaneyah@gmail.com
Equipment Control Systems and Services, Konecranes,
Netherlands
Hankimaa, Helmi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-09
helmi.hankimaa@aalto.fi
Department of Mathematics and Systems Analysis, Aalto
University, Espoo, FinlandHans, Erwin W. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-10
e.w.hans@utwente.nl
Industrial Engineering & Business Information Systems,
University of Twente, fac. Behavioral Management and Social Science, Enschede, Netherlands
Hansen, Bjørn Gunnar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-12
bjorn.gunnar.hansen@tine.no
Tine SA Research and Development Department, Norway
Hansen, Siv Marie Cartland . . . . . . . . . . . . . . . . . . . . . . . . . TC-51
scaha@dtu.dk
Department of Technology, Management and Economics,
DTU, Denmark
Hantoute, Abderrahim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-42
hantoute@ua.es
Mathematics, Universidad de Alicante, Alicante, Spain,
Spain

AUTHOR INDEX

zdenek.hanzalek@cvut.cz
CTU Prague, Praha, Czech RepublicHao, Jin-Kao . . . . . . . . . . . . . . . . . . . . . . . WD-10, WA-15, MB-58
jin-kao.hao@univ-angers.fr
LERIA, Université d’Angers, Angers, France
Hao, Zhongyuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-07
lingyuran881201@163.com
Dongbei University of Finance and Economics, Dalian,
China
Harison, Elad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-61
eladha@shenkar.ac.il
Shenkar College of Design and Engineering, Ramat-Gan, Israel
Harjunen, Ville . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-11
ville.harjunen@helsinki.fi
Helsinki University, Helsinki, Finland
Harper, Paul . . . . . . . . . . . . . . . . . MC-15, TB-15, MC-17, WA-18
harper@cardiff.ac.uk
School of Mathematics, Cardiff University, Cardiff, Wales,
United Kingdom
Harrison, Eric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-09
eric.harrison@vtt.fi
Design and Operation of Energy Systems, VTT TechnicalResearch Centre of Finland, Espoo, Finland
Harrod, Steven . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-40
stehar@dtu.dk
Department of Engineering Technology, Technical University of Denmark, Ballerup, Denmark
Hartisch, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-03, TD-34
michael.hartisch@fau.de
Data Science, Friedrich-Alexander-Universität ErlangenNürnberg, Erlangen, Germany
Hartl, Richard . . . . . . . . . . . . . . . MB-33, TB-33, MB-60, TD-60
richard.hartl@univie.ac.at
Business Decisions & Analytics, University of Vienna, Vienna, Austria
Hartman, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-52
hartman@cs.cas.cz
Department of Complex Systems, The Institute of Computer
Science of the Czech Academy of Sciences, Prague, Czech
Republic
Hartman, Joseph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-29
joseph_hartman@uml.edu
UMass Lowell, United States
Harush, Jacob kobe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-61
kobharos@hotmail.com
IsraelHasannasab, Maryam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-48
hasannasab.tmu84@gmail.com
Accounting and finance, The university of Auckland, Auckland, New ZealandHanzalek, Zdenek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-60

571

AUTHOR INDEXEURO 2024 - Copenhagen

Hasegawa, Daisuke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-55
hasega60@g.ecc.u-tokyo.ac.jp
Center for Real Estate Innovation, The University of Tokyo,
Bunkyo-ku, Tokyo, Japan
Hashim, Fatma. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-52
fatma_hashim@h-eng.helwan.edu.eg
Helwan university, Egypt
Hasiloglu Ciftciler, Melda . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-50
nilsumelda@gmail.com
Blackrock, Please select, Ireland
Hassani, Dana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-21
dhassani@umass.edu
Operations and Information Management, University of Massachusetts Amherst, Amherst, MA, United States
Hassanpourjesri, Zahra Sadat . . . . . . . . . . . . . . . . . . . . . . . TA-24
hasanpourjesri@sharif.edu
IE, Sharif University of Technology, Tehran, Iran, Islamic
Republic oftakaki@kbs.keio.ac.jp
Keio University, Yokohama, Japan
Hayel, Yezekael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-53
yezekael.hayel@uni-avignon.fr
LIA, Avignon University, Avignon, France, France
Häfele, Lorenz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-22
st155105@stud.uni-stuttgart.deInstitute of Energy Economics and Rational Energy Use, University of Stuttgart, Stuttgart, Germany
Hägele, Sascha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-43
s.haegele@con.uni-saarland.de
Chair of Managerial Accounting, Saarland University, Saarbrücken, Germany
Häll, Carl Henrik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-56
carl.henrik.hall@liu.se
Department of Science and Technology, Linköping University, Sweden

Hassanzadeh, Alborz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-21
al.zadeh@hec.ca
HEC Montreal, Canada

Hämäläinen, Raimo P. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-11
raimo.hamalainen@aalto.fi
Systems Analysis Laboratory, Aalto University, School of
Science, Aalto, Finland

Hassin, Yehuda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-49
hassin@jce.ac.il
Software Engineering, Azrieli College of Engineering
Jerusalem, Jerusalem, Israel

Härtull, Oscar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-30
oscar.hartull@abo.fi
Department of Information Technologies, Åbo Akademi University, VAASA, Finland

Hatamimarbini, Adel . . . . . . . . . . . . . . . . . . . . . . . MA-48, WC-48
a.hatamimarbini@hud.ac.uk
Department of Accounting, Finance, Logistics and Economics, University of Huddersfield, Huddersfield, United
Kingdom

Hazir, Oncu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-60
oncu.hazir@rennes-sb.com
Rennes School of Business, France

Hautsch, Nikolaus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-52
nikolaus.hautsch@univie.ac.at
Department of Statistics and Operations Research, Universityof Vienna, Austria
Hayakawa, Norihiro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-48
lonmei@gmail.com
Graduate School of Business Sciences, University ofTsukuba, Bunkyo-ku, Japan
Hayashi, Akiko . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-44
hayashi@inulab.sys.es.osaka-u.ac.jp
Graduate School of Engineering Science, Osaka University,
Toyoaka, Osaka, Japan
Hayashi, Hitomi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-25
usagibird.torirabit@gmail.com
Graduate School of Information Science, University of
Hyogo, Japan
Hayashi, Shunsuke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-43
s_hayashi@hosei.ac.jp
Department of Industrial and System Engineering, Faculty of
Science and Enginering, Hosei University, Koganei, Tokyo,
Japan
Hayashi, Takaki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-51

572

He, Peiyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-21
he-py20@mails.tsinghua.edu.cn
Industrial Engineering, Tsinghua University, Beijing, Beijing, China
Heavey, Cathal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-23
cathal.heavey@ul.ie
University of Limerick, Limerick, Ireland
Heinold, Arne . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-62
arne.heinold@bwl.uni-kiel.de
School of Economics and Business, Kiel University, Kiel,
Please Select (only U.S. / Can / Aus), Germany
Heinold, Arne . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-62
arne.heinold@klu.org
Kühne Logistics University, Germany
Heinrich, Irene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-54
heinrich@mathematik.tu-darmstadt.deMathematics, TU Darmstadt, Darmstadt, Germany
Heinz, Frank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-57
frank.heinz@rwth-aachen.de
E.ON Energy Research Center, RWTH Aachen University,
Aachen, Germany
Heinz, Vilem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-60
heinzvil@fel.cvut.cz

EURO 2024 - Copenhagen
Czech Technical University in Prague, Czech Republic
Heipcke, Susanne . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-30, MD-46
susanneheipcke@fico.com
Xpress Optimization, FICO, Marseille, France
Held, Felix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-37
felix.held@chalmers.se
Chalmers University of Technology, Sweden
Helistö, Niina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-09
niina.helisto@vtt.fi
VTT, Finland
Heller, Till . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-09, MD-49
till.heller@itwm.fraunhofer.de
Optimization, Fraunhofer Institute for Industrial Mathematics ITWM, Germany

AUTHOR INDEX

Statistics and Operational Research, Faculty of Sciences,
University of Lisbon, Lisbon, Lisbon, Portugal
Hentati Kaffel, Rania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-63
rania.kaffel@univ-paris1.fr
CES : Centre d’économie de la Sorbonne, University Paris 1
Panthéon Sorbonne, Paris, France
Hentschel, Dana Josephine . . . . . . . . . . . . . . . . . . . . . . . . . WC-09
djh.eco@cbs.dk
Copenhagen School of Energy Infrastructure (CSEI), Copenhagen Business School, Denmark
Henze, Fabian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-42
fhenze2@uni-koeln.de
Institute for Theoretical Physics, University Cologne,
Cologne, Northrhine Westphalia, Germany

Heloisa Carvalho Machado, Rafaela . . . . . . . . . . . . . . . . . TB-44
rafaela.h.machado@gmail.com
Universidade Federal de Viçosa, Brazil

Heredia, F.-Javier . . . . . . . . . . . . . . . . . . . . . . . . . . WB-34, WD-50
f.javier.heredia@upc.edu
Statistics and Operations Research, Universitat Politècnica
de Catalunya - BarcelonaTech, Barcelona, Catalunya, Spain

Hemmati Golsefidi, Atefeh . . . . . . . . . . . . . . . . . . . . . . . . . . WA-56
ahego@dtu.dk
DTU Managment, Technical University of Denmark (DTU),
Herlev, Copenhagen, Denmark

Herlicka, Lisa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-24
lisa.herlicka@bwl.uni-kiel.de
Christian-Albrechts-Universität, Kiel, Please Select (onlyU.S. / Can / Aus), Germany

Hemmati, Ahmad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-64
ahmad.hemmati@uib.no
University of Bergen, Norway

Hermann, Julian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-19
jhermann@ethz.ch
Chair of Energy Systems Analysis, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland

Hendiani, Sepehr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-47
sepehr.hendiani@om.rwth-aachen.de
School of Business and Economics, Chair of Operations
Management, RWTH Aachen University, Aachen, Germany
Hendrikx, Hadrien . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-32
hadrien.hendrikx@inria.fr
Inria Grenoble, France
Henggeler Antunes, Carlos . . . . . . . . . . . . . . . . . . TD-11, WC-37
ch@deec.uc.pt
INESC Coimbra, Department of Electrical and Computer
Engineering, University of Coimbra, Coimbra, Portugal
Henke, Dorothee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-04
dorothee.henke@uni-passau.de
University of Passau, Germany
Henke, Ilaria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
ilaria.henke@unina.it
Federico II University of Naples, Italy
Henríquez Díaz, Gloria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-15
ghenriquez@dim.uchile.clCenter For Mathematical Modeling (CNRS IRL2807), University of Chile, Santiago, Chile

Hermans, Ben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-43
ben.hermans@kuleuven.be
ORSTAT, KU Leuven, Belgium
Hernández Gress, Eva Selene . . . . . . . . . . . . . . . . . . . . . . . MD-61
evahgress@tec.mx
Industrial Engineering, Tec de Monterrey, Pachuca, Hidalgo,
MexicoHernandez Riaño, Helman Enrique . . . . . . . . . . . . . . . . . MB-58
hhernandez@correo.unicordoba.edu.co
Industrial Engineer, Universidad de Córdoba, Montería, Córdoba, Colombia
Hernandez, Adolfo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-47
adolfher@ucm.es
Financial & Actuarial Economics & Statistics, Complutense
University Madrid, Madrid, Sri Lanka
Hernandez, Monica . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-13
m_huelin@uma.es
Applied Economics (Mathematics), University of Malaga,
Malaga, Spain

Henriques, Carla . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-44
coliv@inescc.pt
INESC Coimbra and ISCAC Coimbra, Coimbra, Portugal

Hernandez-Diaz, Alfredo G. . . . . . . . . . . . . . . . . . . . . . . . . MD-58
agarher@upo.es
Department of Economic, Quantitative Methods and E.H.,
Pablo de Olavide University, Seville, Spain

Henriques, Sofia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-25
sofiasoareshenriques99@gmail.com

Hernández-Pérez, Hipólito . . . . . . . . . . . . . . . . . . . . . . . . . . MC-25hhperez@ull.edu.es

573

AUTHOR INDEXEURO 2024 - Copenhagen

Estadística e Investigación Operativa, Universidad de La Laguna (Tenerife), La Laguna, Canarias, Spain
Hernanz Zajara, Javier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-09
javier.hernanz@repsol.com
Quantum Advisory Team, Repsol, Madrid, Madrid, Spain
Herrala, Olli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-34
olli.herrala@aalto.fi
Systems Analysis Laboratory, Aalto University, Aalto, Finland
Herrera, Hugo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-12
hugo.herrera@uib.no
Geography Department, University of Bergen, Bergen, Norway
Herzallah, Randa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-23
Randa.Herzallah@warwick.ac.uk
Mathematics Institute, The University of Warwick, United
Kingdom
Herzog, Roland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-41
roland.herzog@iwr.uni-heidelberg.de
Interdisciplinary Center for Scientific Computing, Germany
Hesamzadeh, Mohammad Reza . . . . . . . . . . . . . . . . . . . . . . TA-09
mrhesamzadeh@ee.kth.seElectric Power Systems, KTH Royal Institute of Technology,
Stockholm, Sweden
Hess, Christina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-58
christina.hess@univie.ac.at
Business Decisions and Analytics, University of Vienna, Vienna, Austria
Hettiarachchi, Biman Darshana . . . . . . . . . . . . . . . . . . . . WC-23
Biman.Hettiarachchi@uni-kassel.de
FB07 Supply Chain Management - ReTraCE Project, University of Kassel, Kassel, Hessen, Germany
Heuser, Laura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-21
lheuser@ethz.ch
ETH Zurich, Zürich, Switzerland
Heuseveldt, Lisanne . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-54
l.a.heuseveldt@uu.nl
Information and Computing Sciences, Utrecht University,
Netherlands
Hewitt, Mike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-35, MC-61
mhewitt3@luc.edu
Loyola University Chicago, United States
Hezarkhani, Behzad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-24
b.hezarkhani@soton.ac.uk
Business School, Southampton University, United Kingdom
Hidalgo, Rocio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-63
rociohidalgo98@gmail.com
Universitat de València, Spain
Hieber, Peter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-57
peter.hieber@unil.ch
Switzerland

574

Higashikawa, Yuya . . . . . . . . . . . . . . . . . . . . . . . . . . TA-25, WD-25
parklife.parklife@gmail.com
University of Hyogo, Kobe, Japan
Hildebrandt, Thomas Troels . . . . . . . . . . . . . . . . . . . . . . . . MC-27
hilde@di.ku.dk
Computer Science Departement, University of Copenhagen,Denmark
Hillston, Jane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-28
jane.hillston@ed.ac.uk
Informatics, University of Edinburgh, Edinburgh, United
Kingdom
Himstedt, Barbara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-55
barbara.himstedt@bwl.uni-kiel.de
Christian-Albrechts-University, Kiel, Germany
Hinojosa, Miguel A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-36
mahinram@upo.es
Universidad Pablo de Olavide, Seville, Spain
Hirotsu, Nobuyoshi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-16
nhirotsu@hotmail.com
Juntendo University, Inzai, Chiba, Japan
Hirsch, Patrick . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-15
patrick.hirsch@boku.ac.at
Institute of Production and Logistics, University of Natural
Resources and Life Sciences, Vienna, Wien, Austria
Hirschmann, Florian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-14
florian.hirschmann@uni-kassel.de
Energy Management and Power System Operation, University of Kassel, Germany
Hirschprung, Ron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-28
ronyh@ariel.ac.il
Ariel university, Israel
Hlavatý, Robert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-34
hlavaty85@gmail.com
Department of Systems Engineering, CZU Prague, Czech
Republic
Hlinka, Jaroslav . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-52
hlinka@cs.cas.cz
National Institute of Mental Health, Prague, Czech Republic,
Prague, Czech Republic
Ho-Nguyen, Nam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-12
nam.ho-nguyen@sydney.edu.au
The University of Sydney, Sydney, Australia
Hobbie, Hannes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-09
hannes.hobbie@tu-dresden.de
Chair of Energy Economics, TU Dresden, Dresden, Germany
Hobbs, Ben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-09
bhobbs@jhu.edu
Johns Hopkins University, Baltimore, Maryland, UnitedStatesHoberg, Kai. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-50

EURO 2024 - Copenhagen
Kai.Hoberg@the-klu.org
Supply Chain and Operations Strategy, Kühne Logistics University, Hamburg, Germany
Hocine, Amin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-44
amin.hocine@unibg.it
Italy
Hodgett, Richard . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-30, TA-45
r.e.hodgett@leeds.ac.uk
Leeds University Business School, University of Leeds,
Leeds, United Kingdom
Hoffmann, Steffen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-48
steffen.hoffmann@fernuni-hagen.de
Chair of Operations Research, FernUniversität in Hagen(University of Hagen), Hagen, Germany
Hofmann, René . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-19
rene.hofmann@tuwien.ac.at
Institute for Energy Systems and Thermodynamics, TU Wien,
W, Austria
Höhne, Felix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-49
felix.hoehne@itwm.fraunhofer.deFraunhofer Institute for Industrial Mathematics ITWM,
Kaiserslautern, Germany
Hojny, Christopher . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-30
c.hojny@tue.nl
TU Eindhoven, Netherlands
Holmberg, Kaj . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-64
kaj.holmberg@liu.se
Optimization, Mathematics, Linkoping University, Linkoping, Sweden
Homaei, Shamim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-19
shamim.homaie@gmail.com
Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology,
Trondheim, Norway
Honda, Yago . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-28
yagohonda11@gmail.com
Instituto Brasileiro de Informação em Ciência e Tecnologia,
Brasília, DF, Brazil
Hong, I-Hsuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-40
ihong@ntu.edu.tw
Industrial Engineering, National Taiwan University, Taipei,
TaiwanHoogervorst, Rowan . . . . . . . . . . . . . . . . . . . . . . . . . TC-51, WA-54
rowho@dtu.dk
DTU Management, DTU, Kongens Lyngby, Denmark
Horiguchi, Masayuki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-40
horiguchi@kanagawa-u.ac.jp
Department of Mathematics, Faculty of Science, Kanagawa
University, Yokohama, Kanagawa, Japan
Horiyama, Takashi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-25
horiyama@ist.hokudai.ac.jp
Faculty of Information Science and Technology, Hokkaido

AUTHOR INDEX

University, Sapporo, Hokkaido, Japan
Hornberger, Zach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-39
Zth2@illinois.edu
University of Illinois, Urbana, IL, United States
Hörsting, Lena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-43
lena.hoersting@bwl.uni-kiel.deService Analytics, CAU Kiel University, Germany
Horuk, Gökçe Sabriye . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-15
gokce.horuk@gmail.com
Ankara Yıldırım Beyazıt University, Turkey
Hoseinpour, Pooya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-61
p.hoseinpour@aut.ac.ir
Department of Industrial Engineering & Management System, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran, Islamic Republic of
Hosoda, Junko . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-30, WD-50
junko.hosoda.dp@hitachi.com
Research and Development Group, Hitachi, Ltd., Yokohama,
Kanagawa, Japan
Hosseini, Amir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-26
amir.hosseini@uni-passau.de
University of Passau, Passau, Germany
Hosseini, Seyede Saeede . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-35
seyede-saeede.hosseini@polymtl.ca
Polytechnique Montreal, Canada
Hosseinnezhad, Davoud . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-23
davoud.hosseinnezhad@ul.ie
School of Engineering, University of Limerick, Limerick,
Limerick, Ireland
Hosseinzadeh, Mahnaz . . . . . . . . . . . . . . . . . . . . . WA-06, WB-34
m.hosseinzadeh@sheffield.ac.uk
Sheffield, United Kingdom
Hottung, André . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-03, MD-03
andre.hottung@uni-bielefeld.de
Decision and Operation Technologies, Bielefeld University,
Bielefeld, Germany
Hou, Kuo-Lung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-49
klhou@ocu.edu.tw
Department of Marketing and Supply Chain Management,
Overseas Chinese University, Taiwan
Hou, Shixuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-43
hjwshx@gmail.com
Concordia University, London, Ontario, Canada
Hougaard, Jens Leth . . . . . . . . . . . . . . . . . . . . . . . . TD-43, WA-43
jlh@ifro.ku.dk
University of Copenhagen, Denmark
Hrdina, Jakub . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-48
hrdina8@uniba.sk
Faculty of Mathematics, Physics and Informatics, Comenius
University Bratislava, Slovakia

575

AUTHOR INDEXEURO 2024 - Copenhagen

Hrusovsky, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-23
martin.hrusovsky@wu.ac.at
WU Vienna University of Economics and Business, Vienna,
Austria
Hsiao Hua, Ho . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-59
lovelypite@gmail.com
Math, Hsinchu County Er Chong Junior High School,
Hsinchu city, Taiwan
Hsueh, Che-Fu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-53
cfhsueh@uch.edu.twMarketing and Distribution Management, Chien Hsin University of Science and Technology, Taoyuan, Taiwan
Hu, Bin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-45
bin.hu@ait.ac.at
Mobility, AIT Austrian Institute of Technology, Vienna, Austria
Hu, Jiaqiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-35
jiaqiao.hu.1@stonybrook.edu
Applied Mathematics and Statistics, Stony Brook University,
Stony Brook, NY, United States
Hu, Ruoyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-21
s2456147@ed.ac.uk
Business School, the University of Edinburgh, Edinburgh,
Scotland, United Kingdom
Hu, Wenjie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-15
huwenjie@mail.nwpu.edu.cn
School of Management, Northwestern Polytechnical University, Xi’an, Shaanxi, China
Hu, Yaohua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-41
hyh19840428@163.com
School of Mathematical Sciences, Shenzhen University,
Shenzhen, Guangdong Province, China
Huang, He . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-44
he.huang@psi.chLaboratory for Energy Systems Analysis (LEA), Paul Scherrer Institute, Villigen PSI, Aargau, Switzerland

Huang, Yixiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-52
huangyixiao@outlook.com
SF Express, China
Hübner, Alexander . . . . . . . . . . WD-13, MA-50, TD-58, WC-58
alexander.huebner@tum.de
Supply and Value Chain Management, Technical University
Munich, Straubing, Germany
Huchzermeier, Arnd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-50
ah@whu.edu
Production Management, WHU - Otto Beisheim School ofManagement, Vallendar, Germany
Huckebrink, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-19
huckebrink@ee.ruhr-uni-bochum.de
Ruhr-Universität Bochum, Bochum, Germany
Huerta-Barrientos, Aida . . . . . . WD-08, TC-12, WB-28, TA-53
aida.huerta@comunidad.unam.mx
Operations Research, National Autonomous University of
Mexico, Mexico City, D.F., Mexico
Huettner, Frank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-36
info@frankhuettner.de
Skk Gsb, Seoul, Korea, Republic of
Huf, Tobias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-64
tobias.huf@uni-a.deUniversity of Augsburg, Augsburg, Germany
Hufendiek, Kai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-22
kai.hufendiek@ier.uni-stuttgart.de
IER - Institute of Energy Economics and Rational Energy
Use, University of Stuttgart, Stuttgart, Germany
Huh, Tim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-22
tim.huh@sauder.ubc.ca
Sauder School of Business, University of British Columbia„
CanadaHuisman, Dennis . . . . . . . . . . . . TD-46, MA-54, MB-54, WD-54
huisman@ese.eur.nl
Econometric Institute, Erasmus University, Rotterdam,
Netherlands

Huang, Jiangyi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-19
jiahuang@ethz.ch
Chair of Energy Systems Analysis, Department of Mechanical and Process Engineering, ETH Zurich, Switzerland

Huisman, Kuno . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-33
k.j.m.huisman@uvt.nl
Tilburg University, Tilburg, Netherlands

Huang, Tzu-Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-07
huangziyu09@gmail.com
Department of Industrial Engineering and Management, Yuan
Ze University, Taiwan

Hulipalled, Poornima . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-48
poornima@kishkindauniversity.edu.in
MCA, Kishkinda University, Ballari, Ballari, Karnataka, India

Huang, Wei-Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-60
joe06155@gmail.com
Li Jin Engineering Company, Taipei, Taiwan

Hüllermeier, Eyke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-40
eyke@ifi.lmu.de
Institut für Informatik, Ludwig-Maximilians Universität
München, Munich, Bavaria, Germany

Huang, Yi-Fen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-64
fen.mg11@nycu.edu.tw
Department of Transportation and Logistics Management,
National Yang Ming Chiao Tung University, Hsinchu City,
Taiwan576

Hundhammer, Moritz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-50
moritz.hundhammer@stud.ku.de
Business School, Technische Hochschule Ingolstadt, Germany

EURO 2024 - Copenhagen

AUTHOR INDEX

Hurtado-Cayo, Pitehr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-50
pitehr.hurtado@pucv.cl
School of Industrial Engineering, Pontificia Universidad
Catolica de Valparaiso, Valparaiso, Chile

Ii wen, Lai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-59
victorialai63@gmail.com
Accounting department, National Taipei University, Hsinchu
City, Eastern District, Taiwan

Husseis, Anas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-47
anas.husseis@internetoftrust.com
Cyber Security, Internet of Trust, Paris, France

Iida, Tetsuo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-60
iida@komazawa-u.ac.jp
Faculty of Business Administration, Komazawa University,
Tokyo, Japan

Hussien, Abdelazim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-52
abdelazim.hussien@liu.se
Linkoping, Sweden
Huttin, Christine C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-47
chxyinfo@endepusresearchinc.com
Business and Economics of healthcare, Univ Aix Marseilleand Endepusresearch, Inc, Cambridge, MASSACHUSETTS,
United States
Huynh, Thi Anh Phuong . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-02
anhphuonghus@gmail.com
Faculty of Sociology and Social Work, Hue University of
Sciences, Hue, Viet Nam
Huynh, Van Ngai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-41
vangaihuynh@gmail.com
Department of Mathematics, University of Quy Nhon, Viet
Nam
Hvattum, Lars Magnus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-64
hvattum@himolde.no
Faculty of Logistics, Molde University College, Molde, Norway
Hvidegaard Petersen, Vibeke . . . . . . . . . . . . . . . . . . . . . . . WD-34
vibeke.h.petersen@ntnu.no
NTNU, TRONDHEIM, Norway
Hwang, Pin-Rui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-16
pinrui.hwang@gmail.com
Information Management, National United University, MiaoLi City, Taiwan

Ilabaca, Antonia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-60
antonia.ilabaca.c@mail.pucv.cl
School of Industrial Engineering, Pontificia Universidad
Católica de Valparaíso, Valparaíso, Chile
Illés, Tibor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-38
tibor.illes@uni-corvinus.hu
Corvinus University of Budapest, Budapest, Hungary
Imdahl, Christina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-28
c.imdahl@tue.nl
Eindhoven University of Technology, Netherlands
Imdahl, Christina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-61
Christina.Imdahl@the-klu.org
Supply Chain and Operations Management, Kühne Logistics
University, Hamburg, Germany
Imeri, Adhurim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-23
adhurim.imeri@wu.ac.at
Institute for Production Management, WU, Vienna University of Economics and Business, Vienna, Vienna, Austria
Infante, Julian Andrés . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-22
j.infante@uniandes.edu.co
Universidad de los Andes, Colombia
Ingold, Karin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-11
karin.ingold@unibe.chInstitute Political Science, University of Bern, Bern, Switzerland

Ibarra, Sergio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-28
sergibar.1795@gmail.com
Operational Research, UNAM, Coyoacan, CDMX, Mexico

Ingólfsdóttir, Anna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-27
Anna.ingolfsdottir@gmail.com
Department of Computer Science, Reykjavík University, Icelandİbroşka, Baybars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-64
baybars.ibroska@trendyol.com
Business Development, Trendyol Express, Turkey

Inkilä, Ville-Pekka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-16
ville-pekka.inkila@palloliitto.fi
Football Association of Finland, Finland

Iftimie, Bogdan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-63
bogdan.iftimie@csie.ase.ro
Department of Applied Mathematics, Bucharest Universityof Economic Studies, Bucharest, Romania

Innan, Shigeaki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-44
innan@inulab.sys.es.osaka-u.ac.jp
Graduate School of Engineering Science, Osaka University,
Japan

Igualada, Lucia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-22, WB-53
ligualada@irec.cat
Energy Systems Analytics Research Group, Intitut de Recerca en Energia de Catalunya, Sant Adria de Besos, Spain

Innocente, Emma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-56
emma.innocente@uclouvain.beOperations and Information Systems (OIS), Université
Catholique de Louvain, Belgium

Ihler, Alex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-34
ihler@ics.uci.edu
University of California Irvine, United States

Inostroza-Quezada, Ignacio . . . . . . . . . . . . . . . . . . . . . . . . . TB-50
iinostroza@uandes.cl
School of Business and Economics, Universidad de los Andes, Santiago, Chile

577

AUTHOR INDEXEURO 2024 - Copenhagenment, Hit- holon institute of technology, Holon, Israel

Inuiguchi, Masahiro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-44
inuiguti@sys.es.osaka-u.ac.jp
Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka, JapanIvo de Carvalho, Mafalda . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-24
mafalda.i.de.carvalho@tecnico.ulisboa.pt
Engineering and Management, Instituto Superior Técnico,
Lisboa, Lisboa, Portugal

Iori, Manuel . . . . . . . . . . TB-25, MC-29, TD-29, TD-45, WD-49
manuel.iori@unimore.it
DISMI, University of Modena and Reggio Emilia, Reggio
Emilia, Italy

Ivorra, Benjamin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-19
ivorra@ucm.es
Universidad Complutense de Madrid, Spain

Iosifidis, George . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-57
g.iosifidis@tudelft.nl
TU Delft, Netherlands

Iwaji, Nako . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-59
r202270085ta@jindai.jp
Kanagawa university, Japan

Iosifidis, George . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-57
giosifid@amazon.com
SNT, Amazon, Netherlands

Iwazaki, Ana Cláudia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-64
anaclaudiaacih@fab.mil.br
EC4ISR Division, Institute for Advanced Studies - IEAv, SãoJosé dos Campos, São Paulo, Brazil

Iosifidis, George . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-57
giosifid@amazon.nl
Amazon, Netherlands

İyican, Çağrı Doğuş . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-58
cagri.iyican@trendyol.com
Data Science, Trendyol, Turkey

Iovanella, Antonio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-63
antonio.iovanella@unint.eu
Department of Humanistic and International Social Sciences,
Università degli Studi Internazionali di Roma - UNINT,
Roma, Italy

Iyigün, Cem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-03
iyigun@metu.edu.tr
Department of Industrial Engineering, Middle East Technical
University (METU), Ankara, Turkey

Iravani, Foaad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-50
foaad.iravani@walmart.com
CENTROID, Walmart, United States

Izady, Navid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-15, WB-15
navid.izady@city.ac.uk
Bayes Business School, United Kingdom

Irawan, Chandra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-25
chandra.irawan@nottingham.edu.cn
Nottingham Business School, University of Nottingham
China, Ningbo, – Please Select (only U.S. / Can / Aus), China

Izco, Irene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-29
irene.izco@unavarra.es
Statitics, Computer Science, and Mathematics, Public University of Navarre, Pamplona, Navarra, Spain

Iris, Cagatay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-25, TB-62
c.iris@liverpool.ac.uk
Management School, University of Liverpool, Liverpool,
United Kingdom

Iziomo, Giwa Reagan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-31
g.iziomo@aston.ac.uk
Operations and Information Management, Aston University,
Birmingham, West Midlands, United Kingdom

Irnich, Stefan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-60
irnich@uni-mainz.de
Chair of Logistics Management, Gutenberg School of Management and Economics, Johannes Gutenberg University
Mainz, Mainz, Germany

Jabali, Ola . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-52
ola.jabali@polimi.it
Dipartimento di Elettronica, Informazione e Bioingegneria,
Politecnico di Milano, Milano, Italy

Ishikawa, Risa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-25
ishikawa.risa2001@gmail.com
Information Science, University of Hyogo, Japan

Jablonsky, Josef . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-48
jablon@vse.cz
Faculty of Informatics and Statistics, Prague University of
Economics and Business, Praha 3, Czech Republic

Ismail, Asrul Harun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-10
asrul.ismail@port.ac.uk
Centre of Operational Research and Logistics, University ofPortsmouth, Birmingham, West Midlands, United Kingdom

Jackson, George . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-37
g.jackson@imperial.ac.uk
Department of Chemical Engineering, Imperial College London, London, United Kingdom

Isotalus, Hanna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-15
hanna.isotalus@bristol.ac.uk
University of Bristol, United Kingdom

Jackson, Sophie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-13
sophie.jackson@guidedsystemssolutions.com
Guided Systems Solutions, Portsmouth, Hampshire, United
Kingdom

Israeli, Uriel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-49
uriel@hit.ac.il
Faculty of Industrial Engineering and Technology Manage-

578

Jacobovic, Royi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-40
royi.jacobovic@mail.huji.ac.il

EURO 2024 - Copenhagen
Tel-Aviv University, Israel
Jacobson, Sheldon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-39
shj@illinois.edu
Computer Science, University of Illinois, Urbana, Illinois,
United States
Jacquet, Benjamin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-16
benjaminjacquet74@hotmail.fr
ENSTA Paris, Palaiseau, France
Jafari, Nahid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-18
jnahid@hotmail.com
School of Business, SUNY Farmingdale, New York, NY,
United States
Jaggi, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-57
jagg@zhaw.ch
Banking and Finance, Zurich University of Applied Sciences,
Winterthur, Zurich, Switzerland
Jagtenberg, Caroline . . . . . . . . . . . . . . . . . . . . . . . . MB-10, TD-31
c.j.jagtenberg@vu.nl
Supply Chain Analytics, Vrije Universiteit Amsterdam,
Auckland, Auckland, New Zealand
Jahangard, Mahdi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-31
mj661@pgr.aru.ac.uk
Anglia Ruskin University, chelmsford, essex, United Kingdom
Jahnke, Patrick . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-45
p.jahnke@sap.com
Sap Se, Germany
Jakhar, Suresh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-59
skj@iiml.ac.in
Operations Management, Indian Institute of Management
Lucknow, Lucknow, Uttar Pradesh, India
Jakovetic, Dusan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-34
dusan.jakovetic@gmail.com
University of Novi Sad Faculty of Sciences, Serbia
Jalali, Hamed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-23, TC-50
hamed.jalali@neoma-bs.fr
Department of information systems, supply chain management, and decision making, NEOMA Business school, MontSaint-Aignan, France
Jalil, Muhammad Naiman . . . . . . . . . . . . . . . . . . . . . . . . . . MC-21
muhammad.jalil@lums.edu.pk
Suleman Dawood School of Business, Lahore University ofManagement Sciences, Lahore, Pakistan
Jalili Marand, Ata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-61
atajalili@econ.au.dk
Economics and Business Economics, Aarhus university,
Aarhus V, Denmark
Jalloul, Maya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-59
maya.jalloul@hec.edu
Information Systems and Operations Management, HEC
Paris, Paris, France

AUTHOR INDEX

Jalowski, Noah . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-55
noah.jalowski@mailbox.tu-dresden.deTechnische Universität Dresden, Germany
Jamali, Anouar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-02
ma.jamali@ocpafrica.com
OCP Africa, Casablanca, Morocco
Jamili, Negin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-18
jamili@rsm.nl
Erasmus University Rotterdam, Netherlands
Janda, Karel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-19
karel-janda@seznam.cz
Charles University, Praha, Czech Republic
Janda, Pavel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-47
pavel.janda@polimi.it
Italy
Jang, Chanyoung. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-08
jjccyy9741@naver.com
Korea, Republic ofJanin, Jean-Marc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-21
jean-marc.janin@rte-france.com
R&D, RTE, Paris La Defense Cedex, France
Jankovic, Marija . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-17
marija.jankovic@centralesupelec.fr
Laboratoire de Génie Industriel, CentraleSupélec, Universityof Paris Saclay, Gif-sur-Yvette, France
Jankunas, Rimas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-07
rjj10@proton.me
Medical Academy, Lithuanian University of Health Sciences,
Lithuania
Jans, Raf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-49, MA-61
raf.jans@hec.ca
Department of Logistics and Operations Management, HEC
Montreal, Montreal, Quebec, Canada
Janssen, Mart P. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-15
m.janssen@sanquin.nl
Transfusion Technology Assessment, Sanquin Research,
Amsterdam, Netherlands
Janssens, Bram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-28
bram.janssens@ugent.beGhent University, Gent, Belgium
Jaoui, Nadia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-55
nadia.jaoui@um6p.ma
Africa Business School, Mohammed VI Polytechnic University, Rabat, Rabat-Salé-Kénitra, Morocco
Jaramillo-Rios, Juana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-21
jujaramillori@unal.edu.co
Department of Civil Engineering, Universidad Nacional de
Colombia, Colombia
Jartnillaphand, Ponpot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-04
19877688@student.curtin.edu.au
School of Electrical Engineering, Computing and Mathemat-

579

AUTHOR INDEXEURO 2024 - Copenhagen

ical Sciences, Curtin University, Perth, Western Australia,
AustraliaJasa, Hajg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-41
hajg.jasa@protonmail.com
Department of Mathematical Sciences (IMF), Norwegian
University of Science and Technology (NTNU), Trondheim,
Trondelag, Norway
Júdice, Pedro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-34
pedro.judice@iscte-iul.pt
ISCTE Business Research Unit, Lisbon, Portugal
Jäger, Gerold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-25
gerold.jager@umu.se
Mathematics and Mathematical Statistics, Umeå University,Umeå, Sweden
Jäger, Sven . . . . . . . . . . . . . . . . . . . . . . . . . TD-51, WC-51, WD-51
sven.jaeger@math.rptu.de
Department of Mathematics, RPTU Kaiserslautern-Landau,
Kaiserslautern, Rheinland-Pfalz, Germany
Jedidi, Kamel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-11
kj7@gsb.columbia.edu
Columbia University, New York City, United States
Jefroy, Niloofar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-58
niloofar.jefroy@ntnu.no
Mechanical and Industrial Engineering, NTNU Norwegian
University of Science and Technology, Trondheim, Norway
Jelodari, Elham . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-26
ejelodarimamaghani@univ-catholyon.fr
ESDES, France
Jensen, Rune . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-03, MD-62
rmj@itu.dk
IT-University of Copenhagen, Denmark
Jeon, Haejun. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-57
haejun.jeon@gmail.com
Department of Management, Tokyo University of Science,
Chiyoda, Tokyo, Japan
Jeyakumar, V. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-42
v.jeyakumar@unsw.edu.au
The University of New South Wales, Sydney, Australia
Jha, Nivedita. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-18
nivedita.jha_fpm20@bimtech.ac.in
FPM, Birla Institute of Management Technology, Greater
Noida, Uttar Pradesh, India
Jhang, Jhy Ping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-07
jpjhang@cc.hfu.edu.tw
Department of Intelligent Life Technology, Huafan University, New Taipei, Taiwan
Jiang, Bo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-03, WC-19
jiangbo20@nudt.edu.cn
College of Systems Engineering, National University of Defense Technology, China
Jiang, Hongyi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-51

580

hyjiang@link.cuhk.edu.hk
Systems Engineering and Engineering Management, The
Chinese University of Hong Kong, Hong Kong, Hong Kong
Jiang, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-07
l.jiang@polyu.edu.hk
Logistics and Maritime Studies, Hong Kong Polytechnic University, HUng Hom, Kowloon, Hong Kong
Jiang, Xia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-03
summer142857.jiang@gmail.com
Industrial Engineering and Innovation Sciences, Eindhoven
University of Technology, Eindhoven, Netherlands
Jiang, Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-26, WC-51, WA-64
yujiang@dtu.dk
Department of Technology, Management and Economics,
Denmark
Jiarui, Zhang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-51
21114045@bjtu.edu.cn
School of Transportation and Traffic, Beijing Jiaotong University, Beijing, Beijing, China
Jiménez-Piqueras, Celia . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-62
cejipi@upvnet.upv.es
Universitat Politècnica de València, Spain
Jimenez Sanchez, Luis Felipe . . . . . . . . . . . . . . . . . . . . . . . . TD-51
ljimenezs2@ucentral.edu.co
Universidad de los Andes, Colombia
Jimenez, Maritza . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-19
mjimenezz@unal.edu.co
Departamento de Ingeniería de la Organización, Universidad
Nacional de Colombia, Medellin, Colombia
Jimenez-Cordero, M. Asuncion . . . . . . . . . . . . . MA-19, WD-27
asuncionjc@uma.es
Statistics and Operations Research, University of Malaga,
Malaga, Spain
Jin, Feiyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-23
jfyysu2018@163.com
School of Management, Harbin Institute of Technology,
Harbin, Heilongjiang, China
Johanndeiter, Silke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-09
silke.johanndeiter@ruhr-uni-bochum.de
Ruhr-Universität Bochum, Germany
Johansen, Malthe Rauh . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-57
mrj@math.ku.dk
Department of Mathematical Sciences, University of Copenhagen, Copenhagen Ø, Denmark
John, Linda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-17
lindajohn44@gmail.com
Transfusion Medicine, Amrita Institute of Medical Sciences,
Kochi, Kerala, India
Johns, Gemma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-15
gemma.johns3@wales.nhs.uk
Tec cymru, Bridgend, United Kingdom

EURO 2024 - Copenhagen
Joki, Kaisa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-28
kjjoki@utu.fi
Department of Mathematics and Statistics, University of
Turku, Finland
Joliot, Anna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-42
anna.joliot@pasqal.com
Pasqal, Massy, France
Jones, Dylan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-02, MD-20
dylan.jones@port.ac.uk
Mathematics, University of Portsmouth, Portsmouth, Hampshire, United Kingdom
Joosten, Sara. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-58
s.joosten@tum.deTechnical University Munich, Germany
Jorge, Diana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-56
diana.jorge@tecnico.ulisboa.pt
Instituto Superior Técnico, Universidade de Lisboa, Portugal
Jose, Esther . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-29, WA-58
estherjo@buffalo.edu
Industrial and Systems Engineering, University at Buffalo,
Buffalo, New York, United States
Joshi, Harit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-43
harit.joshi@spjimr.org
Operations, Supply Chain and Quantitative Methods,
SPJIMR, Mumbai, Maharashtra, India
Joshi, Kanchan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-54
kanchan.joshi@univie.ac.at
Business Decisions and Analytics, University of Vienna, Vienna, Vienna, Austria
Jouglet, Antoine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-60
antoine.jouglet@hds.utc.fr
Université de Technologie de Compiègne, Heudiasyc UMR
CNRS 7253, Compiègne, France
Jouini, Oualid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-17
oualid.jouini@ecp.fr
Laboratoire Genie Industriel, Ecole Centrale Paris, ChatenayMalabry, France
Juan, Angel A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-64
ajuanp@upv.es
Applied Statistics and Operations Research, Universitat
Politècnica de València, Valencia, Spain
Juanpera, Marc . . . . . . . . . . . . . . . . . WB-13, MB-14, MB-18, 19
marc.juanpera@upc.edu
Department of Management, Universitat Politècnica deCatalunya, Spain
Juneja, Januj . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-08
jjuneja@sdsu.edu
San Diego State University, San Diego, California, UnitedStatesJunova, Jana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-34
junova@karlin.mff.cuni.cz
Department of Probability and Mathematical Statistics,

AUTHOR INDEXCharles University MFF, Praha 8, Czech Republic
Jüttner, Alpár . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-38
alpar.juttner@gmail.com
Dept of Operations Research, ELTE, Budapest, Hungary
Juvigny, Corentin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-28
juvigny@essec.edu
Information Systems, Decision sciences and Statistics, ESSEC Business School, Cergy, France
Kabak, Özgür . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-37, TD-44
kabak@itu.edu.tr
Industrial Engineering Dept., Istanbul Technical University,
Istanbul, Turkey
Kabarcık, Ahmet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-29
a.kabarcik@cankaya.edu.tr
Industrial Engineering, Çankaya University, Turkey
Kabasinskas, Audrius . . . . . . . . . . . . . . . . . . . . . . . TD-02, MD-51
audrius.kabasinskas@ktu.lt
Department of Mathematical Modeling, Kaunas Universityof Technology, Kaunas, Lithuania
Kabgani, Alireza . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-03, WB-41
alireza.kabgani@uantwerpen.be
Department of Mathematics, University of Antwerp,
Antwerp, BELGIUM, Belgium
Kachirayil, Febin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-19
fkachirayil@ethz.ch
ETH Zürich, Zürich, Switzerland
Kaczmarczyk, Waldemar . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-50
waldek@agh.edu.pl
Department of Strategic Management, AGH University of
Krakow, Krakow, Poland
Kadirkamanathan, Visakan . . . . . . . . . . . . . . . . . . . . . . . . MD-13
visakan@sheffield.ac.uk
University of Sheffield, United Kingdom
Kadivar, Marieh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-07
m.kadivar@tue.nl
Industrial engineering, Technical university of Eindhoven,
Eindhoven, -, NetherlandsKadzinski, Milosz . . . . . . . . . . . . MA-11, MB-44, MD-44, TA-44
milosz.kadzinski@cs.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poznan, Poland
Kager, Johannes R. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-37
johannes.kager@tum.de
TUM Campus Straubing for Biotechnology and Sustainability, Weihenstephan-Triesdorf University of Applied Sciences,
Straubing, Germany
Kaibel, Volker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-38
kaibel@ovgu.de
Institut für Mathematische Optimierung, Otto-von-GuerickeUniversität Magdeburg, Magdeburg, Germany
Kal’atová, Monika . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-34

581

AUTHOR INDEXEURO 2024 - Copenhagen

kalatova@karlin.mff.cuni.cz
Department of Probability and Mathematical Statistics,
Charles University, Prague, Czech Republic
Kajántó, Sándor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-41
sandor.kajanto@ubbcluj.roBabes, -Bolyai University, Romania
Kajiji, Nina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-18, WD-18
nina@nkd-group.com
Computer Science and Statistics, University of Rhode Island,
and The NKD Group, Inc., Kingston, RI, United States
Kalauz, Károly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-49
kalauz.k@gmail.com
Digital Development Center, Széchenyi István University,
Győr, Hungary
Kalaycı, Betül . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-06
betulkalayci@gmail.com
Financial Mathematics, Institute of Applied Mathematics,
Ankara, Turkey
Kalcsics, Jörg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-61
joerg.kalcsics@ed.ac.uk
School of Mathematics, University of Edinburgh, Edinburgh,
United Kingdom
Kalczynski, Pawel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-61
pkalczynski@fullerton.edu
Information Systems Decision Sciences, California State
University, Fullerton, Fullerton, CA, United States
Kale, Devendra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-18
dkale@uri.edu
Business - Accounting, University of Rhode Island, UnitedStates
Kaleta, Mariusz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-45
mkaleta@ia.pw.edu.pl
Inistitute of Control & Computation Engineering, Warsaw
University of Technology, Warsaw, Poland
Kallio, Markku . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-02
markku.kallio@aalto.fi
Aalto University School of Business, Espoo, Finland
Kallus, Nathan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-06
kallus@cornell.edu
Cornell University, United States
Kamdem, Bruno . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-18
bgk8384@nyu.edu
Finance and Risk Engineering, New York University, Brooklyn, NY, United States
Kaminska, Barbara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-48
b.kaminska@pwr.edu.pl
Department of Management Systems and Organization Development, Wroclaw University of Science and Technology,
Wroclaw, Poland
Kamioka, Yuta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-40
t231d023@gunma-u.ac.jp
Informatics, Gunma University, Kiryu, Gunma, Japan

582

Kamisli Ozturk, Zehra . . . . . . . . . . . . . . . . . . . . . . TC-12, WC-27
zkamisli@eskisehir.edu.tr
Industrial Engineering Department, Eskisehir Technical University, Eskişehir, Turkey
Kamzolov, Dmitry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-32
kamzolov.dmitry@mbzuai.ac.ae
Machine Learning, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dabi, United Arab Emirates
Kan, Xiaoming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-22
kanxiaoming1@gmail.com
Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
Kanavetas, Odysseas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-40
o.kanavetas@math.leidenuniv.nl
Mathematical Institute, Leiden University, Leiden, Netherlands
Kang, Zhao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-34
z.kang@tue.nl
Department Of Industrial Engineering & Innovation Sciences, Eindhoven University of Technology, Eindhoven,
Netherlands
Kao, Ling-Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-50, WD-50
lingjingkao@mail.ntut.edu.tw
National Taipei University of Technology, Taiwan
Kaparis, Konstantinos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-48
k.kaparis@uom.edu.gr
Business Administration, University of Macedonia, Thessaloniki, Greece
Kapoor, Chaitanya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-45
chaitanya.kapoor@outlook.com
University of Manchester, New Dehi, Delhi, India
Kar, Soummya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-34
soummyak@andrew.cmu.edu
Carnegie Mellon University, Pittsburgh, United States
Kara, Bahar Yetis . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-01, TC-21
bkara@bilkent.edu.tr
Industrial Engineering, Bilkent University, Ankara, Turkey
Kara, Emre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-24
e.kara@tue.nl
Ie & Is, Eindhoven University of Technology, Eindhoven,
Noord-Brabant, Netherlands
Kara, Mehmet Erol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-25
mehmeterol.kara@ogu.edu.tr
Industrial Engineering, Eskisehir Osmangazi University, Eskisehir, Odunpazari, Turkey
Kara, Mert. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-21
mert.k@std.izmirekonomi.edu.tr
Industrial Engineering, Izmir University of Economics, İzmir,
Turkey, Turkey
Karabas, Tolga . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-22

EURO 2024 - Copenhagentolga.karabas@metu.edu.tr
Department of Industrial Engineering, Middle East Technical
University, Ankara, Turkey
Karabatı, Selçuk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-24
skarabati@ku.edu.tr
College of Administrative Sciences and Economics, Koç University, Sarıyer, İstanbul, Turkey
Karacapilidis, Nikos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-08
karacap@upatras.gr
MEAD - IMIS Lab, University of Patras, Rio Patras, Greece
Karademir, Cigdem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-24
c.karademir@tudelft.nl
Delft University of Technology, Delft, Netherlands

AUTHOR INDEX

kasparas.karlauskas@mif.stud.vu.lt
Institute of Data Science and Digital Technologies, Imageand Signal Analysis Group, Vilnius University, Vilnius, Vilnius, Lithuania
Karlsson, Kenneth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-09
kenneth.karlsson@energymodellinglab.com
Energy Modelling Lab, Copenhagen K, Denmark, Denmark
Karmitsa, Napsu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-28
napsu@karmitsa.fi
Department of Computing, University of Turku, Turku, Finland
Karsten, Christian Møller . . . . . . . . . . . . . . . . . . . . . . . . . . MD-57
cvadkarsten@gmail.com
Advanced Analytics, Banking Circle, Denmark

Karaer, Özgen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-24, WB-61
okaraer@metu.edu.tr
Industrial Engineering, Middle East Technical University,
Ankara, Turkey

Karsu, Ozlem . . . . . . . . . . . . . . . . . . . . . . . . . TC-06, TC-21, TA-37
ozlemkarsu@bilkent.edu.tr
Industrial Engineering, Bilkent University, Ankara, Turkey

Karaesmen, Itir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-26
karaesme@american.edu
Kogod School of Business, American University, Washington, DC, United States

Karthicanand, M S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-48
karthicanandm@gmail.com
Department of Management Studies, Indian Institute of Technology Madras, Chennai, Tamilnadu, India

Karagiannis, Giannis . . . . . . . . . . . . . . . . . . . . . . . . WC-44, TD-48
karagian@uom.gr
Economics, University of Macedonia, Thessaloniki, Greece

Karul, Elif . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-15
ekarul19@ku.edu.tr
Operations Management & Information Systems, Koç University, Turkey

Karasakal, Esra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-61
koktener@metu.edu.tr
Industrial Engineering Department, Middle East Technical
University, Ankara, Turkey
Karasakal, Orhan . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-29, TA-61
okarasakal@cankaya.edu.tr
Industrial Engineering Department, Cankaya University,
TurkeyKarbassi, Veis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-24
veis.karbassi@gmail.com
Chair of Operations Management„ RWTH-Aachen University, Aachen, Nordrhein-Westfalen, Germany
Karbhari, Pradnya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-59
pradnya@gmail.com
Swiggy, India
Karimi Mamaghan, Maryam . . . . . . . . . . . . . . . . WC-12, TB-26
m.karimi.mamaghan@vu.nl
Operations Analytics, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
Karimi, Mehdi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-38
mkarim3@ilstu.edu
Mathematics, Illinois State University, Normal, IL, UnitedStates
Karimi, Neman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-21
neman.karimi@sabanciuniv.edu
Industrial Engineering, Sabanci University, Istanbul, Turkey
Karlauskas, Kasparas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-28

Karwasra, Kritika . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-56
kritika.karwasra@iitb.ac.inIndustrial Engineering and Operations Research, Indian Institute of Information Technology, Mumbai, Maharashtra, India
Kasapidis, Grigorios . . . . . . . . . . . . . . . . . . . . . . . . MB-25, WC-26
gkasapid@liverpool.ac.uk
Operations and Supply Chain Management, University ofLiverpool, Liverpool, Merseyside, United Kingdom
Kasimbeyli, Refail . . . . . . . . . . . . . . . . . . . . . . . . . . TC-41, WB-41
refail.kasimbeyli@gmail.com
Industrial Engineering, Eskisehir Technical University, Eskisehir, Turkey
Kaspi, Mor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-54
morkaspi@tauex.tau.ac.il
Industrial Engineering, Tel-Aviv University, Tel-Aviv,
Choose Any State/Province, Israel
Kasturiratne, Dulekha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-45
dulekha.kasturiratne@plymouth.ac.uk
University of Plymouth, Plymouth, United Kingdom
Kat, Bora . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-22
borakat@gmail.com
The Scientific and Technological Research Council of
Türkiye, Turkey
Katoh, Naoki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-25
naoki.katoh@gmail.com
Graduate school of Inforrmation Science, University of
Hyogo, Kobe, Hyogo, Japan583

AUTHOR INDEXEURO 2024 - Copenhagen

Katsikopoulos, Konstantinos . . . . . . . . . . . . . . . . . . . . . . . . WD-11
k.katsikopoulos@soton.ac.uk
Southampton Business School, Southampton, United Kingdom
Kaucic, Massimiliano. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-08
massimiliano.kaucic@deams.units.it
Department of Economics, Business, Mathematics and Statistics, University of Trieste, Italy
Kaur, Prabhjot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-26
prabh.g3@gmail.com
Department of Applied Sciences, University Institute of Engineering and Technology, Chandigarh., Mohali, Punjab, India
Kavis, Ali . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-32
ali.kavis@epfl.ch
EPFL, Lausanne, Switzerland
Kavitha, Veeraruna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-40
vkavitha@iitb.ac.in
IEOR, Indian Institute of Technology Bombay, India
Kawakami, Yuki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-25
kwkm12323yuki@gmail.com
Hokkaido University, Shinagawa, Tokyo, Japan

Department of Wind and Energy Systems, Technical University of Denmark (DTU), Denmark
Kazibudzki, Pawel Tadeusz . . . . . . . . . . . . . . . . . . . . . . . . . . TD-44
p.kazibudzki@po.edu.pl
Department of Corporate Management, e-Business and Electronic Economy, Opole University of Technology, Opole,
PolandKchaou-Boujelben, Mouna . . . . . . . . . . . . . . . . . . . . . . . . . . TA-18
mouna.boujelben@uaeu.ac.ae
United Arab Emirates University, Al Ain, United Arab Emirates
Ke, Dan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-07
dke@whu.edu.cn
Wuhan University, China
Kebrich, Sebastian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-14
s.kebrich@fz-juelich.de
Forschungszentrum Jülich, Jülich, Germany
Keček, Damira . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-48
dkecek@unin.hr
University North, Croatia, Koprivnica, Koprivničkokriževačka, Croatia

Kawanishi, Ken’ichi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-40
kawanisi@gunma-u.ac.jp
Informatics, Gunma University, Kiryu, Japan

Kechagias, Giannis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-10
ioannika@econ.auth.gr
Department of Economics, Aristotle University of Thessaloniki, Thessaloniki, Greece

Kaya Gezmiş, Safiye Şeyma . . . . . . . . . . . . . . . . . . . . . . . . . TD-28
safiyekaya18@ku.edu.tr
İSTANBUL, Turkey

Kedad-Sidhoum, Safia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-49
safia.kedad_sidhoum@cnam.fr
Cnam - Cedric, Paris, Cedex 03, France

Kaya, Anil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-09
anil.kaya@kit.edu
Karlsruhe Institute of Technology, Karlsruhe, Germany

Kedem-Yemini, Sagit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-53
ksagit@gmail.com
Logistics, Sapir Academic College, Israel

Kaya, Murat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-44
mkaya@sabanciuniv.edu
Industrial Engineering, Sabanci University, Istanbul, Turkey

Kee, Taeyoung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-11
taeyoung.kee@aalto.fi
Department of Information and Service Management, Aalto
University, School of Business, Espoo, Uusimaa, Finland

Kaya, Onur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-50, WC-55
onur_kaya@eskisehir.edu.tr
Industrial Engineering, Eskisehir Technical University, Eskişehir, Turkey
Kayacan, Abdullah . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-22
abdullah.kayacan@bogazici.edu.tr
Industrial Engineering, Bogazici University, Istanbul, Turkey,
TurkeyKeehan, Dominic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-34
dominickeehan@gmail.com
Engineering Science, Univeristy of Auckland, Auckland,
New ZealandKeisler, Jeffrey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-11
jeff.keisler@umb.edu
Management Science & Information Systems, University of
Massachusetts Boston, Boston, MA, United States

Kayis, Enis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-23, MB-27
enis.kayis@ozyegin.edu.tr
Industrial Engineering Department, Ozyegin University, Istanbul, International, Turkey

Kekes, Cédric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-54
cedric.kekes@tu-dresden.de
Chair of Traffic Flow Science, TU Dresden, Germany

Kazakov, R. M. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-11, MB-20
r.kazakov@hw.ac.uk
Heriot Watt University, Edinburgh, NA, United Kingdom

Kempter, Stefan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-15
steke@dtu.dk
Technology, Management and Economics, DTU, Germany

Kazempour, Jalal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-35
jalal@dtu.dk

Kenne, Jean-Pierre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-33
jean-pierre.kenne@etsmtl.ca

584

EURO 2024 - Copenhagen
University of Quebec, École de Technologie Supérieure,Montréal, Quebec, Canada
Kepaptsoglou, Konstantinos . . . . . . . . . . . . . . . . . . . . . . . . . TA-56
kkepap@central.ntua.gr
Rural and Surveying Engineering, National Technical University of Athens, Athens, Greece
Kergosien, Yannick . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-51
yannick.kergosien@univ-tours.fr
Université de Tours, Tours, France
Kerr-Delworth, Paul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-30
pkerrdel@mathworks.com
MathWorks, Cambridge, Cambs, United Kingdom
Kerscher, Christoph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-39
christoph.kerscher@tum.de
Logistics and Supply Chain Management, Technical University of Munich, Munich, Germany
Kerschke, Pascal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-55
pascal.kerschke@tu-dresden.de
Big Data Analytics in Transportation, TU Dresden &ScaDS.AI, Dresden, Germany
Kerstens, Kristiaan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-48
k.kerstens@ieseg.fr
IESEG School of Management, Cnrs-lem (umr 9221), Lille,
FranceKeseru, Imre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-54
imre.keseru@vub.be
BUTO, Vrije Universiteit Brussel, Brussels, Belgium
Keskin, Merve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-26
m.keskin@sheffield.ac.uk
Operations Management and Decision Sciences, Sheffield
University Management School, Sheffield, United Kingdom
Ketelaars, Martijn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-33
m.w.ketelaars@tilburguniversity.edu
Econometrics & Operations Research, Tilburg University,
Netherlands
Keutz, Julian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-09
julian.keutz@ewi.uni-koeln.de
EWI, Germany
Keykavoussi, Ashkan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-60
ashkan.keykavoussi@srbiau.ac.ir
Department of Industrial and Technology Management - Faculty of Management and Economics, Science and Research
University of Tehran - Islamic Azad University, Iran, Islamic
Republic of

AUTHOR INDEX

Khandelwal, Ankit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-55
ankitkhandelwal@fico.com
Senior Director - FICO Scores, FICO, Singapore, Singapore
Khaniyev, Taghi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-06
taghi.khaniyev@bilkent.edu.tr
Industrial Engineering, Bilkent Universitesi, Ankara, Turkey
Khayyati, Siamak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-59
s.khayyati@uliege.be
HEC Liege, University of Liege, Belgium
Kheiri, Ahmed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-58
a.o.kheiri@gmail.com
Lancaster University, Lancashire, United Kingdom
Khezerlouye Aghdam, Fatemeh . . . . . . . . . . . . . . . . . . . . . MC-23
khezerlou.fatemeh@gmail.com
Department of Industrial Engineering, Alzahra University,
Iran, Islamic Republic of
Khodaee, Sina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-64
sk2896@bath.ac.uk
School of Management, University of Bath, BATH, Bath andNorth East Somerset, United Kingdom
Khojandi, Anahita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-31
khojandi@utk.edu
University of Tennessee-Knoxville, Knoxville, TN, UnitedStatesKhoshnevis, Pegah . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-48
p.khoshnevis@sheffield.ac.uk
Sheffield University Management School, University of
Sheffield, United Kingdom
Khosravi, Banafsheh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-20
banafsheh.khosravi@port.ac.uk
School of Mathematics and Physics, University ofPortsmouth, Portsmouth, United Kingdom
Khutsishvili, Irina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-47
irina.khutsishvili@tsu.ge
Department of Computer Sciences, Iv.Javakhishvili Tbilisi
State University, Tbilisi, Georgia
Kiavash, Parinaz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-15
pkiavash22@ku.edu.tr
Koç University, Turkey
Kieckhäfer, Karsten . . . . . . . . . . . . . . . . . . . . . . . . MB-24, WA-46
karsten.kieckhaefer@fernuni-hagen.de
Chair of Production and Logistics Management, FernUniversität in Hagen, Hagen, Germany

Khabazian, Aien . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-02
akhabazian2@uh.edu
Department of Industrial Engineering, University of Houston, Houston, TX, United States

Kiesmuller, Gudrun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-25
gudrun.kiesmueller@tum.de
TUM School of Management, TU München, Heilbronn, Germany

Khan, Akhtar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-41
aaksma@rit.edu
School of Mathematical Sciences, Rochester Institute ofTechnology, Rochester, New York, United States

Kilic, Onur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-49
o.a.kilic@rug.nl
Department of Operations, University of Groningen, Netherlands

585

AUTHOR INDEXEURO 2024 - Copenhagen

Kilic, Shabnam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-06
shabnam.kilic@bilkent.edu.tr
TurkeyKing, Douglas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-39
dmking@illinois.edu
University of Illinois, Urbana, IL, United States

Kilinc, Derya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-15
kilinc.derya@mayo.edu
Mayo Clinic Rochester, Rochester, MN, United States

Kirkbride, Christopher . . . . . . . . . . . . . . . . . . . . . WC-35, WD-58
c.kirkbride@lancaster.ac.uk
The Management School, Lancaster University, Lancaster,
Lancashire, United Kingdom

Kim, Byung Soo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-49
bskim@inu.ac.kr
Industrial & Management Engineering, Incheon National
University, Incehon, Korea, Republic ofKim, Chesoong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-39
dowoo@sangji.ac.kr
Department of Business Administration, Sangji University,
Wonju, Gangwon-do, Korea, Republic ofKim, Kwang-Jae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-08
kjkim2208@gmail.com
Korea, Republic ofKim, Minjeong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-43
minki3446@snu.ac.kr
Korea, Republic ofKim, Sunyoung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-30
skim@ewha.ac.kr
Department of Mathematics, Ewha Women’s University,
Sudaemoon-gu, Seoul, Korea, Republic ofKim, Thai-Young . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-50
thaiyoung@cau.ac.kr
International Logistics, Chung-Ang University, Seoul, Korea,
Republic ofKim, Woo Chang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-63
wkim@kaist.ac.kr
Industrial and Systems Engineering, KAIST, Daejeon, Korea,
Republic ofKim, Young Jin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-49
youngk@pknu.ac.kr
Systems Management and Engineering, Pukyong National
University, Busan, Korea, Republic ofKim, YoungGwan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-08
dltmzh1212@naver.com
Korea, Republic ofKim, Youngseo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-38
yk796@cornell.edu
Civil and Environmental Engineering, Cornell University,
Ithaca, NY, United States
Kimms, Alf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-56, MB-62
alf.kimms@uni-due.de
Mercator School of Management, University of DuisburgEssen, Campus Duisburg, Duisburg, Germany
Kimura, Ryusuke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-59
ryusuke_kimura@r.recruit.co.jp
Data Management & Planning Office, Recruit Co., Ltd.,
Chiyoda, Tokyo, Japan

586

Kirpekar-Sauer, David Ajit . . . . . . . . . . . . . . . . . . . . . . . . . . TC-15
dakir@dtu.dk
Denmark
Kirst, Peter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-13
peter.kirst@wur.nl
Wageningen University & Research, Netherlands
Kis, Tamas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-26
kis.tamas@sztaki.mta.hu
Institute for Computer Science and Control, Budapest, Hungary
Kisekka, Isaya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-12
ikisekka@ucdavis.edu
Land, Air and Water Resources, University of California
Davis, Davis, CA, United States
Kitsos Kalyvianakis, Ioannis . . . . . . . . . . . . . . . . . . . . . . . . MA-60
john@kitsos.gr
Management Science & Technology, Athens University of
Economics and Business, Athens, Greece
Kitzing, Lena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-09
lkit@dtu.dk
Wind and Energy Systems, DTU, Roskilde, Denmark
Kivikangas, Matias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-11
matias.kivikangas@aalto.fi
ISM, Aalto University School of Business, Espoo, Finland
Kiviluoma, Juha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-09
juha.kiviluoma@vtt.fi
VTT Technical Research Centre of Finland, Espoo, Finland
Klapez, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
martin.klapez@unimore.it
University of Modena and Reggio Emilia, Italy
Klar, Robert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-62
robert.klar@liu.se
Science and Technology, Linköping University, Sweden
Klatzer, Thomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-09
thomas.klatzer@tugraz.at
Institute of Electricity Economics and Energy Innovation,
Graz University of Technology, Graz, Austria
Klein, Jan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-04
J.G.Klein@cwi.nl
CWI, Netherlands
Klein, Nicklas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-60
nicklas.klein@unibe.ch
Department of Business Administration, University of Bern,EURO 2024 - Copenhagen
Switzerland
Klein, Robert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-54, TD-59
robert.klein@wiwi.uni-augsburg.de
Chair of Analytics & Optimization, University of Augsburg,
Augsburg, Germany
Klein, Vienna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-59
vienna.klein@uni-a.de
Chair of Analytics & Optimization, University of Augsburg,
Augsburg, Germany
Klibi, Walid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-50, WD-55
walid.klibi@kedgebs.com
Operations Management and Information Systems Department, Kedge Bs / Cirrelt, Bordeaux, France
Klingebiel, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-58
martin.klingebiel@prod.uni-hannover.deInstitut für Produktionswirtschaft, Universität Hannover,Hannover, Germany
Klobučar Barišić, Ana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-29
aklobucar@fsb.hr
Mathematics, Faculty of Mechanical Engineering and Naval
Architecture, University of Zagreb, Zagreb, Croatia
Klose, Andreas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-37
aklose@math.au.dk
Department of Mathematics, Aarhus University, Aarhus,
Denmark
Kloster, Konstantin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-52
konstantin.kloster@wiwi.uni-kl.de
Business Studies and Economics, Technische Universität
Kaiserslautern, Germany
Kloster, Oddvar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-54
oddvar.kloster@sintef.noMathematics and Cybernetics, SINTEF, Oslo, Norway
Knio, Omar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-19
omar.knio@kaust.edu.sa
KAUST - King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Ko, Chang Seong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-49
csko@ks.ac.kr
Industrial and Management Engineering, Kyungsung University, Busan, Korea, Republic of
Koßmann, Gereon. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-42
kossmann@physik.rwth-aachen.de
RWTH Aachen, Aachen, Germany

AUTHOR INDEX

koberstein@europa-uni.de
Information and Operations Management, European University Viadrina Frankfurt (Oder), Frankfurt (Oder), Germany
Koca, Esra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-25
esra.koca@sabanciuniv.edu
Industrial Engineering, Sabanci University, Istanbul, Turkey
Kocabiyikoglu, Ayse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-50
ayse.kocabiyikoglu@sabanciuniv.edu
Sabanci University, Turkey
Kocaman, Ayse Selin . . . . . . . . . . . . . . . . MD-22, TA-22, WD-22
selin.kocaman@bilkent.edu.tr
Industrial Engineering, Bilkent University, Ankara, Turkey
Kocatürk, Fatih . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-56
fatihkocaturk438@gmail.com
Business Decisions and Analytics, University of Vienna,
Wien, Austria
Kochems, Johannes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-09
johannes.kochems@dlr.de
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)
German Aerospace Center, Stuttgart, Germany
Kocuk, Burak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-21
burakkocuk@sabanciuniv.edu
Industrial Engineering, Sabanci University, Istanbul, Turkey
Kocvara, Michal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-34
m.kocvara@bham.ac.uk
School of Mathematics, University of Birmingham, Birmingham, N/A, United Kingdom
Kóczy, László . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-36
koczy@krtk.mta.hu
Centre for Economic and Regional Studies, Hungarian
Academy of Sciences, Budapest, Hungary
Koell, Josef . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-19
josef.f.koell@gmail.com
ETH Zurich, Switzerland
Koenen, Melissa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-13
m.f.koenen@tilburguniversity.edu
Zero Hunger Lab, Tilburg University, Tilburg, Netherlands
Kof, Gökhan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-37
kofgokhan@gmail.com
Koç University, Turkey
Kogan, Konstantin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-33
kogank@biu.ac.il
Management, Bar-Ilan University, Israel

Kobayashi, Yuki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-25
kobayashi-y@omu.ac.jp
Graduate School of Engineering, Osaka Metropolitan University, Osaka, Sumiyoshi, Japan

Kojić, Vedran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-43
vkojic@efzg.hr
Faculty of Economics and Business - Zagreb, Zagreb, Croatia

Kober, Tom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-09
tom.kober@psi.ch
Paul Scherrer Institute, Villigen PSI, Switzerland

Kojima, Masakazu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-30
kojima@is.titech.ac.jp
Chuo University, Tokyo, Japan

Koberstein, Achim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-61

Kok, Koen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-19

587

AUTHOR INDEXEURO 2024 - Copenhagen

j.k.kok@tue.nl
Eindhoven, Netherlands
Kokkonda, Prashanth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-51
kprashanth@iisc.ac.in
Civil Engineering, Indian Institute of Science, Hanamkonda,
Telangana, India
Köksal, Burak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-61
burak.kooksal@gmail.com
Industrial Engineering Department, Middle East Technical
University, Turkey
Koliechkina, Liudmyla . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-52
ludaplt1971@gmail.com
Department of Algorithms and Databases, Faculty of Mathematics and Computer Science, University of Lodz, Lodz,
PolandKorpås, Magnus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-09
magnus.korpas@ntnu.no
Department of Electric Power Engineering, Norwegian University of Science and Technology, Trondheim, Norway
Kort, Peter . . . . . . . . . . . . . . . . . . . . . . . . . . MB-33, MC-33, TB-33
kort@tilburguniversity.edu
Tilburg University, Netherlands
Kosolap, Anatolii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-41
anivkos@ua.fm
Computer System, University of Chemical Engineering, Dnepropetrovsk, Ukraine
Kostami, Vasiliki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-59
kostami@hec.fr
HEC Paris, France

Kolisch, Rainer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-25
rainer.kolisch@tum.de
TUM School of Management, Technical University of Munich, Muenchen, Germany

Kostyukova, Olga . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-38
kostyukova@im.bas-net.by
Mathematical System Theory, Institute of Mathematics, National Academy of Sciences of Belarus, Minsk, Belarus

Koller, Hannes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-45
hannes.koller@ait.ac.at
Center of Energy, AIT Austrian Institute of Technology
GmbH, Austria

Kotecha, Niki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-45
nk3118@ic.ac.uk
Imperial College London, London, United Kingdom

Komoszynska, Magdalena . . . . . . . . . . . . . . . . . . . . . . . . . . WD-09
mk@planenergi.dk
PlanEnergi, Denmark
Konstantinopoulos, Konstantinos . . . . . . . . . . . . . . . . . . . WD-08
kostas@coffeeisland.gr
University of Patras, Greece
Koo, Pyung-Hoi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-49
phkoo@pknu.ac.kr
Systems Management and Engineering, Pukyong National
University, Busan, Korea, Republic of
Kool, Wouter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-64
wouter.kool@ortec.com
ORTEC, Netherlands
Koole, Ger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-40
ger.koole@vu.nl
Mathematics, Vrije Universiteit Amsterdam, Amsterdam,
Netherlands
Kopa, Milos . . . . . . . . . . . . . . . . . . TC-01, TC-34, WB-35, MD-51
kopa@karlin.mff.cuni.cz
Department of Probability and Mathematical Statistics,
Charles University in Prague, Faculty of Mathematics andPhysics, Prague, Czech Republic
Korhonen, Antti . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-57
antti.korhonen@protonmail.com
Detech Decision Technologies, Espoo, Finland
Koronakos, Gregory . . . . . . . . . . . . . . . . . . . . . . . . WC-08, MD-48
gkoron@unipi.gr
Department of Informatics, University of Piraeus, Piraeus,
Greece588

Kotenkoff, Alexandre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-42
alexandre.kotenkoff@mbda-systems.com
MBDA, France
Kotrogiannis, Vasilis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-06
vkotrogiannis@epu.ntua.gr
Decision Support Systems Laboratory, School of Electricaland Computer Engineering, National Technical University ofAthens, Zografou, Athens, Greece
Kou, Shuhan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-29
shkou@terpmail.umd.edu
Riverdale, MD, United States
Kouaissah, Noureddine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-44
noureddine.kouaissah@um6p.ma
Africa Business School, Mohammed VI Polytechnic University, Rabat, Morocco, Morocco
Kouchaki-Penchah, Hamed . . . . . . . . . . . . . . . . . . . . . . . . . WA-22
hamed.kouchaki-penchah@hec.ca
GERAD and Decision Sciences, HEC Montréal, Canada
Kouchakkapourchali, Amirbahador . . . . . . . . . . . . . . . . . MC-18
amkouchak825@gmail.com
DIST, Politecnico di Torino, TORINO, TORINO, Italy
Koukoumialos, Stelios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-10
skoukoum@teilar.gr
Business Administration, University of Thessaly, Larissa,
Thessaly, Greece
Kountouris, Ioannis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-09
iokoun@dtu.dk
DTU management, DTU, København, Denmark
Kourtzidis, Stavros . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-48

EURO 2024 - Copenhagens.kourtzidis@dundee.ac.uk
School of Business, University of Dundee, Dundee, Scotland,
United Kingdom
Koushan, Mona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-10
mona.koushan@canterbury.ac.nz
Management, Marketing & Tourism, University of Canterbury, Christchurch, Canterbury, New Zealand
Kouvelis, Panos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-12
kouvelis@wustl.edu
Olin Business School, Washington University in St.Louis,
St.Louis, Missouri, United States
Kovacevic, Raimund . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-35
raimund.kovacevic@donau-uni.ac.at
Department für Wirtschaft und Gesundheit, Universität
Krems, Krems, Austria
Kovalchik, Stephanie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-16
skovalchik@zelusanalytics.com
Zelus Analytics, Germany
Kovalenko, Illia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-51
illia.kovalenko@ul.ie
Kemmy Business School, University of Limerick, Limerick,Ireland
Koyama, Akira . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-50
ak.koyama.ch@logisteed.com
LOGISTEED Solutions, Ltd., Japan
KoyuncuoĞlu, Mehmet Ulaş . . . . . . . . . . . . . . . . . . . . . . . . WC-06
ulas@pau.edu.tr
Department of Management Information Systems, Pamukkale University, Denizli, Turkey

AUTHOR INDEX

Kresta, Aleš . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-51
ales.kresta@vsb.cz
Department of Finance, Faculty of Economics, VSBTechnical University of Ostrava, Ostrava, Czech Republic
Krestová, Terezie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-31
terezie.krestova@vsb.cz
VSB-Technical University of Ostrava, Ostrava, Česká Republika, Czech Republic
Krishan Lal, Manish . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-42
manish.krishanlal@tum.de
TU Munich, Germany
Krishnakumar, Malavika . . . . . . . . . . . . . . . . . . . MD-17, WA-18
malavikakrishnakumar2017@gmail.com
Mathematics, Amrita Vishwa Vidyapeetham, Kollam, Kollam, India
Krishnan, M Sivarama Anandha . . . . . . . . . . . . . . . . . . . . MB-59
sivaramsrak@gmail.com
Department of Management Studies, Indian Institute of Technology Madras, Madras, Tamil Nadu, India
Krishnan, Ramesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-24
ramesh.krishnan@iimk.ac.in
QMOM, Indian Institute Of Management, Kozhikode, India
Kristaly, Alexandru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-41
alexandrukristaly@yahoo.com
Babes-Bolyai University, Cluj-Napoca, Romania
Krmac, Evelin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-48
evelin.krmac@fpp.uni-lj.si
Faculty of Maritime studies and transport, University of
Ljubljana, Portorož, Slovenia

Kozanidis, George . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-39
gkoz@mie.uth.gr
Mechanical Engineering, University of Thessaly, Volos,
Magnisia, Greece

Kroesen, Maarten . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-11
m.kroesen@tudelft.nl
Delft, Netherlands

Kraft, Volker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-16
volker.kraft@jmp.com
Global Academic Program, JMP Statistical Discovery, Heidelberg, Germany

Krogh Boomsma, Trine . . . . . . . . . . . . . MA-03, MB-21, MC-52
trine@math.ku.dk
Department of Mathematical Sciences, University of Copenhagen, København Ø, Denmark

Krass, Dmitry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-24
krass@rotman.utoronto.ca
Rotman School of Mgmt, University of Toronto, Toronto,
Ontario, Canada

Kronqvist, Jan . . . . . . . . . . . . . . . . . . . . . . TA-04, WA-04, WC-04
jankr@kth.se
Mathematics, KTH Royal Institute of Technology, Sweden

Kraus, Mathias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-31
mathias.kraus@ur.de
University of Regensburg, Germany
Krauth, Maurice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-55
maurice.krauth@tu-dresden.de
Chair of Railway Operations, Technische Universität Dresden, Germany
Kremantzis, Marios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-48
marios.kremantzis@bristol.ac.uk
University of Bristol Business School, University of Bristol,
Bristol, United Kingdom

Krpan, Mira . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-43
moraic@net.efzg.hr
Faculty of Economics and Business - Zagreb, Croatia
Kruit, Myrthe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-25
m.n.kruit@student.utwente.nl
University of Twente, Netherlands
Krumke, Sven. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-53
krumke@mathematik.uni-kl.deMathematics, University of Kaiserslautern, Kaiserslautern,
Germany
Krushinsky, Dmitry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-56

589

AUTHOR INDEXEURO 2024 - Copenhagend.krushinsky@gmail.com
Operations Research and Logistics (ORL), Wageningen University & Research, Wageningen, Netherlands
Kuchler, Johannes . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-07, MD-11
johannes.kuchler@th-deg.de
Deggendorf, Germany
Kuckelberg, Alexander . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-58
a.kuckelberg@via-con.de
VIA Consulting & Development GmbH, Germany
Kucukaydin, Hande . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-61
hande.kucukaydin@mef.edu.tr
Industrial Engineering, MEF University, Istanbul, Turkey
Kucukazici, Beste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-15
beste.kucukyazici@queensu.ca
Smith Business School, Queen’s University, Kingston, Ontario, Canada
Kueng, Richard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-42
richard.kueng@jku.at
JKU Linz, Austria
Kuennen, Jan-Rasmus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-02
jan-rasmus.kuennen@whu.edu
WHU, Hamburg, Germany
Kuhn, Heinrich . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-50
heinrich.kuhn@ku.de
Supply Chain Management & Operations, Catholic University of Eichstaett-Ingolstadt, Ingolstadt, Bavaria, Germany
Kuhn, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-33, TC-33
michael.kuhn@oeaw.ac.at
Vienna Institute of Demography, Vienna, Austria
Kuiper, Alex . . . . . . . . . . . . . . . . . . . . . . . . . TB-10, TB-15, WA-39
a.kuiper@uva.nl
Business Analytics, University of Amsterdam, Amsterdam,
Netherlands
Kulak, Osman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-23
osman.kulak@tum.de
TUM School of Management, Technical University of Munich, Munich, Germany

aamiku@amazon.com
SNT, Amazon, Luxembourg
Kumar, Amit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-57, WA-57
aamiku@amazon.lu
Amazon, Luxembourg
Kumar, Kunal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-54
kunal.kumar@klm.com
KLM Royal Dutch Airlines, Netherlands
Kumar, Maneesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-15
kumarm8@cardiff.ac.uk
Cardiff Business School, United Kingdom
Kumar, Prashant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-53
prashant95kumar@gmail.com
Department of Mechanical Engineering, Indian Institute ofTechnology (IIT), Delhi, New Delhi, Delhi, India
Kumar, Ram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-24
ram@iimk.ac.in
QM & OM Area, IIM Kozhikode, CALICUT, KERALA, India
Kumar, Satish . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-26
S.Kumar@leedsbeckett.ac.uk
School of Built Environment, Engineering, and Computing,Leeds Beckett University, Leeds, Yorkshire, United Kingdom
Kumar, Sushil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-44
sk@iiml.ac.in
Operations Management, Indian Institute of Management
Lucknow, Lucknow, Uttar Pradesh, India
Kumari, Arunima . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-41
arunimamishra@bpitindia.com
Mathematics, Bhgwan Parshuram Institute of Technology,
Delhi, Delhi, India
Kumawat, Govind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-62
govind.kumawat@iimu.ac.in
IIM Udaipur, India
Kumbria, Kristina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-60
kristina.kumbria@hds.utc.fr
Laboratoire Heudiasyc, Université de Technologie de Compiègne, Compiegne, France

Kulich, Miroslav . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-58
kulich@cvut.cz
Czech Institute of Informatics, Robotics and Cybernetics,
Czech Technical University in Prague, Prague, Czech Republic

Kummer, Sebastian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-50
sebastian.kummer@wu.ac.at
Institute of Transport and Logistics Management, Vienna
University of Business and Economics, Vienna, Austria

Kuller, Martijn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-11
martijn.kuller@eawag.ch
Environmental Social Sciences (ESS), Eawag: Swiss Federal
Institute of Aquatic Science and Technology, Switzerland

Kunc, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-11, WC-20
m.h.kunc@soton.ac.uk
Southampton Business School, University of Southampton,
United Kingdom

Kumar Suman, Hemant . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-51
hemant@ct.iitr.ac.in
Centre for Transportation System, Indian Institute of Technology Roorkee (IITR), Roorkee, Uttarakhand, India

Kung, Yen-Ying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-47
yykung@vghtpe.gov.tw
National Yang Ming Chiao Tung University, Taiwan

Kumar, Amit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-57

590

Kuo, Yong-Hong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-06
yhkuo@hku.hk

EURO 2024 - Copenhagen
Department of of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, Hong
Kong
Kułakowski, Konrad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-44
konrad.kulakowski@agh.edu.pl
Applied Computer Science, AGH University of Science andTechnology, Kraków, Lesser Poland, Poland
Kuri, Sabrina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-13
sabrina.kuri@wfp.org
World Food Programme, Italy
Kurishchenko, Kseniia . . . . . . . . . . . . . . . . . . . . . . WC-12, MB-27
kk.eco@cbs.dk
Economics, Copenhagen Business School, Frederiksberg,
Denmark
Kuroiwa, Ryo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-30
ryo.kuroiwa@mail.utoronto.ca
Mechanical & Industrial Engineering, University of Toronto,
Toronto, Ontario, Canada
Kurtz, Jannis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-27
j.kurtz@uva.nl
Amsterdam Business School, University of Amsterdam,
Netherlands

AUTHOR INDEXSAMK, Pori, Finland
Kyngäs, Nico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-26
nico.kyngas@samk.fi
Satakunta University of Applied Sciences, Pori, Finland
Kyrgiakos, Leonidas Sotirios . . . . . . . . . . . . . . . . . . . . . . . . WC-48
lkyrgiakos@uth.gr
University of Thessaly, Volos, Greece
Kyriakidis, Epaminondas . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-64
ekyriak@aueb.gr
Statistics, Athens University of Economics and Business,Athens, Greece
Kytagias, Christos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-45
ckyt@uniwa.gr
University of West Attica, Greece
L’Ecuyer, Pierre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-06
lecuyer@iro.umontreal.ca
DIRO, Université de Montréal, Montreal, QC, Canada
Labarrière, Hippolyte . . . . . . . . . . . . . . . . . . . . . . MD-32, MB-34
hippolyte.labarriere@edu.unige.it
Università di Genova, Italy

Kuryatnikova, Olga . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-22
kuryatnikova@ese.eur.nl
Erasmus University Rotterdam, Netherlands

Labbé, Martine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-27, TA-28
mlabbe@ulb.ac.becomputer Science, Université Libre de Bruxelles, Bruxelles,
Belgium

Kurz, Sascha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-36
sascha.kurz@uni-bayreuth.deMathematics, Physics and Computer Science, University of
Bayreuth, Bayreuth, Bavaria, Germany

Labib, Ashraf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-02, MD-20
ashraf.labib@port.ac.uk
Strategy and Business Systems, University of Portsmouth,
Portsmouth, United Kingdom

Kuscu, Elif . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-24
elif.kuscu@business.uzh.ch
Sustainable Operations Management, University of Zurich,
Zurich, Switzerland

Labijak-Kowalska, Anna . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-44
anna.labijak@cs.put.poznan.pl
Institute of Computing Science, Poznań University of Technology, Poznań, Poland

Kushwaha, Shivam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-48
shivamkushwaha@irma.ac.in
Pom & Qt, Institute of Rural Management Anand, Anand,
Gujarat, India

Labrecciosa, Paola . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-36
paola.labrecciosa@essca.fr
ELS, ESSCA School of Management, France

Kuziak, Dorota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-29
dorota.kuziak@uca.es
University of Cadiz, Spain

Lachout, Petr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-35
lachout@karlin.mff.cuni.cz
Dept. Probability and Statistics, Charles University in Praha,
Praha, Czech Republic

Kuznetsov, Anatoliy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-04
akuznetsov3@gatech.edu
Georgia Institute of Technology, United States

Lacomme, Philippe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-42
placomme@isima.fr
ISIMA, LIMOS, AUBIERE, AUVERGNE, France

Kvasov, Dmitri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-41
kvadim@dimes.unical.it
DIMES, University of Calabria, Rende (CS), Italy

Lacroix, Mathieu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-25
lacroix@lipn.fr
LIPN, Université Sorbonne Paris Nord, France

Kwon, Roy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-63
roy.kwon@utoronto.ca
University of Toronto, Toronto, Ontario, Canada

Ladefoged, Magnus Berg . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-45
mabela@mp.aau.dk
Materials and Production, Aalborg University, Aalborg, Denmark

Kyngäs, Jari . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-26
jari.kyngas@samk.fi

Laganà, Demetrio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-64

591

AUTHOR INDEXEURO 2024 - Copenhagen

demetrio.lagana@unical.it
Department of Mechanical, Energy and Management Engineering, University of Calabria, Rende, Italy
Lagos, Tomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-12
tomasignacio.lagos@sydney.edu.au
Discipline of Business Analytics, The University of Sydney,
Sydney, NSW, Australia
Lahav, Yael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-61
ylahav@gmail.com
Management, School of Industrial Engineering & Management, Bar-Ilan University and Shenkar College, Ramat Gan,
IsraelLahtinen, Tuomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-11
tuomas.j.lahtinen@aalto.fi
Systems Analysis Laboratory, Aalto University School of
Science, Finland
Lai, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-28
d.lai@soton.ac.uk
University of Southampton Business School, United Kingdom
Lai, Guan-Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-28
gavin880311@gmail.com
Information Management and Finance, National Yang Ming
Chiao Tung University, Taiwan
Lai, Kexin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-61
kl4g16@soton.ac.uk
Mathematical Sciences, University of Souhampton,
Southampton, Hampshire, United Kingdom
Lai, Kin Keung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-57
mskklai@outlook.com
International Business School, Shaanxi Normal University,
China
Laksá, Sanna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-43
sanna@vardin.fo
P/F Varðin, Faroe Islands
Lakshay, Lakshay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-51
lakshay.mec@iitbhu.ac.in
Department of Mechanical Engineering, Indian Institute ofTechnology, Banaras Hindu University IIT (BHU), Varanasi,
221005, Uttar Pradesh, India
Lalla-Ruiz, Eduardo . . . . . . . . . . . . . . . . . . . . . . . WD-56, MD-62
e.a.lalla@utwente.nl
Faculty of Behavioural, Management and Social Sciences,
University of Twente, Enschede, NetherlandsLamé, Guillaume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-17
guillaume.lame@centralesupelec.fr
Laboratoire de Génie Industriel, CentraleSupélec, France
Lambert, Jo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-17
jo.lambert@uzgent.be
Department of Dermatology, Ghent University Hospital,
Ghent, Belgium
Lambin, Xavier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-14592

lambin@essec.edu
cergy, France, France
Lami, Isabella . . . . . . . . . . . . . . . MA-13, MC-13, MC-44, TB-47
isabella.lami@polito.it
DIST, Politecnico di Torino, Torino, Torino, Italy
Lammel, Ina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-37
ina.lammel@itwm.fraunhofer.de
Optimization, Fraunhofer ITWM, Kaiserslautern, Germany
Lan, Leon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-64
l.lan@vu.nl
Vrije Universiteit Amsterdam, Amsterdam, Netherlands
Lancini, Emiliano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-25
emiliano.lancini@lamsade.dauphine.fr
LAMSADE, Université Paris Dauphine PSL, Paris, France
Landete, Mercedes . . . . TB-26, WD-27, TD-52, MA-61, TB-64
landete@umh.es
Departamento de Estadística y Matemática Aplicada, University Miguel Hernández of Elche, Elche, Alicante, Spain
Landi, Germana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-32
germana.landi@unibo.it
Department of Mathematics, University of Bologna, Italy
Landini, Elisa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
elisa.landini@unimore.it
University of Modena and Reggio Emilia, Italy
Landini, Simone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-63
landini@ires.piemonte.it
Economics, IRES Piemonte, Torino, Italy
Lane, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-20
d.c.lane@henley.ac.uk
Prof of Business Informatics, Henley Business School, Reading, United Kingdom
Lange, Jan-Hendrik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-57
jhlange@amazon.com
Amazon, Germany
Lange, Nina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-29, MA-57
ntl@math.ku.dk
Department of Mathematical Sciences, University of Copenhagen, Denmark, Denmark
Langer, Lissy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-09
lilang@dtu.dk
Technical University of Denmark (DTU), Denmark
Langevin, André . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-26
andre.langevin@polymtl.ca
Mathematics and Industrial Engineering Department, École
Polytechnique de Montréal, Montréal, Canada
Lannez, Sebastien . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-63
sebastienlannez@fico.com
Xpress Optimization, FICO, Tours, Indre-Et-Loire, France
Lantz, Frederic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-19
frederic.lantz@ifpen.fr

EURO 2024 - Copenhagen

AUTHOR INDEXIFP-School, Rueil-Malmaison, France
Lanza, Alessandro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-32
alessandro.lanza2@unibo.it
University of Bologna, Italy
Lanzarone, Ettore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-58
ettore.lanzarone@unibg.it
DIGIP, University of Bergamo, Dalmine (BG), Italy
Laporte, Gilbert . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-21, MD-64
gilbert.laporte@cirrelt.ca
HEC Montreal, Montreal, Canada
Lappas, Theodoros . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-26
ted@aueb.gr
Greece
Lapucci, Matteo . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-12, MB-32
matteo.lapucci@unifi.it
Department of Information Engineering, University of Florence, Firenze, Italy
Larsen, Allan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-26
alar@dtu.dk
Department of Management, Technology and Economics,
Technical University of Denmark, Kgs. Lyngby, Denmark
Larsen, Andreas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-45
ankula@mp.aau.dk
Materials and Production, Aalborg University, Aalborg, Denmark
Larsen, Christian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-16
chl@econ.au.dk
Economics, CORAL, Aarhus School of Business, Aarhus
University, Aarhus V, Denmark
Larsen, Erik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-19
erik.larsen@mgmt.au.dk
Department of Management, Aarhus University, Aarhus V,
Denmark
Larsson, Torbjörn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-37
torbjorn.larsson@liu.se
Mathematics, Linköping University, Linköping, Sweden
Latafat, Puya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-41
puya.latafat@kuleuven.beElectrical Engineering (ESAT) STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU
LEuven, Leuven, Belgium
Lau, Hoong Chuin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-26
hclau@smu.edu.sg
School of Information Systems, Singapore Management University, Singapore, Singapore
Laude, Emanuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-41
emanuel.laude@esat.kuleuven.be
KU Leuven, Belgium
Lauga, Guillaume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-32
guillaume.lauga@ens-lyon.fr
Informatique, ENS Lyon, France

Laurent, Monique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-38
M.Laurent@cwi.nl
Centrum Wiskunde & Informatica Amsterdam and Tilburg
University, Amsterdam, Netherlands
Lauria, Davide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-51
davide.lauria@unibg.it
Dipartimento di Scienze aziendali, economiche e metodi
quantitativi, University of Bergamo, Milano, Italy, Italy
Laurini, Mattia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-18
mattia.laurini@unipr.it
Department of Engineering and Architecture, University ofParma, Parma, Emilia-Romagna, Italy
Lauven, Lars-Peter . . . . . . . . . . . . . . . . . . . . . . . . . . MA-14, TC-37
lars.lauven@uni-kassel.de
Chair of Energy Management and Power System Operation,
University of Kassel, Kassel, Germany
Lawless, Connor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-27
cal379@cornell.edu
Cornell University, Ithaca, NY, United States
Lämmel, Sebastian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-41
sebastian.laemmel@mathematik.tu-chemnitz.de
Chemnitz University of Technology, Chemnitz, Germany
Lazaro, Angel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-43
angel.lazaro@wur.nl
Urban Economics / Environmental Economics and Natural
Resources Group, Wageningen University, Netherlands
Le Cadre, Hélène . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-14
helene.le-cadre@inria.fr
Inria, France
Le Digabel, Sébastien . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-30
sebastien.le-digabel@polymtl.ca
Polytechnique Montréal, Montréal, QC, Canada
Le Josse, Nicolas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-20
nicolaslj@fr.thalesgroup.com
Thales, Brest, Bretagne, France
Le Roux, Gavin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-55
gjleroux@sun.ac.za
Department of Logistics, Stellenbosch University, Stellenbosch, Western Cape, South Africa
Le Thi, Hoai An . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-41
hoai-an.le-thi@univ-lorraine.fr
Computer Science, University of Lorraine, Metz, France
Le, Thi Hoa Sen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-02
sen.lethihoa@huaf.edu.vn
Hue University of Agriculture and Forestry (HUAF), Viet
Nam
Le, Thi My Le . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-41
thi-my-le.le@univ-lorraine.fr
Computer Science, University of Lorraine, METZ, France
Leal Palazón, Marina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-27

593

AUTHOR INDEXEURO 2024 - Copenhagenm.leal@umh.es
Statistics, Mathematics and Informatics, Universidad Miguel
Hernandez, Elche, Spain

Graduate school of Industrial Management, National Cheng
Kung University, Tainan, Taiwan

Leal, Alonso. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-31
aleal@miuandes.cl
University of los Andes, Santiago, Chile

Lee, Sangmin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-03
sangmin.lee@maersk.com
Mathematical Sciences, University of Copenhagen, Maersk,
København SV, Denmark

Leander, John . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-13
john.leander@byv.kth.se
Civil and Architectural Engineering, Royal Institute of Technology, KTH, Stockholm, Sweden

Lee, Seung Jae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-49
lsj0804@inu.ac.kr
Industrial & Management Engineering, Incheon National
University, Incheon, N/A, Korea, Republic of

Leban, Vasja . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-12
vasja.leban@bf.uni-lj.si
Department of Forestry and Renewable Forest Resources,
University of Ljubljana, Biotechnical Faculty, Ljubljana,
Slovenia

Lee, Shine-Der . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-29
sdlee@mail.ncku.edu.tw
Industrial & Information Management, National Cheng Kung
University, Tainan city, Taiwan, Taiwan

Leclere, Vincent. . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-22, WD-27
vincent.leclere@enpc.fr
CERMICS, Ecole des Ponts, Champs-sur-Marne, —, France
Lee, Dae-Jin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-15
daelee@faculty.ie.edu
IE University, Spain
Lee, Ilbin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-31
ilbin@ualberta.ca
University of Alberta, Canada
Lee, Jae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-25
jaelee831@gmail.com
Mathematics and Statistics, University of Melbourne, Australia
Lee, Jon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-04
jonxlee@umich.edu
Industrial and Operations Engineering Department, University of Michigan, Ann Arbor, Michigan, United States
Lee, Joosung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-58
joosung.lee@ed.ac.uk
University of Edinburgh, United Kingdom
Lee, Junkyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-34
junkyul@ics.uci.edu
IBM Research, United States
Lee, JunSeop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-23
wnstjq322@naver.com
yonsei.uni, Korea, Republic of
Lee, Junyong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-04
jy8020@korea.ac.kr
Department of Industrial and Management Engineering, Korea University, Korea, Republic of
Lee, Lauren Ye Seol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-37
lauren.lee@ucl.ac.uk
Department of Chemical Engineering, University College
London, United Kingdom
Lee, Peggy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-29
z8108151@email.ncku.edu.tw

594

Leeftink, Gréanne . . . . . . . . . . . . . . . . . . . . . . . . . . WC-10, WD-10
a.g.leeftink@utwente.nl
University of Twente, Netherlands
Lefebvre, Henri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-02
lefebvre@uni-trier.de
Department of Mathematics, Universität Trier, Germany
Lefgoum, Raian Noufel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-09
raian.lefgoum@enpc.fr
Cermics and Persee, Champs-sur-Marne, France
Legato, Pasquale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-50
legato@dimes.unical.it
DIMES, University of Calabria, Rende, CS, Italy
Legros, Benjamin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-39
benjamin.legros@centraliens.net
Ecole de Management de Normandie, France
Lehtonen, Matti . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-19
matti.lehtonen@aalto.fi
Aalto University, Finland
Leijenhorst, Nando . . . . . . . . . . . . . . . . . . . . . . . . . MC-38, WA-38
n.m.leijenhorst@tudelft.nl
Delft University of Technology, Netherlands
Leinen, Willi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-52
leinen@hsu-hh.deMechanical and Civil Engineering, Helmut Schmidt University, Hamburg, Germany
Leithäuser, Neele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-09
neele.leithaeuser@itwm.fraunhofer.de
Optimization, Fraunhofer ITWM, Kaiserslautern, Germany
Leitner, Markus . . . . . . . . . . . . . . . . . . . . . WA-15, MB-52, TA-61
m.leitner@vu.nl
Department of Operations Analytics, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
Leitner, Stephan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-43
stephan.leitner@aau.at
Department of Management Control and Strategic Management, University of Klagenfurt, Klagenfurt, Austria

EURO 2024 - Copenhagen
Lejeune, Miguel . . . . . . . . . . . . . . . . . . . . . MB-21, MC-29, TC-35
mlejeune@gwu.edu
George Washington University, United States
Lellis Moreira, Miguel Ângelo . . . . . . . . . . . . . . . WD-44, TA-45
miguellellis@hotmail.com
Production Engineering, Fluminense Federal University,
Niterói, Rio de Janeiro, Brazil
Leloup, Emeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-07
emeline.leloup@uliege.be
HEC - Management School, University of Liège, Belgium
Lemahieu, Wilfried . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-08
wilfried.lemahieu@kuleuven.be
Faculty of Economics and Business, KU Leuven, Belgium
Lemos, Felipe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-52
fcrlms@proton.me
Universidade Federal da Paraíba, João Pessoa, Paraíba, Brazil
Leonard, Thaddeus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-02
thaddeus.leonard@renault.com
Renault Group, France
Leopold-Wildburger, Ulrike . . . . . . . . . . . . . . . . WB-40, MD-65
ulrike.leopold@uni-graz.at
Department of Operations and Information Systems, University of Graz, Graz, Austria
Leppanen, Ilkka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-07
ilkka.j.leppanen@aalto.fi
School of Business, Aalto University, Espoo, Finland
Lerede, Daniele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-19
daniele.lerede@unito.it
Italy
Lesaja, Goran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-38
goran@georgiasouthern.edu
Mathematical Sciences, Georgia Southern University, Statesboro, Georgia, United States
Leshman, Ori . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-28
orileshman@gmail.com
psychology, Reichman University, Savyon, israel, Israel
Lespay, Hernán . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-60, WB-64
hernan.lespay@ucn.cl
Departamento de Ingeniería Industrial, Universidad Católica
del Norte, Antofagasta, Antofagasta, Chile
Lessmann, Stefan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-31
stefan.lessmann@hu-berlin.de
School of Business and Economics, Humboldt-University of
Berlin, Berlin, Germany
Letizia, Paolo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-23
pletizia@utk.edu
University of Tennessee, United States
Letsios, Dimitrios . . . . . . . . . . . . . . . . . . . . . . . . . . MA-49, WB-64
dimitrios.letsios@kcl.ac.uk
Department of Informatics, King’s College London, United
Kingdom

AUTHOR INDEX

Leung, Eric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-15
khleung@liverpool.ac.uk
United Kingdom
Leung, Janny . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-20
jannyleung@um.edu.moState Key Lav of Internet of Things for Smart City, The University of Macau, Taipa, Macao, CHINA, Macao
Leus, Roel . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-21, WA-43, TC-60
roel.leus@kuleuven.be
ORSTAT, KU Leuven, Leuven, Belgium
Levantesi, Susanna . . . . . . . . . . . . . . . . . . . . . . . . . . MB-63, TA-63
susanna.levantesi@uniroma1.it
Sapienza University of Rome, roma, Italy
Levering, Nikki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-40
nikki.levering@laas.fr
Irit-cnrs, Laas-cnrs, Toulouse, France
Levinson, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-10
david.levinson@sydney.edu.au
Civil Engineering, University of Sydney, Sydney, NSW, Australia
Levy, Priel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-08
priel.levy@biu.ac.il
Bar Ilan University, Israel
Ley, Eva . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-04
eva.ley@tu-braunschweig.de
Institute for Mathematical Optimization, Braunschweig, Germany
Ley, Eva . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-04
eva.ley@tu-braunschweig.de
Institute for Mathematical Optimization, TU Braunschweig,
Braunschweig, Germany
Leyer, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-11
michael.leyer@wiwi.uni-marburg.de
Chair of Digitalisation and Process Management, Universityof Marburg, Marburg, Germany
Leyman, Pieter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-23
pieter.leyman@ugent.beIndustrial Systems Engineering and Production Design,
Ghent University, Zwijnaarde, Belgium
Li, Boda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-22
boda.li@de.abb.com
DECRC, ABB, Mannheim, Germany
Li, Bo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-24
libo0410@tju.edu.cn
College of Management and Economics, Tianjin University,
China
Li, Chen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-53
chen.li@tju.edu.cn
Department of Operations and Supply Chain Management,
Tianjin University, Tianjin, Tianjin, China

595

AUTHOR INDEXEURO 2024 - Copenhagen

Li, Churui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-57
churui.li@kuleuven.be
Faculty of Economics and Business, KU Leuven, Leuven,
BelgiumLi, Dong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-15
dong.li@lancaster.ac.uk
Management Science, Lancaster University, Lancaster,
United Kingdom
Li, Feng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-29
omlifeng@163.com
Huazhong University of Science and Technology, China
Li, Guoyin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-42
g.li@unsw.edu.au
University of New South Wales, Sydney, Australia
Li, Haitao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-60
lihait@umsl.edu
University of Missouri - St. Louis, St. Louis, Missouri,
United States

VU University Amsterdam, Amsterdam, Netherlands
Li, Tianqi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-51
tianqi1205@gmail.com
School of traffic and transportation, Beijing Jiaotong University, Beijing, Beijing, China
Li, Tian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-15
litian@ecust.edu.cn
East China University of Science and Technology, China
Li, Weiqi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-25
weli@umich.edu
School of Management, University of Michigan-Flint, Flint,
Michigan, United States
Li, Xiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-32
xiang.li@inf.ethz.ch
Department of Computer Science, ETH Zurich, Switzerland
Li, Xinyi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-45
xinyi.li@plymouth.ac.uk
University of Plymouth, Plymouth, United Kingdom

Li, Hao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-61h99li@uwaterloo.ca
Department of Management Science and Engineering, University of Waterloo, Waterloo, Ontario, Canada

Li, Yongfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-29
liyongfeng10@huawei.com
Huawei Technologies Co., Ltd., Theory Lab, China

Li, Jianshu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-29
lijianshu2@huawei.com
China

Li, Yuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-57
yyuanli@amazon.com
Amazon, United States

Li, Jing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-33
rbslijing2@163.com
Renmin University of China, China

Li, Yuchen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-26
lycnbb@126.com
Economics and Management, Beijing University of Technology, China

Li, Luyao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-07
lild19@mails.tsinghua.edu.cn
Industrial Engineering, Tsinghua University, Beijing, China
Li, Miao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-16
Miao.li@ugent.be
Department of Business Informatics and Operations Management, Ghent University, Gent, East Flanders, Belgium
Li, Ping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-13
p.li@sheffield.ac.uk
University of Sheffield, United Kingdom
Li, Ru-Ying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-15
prettydora50525@gmail.com
TaiwanLi, Runjie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-58
rlibu@connect.ust.hk
Hong Kong University of Science and Technology, Hong
Kong, Hong Kong

Li, Zhening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-34
zheningli@gmail.com
Department of Mathematics, University of Portsmouth,
Portsmouth, United Kingdom
Liakopoulos, Nikos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-57
liakopoulosnp@gmail.com
ATS, Amazon, Luxembourg, Luxembourg
Liang, Gongqian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-52
lgqian@nwpu.edu.cn
Northwestern Polytechnical University, China
Liao, Chia-Wen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-47
cwliao@cute.edu.tw
Department of Civil Engineering and Hazard Mitigation,
China University of Technology, Taipei, Taiwan
Liao, Ruiwen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-49
helia.liao.ruiwen@gmail.com
LISN Paris-Saclay University, France

Li, Siqiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-56
20114044@bjtu.edu.cn
School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China

Liao, Yubin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-62
2110474158@email.szu.edu.cn
Shenzhen university, China

Li, Siqiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-40
siqiao@ccmath.com

Liao, Zhengwen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-54
zwliao@bjtu.edu.cn

596

EURO 2024 - Copenhagen
School of Traffic and Transportation, Beijing Jiaotong University, China
Liao, Zhiqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-41
zhiqiang.liao@aalto.fi
Aalto Univerdity, Espoo, Finland
Liapakis, Anastasios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-08
aliapakis@uniwa.gr
Department of Archival, Library & Information Studies(ALIS), University of West Attica, Athens (Egaleo), Athens,
GreeceLiapis, Georgios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-27
ucecgli@ucl.ac.uk
Chemical Engineering, University College London, London,
United Kingdom
Liberti, Leo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-04, WB-04
leoliberti@gmail.com
Cnrs Lix, Ecole Polytechnique, Palaiseau, France
Lidén, Tomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-51
tomas.liden@vti.se
Traffic Analysis and Logistics, VTI - Swedish National Road
and Transport Research Institute, Stockholm, Sweden
Lidbetter, Thomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-36
tlidbetter@business.rutgers.edu
Rutgers Business School, Rutgers University, Newark, NJ,
United States
Lidvall, Johan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-50
johan.lidvall@volvo.com
SML, Volvo Group, Gothenburg, Sweden
Lienert, Judit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-11, TD-11
judit.lienert@eawag.ch
Environmental Social Sciences (ESS), Eawag: Swiss Federal
Institute of Aquatic Science and Technology, Duebendorf,
Switzerland
Lienkamp, Benedikt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-54
benedikt.lienkamp@tum.de
School of Management, Technical University of Munich,
Munich, Germany

AUTHOR INDEX

Lillo Rodríguez, Rosa Elvira. . . . . . . . . . . . . . . . . WB-27, TD-39
rosaelvira.lillo@uc3m.es
Statstics, Universidad Carlos III de Madrid, Las Rozas deMadrid 28232, Madrid, Spain
Lillo, Fabrizio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-02, WD-40
fabrizio.lillo@sns.it
Scuola Normale Superiore and Università di Bologna, Italy
Lima Azevedo, Carlos M. . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-51
climaz@dtu.dk
Technical University of Denmark, Department of Technology, Management, and Economics, 2800 Kongens Lyngby,
Denmark, Denmark
Limbourg, Sabine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-26
sabine.limbourg@uliege.be
HEC Liège Management School, ULiège, Belgium
Lin, Ching-Torng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-44
charllin@mail.dyu.edu.tw
Information Management, Dayeh University, Changhua, Taiwan
Lin, Jane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-55
janelin@uic.edu
Department of Civil and Materials Engineering, University
of Illinois at Chicago, Chicago, Illinois, United States
Lin, Jen-Yen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-49
jylinor@gmail.com—, Taiwan
Lin, Li-Chiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-49
chiao@ncut.edu.tw
Department of Business Administration, National Chinyi
University of Technology, Taiwan
Lin, Min-Bin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-31
min-bin.lin@kit.edu
Institute AIFB, Karlsruhe Institute of Technology, Karlsruhe,
Baden-Württemberg, Germany
Lin, Ming-Hua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-60
mhlin@utaipei.edu.tw
Department of Urban Industrial Management and Marketing,
University of Taipei, Taipei, Taipei, Taiwan

Liers, Frauke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-04, TD-34
frauke.liers@math.uni-erlangen.de
Department Mathematik, FAU Erlangen-Nuremberg, Erlangen, Germany

Lin, Yang Kuei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-15
yklin@mail.fcu.edu.tw
TaiwanLiesiö, Juuso . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-11, MD-11
juuso.liesio@aalto.fi
Department of Information and Service Management, Aalto
University School of Business, Aalto, Finland

Lin, Yong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-07
y.lin@bham.ac.uk
University of Birmingham, Birmingham, England, United
Kingdom

Lifshitz, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-49, WC-49
daniel.lifshitz@gmail.com
Ben Gurion University of the Negev, Israel

Lin, Yueh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-28
y.lin@ieseg.fr
Quantitative Methods, IESEG school of management, Lille,
FranceLigny, Lucas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-08
lligny@localsolver.com
LocalSolver, France

Lin, Yunzhu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-53, MC-56
mschang@cycu.edu.tw
Department of Civil Engineering, Chung Yuan Christian Uni-

597

AUTHOR INDEXEURO 2024 - Copenhagenversity, Taoyuan, Taiwan
Lin, Zhiyuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-54, TA-54
z.lin@leeds.ac.uk
Institute for Transport Studies, University of Leeds, Leeds,
United Kingdom
Lindahl, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-46, TA-46
miclindahl@gmail.com
Management Engineering, Technical University of Denmark,Denmark

University of Strathclyde, United Kingdom
Liu, Chang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-03
changliu@hkaift.com
City University of Hong Kong, HK, Hong Kong
Liu, Haichao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-10
haichaol475@gmail.com
School of Management, Northwestern polytechnical university, Xian, — Select One —, China

Linders, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-57
d.h.linders@uva.nl
University of Amsterdam, Netherlands

Liu, Jiyin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-62
j.y.liu@lboro.ac.uk
School of Business and Economics, Loughborough University, Loughborough, Leicestershire, United Kingdom

Lindgren, Charlie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-31
clg@du.se
Economics, Dalarna University, Borlänge, Dalarna, Sweden

Liu, Junyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-29
liu.junyan1@huawei.com
Huawei Hong Kong Research Center, Hong Kong

Lindner, Fabian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-23
fabian.lindner@hszg.de
Faculty of Business Administration and Engineering, Zittau/Görlitz University of Applied Sciences, Germany

Liu, Lindong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-64
ldliu@ustc.edu.cn
Management Science, University of Science and Technology
of China, China

Linker, Raphael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-12
linkerr@tx.technion.ac.il
The Technion - Israel Instiute of Technology, Haifa, Israel

Liu, Mingda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-52
lmd20@mails.tsinghua.edu.cn
Tsinghua University, China

Liou, James . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-53
jamesjhliou@gmail.com
Industrial Engineering and Management, National Taipei
University of Technology, Taipei, Taiwan

Liu, Qipeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-24
qipli@dtu.dk
Chemical and Biochemical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark

Liow, Kong You . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-60
kongyouliow@gmail.com
Decision Science Services, Ørsted, Gentofte, Denmark

Liu, Qiyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-24
qiyanl@umich.edu
Civil and Environmental Engineering, University of Michigan, Ann Arbor, Ann Arbor, MI, United States

Lippi, Marco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-25
marco.lippi@unifi.it
Department of Information Engineering, University of Florence, Florence, Italy
Lisboa, Adriano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-36
adriano.lisboa@gaiasd.com
Gaia, solutions on demand, Belo Horizonte, Minas Gerais,
BrazilLitsiou, Dina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-45
k.litsiou@mmu.ac.uk
Marketing, International Business and Tourism, Manchester
Metropolitan University, Manchester, United Kingdom
Litvinchev, Igor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-07
igorlitvinchev@gmail.com
Universidad Autónoma de Nuevo León, San Nicolás de losGarza, Nuevo León, Mexico
Liu, Baolong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-07
liubl@shanghaitech.edu.cn
School of Entrepreneurship and Management, ShanghaiTech
University, Shanghai, China
Liu, Bin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-39
b.liu@strath.ac.uk

598

Liu, Shaofeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-45
shaofeng.liu@plymouth.ac.uk
Plymouth Business School, University of Plymouth, Plymouth, United Kingdom
Liu, Shiang-Tai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-48
stliu@vnu.edu.tw
Graduate School of Business & Management, Vanung University, Taoyuan, Taiwan
Liu, Sining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-14
sining.liu@psi.chLaboratory for Energy Systems Analysis, Paul Scherrer Institute; Department of Mechanical and Process Engineering,ETH Zurich, Switzerland
Liu, Songsong . . . . . . . . . . . . . . . TA-15, WB-23, WA-24, WA-36
s.liu@hit.edu.cn
School of Management, Harbin Institute of Technology,
Harbin, China
Liu, Song . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-23
liusong_andrew3@outlook.com
IESEG School of Management, Univ. Lille, CNRS, UMR
9221 - LEM - Lille Economie Management, FranceEURO 2024 - Copenhagen
Liu, Wendy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-10
wendylll@icloud.com
Tilburg University, Netherlands
Liu, Wenqian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-64
iewliu@ust.hk
Industrial Engineering and Decision Analytics, The Hong
Kong University of Science and Technology, Hong Kong,
Hong Kong
Liu, Xiaoqian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-32
xiaoqian.liu1025@gmail.com
The University of Texas MD Anderson Cancer Center, Houston, TX, United States
Liu, Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-28
yu.2.liu@aalto.fi
Department of Mathematics and Systems Analysis, Aalto
University, Espoo, Finland

AUTHOR INDEX

Locatelli, Marco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-18, TA-52
marco.locatelli@unipr.it
Department of Engineering and Architecture, University ofParma, Italy
Loch-Temzelides, Ted . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-33
tedt@rice.edu
Rice University, United States
Lodi, Andrea. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-27
andrea.lodi@cornell.edu
D.E.I.S., University of Bologna, Bologna, Italy
Lodi, Andrea. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-52
andrea.lodi@polymtl.ca
Ecole Polytechnique, Montreal, Canada
Logothetis, Dimitrios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-40
d.logothetis@math.leidenuniv.nl
Mathematical Institute, Leiden University, Leiden, Netherlands

Liu, Zhengfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-61
221019039@link.cuhk.edu.cn
School of Science and Engineering,The Chinese Universityof Hong Kong,Shenzhen, Big Data Intelligent Application
Technology Research Institute, ShenzhenResearch Instituteof Big Data, Shenzhen, Shen Zhen, Guangdong Province,
China

Logothetis, Dimitrios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-40
dlogothetis@math.uoa.gr
Mathematics, National and Kapodistrian University ofAthens, Athens, Greece

Liuzzi, Giampaolo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-32
giampaolo.liuzzi@diag.uniroma1.it
DIAG - Sapienza University of Rome, Rome, Italy

Löhne, Andreas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-37
andreas.loehne@uni-jena.de
Institut für Mathematik, FSU Jena, Jena, Germany

Liuzzi, Giampaolo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-32
giampaolo.liuzzi@uniroma1.it
DIAG, Sapienza Univ. of Rome, Rome, RM, Italy

Lohr, Alexander . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-49
alexander.lohr@uibk.ac.at
Information Systems, Production and Logistics Management,
University of Innsbruck, Innsbruck, Austria

Ljubic, Ivana . MD-29, TC-29, TD-29, WC-35, TA-52, TB-52,
TD-52
ivana.ljubic@essec.edu
IDS, ESSEC Business School of Paris, Cergy-Pontoise,FranceLlagas, Jahir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-56
jahir.llagas@econ.au.dk
Department of Economics and Business Economics, Aarhus
University, Aarhus V, Aarhus, Denmark
Llagostera Blasco, Pol . . . . . . . . . . . . . . . . . . . . . . . . TA-12, TC-12
pol.llagostera@udl.cat
Mathematics, University of Lleida, Lleida, Catalonia, Spain
Lleo, Sebastien . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-57, TA-57
sebastien.lleo@neoma-bs.fr
Finance Department, NEOMA Business School, Reims,
FranceLo, Mei-Chen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-47
meichen.md10@nycu.edu.tw
National Yang Ming Chiao Tung University & National
United University, Taiwan
Locatelli, Alberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-49
alberto.locatelli@unimore.it
Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Travesio, PN, Italy

Lohrmann, Christoph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-31
christophl@ru.is
Department of Computer Science, Reykjavik University, IcelandLoição, Lilite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-17
lilite.loicao@infarmed.pt
INFARMED, Portugal
Loick, Philipp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-57
loickp@amazon.lu
Amazon, Luxembourg
Lok - Visser, Jedidja . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-10
j.lok-visser@utwente.nl
CHOIR, University of Twente, Zwolle, Netherlands
Lokachari, Prakash Sai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-48
lps@iitm.ac.in
Department of Management Studies, Indian Institute of Technology Madras, India
Lombard, Augustin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-57
auguslom@amazon.fr
Amazon, France
Lombard, Simone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-11
sl2130@hw.ac.uk

599

AUTHOR INDEXEURO 2024 - Copenhagen

Edinburgh Business School, Heriot-Watt University, Germany
Lombardi, Francesco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-38
f.lombardi@tudelft.nl
TU Delft, Netherlands
Lombardi, Patrizia . . . . . . . . . . . . . . . . . MD-18, WC-23, MD-47
patrizia.lombardi@polito.it
DIST, Polytechnic of Turin, Turin, Italy
Longaray, Andre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-37
andrelongaray@furg.br
Federal University of Rio Grande, Brazil

López Rodríguez, Gabriel . . . . . . . . . . . . . . . . . . . . . . . . . . WB-19
gabriel.lopez@die.uhu.es
Departamento Ingeniería Eléctrica y Térmica, de Diseño yProyectos, Universidad de Huelva, Huelva, Spain
López, Hugo A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-27
hulo@dtu.dk
DTU Compute, Technical University of Denmark, Kongens
Lyngby, Denmark
Lopez, Julio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-58
julioloa@inditex.com
Analytics, Inditex, Arteixo, A Coruña, Spain

Longhi, Andrea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-18
andrea.longhi@polito.it
DIST, Politecnico di Torino, Torino, Italia, Italy

Lopez, Maria Luz . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-34, MB-56
marialuz.lopez@uclm.es
matemáticas, Universidad de Castilla La Mancha, Ciudad
Real, Ciudad Real, Spain

Longo, Giovanni . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-56
giovanni.longo@dia.units.it
DEAMS - Department of Economics, Business, Mathematics
and Statistics, University of Trieste, Trieste, Italy

López-Cerdá, Marco A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-42
marco.antonio@ua.es
Statistics and Operations Research, Alicante University, Alicante, Spain

Lopes Gerum, Pedro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-50
p.lopesgerum@csuohio.edu
Cleveland State University, United States

López-Ospina, Héctor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-45
hhlopez@uninorte.edu.co
Departamento de Ingeniería Industrial, Universidad delNorte, Barranquilla, Colombia

Lopes, Fernando . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-09
fernando.lopes@lneg.pt
LNEG, Portugal
Lopes, Manuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-07
mpl@isep.ipp.pt
INESC TEC and School of Engineering, Polytechnic ofPorto, Porto, Portugal
Lopes, Maria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-10
maria.lopes.martins@tecnico.ulisboa.pt
CEGIST, Instituto Superior Técnico, Carcavelos, Lisboa,
Portugal
Lopes, Marta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-11, TD-11
mlopes@esac.pt
IPC-ESAC, INESC Coimbra, Coimbra, Portugal
Lopes, Rui Borges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-49
rui.borges@ua.pt
Dep. of Economics, Management and Industrial Engineering, CIDMA / University of Aveiro, Aveiro, Portugal
Lopez Moya, Estrella . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-13
elopezmoya@icatalist.eu
ICATALIST, Las Rozas, MADRID, Spain
López Pereira, Jorge Mario . . . . . . . . . . . . . . . . . . . . . . . . . MB-58
jotalopez@correo.unicordoba.edu.co
Industrial Engineer, Universidad de Córdoba, Montería, Córdoba, Colombia
Lopez Redondo, Juana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-22
jlredondo@ual.es
Department of Informatics, University of Almeria, Almeria,
Spain

600

López-Plata, Israel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-62
ilopezpl@ull.edu.es
Ingeniería Informática y Sistemas, Universidad de La Laguna, Spain
Lorca, Alvaro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-19
alvarolorca@uc.cl
Electrical Engineering - Industrial and Systems Engineering, Pontificia Universidad Catolica de Chile, Santiago, RM,
Chile
Lorentziadis, Panos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-36
paloren@otenet.gr
Business Administration, Athens University of Economics
and Business, ATHENS, Greece
Lorenz, Lisa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-09
lisa.lorenz@tu-dresden.de
Chair of Energy Economics, TU Dresden, Germany
Lorenzo-Freire, Silvia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-45
silvia.lorenzo@udc.es
University of A Coruña, Spain
Loris, Ignace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-34
ignace.loris@ulb.be
Mathematics Department, Université libre de Bruxelles,
Bruxelles, Belgium
Lotfi, Aymane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-58
lotfiaymane1@gmail.com
Google, Paris, France
Lotfi, Somayyeh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-63
slotfi01@ucy.ac.cy
Department of Accounting and Finance, University ofEURO 2024 - Copenhagen
Cyprus, Cyprus
Lotfian, Elaheh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-03
e.lotfian@gmail.com
Statistics, Tarbiat modares university, Tehran, Iran, Islamic
Republic ofLotfipoor, Ashkan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-28
a.lotfipoor@surrey.ac.uk
Surrey Business School, University of Surrey, Guildford,
Surrey, United Kingdom
Louhichi, Wael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-51
Wael.louhichi@essca.fr
Finance, ESSCA, BOULOGNE-BILLANCOURT, France
Lourenço, Madalena Alexandre . . . . . . . . . . . . . . . . . . . . . TB-44
madalena.lourenco@tecnico.ulisboa.pt
Instituto Superior Técnico, ULisboa, Lisbon, Portugal
Louveaux, Quentin . . . . . . . . . . . . . . . . . . . . . . . . . . TC-38, WC-38
q.louveaux@ulg.ac.beMontefiore Institute, Université de Liège, Liège, Belgium
Lowery, Ben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-50
b.lowery@lancaster.ac.uk
Department of Mathematics and Statistics, Lancaster University, Lancaster, Lancashire, United Kingdom
Lowing, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-22
david.lowing@outlook.com
GATE Univ. Lyon-St-Etienne, France
Lozano, Leonardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-20
lozanolo@ucmail.uc.edu
University of Cincinnati, Cincinnati, OH, United States
Lu, Baile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-40
lubaile@nudt.edu.cn
College of Systems Engineering, National University of Defense Technology, Changsha, Hunan, China
Lu, Chixiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-48
c.lu@surrey.ac.uk
Surrey Business school, University of Surrey, Guildford, Surrey, United Kingdom
Lu, Hua-An . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-25
halu@ntou.edu.tw
TaiwanLu, Jizhou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-39, MB-57
jizhoulu@cufe.edu.cn
China
Lu, Lijue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-33, TC-50
lijue.lu@neoma-bs.fr
Information System, Supply Chain Management & Decision
Support Department, NEOMA Business School, France
Lu, Xuefei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-27
xuefei.lu@skema.edu
Skema Business School, France
Lu, Yahan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-51

AUTHOR INDEX

y.lu-5@tudelft.nl
Delft University of Technology, Netherlands
Lu, Yahan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-51
yahanlu@tudelft.nl
Delft University of Technology, Delft, Netherlands
Luan, Xiaojie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-51, TA-54
xjluan@bjtu.edu.cn
Beijing Jiaotong University, Beijing, China
Lubjuhn, Sebastian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-19
sebastian.lubjuhn@socecon.rwth-aachen.de
Decision Analysis and Socio-Economic Assessment, RWTH
Aachen, Aachen, Germany
Lucarelli, Caterina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-63
c.lucarelli@univpm.it
Department of Management, Università Politecnica Marche,
Ancona, Italy
Luce, Robert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-30
luce@gurobi.com
Gurobi, Monheim am Rhein, Germany
Lucidi, Stefano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-03, MB-32
lucidi@dis.uniroma1.it
Department of Computer, Control, and Management Science,
University of Rome "La Sapienza", Rome, Italy
Lücker, Florian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-59
florian.lucker@city.ac.uk
Bayes Business School, City, University of London, London,
United Kingdom
Lüer-Villagra, Armin . . . . . . . . . . . . . . . . . . . . . . . MA-61, MC-61
armin.luer@unab.cl
Department of Engineering Sciences, Universidad Andres
Bello, Concepción, Chile
Luhn, Gerhard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-23
gerhard.luhn@systema.com
SYSTEMA Systementwicklung Dipl.-Inf. Manfred Austen
GmbH, Dresden, Germany
Lukac, Zrinka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-43
zlukac@efzg.hr
Faculty of Economics and Business - Zagreb, Zagreb, Croatia
Lukasiak, Piotr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-20
piotr.lukasiak@cs.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poznan, Poland
Lund, Henrik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-09
lund@plan.aau.dk
University of Aalborg, Aalborg, Northern Jutland, Denmark
Lundberg, Kristian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-37
kristian.lundberg@saabgroup.com
Saab AB, Sweden
Lundell, Andreas . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-04, WA-30
alundell@abo.fi
Department of Information Technologies, Åbo Akademi Uni-

601

AUTHOR INDEXEURO 2024 - Copenhagenversity, Turku, Finland
Luo, Jiabin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-31
j.luo2@aston.ac.uk
Operations and Information Management, Aston University,
Birmingham, West Midlands, United Kingdom
Luo, Tanxiaosi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-43, TC-43
luotanxiaosi@csu.edu.cn
Business School, Central South University, China
Lupi, Marino . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-56
marino.lupi@unipi.it
Department of Civil and Industrial Engineering, Universityof Pisa, Pisa, Italy
Lupoli, Mario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-63
ml274@st-andrews.ac.uk
BNP Paribas, London, United Kingdom
Luque, Mariano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-44
mluque@uma.es
Applied Economics (Mathematics), Universidad de Malaga,
Malaga, Spain
Lurkin, Virginie . . . . . . . . . . . . . . . . . . . . MD-52, WB-58, MD-59
virginie.lurkin@unil.ch
HEC, University of Lausanne, Eindhoven, Switzerland
Lusby, Richard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-22
rmlu@dtu.dk
DTU Management, Technical University of Denmark, Kgs
Lyngby, Denmark
Luteberget, Bjørnar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-54
bjornar.luteberget@sintef.noMathematics and Cybernetics, SINTEF Digital, Norway
Luxton, Richard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-15
Richard.Luxton@uwe.ac.uk
UWE Bristol, United Kingdom
Luz, Aidee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-12
aideeluzbustos@gmail.com
Faculta De Ingenieria, UNAM, Cidad de Mexico, Ciudad de
Mexico, Mexico
Luzon, Yossi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-02
YossefL@afeka.ac.il
Afeka College of Engineering, Tel Aviv, Israel
Lyngesen, Mark . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-52
lyngesen@econ.au.dk
Department of Economics and Business Economics, Aarhus
University, Aarhus, Denmark, Aarhus Kommune, Denmark
Lysgaard, Jens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-58, TD-58
lys@econ.au.dk
Department of Economics and Business Economics, Aarhus
University, Aarhus V, Denmark
Lyu, Chang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-24
1427598731@qq.com
School of Management, Harbin Institute of Technology,
Harbin, Heilongjiang Province, China

602

Lyu, Xiaohuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-62
x.lyu-1@tudelft.nl
MTT, TU Delft, Stieltjesweg, Netherlands
Lyu, Zhengwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-61
lvzhengwei@bjtu.edu.cn
School of Economics and Management, Beijing Jiaotong
University, Beijing, Beijing, China
Lyu, Zhipeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-29
zhipeng.lv@hust.edu.cn
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
M’Hallah, Rym. . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-54, MB-59
rymmha@yahoo.com
Engineering, King’s College London, London, England,
United Kingdom
Ma, Cheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-23
mc_0812@163.com
Qingdao University, China
Ma, Guiyuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-02
guiyuanma@xjtu.edu.cn
School of Economics and Finance, Xi’an Jiaotong University,
China
Ma, Jihui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-54
jhma@bjtu.edu.cn
Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry ofTransport, Beijing Jiaotong University, China
Ma, Li-Ching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-03
lcma@nuu.edu.tw
Department of Information Management, National United
University, Miaoli, Taiwan, Taiwan
Ma, Shoufeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-34
sfma@tju.edu.cn
College of Management and Economics, Tianjin University,
Tianjin, Tianjin, China
Ma, Shumin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-28
smma.cuhk@gmail.com
BNU-HKBU United International College, Hong Kong
Ma, Tai-yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-56
tai-yu.ma@liser.lu
Urban Development and Mobility, Luxembourg Institute of Socio-Economic Research (LISER), Esch-surAlzette/Belval, Luxembourg
Ma, Tiejun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-28
tiejun.ma@gmail.com
Inforamatics, University of Edinburgh, United Kingdom
Maag, Volker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-37
maag@itwm.fhg.de
Optimization,
Fraunhofer-Institut für Techno- undWirtschaftsmathematik, Kaiserslautern, Germany
Macedo, Eloisa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-56

EURO 2024 - Copenhagen
macedo@ua.pt
Department of Mechanical Engineering, University of
Aveiro, Aveiro, Aveiro, Portugal
Macedo, Marcelo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-37
marcelo.macedo@ufsc.br
Federal University of Santa Catarina, Brazil
Macharis, Cathy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-55
Cathy.Macharis@vub.ac.be
BUTO-MOBI, Vrije Universiteit Brussel, Brussels, Belgium
Macharis, Cathy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-44
cathy.macharis@vub.be
BUTO-Mobilise, Vrije Universiteit Brussel, Brussels, Belgium
Machowska, Dominika . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-33
machowska@mimuw.edu.pl
Institute of Applied Mathematics and Mechanics, Universityof Warsaw, Poland
Macq, Johan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-63
johan.macq@gmail.com
PRISM Sorbonne, Université Paris 1 Panthéon-Sorbonne,
Vivienne Investissement, France
MacSwiney Brugha, Cathal . . . . . . . . . . . . . . . . . . . . . . . . . MC-20
cathal.brugha@ucd.ie
Centre for Business Analytics, University College Dublin,
Dublin 4, Ireland
Maculan, Nelson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-58
maculan@cos.ufrj.br
Ufrj-coppe / Pesc, Universidade Federal do Rio de Janeiro,
Rio de Janeiro, RJ, Brazil
Madaan, Jitender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-53
jmadaan@iitd.ac.in
Department of Management studies, Indian Institute of Technology Delhi, New Delhi, Delhi, India
Madani, Seyedehsima . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-54
seyedehsima.madani@vub.be
Mobilise Research Group, Vrije Universiteit Brussel, Brussels, Belgium
Madarasi, Péter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-38
madarasi@cs.elte.hu
Eötvös Loránd University, Hungary
Madas, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-62
mmadas@uom.gr
Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece
Maddah, Bacel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-50
bm05@aub.edu.lb
Industrial Engineering and Management, American University of Beirut, Beirut, Lebanon
Maden, Ayse Dilek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-29
aysedilekmaden@selcuk.edu.tr
Mathematics, Selcuk University, Turkey

AUTHOR INDEXMader, Simon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-64
simon.mader@ku.de
Ingolstadt School of Management, Catholic University ofEichstätt-Ingolstadt, Germany
Madlener, Reinhard . . . . . . . . . . . . . . . . . WC-06, TA-14, WD-57
rmadlener@eonerc.rwth-aachen.de
School of Business and Economics / E.ON Energy ResearchCenter, RWTH Aachen University, Aachen, Germany
Maecker, Dennis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-07
dennis.maecker@dfki.de
German Research Center for Artificial Intelligence, Germany
Maenhout, Broos . . . . . . . . . . . . . . . . . . . WB-10, WA-15, WB-26
Broos.Maenhout@Ugent.be
Business Informatics and Operations Management, Ghent
University, Gent, Belgium
Maggioni, Francesca . . . . . . . . . MC-16, MA-35, MC-35, TD-35
francesca.maggioni@unibg.it
Department of Management, Information and Production Engineering, University of Bergamo, Bergamo, Italy
Magistri, Simone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-37
simone.magistri@unifi.it
Information Engineering (DINFO), University of Florence,
Italy, Italy
Magnanti, Thomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-22
magnanti@mit.edu
Provosts Office, MIT, Cambridge, Massachusetts, UnitedStatesMagni, Carlo Alberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-29
carloalberto.magni@unimore.it
Economics, School of Doctorate E4E (Engineering for Economics - Economics for Engineering) - University of Modena
and Reggo Emilia, Modena, Italy
Magnot, Jean-Pierre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-44
jp.magnot@gmail.com
LAREMA, Université d’Angers, France
Mahabhashyam, Sai Rajesh . . . . . . . . . . . . . . . . . . . . . . . . WB-50
sairajesh.mahabhashy@walmart.com
Supply Chain Strategy Data Science, Walmart, Inc., Irving,
TX, United States
Mahajan, Ashutosh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-61
amahajan@iitb.ac.in
IEOR Department, IIT Bombay, Mumbai, Maharashtra, India
Maharani, Anisha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-10
anisha.maharani@uliege.be
HEC Liège - Management School, University of Liège,
Liège, Belgium
Maher, Stephen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-30
stephen@sjmsolutions.co.uk
GAMS Software GmbH, United Kingdom
Mahes, Roshan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-39
roshan-1@live.com
Amsterdam, Noord-Holland, Netherlands

603

AUTHOR INDEXEURO 2024 - Copenhagen

Mahmutogullari, Irfan . . . . . . . . . . . . . . . . . . . . . . WB-03, TC-21
irfan.mahmutogullari@kuleuven.be
Dept. of Computer Science, KU Leuven, Leuven, Choose aState or Province, Belgium
Mahmutogullari, Ozlem . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-24
o.mahmutogullari@tue.nl
Eindhoven University of Technology, Netherlands
Maia, Francisco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-50
francisco.l.maia@inesctec.pt
Industrial Engineering and Management, INESC TEC and
FEUP, Porto, Porto, Portugal
Maier, Sebastian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-35
sebastian.maier@imperial.ac.uk
Imperial College London, London, United Kingdom
Main, Alastair . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-62, WA-62
aroma@dtu.dk
Management, Technical University of Denmark, Kgs. Lyngby, Denmark
Maiz, Santiago . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-21
santiago.maiz@uclm.es
Electrical Engineering, Universidad de Castilla-La Mancha,
Ciudad Real, Spain
Majchrzak, Maciej . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-20
maciej.majchrzak@doctorate.put.poznan.pl
Poznan University of Technology, Poland
Majumder, Pinki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-16
pinki.mjmdr@rediffmail.com
MATHEMATICS, Nit Agartala, AGARTALA, India
Maknoon, Yousef . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-11
M.Y.Maknoon@tudelft.nl
Delft University of Technology, Delft, Netherlands
Malagon, Homero . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-08
malagonhomer@gmail.com
UNAM, Mexico
Malaguti, Enrico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-25
enrico.malaguti@unibo.it
DEI, University of Bologna, Bologna, Italy
Malaspina, Greta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-34
greta.malaspina@dmi.uns.ac.rs
Department of Industrial Engineering, Università di Firenze,
Firenze, Italy
Malcato, Hugo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-16
hapm@ua.pt
DEGEIT, University of Aveiro, Aveiro, Portugal

Department of Management Control and Information Systems, University of Chile, Santiago, Chile
Maleas, Zisis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-18
zisismaleas@certh.gr
HIT, CERTH, Thessaloniki, Central Macedonia, Greece
Maleki Vishkaei, Behzad . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-23
behzad.maleki@unibocconi.it
Bocconi university, Milan, Italy
Malekipirbazari, Milad . . . . . . . . . . . . . . . . . . . . . . TB-35, WB-35
miladma@chalmers.se
Computer Science and Engineering, Chalmers University ofTechnology, Göteborg, Sweden
Malevergne, Yannick . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-63
yannick.malevergne@univ-paris1.fr
PRISM Sorbonne, Université Paris 1 Panthéon-Sorbonne,
Paris, France
Malheiros, Igor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-35
igormalheiros92@gmail.com
Cnrs, Lirmm, France
Malighetti, Paolo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-35
paolo.malighetti@unibg.it
Department of Management, Information and Production Engineering, University of Bergamo, Italy
Mallor, Fermin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-07
mallor@unavarra.es
Institute of Smart Cities, Public University of Navarre, Pamplona, Spain
Malo, Pekka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-11, MC-11
pekka.malo@aalto.fi
Information and Service Economy, Aalto University Schoolof Business, Helsinki, Finland
Mamei, Marco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
marco.mamei@unimore.it
University of Modena and Reggio Emilia, Italy
Mammetti, Veronica . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-63
veronica.mammetti@uniroma1.it
Department of Economic and Social Sciences, La Sapienza
Università di Roma, Italy
Manabe, Rikutaro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-18
rik@up.t.u-tokyo.ac.jp
The University of Tokyo, Japan
Manafi, Ehsan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-14
ehsan.manafi@upc.edu
Institute of Industrial and Control Engineering (IOC), Universitat Politècnica de Catalunya - BarcelonaTech (UPC),
Barcelona, Catalonia, Spain

Maldonado, Felipe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-28
felipe.maldonado@essex.ac.uk
Mathematical Sciences, University of Essex, Colchester, Essex, United Kingdom

Manarin, Silvia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-47
silvia.manarin@phd.unipd.it
University of Padova, Italy

Maldonado, Sebastian . . . . . . . . . . . . . . . WA-27, MB-31, WA-31
sebastianm@fen.uchile.cl

Mañé, Ignasi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-34
ignasi.mane.bosch@gmail.com

604

EURO 2024 - Copenhagen
Statistics and Operations Research, Universitat Politècnica
de Catalunya - BarcelonaTech, Barcelona, Spain
Manca, Benedetto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-04
bmanca@unica.it
Mathematics and Computer Science, Università degli Studi
di Cagliari, cagliari, Italy
Mancini, Simona . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-35, WC-58
simona.mancini@unipa.it
Dipartimento di Ingegneria, Università degli Studi diPalermo, Palermo, Italy, Italy
Mancuso, Andrea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-12
andrea.mancuso@unina.it
Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Napoli, Napoli,
Italy
Mandjes, Michel . . . . . . . . . . . . . . . . . . . . WB-34, TA-40, WB-56
m.r.h.mandjes@uva.nl
University of Amsterdam, Amsterdam, Netherlands

AUTHOR INDEX

Manrique-Saavedra, Danna . . . . . . . . . . . . . . . . . . . . . . . . . TC-58
danna2191285@correo.uis.edu.co
School of Industrial and Business Studies, Universidad Industrial de Santander, Piedecuesta, Santander, Colombia
Mansini, Renata . . . . . . . . . . . . MC-10, MA-26, MC-35, WA-37
renata.mansini@unibs.it
Department of Information Engineering, University of Brescia, Brescia, Italy
Manson, Kerry. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-25
kerry.manson@pg.canterbury.ac.nz
University of Canterbury, New ZealandMansouri Kaleibar, Mozhgan . . . . . . . . . . . . . . . . . . . . . . . WB-48
mozhgan.mansouri.kaleibar@fpp.uni-lj.si
Faculty of Maritime studies and transport, University of
Ljubljana, Portorose, Slovenia
Mansouri, Afshin . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-21, TA-23
Afshin.Mansouri@brunel.ac.uk
Brunel Business School, Brunel University London,
Uxbridge, United Kingdom

Manerba, Daniele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-35
daniele.manerba@unibs.it
Dept. of Information Engineering, Università degli Studi diBrescia, Brescia, Italy

Mansouri, Nadere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-56
nmansouri@smu.edu
Blue Yonder Inc, Coppell, TX, United States

Manger, Robert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-29
manger@math.hr
Department of Mathematics, Faculty of Science, Universityof Zagreb, Zagreb, Croatia

Mansueto, Pierluigi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-37
pierluigi.mansueto@unifi.it
Department of Information Engineering, University of Florence, Florence, Italy

Mankowski, Michal . . . . . . . . . . . . . . . . . . . . . . . . . . TC-10, TD-10
michal.mankowski@kaust.edu.sa
King Abdullah University of Science and Technology, Saudi
ArabiaMantilla Rosas, Javier Enrique . . . . . . . . . . . . . . . . . . . . . WC-28
jemantillar@udistrital.edu.co
CUNDINAMARCA, Universidad Distrital Francisco JoseDe Caldas, BOGOTÁ, COLOMBIA, Colombia

Manlove, David . . . . . . . . . . . . . . . . . . . . . . . TC-10, TD-10, TC-25
david.manlove@glasgow.ac.uk
School of Computing Science, University of Glasgow, Glasgow, United Kingdom

Mantke, Henrik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-14
mantke@izes.de
Energy Markets, IZES gGmbH, Saarbrücken, Saarland, Germany

Mannhardt, Jacob . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-09
jmannhardt@ethz.ch
ETH Zurich, Switzerland

Manzi, Sean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-10
sean.manzi@dartington.org.uk
Dartington Service Design Lab, Buckfastleigh, Devon,
United Kingdom

Manni, Emanuele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-64
emanuele.manni@unisalento.it
Department of Engineering, University of Salento, Lecce,
Italy
Mannino, Carlo . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-51, MD-54
Carlo.Mannino@sintef.no
SINTEF, Oslo, Norway
Manou, Athanasia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-40
amanou@math.uoa.gr
Mathematics, University of Athens, Greece
Manousakis, Eleftherios . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-60
lmanousakis@aueb.gr
Department of Management Science & Technology, Athens
University of Economics and Business, Keratsini, Greece

Mao, Kun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-29
maokun@huawei.com„ China
Mar-Ortiz, Julio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-62
jmar@docentes.uat.edu.mx
Faculty of Engineering, Universidad Autonoma de Tamaulipas, Tampico, Tamaulipas, Mexico
Maragno, Donato . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-27
d.maragno@uva.nl
Business Analytics, University of Amsterdam, Amsterdam,
North-Holland, Netherlands
Marandi, Ahmadreza . . . . . . . . . . . . . . . . . . . . . . . WD-13, TD-34
a.marandi@tue.nl
Industrial Engineering, Eindhoven University of Technology,

605

AUTHOR INDEXEURO 2024 - Copenhagen

Netherlands
Marathe, Rahul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-59
rrmarathe@gmail.com
Management Studies, Indian Institute of technology Madras,
Chennai, TamilNadu, India
Marchais-Roubelat, Anne . . . . . . . . . . . . . . . . . . . WB-11, WC-11
anne.roubelat@lecnam.net
Lirsa, Conservatoire National des Arts et Métiers, Paris,
FranceMarchese, Dario Nicola . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-10
dario.marchese@unibg.it
Management, Information and Production Engineering, University of Bergamo, Dalmine, Bergamo, Italy
Marchese, Marco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-22
marco.marchese@totalenergies.com
OT/RD/H&S/CSO, TotalEnergies, France
Marcianò, Attilio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-40
attilio.marciano@unirc.it
DIIES, Università "Mediterranea" di Reggio Calabria, Italy
Marconi, Giada . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-56
g.marconi10@studenti.unipi.it
University Centre of Logistic Systems, University of Pisa,
Livorno, Italy
Marcotte, Sibylle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-32
sibylle.marcotte@ens.fr
DMA, ENS, Paris, Île-de-France, France
Marek, Lubos. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-31
marek@vse.cz
Statistics and Probability, University of Economics, Prague,
Prague, Czech Republic, Czech Republic
Marenco, Javier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-27
javier.marenco@utdt.edu
Business School, Universidad Torcuato Di Tella, Buenos
Aires, Buenos Aires, Argentina
Margaritis, Dimitris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-48
d.margaritis@auckland.ac.nz
Accounting and Finance, University of Auckland, Auckland,
New Zealand
Margonari, Mattia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-02
mattia.margonari@uniroma1.it
Sapienza Universita’ di Roma, Italy
Maria Rosaria, Pappalardo . . . . . . . . . . . . . . . . . . . . . . . . . MB-47
maria.pappalardo1@unict.it
Department of Economics and Business, University of Catania, Catania, Italy, Italy
Marianov, Vladimir . . . . . . . . . . . . . . . . . . . . . . . . MA-61, MC-61
marianov@uc.cl
Electrical Engineering, Pontificia Universidad Catolica deChile, Santiago, Chile
Marin-Navarro, Samuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-15
smarin@cmm.uchile.cl

606

University of Chile, Chile
Marinelli, Mario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
mario.marinelli@poliba.it
Polytechnic University of Bari, Italy
Marinescu, Radu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-34
radu.marinescu@ie.ibm.com
Drl Ai, IBM Research Europe, Dublin, Ireland
Maring, Jan Gerard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-10
j.g.maring@isala.nl
Clinical Pharmacy & Connected Care, Isala, Zwolle, Netherlands
Marino, Zelda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-16, TB-32
zelda.marino@uniparthenope.it
University of Naples Parthenope, Naples, Italy
Marins, Fernando . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-28
fernando.marins@outlook.com
Production, UNESP - São Paulo State University,
Guaratinguetá, SP, Brazil
Mariotti, Thiago . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-15
thiago.mariotti@usp.br
ICMC/USP, Brazil
Marjanović, Ivana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-18
ivana.veselinovic@eknfak.ni.ac.rs
University of Nis, Faculty of Economics, Serbia
Markhorst, Berend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-35
berend.markhorst@cwi.nl
Stochastics Group, CWI, Amsterdam, Noord-Holland,
Netherlands
Marklund, Johan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-50
johan.marklund@iml.lth.se
Industrial Management and Logistics, Lund University,
Lund, Sweden
Marlière, Gregory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-51
gregory.marliere@univ-eiffel.fr
Univertsité Gustave Eiffel, COSYS-ESTAS, F-59650 Villeneuve d’Ascq, France, France
Mármol, Amparo . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-36, TA-36
amarmol@us.es
Economia Aplicada III and IMUS, Universidad de Sevilla,
Sevilla, Spain
Maroto, Concepción . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-47
cmaroto@eio.upv.es
Applied Statistics, Operations Research and Quality, Universitat Politecnica de Valencia, Valencia, Spain
Marquardt, Teresa . . . . . . . . . . . . . . . . . . . . . . . . . MA-62, WB-62
t.marquardt@bwl.uni-kiel.deService Analytics, CAU Kiel University, Germany
Marques, Francisco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-31
franciscocmarques@ua.pt
Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro, Aveiro, Portugal

EURO 2024 - Copenhagen

Marques, Nuno . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-60
up201503975@edu.fe.up.pt
Faculty of Engineering, University of Porto, Portugal
Marques, Susete . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-12
smarques@isa.ulisboa.pt
Forest Research Center, University of Lisbon, School of
Agriculture, Lisbon, Portugal
Marques, Tomás . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-25
tmarques0580@gmail.com
Institute of Polymers and Composites, Guimarães, Portugal
Marshall, Charlotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-15
marshallcl@cardiff.ac.uk
Cardiff University, United Kingdom
Marszalec, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-43
daniel.marszalec@icu.ac.jp
Japan
Martín-Baos, José Ángel . . . . . . . . . . . . . . . . . . . . . TA-34, MB-56
joseangel.martin@uclm.es
Mathematics, University of Castilla-La Mancha, Ciudad
Real, Spain
Martínez, José Mario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-32
martinez@ime.unicamp.br
Dept. Applied Mathematics, University of Campinas, Campinas, SP, Brazil
Martínez, Karim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-15
karimperezmartinez@gmail.com
ExPretio Technologies, Canada
Martínez-Merino, Luisa I. . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-61
luisa.martinez@uca.es
Departamento de Estadística e Investigación Operativa, Universidad de Cádiz, Spain

AUTHOR INDEX

mathis.martin@emse.fr
SFL, Ecole des Mines de Saint-Etienne, France
Martin, Patrick . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-22
patrick.martin@iws.uni-stuttgart.de
IWS LS3 (SC SimTech), University of Stuttgart, Stuttgart,
Baden-Wuerttemberg, Germany
Martin-Campo, F. Javier . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-25
javier.martin.campo@mat.ucm.es
Estadistica e Investigacion Operativa, Universidad Complutense de Madrid, Madrid, Spain
Martin-Iradi, Bernardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-54
bernardo.martin-iradi@ivt.baug.ethz.ch
Institute for Transport Planning and Systems, ETH Zurich,
Zurich, Switzerland
Martinez Sykora, Antonio . . . . . . . . . . . . . . . . . . . MD-10, TD-24
A.Martinez-Sykora@soton.ac.uk
Management School, University of Southampton, Southampton, United Kingdom
Martinez, Aurea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-33, TB-33
aurea@dma.uvigo.es
Matematica Aplicada II, Universidade de Vigo, Vigo, Pontevedra, Spain
Martinez, Ricardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-36
ricardomartinez@ugr.es
Department of Economics, University of Granada, Spain
Martinez-Felipe, Alfonso . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-19
a.martinez-felipe@abdn.ac.uk
Chemical Materials and Processes Group, Just Transition
Lab, School of Engineering, University of Aberdeen, United
Kingdom
Martino, Manuel Luis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-02
manuelluis.martino@uniroma3.it
Business Studies, Roma Tre University, Rome, Italy, Italy

Martínez-Salazar, Iris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-29
irisabril@gmail.com
Graduate Program in Systems Engineering, Universidad
Autónoma de Nuevo León, Mexico

Martino, Simone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-18
simone.martino@compagniadisanpaolo.it
Fondazione Compagnia di SanPaolo, Torino, Italy, Italy

Martelli, Duccio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-63
duccio.martelli@unipg.it
Dept. of Economics, University of Perugia, Perugia, Italy

Martinovic, John . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-07
john.martinovic@tu-dresden.deMathematik, Technische Universität Dresden, Germany

Martin Barragan, Belen . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-27
belen.martin@ed.ac.uk
University of Edinburgh Business School, The University of
Edinburgh, Edinburgh, United Kingdom, United Kingdom

Martins dos Santos, Maria João . . . . . . . . . . . . . MC-23, MA-50
mariajoaosantos19@hotmail.com
Inesc Tec, Portugal

Martin Melero, Inigo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-61
inigo.martin@goumh.umh.es
UMH, Elche, Alicante, Spain
Martin, Layla . . . . . . . . . . . . . . . . . . . . . . . . TC-51, TB-58, MD-64
l.martin@tue.nl
Operations, Planning, Accounting and Control, Eindhoven
University of Technology, Eindhoven, Netherlands

Martins Ton, Vinicius . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-10
vinicius.martins-ton.1@ulaval.ca
Department of Operations and Decision Systems, Université
Laval, QUÉBEC, Québec, Canada
Martins, Isabel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-12
isabelinha@isa.ulisboa.pt
Center for Mathematics, Fundamental Applications and Operations Research, School of Agriculture, University of Lisbon, Lisbon, Portugal

Martin, Mathis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-60

607

AUTHOR INDEXEURO 2024 - Copenhagen

Martins, Roberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-29
ram@dep.ufscar.br
Federal University of Sao Carlos, Brazil
Martins, Sara . . . . . . . . . . . . . . . . . . . . . . . MC-23, MA-50, MB-50
sara.martins@fe.up.pt
Inesc Tec, Porto, Portugal
Martire, Antonio Luciano . . . . . . . . . . . . . . . . . . . . . . . . . . WC-39
antonioluciano.martire@uniroma1.it
MEMOTEF, Sapienza University of Rome, Italy
Martos-Barrachina, Francisco . . . . . . . . . . . . . . . . . . . . . . WC-13
fmeco@uma.es
Programa de Doctorado de Economía y Empresa, Universidad de Málaga, Málaga, Andalucía, Spain
Marttin, İrem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-37
irem.marttin@tupras.com.tr
R&D, Turkish Petroleum Refineries Corp. (TUPRAS), İstanbul, Turkey
Martyn, Krzysztof . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-44
kmartyn@cs.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poland
Martyn, Magdalena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-44
mmartyn@cs.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poland
Martyshev, Pavlo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-21
pmartyshev@kse.org.ua
Center for Food and Land Use Research, Kyiv School of
Economics, Kyiv, Ukraine
Marucho, Nicolás . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-16
nicolasmarucho@live.com.ar
IC/UBA, Argentina
Marx, Rebecca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-04
r.marx@tu-braunschweig.de
Institut für Mathematische Optimierung, TU Braunschweig,
Germany
Maschke, Hendrik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-37
hendrik.maschke@iee.fraunhofer.deFraunhofer IEE, Kassel, Germany
Masola, Alessio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
alessio.masola@unimore.it
University of Modena and Reggio Emilia, Italy

gmastroeni@di.unipi.it
Computer Science, University of Pisa, Pisa, Italy
Mastroeni, Loretta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-63
loretta.mastroeni@uniroma3.it
Dept. of Economics, University of Roma TRE, Rome, Italy
Masui, Yuki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-16
yuki.m3240@gmail.com
Juntendo University, Japan
Mathew, Merin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-17, WA-18
merinmarymathew12@gmail.com
Mathematics, Amrita Vishwa Vidyapeetham, Kollam, Kerala, India
Matoušková, Monika . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-35
matouskova@karlin.mff.cuni.cz
Department of probability and mathematical statistics,
Charles University, Slavičky, Czech, Czech Republic
Matsypura, Dmytro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-12
dmytro.matsypura@sydney.edu.au
Discipline of Business Analytics, Business School, The University of Sydney, Sydney, NSW, Australia
Mattia, Sara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-43
sara.mattia@iasi.cnr.it
Istituto di Analisi dei Sistemi ed Informatica, Consiglio
Nazionale delle Ricerche, Roma, Italy
Maturana, Sergio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-12
smaturan@uc.cl
Ingenieria Industrial y de Sistemas, P. Universidad Catolica
de Chile, Santiago, Chile
Maturana-Ross, Javier . . . . . . . . . . . . . . . . . . . . . . WB-56, TB-62
javier.maturana@pucv.cl
School of Industrial Engineering, Pontificia Universidad
Catolica de Valparaiso, Valparaiso, Valparaiso, Chile
Matuschke, Jannik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-60
jannik.matuschke@kuleuven.be
KU Leuven, Leuven, Belgium
Maureira-Alegría, Grace . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-50
grace.maureira@gmail.com
Statistics and Operations Research, Universitat Politecnica
de Catalunya, Barcelona, Barcelona, Spain
Mauttone, Antonio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-55
mauttone@fing.edu.uy
Operations Research Department, Universidad de la
República, Montevideo, Uruguay

Mason, Andrew J . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-31, TD-34
a.mason@auckland.ac.nz
Dept Engineering Science, University of Auckland, Auckland, New Zealand

Mavrodi, Fenia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-48
a.mavrodi11@gmail.com
University of Macedonia, Thessaloniki, Greece

Masone, Adriano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-12
adriano.masone@unina.it
Department of Electrical Engineering and Information Technology, University "Federico II" of Naples, Italy

May, Ross. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-31
rom@du.se
School of Information and Engineering, Dalarna University,
Falun, Sweden

Mastroeni, Giandomenico . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-04

Mørkved, Kenneth André Inghelm . . . . . . . . . . . . . . . . . . MB-61

608

EURO 2024 - Copenhagen
kenneth.a.i.morkved@ntnu.no
NTNU, NorwayMårtensson, Philip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-50
philip.martensson@volvo.com
Service Market Logistics, Volvo Group, Gothenburg, Sweden
Maziotis, Alexandros . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-53
alexandrosmaziotis@gmail.com
Pontificia Universidad Catolica de Chile, Santiago, None Selected, Chile
Mazurek, Jiri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-44
mazurek@opf.slu.cz
School of Business Administration in Karvina, Czech Republic
Mazurencu Marinescu Pele, Miruna . . . . . . . . . . . . . . . . WC-31
miruna.mazurencu@ase.ro
Statistics and Econometrics, Bucharest University of Economic Studies, Bucharest, Romania
Mazza, Rina Mary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-50
rmazza@dimes.unical.it
DIMES, University of Calabria, Rende, CS, Italy
Mazzarisi, Piero . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-40
piero.mazzarisi@unisi.it
Università di Siena, Italy
Mazzoccoli, Alessandro. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-63
alessandro.mazzoccoli@gmail.com
Economics, Roma TRE University, Rome, Italy

AUTHOR INDEX

Meadows, Maureen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-11
ac3495@coventry.ac.uk
Centre for Business in Society, Coventry University, Coventry, United Kingdom
Meca, Ana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-40
ana.meca@umh.es
Operations Research Center, Universidad Miguel Hernández,
Elche, Alicante, Spain
Mecca, Beatrice . . . . . . . . . . . . . . . . . . . . . MA-13, MB-18, TB-47
beatrice.mecca@polito.it
Interuniversity Department of Regional and Urban Studies
and Planning (DIST), Politecnico di Torino, Torino, Torino,
Italy
Mecca, Umberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-18
umberto.mecca@polito.it
DIST, Politecnico di Torino, Torino, Italia, Italy
Medda, Francesca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-40
f.medda@ucl.ac.uk
UniversUniversity College London and Commissione
Nazionale per le Società e la Borsa, United Kingdom
Medjroubi, Wided . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-53
Wided.Medjroubi@dlr.deInstitute of Networked Energy Systems, German Aerospace
Center, Oldenburg, Germany
Medwenitsch, Karina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-19
karina.medwenitsch@forschung-burgenland.at
Forschung Burgenland GmbH, Eisenstadt, Austria

McCrorie, Roderick . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-63
mccrorie@st-andrews.ac.uk
Department of Economics, University of St Andrews Business School, St Andrews, Fife, United Kingdom

Meghazi, Imran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-42
imeghazi@lirmm.fr
LIRMM, France

McGarraghy, Seán . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-06
sean.mcgarraghy@ucd.ie
Management Information Systems, University College
Dublin, Dublin 4, Ireland

Mehbali, Mohamed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-52
mehbalim@lsbu.ac.uk
Centre for Research Informed Teaching, London South Bank
University, London, United Kingdom

McGarvey, Ronald . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-61
r.mcgarvey@ieseg.fr
IESEG, Paris-La Defense, France

Meijer, Mirjam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-12, MD-39
mirjam.meijer@klu.org
Kühne Logistics University, Germany

Mcgibney, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-17
dmcgibney@bus.miami.edu
University of Miami, Coral Gables, Florida, United States

Meisel, Frank . . . . . . . . MB-24, MC-24, TC-55, MA-62, WB-62
meisel@bwl.uni-kiel.de
Christian-Albrechts-University, Kiel, Germany

McGregor, Adam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-07
adam.mcgregor@deakin.edu.au
School of Information Technology, Deakin University, Diggers Rest, Vic, Australia

Meisen, Tobias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-03
meisen@uni-wuppertal.deInstitute for Technologies and Management of Digital Transformation, University of Wuppertal, Wuppertal, NRW, Germany

McKenna, Russell . . . . . . . . . . . . . . . . . . . . . . . . . . MC-19, MD-44
rmckenna@ethz.ch
ETH Zürich, Zürich, Switzerland
McKenna, Russell . . . . . . . . . . . . . . . . . . . TA-09, MB-14, MC-19
russell.mckenna@psi.chLaboratory for Energy Systems Analysis, Paul Scherrer Institute, Switzerland

Meissner, Joern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-03
joe@meiss.com
Kuehne Logistics University, Hamburg, Germany
Melachrinoudis, Emanuel . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-50
emelas@coe.neu.edu
Mechanical and Industrial Engineering, Northeastern Univer-

609

AUTHOR INDEXEURO 2024 - Copenhagensity, Boston, MA, United States

Hildesheim, Germany

Melashenko, Oksana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-07
o.p.melashenko@gmail.co
Institute of Mechanical Engineering Problems of the National
Academy of Sciences of Ukraine, Kharkiv, Ukraine

Mendoza Pardo, Christine . . . . . . . . . . . . . . . . . . . WA-13, TD-24
christine.mendoza-pardo@uni-bayreuth.de
Chair of Food Supply Chain Management, University of
Bayreuth, Kulmbach, Bavaria, Germany

Melguizo Padial, Miguel Angel . . . . . . . . . . . . . . . . . . . . . . TB-42
ma.mp@ua.es
Mathematics, University of Alicante, Alicante, Comunidad
Valenciana, Spain

Mendoza, Jorge E. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-51
jorge.mendoza@hec.ca
HEC Montréal, Montréal, Canada

Melnikov, Vitalik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-40
melnikov@mail.uni-paderborn.de
Software Innovation Campus Paderborn, Paderborn University, Paderborn, Germany
Melo, João . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-25
pg50466@alunos.uminho.pt
Institute of Polymers and Composites, Portugal
Meloni, Carlo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-60
carlo.meloni@uniroma1.it"La Sapienza" University of Roma, Roma, Italy

Menezes, Mozart . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-50, WA-60
mozartmenezes@me.com
Operations Management & Information Systems, NEOMA
Business School -Bordeaux, Mont-Saint-Aignan, France
Meng, Lingyun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-54
menglingyun2001@hotmail.com
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China
Meng, Qiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-55, MC-56
ceemq@nus.edu.sg
Civil and Environment Engineering, National University ofSingapore, Singapore

Men, Changyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-07, TA-11
changyu.men@kuleuven.be
Research Center for Operations Management, KU Leuven,
Leuven, Belgium

Meng, Zhu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-34
mengzhu980909@tju.edu.cn
China

Mencarelli, Luca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-04
luca.mencarelli@unipi.it
Dipartimento di Informatica, Università di Pisa, Italy

Menke, Ruben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-57
rum@bankingcircle.com
Banking Circle ApS, Copenhagen, Denmark

Mencaroni, Andrea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-23
andrea.mencaroni@ugent.beIndustrial System Engineering and Product Design, Ghent
University, Zwijnaarde, Belgium

Menlik, Berfin Osedya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-15
osedya123@gmail.com
Ankara Yıldırım Beyazıt University, Turkey

Mendelson, Gal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-20
galmen@technion.ac.il
Technion - Israel Institute of Technology, Israel

Menon, Radhika . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-18
amritarite.rmenon@gmail.com
Center for Research in Analytics, Technologies & Education,
Amrita Vishwa Vidyapeetham, Kollam, Kerala, India

Mendes, Ana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-25
asmendes96@gmail.com
Data Science, Closer, Portugal

Menzietti, Massimiliano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-63
massimiliano.menzietti@unical.it
University of Calabria, Cosenza, Italy

Mendes, André Bergsten . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-12
andbergs@usp.br
Departamento de Engenharia Naval e Oceânica, Universidade de São Paulo, Sao Paulo, Sao Paulo, Brazil

Mercado Fernandez, Rodrigo . . . . . . . . . . . . . . . . . . . . . . . . TC-24
rodmerfdez@gmail.com
Tecnologico de Monterrey, Mexico

Mendes, Renata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-52
renatampc030802@gmail.com
Universidade Federal da Paraíba, CABEDELO, PB, Brazil

Mercuri, Lorenzo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-63
lorenzo.mercuri@unimi.it
University of Milan, Milan, Italy

Mendez-Vogel, Gonzalo . . . . . . . . . . . . . . . . . . . . . MA-61, MC-61
gonzalo.mendez@unab.cl
Department of Engineering Sciences, Universidad Andres
Bello, Concepción, Chile

Merino, María . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-15
maria.merino@ehu.es
Mathematics, University of the Basque Country-UPV/EHU;
Basque Center for Applied Mathematics-BCAM, Leioa; Bilbao, Vizcaya, Spain

Mendl, Ann-Kathrin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-60
mendl@bwl.uni-hildesheim.deOperations Research Group, Institute for Business Administration and Information Systems, University of Hildesheim,

Merkert, Maximilian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-04
m.merkert@tu-braunschweig.de
Institute for Mathematical Optimization, TU Braunschweig,
Braunschweig, Niedersachsen, Germany

610

EURO 2024 - Copenhagen

Merten, Sebastian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-03
sebastian.merten@uni-passau.deBusiness Decisions and Data Science, University of Passau,
Germany
Mes, Martijn . . . . . . . . . . . . . . . . . . . . . . . MD-23, WD-56, MA-58
m.r.k.mes@utwente.nl
IEBIS, University of Twente, Enschede, Netherlands
Mesquita-Cunha, Mariana . . . . . . . . . . . . . . . . . . WA-12, WD-37
mariana.cunha@tecnico.ulisboa.pt
Departamento de Engenharia e Gestão, Instituto Superior
Técnico, Universidade de Lisboa, Lisboa, Portugal
Messina, Enza . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-27, MC-35
messina@disco.unimib.it
DISCo - Department of Informatics, Systems and Communication, University of Milano Bicocca, Milano, Italy
Messmann, Lukas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-24
lukas.messmann@uni-a.deResource Lab, University of Augsburg, Augsburg, Bavaria,
Germany
Metulini, Rodolfo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-16
rodolfo.metulini@unibg.it
Department of Economics, University of Bergamo, bergamo,
Italy
Metzlaff, Tobias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-38
tobias.metzlaff@inria.fr
Germany
Meuche, Katja . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-57
kmeuche@gatech.edu
Georgia Institute of Technology, Atlanta, Georgia, UnitedStatesMeydani, Roya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-13
meidani@kth.se
Civil and Architectural Engineering, Royal Institute of Technology, Stockholm, Sverige, Sweden
Meyer, Jörn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-24
jorn.meyer@om.rwth-aachen.de
Chair of Operations Management, RWTH Aachen University, Aachen, NRW, Germany
Meyer, Jörn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-24
jorn.meyer@rwth-aachen.de
Chair of Operations Mangement, RWTH Aachen University,
Aachen, Germany
Meza, Armando . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-45
admeza@miuandes.cl
Ingeniería y Ciencias Aplicadas, Universidad de los Andes,
Santiago, RM, Chile
Mezali, Hakim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-06
hm2908@gmail.com
Department of Computing, Glasgow Caledonian University,
London, London, United Kingdom
Miao, Jianrui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-54

AUTHOR INDEX

jrmiao@bjtu.edu.cn
State key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China
Miaomiao, Zhang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-28
zhangmiaomiao@csair.com
Information Center, China Southern Airlines Company Limited, GuangZhou, GuangDong, China
Michelotto, Federico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-25
federico.michelotto2@unibo.it
DEI, University of Bologna, Italy
Michels, Rouven . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-16
r.michels@uni-bielefeld.deBielefeld University, Germany
Michna, Zbigniew . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-39
zbigniew.michna@pwr.edu.pl
Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, Wroclaw, Poland
Miclea, Adriana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-44
adriana.mihnea@fabiz.ase.ro
UNESCO Department for Business Administration,
Bucharest University of Economic Studies, Bucharest, Romania
Mieskes, Margot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-31
margot.mieskes@h-da.de
University of Applied Sciences Darmstadt, Germany
Mieth, Robert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-35
robert.mieth@rutgers.edu
Industrial and Systems Engineering, Rutgers University, New
Brunswick, New Jersey, United States
Miettinen, Kaisa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-11
kaisa.miettinen@jyu.fi
Faculty of Information Technology, University of Jyväskyla,
University of Jyväskyla, Finland
Miftari, Bardhyl . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-38, WC-38
bmiftari@uliege.be
SmartGrids, University of Liege, Liege, Liege, Belgium
Miglionico, Giovanna . . . . . . . . . . . . . . . . MD-03, MB-35, TC-41
gmiglionico@dimes.unical.it
DIMES, Università della Calabria, Arcavacata di Rende, Italy
Miguéis, Vera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-28
vmlo@fe.up.pt
INESC TEC, Faculty of Engineering, University of Porto,
PortugalMiguel, Carlos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-49
carlos.miguelpalao@ugent.beIndustrial Systems Engineering and Product Design, Ghent
University, Gent, Flanders, Belgium
Mikkelsen, Jesper Bang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-53
jbmikkelsen@econ.au.dk
Department of Economics and Business Economics, Aarhus
University, Aarhus V, Denmark

611

AUTHOR INDEXEURO 2024 - Copenhagen

Miklas-Kalczynska, Malgorzata . . . . . . . . . . . . . . . . . . . . . TC-61
mmiklas-kalczynska@fullerton.edu
Information Systems and Decision Sciences, California State
University, Fullerton, United States

Miroforidis, Janusz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-45
janusz.miroforidis@ibspan.waw.pl
Department of Decision Support in the Presence of Risk, Systems Research Institute, Polish Academy of Sciences, Poland

Milanesi, Simone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-26
simone.milanesi01@universitadipavia.it
Italy

Mishra, Ajay Kumar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-51
akmishra@vgsom.iitkgp.ac.in
Vinod Gupta School Of Management, Iit Kharagpur,
KHARAGPUR, West Bengal, India

Milanović, Sandra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-18
sandra.milanovic@eknfak.ni.ac.rs
University of Niš, Faculty of Economics, Niš, Serbia

Mishra, Onkar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-44
mishra.onkar@gmail.com
Information System, SmartBots, India

Milazzo, Alessandro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-63
alessandro.milazzo@unito.it
University of Torino, Italy

Mishra, Ruchi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-44
ruchimishra@irma.ac.in
Production Operations Management & QT, Institute of Rural
Management Anand, Anand, Gujarat, India

Milenkovic, Marko . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-52
marko.02.milenkovic@gmail.com
Faculty of Science and Mathematics, University of Nis, Serbia
Miller-Hooks, Elise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-53
miller@gmu.edu
Civil, Environmental and Infrastructure Engineering, George
Mason University, Fairfax, VA, United States

Misiç, Arya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-21
aryasevgen@gmail.com
Industrial Engineering, İzmir University of Economics, İzmir,
TurkeyMisitano, Giovanni . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-11
giovanni.a.misitano@jyu.fi
P.O. Box 35, University of Jyväskylä, 40014 Jyväskylä, Finland

Milstein, Irena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-09
irenam@hit.ac.il
Faculty of Management of Technology, Holon Institute ofTechnology, Holon, Israel

Mislej, Pablo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-16
pmislej@gmail.com
Statistics, IC/UBA, Buenos Aires, CABA, Argentina

Miltenberger, Matthias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-30
miltenberger@gurobi.com
Gurobi Optimization, Germany

Missaoui, Ahmed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-19
ahmed.missaoui@insight-centre.org
Computer science, University college Cork, Cork, Ireland

Minner, Stefan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-39, TB-45
stefan.minner@tum.de
TUM School of Management, Technische Universität
München, Munich, Germany

Missbauer, Hubert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-49
hubert.missbauer@uibk.ac.at
Information Systems, Production and Logistics Management,
University of Innsbruck, Innsbruck, Austria

Minuyee, Mahnoush . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-10
mahnoush.minuyee@ucalgary.ca
University of Calgary, CALGARY, AB, Canada

Mitici, Mihaela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-55
m.a.mitici@uu.nl
Utrecht University, Netherlands

Mir Djawadi, Behnud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-40
behnud.mir.djawadi@uni-paderborn.de
Department Management, Paderborn University, Paderborn,
Northrhine-Westphalia, Germany

Mitkova, Veronika. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-28
mitkova@fses.uniba.sk
Institute of Economics, Faculty of Social and Economic Sciences, Comenius University in Bratislava, Bratislava, Slovakia

Miranda, Francis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-03
franzmiranda@yahoo.com
Business School Lausanne, Philippines
Miranda, Jaime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-51
jmirandap@fen.uchile.cl
Department of Management Control and Information Systems, Universidad de Chile, Chile
Miranda, Joao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-16, WA-49
jlm.estgp08@yahoo.com
ESTGD and VALORIZA/IPPortalegre; CERENA/IST, Portugal

Mitridati, Lesia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-09
lemitri@dtu.dk
DTU, Lyngby, Denmark
Mittal, Meenakshi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-26
meenakshir.fzr@gmail.com
Mathematics, LRDAVcollege, Jagraon, Punjab, India
Mittas, Nikolaos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-45
nmittas@chem.ihu.gr
Chemistry, International Hellenic University, Kavala, Greece
Mittelmann, Hans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-30

612

EURO 2024 - Copenhagen
mittelmann@asu.edu
School of Math&Stats, Arizona State University, Tempe, AZ,
United States
Mittleman, Gur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-02
gurm@afeka.ac.il
Afeka College of Engineering, Tel Aviv, Israel
Miyamoto, Yuichiro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-55
miyamoto@sophia.ac.jp
Sophia University, Chiyoda-ku, Tokyo, Japan
Miyashita, Naoko . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-50
naoko.miyashita.sq@hitachi.com
Research and Development Group, Hitachi, Ltd., Japan
Miyazaki, Shuichi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-25
shuichi@sis.u-hyogo.ac.jp
Graduate School of Information Science, University of
Hyogo, Kobe, Hyogo, Japan
Mlekusch, Johanna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-60
mlekuschjohanna@gmail.com
Department of Business Decisions and Analytics, Universityof Vienna, Wien, Austria
Mlinar, Tanja . . . . . . . . . . . . . . . . . . . . . . . TD-23, WA-23, MA-33
t.mlinar@ieseg.fr
IESEG School of Management, Univ. Lille, CNRS, UMR
9221 - LEM - Lille Economie Management, Lille, Sélectionnez un élément, France
Moazzeni, Sahar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-55
sahar.moazzeni@nhh.no
Department of Business and Management Science, NHH
Norwegian School of Economics, BERGEN, Norway
Moench, Lars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-24
lars.moench@fernuni-hagen.de
FernUniversität in Hagen, Hagen, Germany
Moews, Ben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-06
ben.moews@ed.ac.uk
Business School, University of Edinburgh, United Kingdom
Mogre, Riccardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-39
riccardo.mogre@durham.ac.uk
Business School, Durham University, Durham, United Kingdom
Mohammadi, Ebrahim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-56
e.mohammadi@rsm.nl
Rotterdam School of Management, Erasmus University Rotterdam, Rotterdam, South Holland, Netherlands

AUTHOR INDEX

Mohan, Deepti . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-25
deeptimohan@gim.ac.in
IT & Operations Management, Goa Institute of Management,
Goa, Goa, India
Mohanta, Kshitish Kumar . . . . . . . . . . . . . . . . . . . . . . . . . . WC-48
kshitishkumar.math@gmail.com
Mathematics, Indira Gandhi National Tribal University,
Amarkantak, Amarkantak, Madhya Pradesh, India
Molenbruch, Yves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-54
yves.molenbruch@vub.be
MOBI Research Centre, Vrije Universiteit Brussel (VUB),
Brussel, Belgium
Molero-Río, Cristina . . . . . . . . . . . . . . . . . . . . . . . MA-02, WD-27
molero@lix.polytechnique.fr
École Polytechnique, France
Molho, Elena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-41
elena.molho@unipv.it
Dipartimento di Scienze Economiche e Aziendali, Università
di Pavia, Pavia, Italy
Molica Bisci, Giovanni . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-41
giovanni.molicabisci@uniurb.it
University of Urbino Carlo Bo, Italy
Molinari, Cesare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-32
molinari@dima.unige.it
Università di Genova, Genova, GE, Italy
Molinero, Xavier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-36
xavier.molinero@upc.edu
Mathematics, Universitat Politècnica de Catalunya ’BarcelonaTech, Terrassa, Barcelona, Spain
Molinos-senante, Maria . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-53
maria.molinos@uva.es
Spain
Molinos-Senante, Maria . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-44
mmolinos@uc.cl
Pontificia Universidad Católica de Chile, Santiago, Santiago,
Chile
Mommens, Koen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-55
koen.mommens@vub.be
Vrije Universiteit Brussels, Department of Business Technology and Operations, Belgium
Mommessin, Clément . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-26
clement.mommessin@imt-atlantique.fr
IMT Atlantique Nantes, France

Mohammadi, Mehrdad . . . . . . . . . . . . . . . . . . . . . . TB-26, WD-58
m.mohammadi1@tue.nl
Industrial Engineering and Innovation Sciences, Eindhoven
University of Technology, Eindhoven, Netherlands

Momsen, Katharina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-07
katharina.momsen@awi.uni-heidelberg.de
University of Heidelberg, Germany

Mohammadi, Mehrdad . . . . . . . . . . . . . . . . . . . . . MD-52, WB-58
mehrdadmohamadi1013@gmail.com
Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven,
Netherlands

Monaci, Marta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-27, TB-52
marta.monaci@uniroma1.it
Department of Computer, Control and Management Engineering "Antonio Ruberti" (DIAG), Sapienza University ofRome, Rome, Lazio, Italy

613

AUTHOR INDEXEURO 2024 - Copenhagen
Technology, Atlanta, GA, United States

Monaci, Michele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-25
michele.monaci@unibo.it
DEI, University of Bologna, Bologna, Italy
Monardes, Carlos . . . . . . . . . . . . . . . . . . . . . . . . . . MD-12, MC-48
cmonardes@ucn.cl
School of Engineering, Universidad Católica del Norte, Coquimbo, Coquimbo, Chile
Moni, Merlin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-18
drmerlin.blessan@gmail.com
Infectious Disease, Amrita Institute of Medical Sciences,
Kochi, Kerala, India
Moniz, Samuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-07
samuel.moniz@dem.uc.pt
Mechanical, University of Coimbra, Coimbra, Coimbra, Portugal
Monroy, Luisa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-36
lmonroy@us.es
Economia Aplicada III, Universidad de Sevilla, Sevilla, Spain
Montalva, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-44
martin.montalva@usach.cl
Faculty of Arquitecture and Built Environment, University
Of Santiago Of Chile, SANTIAGO, Region Metropolitana,
Chile
Montanari, Giulia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-14
giulia.montanari@polito.it
Energy, Politecnico di Torino, Torino, Italy, Italy
Monteiro, Ana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-32
ammm@fe.uc.pt
School of Economics, University of Coimbra, Coimbra, PortugalMonteiro, Helena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-17
Helena.Monteiro@infarmed.pt
INFARMED, PortugalMonteiro, Tiago . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-64
tiagomonteiro.trpm@gmail.com
Inesc Tec & Feup, Porto, PortugalMontes, Ana Maria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-22
a.montesf@uniandes.edu.co
Administración, Universidad de los Andes, Colombia
Montes, Tomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-22, WB-53
tmontes@irec.cat
Energy Systems Analytics Research Group, Institut de Recerca en Energia de Catalunya, Sant Adria Besos, South
Carolina, Spain
Montibeller, Gilberto . . . . . . . . . . . . . . . . MC-11, TA-11, WA-46
g.montibeller@lboro.ac.uk
Management Science and Operations Group, Loughborough
University, Loughborough, United Kingdom
Montreuil, Benoit . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-50, TD-57
benoit.montreuil@isye.gatech.edu
Industrial and Systems Engineering, Georgia Institute of

614

Montt, Cexcilia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-50
ceciliamontt25@gmail.com
Ingeniería Industrial, Universidad de Santiago de Chile,
PROVIDENCIA, Metropolitana, Chile
Moon, Ilkyeong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-43
ikmoon@snu.ac.kr
Industrial Engineering, Seoul National University, Seoul, Korea, Republic of
Moosavi, S. Iman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-11
s.i.moosavi@tilburguniversity.edu
Information Systems and Operations Management, Tilburg
University, Tilburg, Noord Brabant, Netherlands
Mor, Baruch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-26, TD-36
baruchm@ariel.ac.il
Department of Economics and Business Administration,
Ariel University, Ariel, Israel
Morabito, Reinaldo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-52
morabito@ufscar.br
Dept. of Production Engineering, Federal University of São
Carlos, Sao Carlos, Sao Paulo, Brazil
Moraes, Felipe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-31
felipe.moraes@booking.com
Booking.com, Netherlands
Morala, Pablo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-27
pablo.morala@uc3m.es
Department of Statistics, Universidad Carlos III de Madrid,
Spain
Morales, Juan Miguel . . . . . . . . MA-19, TA-27, WD-27, WA-35
juan.morales@uma.es
Applied Mathematics, University of Málaga, Málaga, Spain
Morales-Espana, Germán. . . . . . . . . . . . . . . . . . . . . . . . . . . WA-09
german.morales@tno.nl
Energy Policy Studies, ECN part of TNO, Amsterdam,
Netherlands
Moralles, Herick . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-29
herickmoralles@dep.ufscar.br
Federal University of Sao Carlos, Brazil
Moran, Diego . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-04
morand@rpi.edu
Industrial and systems engineering, Rensselaer Polytechnic
Institute, Troy, NY, United States
Morande, Juan Pablo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-23
jpm0112@auburn.edu
Industrial and Systems engineering, Auburn University,
Auburn, Alabama, United States
Morandi, Valentina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-26
valentina.morandi1@unibs.it
Economics and Management, University of Brescia, Brescia,
Brescia, Italy
Moreau, Yves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-41

EURO 2024 - Copenhagen
Yves.Moreau@esat.kuleuven.be
KU Leuven, Leuven, Belgium
Moreira, Fábio . . . . . . . . . . . . . . . . . . . . . . MC-07, TD-50, WA-50
fnssm26@gmail.com
INESC TEC, INEGI, Faculty of Engineering, University ofPorto, Porto, Portugal
Morelli, Giacomo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-63
giacomo.morelli@uniroma1.it
Statistics, Sapienza University, roma, Roma, Italy
Morello, Eugenio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-18
eugenio.morello@polimi.it
Architecture and urban studies (Dastu), Politecnico di Milano, Milan, Italy
Moreno Baca, Fabricio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-62
fabricio.moreno@upaep.edu.mx
Logistics & Supply Chain Management, UPAEP, Puebla,
Puebla, Mexico
Moreno, Eduardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-27, TD-29
eduardo.moreno@uai.cl
Faculty of Engineering and Sciences, Universidad Adolfo
Ibañez, Santiago, Chile
Moreno-Ternero, Juan D. . . . . . . . . . . . . . . . . . . . . MB-36, TD-43
jdmoreno@upo.es
Economics, Universidad Pablo de Olavide, Spain
Moreno-Vega, Marcos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-29
jmmoreno@ull.es
Ingeniería Informática y de Sistemas, Universidad de La Laguna, San Cristóbal de La Laguna, Islas Canarias, Spain
Moreschini, Lorenzo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-26
lorenzo.moreschini@unibs.it
Department of Information Engineering, University of Brescia, Brescia, Italy
Moretto, Michele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-33
michele.moretto@unipd.it
Ecomics and Management, University of Padova, Italy
Morigi, Serena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-32
serena.morigi@unibo.it
University of Bologna, Italy
Morini, Benedetta . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-32, MB-34
benedetta.morini@unifi.it
Dipartimento di Ingegneria Industriale, Universita di Firenze,
Firenze, Italy
Morita, Hiroshi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-28
morita@ist.osaka-u.ac.jp
Department of Information and Physical Sciences, Osaka
University, Suita, Japan
Mortimer, Katie. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-20
ktmortimer@outlook.com
Defence Science and Technology Group, VIC, Australia
Morton, Alec . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-13
alec.morton@strath.ac.uk

AUTHOR INDEXManagement Science, University of Strathclyde, Glasgow,
United Kingdom
Mosalla Nezhad, Behzad . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-25
b.mosallanezhad@utwente.nl
Industrial Engineering & Business Information Systems(IEBIS), University of Twente, Enschede, Overijssel, Netherlands
Mosheiov, Gur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-26, WA-26
msomer@huji.ac.il
School of Business, Hebrew University, Jerusalem, Israel
Mosidze, Aleksandre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-33
a.mosidze@freeuni.edu.ge
Mathematics And Computer Sciences, Free University Of
Tbilisi, Tbilisi, Georgia
Mosquera-Rodríguez, Manuel Alfredo . . . . . . . . . . . . . . MC-36
mamrguez@uvigo.es
Estatística e Investigación Operativa, Universidade de Vigo,
Ourense, Spain
Möst, Dominik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-09
dominik.moest@tu-dresden.de
Chair of Energy Economics, Technische Universität Dresden,
Dresden, Germany
Mostafayi Darmian, Sobhan . . . . . . . . . . . . . . . . . . . . . . . . . TC-23
sobhan.m.darmian@ntnu.no
Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim,
NorwayMota, Bruna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-24, MD-24
Bruna.Mota@tecnico.ulisboa.pt
CEG-IST, Instituto Superior Técnico, Portugal
Mounier, Jean-Christophe . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-18
jean-christophe.mounier@alumni.univ-avignon.fr
Avignon University, AVIGNON, France
Moura, Alexandra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-51
amoura@iseg.ulisboa.pt
CEMAPRE and ISEG - Lisbon School of Economics andManagement, Lisbon, Portugal
Mourtos, Yiannis . . . WA-25, WC-26, WD-29, MD-49, WB-49
mourtos@aueb.gr
Management Science & Technology, Athens University of
Economics & Business, Greece
Mousavi, Mohammad Mahdi . . . . . . . . . . . . . . . . . . . . . . . . TA-14
m.mousavi@bradford.ac.uk
School of Management, University of Bradford, Bradford,
England, United Kingdom
Mousseau, Normand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-14
normand.mousseau@umontreal.ca
University of Montréal, Canada
Mouton, Jeanne . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-27
jeanne.mouton@laposte.net
Université Nice Côte d’Azur, France

615

AUTHOR INDEXEURO 2024 - Copenhagen

Mouysset, Sandrine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-55
sandrine.mouysset@toulouse-inp.fr
IRIT, INP Toulouse, France
Mouzakitis, Apostolos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-08
a.mouzakitis@outlook.com
PhD Studies, New York College Greece, Athens, Greece
Moylan, Emily . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-10
emily.moylan@sydney.edu.au
School of Civil Engineering, University of Sydney, Sydney,
NSW, Australia
Mpourdakos, Ilias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-49
t8210226@aueb.gr
Management Science And Technology, Athens University Of
Economics And Business, Athens, Attika, Greece
Muccio, Eugenio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-47
eugenio.muccio@unina.it
Department of Architecture (DiARC), University of Naples
Federico II, NetherlandsMues, Christophe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-31
c.mues@soton.ac.uk
Southampton Business School, University of Southampton,
Southampton, United Kingdom
Muesgens, Felix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-19
muesgens@b-tu.de
Chair of Energy Economics, BTU Cottbus-Senftenberg, Cottbus, Germany
Mukherjee, Sanmoy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-63
sanmoy.mukherjee@edu.unige.it
Department of Economics, University of Genoa, Genova,
Liguria, Italy
Mukherjee, Saral . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-43
saralm@iima.ac.in
Production and Quantitative Methods, Indian Institute ofManagement, Ahmedabad, Gujarat, India
Müller, Benjamin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-30
benjamin.mueller@zib.de
COPT GmbH, Berlin, Germany
Müller, Christian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-59
Christian.Mueller.9@uni-due.de
Mercator School of Management, University of DuisburgEssen, Duisburg, Germany
Müller, Wieland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-07
wieland.mueller@uni-rostock.deInstitute for Business Administration, University of Rostock,Germany

Production Engineering Department, Federal University of
Sao Carlos, Sao Carlos, SP, Brazil
Münch, Christin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-56
christin.muench@uni-due.de
Mercator School of Management, University of DuisburgEssen, Duisburg, NRW, Germany
Muñoz-Delgado, Gregorio . . . . . . . . . . . . . . . . . . . . . . . . . . MA-21
Gregorio.Munoz@uclm.es
University of Castilla-La Mancha, Spain
Muñoz-Hernandez, Jose Ignacio . . . . . . . . . . . . . . . . . . . . MD-19
joseignacio.munoz@uclm.es
Universidad de Castilla-La Mancha, Albacete, Spain
Munoz-Marquez, Manuel . . . . . . . . . . . . . . . . . . . . . . . . . . WD-29
manuel.munoz@uca.es
Statistics and Operations Research, Cadiz University, Puerto
Real, Cádiz, Spain
Muñoz-Ocaña, Juan Manuel . . . . . . . . . . . . . . . . . . . . . . . . TB-26
juanmanuel.munoz@uca.es
Estadística e IO, University of Cadiz, Spain
Münster, Marie . . . . . . . . . . . . . WC-09, WD-09, MA-19, MB-21
maem@dtu.dk
DTU Management Engineering, Technical University ofDenmark, Kgs. Lyngby, Denmark
Murayama, Akito . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-18
murayama@up.t.u-tokyo.ac.jp
Department of Urban Engineering, The University of Tokyo,
Bunkyo-Ku, Tokyo, Japan
Murphy, Ken . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-16
murphyke@uci.edu
Merage School of Business, UC Irvine, Irvine, CA, UnitedStatesMurua, Maialen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-28
maialen.murua@tecnalia.com
TECNALIA, Spain
Musmanno, Roberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-64
roberto.musmanno@unical.it
Department of Mechanical, Energy, and Management Engineering, University of Calabria, Rende, Italy
Mutlu, Nevin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-24
n.r.mutlu@tue.nl
Eindhoven University of Technology, Netherlands
Muttoni, Maddalena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-33
mmuttoni@math.unipd.it
Department of Mathematics "Tullio Levi-Civita", Universityof Padova, Italy

Mullick, Pratik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-56
pratik.mullick@pwr.edu.pl
Operations Research and Business Intelligence, Wroclaw
University of Science and Technology, Wroclaw, Lower Silesia, Poland

Muyldermans, Luc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-55
luc.muyldermans@nottingham.ac.uk
Business School, Nottingham University, Nottingham,
United Kingdom

Munari, Pedro . . . . . . . . . . . . . . . . . . . . . . MA-35, MB-52, WB-64
munari@dep.ufscar.br

Mwasha, Genuine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-20
genuine@theroofofafrica.org

616

EURO 2024 - Copenhagen
The Roof of Africa, Edinburgh, United Kingdom
Nácher, Lorena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-27
lnacher@umh.es
University Miguel Hernández of Elche, Spain
Nachtigall, Karl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-54, WD-54
karl.nachtigall@tu-dresden.de
Faculty of Transport and Traffic Sciences, Institut for Logistics and Aviation, Technical University of Dresden, Dresden,
Sachsen, Germany
Nacry, Florent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-42
florent.nacry@univ-perp.fr
Université Perpignan, France
Nadar, Emre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-22, TA-22
emre.nadar@bilkent.edu.tr
Bilkent University, Ankara, Ankara, Turkey
Nafar, Mohsen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-03
mohsen.nafar@uni-bielefeld.deBielefeld University, Bielefeld, Germany
Nagaiwa, Kenichiro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-55
k-nagaiwa@kagoshima-ct.ac.jp
Department of Infomation Engineering, Kagoshima College,Japan
Nagar, Gajendra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-51
mez208825@iitd.ac.in
Department of Mechanical Engineering, Indian Institute ofTechnology Delhi (IITD), New Delhi, Delhi, India
Nagarajan, Senthil Murugan . . . . . . . . . . . . . . . . . . . . . . . . TD-16
senthil.nagarajan@uni.lu
Department of Mathematics, University of Luxembourg,
Esch Sur Alzette, Luxembourg
Nagel, Jesse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-36
jesse.nagel@cwi.nl
Stochastics, CWI, Amsterdam, Netherlands
Naghmouchi, M. Yassine . . . . . . . . . . . . . . . . . . . . MA-42, MC-42
yassine.naghmouchi@pasqal.com
PASQAL, France
Nagurney, Anna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-12, TD-21
nagurney@isenberg.umass.edu
Department of Operations and Information Management,
University of Massachusetts Amherst, Amherst, Massachusetts, United States
Nagy, Mariana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-49
mnagy62@yahoo.com
UAV Arad, Romania
Nair, Rahul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-17
rahul.nair@ie.ibm.com
IBM Research - Ireland, Dublin, Ireland
Nait Saada, Juba . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-57
jubans@amazon.com
Amazon, United Kingdom

AUTHOR INDEX

Najafi-Zangeneh, Saeed . . . . . . . . . . . . . . . . . . . . . WC-15, WA-45
saeed.najafi@ensae.fr
ENSAE-Ecole Polytechnique, GIF-SUR-YVETTE, France
Naka, Garoufalia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-47
g.naka@uoi.gr
Department of Economics, University of Ioannina, Ioannina,
GreeceNakamura, Ayane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-40
s2230122@s.tsukuba.ac.jp
Japan
Nakayama, Marvin K. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-06
Marvin.Nakayama@njit.edu
Computer Science, New Jersey Institute of Technology,
Newark, New Jersey, United States
Nakhla, Michel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-03
michel.nakhla@mines-paristech.fr
CGS-I3-MINES-ParisTech-Université
ParisSaclayAgroParisTech, Paris, France
Naldi, Maurizio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-63
m.naldi@lumsa.it
GEPLI, LUMSA University, Roma, Italy
Naldini, Federico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-51
federico.naldini@gmail.com
COSYS-ESTAS, Université Gustave Eiffel, Villeneuved’Ascq, France
Namazian, Zahra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-28
zahra.namazian@monash.edu
Optimization, Data Science and AI, Faculty of Information
Technology, Faculty of IT, Monash University, Melbourne,
Victoria, Australia
Nami, Nazanin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-24
nazanin.nami@postgrad.manchester.ac.uk
AMBS, University of Manchester, United Kingdom
Napoletano, Antonio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-58
antonio.napoletano@optit.net
Optit.srl, Italy
Narayanaswami, Sundaravalli . . . . . . . . . . . . . . . . . . . . . . WC-18
sundaravallin@iima.ac.in
IIM Ahmedabad, Ahmedabad, Gujarat, India
Narushima, Yasushi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-41
narushima@keio.jp
Department of Industrial and Systems Engineering, Keio
University, Yokohama, Kanagawa, Japan
Nasca, Ludovica . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-47, TD-47
ludovica.nasca@phd.unict.it
Department of Civil Engineering and Architecture, University of Catania, Catania, Italy, Italy
Nasiadka, Julia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-48
249835@student.pwr.edu.pl
Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology,
Poland617

AUTHOR INDEXEURO 2024 - Copenhagen

Nasini, Stefano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-28
s.nasini@ieseg.fr
IESEG School of Management, Lomme, France
Nath, Rito Brata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-51
ritonath@iisc.ac.in
Civil Engineering, Indian Institute of Science (IISc), Bangalore, Karnataka, India

Nesterov, Yurii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-32
yurii.nesterov@uclouvain.be
CORE, Université catholique de Louvain (UCL), Louvain-laneuve, Belgium
Neufeld, Ariel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-03
ariel.neufeld@ntu.edu.sg
School of Physical and Mathematical Sciences, Nanyang
Technological University, Singapore

Naumov, Vitalii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-60
vitalii.naumov@pk.edu.pl
Transportation Systems Department, Cracow University ofTechnology, Krakow, malopolskie, Poland

Neufeld, Janis Sebastian . . . . . . . . . . . . . . . . . . . . MB-54, WD-60
Janis_Sebastian.Neufeld@tu-dresden.deIndustrial Management, TU Dresden, Dresden, Germany

Navarro García, Manuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-27
mannavar@est-econ.uc3m.es
Departamento de Estadística, Universidad Carlos III de
Madrid, Madrid, Spain

Neumann, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-43
martneum@sdu.dk
Language, culture and communication, University of Southern Denmark, Slagelse, Denmark

Neděla, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-51
ned0043@vsb.cz
Finance, VSB-TU Ostrava, Ostrava, Czech Republic

Neumann, Philipp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-52
philipp.neumann@hsu-hh.deMechanical and Civil Engineering, Helmut Schmidt University, Hamburg, Germany

Nedungadi, Prema . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-18
prema@am.amrita.edu
Center for Research in Analytics, Technologies & Education,
Amrita Vishwa Vidyapeetham, Kollam, Kerala, India

Neves, Carlos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-52
carlosneves@eng.ci.ufpb.br
Universidade Federal da Paraíba, João Pessoa, Paraíba, Brazil

Negreiros, Marcos José . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-58
negreiro@graphvs.com.br
Computer Science, Universitade Estadual do Ceara, Fortaleza, Ceara, Brazil

Ng, Wai Leong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-02
wlng@hsu.edu.hk
Department of Mathematics, Statistics and Insurance, The
Hang Seng University of Hong Kong, Hong Kong

Nel, Stephan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-25
gsnel@sun.ac.za
Department of Industrial Engineering, Stellenbosch University, Stellenbosch, Western Cape, South Africa

Nguewouo, Franklin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-06
nguewouo.franklin@hydroquebec.com
Hydroquébec, Montreal, Québec, Canada

Nemeth, Sandor Zoltan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-41
s.nemeth@bham.ac.uk
School of Mathematics, University of Birmingham, Birmingham, United Kingdom
Nemoto, Toshio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-55
nemoto@shonan.bunkyo.ac.jp
Bunkyo University, Chigasaki, Kanagawa, Japan
Nepomuceno, Thyago . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-48
thyago.nepomuceno@ufpe.br
Department of Statistics, Federal University of Pernambuco,
BrazilNeron, Emmanuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-18
neron@univ-tours.fr
Polytech Tours, University of Tours, Tours, France
Neroni, Mattia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-57
nemattia@amazon.com
Amazon, Barcelona, Spain, Spain
Neshat, Najmeh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-14
najmeh_neshaat@yahoo.com
GERAD and Department of Decision Sciences, HEC Montréal, Canada

618

Nguyen, Dang Viet Anh . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-26
andng@dtu.dk
Department of Technology, Management and Economics,
Technical University of Denmark, Copenhagen, Denmark
Nguyen, Le Khanh Ngan. . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-43
nguyen-le-khanh-ngan@strath.ac.uk
Management Science, University of Strathclyde, Glasgow,
Scotland, United Kingdom
Nguyen, Phuong Le . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-02
lehanoi2005@yahoo.com
Hanoi University of Agriculture (HUA), Hanoi, Viet Nam
Nguyen, Thi Thu Ba . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-02
thuba.nguyen@beds.ac.uk
University of Bedfordshire, United Kingdom
Nguyen, Tri-Dung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-36
t.nguyen-264@kent.ac.uk
The University of Kent, United Kingdom
Nguyen, Trung Thanh. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-26
thanh.nguyentrung@phenikaa-uni.edu.vn
Phenikaa University, Viet Nam
Nguyen, Tuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-17

EURO 2024 - Copenhagen
phi2lu4@gmail.com
Edwards LifeSciences, Emeryville, California, United States
Nickel, Stefan . . . . . . . . . . . . . . . . . . . . . . . . WD-45, TB-56, TD-61
stefan.nickel@kit.edu
Institute for Operations Research (IOR), Karlsruhe Instituteof Technology (KIT), Karlsruhe, Germany
Nicolle, Adrien . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-22
adrien.nicolle@centralesupelec.fr
FranceNie, Lei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-51
leinie_@bjtu.edu.cn
School of traffic and transportation, Beijing Jiaotong University, China
Nielsen, Otto Anker . . . . . . . . . . . . . . . . . MA-26, WC-51, WA-64
oani@dtu.dk
DTU, Kopenhagen, Germany
Nielsen, Peter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-10, TB-39
peter@mp.aau.dk
Aalborg University, Denmark
Nielson, Lise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-16
Lise.Nielson@nordicenergy.org
Lise Nielson, Denmark
Niemann, Tim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-04
tim.niemann@tu-braunschweig.de
Institute for Mathematical Optimization, Technische Universität Braunschweig, Braunschweig, Germany
Nienhaus, Kristina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-09
kristina.nienhaus@dlr.de
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)
German Aerospace Center, Germany
Nietert, Bernhard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-07
nietert@wiwi.uni-marburg.de
Finance and Banking, Philipps Universitaet Marburg, Marburg, Hesse, Germany
Niffoi, Louis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-17
louis.niffoi@centralesupelec.fr
Laboratoire de Génie Industriel, CentraleSupélec, Gif syr
Yvette, France
Nijsten, Tygo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-56
t.g.c.nijsten@uva.nl
Korteweg-de Vries Institute for Mathematics, University of
Amsterdam, AMSTERDAM, NOORD-HOLLAND, Netherlands
Nikolić, Ognjen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-48
ognjen.nikolic@fon.bg.ac.rs
Department of Operations Research and Statistics, Faculty ofOrganizational Sciences, University of Belgrade, Belgrade,
Serbia, Serbia
Nikolopoulos, Konstantinos . . . . . . . . . . . . . . . . . . TB-45, WD-59
kostas.nikolopoulos@durham.ac.uk
Marketing and Management, Durham University, Durham,
United Kingdom

AUTHOR INDEX

Nimana, Nimit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-41
nimitni@kku.ac.th
Department of Mathematics, Khon Kaen University, Khon
Kaen, Thailand
Ning, Yina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-40
yinaning@hkaift.com
Laboratory for AI-Powered Financial Technologies, China
Ninh, Anh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-17
atninh@wm.edu
Mathematics, William & Mary, WILLIAMSBURG, VA,
United States
Niño Báez, José Edgardo . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-55
jenino@uc.cl
Ingeniería de Transporte y Logística, Pontificia Universidad
Católica de Chile, Santiago de Chile, Región Metropolitana,
Chile
Nishide, Katsumasa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-51
knishide@waseda.jp
Graduate School of Business and Finance, Waseda University, Japan
Nishihara, Michi . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-57, TA-59
nishihara@econ.osaka-u.ac.jp
Graduate School of Economics, Osaka University, Osaka,Japan
Nishii, Ayano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-25
ad23f047@guh.u-hyogo.ac.jp
Graduate School of Information Science, University of
Hyogo, Kobe, Hyogo, Japan
Nishikawa, Takahiro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-50
ta-nishikawa@logisteed.com
LOGISTEED, Ltd., Japan
Nishimura, Etsuko . . . . . . . . . . . . . . . . . . . . . . . . . . MD-21, TC-55
e-nisi@maritime.kobe-u.ac.jp
Graduate School of Maritime Sciences, Kobe University,
Kobe, Japan
Nitanai, Ryoichi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-18
ryo_nita@up.t.u-tokyo.ac.jp
The University of Tokyo, Japan
Nivievskyi, Oleg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-21
onivievskyi@kse.org.ua
Center for Food and Land Use Research, Kyiv School of
Economics, Kyiv, Ukraine
Nogueira do Nascimento, Douglas . . . . . . . . . . . . . . . . . . . TA-29
douglas.nogueira@unesp.br
Department of Industrial Engineering, Universidade EstadualPaulista - UNESP, Brazil
Nogueira, Conceição . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-49
conceicao.veloso@ipleiria.pt
Matemática, Instituto Politécnico de Leiria, LEIRIA, Portugal
Nolz, Pamela C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-08

619

AUTHOR INDEXEURO 2024 - Copenhagenpamela.nolz@fhstp.ac.at
St. Poelten University of Applied Sciences, St. Poelten, Austria
Nomer, Ahmet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-37
ahmet.nomer@tupras.com.tr
Turkish Petroleum Refineries Corp. (TUPRAS), Turkey
Nonato, Maddalena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-25
nntmdl@unife.it
EndIF, Universita’ di Ferrara, Ferrara, Italy
Noorafza, Mahdi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-43
m.noorafza@tudelft.nl
Delft University of Technology, Delft, Netherlands
Notte, Gastón . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-12
notteg@gmail.com
UdelaR, Paysandú, Paysandú, Uruguay
Nouaouri, Issam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-10
issam.nouaouri@univ-artois.fr
University of Artois / LGI2A, BETHUNE, France
Nourmohammadi, Amir . . . . . . . . . . . . . . . . . . . . WB-49, WB-58
amir.nourmohammadi@his.seIntelligent Production Systems, School of Engineering Science, University of Skövde, Skövde, Västra Götaland, Sweden
Novoa, Luis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-44
novoadlj@jmu.edu
James Madison University, Harrisonburg, VA, United States
Nowak, Wolfgang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-22
wolfgang.nowak@iws.uni-stuttgart.de
IWS LS3 (SC SimTech), University of Stuttgart, Stuttgart,
Baden-Wuerttemberg, Germany
Nowakowski, Andrzej . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-33
andrzej.nowakowski@wmii.uni.lodz.pl
Mathematics & Computer Sciences, University of Lodz,
Łódź, Łódzkie, Poland
Ntim, Collins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-31
C.G.Ntim@soton.ac.uk
Southampton Business School, University of Southampton,
Southampton, Hampshire, United Kingdom
Nucamendi-Guillén, Samuel . . . . . . . . . . . . . . . . . . . . . . . . MD-29
snucamendi@up.edu.mx
Facultad de Ingeniería, Universidad Panamericana, Zapopan,
Jalisco, Mexico
Nunes Braga, Tiago Emmanuel . . . . . . . . . . . . . . . . . . . . . WC-28
tiagobraga@ibict.br
Instituto Brasileiro de Informação em Ciência e Tecnologia,
Brasília, DF, Brazil
Nuñez, Luciano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-62
luciano.nunez.c@mail.pucv.cl
Escuela de Ingeniería de Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Chile
Nunez, Nicolas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-31

620

nnunezm@pucp.edu.pe
Centrum PUCP, PUCP, Peru
Nur, Hasan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-61
hnur536@aucklanduni.ac.nz
Information System and Operation Management, Univeristyof Auckland, Grafton, New ZealandNurmi, Kimmo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-26
cimmo.nurmi@samk.fi
Research and Development, Satakunta University of Applied
Sciences, Pori, Finland
Nutzel, Maike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-28
maike.nutzel@gmail.com
Vrije Universiteit Amsterdam, Amsterdam, Netherlands
O’Brien, Frances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-11
frances.o-brien@wbs.ac.uk
Warwick Business School, University of Warwick, Coventry,
United Kingdom
O’Sullivan, Barry . . . . . . . . . . . . . . . . . . . . . . . . . . MC-10, WC-19
b.osullivan@cs.ucc.ie
School of Computer Science & IT, University College Cork,
Cork, Ireland
Oakey, Andy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-10, TD-24
a.oakey@soton.ac.uk
Transportation Research Group, University of Southampton,
Southampton, United Kingdom
Obasohan, Phillips Edomwonyi . . . . . . . . . . . . . . . . . . . . . MB-15
philiobas@yahoo.com
Liberal Studies, Niger State Polytechnic, Bida, Niger, Nigeria
Obata, Tsuneshi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-44, TD-44
t-obata@haruka.otemon.ac.jp
Otemon Gakuin University, Ibaraki, Osaka Pref., Japan
Oberdieck, Richard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-57
rico@bankingcircle.com
Banking Circle ApS, Copenhagen, Denmark
Obradovic, Gabrijela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-37
gabobr@chalmers.se
Chalmers University of Technology, Sweden
Obreque, Carlos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-64
cobreque@ubiobio.cl
Industrial Enginnering, Universidad del Bío-Bío, Concepción, Chile
Oddershede, Astrid . . . . . . . . . . MB-18, WB-44, WA-50, TA-60
astrid.oddershede@usach.cl
Industrial Engineering, University Of Santiago Of Chile,
Santiago, Santiago, Chile
Odegaard, Fredrik . . . . . . . . . . . . . . . . . . . . . . . . . . MA-59, TB-59
fodegaard@ivey.uwo.ca
Ivey Business School, Western University, London, Ontario,
CanadaOetting, Andreas . . . . . . . . . . . . . . . . . . . . WB-51, TD-54, MB-56
Oetting@verkehr.tu-darmstadt.deEURO 2024 - Copenhagen
TU Darmstadt, Darmstadt, Germany

AUTHOR INDEXOf Chile, SANTIAGO, Region Metropolitana, Chile

Ogazón, Esteban . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-55
estebanogazon@gmail.com
OSD, Université Laval, Canada

Olivares-Nadal, Alba V . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-33
a.olivares_nadal@unsw.edu.au
UNSW Business School, Sydney, Australia

Oggioni, Giorgia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-19
giorgia.oggioni@unibs.it
Department of Economics and Management, University of
Brescia, Italy, Brescia, Italy, Italy

Olivari, Erika. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-62
erika.olivari@polito.it
Politecnico di Torino, Torino, Torino, Italy

Ogier, Maxime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-52
maxime.ogier@centralelille.fr
CRIStAL laboratory, Centrale Lille, FranceÖgrük, Akin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-24
akin.oegruek@tuhh.de
Hamburg University of Technology, Hamburg, Germany
Oguz, Ceyda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-13, WD-28
coguz@ku.edu.tr
Department of Industrial Engineering, Koc University, Istanbul, Turkey
Ogwang, Tomson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-56
togwang@brocku.ca
Economics, Brock University, St. Catharines, Ontario,
CanadaOikonomidis, Konstantinos . . . . . . . . . . . . . . . . . . . . . . . . . WA-41
konstantinos.oikonomidis@kuleuven.be
Dept. Electrical Engineering (ESAT), KU Leuven, Belgium
Okumusoglu, Bahar Cennet . . . . . . . . . . . . . . . . . . . . . . . . . TA-49
bahar.okumusoglu@tum.de
TUM School of Management, Technical University of Munich, Germany
Oladzad, Nastaran . . . . . . . . . . . . . . . . . . . . . . . . . . MC-29, WA-58
noladzad@buffalo.edu
Industrial and Systems Engineering, University at Buffalo,
Buffalo, NY, United States
Olama, Alireza . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-04, WA-30
alireza.olama@abo.fi
Department of Information Technology, Åbo Akademi University, Vaasa, Finland
Olfati, Marjan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-23
marjan.olfati@brunel.ac.uk
Brunel Business School, Brunel University London,
Uxbridge, London, United Kingdom
Oliva, Immacolata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-39
immacolata.oliva@uniroma1.it
Methods and Models for Economics, Territory and Finance,
Sapienza University of Rome, Rome, Rome, Italy
Olivares, Marcelo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-43
marceloolivar@gmail.com
Ingenieria Industrial, Universidad de Chile, Santiago, Chile
Olivares, Victor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-44
victor.olivares@usach.cl
Industrial Engineering Department, University Of Santiago

Oliveira da Ponte, Gabriel . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-04
gabponte@umich.edu
Industrial and Operations Engineering, University of Michigan and UFRJ, Ann Arbor, Michigan, United States
Oliveira, Aurelio . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-10, WB-30
aurelio@ime.unicamp.br
Computational & Applied Mathematics, UNICAMP, Campinas, SP, Brazil
Oliveira, Carlos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-51, TA-57
carlosoliveira@iseg.ulisboa.pt
Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology,
Trondheim, Norway
Oliveira, Fabricio . . . . . . . . . . . MB-09, MC-21, WC-28, WA-34
fabricio.oliveira@aalto.fi
Mathematics and Systems Analysis, Aalto University,
AALTO, Finland
Oliveira, José Fernando . . . . . MB-07, MC-07, MD-08, TB-28,
TA-29, TB-29, MB-59
jfo@fe.up.pt
INESC TEC, Faculty of Engineering, University of Porto,
Porto, Portugal
Oliveira, Mónica . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-07
monica.oliveira@ist.utl.pt
Center for Management Studies of Instituto Superior Técnico, Technical University of Lisbon, Lisboa, Portugal
Olteanu, Alexandru . . . . . . . . . . . . . . . . . . . . . . . . MD-20, WC-55
alexandru.olteanu@univ-ubs.fr
Lab-STICC, UMR 6285, CNRS, Université Bretagne Sud,
Lorient, France
Omedes, Jason . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-57
jomedes@amazon.es
Amazon, Spain
Omirolis, Angelos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-60
omirolisangelos@gmail.com
Department of Management Science and Technology, Athens
University of Economics and Business, Greece
Onal, Mehmet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-49
mehmet.onal@ozyegin.edu.tr
Industrial Engineering, Ozyegin University, Istanbul, İstanbul, Turkey
Onat, Melike Kamuran . . . . . . . . . . . . . . . . . . . . . TD-37, WD-52
melikekamuranonat@gmail.com
R&D, TÜPRAŞ, Turkey

621

AUTHOR INDEXEURO 2024 - Copenhagen

Onay, Anita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-11
anita.onay@mci.edu
Industrial Engineering, Management Center Innsbruck, Innsbruck, AustriaÖncel, Kerem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-19
koncel17@ku.edu.tr
Fraunhofer ISE, Turkey

Business Analytics, The University of Sydney, The University Of Sydney, NSW, Australia
Orosei, Roberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-25
roberto.orosei@inaf.it
Institute of Radioastronomy, Italian National Institute of Astrophysics, Bologna, Italy

Önen-Dumlu, Zehra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-17
zod23@bath.ac.uk
School of Management, University of Bath, United Kingdom

Ortmann, Janosch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-21
ortmann.janosch@uqam.ca
AOTI, University of Quebec in Montreal, Montreal, Quebec,
CanadaOneto, Alfredo Ernesto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-19
aalfredo@ethz.ch
D-MAVT, ETH Zurich, Zurich, Zurich, Switzerland

Ortobelli Lozza, Sergio . . . . . . . . . . . . . MA-44, MA-51, MD-51
sergio.ortobelli@unibg.it
University of Bergamo, Bergamo, Bergamo, Italy

Onyi, Charles Emeka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-29
charles.onyi@strath.ac.uk
Management Science, University of Strathclyde, Glasgow,
Scotland, United KingdomÓskarsdóttir, María . . . . . . . . . . . . . . . . MD-08, WA-27, MD-31
mariaoskars@ru.is
Department of Computer Science, Reykjavik University, Iceland

Oosterwijk, Tim. . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-25, TA-36
t.s.oosterwijk@gmail.com
Operations Analytics, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

Osorio-Mora, Alan . . . . . . . . . . . . . . . . . . . . . . . . . . TA-26, WD-56
alan.osorio2@unibo.it
DEI, Universitá di Bologna, Bologna, BO, Italy

Oostra, Dorien . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-07
Dorien.Oostra@radboudumc.nl
Geriatrics, Radboudumc, Nijmegen, Netherlands
Oppio, Alessandra . . . . MB-13, MC-47, MD-47, TC-47, TD-47
alessandra.oppio@polimi.it
Department of Architecture and Urban Studies, Politecnicoof Milano, Milano, Italy
Oral, Muhammed Enes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-58
enes.oral@trendyol.com
Technology, Trendyol, Turkey
Orejuela Cabrera, Juan Pablo . . . . . . . . . . . . . . . . . . . . . . WC-28
juan.orejuela@correounivalle.edu.co
Ing. Industrial., Universidad del Valle, Cali, Valle del cauca,
Colombia
Orhan, Cosku Can . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-18
cosku.orhan@nhh.no
Business and Management Science, NHH Norwegian Schoolof Economics, Bergen, Norway
Oriolo, Gianpaolo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-12
oriolo@disp.uniroma2.it
Universita’ Roma Tor Vergata, Italy
Orjuela Castro, Javier Arturo . . . . . . . . . . . . . . . . . . . . . . WC-28
jaorjuelac@unal.edu.co
Engineering, Universidad Distrital, Bogota, Colombia
Orlin, James . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-26
jorlin@mit.edu
ORC and Sloan School of Management, MIT, Cambridge,
MA, United States
Oron, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-60
daniel.oron@sydney.edu.au

622

Ospina-Forero, Luis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-06
luis.ospina-forero@manchester.ac.uk
Alliance Manchester Business School-3.046, University ofManchester, United Kingdom
Ossandón, Joaquín . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-23
joaquin.ossandon.e@mail.pucv.cl
Pontificia Universidad Católica de Valparaíso, Chile
Oster, Johanna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-16
johanna.oster@hs-wismar.deWismar Business School, Hochschule Wismar / Universität
Rostock, GermanyØsterdal, Lars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-43
lpo.eco@cbs.dk
Copenhagen Business School, Copenhagen, Denmark
Ostermann, Birte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-42
birte.ostermann@tu-braunschweig.de
TU Braunschweig, Germany
Ostermeier, Manuel . . . . . . . . . . . . . . . . . MA-50, TD-58, MB-64
manuel.ostermeier@uni-a.deUniversity of Augsburg, Augsburg, Germany
Ostroukhov, Petr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-32
postroukhov12@gmail.com
Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, Masdar City, Abu-Dhabi, United ArabEmirates
Osuna-Gómez, Rafaela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-41
rafaela@us.es
University of Seville, Sevilla, Spain
Otero, Ricardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-06
ricotero@live.com
KU Leuven, Belgium

EURO 2024 - Copenhagen

Otto, Alena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-26
alena.otto@uni-passau.de
University of Passau, Passau, Germany

AUTHOR INDEXÖzbek, Berker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-50
berker.ozbek@trendyol.com
Data Science, Trendyol, Turkey

Ottomanelli, Michele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
michele.ottomanelli@poliba.it
DICATECh, Politecnico di Bari, Bari, ItalyÖzcan, İsmail . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-40
iozcan@umh.es
Operations Research Center, Universidad Miguel Hernández,
Elche, Alicante, Spain

Ouelhadj, Djamila . . . . . . . . . . . . . . . . . . . . . . . . . . MD-10, TB-55
djamila.ouelhadj@port.ac.uk
Maths, University of Portsmouth, Portsmouth, United Kingdom

Ozcelik, Mert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-56
ozcelikmert@sabanciuniv.edu
Faculty of Engineering and Natural Sciences, Sabancı University, Tuzla, Istanbul, Turkey

Ouenniche, Jamal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-58
jamal.ouenniche@ed.ac.uk
Management School and Economics, Edinburgh University,
Edinburgh, Scotland, United KingdomÖzçetin, Erdener . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-41
eozcetin@gmail.com
Industrial Engineering, Hitit University, Corum, Turkey

Oukassou, Ahmed-Youssef . . . . . . . . . . . . . . . . . . . . . . . . . . MA-48
a.oukassou@ieseg.fr
Economics & Quantitative Methods, Ieseg School Of Management, Lille, France
Ouyang, Zizhou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-27
zizhou.ouyang@ed.ac.uk
Business School, University of Edinburgh, United Kingdom
Ouzana, Noam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-12
noam.ouzana@campus.technion.ac.il
Environmental engineering, Technion, Haifa, Israel
Ovchinnikov, Anton . . . . . . . . . . . . . . . . . . . . . . . . MB-23, WC-24
anton.ovchinnikov@queensu.ca
School of Business, Queen’s University, Kingston, ON,
CanadaOverbeek, Brian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-10
b.overbeek2@vu.nl
Operations Analytics, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
Overmars, Maik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-10
m.g.overmars@utwente.nl
Mathematics of Operations Research, University of Twente,
Enschede, Overijssel, Netherlands

Ozekici, Suleyman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-39
sozekici@ku.edu.tr
Department of Industrial Engineering, Koç University, Istanbul, Turkey
Ozener, Okan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-60
orsan.ozener@ozyegin.edu.tr
Industrial Engineering, Ozyegin University, Istanbul, Turkey
Ozer, Ege Naz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-13
ege.ozer@wur.nl
Operations Research and Logistics, Wageningen University,
Wageningen, Gelderland, Netherlands
Ozkan, Yusufcan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-63
yusufcan.ozkan@riskoptima.com.tr
R&D, Riskoptima Wealth Tech Corp., Istanbul, N/A, Turkey
Ozogur-Akyuz, Sureyya . . . . . . . . . . . . . MB-04, MD-04, WC-27
sureyya.akyuz@eng.bau.edu.tr
Department of Mathematics Engineering, Bahcesehir University, Istanbul, Turkey
Ozpeynirci, Ozgur . . . . . . . . . . . . . . . . . . . . . . . . . . MB-58, MC-64
ozgur.ozpeynirci@ieu.edu.tr
Department of Logistics Management, Izmir University of
Economics, Izmir, Turkey

Ovestad, Thomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-57
tovestad@amazon.com
Amazon, SpainÖzpeynirci, Selin . . . . . . . . . . . . . . . . . . . . WA-21, MB-58, MC-64
selin.ozpeynirci@ieu.edu.tr
Industrial Engineering, Izmir University of Economics, Izmir,
TurkeyOwen, Art B. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-06
owen@stanford.edu
Department of Statistics, Stanford University, Stanford, CA,
United States

Ozturk, Cemalettin . . . . . . . . . . . . . . . . . . WC-19, TC-49, MD-55
cemalettin.ozturk@mtu.ie
Process, Energy & Transport, Munster Technological University, Cork, Ireland

Oyebolu, Folarin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-15
folarin.oyebolu@mrc-bsu.cam.ac.uk
MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom

Ozturk, Gurkan . . . . . . . . . . . . . . . . . . . . . TC-12, WC-27, WC-55
gurkan.o@eskisehir.edu.tr
Industrial Engineering, Eskisehir Technical University, Eskisehir, Turkey

Oz, Binyamin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-36
binyamin.oz@mail.huji.ac.il
The Hebrew University of Jerusalem ., Jerusalem, Israel

P S, Rejitha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-18
rejtaps123@gmail.com
Infectious Disease, Amrita Institute of Medical Sciences,
Kochi, Kerala, India

623

AUTHOR INDEXEURO 2024 - Copenhagen

P, Preetha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-18
preethap@aims.amrita.edu
Infectious Disease, Amrita Institute of Medical Sciences Hospital, Kochi, Kerala, India
Pac, Burak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-10, WA-25
burakpac@gtu.edu.tr
Industrial Engineering, Gebze Technical University, Kocaeli,
TurkeyPacciarelli, Dario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-51
pacciarelli@dia.uniroma3.it
Dipartimento di Ingegneria, Roma Tre University, Roma,
Italy
Pachamanova, Dessi . . . . . . . . . . . . . . . . . . . . . . . . WA-10, MA-20
dpachamanova@babson.edu
Mathematics, Analytics, Science and Technology, Babson
College, Babson Park, MA, United States
Pacheco Paneque, Meritxell . . . . . . . . . . TD-45, WD-51, MC-58
meritxell.pacheco@unifr.ch
DS&OR, Univeristy of Fribourg, Fribourg, Switzerland
Pacheco, Joaquín . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-15
jpacheco@ubu.es
Applied Economy, University of Burgos, Burgos, Spain
Paciarotti, Claudia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-24
c.paciarotti@univpm.it
DIISM, Università Politecnica delle Marche, Ancona, Italy

Pahikkala, Tapio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-28
aatapa@utu.fi
Department of Future Technologies, University of Turku,
Finland, Turku, Finland
Pahlevani, Mahsa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-15
MPahlevani@dal.ca
Dalhousie University, Canada
Paias, Ana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-25, WA-58
ampaias@fc.ul.pt
Deio - Cmafcio, Universidade de Lisboa, Faculdade de Ciências, Portugal
Pala, Anna Laura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-53
pala@diag.uniroma1.it
DIAG, Sapienza Università di Roma, Vicalvi, Italy
Palagi, Laura . . . . . . . . . . . . . . . . . TD-27, MA-32, TA-52, TB-52
palagi@diag.uniroma1.it
Department of Computer, Control, and Management Engineering A. Ruberti, Sapienza University of Rome, Rome,
Italy
Palhano, Augusto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-58
augustopalhano@gmail.com
Consulting and Development, GRAPHVS Ltda, Fortaleza,
CE, Brazil
Palma, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-30
d.palma@leeds.ac.uk
Management, Leeds University Business School, Leeds,
West Yorkshire, United Kingdom

Pacino, Dario . . . . . . . . . . . . . . . . MA-62, MB-62, TA-62, WA-62
darpa@dtu.dk
Department of Management, Technology and Economics,
Technical University of Denmark, Kgs. Lyngby, Denmark

Palmer, Geraint . . . . . . . . . . . . . . . . . . . . . TB-15, MC-17, WA-18
palmergi1@cardiff.ac.uk
School of Mathematics, Cardiff University, United Kingdom

Padilla, Maria Paula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-15
mayibupa@gmail.com
Unicamp, Colombia

Palominos, Pedro . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-18, TA-60
pedro.palominos@usach.cl
University of Santiago of Chile, Santiago, Santiago, Chile

Padron, Silvia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-55
s.padron@tbs-education.fr
TBS Business School, France

Paltayian, George . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-48
gpaltag@uom.edu.gr
Department of Business Administration, University of Macedonia, Thessaloniki, Thessaloniki, Greece

Paelinck, Marc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-54
Marc.paelinck@klm.com
KLM Royal Dutch Airlines, Netherlands
Paetow, Thomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-07
thomas.paetow@hs-wismar.deWismar Business School, Wismar University of Applied Sciences, Wismar, MV, Germany
Pagano, Alessandro . . . . . . . . . . . . . . . . . . . . . . . . . MB-13, TA-13
alessandro.pagano@ba.irsa.cnr.it
Water Research Institute, National Research Council, Bari,
Italy
Pagnier, Celine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-56
celine.pagnier@ntnu.com
Industrial Economics and Technology Management, NTNU,
Trondheim, Norway

624

Pamplona, João Vitor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-28
pamplona@uni-trier.de
Economic and Social Statistics Department, Trier University,
Trier, Germany
Pan, Chao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-10
chao.pan@kuleuven.be
Department of Decision Sciences and Information Management, KU Leuven, 3000, Belgium
Pan, Kai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-09, WA-29
kai.pan@polyu.edu.hk
Department of Logistics and Maritime Studies, The Hong
Kong Polytechnic University, Hung Hom, Kowloon, Hong
Kong
Pan, Qiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-10
panstpan@gmail.com

EURO 2024 - Copenhagen
Colledge of Management and Economics, Tianjin University,
Tianjin, Tianjin, China
Pan, Shaohua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-41
shhpan@scut.edu.cn
School of Mathematics, South China University of Technology, Guangzhou, Tianhe district, China
Pan, Stefania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-09
stefania.pan@artelys.com
Artelys Italia, Italy
Panagakos, George . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-18
geopan@dtu.dk
Department of Technology, Management and Economics,
Technical University of Denmark, Kongens Lyngby, Denmark
Panagopoulos, Andreas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-13
a.panagopoulos@swri.gr
Soil And Water Resources Institute, Hellenic Agricultural
Organisation, Kalamaria, Greece
Panda, Nanda Kishor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-22
n.k.panda@tudelft.nl
Delft University of Technology, Netherlands
Pande, Shashwat M. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-11
s.m.pande@lboro.ac.uk
Centre for Information Management, Loughborough University, Loughborough, Leicestershire, United Kingdom
Pandelis, Dimitrios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-39
d_pandelis@mie.uth.gr
Mechanical Engineering, University of Thessaly, Volos,
GreecePandey, Adya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-53
adya.pandey@mech.iitd.ac.in
India
Pandey, Utsav . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-48
utsav.pandey@iiml.ac.in
Decision Sciences, Indian Institute of Management Lucknow,
Lucknow, Uttar Pradesh, India
Pandey, Vaishnavi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-53
vp986@snu.edu.inManagement, Shiv Nadar Institution of Eminence, Greater
Noida, Uttar Pradesh, India
Pang, Gu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-07, MC-29
g.pang.1@bham.ac.uk
Business School, The University of Birmingham, Birmingham, West Midland, United Kingdom
Panja, Debabrata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-54
d.panja@uu.nl
Computer Science, Utrecht University, Netherlands
Pankratov, Alexander . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-07
impankratov@mail.ru
Department of Mathematical Modeling and Optimal Design,
Institute for Mechanical Engineering Problems of the National Academy of Sciences of Ukraine, Kharkov, Ukraine

AUTHOR INDEX

Pantuso, Giovanni . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-34, TC-35
giopantuso@gmail.com
Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
Paolo, Dell’Olmo . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-26, WA-37
paolo.dellolmo@uniroma1.it
Dipartimento di Statistica, Probabilità, Statistiche applicate,
University of Rome La Sapienza, Roma, Italy
Papageorgiou, Lazaros . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-27
l.papageorgiou@ucl.ac.uk
Chemical Engineering, University College London, London,
United Kingdom
Papakyriakopoulos, Dimitris . . . . . . . . . . . . . . . . . . . . . . . . MD-45
dpapak@uniwa.gr
Business administration, University of West Attica, Greece
Papamichail, K.Nadia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-11
nadia.papamichail@manchester.ac.uk
Alliance Manchester Business School, The University ofManchester & The Alan Turing Institute, Manchester, United
Kingdom
Papanagnou, Christos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-23
chrobo@yahoo.com
Engineering Systems and Supply Chain Management, Aston
University, Birmingham, United Kingdom
Papapostolou, Aikaterini . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-06
kpapap@epu.ntua.gr
Decision Support Systems Laboratory, School of Electricaland Computer Engineering, National Technical University ofAthens, Athens, Greece
Papasalouros, Andreas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-64
andpapas@aegean.gr
MATHEMATICS, University Of The Aegean, SAMOS,
GreecePapathanasiou, Dimitrios . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-49
t8210114@aueb.gr
Department of Management Science and Technology, Athens
University of Economics and Business, PIRAEUS, Greece
Papathanasiou, Maria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-53
maria.papathanasiou11@imperial.ac.uk
Imperial College London, London, United Kingdom
Papavasiliou, Anthony . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-09
anthony.papavasiliou@uclouvain.be
CORE, UCLouvain, Louvain la Neuve, Belgium
Paquay, Célia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-07, TA-29
cpaquay@uliege.be
HEC Liège-Management School of the Université of Liège,
Liège, Liège, Belgium
Paquet, Catherine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-28
catherine.paquet@fsa.ulaval.ca
Marketing, Universite Laval, Quebec, Quebec, Canada
Parada, Víctor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-21

625

AUTHOR INDEXEURO 2024 - Copenhagen

victor.parada@usach.cl
Universidad de Santiago de Chile, Santiago, RM, Chileaxel.parmentier@cermics.enpc.fr
Cermics, Champs sur Marne, France

Paradiso, Rosario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-35
r.paradiso@vu.nl
Vrije Universiteit Amsterdam, Italy

Parmentier, Axel . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-02, WD-27
axel.parmentier@enpc.fr
CERMICS, Ecole des Ponts ParisTech, France

Paraskevas, Antonios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-62
aparaskevas@uom.edu.gr
Dept. of Applied Informatics, University of Macedonia,
Thessaloniki, Greece

Paronuzzi, Paolo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-25
paolo.paronuzzi@unibo.it
DEI, University of Bologna, Bologna, Italy

Paraskevopoulos, Dimitris . . . . . . . . . . . . . . . . . . . . . . . . . . MD-15
dimi@city.ac.uk
Bayes Business School (formerly Cass), City, University of
London, London, United Kingdom
Paraskevopoulos, Dimitris . . . . . . . . . . . . . . . . . . . . . . . . . . WC-26
Dimitris.Paraskevopoulos@city.ac.uk
Bayes Business School (formerly Cass), City, University of
London, London, Uk, United Kingdom

Parpas, Panos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-34
panos.parpas@imperial.ac.uk
Computing, Imperial College London, London, United Kingdom
Parr, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-13
martin.parr@guidedsystemssolutions.com
Guided Systems Solutions, London, London, United Kingdom

Paraskevopoulos, Ioannis . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-22
yparaskevopoulos@comillas.edu
Spain

Parra, Juan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-42
parra@umh.es
Operations Research Center, Miguel Hernández University,
Elche, Alicante, Spain

Pardo, Eduardo G. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-52
eduardo.pardo@urjc.es
Informática y Estadística, Universidad Rey Juan Carlos,
Spain

Parragh, Sophie . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-37, MC-52
sophie.parragh@jku.at
Institute of Production and Logistics Management, Johannes
Kepler University Linz, Linz, Austria

Paredes-Belmar, Germán . . . . . . . . . . . . . . . . . . . . TB-23, MA-60
german.paredes@pucv.cl
School of Industrial Engineering, Pontificia Universidad
Católica de Valparaíso, Chile

Parsa, Iman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-21
iman.parsa@insead.edu
INSEAD, France

Parikh, Jinal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-16, WB-18
jinal.parikh@ahduni.edu.inTechnology, Operations & Decision Sciences, Amrut Mody
School of Management, Ahmedabad University, Ahmedabad,
Gujarat, India
Pariota, Luigi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
luigi.pariota@unina.it
Federico II University of Naples, Italy
Park, Jisoo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-50
jisoopark@gatech.edu
Industrial and Systems Engineering, Georgia Institute ofTechnology, Atlanta, Georgia, United States
Park, Kyeonggeun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-40
anandh@korea.ac.kr
Department of Battery-Smart Factory, Korea University,
SEOUL, KOREA, Korea, Republic of
Park, Ye-won . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-08
jk07045@naver.com
Korea, Republic of
Park, You-Jin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-28
yjpark@mail.ntut.edu.tw
National Taipei University of Technology, Taiwan
Parmentier, Axel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-27

626

Parvasi, Seyed Parsa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-08
parsa.parvasi@gmail.com
Management, Technical University of Denmark (DTU), Kongens Lyngby, Denmark
Parzanese, Giovanni . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-42
gpzes@yahoo.it
Universidad de Alicante, GENOVA, GE, Italy
Pascariu, Bianca . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-51, TB-52
bianca.pascariu@univ-eiffel.fr
COSYS-ESTAS, Université Gustave Eiffel, France
Paschos, Georgios . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-57, WA-57
paschosg@amazon.lu
Amazon, Luxembourg
Pascoal, Marta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-52
marta.brazpascoal@polimi.it
DEIB, Politecnico di Milano; University of Coimbra, Milano,
Italy
Pasha, Adnan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-62
apasha@ce.iitr.ac.in
Department of Civil Engineering, Indian Institute of Technology Roorkee, India
Pashapour, Amirreza . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-21
apashapour20@ku.edu.tr
IEOM, KOC University, Turkey

EURO 2024 - Copenhagen

Passacantando, Mauro . . . . . . . . . . . . . . TD-27, WC-43, WD-43
mauro.passacantando@unimib.it
Department of Business and Law, University of MilanoBicocca, Milano, Italy
Pastor, Rafael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-14, MB-18
rafael.pastor@upc.edu
Technical University of Catalonia, Barcelona, Spain
Pasupathy, Kalyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-15
pasupathyk@mayo.edu
Kern Center for the Science of Health Care Delivery, Mayo
College of Medicine, Rochester, Minnesota, United States
Patel, Jigar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-03
jigar.patel@bus.miami.edu
Management Science, University of Miami, Coral Gables,
FL, United States
Pathak, Sheeba . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-23
sp01762@surrey.ac.uk
Business Analytics and Operations, Surrey Business School,
Guildford, Surrey, United Kingdom

AUTHOR INDEX

Department of Data and Information Management, Dalarna
University, Sweden
Päprer, Paul. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-54
paul.paeprer@tu-dresden.de
Business Administration, esp. Industrial Management, TU
Dresden, Dresden, Germany
Pérez Porras, Fernando . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-45
o12pepof@uco.es
Deapartamento de Ingeniería Gráfica y Geomática, Universidad de Córdoba, Córdoba, Spain
Pérez Rubio, Ana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-58
ana.perez@oga.ai
oga, Spain
Pérez, Luis Fernando . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-42
l.perezarmas@ieseg.fr
IESEG, France
Pérez, M. Angeles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-10
angeles.perez@uv.es
Mathematics for Ecomomy, University of Valencia, Valencia,
Spain

Patrício, Lia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-28
lpatric@fe.up.pt
Industrial Engineering and Management, INESC TEC and
Faculty of Engineering, University of Porto, Porto, Portugal

Päsler, Lars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-21
lars.pasler@student.uva.nl
Universiteit van Amsterdam, Netherlands

Patria, Daniele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-37
daniele.patria@uniroma1.it
DIAG, Sapienza, University of Rome, Rome, RM, Italy

Pearson, Justin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-56
justin.pearson@it.uu.se
Institutionen for IT, Uppsala universitet, Uppsala, Sweden

Patrinos, Panagiotis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-41
panos.patrinos@esat.kuleuven.beElectrical Engineering, KU Leuven, Leuven, Belgium

Pecci, Filippo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-09
f.pecci14@imperial.ac.uk
Imperial College London, United Kingdom

Pattanaik, Piyalee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-54
piyalee.pattanaik@aalto.fi
Mathematics and System analysis, Aalto University,
Helsinki, Finland

Pecora, Jose Eduardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-10pecora@ufpr.br
Administracao Geral e Aplicada, Universidade Federal do
Parana, Curitiba, PR, Brazil

Pavlenishvili, Levan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-19
levan.pavlenishvili@gmail.com
Director, Operational Research Institute, Tbilisi, Georgia,
Georgia

Pedemonte, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-12
mpedemon@fing.edu.uy
UdelaR, Montevideo, Uruguay

Pavlikov, Konstantin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-58
kop@sam.sdu.dk
Department of Business and Management, University ofSouthern Denmark, Denmark
Pavlov, Valery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-61
v.pavlov@auckland.ac.nz
Information Systems and Operations Management, University of Auckland, Auckland, New Zealand
Pawlak, Grzegorz . . . . . . . . . . . . . . . . . . . . . . . . . . WB-26, MA-48
grzegorz.pawlak@cs.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poznan, Poland
Pääkkönen, Joonas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-16
jpa@du.se

Pedroso, João Pedro . . . . . . . . . . . . . . . . . . . . . . . . . TC-10, WA-64
jpp@fc.up.pt
Campus da FEUP, INESC TEC and Faculdade de Ciencias,
Universidade do Porto, Porto, Portugal
Peecock, Anna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-19
a.peecock.21@abdn.ac.uk
School of Engineering, University of Aberdeen, United Kingdom
Peeperkorn, Jari . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-27
jari.peeperkorn@kuleuven.be
Research Center for Information Systems Engineering(LIRIS), KU Leuven, Leuven, Belgium
Peer, Rebecca . . . . . . . . . . . . . . . . . . . . . . . TB-19, WA-19, MC-53
rebecca.peer@canterbury.ac.nz
Civil and Natural Resources Engineering, University of Can-627

AUTHOR INDEXEURO 2024 - Copenhagen

terbury, Christchurch, New Zealand

DIST, Politecnico di Torino, Torino, Italy

Peet, Thomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-27
tomaspemora@uchile.cl
EOL, Universidad de Chile, Santiago, Metropolitana de Santiago, Chile

Pemy, Moustapha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-18
mpemy@towson.edu
Mathematics, Towson University, Towson, MD, UnitedStatesPeirano, Lorenzo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-29
lorenzo.peirano@unibs.it
University of Brescia, Italy

Peña, Javier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-38
jfp@andrew.cmu.edu
Carnegie Mellon University, United States

Peiró, Juanjo . . . . . . . . . . . . . . . . . . . . . . . . . TB-26, TD-52, TB-64
juanjo.peiro@uv.es
Estadística i Investigació Operativa, Universitat de València,
Burjassot, Valencia, Spain

Penagos, Paula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-21
pph4d@umsl.edu
University of Missouri-St. Louis, United States

Peitz, Sebastian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-37
sebastian.peitz@upb.de
Department of Computer Science, University of Paderborn,
Paderborn, Germany
Pekár, Juraj . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-53
juraj.pekar@euba.sk
Department of Operations Research and Econometrics, University of Economics in Bratislava, Bratislava, Slovakia
Pekar, Viktor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-31
v.pekar@aston.ac.uk
Operations & Information Management, Aston University,
Birmingham, West Midlands, United Kingdom
Pekec, Sasa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-43, WA-43
pekec@duke.edu
Duke University, Durham, NC, United States
Peker, Meltem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-22
meltem.peker@bilkent.edu.tr
Department of Industrial Engineering, Bilkent University,
Ankara, Turkey
Pele, Daniel Traian . . . . . . . . . . . . . . . . . . . . . . . . . WC-31, WD-31
danpele@ase.ro
Bucharest University of Economic Studies. Institute for Economic Forecasting, Romanian Academy, Bucharest, Romania
Pelissari, Renata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-44
renatapelissari@gmail.com
Engineering School, Mackenzie Presbyterian University, Jundiaí, SP, Brazil
Pelizzari, Cristian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-39
cristian.pelizzari@unibs.it
Department of Economics and Management, University of
Brescia, Brescia Bs, Italy
Pellegrini, Paola . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-51, TB-52
paola.pellegrini@univ-eiffel.fr
Université Gustave Eiffel, France
Pellegrino, Carlo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-47
carlo.pellegrino@unipd.it
University of Padova, Italy
Pellerey, Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-18
virginia.pellerey@polito.it

628

Peng, Bo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-32, TB-52
bo.peng@univie.ac.atUniversity of Vienna, Vienna, Vienna, Austria
Peng, Wenxiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-62
pengwenxiang@126.com
Shenzhen, — Select One —, China
Peng, Yangyang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-61
yypeng@sribd.cn
Big Data Intelligent Application Technology Research Institute, Shenzhen Research Institute of Big Data, Shenzhen,
Shenzhen, China
Peng, Yan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-07
pengyan@tju.edu.cn
Tianjin University, China
Perakis, Georgia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-20
georgiap@mit.edu
MIT Sloan School of Management, Cambridge, Massachusetts, United States
Perau, Christian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-19
christian.perau@kit.edu
Chair of Energy Economics, KIT, Karlsruhe, Germany
Perchiazzo, Andrea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-63
andrea.perchiazzo@unimi.it
Department of Economics, Management and Quantitative
Methods, University of Milan, Italy
Perea, Federico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-49, WA-60
perea@us.es
Departamento de Matemática Aplicada II, Universidad de
Sevilla, Sevilla, Spain
Pereira Coutinho, Walton . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-35
walton.coutinho@ufpe.br
Industrial Engineering - CAA, Federal University of Pernambuco, Caruaru, Pernambuco, Brazil
Pereira de Oliveira, Fernando Luiz . . . . . . . . . . . . . . . . . . TA-51
fernandoluiz@ufop.edu.br
Statistics, Universidade Federal de Ouro Preto, Ouro Preto,
Minas Gerais, Brazil
Pereira, Francisco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-56
camara@dtu.dk
Management Engineering, DTU, Kongens Lyngby, Denmark

EURO 2024 - Copenhagen

Pereira, Luis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-56
luis.m.pereira@live.ua.pt
University of Aveiro, Portugal
Pereira, Miguel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-44
miguelalvespereira@tecnico.ulisboa.pt
Instituto Superior Técnico (Universidade de Lisboa), Lisboa,
Portugal
Pereira, Teresa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-45
mtp@isep.ipp.pt
CIDEM, School of Engineering, Polytechnic Institute ofPorto, Porto, Portugal
Perez Valdes, Gerardo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-23
gerardoa.perez-valdes@sintef.no
Economics and Technology Management, SINTEF Technology and Society, Trondheim, Norway
Perez, Juan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-45
jperez@uandes.cl
Industrial Engineering, Universidad de Los Andes, Chile,
Santiago, Región Metropolitana, Chile
Perez, Luis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-42
luisfernandopa1212@gmail.com
Operations Management, IESEG, lille, Nord, France
Perkkiö, Ari-Pekka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-35
perkkioe@math.lmu.de
Germany
Perry, Marieke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-07
marieke.perry@radboudumc.nl
Geriatrics, Radboudumc, Nijmegen, Netherlands
Perumal, Vamanie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-31
ed20d007@smail.iitm.ac.in
Engineering Design, Indian Institute of Technology Madras,
Chennai, Tamil Nadu, India
Perzina, Radomir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-44
perzina@opf.slu.cz
Department of Informatics and Mathematics, Silesian University, School of Business, Karvina, Czech Republic
Pessoa, Artur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-35
arturpessoa@id.uff.br
Universidade Federal Fluminense, Niterói, Brazil
Peters, Sven . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-45
peters@efi.rwth-aachen.de
RWTH | EFi, GermanyPetersen, Bjørn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-30, WB-64
bjorn@flowty.ai
Flowty, Denmark
Petersen, Niels Christian . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-58
ncp@sam.sdu.dk
Department of Business and Economics, University of Southern Denmark, Odense, Denmark
Peterson, Anders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-56

AUTHOR INDEX

anders.peterson@liu.se
Dept of Science and Technology, Linköping University, Norrköping, Sweden
Petric Maretic, Hermina . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-57
maretich@amazon.com
United Kingdom
Petris, Matteo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-35, WD-58
matteopetris@gmail.com
ESSEC Business School, France
Petroczy, Dora Greta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-16
petroczyd@mnb.hu
Central Bank of Hungary, Hungary
Petrot, Narin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-43
narinp@nu.ac.th
Mathematics, Naresuan University, MuangPhitsanulok, Phitsanulok, Thailand
Petrova, Penka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-20
events@worldbusinessweb.net
World Business Web Association, Sofia, Bulgaria
Petrovic, Sanja . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-55
sanja.petrovic@nottingham.ac.uk
Division of Operations Management and Information Systems, Nottingham University Business School, Nottingham,
United Kingdom
Pettersson, William . . . . . . . . . . . . . . . . . . . . . . . . . . TD-10, TC-25
william.pettersson@glasgow.ac.uk
University of Glasgow, Glasgow, United Kingdom
Petukhina, Alla . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-31
alla.petukhina@htw-berlin.de
Germany
Peura, Heikki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-22
heikki.peura@aalto.fi
Information and Service Department, Aalto University
School of Business, Finland
Pezdevšek Malovrh, Špela . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-12
spela.pezdevsek.malovrh@bf.uni-lj.si
Biotechnical Faculty, University of Ljubljana, Ljubljana,
Slovenia, Slovenia
Pezeshki, Bobak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-34
pezeshkb@uci.edu
University of California Irvine, United States
Pezoa, Raul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-56
raul.pezoa@udp.cl
Universidad Diego Portales, Chile
Pfeiffer, Christian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-54
christian.pfeiffer@uni-hamburg.de
Institute of Operations Management, University of Hamburg,
Hamburg, Hamburg, Germany
Pfenninger, Stefan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-09
S.Pfenninger@tudelft.nl
TU Delft, Delft, Netherlands

629

AUTHOR INDEXEURO 2024 - Copenhagen

Pferschy, Ulrich . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-53
ulrich.pferschy@uni-graz.at
Department of Operations and Information Systems, University of Graz, Graz, Austria

Piccotto, Filippo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-08
filippo.piccotto@phd.units.it
Department of Economics, Business, Mathematics and Statistics, University of Trieste, Latisana, Udine, Italy

Pflug, Georg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-35, TA-35
georg.pflug@univie.ac.at
Department of Statistics and Decision Support Systems, University of Vienna, Vienna, Austria

Pichon, Adrien . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-55
adrien.pichon@univ-ubs.fr
Lab-STICC, UMR 6285, CNRS, Université Bretagne Sud,
Lorient, Morbihan, France

Pham, Hoai Chung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-02
chung.tdsi@gmail.com
Transport and Development Strategy Institute, Hanoi, Viet
Nam

Picone, Marco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
marco.picone@unimore.it
University of Modena and Reggio Emilia, Italy

Pham, San Tu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-04
san.pham@kuleuven.be
Computer Science, KU Leuven, Kortrijk, Belgium
Phan, Vuong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-42
t.v.phan@soton.ac.uk
University of Southampton, Southampton, United KingdomPhùng, Duc Chính . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-02
ducchinh.imh@gmail.com
Viet Nam Institute Of Meteorology, Hydrology And Climate
Change, Ha Noi, Hà N.i, Viet Nam
Phillips, Christina . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-13, WD-46
c.j.phillips@ljmu.ac.uk
LBS, Liverpool John Moores University, Liverpool, Merseyside, United Kingdom
Philpott, Andy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-34
a.philpott@auckland.ac.nz
Engineering Science, The University of Auckland, Auckland,
New Zealand
Phosavanh, Johnson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-60
johnson.phosavanh@sydney.edu.au
Discipline of Business Analytics, The University of Sydney,
Sydney, NSW, Australia
Phung-Duc, Tuan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-40
tuan@sk.tsukuba.ac.jp
University of Tsukuba, Tsukuba, Japan
Piazza, Marco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-27
m.piazza23@campus.unimib.it
Computer Science, University of Milan-Bicocca, Italy
Picchi, Marinella . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-16
marinella.picchi@scuola.istruzione.it
IIS Antonietti, Iseo, Italy
Piccialli, Veronica . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-01
veronica.piccialli@uniroma1.it
DIAG, Università degli Studi di Roma "La Sapienza", Rome,
Italy
Piccolo, Carmela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-61
carmela.piccolo@unina.it
Department of Industrial Engineering, University of Naples
Federico II, Napoli, Italy

630

Pidnebesna, Anna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-52
pidnebesna@cs.cas.cz
The Institute of Computer Science of the Czech Academy of
Sciences, Prague, Czech Republic
Piest, Sebastian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-25
j.p.s.piest@utwente.nl
Industrial Engineering & Management (IEBIS), Universityof Twente, Enschede, Netherlands
Pignatelli, Maurizia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-47
maurizia.pignatelli@polito.it
DIST, Politecnico di Torino, Turin, Italy
Pigné, Yoann . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-26
yoann.pigne@univ-lehavre.fr
LITIS, Université Le Havre Normandie, Le Havre, France
Pigozzi, Gabriella . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-47
gabriella.pigozzi@lamsade.dauphine.fr
Cnrs - Lamsade, Université Paris Dauphine - PSL, Paris,
FrancePimenta, João . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-17
joao.felix.pimenta@tecnico.ulisboa.pt
Instituto Superior Técnico, Universidade de Lisboa, Portugal
Pimentel, Rita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-57, TB-57
rita.pimentel@ntnu.no
Industrial Economics and Technology Management, NTNU,
Trondheim, Trondelag, Norway
Pina Pardo, Juan Carlos . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-50
juanpina@mit.edu
Center for Transportation and Logistics, Massachusetts Institute of Technology, CAMBRIDGE, MA, United States
Pinar, Mustafa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-26
mustafap@bilkent.edu.tr
Department of Industrial Engineering, Bilkent University,
Ankara, Turkey
Pincheira, Pablo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-31
pablo_sps@outlook.es
ACHS, Santiago, Chile
Pineau, Pierre-Olivier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-22
pierre-olivier.pineau@hec.ca
HEC Montréal, Montréal, Canada

EURO 2024 - Copenhagen

AUTHOR INDEX

Pineda Morente, Salvador . . . . . . . . . . . . . . . . . . MA-19, WD-27
spinedamorente@gmail.com
Electrical Engineering, University of Málaga, Málaga, Spain

Pinto, Mariana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-16
marianafpinto@ua.pt
DEGEIT, University of Aveiro, Aveiro, Portugal

Pinedo, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-60
mlp5@stern.nyu.edu
Leonard N. Stern School of Business, New York University,
United States

Pinto, Xavier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-16
xavier.pinto@ua.pt
DEGEIT, University of Aveiro, Aveiro, Portugal

Pinheiro, Maria Eduarda . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-28
pinheiro@uni-trier.deMathematics, Trier University, Trier, Germany
Pinsach Batet, Ferran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-22
fpinsach@irec.cat
Energy Systems Analytics Research Group, Institut deRecerca en Energia de Catalunya, Sant Adrià del Besòs,Barcelona, Spain
Pinson, Pierre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-22, WA-35
ppinson@ic.ac.uk
Dyson School of Design Engineering, Imperial College London, United Kingdom
Pintér, Miklós . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-36
pmiklos@protonmail.com
Corvinus Center for Operational Research, Corvinus University of Budapest, Budapest, Hungary
Pinto de Lima, Ricardo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-19
ricardo.lima@kaust.edu.sa
Computer, Electrical and Mathematical Sciences and Engineering Division, KAUST - King Abdullah University of
Science and Technology, Thuwal, Saudi Arabia

Pipicelli, Eduardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-61
eduardo.pipicelli@unina.it
Department of Industrial Engineering, University of Naples
Federico II, Naples, Italy
Piplani, Rajesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-26
mrpiplani@ntu.edu.sg
Systems and Engineering Mgmt, Nanyang Technological
University, Singapore, Singapore
Pirnia, Mehrdad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-14
mpirnia@uwaterloo.ca
Management Sciences, University of Waterloo, Waterloo,
Ontario, Canada
Pironet, Thierry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-07
thierry.pironet@uliege.be
Operations, HEC-University of Liège, Liège, Liège, Belgium
Pisanu, Francesco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-25
pisanu@lipn.fr
LIPN, Université Sorbonne Paris Nord, France
Piscopo, Gabriella . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-63
gabriella.piscopo@unina.it
University of Naples Federico II, Naples, Italy

Pinto de Pina Ferreira, Vitor Luiz . . . . . . . . . . . . . . . . . . . TC-22
vitor.pinto-de-pina-ferreira@enpc.fr
CERMICS, École des Ponts ParisTech, Champs-sur-Marne,
Seine-et-Marne, France

Pishchulov, Grigory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-24
grigory.pishchulov@manchester.ac.uk
Alliance Manchester Business School, University of Manchester, United Kingdom

Pinto Varela, Tânia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-07
tania.pinto.varela@tecnico.ulisboa.pt
Engeneering and Management, Instituto Superior Tecnico,
Lisboa, Portugal

Pisinaras, Vassilios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-13
v.pisinaras@swri.gr
Soil & Water Resources Institute, Hellenic Agricultural Organization - DIMITRA, Sindos, Thessaloniki, Greece

Pinto, Diego Maria . . . . . . . . . . . . . . . . . . . . . . . . . . MA-18, TB-35
diegomaria.pinto@iasi.cnr.it
Istituto di Analisi dei Sistemi ed Informatica "Antonio Ruberti", Consiglio Nazionale delle Ricerche, Italy

Pisinger, David . . . . . . MB-21, MA-26, MB-26, WA-26, MA-29
dapi@dtu.dk
Management Engineering, DTU, Lyngby, Denmark

Pinto, Gavriel David . . . . . . . . . . . . . . . . . . . . . . . . TD-49, WC-49
gabipi@jce.ac.il
Industrial Engineering and Management, Azrieli College ofEngineering Jerusalem, Jerusalem, Israel
Pinto, Maria Cristina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-47
mariacristina.pinto@rse-web.it
Interuniversity Department of Regional and Urban Studies
and Planning, Politecnico di Torino - RSE SpA, Italy
Pinto, Maria José . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-64
mariajosemjp@fab.mil.br
EC4ISR Division, Institute for Advanced Studies (IEAv), SãoJosé dos Campos, São Paulo, Brazil

Pitea, Ariana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-41
arianapitea@yahoo.com
Mathematics and Informatics, National University of Scienceand Technology Politehnica of Bucharest, Bucharest, Romania
Pithan, Philipp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-50
philipp.pithan@whu.edu
WHU - Otto Beisheim School of Management, Germany
Pitt, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-15
m.pitt@exeter.ac.uk
Medical School, University of Exeter, Exeter, Devon, United
Kingdom
Pizzari, Emanuele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-35

631

AUTHOR INDEXEURO 2024 - Copenhagen

emanuele.pizzari@iasi.cnr.it
Istituto di analisi dei sistemi ed informatica "Antonio Ruberti" (IASI), Consiglio Nazionale delle Ricerche (CNR),
Rome, Rome, Italy
Pla, LluisM . . . . . . . . . . . . . . . . . . . . . . . . . . MC-12, TA-12, TC-12
lmpla@matematica.udl.es
Mathematics, University of Lleida, Lleida, Spain
Plaga, Leonie Sara . . . . . . . . . . . . . . . . . . MA-09, TC-19, MA-21
leonie.plaga@ruhr-uni-bochum.de
Ruhr-Universität Bochum, Germany
Plana, Isaac . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-64
isaac.plana@uv.es
Matemáticas para la Economía y la Empresa, University of
Valencia, Valencia, Spain
Plischke, Elmar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-11
epl@tu-clausthal.deInstitut für Endlagerforschung, TU Clausthal, ClausthalZellerfeld, Germany
Plitsos, Stathis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-29
stathisp@unipi.gr
Industrial Management and Technology, University of Piraeus, Greece
Plotz, Patrick . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-57
patrick.ploetz@isi.fraunhofer.deFraunhofer Institute for Systems and Innovation Research
ISI, Germany
Plougonven, Riwal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-22
riwal.plougonven@lmd.ipsl.fr
LMD-IPSL, Ecole Polytechnique, Institut Polytechnique deParis, ENS, PSL Research University, Sorbonne Université,
CNRS, France
Pluchinotta, Irene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-13
i.pluchinotta@ucl.ac.uk
The Bartlett, University College London, London, United
KingdomPöch, Niklas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-58
niklas.poech@prod.uni-hannover.deInstitute of Production Management, Leibniz University Hannover, Germany
Pohl, Erik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-44
erik.pohl@uni-due.de
Business Administration and Production Management, Universität Duisburg-Essen, Duisburg, Germany
Poirion, Pierre-Louis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-04
pierre-louis.poirion@riken.jp
RIKEN-AIP, Japan
Pokorna, Aneta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-52
pokorna@iuuk.mff.cuni.cz
The Institute of Computer Science of the Czech Academy of
Sciences, Prague, Czech Republic
Pokorny, Petr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-23
pokornyp@vse.cz

632

Department of Econometrics, Prague University of Economics and Business, Czech Republic
Pokou, Frédy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-14
fredy-vale-manuel.pokou@inria.fr
INOCS, INRIA, Villeneuve-d’Ascq, Hauts-de-France,
FrancePokutta, Sebastian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-34
pokutta@zib.de
Zuse Institute Berlin, Berlin, Germany
Polak, Sven . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-38
s.c.polak@tilburguniversity.edu
Tilburg University, Netherlands
Polanco, Luis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-22
luis.polanco@cicy.mx
Unidad de Energía Renovable, Centro de Investigación Científica de Yucatán A.C., Merida, Yucatan, Mexico
Polat, Ezgi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-09
eezgipolat@gmail.com
Industrial Engineering, Yıldız Technical University, Turkey
Poldi, Kelly Cristina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-07
kelly@ime.unicamp.br
Institute of Mathematics, Statistics and Scientific Computing (IMECC), State University of Campinas (UNICAMP),
Campinas-SP, São Paulo, Brazil
Poli, Giuliano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-13, TD-47
giuliano.poli@unina.it
Department of Architecture, University of Naples Federico
II, Naples, Italy
Polla, Giorgio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-57
giopolla@amazon.com
Amazon, Spain
Polo, Cristina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-48
cristinapf@unex.es
Universidad de Extremadura, BADAJOZ, Badajoz, Spain
Polyakovskiy, Sergey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-07
sergey.polyakovskiy@deakin.edu.au
Deakin University, Australia
Ponce, Diego . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-29
dponce@us.es
Institute of Mathematics of the University of Seville (IMUS),
Universidad de Sevilla, Sevilla, Spain
Ponsich, Antonin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-19
antonin.sebastien.ponsich@upc.edu
Sistemas, Universidad Autónoma Metropolitana - Azcapotzalco, MEXICO, Distrito Federeal, Mexico
Pontes, António . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-25
pontes@dep.uminho.pt
Institute for Polymers and Composites, Portugal
Pontes, Lara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-52
laradicp@gmail.com
Centro de Informática, Universidade Federal da Paraíba, JoãoEURO 2024 - Copenhagen
Pessoa, Paraíba, Brazil

AUTHOR INDEXKLM Royal Dutch Airlines, Netherlands

Pop, Adrian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-52
Adrian.Pop@liu.se
Linkoping University, Sweden

Povh, Janez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-42
janez.povh@rudolfovo.eu
Rudolfovo, Novo Mesto, Slovenia

Popovic, Alexander Niclas . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-50
niclas.popovic@whu.edu
Chair of Production Management, WHU - Otto Beisheim
School of Management, Vallendar, Germany

Pozas, Diego . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-60
diego.pozas@usach.cl
Department of Industrial Engineering, University of Santiago
of Chile, Santiago, Chile

Poropudas, Jirka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-16
jirka.poropudas@gmail.com
SportIQ, Helsinki, FinlandPozzi, Matteo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-22
matteo.pozzi@optit.net
Optit srl, Bologna, Italy

Porras Cabrera, Álvaro . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-19
alvaroporras19@gmail.com
OASYS Group, University of Málaga, Málaga, Spain

Prabhakar, Aditya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-23
f20adityap@iimidr.ac.in
Operations Management and Quantitative Techniques, Indian
Institute of Management Indore, Indore, Madhya Pradesh, India

Porta, Federica . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-34
federica.porta@unimore.it
Universita’ di Modena e Reggio Emilia, Italy
Porumbel, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-38
daniel.porumbel@cnam.fr
CEDRIC CS Lab, CNAM, Paris, France
Poss, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-35
michael.poss@lirmm.fr
Cnrs, Lirmm, France
Posth, Alexander . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-57
posh@zhaw.ch
Department of Banking, Finance, Insurance, ZHAW Schoolof Management and Law, Winterthur, Switzerland
Potgieter, Linke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-10
lpotgieter@sun.ac.za
Logistics, Stellenbosch University, Matieland, Western Cape,South Africa
Pougkakiotis, Spyridon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-02
s.pougkakiotis@sms.ed.ac.uk
School of Mathematics, University of Edinburgh, Edinburgh,
United Kingdom
Pouliasi, Aliki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-56
apouliasi@mail.ntua.gr
Rural and Surveying Engineering, National Technical University of Athens, Athens, Greece
Pourahmadi, Farzaneh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-03
farpour@dtu.dk
Technical University of Denmark, Lyngby, Denmark
Pourakbari-Kasmaei, Mahdi . . . . . . . . . . . . . . . . . . . . . . . MD-19
mahdi.pourakbari@aalto.fi
Aalto University, Finland
Pournin, Lionel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-42
lionel.pournin@univ-paris13.fr
Université Paris 13, Villetaneuse, France, France
Poursaeidesfahani, Ali . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-54
ali.poursaeidesfahani@klm.com

Pradenas, Lorena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-21
lpradena@udec.cl
Ingeniería Industrial, Universidad de Concepción, Concepción, Concepción, Chile
Prak, Dennis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-58
d.r.j.prak@utwente.nl
University of Twente, Netherlands
Prakhya, Karthik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-04
karthik.prakhya@umu.seUmeå University, Umeå, Sweden
Prangprakhon, Mootta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-41
mootta_prangprakhon@hotmail.com
Department of Mathematics, Khon Kaen University, Khon
Kaen, Thailand
Pranzo, Marco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-60
pranzo@dii.unisi.it
Dipartimento di Ingegneria dell’Informazione, Università di
Siena, Siena, Italy
Prasad, Rahul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-49
prasadrahul991@gmail.com
Management Studies, IIT Madras, Chennai, Tamil Nadu, India
Prasad, Srinivas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-44
prasad@gwu.edu
Decision Sciences, The George Washington University,
Washington, DC, United States
Prat, Eléa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-22
emapr@dtu.dk
DTU, Denmark
Prato, Marco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-34
marco.prato@unimore.it
Università degli studi di Modena e Reggio Emilia, Italy
Praxedes, Rafael . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-18, WA-58
rafael.praxedes@unipr.it
Department of Engineering and Architecture, University of633

AUTHOR INDEXEURO 2024 - Copenhagen

Parma, Parma, Italy
Preis, Henning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-55
henning.preis@tu-dresden.de
Faculty of Traffic and Transportation Sciences, Working
Group Transport Logistics, Technische Universität Dresden,
Germany
Pries, Joris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-04, TD-28
jorispries@gmail.com
CWI, Netherlands
Prieto-Curiel, Rafael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-33
prieto-curiel@csh.ac.atComplexity Science Hub, Austria
Prigent, Jean-Luc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-51
jean-luc.prigent@u-cergy.fr
ThEMA, University of Cergy-Pontoise, Cergy-Pontoise,FrancePrins, Renske . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-24
prins@rsm.nl
Technology and Operations Management, RSM Erasmus
University, Munich, Bavaria, Germany
Prodhon, Caroline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-24
caroline.prodhon@utt.fr
University of Technology of Troyes, Troyes, France
Proença, Hugo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-31
hugo.proenca@booking.com
Booking.com, Netherlands
Prokopyev, Oleg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-12
droleg@pitt.edu
University of Pittsburgh, Pittsburgh, PA, United States
Proske, Frederik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-30
fproske@gams.com
GAMS Software GmbH, Frechen, Germany
Pruyn, Jeroen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-35
J.F.J.Pruyn@tudelft.nl
Maritime and transport technology, Delft University of Technology, Delft, Netherlands
Psaraftis, Harilaos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-08
hnpsar@dtu.dk
Technical University of Denmark, Lyngby, Denmark
Psaromiligkos, Yannis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-45
yannis.psaromiligkos@uniwa.gr
Business Administration, University of West Attica, Athens,
GreecePsarras, John . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-06
john@epu.ntua.gr
Electrical & Computer Engineering, National Technical University of Athens, Athens, Greece
Psarros, Athanasios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-49
apsaros@elval.com
Elval, aluminium rolling division of ElvalHalcor SA, Oinofyta, Viotia, Greece

634

Pucci, Davide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-32
davide.pucci@unifi.it
Department of Information Engineering, University of Florence, Firenze, Italy
Puche, Julio Cesar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-15
jcpuche@ubu.es
University Of Burgos, BURGOS, Spain
Puchert, Christian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-60
christian.puchert@ab-ovo.com
Ab Ovo Deutschland GmbH, Düsseldorf, Germany
Puerto, Justo . . . . . . . . . TD-26, WA-28, TC-29, WA-37, WB-49
puerto@us.es
Estadistica e I.O., Universidad de Sevilla, Sevilla, Spain
Pulido Bravo, Belén . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-27
belen.pulido@uc3m.es
uc3m-Santander Big Data Institute, Universidad Carlos III de
Madrid, Getafe, Madrid, Spain
Pun, Hubert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-61
hpun@ivey.ca
Ivey Business School, London, Canada
Punnen, Abraham . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-29
apunnen@sfu.ca
Simon Fraser University, Canada
Puron, Antonio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-27
antonio.puron@gmail.com
Inoma / Tak-Tak-Tak, Mexico, Ciudad de México, Mexico
Purshouse, Robin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-13
r.purshouse@sheffield.ac.uk
University of Sheffield, United Kingdom
Pustelnik, Nelly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-32
nelly.pustelnik@ens-lyon.fr
Univ Lyon, Ens de Lyon, Univ Lyon 1, CNRS, Laboratoirede Physique, Lyon, Lyon, France
Puttemans, Inez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-61
inez.puttemans@uhasselt.beHasselt University, Belgium
Qi, Xiangtong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-58, MB-64
ieemqi@ust.hk
HKUST, HKUST, Hong Kong, China, Hong Kong
Qin, Yicong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-28
qinyc@csair.com
China Southern Airlines, China
Qiu, Feng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-22
qiu.d.feng@gmail.com
Argonne National Laboratory, United States
Quach, Pascal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-35
pascal.quach@centralesupelec.fr
CentraleSupélec, France
Quagliato Scarelli Cava, Diego Eduardo . . . . . . TA-06, TD-28

EURO 2024 - Copenhagen

AUTHOR INDEX

diego.e.cava@unesp.br
São Paulo State University, Guaratinguetá, São Paulo, Brazil

Industrial Engineering & Management, Ben-Gurion University, Tel Aviv, Israel

Quaglietta, Egidio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-51
e.quaglietta@tudelft.nl
Delft University of Technology, Netherlands

Rabta, Boualem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-39
boualem.rabta@wu.ac.at
WU Vienna, Austria

Quariguasi Frota Net, Joao . . . . . . . . . . . . . . . . . . . . . . . . . WD-24
joao.quariguasifrotanet@manchester.ac.uk
AMBS, University of Manchester, Manchester, United Kingdom

Rachah, Amira . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-23
Amira.Rachah@sintef.noSustainable Energy Technology, SINTEF, Trondheim, Trøndelag, Norway

Quariguasi Frota Neto, Joao . . . . . . . . . . . . . . . . . . . . . . . . WD-59
joao.quariguasifrotaneto@manchester.ac.uk
Alliance Manchester Business School, University of Manchester, Manchester, United Kingdom

Rachaniotis, Nikolaos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-15
nraxan@unipi.gr
Industrial Management & Technology, University of Piraeus,
GreeceQuattrone, Michele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-46
michele.quattrone@airliquide.com
Air Liquide R&d, Les loges en josas, France

Raciti, Fabio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-43
fraciti@dmi.unict.it
Mathematics and Computer Science, University of Catania,
Catania, Italy

Queiroga, Eduardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-58
eduardovqueiroga@gmail.com
Universidade Federal da Paraíba, Brazil
Quesnel, Frédéric . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-29, TB-51
frederic.quesnel@gerad.ca
GERAD, Canada
Quezada, Franco . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-19, TB-49
franco.quezada@usach.cl
Industrial Engineering Department, University of Santiago of
Chile, Chile
Quezada, Luis . . . . . . . . MB-18, WB-44, TA-45, WA-50, TA-60
luis.quezada@usach.cl
Department of Industrial Engineering, University Of Santiago Of Chile, SANTIAGO, SANTIAGO, Chile
Quintanilla, Sacramento . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-10
Maria.Quintanilla@uv.es
Matemáticas para la Economía y la Empresa, University of
Valencia, Valencia, Spain
Quttineh, Nils-Hassan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-37
nils-hassan.quttineh@liu.se
Department of Mathematics, Linköping, Sweden
Raa, Birger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-23, WC-58
birger.raa@ugent.beIndustrial Systems Engineering and Product Design, Ghent
University, Zwijnaarde, Oost-Vlaanderen, Belgium
Raaijmakers, Jonan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-11
jonan.raaijmakers@ru.nl
Nijmegen School of Management, Radboud University,
Netherlands
Raaymann, Sophia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-24
sophia.raaymann@whu.edu
WHU - Otto Beisheim School of Management, Frankfurt,
Germany
Rabinowitz, Gad . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-49, WC-49
rgadi@bgu.ac.ilRačko, Lukáš . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-35
racko@karlin.mff.cuni.cz
Department of probability and mathematical statistics, Faculty of mathematics and physics, Charles university, Prague,
Czech Republic
Radanovic, Luka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-52
lukaradanovich@gmail.com
Faclty of Mathematics, University of Belgrade, Serbia
Radanovits, Regina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-36
regina.radanovits@stud.uni-corvinus.hu
Corvinus University of Budapest, Hungary
Radovanović, Sandro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-48
sandro.radovanovic@fon.bg.ac.rs
Faculty of Organizational Sciences, University of Belgrade,
Belgrade, Serbia
Raffaele, Alice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-16, MD-64
alice.raffaele@unical.it
Department of Mechanical, Energy, and Management Engineering, University of Calabria, Rende, Italy
Raffler, Clemens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-24
c.raffler@tbwresearch.org
tbw research GesmbH, Austria
Rahman, Shimanto. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-28
shimanto.rahman@ugent.be
Economics and Business Administration, Ghent University,
Ghent, East-Flanders, Belgium
Rai, Devansh. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-24
ms21s006@smail.iitm.ac.in
Department of Management Studies, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
Rajendran, Chandrasekharan . . . . . . . . . . . . . . . . . . . . . . . TA-24
crajiitm@gmail.com
Department of Management Studies, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India

635

AUTHOR INDEXEURO 2024 - Copenhagen

Rakhis, Ilyas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-02
I.RAKHIS@ocpgroup.ma
OCP Group, Casablanca, Morocco
Ralphs, Theodore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-30
ted@lehigh.edu
United States
Ramadasu, Venugopal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-23
venugopal.ramadasu@wu.ac.at
Department of Information Systems and Operations Management, Vienna University of Economics and Business, Vienna,
Austria
Ramaekers, Katrien . . . . . . . . . . . . . . . . . WA-15, TA-58, MA-64
katrien.ramaekers@uhasselt.beResearch group Logistics, Hasselt University, Diepenbeek,
Belgium

Ramirez, Zaira . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-53
zairaramirez72@aragon.unam.mx
Ingeniería de Sistemas, Fi Unam, Mexico, Mexico, Mexico
Ramos, Andres . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-19
andres.ramos@comillas.edu
Departamento de Organizacion Industrial - Instituto de Investigacion Tecnologica, Universidad Pontificia Comillas,
Madrid, Spain
Ramos, Angel Manuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-19
angel@mat.ucm.es
Universidad Complutense de Madrid, Madrid, Spain
Ramos, António . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-07
agr@isep.ipp.pt
INESC TEC and School of Engineering, Polytechnic ofPorto, Porto, Portugal

Ramamohan, Varun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-17
varunr@mech.iitd.ac.in
Department of Mechanical Engineering, Indian Institute ofTechnology Delhi, New Delhi, Delhi, India

Ramos, Tania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-35, TB-56
tania.p.ramos@tecnico.ulisboa.pt
IST-UL, Portugal

Raman, Raghu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-18
raghu@amrita.edu
Center for Research in Analytics, Technologies & Education,
Amrita Vishwa Vidyapeetham, Kollam, Kerala, India

Ramosaj, Agneta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-28
agneta.ramosaj@unifr.chInformatique, Universty of Fribourg, Fribourg, Fribourg,
Switzerland

Ramírez, Víctor M. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-22
victor.ramirez@cicy.mx
Centro de Investigación Científica de Yucatán, Mérida, Yucatán, Mexico

Ramosaj, Nicolas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-28
nicolas.ramosaj@hefr.ch- Mechanical Engineering, University of Applied Sciences
and Arts, Western Switzerland, Fribourg, Fribourg, Switzerland

Ramírez-Ayerbe, Jasone . . . . . . . . . . . . . . . . . . . . . MD-27, TA-27
mrayerbe@us.es
Instituto de Matemáticas de la Universidad de Sevilla, Spain
Ramírez-Cobo, Pepa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-39
pepa.ramirez@uca.es
Universidad de Cádiz, Cádiz, Spain
Rambau, Jörg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-28
joerg.rambau@uni-bayreuth.de
Fakultät für Mathematik, Physik und Informatik, LS
Wirtschaftsmathematik, Bayreuth, Bayern, Germany
Rambha, Tarun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-51, WD-51
tarunrambha@iisc.ac.in
Civil Engineering, Indian Institute of Science, Bengaluru,
Karnataka, India

Ramponi, Alessandro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-63
alessandro.ramponi@uniroma2.it
Economics and Finance, Università di Roma Tor Vergata,Rome, Lazio, Italy
Ramu, Palaniappan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-31
palramu@iitm.ac.in
Engineering Design, Indian Institute of Technology Madras,
Chennai, Tamil Nadu, India
Rana, Jeetu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-44
jeeturana@iiml.ac.in
Operations Management, Indian Institute of Management
Lucknow, Lucknow, Uttar Pradesh, India

Ramezanpour Shalmani, Shaghayegh . . . . . . . . . . . . . . . WD-49
b00780596@essec.edu
Operations and data analytics, Essec Business School,
CERGY, France

Ranaboldo, Matteo . . . . . . . . . . . . . . . . . . . . . . . . . MB-14, MB-19
matteo.ranaboldo@upc.edu
Centre d’Innovació Tecnològica en Convertidors Estàtics
i Accionaments (CITCEA), Universitat Politècnica deCatalunya - BarcelonaTech (UPC), Barcelona, Catalonia,
Spain

Ramirez Cobo, Pepa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-27
pepa.ramirez@gm.uca.es
Universidad de Cádiz, Spain

Rancourt, Marie-Ève . . . . . . . . . . . . . . . . . . . . . . . . TB-21, TD-21
marie-eve.rancourt@hec.ca
HEC Montreal, Montreal, Canada

Ramirez, Hector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-42
hramirez@dim.uchile.clCenter for Mathematical Modeling, Universidad de Chile,
Santiago, RM, Chile

Rand, Graham . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-01, WC-20
g.rand@lancaster.ac.uk
retired, United Kingdom

636

EURO 2024 - Copenhagen
Randellini, Niso . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-47
niso.randellini@polimi.it
Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Milano, Italy, Italy
Rangaraj, Narayan . . . . . . . . . . . . . . . . . . . . . . . . . MD-50, WC-56
narayan.rangaraj@iitb.ac.inIndustrial Engineering and Operations Research, Indian Institute of Technology Bombay, Mumbai, India
Ranza, Filippo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-37
filippo.ranza@unibs.it
Department of Information Engineering, University of Brescia, Brescia, Italy
Rapisarda, Massimo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-23
massimo.rapisarda@outlook.it
DIGEP, Politecnico di Torino, Turin, Piemonte, Italy
Ras, Charl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-25
cjras@unimelb.edu.au
School of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia
Rashidipour, Azin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-26
azin.rashidipour@ugent.be
Economic and Business Administration, Gent University,
Gent, Belgium
Rasku, Topi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-09
topi.rasku@vtt.fi
Design and Operation of Energy Systems, VTT TechnicalResearch Centre of Finland, Espoo, Finland
Rastani, Sina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-26
s.rastani@sheffield.ac.uk
Operations Management and Decision Sciences, Sheffield
University Management School, Sheffield, United Kingdom
Rastogi, Rajat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-62
rajat.rastogi@ce.iitr.ac.in
Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Ratti, Luca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-34
luca.ratti5@unibo.it
University of Bologna, Italy
Rauhaus, Davina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-59
davina.hartmann@uni-due.de
Mercator School of Management, University of DuisburgEssen, Duisburg, Germany
Rault, Tifenn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-18
tifenn.rault@univ-tours.fr
Polytech Tours, University of Tours, Tours, France
Rauniar, Rupak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-08
rauniarr@uhd.edu
Supply Chain, University of Houston-Downtown, Houston,
TX, United States
Rauschert, Felix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-55
felix.rauschert@tu-dresden.de
Big Data Analytics in Transportation, TU Dresden, Germany

AUTHOR INDEX

Rautio, Siiri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-34
siiri.rautio@helsinki.fi
University of Helsinki, Finland
Ravagnani, Adele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-40
adele.ravagnani@sns.it
Scuola Normale Superiore, Pisa, Italy
Ravaja, Niklas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-11
niklas.ravaja@helsinki.fi
Helsinki University, Helsinki, Finland
Ravanos, Panagiotis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-44
panagiotis.ravanos@ec.europa.eu
Directorate S: Innovation in Science and Policymaking, Unit
S3: Science for Modelling, Monitoring and Evaluation, European Commission, Joint Research Center, ISPA, Italy
Ravner, Liron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-40
lravner@stat.haifa.ac.il
Statistics, University of Haifa, Israel
Ray, Devpriyo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-24
phd20devpriyor@iima.ac.in
Indian Institute of Management, Ahmedabad, Ahmedabad,
Gujarat, India
Rêgo, Leandro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-36, WB-36
leandro@dema.ufc.br
Statistics and Applied Math Department, Universidade Federal do Ceará, Fortaleza, Ceará, Brazil
Rayzer, Lucas Pietro Biasi . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-15
lucas.rayzer7733@edu.udesc.br
Universidade do Estado de Santa Catarina, Brazil
Razavi, Saiedeh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-62
razavi@mcmaster.ca
McMaster University, Canada
Rea, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-20
dar421@lehigh.edu
DATA, Lehigh University, Bethlehem, Pennsylvania, UnitedStatesRebaudengo, Manuela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-18
manuela.rebaudengo@polito.it
DIST, Politecnico di Torino, Torino, Italia, Italy
Rebegoldi, Simone . . . . . . . . . . . . . . . . . . . TD-32, MA-34, MB-34
simone.rebegoldi@unimore.it
Dipartimento di Scienze Fisiche, Informatiche e Matematiche, Università di Modena e Reggio Emilia, Italy
Rebennack, Steffen . . . . . . . . . . . . . . . . . . . . . . . . . . MD-08, TA-09
steffen.rebennack@kit.edu
Institute of Operations Research (IOR), Karlsruhe Institute ofTechnology (KIT), Karlsruhe, Germany
Recupero, Giuseppe Antonio . . . . . . . . . . . . . . . . . . . . . . . . TA-32
giuseppe.recupero3@unibo.it
Mathematics, University of Bologna, Bologna, Italy
Rehman, Masood Ur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-29

637

AUTHOR INDEXEURO 2024 - Copenhagenmasoodqau27@gmail.com
Physics and Mathematics, Anqing Normal University, Anqing, China

susana.relvas@tecnico.ulisboa.pt
CEG-IST, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

Rei, Walter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-21
rei.walter@uqam.ca
CIRRELT and UQAM, Montreal, Canada

Remijnse, Marloes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-13
m.remijnse@tue.nl
Industrial Engineering and Innovation Science, Eindhoven
University of Technology, Netherlands

Reicherz, Ricardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-51
reicherz@rhrk.uni-kl.de
RPTU Kaiserslautern-Landau, Kaiserslautern, Germany
Reiff, Marian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-53
reiff.marian@gmail.com
Department of Operations Research and Economics, University of Economics in Bratislava, Slovakia
Reiffers, Alexandre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-40
alexandre.reiffers-masson@imt-atlantique.fr
FranceReihaneh, Mohammad . . . . . . . . . . . . . . . . . . . . . . TB-15, MB-25
m.reihaneh@ieseg.fr
Operations Management, IESEG School of Management(LEM-CNRS 9221), France
Reijnen, Robbert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-03
r.v.j.reijnen@tue.nl
IE&IS, TU Eindhoven, Netherlands
Reimann, Marc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-23
marc.reimann@uni-graz.at
Operations and Information Systems, Universität Graz, Graz,
Austria

Remoy, Hilde . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-47
h.t.remoy@tudelft.nl
Management in the Built Environment, TU Delft, Delft,
Netherlands
Ren, Xiaorong. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-56
xiaorongren1218@163.com
Northwestern Polytechnical University, Xian, China
Ren, Xuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-52
renxuan@mail.nwpu.edu.cn
School of Management, Northwestern Polytechnical University, Xi’an, Shaanxi, China
Rener, Elena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-60
elena.rener@unibe.ch
University of Bern, Switzerland
Repoussis, Panagiotis . . . . . . . . . . . . . . . . . . . . . . . . TB-26, WC-26
prepousi@aueb.gr
Department of Marketing and Communication, Athens University of Economics and Business, Greece
Repusseau, Felix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-18
felix.repusseau@etu.univ-tours.fr
Polytech Tours, University of Tours, Tours, France

Reiner, Gerald . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-23, WD-23
gerald.reiner@wu.ac.at
Department of Information Systems and Operations Management, Vienna University of Economics and Business, Vienna,
Austria

Requejo, Cristina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-25
crequejo@iseg.ulisboa.pt
CEMAPRE/ISEG School of Economics and Management,
University of Lisbon, Lisboa, Portugal

Reinhardt, Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-62, MD-62
liner@ruc.dk
Institute for people and technology, Roskilde universitet,
Roskilde, Denmark

Resat, Giray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-55
giray.resat@ieu.edu.tr
Industrial Engineering, Izmir University of Economics,
TurkeyReis, Bernardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-44
bernardosouzareis@gmail.com
Universidade Federal de Minas Gerais, Brazil

Resheff, Yehezkel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-55
hezi.resheff@gmail.com
Business School, Hebrew University, Israel

Reisach, Ulrike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-20
ulrike.reisach@hnu.de
Information Management Department, Neu-Ulm Universityof Applied Sciences, Neu-Ulm, Bavaria, Germany

Rettinger, Moritz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-45
moritz.rettinger@tum.de
Logistics and Supply Chain Management, TUM School ofManagement, Technical University of Munich, Munich, Germany

Reiter, Kendra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-54
kendra.reiter@uni-wuerzburg.de
Institute for Computer Science, University of Würzburg,
Würzburg, Germany
Relund Nielsen, Lars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-52
larsrn@econ.au.dk
Economics, Aarhus University, Aarhus, Denmark
Relvas, Susana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-24

638

Reusens, Manon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-31
manon.reusens@kuleuven.be
Faculty of Business and Economics, KU Leuven, Leuven,
Vlaams Brabant, Belgium
Reusken, Meike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-64
M.C.D.Reusken_1@tilburguniversity.edu
Tilburg University, Rotterdam, Zuid-Holland, Netherlands

EURO 2024 - Copenhagen
Reuter-Oppermann, Melanie . . . . . . . . . . . . . . . . . . . . . . . MB-10
m.n.reuter-oppermann@utwente.nl
BMS, University of Twente, Enschede, Netherlands
Rey, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-29
d.rey@unsw.edu.au
School of Civil and Environmental Engineering, UNSW,
Sydney, NSW, Australia
Reyes, Nicolás . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-12
nicolasreyes@utalca.cl
Programa de Doctorado en Sistemas de Ingeniería, Universidad de Talca, Curicó, Chile
Reyes, Patricio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-09
patricio.reyes@bsc.es
CASE, BSC, Spain
Reyes, Valentina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-31
vareyes@ug.uchile.cl
Depertment of Industrial Engineering, University of Chile,
Santiago, Chile
Reynolds, Eleanor . . . . . . . . . . . . . . . . . . . . . . . . . . WB-11, MC-13
eleanor.reynolds@wbs.ac.uk
Warwick Business School, University of Warwick, Coventry,
West Midlands, United Kingdom
Reza-Gharehbagh, Raziyeh . . . . . . . . . . . . . . . . . . . . . . . . . . TC-53
rreza-gharehbagh@em-normandie.fr
Supply Chain & Management Digital, EM Normandie Business School, Le Havre, France
Rezaee Vessal, Sara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-49
rezaee@essec.edu
Operations Management, ESSEC Business School, CergyPontoise, Not Applicable, France
Rezaei, Jafar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-11, MB-44
j.rezaei@tudelft.nl
Engineering Systems and Services, Delft University of Technology, Delft, Netherlands
Rezaei, Mostafa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-31
mrezaei@escp.eu
Information and Operations Management, ESCP Business
School, Paris, France

AUTHOR INDEX

Ricart, Sandra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-43
sandra.ricart@polimi.it
Department of Electronics, Information and Bioengineering,
Politecnico di Milano, Milan, Italy
Ricci, Cristiano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-33
cristiano.ricci@ec.unipi.it
Università di Pisa, Pisa, Italy
Ricci, Nadia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-63
nadia.ricci@studenti.unipd.it
DII-Management, Università degli studi di Padova-Università
Politecnica delle Marche, Campello Sul Clitunno, PERUGIA,
Italy
Riccietti, Elisa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-32
elisa.riccietti@ens-lyon.fr
LIP, ENS Lyon, France
Richtarik, Peter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-32
richtarik@gmail.com
Computer Science, KAUST, Thuwal, Saudi Arabia
Richter, Rónán Rian Carl . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-28
ronan.richter@uni-bayreuth.de
LS Wirtschaftsmathematik, Universität Bayreuth, Bayreuth,
Germany
Rieck, Julia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-54, MC-60
rieck@bwl.uni-hildesheim.deOperations Research Group, University of Hildesheim,
Hildesheim, Germany
Rieger, Paul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-54
rieger@verkehr.tu-darmstadt.deInstitute for Railway Engineering, TU Darmstadt, Darmstadt,
Hessen, Germany
Riepin, Iegor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-09, TD-30
iegor.riepin@gmail.com
Digital Transformation in Energy Systems, Technical University of Berlin, Berlin, Germany
Riera, Jefferson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-19
jefferson.riera@kaust.edu.sa
King Abdullah University of Science and Technology, Saudi
ArabiaRezeq, Mohanad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-21
mohanadim.rezeq@ugent.be
Department of Business Informatics and Operations Management, Ghent University, Gent, Belgium

Riera-Ledesma, Jorge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-25
jriera@ull.edu.es
University of La Laguna, Spain

Ribeiro, Glaydston . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-55
glaydston@pet.coppe.ufrj.br
COPPE, Federal University of Rio de Janeiro, Rio de Janeiro,
RJ, Brazil

Ries, Jana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-62
jana.ries@port.ac.uk
Portsmouth Business School, University of Portsmouth,
United Kingdom

Ribesmeier, Matthias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-55
matthias.ribesmeier@tu-dresden.de
Chair Of Railway Operations, TU Dresden, Germany

Riesenegger, Lena . . . . . . . . . . . . . . . . . . . . . . . . . . WD-13, MA-50
lena.riesenegger@tum.de
Supply and Value Chain Management, Technical Universityof Munich, Germany

Ricard, Léa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-52
lea.ricard@epfl.ch
Epfl Enac Iic Transp-or, Lausanne, Switzerland

Rigó, Petra Renáta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-38
petra.rigo@uni-corvinus.hu

639

AUTHOR INDEXEURO 2024 - Copenhagen

Corvinus University of Budapest, Budapest, Hungary
Rijal, Arpan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-64
a.rijal@rug.nl
University of Groningen, Groningen, Netherlands
Rincon-Zapatero, Juan Pablo . . . . . . . . . . . . . . . . . . . . . . . MB-41
jrincon@eco.uc3m.es
Economia, Universidad Carlos III de Madrid, Getafe, Madrid,
Spain
Rios, Ignacio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-43
ignacio.riosuribe@utdallas.edu
University of Texas at Dallas, Richardson, TX, United States
Riquelme, Fabian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-36
fabian.riquelme@uv.cl
Escuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso, Chile
Riquelme, Victor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-15
vriquelme@dim.uchile.clCenter for Mathematical Modeling, Universidad de Chile,
Santiago, Chile

Almenara, Minas Gerais, Brazil
Rocha, Pedro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-07
pmonteirorocha@sapo.pt
INESCTEC and School of Engineering, Polytechnic of Porto,
Porto, Portugal
Rocha, Tomás . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-56
tomasrocha@tecnico.ulisboa.pt
IST, Portugal
Rodomanov, Anton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-32
anton.rodomanov@cispa.de
CISPA Helmholtz Center for Information Security, Select,
Germany
Rodríguez Álvarez, Margarita . . . . . . . . . . . . . . . . . . . . . . WA-42
marga.rodriguez@ua.es
Dpto. Matemáticas, Universidad de Alicante, San Vicentedel Raspeig, Alicante, Spain
Rodríguez Ballesteros, Sofía . . . . . . . . . . . . . . . . . . . . . . . . MD-60
sofia.rodriguezb@umh.es
Center of Operations Research, Miguel Hernández University of Elche, ELCHE/ELX, ALICANTE/ALACANT, Spain

Risstad, Morten . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-57
morten.risstad@ntnu.no
Industrial Economics and Technology Management, NTNU,
Trondheim, Norway

Rodríguez Barreiro, Marta . . . . . . . . . . . . . . . . . . . . . . . . . MC-45
marta.rodriguez.barreiro@udc.es
Universidade da Coruña, Spain

Ritter, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-37
michael.ritter@tum.de
Department of Mathematics, Technical University of Munich, Germany

Rodríguez Martín, Inmaculada . . . . . . . . . . . . . . . . . . . . . MC-25
irguez@ull.edu.es
Matemáticas, Estadística e Investigación Operativa, Universidad de La Laguna, La Laguna, Tenerife, Spain

Ritzinger, Ulrike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-45
ulrike.ritzinger@ait.ac.at
Center for Energy, AIT Austrian Institute of Technology
GmbH, Vienna, Vienna, Austria

Rodríguez-Acevedo, Iria . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-30
iriarodriguez.acevedo@usc.es
University of Santiago de Compostela, Spain

Rizzini, Giorgio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-02
giorgio.rizzini@sns.it
Italy
Roa, Joaquín . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-27, MB-31
joaquin.roa.l@uchile.cl
Centre for Public Systems, University of Chile, Santiago,
Chile
Robert, Anna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-22
anna.robert@totalenergies.com
TotalEnergies R&D, Paris, France
Roberti, Roberto . . . . . . . . . . . . . . . . . . . . MD-52, TD-58, MD-64
roberto.roberti@unipd.it
Information Engineering, University of Padova, Padova, Italy
Robine, André . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-51
andre.robine@gmail.com
Production enginnering, Universidade Federal de Ouro Preto,
Ouro Preto, Minas Gerais, Brazil
Rocha, Luiz Celio Souza . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-19
luizrochamg@hotmail.com
Management, Federal Institute of Northern Minas Gerais,

640

Rodrigues, Filipe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-62
frodrigues@iseg.ulisboa.pt
ISEG, University of Lisbon, Portugal
Rodrigues, Filipe . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-51, WA-62
rodr@dtu.dk
DTU Management, Technical University of Denmark, Kgs.
Lyngby, Denmark
Rodriguez, Joaquin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-51
joaquin.rodriguez@univ-eiffel.fr
Cosys-Estas, University Gustave EIffel, Villeneuve d’Ascq,
FranceRodriguez-Benitez, Luis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-34
luis.rodriguez@uclm.es
Technologies and Information Systems, University ofCastilla-La Mancha, Spain
Rodriguez-Chia, Antonio Manuel . . . . . WA-12, TB-26, TA-61
antonio.rodriguezchia@uca.es
Estadistica e IO, Universidad de Cádiz, Puerto Real (Cadiz),
Cadiz, Spain
Roets, Bart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-07, TA-11
bart.roets@infrabel.beEURO 2024 - Copenhagen
Traffic Management & Services, Infrabel, Brussels, NA, Belgium
Rogetzer, Patricia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-23
p.b.rogetzer@utwente.nl
Department Industrial Engineering & Business Information
Systems, University of Twente, Enschede, Netherlands
Rognes, Christian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-34
christian.rognes@ntnu.no
NTNU, Norway
Rohmer, Sonja . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-13, WD-13
sonja-ursula-katharina.rohmer@hec.ca
Department of Logistics and Operations Management, HEC
Montreal, Montreal, Canada

AUTHOR INDEX

Rondepierre, Aude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-34
aude.rondepierre@insa-toulouse.fr
Département Génie Mathématiques et Modélisation, INSA
Toulouse, France
Roodhooft, Filip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-17
filip.roodhooft@vlerick.com
Accounting & Finance, Vlerick Business School, Ghent, Belgium
Ropke, Stefan . . . . . . . . . . . . . . . . . . . . . . MA-57, MC-58, WA-64
ropke@dtu.dk
Department of Management Engineering, Technical University of Denmark, Kgs. Lyngby, Danmark, Denmark

Rohmer, Sonja . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-64
sonja.rohmer@yahoo.com
HEC Montréal, Montreal, Québec, Canada

Rosehart, William D. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-53
rosehart@ucalgary.ca
Department of Electrical and Software Engineering, University of Calgary, CALGARY, Alberta, Canada

Rohner, Jakob . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-53
jakob.rohner@rwth-aachen.de
School of Business and Economics, RWTH Aachen University, Aachen, Germany

Rosell, Francisca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-12
francisca.rosell@eurecat.org
Applied Artificial Intelligence Unit, Eurecat, Barcelona, Catalonia, Spain

Rolfes, Jan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-04
jrolfes@kth.se
Optimization and System Theory, Royal Institute of Technology (KTH) Stockholm, Stockholm, Sweden

Rosenberg, Sonja . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-21, TA-23
sonja.rosenberg@kit.edu
Institute for Industrial Production (IIP / KIT), KIT, Karlsruhe,
BW, Germany

Roma, Marco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-25
marco.roma@unifi.it
Dipartimento di Ingegneria dell’Informazione, Università diFirenze, Codigoro, Italy

Rosenova, Yavora . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-20
yavorakazakova@gmail.com
Scenario Simlab, Bulgaria

Romano, Giulia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-44
giulia.romano@unipi.it
Via Ridolfi, 10, Department of Economics and Management,
Pisa, Italy
Romanova, Tatiana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-07
sherom@kharkov.ua
Department of Mathematical Modeling and Optimal Design,
Institute for Mechanical Engineering Problems of the National Academy of Sciences of Ukraine, Kharkov, Ukraine
Romanova, Tetyana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-07
tarom27@yahoo.com
Leeds University Business School, University of Leeds,
Leeds, United Kingdom
Romeijnders, Ward . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-35
w.romeijnders@rug.nl
Department of Operations, University of Groningen, Netherlands

Roshandel, Yasmin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-49
yasmin.roshandel@pon.com
Analytics & Optimisation, VU Amsterdam, Amsterdam,
Noord-Holland, Netherlands
Roshani, Abdolreza . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-31
a.roshani@surrey.ac.uk
Business Analytics and Operations, Surrey Business School,
Guildford, Surrey, United Kingdom
Rosolia, Ugo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-57
ugo.rosolia@gmail.com
Luxembourg
Rossi, Roberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-49
robros@gmail.com
Business School, University of Edinburgh, Edinburgh, United
Kingdom
Rossini, Enrico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
enrico.rossini@unimore.it
University of Modena and Reggio Emilia, Italy

Römer, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-03
michael.roemer@uni-bielefeld.de
Decision Analytics Group, Bielefeld University, Bielefeld,
Germany

Rossitti, Marco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-47
marco.rossitti@polimi.it
DAStU, Politecnico di Milano, Milano, Lombardia, Italy

Romero Morales, Dolores . . . . . . . . . . . . MB-27, MD-27, TA-27
drm.eco@cbs.dk
Copenhagen Business School, Frederiksberg, Denmark

Rotella Junior, Paulo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-19
paulo.rotela@gmail.com
Faculty of Social Sciences, Charles University, Czech Re-

641

AUTHOR INDEXEURO 2024 - Copenhagen

public, and Federal University of Paraiba - UFPB, Brazil
Roth, Sarah . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-51
s.roth@rptu.de
Germany
Rottembourg, Benoit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-27
rottembourg@yahoo.com
Regalia, INRIA, Boulogne Billancourt, France
Rottner, Cecile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-25
cecile.rottner@gmail.com
Edf R&d, France
Rotundo, Giulia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-63
giulia.rotundo@uniroma1.it
Statistical sciences, Sapienza University of Rome, Roma,
RM, Italy
Roubelat, Fabrice . . . . . . . . . . . . . . . . . . . . . . . . . . WB-11, WC-11
fabrice.roubelat@univ-poitiers.fr
Unesco chair on Foresight and strategic international intelligence, University of Poitiers - IAE - Cerege, Niort, France
Roudneshin, Maryam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-23
rudneshinmaryam@yahoo.com
University College Dublin, dublin, Dublin, Ireland
Rougerie, Tiphaine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-57
tiphaine.rougerie@outlook.fr
LocalSolver, Paris, France
Roura, Salvador . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-36
roura@cs.upc.edu
Computer Science, Universitat Politècnica de Catalunya BarcelonaTech, Barcelona, Spain
Rouse, Paul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-48
p.rouse@auckland.ac.nz
Accounting & Finance, University of Auckland, Auckland,
New Zealand
Roussanaly, Simon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-19
Simon.Roussanaly@sintef.noGas Technology, SINTEF Energy Research, Norway
Rousseau, Louis-Martin . . . . . . . . . . . . MB-04, MA-35, MD-52
louis-martin.rousseau@polymtl.ca
Mathematic and Industrial Engineering, École Polytechniquede Montréal, Montréal, QC, Canada
Rouwette, Etienne . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-11, TC-11
e.rouwette@fm.ru.nl
Nijmegen School of Management, Radboud University Nijmegen, Nijmegen, Netherlands
Roux, Rachel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-42
rachel.roux@mbda-systems.com
MBDA, France
Rowland, Nicholas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-11
njr12@psu.edu
Penn State University, United States
Roy, Debjit . . . . . . . . . . . . . . . . . . MA-29, MD-50, TA-58, WD-62

642

debjit@iima.ac.in
Production and Quantitative Methods, Indian Institute ofManagement Ahmedabad, Ahmedabad, Gujarat, India
Roy, Jagannath . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-58
jroy@nitw.ac.in
MATHEMATICS, Nit Warangal, WARANGAL, TELANGANA, India
Roy, Santanu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-08
rsan58@yahoo.co.uk
Icfai Business School (ibs) Dehradun, The Icfai University,
DEHRADUN, UTTARAKHAND, India
Rroji, Edit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-63
edit.rroji@unimib.it
University of Milano-Bicocca, Milan, Italy
Rubiales Caballero, Victoriana . . . . . . . . . . . . . . . . . . . . . . MC-36
vrubiales@us.es
Economía Aplicada III, Universidad de Sevilla, Sevilla, España, Spain
Rubino, Giacomo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-53
giacomo.rubino@unige.ch
Renewable Energy Systems, University of Geneva, Genève,
Switzerland
Rubio-Herrero, Javier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-50
javier.rubioherrero@unt.edu
Information Technology and Decision Sciences, Universityof North Texas, Denton, TX, United States
Rudby, Julia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-55
julia.rudby@ntnu.no
Department of Industrial Economics and Technology Management, Norwegian Univrsity of Science and Technology,
NorwayRudholm, Niklas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-31
niklas.rudholm@hfi.se
Gävle, Sweden
Rudi, Andreas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-61
andreas.rudi@kit.edu
Institute for Industrial Production (IIP), Karlsruhe Institute ofTechnology (KIT), Karlsruhe, BW, Germany
Rudolf, Janja . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-45
janja.rudolf@bf.uni-lj.si
Department of Agriculture, Biotechnical Faculty, Ljubljana,
Slovenia
Ruehmkorf, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-18
s223309@student.dtu.dk
Technical University of Denmark, Denmark
Ruela, Victor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-19
victor.ruela@tuwien.ac.at
Institute of Energy Systems and Thermodynamics, TU Wien,
Vienna, Austria
Ruffini, Alessandra . . . . . . . . . . . . . . . . . . . . . . . . . . WA-02, TD-39
alessandra.ruffini@unibs.it
Department of Economics and Management, University ofEURO 2024 - Copenhagen
Brescia, Brescia BS, Italy

AUTHOR INDEXMathematics, University of Padova, Padova, Italy

Rufián-Lizana, Antonio . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-41
rufian@us.es
University of Seville, Sevilla, Spain

Russo, Antonio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-40
a.russo@consob.it
Commissione Nazionale per le Società e la Borsa, Italy

Ruggiero, Valeria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-34
valeria.ruggiero@unife.it
Università di Ferrara, Italy

Russo, Davide Donato . . . . . . . . . . . . . . . . . . . . . . MA-18, WC-53
davide.russo@unimol.it
University of Molise, Italy

Ruggles, Tyler H . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-14
truggles@carnegiescience.edu
Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, United States

Ryan, Jennifer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-39
jennifer.ryan@unl.edu
College of Business Administration, University of NebraskaLincoln, Lincoln, NE, United States

Ruhnau, Oliver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-09
ruhnau@hertie-school.org
Hertie School, Berlin, Germany

Rybarczyk, Agnieszka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-20
arybarczyk@cs.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poznan, Poland

Ruiz, Ana Belen . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-11, WC-44
abruiz@uma.es
Applied Economics (Mathematics), University of Malaga,
Malaga, Spain
Ruiz, Angel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-10
angel.ruiz@fsa.ulaval.ca
Opérations et systèmes de décision, Université Laval,
Québec, Quebec, Canada
Ruiz, Angel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-55
Angel.Ruiz@osd.ulaval.ca
Department of Operations and Decision Systems, Universite
Laval, Quebec, Canada
Ruiz, Carlos . . . . . . . . . . . . . . . . . . . . . . . . WA-02, MA-19, MB-35
caruizm@est-econ.uc3m.es
Universidad Carlos III de Madrid, Leganes, Choose Any
State/Province, Spain
Ruiz, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-55
daniel.ruiz@toulouse-inp.fr
IRIT, INP Toulouse, France
Ruiz, Francisco. . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-11, WC-44
rua@uma.es
Applied Economics (Mathematics), University of Málaga,
Málaga, Spain
Ruiz, Manuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-22
manuel.ruiz@rte-france.com
RTE, Paris La Défense Cedex, France
Ruiz, Rubén . . . . . . . . . . . . . . . . . . . . . . . . . MC-01, WC-46, TC-62
rruiz@eio.upv.es
Departamento de Estadistica e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Spain
Ruiz-Garzón, Gabriel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-41
gabriel.ruiz@uca.es
Estadistica e I.O., University of Cadiz, Jerez de la Frontera,
Cádiz, Spain
Runggaldier, Wolfgang . . . . . . . . . . . . . . . . . . . . . . MC-57, TA-57
runggal@math.unipd.it

Rygg, Erlend Stegavik. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-57
erlendrygg@gmail.com
NorwayRyner, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-04
martinrr@kth.se
KTH Royal Institute of Technology, Sweden
Rytter, Niels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-11
ngry@iti.sdu.dk
SDU, Denmark
Saavedra-Nieves, Alejandro . . . . . . . . . . . . . . . . . MC-36, MD-36
alejandro.saavedra.nieves@usc.es
Estatística, Análise Matemática e Optimización, Universidade de Santiago de Compostela, Santiago de Compostela,
Spain
Saban, Daniela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-43
dsaban@dc.uba.ar
UBA, Buenos Aires, Argentina
Saberi, Sara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-23, TC-24
ssaberi@wpi.edu
Business School, Worcester Polytechnic Institute, Worcester,
Massachusetts, United States
Sabino, Emerson R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-36
emerson.rodrigues055@gmail.com
Management Engineering, Federal University of Pernambuco, Recife, Pernambuco, Brazil
Sabouri, Alireza . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-07
alireza.sabouri@haskayne.ucalgary.ca
University of Calgary, Canada
Saccani, Irene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-18
irene.saccani@unipr.it
Department of Engineering and Architecture, University ofParma, Parma, PR, Italy
Sacco, Sabrina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-13
sabrina.sacco@unina.it
Architecture, University of Naples Federico II, Portici,
Napoli, Italy

643

AUTHOR INDEXEURO 2024 - Copenhagen

Sachan, Swati . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-04, TD-37
swati.sachan@liverpool.ac.uk
Management School, University of Liverpool, Liverpool, Choose a state-, United Kingdom

Sahin Zorluoglu, Ozge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-44
ozgesahinzorluoglu@arel.edu.tr
Industrial Engineering, Istanbul Arel University, İstanbul, İstanbul, Turkey

Sachs, Anna-Lena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-50
a.sachs@lancaster.ac.uk
Management Science, Lancaster University, United Kingdom

Sahin, Halenur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-21
halenursahin@gmail.com
Industrial Engineering Dept., Tobb Etu, Turkey

Sacone, Simona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-62
simona.sacone@unige.it
DIST, University of Genova, Genova, Italy

Sahin, Ocan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-52
ocansahin@outlook.com
R&D, Tüpraş, Kocaeli, Korfez, Turkey

Sadanoglu, Eylul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-18
eylulsadanoglu@hotmail.co.uk
Business, University of Greenwich, London, United Kingdom

Sahin, Umit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-22
umit.sahin@sabanciuniv.edu
Sabancı Üniversity, İstanbul Policy Center, Turkey

Saddikuti, Venkataramanaiah . . . . . . . . . . . . . . . . . . . . . . MC-21
svenkat@iiml.ac.in
Operations Management, Indian Institute of Management
Lucknow, Noida, Uttar Pradesh, India
Sadeghi, Jafar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-48
jsadegh@uwo.ca
Ivey Business School, Western University, London Ontario,
CanadaSadeghi, Niloufar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-59
nsadeghi@mail.uni-mannheim.de
Chair of Service Operations Management, University ofMannheim, Mannheim, Germany, Germany
Sadeh, Arik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-59
sadeh@hit.ac.il
Industrial Engineering and Technology Management Faculty,
HIT Holon Institute of Technology, Holon, Israel
Sadoine, Louise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-09
535862@umons.ac.beMathematics, Université de Mons (UMONS), Mons, Hainaut, Belgium
Saeednia, Mahnam . . . . . . . . . . . . . . . . . . . . . . . . . . TD-62, WA-62
m.saeednia@tudelft.nl
Tu delft, Delft, Netherlands
Saftiuc, Bogdan Paul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-31
bobbysaftiuc@gmail.com
Bucharest University of Economic Studies, Romania
Sage, Felix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-16
felix.sage@uni-due.de
Mercator School of Management, University of DuisburgEssen, Duisburg, Germany
Sager, Sebastian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-38
sebastian.sager@iwr.uni-heidelberg.de
Faculty of Mathematics, Otto-von-Guericke-Universität
Magdeburg, Magdeburg, Germany
Sahay, Satyam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-24
SahaySatyam@JohnDeere.com
John Deere, Pune, India

644

Sahinidis, Nikolaos . . . . . . . . . . . . . . . . . . . . . . . . . WB-04, MB-30
nikos@gatech.edu
Georgia Institute of Technology, Atlanta, GA, United States
Sahinyazan, Feyza . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-21, TC-21
feyza_sahinyazan@sfu.ca
Technology & Operations Management, Simon Fraser University, Burnaby, Canada
Sahli, Abderrahim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-60
abderrahim.sahli@esiee.fr
UGE, ESIEE Paris, France
Sahling, Florian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-24
florian.sahling@wiwi.uni-kl.de
Chair of Production Management, University of Kaiserslautern, Kaiserslautern, Germany
Sahoo, Saumyaranjan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-45
saumyaranjans@iimsambalpur.ac.in
Operations Management & Decision Sciences, Indian Institute of Management Sambalpur, Sambalpur, Odisha, India
Saint-Pierre, Cecilia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-27
csaintpierre@uchile.cl
Universidad de Chile, Santiago, Santiago, Chile
Sajadi, Seyed Mojtaba . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-23
s.sajadi@aston.ac.uk
Operations and Information Department, Aston University,
United Kingdom
Saka, Onur Can . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-57
sakonur@amazon.com
SNT, Amazon, Luxembourg
Sakalauskas, Leonidas . . . . . . . . . . . . . . . . . . . . . . WD-07, MD-65
leonidas.sakalauskas@mif.vu.lt
Applied Informatics, Vytautas Magnus University, Kaunas,
Lithuania
Sakarya, Edhem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-13
i.e.sakarya@tue.nl
Industrial Engineering and Innovation Sciences, Eindhoven
University of Technology, Netherlands
Sala-Garrido, Ramon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-53

EURO 2024 - Copenhagensala@uv.es
University of Valencia, Valencia, Spain
Salameh, Osama . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-39
osama.salameh@aaup.edu
Faculty of Engineering, Arab American University, Palestine,
State of
Salani, Matteo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-26
matteo.salani@idsia.ch
Idsia - Usi/supsi, Lugano, Switzerland
Salanova Grau, Josep Maria . . . . . . . . . . . . . . . . . . . . . . . . MC-18
jose@certh.gr
CERTH-HIT, Greece
Salari, Majid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-29
msalari@um.ac.ir
Department of Industrial Engineering, Ferdowsi Universityof Mashhad, Mashhad, Iran, Islamic Republic ofSalassa, Fabio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-60
fabio.salassa@polito.it
DAUIN, Politecnico di Torino, Italy
Salazar González, Juan José . . . . . . . . . . . . . . . . . TA-26, MD-29
jjsalaza@ull.es
Estadística e Investigación Operativa, Universidad de La Laguna (Tenerife), La Laguna, Tenerife, Spain
Saldanha, Rodney . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-36
rodney@cpdee.ufmg.br
Departamento de Engenharia Elétrica, Universidade Federal
de Minas Gerias, Belo Horizonte, Minas Gerais, Brazil

AUTHOR INDEXSalvagnin, Domenico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-58
dominiqs@gmail.com
DEI, University of Padova, Padova, PD, Italy
Sama, Miguel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-41
msama@ind.uned.es
Matemática Aplicada, Universidad Nacional de Educación aDistancia, UNED, Madrid, Spain
Samaniego, Dani . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-36
daniel.samaniego.vidal@upc.edu
Matemàtiques, Universitat Politècnica de Catalunya, Spain
Samà, Marcella . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-60
marcella.sama@uniroma3.it
Roma Tre University, Roma, Italy
Samà, Marcella . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-51
sama@dia.uniroma3.it
Roma Tre University, Italy
Samaranayake, Samitha . . . . . . . . . . . . . . . . . . . . WC-38, WA-56
samitha@berkeley.edu
UC Berkeley, Berkeley, United States
Samaris, Michalis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-29
michsam@aueb.gr
Department of Science & Technology, Athens University of
Economics and Business, Kaisariani, -Select-, Greece
Sammarra, Marcello . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-03
sammarra@icar.cnr.it
Istituto di Calcolo e Reti ad Alte Prestazioni, Consiglio
Nazionale delle Ricerche, Rende (CS), Italy

Saldanha-da-Gama, Francisco . TC-21, TA-26, TB-26, MD-60
francisco.saldanha-da-gama@sheffield.ac.uk
Sheffield University Management School, Sheffield, United
Kingdom

Samur, Mustafa Oktay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-52
oktay.samur@tupras.com.tr
Digital Solutions, TÜPRAŞ, Kocaeli, Turkey

Salice, Andrea. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-18
andrea.salice@compagniadisanpaolo.it
Fondazione Compagnia di San Paolo, Torino, Italy, Italy

San Segundo, Pablo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-52
pablo.sansegundo@upm.es
Polytechnic University of Madrid, Madrid, Spain

Saliju, Suad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-24
suad.saliju@uni-bayreuth.de
Chair of Food Supply Chain Management, University of
Bayreuth, Kulmbach, Germany

Sanchez Pineda, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-24
daniel.sanchez-pineda@imt-atlantique.fr
Département Automatique, Productique et Informatique(DAPI), IMT Atlantique, Colombia

Salman, Sibel . . . . . . . . . . . . . . . . . . . . . . . . WC-15, TA-21, TC-21
ssalman@ku.edu.tr
Industrial Engineering, Koc University, Istanbul, Turkey

Sanchez Sanchez, Ana Maria . . . . . . . . . . . . . . . . . . . . . . . MA-47
anamas11@ucm.es
Economía Financiera y Actuarial y Estadística, Universidad
Complutense de Madrid, Madrid, Spain

Salo, Ahti . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-21, WA-34
ahti.salo@aalto.fi
Systems Analysis Laboratory, Aalto University School of
Science, Aalto, Finland
Salomon, Ludovic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-37
ludovic.salomon@polymtl.ca
MAGI, Polytechnique Montréal, Montreal, Quebec, Canada
Salomon, Valerio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-18
valerio.salomon@unesp.br
Sao Paulo State University, Brazil

Sanchez Sanchez, Carlos . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-57
carlosss@amazon.es
Amazon, Spain
Sánchez-Soriano, Joaquin . . . . . . . . . . . . . . . . . . . MB-36, MA-61
joaquin@umh.es
Centro de Investigación Operativa, Universidad MiguelHernández, Elche, Alicante, Spain
Sanchis, Jose Maria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-64
jmsanchis@mar.upv.es

645

AUTHOR INDEXEURO 2024 - Copenhagen

Matemática aplicada, Universidad Politécnica de Valencia,
Valencia, Spain
Sanchis, Jose Maria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-64
jmsanchis@mat.upv.es
Departamento de Matematica Aplicada, Universidad Politecnica de Valencia, Spain
Sanci, Ece . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-21
ecesanci@amazon.co.uk
Amazon, United Kingdom
Sandhiya, E . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-48
sandhiya.d21202@iimtrichy.ac.in
Operations Management & Decision Sciences, Indian Institute of Management Tiruchirappalli, Trichy, Tamil Nadu,
India
Sandoval Bravo, Salvador . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-53
salvador.sandoval@academicos.udg.mx
Metodos Cuantitativos, Universidad De Guadalajara, ZAPOPAN, JALISCO, Mexico
Sang, Xiaotong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-51
xiaotong.sang@vsb.cz
FINANCE, VSB-Technical University of Ostrava, Ostrava,
Czech Republic, Czech Republic
Sangare, Mariam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-36
mariam.sangare@lirmm.fr
Francebastián, Gipuzkoa, Spain
Santander, Pavlo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-23
pavlo.santander@usach.cl
Universidad de Santiago de Chile, Chile
Santini, Alberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-25
alberto@santini.in
Department of Economics and Business, Universitat Pompeu
Fabra, Barcelona, Spain
Santoro, Stefania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-13
stefaniasantoro@cnr.it
Italy
Santos Silva, Carlos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-44
carlos.santos.silva@tecnico.ulisboa.pt
IN+, Instituto Superior Técnico, ULisboa, Lisbon, Portugal
Santos, Antonio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-32
aasantos@fe.uc.pt
Economics, University of Coimbra, Coimbra, Portugal
Santos, Bruno Filipe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-43
b.f.santos@tudelft.nl
Delft University of Technology, Delft, Netherlands
Santos, Cipriano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-61
cipriano.santos@tec.mx
Tecnologico de Monterrey, United States

Sanguineti, Marcello . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-43
marcello.sanguineti@unige.it
DIBRIS, University of Genoa, Genova, Italy

Santos, Daniel . . . . . . . . . . . . . . . . . . . . . . . MC-10, MA-58, TA-64
daniel.rebelo.santos@tecnico.ulisboa.pt
Instituto Superior Técnico, Universidade de Lisboa, Lisboa,
Portugal

Sanisoğlu, Meltem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-45
meltem.sanisoglu@trendyol.com
TurkeySantos, Fellipe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-36
fellipe.santos@cemig.com.br
PC/AR, CEMIG, Belo Horizonte, Brazil

Sankaranarayanan, Sriram . . . . . . . . . . . . . . . . . . . . . . . . . WA-24
srirams@iima.ac.in
Indian Institute of Management, Ahmedabad, India

Santos, Gonçalo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-56
gdsantos@uc.pt
Department of Civil Engineering, University of Coimbra,
Portugal

Sanlaville, Eric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-26
eric.sanlaville@univ-lehavre.fr
UFR Sciences et Techniques, LITIS, Le Havre, France
Sansavini, Giovanni . WA-09, MA-14, MB-14, MC-19, WD-19,
WC-38, WA-53
sansavig@ethz.ch
ETH, Zurich, Switzerland
Sanson, François . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-09
francois.sanson@lhyfe.com
Lhyfe, Nantes, France
Santana Tovar, Daniela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-47
daniela.santana@polito.it
Piedmont, Politecnico di Torino, Turin, Turin, Italy
Santana, Roberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-28
roberto.santana@ehu.eus
Department of Computer Science and Artificial Intelligence,
University of the Basque Country (UPV/EHU), San Se-

646

Santos, Marcos dos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-44
marcosdossantos_doutorado_uff@yahoo.com.br
Military Engineering Institute (IME), Rio de Janeiro, RJ,
BrazilSantos, Marcos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-17, TA-45
marcosdossantos@ime.eb.br
Systems Engineering, Casnav - Ime, Rio de Janeiro, Rio deJaneiro, Brazil
Santos, Maria João . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-50
mjsantos@inesctec.pt
Inesc Tec, Portugal
Santos, Maristela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-15
mari@icmc.usp.br
Department of Applied Mathematics and Statistics, University of Sao Paulo, São Carlos, São Paulo, Brazil
Sapucaia, Allan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-20, WB-57

EURO 2024 - Copenhagenallansapucaia@gmail.com
Amazon, Luxembourg
Sarkis, Miriam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-53
miriam.sarkis16@imperial.ac.uk
Imperial College London, United Kingdom
Sartor, Giorgio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-51, MD-54
giorgio.sartor@sintef.noMathematics and Cybernetics, SINTEF Digital, Oslo, Norway
Saruwatari, Yasufumi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-48
saru@gssm.otsuka.tsukuba.ac.jp
Faculty of Business Sciences, University of Tsukuba, Tokyo,
Japan

AUTHOR INDEX

Savic, Gordana. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-48
goca@fon.bg.ac.rs
Faculty of Organizational Sciences, University of Belgrade,
Belgrade, Serbia
Savoldi, Laura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-09
laura.savoldi@polito.it
Politecnico di Torino, Italy
Sawada, Kiyoshi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-29
kiyoshi_sawada@red.umds.ac.jp
Department of Economic Information, University of Marketing and Distribution Sciences, Kobe, Japan

Sasaninejad, Sara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-40
Sara.Sasaninejad@ugent.beGhent University, Belgium

Sawik, Bartosz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-29
B_Sawik@cal.berkeley.edu
Department of Business Informatics and Engineering Management, AGH University of Science and Technology,
Kraków, Poland

Sasanpour, Shima . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-19, WA-53
shima.sasanpour@dlr.de
Energy Systems Analysis, German Aerospace Center (DLR),
Stuttgart, Germany

Sayin, Serpil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-37
ssayin@ku.edu.tr
College of Administrative Sciences and Economics, Koc University, Istanbul, Turkey

Sass, Jörn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-57
joern.sass@rptu.deMathematics, RPTU Kaiserslautern-Landau, Kaiserslautern,
Germany

Sørøy, Brede . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-60
brede.soroy@ntnu.no
Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology,
NorwayŞatir Akpunar, Özge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-25
ozge.satirakpunar@ogr.deu.edu.tr
The Graduate School of Natural and Applied Sciences,
Dokuz Eylül University, İZMİR, Turkey
Sato, Kimitoshi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-59, TC-59
k-sato@kanagawa-u.ac.jp
Kanagawa university, Yokohama, Kanagawa, Japan
Satoglu, Sule Itir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-18, TD-37
onbaslis@itu.edu.tr
Industrial Engineering, ITU, Istanbul, Turkey, Turkey
Saunders, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-63
dsaunders@uwaterloo.ca
Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada
Saungweme, Patience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-45
patience.saungweme@whu.edu
Logistics Management, WHU Otto Beisheim School of Management, Germany
Saupe, Jonas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-60
jonas.saupe@unibe.ch
University of Bern, Bern, Switzerland
Saure, Denis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-59
dsaure@dii.uchile.cl
University of Chile, Santiago, Chile
Saure, Denis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-16
dsaure@gmail.com
Industrial Engineering, University of Chile, Santiago, Chile

Sørensen, Siv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-26
sivs@dtu.dk
DTU Management, Technical University of Denmark, Denmark
Sbaraglia, Simone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-02
sbaraglia@unica.it
Dept. of Economics and Business Sciences, University of
Cagliari, Cagliari, Italy
Scagliotti, Alessandro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-32
scag@ma.tum.de
CIT School, TU Munich, and MCML, Garching b.
Muenchen, Bavaria, Germany
Scaillet, Olivier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-63
olivier.scaillet@unige.ch
University of Geneva, Switzerland
Scatamacchia, Rosario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-26
rosario.scatamacchia@polito.it
DIGEP, Politecnico di Torino, Italy
Schaipp, Fabian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-32
fabian.schaipp@gmail.com
Mathematics, Technical University of Munich, Germany
Schauer, Joachim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-53
joachim.schauer@fh-joanneum.at
Department of Software Design und Security, FH Joanneum,
Austria
Schäfer, Fabian. . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-50, WC-58

647

AUTHOR INDEXEURO 2024 - Copenhagenfab.schaefer@tum.de
Supply and Value Chain Management, Technical Universityof Munich, Straubing, Germany
Schälicke, Maik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-54
maik.schaelicke1@tu-dresden.de
TU Dresden, Germany
Schär, Sebastian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-11, TA-44
sebastian.schaer@uni-due.de
Chair of Business Administration and Production Management, University of Duisburg-Essen, Duisburg, Germany

Schlembach, Robin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-24
robin.schlembach@uni-a.deFaculty of Business and Economics, University of Augsburg,
Augsburg, Bavaria, Germany
Schlereth, Christian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-50
schlereth@wiwi.uni-frankfurt.de
Faculty of Business and Economics, University of Frankfurt,Frankfurt, Germany
Schlosser, Rainer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-59
rainer.schlosser@hpi.de
Hasso Plattner Institute, University of Potsdam, Germany

Schede, Elias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-03
elias.schede@uni-bielefeld.de
Decision and Operation Technologies, Bielefeld University,
Bielefeld, Germany

Schmand, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-36
schmand@uni-bremen.de
University of Bremen, Germany

Scheinhardt, Werner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-62
w.r.w.scheinhardt@utwente.nl
Applied Mathematics, University of Twente, Netherlands

Schmeck, Maren Diane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-57
maren.schmeck@uni-bielefeld.de
IMW, Bielefeld University, Germany

Schiewe, Philine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-54
philine.schiewe@aalto.fi
Department of Mathematics and Systems Analysis, Aalto
University, Finland

Schmid, Eberhard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-24
eberhard.schmid@hwr-berlin.de
Wirtschaftswissenschaften, HWR Berlin, Berlin, Germany

Schiffels, Sebastian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-24
sebastian.schiffels@uni-a.deWirtschaftswissenschaftliche Fakultät, Universität Augsburg,
Augsburg, Germany

Schmidbaur, Simon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-54
simon.schmidbaur@unibw.de
Chair of Business Analytics & Management Science, University of the Bundeswehr Munich, Neubiberg, Bavaria, Germany

Schiffer, Maximilian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-54
schiffer@tum.de
School of Management & Munich Data Science Institute,
Technical University of Munich, Munich, Germany

Schmidt, Christiane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-56
christiane.schmidt@liu.se
Department of Science and Technology, Linköping University, Sweden

Schiffling, Sarah . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-21
sarah.schiffling@hanken.fi
HUMLOG Institute, Hanken School of Economics, Finland

Schmidt, Hendrik Jon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-07
hendrik.schmidt@dfki.de
SEE, Deutsches Forschungszentrum für Künstliche Intelligenz, Osnabrück, Lower Saxony, Germany

Schillaci, Francesco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-40
g.schillaci@bssweb.it
Business Software Solution srl, Italy
Schimeczek, Christoph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-09
christoph.schimeczek@dlr.deInstitute of Networked Energy Systems, German Aerospace
Center, Stuttgart, Germany

Schmidt, Johannes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-25
johannes.schmidt@b-tu.de
Brandenburg University of Technology Cottbus-Senftenberg,
Cottbus, Germany
Schmidt, Marie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-54, MD-56
marie.schmidt@uni-wuerzburg.de
Institute for Computer Science, University of Würzburg,
Würzburg, Germany

Schindl, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-58
david.schindl@hesge.ch
Economie d’entreprise, HEG Genève, HES-SO Universityof Applied Sciences and Arts Western Switzerland, Carouge,
GE, Switzerland

Schmidt, Martin . . . . . . . . . . . . . . . . . . . . . TA-19, MB-21, MB-28
martin.schmidt@uni-trier.de
Department of Mathematics, Trier University, Trier, Germany

Schindler, Markus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-19
markus.schindler@forschung-burgenland.at
Forschung Burgenland GmbH, Eisenstadt, Austria

Schmidt, Regina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-38
regina.schmidt@math.uni-augsburg.de
University of Augsburg, Augsburg, Germany

Schinke-Nendza, Aiko . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-09
aiko.schinke-nendza@wiwinf.uni-due.de
Chair for Energy Economics, House of Energy Markets andFinance, University of Duisburg-Essen, Essen, Germany

Schmitt, Andreas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-46
aschmitt@mathematik.tu-darmstadt.de
TU Darmstadt, Germany
Schneider, Germar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-23

648

EURO 2024 - Copenhagen
germar.schneider@infineon.com
Infineon Technologies GmbH & Co. KG, Dresden, Germany
Schneider, Lorenz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-57
schneider@em-lyon.com
Economics, Finance, Accounting, EMLYON Business
School, Ecully, France
Schöbel, Anita . . . . . . . WC-37, TD-51, WA-51, WC-51, WD-51
schoebel@mathematik.uni-kl.de
Department of Mathematics, University of KaiserslauternLandau, Kaiserslautern, Germany
Schoen, Cornelia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-59
cschoen@mail.uni-mannheim.de
Chair of Service Operations Management, University ofMannheim, Mannheim, Germany

AUTHOR INDEX

m.schroder@maastrichtuniversity.nl
Quantitative Economics, Maastricht University, Maastricht,
Limburg, Netherlands
Schrotenboer, Albert . . . . . . . . . . . . . . . MA-56, WC-61, MD-64
a.h.schrotenboer@tue.nl
Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven,
Netherlands
Schryen, Guido . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-19, TC-64
guido.schryen@upb.de
Department of Management Information Systems, Paderborn
University, Germany
Schubert, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-59
schubertdaniel@gmx.net
University of Antwerp, Denmark

Schoen, Fabio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-37
fabio.schoen@unifi.it
Dipartimento di Ingegneria dell’Informazione, Universitàdegli Studi di Firenze, Firenze, Italy

Schüller, Markus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-15
markus.schueller@uni-a.deUniversität Augsburg, Augsburg, Germany

Schoenfelder, Jan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-15
j.schoenfelder@lancaster.ac.uk
LUMS, Lancaster University Leipzig, Leipzig, Saxony, Germany

Schulte, Frederik . . . . . . . . . . . . . . . . . . . MC-62, WA-62, WB-62
f.schulte@tudelft.nl
Transport Engineering and Logistics, Delft University ofTechnology, Delft, Netherlands

Schoepf, Vivien . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-23
vivien.schoepf@whu.edu
WHU - Otto Beisheim School of Management, Germany

Schulte, Jakob . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-07
jakob.schulte@uni-bielefeld.de
Decision Analytics Group, Bielefeld University, Germany

Schol, Dennis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-39
c.schol@tue.nl
Eindhoven University of Technology, Eindhoven, Netherlands

Schultmann, Frank . . . . . . . . . . . MD-21, TA-23, TD-61, WA-61
frank.schultmann@kit.edu
Institute for Industrial Production, Karlsruhe Institute ofTechnology (KIT), Karlsruhe, Germany

Scholten, Lisa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-11
l.scholten@tudelft.nl
Faculty of Technology, Policy and Management, Delft University of Technology, Delft, Netherlands

Schultz, Tailini . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-15
tailini.schultz@edu.udesc.br
Universidade do Estado de Santa Catarina, Brazil

Scholz, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-07, MD-11
michael.scholz@th-deg.de
Deggendorf Institute of Technology, Germany
Schönheit, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-49
martin.schoenheit@tu-dresden.de
Chair of Business Administration, esp. Logistics, TU Dresden, Dresden, Saxony, Germany
Schönlieb, Carola-Bibiane . . . . . . . . . . . . . . . . . . . . . . . . . . MD-32
cbs31@cam.ac.uk
DAMTP, University of Cambridge, United Kingdom
Schorn, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-24
daniel-sascha.schorn@fernuni-hagen.deEnterprise-wide Software Systems, FernUniversität in Hagen, Hagen, North Rhine-Westphalia, Germany
Schouten, Jop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-36
J.Schouten@uvt.nl
Tilburg University, Tilburg, Netherlands
Schröder, Marc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-36

Schulz, Arne . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-54
arne.schulz@uni-hamburg.de
Institute of Quantitative Logistics, Helmut Schmidt University, Hamburg, Hamburg, Germany
Schulz, Jens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-30
jensschulz@fico.com
FICO, Bensheim, Hessen, Germany
Schur, Rouven . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-59
rouven.schur@uni-due.de
University of Duisburg-Essen, Duisburg, Germany
Schütz, Peter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-55, MB-61
peter.schutz@ntnu.noDept. of Industrial Economics and Technology Management,
NTNU, Trondheim, Norway
Schwartz, Alexandra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-19
alexandra.schwartz@tu-dresden.de
Faculty of Mathematics, TU Dresden, Dresden, Germany
Schwartz, Eduardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-63
eschwart@anderson.ucla.edu

649

AUTHOR INDEXEURO 2024 - Copenhagen

Anderson School, UCLA, Los Angeles, CA, United States
Schwartzlose, Hans . . . . . . . . . . . . . . . . . . . . . . . . . MA-46, TC-60
hschw@orsted.com
Decision Science Services, Orsted A/S, Søborg, Denmark
Schwendner, Peter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-57
scwp@zhaw.ch
Zhaw University, Zurich, CH, Switzerland

Seidl, Andrea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-33
andrea.seidl@uni-seeburg.at
Seeburg Castle University, Seekirchen, Austria
Seifert, Matthias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-11
matthias.seifert@ie.edu
Operations & Technology, IE Business School, Madrid,
Spain

Schwindt, Christoph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-23
christoph.schwindt@tu-clausthal.deInstitute of Management and Economics, Clausthal University of Technology, Clausthal-Zellerfeld, Germany

Seifert, Paul. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-14
paules@ntnu.no
Industrial Economy and Technology Management, Norwegian University of Science and Technology, Trondheim, Norway

Schwonnek, Rene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-42
r.schwonnek@gmail.com
ITP, Leibniz University Hannover, Hannover, Germany

Selicato, Laura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-13
laura.selicato@uniba.it
Mathematics, Universiy of Bari Aldo Moro, Bari, Italy

Scieur, Damien . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-32
damien.scieur@gmail.com
Samsung SAIL Montreal, Outremont, Québec, Canada

Seljom, Pernille . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-56
pernille.seljom@ife.no
Renewable Energy Systems, Institute for Energy Technology(IFE), Kurland, Norway

Scopelliti, Domenico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-40
domenico.scopelliti@unibs.it
University of Brescia, Italy
Scrimali, Laura Rosa Maria . . . . . . . . . . . . . . . . . . . . . . . . WA-40
scrimali@dmi.unict.it
DMI, Università di Catania, Catania, Italy
Sebastian Piñeros, Juan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-29
spgsa@hotmail.com
Administración Logística, Universidad Iberoamericana, Bogotá, Colombia

Sengupta, Raja . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-28
raja.sengupta@mcgill.ca
McGill University, Canada
Senneret, Marc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-63
msenneret@vivienne-im.com
Research Department, Vivienne Investissement, France
Seragiotto, Clovis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-08
clovis.seragiotto@ait.ac.at
Austrian Institute of Technology, Austria

Sebastiani, Andrea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-34
asebastiani@unimore.it
Department of Physics, Informatics and Mathematics, University of Modena and Reggio Emilia, Modena, Italy

Sergeyev, Yaroslav . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-41
yaro@dimes.unical.it
DIMES, University of Calabria, Rende (CS), Italy

Secchi, Davide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-43
secchi@sdu.dk
Dept. of Language and Culture, University of Southern Denmark, Slagelse, Denmark

Serna, Maria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-36
mjserna@cs.upc.edu
Computer Science Dept. and Institute of Mathematics of
UPC-BarcelonaTech, Universitat Politècnica de Catalunya,
Barcelona, Catalonia, Spain

Sederlin, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-51
michael.sederlin@vti.se
Stockholm, Sweden

Serra, Domenico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-52
dserra@unisa.it
Department of Mathematics, University of Salerno, Italy

Segura Martínez, Paula . . . . . . . . . . . . . . . . . . . . . . WA-12, TB-64
psegmar@upvnet.upv.es
Universitat Politècnica de València, Valencia, Spain

Serral, Estefanía . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-27
estefania.serralasensio@kuleuven.be
Research Centre for Information Systems Engineering(LIRIS), KU Leuven, Leuven, Belgium

Segura, Baldomero . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-47
bsegura@upvnet.upv.es
Economía y Ciencias Sociales, Universidad Politécnica deValencia, Valencia, Spain
Segura, Marina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-47
masegu04@ucm.es
Department Of Financial And Actuarial Economics And
Statistics, Universidad Complutense de Madrid, Pozuelo De
AlarcÓn, MADRID, Spain

Serrano Borja, Manel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-53
manelsb4@gmail.com
Spain
Serrano, Adrian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-29
adrian.serrano@unavarra.es
Statistics and Operations Research, Public University ofNavarre, Spain
Servadei, Raffaella . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-41

650

EURO 2024 - Copenhagenraffaella.servadei@uniurb.it
Università degli Studi di Urbino Carlo Bo, Italy
Sessa, Valentina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-43
valentina.sessa@minesparis.psl.eu
Centre for Applied Mathematics, Mines Paris - PSL, FranceŠestanović, Tea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-57
tea.sestanovic@efst.hr
Department of Quantitative Methods, University of Split,
Split, Croatia
Seth, Niyati . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-28
niyati.seth@ucdconnect.ie
Mathematics and Statistics, University College Dublin,
Dublin, Dublin, Ireland
Sethuraman, Jay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-43
jay@ieor.columbia.edu
IEOR, Columbia University, New York, NY, United States
Seuring, Stefan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-23
seuring@uni-kassel.de
University of Kassel, Kassel, Germany
Sevaux, Marc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-20, WC-55
marc.sevaux@univ-ubs.fr
Lab-STICC, UMR 6285, CNRS, Université Bretagne Sud,
Lorient, France
Seyeddizaji, Seyedehhaleh . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-40
seyedehhaleh.seyeddizaji@aau.at
Austria
Sfeir, G. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-51
geosaf@dtu.dk
Technical University of Denmark, Department of Technology, Management, and Economics, 2800 Kongens Lyngby,
Denmark, Denmark
Sgalambro, Antonino . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-61
a.sgalambro@sheffield.ac.uk
School of Management, University of Sheffield, Sheffield,
United Kingdom
Sgarbossa, Fabio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-23, TA-58
fabio.sgarbossa@ntnu.no
Department of Mechanical and Industrial Engineering,
NTNU, Trondheim, Norway
Shafi, Nadeem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-31
nshafi@uthsc.edu
University of Tennessee Health Science Center, United States
Shafieezadeh Abadeh, Soroosh . . . . . . . . . . . . . . . . . . . . . . WC-38
shafiee@cornell.edu
Operations Research and Information Engineering, Cornell
University, United States
Shah, Bhavin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-59
bhavinj@iimidr.ac.in
Operations Management and Quantitative Techniques Area,
Indian Institute of Management Indore, INDORE, Madhya
Pradesh, India

AUTHOR INDEX

Shah, Nilay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-53
n.shah@imperial.ac.uk
Imperial College London, London, United Kingdom
Shahsaheni, Seyedmohammadreza . . . . . . . . . . . . . . . . . . TD-07
seyedmohammadreza.sh@ucalgary.ca
Haskayne School of Business, University of Calgary, Calgary, Alberta, Canada
Shaikh, Azharuddin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-16
azharuddin.math@gmail.com
MATHEMATICS, Nit Agartala, India
Shakhlevich, Natalia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-26
n.shakhlevich@leeds.ac.uk
School of Computing, University of Leeds, Leeds, United
Kingdom
Shamohammadi, Mehdi . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-48
mehdi.sh@inha.ac.kr
International Business and Trade (IBT), Inha University, Incheon, Incheon, Korea, Republic of
Shams, Naser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-45
nshams@aut.ac.ir
Amirkabir University of Technology, Iran, Islamic RepublicofSharif Azadeh, Shadi . . . . . . . . . . . . . . . WA-51, WD-51, WA-54
s.sharifazadeh@tudelft.nl
Transport & Planning, TU Delft, Delft, Netherlands
Sharma, Aishna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-17
aishnas@gmail.com
Economics and Public Policy, Shiv Nadar Institute of Eminence deemed to be University, Gautam Bodda Nagar, Uttar
Pradesh, India
Sharma, Charupriya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-57
charupr@amazon.lu
Amazon, Luxembourg
Sharma, Kartikey . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-03, TD-34
kartikey.sharma@zib.de
AISST, Zuse Institute Berlin, Berlin, Berlin, Germany
Sharma, Madhav . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-22
madhavsh@iitk.ac.inIndustrial and Management Engineering, Indian Institute ofTechnology Kanpur, Kanpur, UP, India
Sharma, Preeti . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-06
medpshar@leeds.ac.uk
Leeds Institute for Data Analytics, University of Leeds,
Leeds, West Yorkshire, United Kingdom
Sharpanskykh, Alexei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-43
o.a.sharpanskykh@tudelft.nl
Delft University of Technology, Delft, Netherlands
Shashaani, Sara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-34
sshasha2@ncsu.edu
Industrial and Systems Engineering, North Carolina State
University, Raleigh, North Carolina, United States

651

AUTHOR INDEXEURO 2024 - Copenhagen

Shaukat, Syed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-26
s.shaukat@unsw.edu.au
Aviation, University of New South Wales, Sydney, NewSouth Wales, Australia
Shavandi, Hassan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-14
hassan.shavandi@uwaterloo.ca
University of Waterloo, Canada
Shaw, Luke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-17
luke.shaw4@nhs.net
Nhs Bnssg Icb, United Kingdom
Shaydurova, Daria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-38
daria.shaydurova@ovgu.de
Faculty of Mathematics, Otto-von-Guericke-Universität
Magdeburg, Magdeburg, Saxony-Anhalt, Germany
Shen, Jian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-57
jeinshen@amazon.co.uk
Amazon, United Kingdom
Shen, Kao-Yi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-44
atrategy@gmail.com
Department of Banking & Finance (SCE), Chinese Culture
University, Taipei, Taiwan
Shenoy, Veena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-17
drveenashenoy@gmail.com
Transfusion Medicine, Amrita Institute of Medical Sciences,
Kochi, Kerala, India
Sher, Mali . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-55
mali_sher@yahoo.com
Trafic Police, Israel Police, Israel

Juntendo University, Japan
Shinano, Yuji . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-30
shinano@zib.de
Optimization, Zuse Institue Berlin, Berlin, Berlin, Germany
Shintani, Koichi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-55
shintani@tokai.ac.jp
School of Marine Science and Technology, Tokai University,
Shizuoka, Shizuoka, Japan
Shiraishi, Shunsuke . . . . . . . . . . . . . . . . . . . . . . . . . . TC-44, TD-44
shunke.shira@gmail.com
Faculty of Applied Information Science, Hiroshima Instituteof Technology, Hiroshima, Japan
Shiri, Davood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-21
d.shiri@sheffield.ac.uk
Operations Management and Decision Sciences, The University of Sheffield, United Kingdom
Shnaiderman, Matan . . . . . . . . . . . . . . . . . . . . . . . . TC-33, WC-61
matan.shniderman@biu.ac.il
Management, Bar-Ilan University, Ramat-Gan, Israel
Shone, Rob . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-55
r.shone@lancaster.ac.uk
Management Science, Lancaster University, Lancaster,
United Kingdom
Shorakaei, Hasan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-61
hshorakaei.phd@ivey.ca
Business Administration, Ivey Business School, London,
ON, Canada

Sheth, Harsh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-59
hts2112@columbia.edu
Columbia University, United States

Shoujie, Cai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-45
230209147@seu.edu.cn
School of Economics and Management, Southeast University, Nanjing, Jiangsu province, China

Shi, Guo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-39
guo.shi@strath.ac.uk
Management Science, University of Strathclyde, Glasgow,
Other, United Kingdom

Shroff, Arvind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-59
arvind.shroff@iiml.ac.in
Operations Management, Indian Institute of Management
Lucknow, Lucknow, Uttar Pradesh, India

Shi, You . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-24
shyo@dtu.dk
Wind and Energy Systems, Technical University of Denmark,Kongens Lyngby, Denmark

Shrot, Tammar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-52
tammash@sce.ac.il
Software Engineering, SCE, Ashdod, Israel

Shibata, Takashi . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-57, TA-59
tshibata@tmu.ac.jp
Graduate School of Management, Tokyo Metropolitan University, Hachioji, Tokyo, Japan
Shikhman, Vladimir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-41
vladimir.shikhman@mathematik.tu-chemnitz.de
Chemnitz University of Technology, Germany
Shim, Sung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-40
sung.shim@shu.edu
Seton Hall University, South Orange, NJ, United States
Shimasaki, Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-16
yshimasa@juntendo.ac.jp

652

Shu, Shengnan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-58
shengnan.shu@connect.polyu.hk
The Hong Kong Polytechnic University, China
Shui, Chin Sum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-49
csshui@nctu.edu.tw
Transportation and Logistics Management, National Yang
Ming Chiao Tung University, Hsinchu City, Taiwan, Taiwan
Si Hammou, Nabil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-02
Nabil.SIHAMMOU@ocpgroup.ma
OCP Group, Casablanca, Morocco
Siadati, Saba . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-58
s.siadati@tue.nl
Industrial Engineering & Innovation Sciences, Eindhoven

EURO 2024 - Copenhagen
University of Technology, Eindhoven, Netherlands
Sibanda, Mabutho . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-18
sibandam@ukzn.ac.za
Accounting, Economics & Finance, University of KwaZuluNatal, Durban, KwaZulu-Natal, South Africa
Sibdari, Soheil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-59
ssibdari@umassd.edu
University of Massachusetts, North Dartmouth, MA, UnitedStatesSibul, Gleb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-55
gleb.sibul@ntnu.no
TRONDHEIM, Norway
Sica, Francesco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-47
francesco.sica@uniroma1.it
Department of Architecture and Design, Sapienza University,
Rome, Italy, Italy
Sicilia, Gabriela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-48
gsicilia@ull.edu.es
University of La Laguna, Spain
Sidford, Aaron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-26
sidford@stanford.edu
Departments of Computer Science and MS&E, Stanford University, Stanford, CA, United States
Siegrist, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-11
michael.siegrist@hest.ethz.ch
D-USYS, ETH Zürich, Zürich, Zürich, Switzerland
Signori, Andrea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-63
andrea.signori@unicatt.it
Università Cattolica del Sacro Cuore, Italy
Silbermayr, Lena . . . . . . . . . . . . . . . . . . . . TA-23, WD-23, TB-50
lena.silbermayr@wu.ac.at
Department of Information Systems and Operations, WU Vienna University of Economics and Business, Vienna, Austria
Sildir, Hasan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-35
hasansildir@gtu.edu.tr
Chemical Engineering, Izmir Institute of Technology, Turkey
Silei, Mattia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-34
mattia.silei@unifi.it
Dipartimento di Matematica ed Informatica "Ulisse Dini",
Università degli Studi di Firenze, Firenze, Italy
Silva Alvarez, Francisco José . . . . . . . . . . . . . . . . . . . . . . . . TD-42
fchucksilva@gmail.com
Université de Limges, France
Silva Marins, Fernando Augusto . . . . . . . . . . . . . . . . . . . . . TA-06
fernando.marins@unesp.br
São Paulo State University, Guaratinguetá, São Paulo, Brazil
Silva Pinto, Francisco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-47
francisco.pinto@ulusofona.pt
Fe / Rcm2+, Lusofona University, Lisboa, Portugal
Silva, Aneirson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-06

AUTHOR INDEX

aneirson@yahoo.com.br
UNESP, Guaratinguetá, SP, Brazil
Silva, Cristóvão . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-07
cristovao.silva@dem.uc.pt
Mechanical Engineering, University of Coimbra, Coimbra,
Portugal
Silva, Deivdy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-28
deivdy.william@gmail.com
Instituto Brasileiro de Informação em Ciência e Tecnologia,
Brasília, DF, Brazil
Silva, Eduardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-49
eduardo.msilva094@gmail.com
ICT, UNIFESP, São José dos Campos, São Paulo, Brazil
Silva, Elsa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-07
elsa@dps.uminho.pt
Departament of Production and Systems, University of
Minho, Braga, Portugal
Silva, Elsa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-29
emsilva@inesctec.pt
Inesc Tec, Porto, Portugal
Silva, Everton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-32
ejo.silva@campus.fct.unl.pt
Mathematics, NOVA School of Science and Technology, Almada, Portugal
SIlva, Jaime Gabriel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-53
jgs@isep.ipp.pt
Águas do Douro e Paiva, Portugal
Silva, Victor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-18
v.silvafebre@uandresbello.edu
Ciencias de la Ingenieria, Universidad Andrés Bello, Santiago, Region metropolitana, Chile
Silveira, Tiago . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-07
tiago.silveira@unifal-mg.edu.br
Computer Science, Universidade Federal de Alfenas, Alfenas, Minas Gerais, Brazil
Silvennoinen, Johanna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-11
johanna.silvennoinen@jyu.fi
University of Jyvaskyla, Finland
Silveti-Falls, Antonio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-32
tonys.falls@gmail.com
Centre for Visual Computing, CentraleSupélec, palaiseau,
essonne, France
Simancas, Rosa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-48
rsimancas@unex.es
Universidad de Extremadura, BADAJOZ, Spain
Simhandl, Julia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-24
j.simhandl@tbwresearch.org
tbw research GesmbH, Austria
Simoes Gomes, Carlos Francisco . . . . . MD-17, WD-44, TA-45
cfsg1@bol.com.br
Production Engineering, Fluminense Federal University,

653

AUTHOR INDEXEURO 2024 - Copenhagen

Niteroi, Rio de janeiro, Brazil
Singh Patel, Shivshanker . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-06
ssp.msopt@gmail.com
Decision Sciences, Indian Institute of management visakhapatnam, India
Singh, Anoop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-22
anoops@iitk.ac.in
Department of Management Science (DOMS), Indian Institute of Technology Kanpur (IITK), Kanpur, Uttar Pradesh,
India
Singh, Rajani . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-33, WC-41
rs.digi@cbs.dk
Department of Digitalization, Copenhagen Business School,
Denmark
Singh, Rajesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-44
rajesh.singh@mdi.ac.in
Operations Management, Management Development Institute, India
Singh, Reetendra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-06
reetendra.singh21-02@iimv.ac.in
Decision Sciences, Indian Institute of Management Visakhapatnam, Visakhapatnam, Andhra Pradesh, India
Singh, Sanjeet . . . . . . . . . . . . . . . . . . . . . . . TB-48, WB-48, TC-53
sanjeet@iiml.ac.in
Decision Sciences, Indian Institute of Management Lucknow,
Lucknow, Uttar Pradesh, India
Singh, Shalabh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-64
shalabh.singh@iiml.ac.in
Decision Sciences, Indian Institute of Management, Lucknow, India

Sinnl, Markus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-37, MA-61
markus.sinnl@jku.at
Institute of Business Analytics and Technology Transformation, Johannes Kepler University Linz, Linz, Austria
Sinuany-Stern, Zilla . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-53
zilla@bgu.ac.il
Industrial Engineering and Management, Ben Gurion University of the Negev, Beer-Sheva, Israel
Sipahioglu, Aydin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-25
asipahi@ogu.edu.tr
Industrial Engineering, Osmangazi University, Eskisehir,TurkeySir, Mustafa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-15
mustafa.y.sir@gmail.com
Amazon, Seattle, WA, United States
Siraj, Sajid . . . . . . . . . . . . . . . . . . . . . . . . . . TA-06, WA-06, WA-28
s.siraj@leeds.ac.uk
Leeds University Business School, University of Leeds,
Leeds, England, United Kingdom
Sirbiladze, Gia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-47
gia.sirbiladze@tsu.ge
Department of Computer Sciences, Iv.Javakhishvili Tbilisi
State University, Tbilisi, Georgia
Siskos, Eleftherios . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-44, TA-44
esiskos@tuc.gr
School of Production Engineering and Management, Technical University of Crete, Chania, Greece
Sitters, René . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-25
r.a.sitters@vu.nl
Vrije Universiteit Amsterdam and CWI, Amsterdam, Netherlands

Singh, Shivam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-18
shivam040396@gmail.com
Department of Mechanical Engineering, Indian Institute ofTechnology Delhi, New Delhi, New Delhi, India

Siu Lee Lam, Jasmine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-08
jasmlam@dtu.dk
Technical University of Denmark, Denmark

Singh, Sonia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-55, MC-64
sonia@iiml.ac.in
Decision Sciences, Indian Institute of Management, Lucknow, Lucknow, Uttar Pradesh, India

Siu, Chi Chung. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-02
ccsiu@hsu.edu.hk
Department of Mathematics, Statistics and Insurance, The
Hang Seng University of Hong Kong, Shatin, Hong Kong

Singh, Tilakraj . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-50
meettilak@gmail.com
Unilever, India

Sivertsen, Agnieszka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-62
avts@ruc.dk
Denmark

Singh, Vartika . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-40
vsvartika@iitb.ac.in
Indian Institute of Technology Bombay (IIT Bombay, IITB),
India

Skali Lami, Omar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-20
oskali@mit.edu
McKinsey & Co, Cambridge, MA, United States

Sinha, Amitabh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-57
amitabsi@amazon.com
Amazon, United States
Sinjorgo, Lennart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-38
l.m.sinjorgo@tilburguniversity.edu
Tilburg University, Tilburg, Netherlands

Slootweg, Joris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-36, TA-39
joris.slootweg@cwi.nl
Stochastics, CWI, Amsterdam, Netherlands
Slot, Lucas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-38
lucas.slot@inf.ethz.ch
ETH Zürich, Zurich, Switzerland
Slowinski, Roman . . . . . . . . . . . . . . . . . . . . . . . . . . MD-08, MA-44

654

EURO 2024 - Copenhagenroman.slowinski@cs.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poznan, Poland
Small, Ellie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-40
esmall1@drew.edu
Mathematics and CS, Drew University, Madison, NJ, UnitedStatesSmeets, Tom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-10
tjsmeets@outlook.com
Tilburg University, Netherlands
Smet, Pieter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-58
pieter.smet@cs.kuleuven.be
Computer Science, KU Leuven, Gent, Belgium
Smets, Ruben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-03
ruben.smets1@kuleuven.be
Belgium
Smeulders, Bart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-10, TD-10
b.smeulders@tue.nl
Mathematics and Computer Science, Eindhoven Universityof Technology, Einhoven, Netherlands
Smid, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-35
cyberklezmer@gmail.com
Faculty of Art, Charles Unifersity, Praha 1, Czech Republic
Smilgins, Aleksandrs . . . . . . . . . . . . . . . . . . . . . . . . TD-43, WA-43
asm.eco@cbs.dk
Department of Economics, Frederiksberg, Denmark
Smit, Igor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-03
i.g.smit@tue.nl
Eindhoven University of Technology, Netherlands
Smith, Chris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-13
christopher.smith@manchester.ac.uk
Management Science and Marketing, University of Manchester, Manchester, United Kingdom
Snelder, Maaike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-56
maaike.snelder@tno.nl
Civil Engineering, Delft University of Technology, Delft,
Zuid Holland, Netherlands
Snoeck, Monique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-27
Monique.Snoeck@kuleuven.be
KU Leuven, Leuven, Belgium
So, Mee Chi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-07
m.so@soton.ac.uk
Southampton Business School, University of Southampton,
Southampton, United Kingdom
Soeffker, Ninja . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-56
ninja.soeffker@univie.ac.atUniversity of Vienna, Vienna, Austria
Solano-Blanco, Alfaima L. . . . . . . . . . . . . . . . . . . . . . . . . . . WC-13
a.l.solano.blanco@tue.nl
Industrial Engineering and Innovation Sciences, Eindhoven
University of Technology, Eindhoven, NL, Netherlands

AUTHOR INDEX

Solà Vilalta, Albert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-34
albert.sola.vilalta@upc.edu
Departament d’Estadística i Investigació Operativa, Universitat Politècnica de Catalunya, Spain
Solazzo, Gianluca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-64
gianluca.solazzo@unisalento.it
Engineering for Innovation, University of Salento, Lecce,
Italy
Soler-Noguera, Albert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-18
albert.soler@creuroja.org
Creu Roja Terrassa, Terrassa, Spain
Solis Hidalgo, Jonathan . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-30
j235841@dac.unicamp.br
Universidade Estadual de Campinas, Brazil
Sölter, Yordan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-50
yordan.soelter@whu.edu
Chair of Production Management, WHU - Otto Beisheim
School of Management, Vallendar, Germany
Solymosi, Tamás . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-36
tamas.solymosi@uni-corvinus.hu
Operations Research and Actuarial Sciences, Corvinus University of Budapest, Budapest, Hungary
Sommerseth, Jon Kristian . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-12
jon.kristian.sommerseth@tine.no
Tine SA Research and Development Department, Norway
Son, Jeena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-04
a-jeen0722s@korea.ac.kr
Department of Industrial and Management Engineering, Korea university, Seoul, Korea, Republic ofSong, Changle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-10
songchanglele@gmail.com
The University of Sydney, Ultimo, NSW, Australia
Song, Chen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-22
chen.song@de.abb.com
DECRC, ABB Corporate Research Center, Germany
Song, Dongping . . . . . . . . . . . . . . . MA-03, TD-59, TB-62, TD-62
dongping.song@liverpool.ac.uk
School of Management, University of Liverpool, Liverpool,
United Kingdom
Song, Guopeng . . . . . . . . . . . . . . . . . . . . . . WB-04, MC-21, TC-60
guopeng.song@nudt.edu.cn
National University of Defense Technology, Changsha, China
Song, Seunghwan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-04
ss-hwan@korea.ac.kr
Department of Industrial and Management Engineering, Korea University, Korea, Republic ofSong, Xiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-10
xiang.song@port.ac.uk
School of Mathematics, University of Portsmouth,
Portsmouth, United Kingdom655

AUTHOR INDEXEURO 2024 - Copenhagen

Song, Yanqiu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-43, TC-43
sunny_syq@163.com
School of Management Science and Engineering, Central
university of finance and economics, Beijing, China
Song, Yunya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-06
yunyasong@hkbu.edu.hk
Department of Journalism, Hong Kong Baptist University,
China
Sonntag, Danja . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-23, TB-50
danja.sonntag@iml.lth.se
Lund University, Sweden
Sonveaux, Candy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-20
candy.sonveaux@unamur.be
Naxys, University of Namur, Belgium
Soppert, Matthias . . . . . . . . . . . . . . . . . . . . . . . . . . MA-02, WA-59
matthias.soppert@unibw.de
Chair of Business Analytics & Management Science, University of the Bundeswehr Munich, Neubiberg, Germany

Sousa, Mariana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-50
up201704217@fe.up.pt
Industrial Engineering and Management, Faculty of Engineering of the University of Porto, Porto, Porto, Portugal
Souza, Edson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-33
e.goncalvesdesouza@ieseg.fr
Operations Management, IESEG School of Management,
Lille, France
Souza, Gustavo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-51
gdesouza@ufop.edu.br
Mathematics, Universidade Federal de Ouro Preto, Ouro
Preto, Minas Gerais, Brazil
Spaniol, Matthew . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-11
matt@ruc.dk
RUC, Denmark
Spanninger, Thomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-54
Thomas.Spanninger@ivt.baug.ethz.ch
D Baug, ETH Zurich, Zurich, Switzerland

Sopun, Benjawan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-50
benjawansopun@gmail.com
International Logistics, Chung-Ang University, Seoul, Korea,
Republic ofSpeckenmeyer, Philipp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-64
philipp.speckenmeyer@uni-paderborn.de
Paderborn University, Germany

Sorbera, Enrico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-04
enrico.sorbera@hotmail.it
Dipartimento di Matematica, Università di Pisa, Pisa, Italy

Speh, Hendrik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-51
hendrik.speh@gmx.de
TU Darmstadt, Darmstadt, Germany

Sörensen, Kenneth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-54
kenneth.sorensen@uantwerpen.be
Faculty of Applied Economics, University of Antwerp,
Antwerpen, Belgium

Speranza, M. Grazia . . . . . . . . . . . . . . . . . . . . . . . . WA-26, MB-29
speranza@eco.unibs.it
Dept. of Quantitative Methods, University of Brescia, Brescia, Italy

Sorgente, Carmine . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-29, TD-52
csorgente@unisa.it
Department of Mathematics, University of Salerno, Italy

Sperber, Evelyn . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-09, MC-43
Evelyn.Sperber@dlr.deInstitute of Networked Energy Systems, German Aerospace
Center (DLR), Germany

Sosa, Amanda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-23
amanda.sosa@ucd.ie
School of Biosystems Engineering, University College
Dublin, Dublin, Ireland
Sotiros, Dimitrios-Georgios . . . . . . . . . . . . . . . . . MB-48, MD-48
dsotiros@unipi.gr
Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, Wroclaw, Poland
Sotirov, Renata . . . . . . . . . . . . . . . . . . . . . . TB-38, WA-38, WB-38
r.sotirov@uvt.nl
Tilburg University, Tilburg, Outside of Canada & USA,
Netherlands
Soto-Silva, Wladimir E.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-12
wsoto@ucm.cl
Departamento de Computación e Informática, Universidad
Católica del Maule, Talca, Región del Maule, Chile
Soumis, Francois . . . . . . . . . . . . . . . . . . . MC-02, WA-25, WB-29
francois.soumis@gerad.ca
GERAD, Montreal, Québec, Canada

656

Speth, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-57
daniel.speth@isi.fraunhofer.deInstitute for Systems and Innovation Research ISI, Germany
Spieckermann, Caroline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-39
caroline.spieckermann@tum.de
TUM School of Management, Technical University of Munich, Munich, Germany
Spieksma, Flora . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-40
spieksma@math.leidenuniv.nl
Universiteit Leiden, Leiden, Netherlands
Spieksma, Frits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-10
f.c.r.spieksma@tue.nl
Mathematics and Computer Science, Eindhoven Universityof Technology, Eindhoven, Netherlands
Spilger, Maike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-09
maike.spilger@uni-due.de
Universität Duisburg-Essen, Germany
Spinelli, Andrea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-35

EURO 2024 - Copenhagen
andrea.spinelli@unibg.it
Department of Management, Information and Production Engineering, University of Bergamo, Dalmine, Italy, Italy
Spinler, Stefan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-24, WC-45
stefan.spinler@whu.edu
Kuehne Foundation Endowed Chair in Logistics Management, WHU - Otto Beisheim School of Management, Vallendar, Germany
Spitzer, Jiří . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-51
jiri.spitzer@seznam.cz
Technical University of Denmark, Czech Republic
Spitzer, Victor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-09
victor.spitzer@universite-paris-saclay.fr
LISN, Université Paris Saclay, France
Spliet, Remy . . . . . . . . . . . . . . . . . . . . . . . . WD-13, MB-29, TA-53
Spliet@ese.eur.nl
Econometric Institute, Erasmus University Rotterdam, Rotterdam, Netherlands
Spomer, Andreas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-38
andreas.spomer@uni-koeln.de
University of Cologne, Cologne, Germany
Spoorendonk, Simon . . . . . . . . . . . . . . . . . . . . . . . MD-30, WB-64
simon@flowty.ai
Flowty, Denmark
Sprenkels, Laura . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-40, TA-59
l.n.sprenkels@tue.nl
Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Netherlands
Springael, Johan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-25
johan.springael@uantwerpen.be
Engineering Management, University of Antwerp, Antwerp,
Belgium
Spyridakos, Athanasios . . . . . . . . . . . . . . . . . . . . . MD-45, MA-47
tspyr@uniwa.gr
Department of Business Administration, University of West
Attica, Aigaleo, Athens, Greece
Srinivasa, Murari . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-54
murari.srinivasa20@iimb.ac.in
Decision Sciences, Indian Institute Of Management Bangalore, Bengaluru, Karnataka, India
Sriram, Madhavan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-57
madhavan.sriram@gmail.com
Amazon Transportation Services, Amazon, Luxembourg,
Luxembourg
Sroka, Louis Vincent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-24
vincent.sroka@bwl.uni-kiel.de
Supply Chain Management, Christian-Albrechts-University,
Kiel, Germany
Stabile, Gabriele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-63
gabriele.stabile@uniroma1.it
Dipartimento di Metodi e Modelli per l’Economia, il Territorio e la Finanza, Sapienza-Università di Roma, Rome, Italy

AUTHOR INDEX

Stadlerova, Sarka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-61
stadlerova.sarka@gmail.com
Departementet of Industrial Economy and Technology Management, NTNU, Trondheim, Norway, Norway
Stadnichuk, Vladimir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-19
vladimir.stadnichuk@om.rwth-aachen.de
Lehrstuhl für Operations Managemnt, RWTH Aachen,
Aachen, nrw, Germany
Stampa, Dominik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-56
stampa@fzi.de
FZI Forschungszentrum Informatik, Germany
Stanciu, Monica . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-41
stanciu_monica23@yahoo.com
Departament of Mathematics-Informatics, National University for Science and Technology Politehnica Bucharest,
Bucharest, Romania, Romania
Stanković, Jelena J. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-18
jelena.stankovic@eknfak.ni.ac.rs
University of Niš, Faculty of Economics, Nis, Serbia
Starczewski, Tomasz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-44
tomasz.starczewski@pcz.pl
Department of Mathemtics, Czestochowa Uniwersity ofTechnology, Poland
Starita, Stefano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-25
stefano.starita@sasin.edu
Sasin School of Management, Bangkok, Krung Thep Maha
Nakhon Bangkok, Thailand
STauffer, Gautier . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-12, TB-21
gautier.stauffer@unil.ch
HEC Lausanne, Switzerland
Stavropoulou, Foteini . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-21
f.stavropoulou@ljmu.ac.uk
Liverpool Business School, Liverpool John Moores University, Liverpool, Liverpool, United KingdomStålhane, Magnus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-34
magnus.staalhane@ntnu.no
NTNU, Trondheim, Norway
Stecca, Giuseppe . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-35, WD-53
giuseppe.stecca@iasi.cnr.it
Istituto di Analisi dei Sistemi ed Informatica "Antonio Ruberti", Consiglio Nazionale delle Ricerche, Roma, Italy
Steenmans, Ine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-13
ine.steenmans@ucl.ac.uk
Science, Tehcnolog, University College London, London,
London, United Kingdom
Stefan, Gislind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-56
gislind.stefan@univie.ac.at
Business Decisions and Analytics, University of Vienna,
Wien, Wien, Austria
Stefan, Matthias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-07
matthias.stefan@uibk.ac.at657

AUTHOR INDEXEURO 2024 - Copenhagen

Department of Banking and Finance, University of Innsbruck, Innsbruck, Austria
Stefanelli, Kevyn . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-63, TA-63
kevyn.stefanelli@uniroma1.it
Economics and Finance, Sapienza, Rome, Italy, Italy
Stefanelli, Rosario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-55
ro.stefanelli@studenti.unina.it
Department of Civil, Architectural and Environmental Engineering, Federico II University of Naples, Italy
Stefanelli, Valeria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-53
valeria.stefanelli@unisalento.it
Economic Sciences, Università del Salento, Italy
Steffensen, Mogens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-57
mogens@math.ku.dk
København Ø, Denmark
Steidl, Stella Nadine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-53
stellanadine.steidl@pg.canterbury.ac.nz
Civil and Natural Resources Engineering, University of Canterbury, Christchurch, Canterbury, New Zealand
Steinbach, Cedric . . . . . . . . . . . . . . . . . . WD-45, WB-51, MA-54
steinbach@verkehr.tu-darmstadt.de
Department of Civil and Environmental Engineering, Technical University of Darmstadt, Institute of Railway Engineering, Germany
Steingrube, Annette . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-19
annette.steingrube@ise.fraunhofer.deFraunhofer ISE, Germany
Steinhardt, Claudius . . . . . . . . . . . . . . . . WC-54, TD-59, WA-59
claudius.steinhardt@unibw.de
Chair of Business Analytics & Management Science, University of the Bundeswehr Munich (UniBw), Neubiberg,
Germany

thst@dtu.dk
DTU-Management, Technical University of Denmark, Denmark
Stieglis, Remy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-31
r.stieglis@amsterdamumc.nl
Amsterdam UMC, Amsterdam, Netherlands
Stiglmayr, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-54
stiglmayr@math.uni-wuppertal.deSchool of Mathematics and Natural Sciences, University ofWuppertal, Wuppertal, Germany
Stiller, Sebastian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-04
sebastian.stiller@tu-braunschweig.de
TU Braunschweig, Braunschweig, Germany
Stingl, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-34
stingl@am.uni-erlangen.deInstitut fuer Angewandte Mathematik 2, FriedrichAlexander-Universitaet-Erlangen-Nuernberg, Erlangen, Germany
Stip, Joan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-34
joan.stip@asml.com
ASML, Netherlands
Stivan, Samira . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
eliana.stivan@a2a.it
A2A company, Italy
Stolletz, Raik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-39
stolletz@bwl.uni-mannheim.de
Chair of Production Management, University of Mannheim,
Mannheim, Germany
Strack, Alexander . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-57
alexander.strack@uni-a.deAugsburg, Germany

Steininger, Bertram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-59
bertram.steininger@abe.kth.se
KTH Royal Institute of Technology, Stockholm, Sweden

Strasdat, Nico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-07
nico.strasdat@tu-dresden.de
Department of Mathematics, Technische Universität Dresden, Dresden, Germany

Sterle, Claudio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-12
claudio.sterle@unina.it
Department of Electrical Engineering and Information Technology, Università Federico II di Napoli, Napoli, Italia, Italy

Strauss, Arne . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-23, TC-64
arne.strauss@whu.edu
WHU - Otto Beisheim School of Management, Vallendar,
Choose Any State/Province, Germany

Sternbeck, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-50
Michael.Sternbeck@ku-eichstaett.de
Supply Chain Management and Operations, Catholic University of Eichstaett-Ingolstadt, Ingolstadt/Donau, Germany

Strömberg, Ann-Brith . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-37
anstr@chalmers.se
Mathematical Sciences, Chalmers University of Technology,
Göteborg, Sweden

Stevanović, Dragan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-52
dragance106@yahoo.com
Mathematical Institute, Serbian Academy of Sciences andArts, Serbia

Strömer, Stefan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-09
stefan.stroemer@ait.ac.at
Center of Energy - Integrated Energy Systems & Technology,
Policy and Management, AIT Austrian Institute of Technology GmbH & Delft University of Technology, Vienna,
Austria

Stevens, Alexander . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-27
alexander.stevens@kuleuven.be
KULeuven, Belgium
Stidsen, Thomas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-37

658

Strugariu, Radu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-41
rstrugariu@tuiasi.ro
Mathematics, Gheorghe Asachi Technical University & Oc-

EURO 2024 - Copenhagen

AUTHOR INDEX

tav Mayer Institute of Mathematics, Iasi, Iasi, Romania
Strusevich, Vitaly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-60
V.Strusevich@greenwich.ac.uk
School of Computing and Mathematical Sciences, Universityof Greenwich, London, United Kingdom
Stuckey, Peter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-07, WB-28
peter.stuckey@monash.edu
Faculty of Information Technology, Monash University,
Clayton, Australia
Stummer, Christian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-43
christian.stummer@uni-bielefeld.de
Department of Business Administration and Economics,
Bielefeld University, Bielefeld, Germany
Su, Jack C.P. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-40
jackcpsu@unm.edu
Andersen School of Management, University of New Mexico, Albuquerque, New Mexico, United States
Su, Weizhao. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-55
ws1f20@soton.ac.uk
Department of Mathematical Science, University ofSouthampton, Southampton, United Kingdom
Su, Yue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-27
yue.su@enpc.fr
CERMICS, Ecole des Ponts ParisTech, Champs-sur-Marne,
FranceSuban, Valter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-48
valter.suban@fpp.uni-lj.si
Faculty of Maritime Studies, University of Ljubljana, Portoroz, Slovenia
Subramanian, Anand . TA-18, WB-20, TD-35, TD-45, WA-52,
MC-58, WA-58
anand@ci.ufpb.br
Universidade Federal da Paraíba, João Pessoa, Paraíba, Brazil
Suchan, Karol. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-62
karol.suchan@mail.udp.cl
Escuela de Informática y Telecomunicaciones, Universidad
Diego Portales, Santiago, Region Metropolitana, Chile
Sudoso, Antonio M. . . . . . . . . . . . . . . . . . . . . . . . . MA-18, MA-32
antoniomaria.sudoso@uniroma1.it
Sapienza University of Rome, Rome, Italy

Sun, Fabo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-29
nfabo@huawei.com
Huawei, Hong Kong
Sun, Geqie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-11
g.sun-3@tudelft.nl
TPM, Delft University of Technology, Delft, NetherlandsSun, Hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-56, WB-56
hui.sun@seu.edu.cn
Industrial Engineering, Southeast University, Nanjing, China
Sun, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-29
riosun@gmail.com
Huawei Hong Kong Research Center, Hong Kong
Sun, Qinghe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-62
qinghe.sun@polyu.edu.hk
The Hong Kong Polytechnic University, Hong Kong
Sun, Qi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-23
sqd124@gmail.com
School Of Business, University of Dundee, Dundee, Dundee,
United Kingdom
Sun, Shudong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-49
sdsun@nwpu.edu.cn
Industrial Engineering, Northwestern Polytechnical Uni.,
Xi’an, China
Sun, Xiaoyang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-26
scxs@leeds.ac.uk
University of Leeds, United Kingdom
Sun, Yige . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-28
yige-jessica.sun@connect.polyu.hk
ChinaSun, Yiting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-49
syt209054@tju.edu.cn
Tianjin, China
Sun-weng., Huang . . . . . . . . . . . . . . . . . . . TB-07, MC-53, WC-59
cindyhuangxia05@gmail.com
Department of Health Care Management, National Taipei
University of Nursing and Health Sciences, Taipei, Taiwan
Sundström, Jerker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-56
jerker.sundstrom@infranord.se
Infranord, Sweden

Suen, Tsz Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-20
tsz.suen@port.ac.uk
Faculty of Technology, University of Portsmouth, Portsmout,
United Kingdom

Sung, Inkyung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-10
inkyung_sung@mp.aau.dk
Materials and Production, Aalborg University, Denmark

Summers, Francoise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-18
fsummers@metu.edu.tr
Architecture Department, Middle East Technical University,
Ankara, Turkey

Sunil, Deena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-48
sunil.deena007@gmail.com
Mathematics, Indira Gandhi National Tribal University,
Amarkantak, Amarkantak, Madhya Pradesh, India

Sun, Cong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-42
suncong@lsec.cc.ac.cn
School of Science, Beijing University of Posts and Telecommunications, Beijing, China

Suresh, Karthik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-13
k.suresh@jayamony.co.uk
University of Hull, United Kingdom

659

AUTHOR INDEXEURO 2024 - Copenhagen

Suresh, Sruthi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-17
sruthisuresh@medical.aims.amrita.edu
Pediatrics, Amrita Institute of Medical Sciences, Kochi, Kerala, India

T. S., Dipu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-18
diputsmck@gmail.com
Infectious Disease, Amrita Institute of Medical Sciences,
Kochi, Kerala, India

Suryapeta, Akshitha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-59
phd21025@iiml.ac.in
Operations Management, IIM Lucknow, Lucknow, Uttar
Pradesh, India

Tabares, Alejandra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-22
a.tabaresp@uniandes.edu.co
Industrial engineering, Los Andes University, Bogotá, Cundinamarca, Colombia

Sutiene, Kristina . . . . . . . . . . . . . . . . . . . . MD-51, TA-51, WC-59
kristina.sutiene@ktu.lt
Department of Mathematical Modeling, Kaunas Universityof Technology, Kaunas, Lithuania

Taccini, Marco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-45
marco.taccini@unimore.it
Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Reggio Emilia, Italy

Şüyün, Oğuz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-37
oguz.suyun@tupras.com.tr
Turkish Petroleum Refineries Corp. (TUPRAS), Turkey

Tadano, Yara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-28
yaratadano@utfpr.edu.br
Federal University of Technology - Parana, Ponta Grossa,
PR, Brazil

Suzuki, Ken-ichi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-60
kenichi.suzuki.b2@tohoku.ac.jp
Graduate School of Economics and Management, Tohoku
University, Sendai, Miyagi, Japan

Tadaros, Marduch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-64
marduch.tadaros@liu.se
Department of Science and Technology, Linköping University, Sweden

Swaminathan, Kritika . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-61
phd22028@iiml.ac.in
Operations Management, Indian Institute of Management
Lucknow, Lucknow, Uttar Pradesh, India

Tadumadze, Tamaz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-33
tamaz.tadumadze@tsu.ge
Tbilisi State University, Tbilisi, Georgia, Georgia

Swiercz, Aleksandra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-20
aswiercz@cs.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poznan, Poland

Taghavi, Majid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-15
majid.taghavi@smu.ca
Sobey School of Business, Saint Mary’s University, Halifax,
NS, Canada

Szachniuk, Marta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-20
euro2024@cs.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poznan, Poland

Taghikhah, Rosa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-11
firouzeh.taghikhah@sydney.edu.au
University of Sydney, Australia

Szczecinski, Leszek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-16
leszek.szczecinski@inrs.ca
INRS, Montreal, Quebec, Canada
Sziklai, Balázs R. . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-16, TB-36
sziklai.balazs@krtk.mta.hu
Centre for Economic and Regional Studies, Hungarian
Academy of Sciences, Budapest, HU, Hungary
Szögi, Evelin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-26
szogi.evelin@sztaki.hu
Institute for Computer Science and Control, Hungary
Szymula, Christopher . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-54
christopher.szymula@tu-dresden.deTraffic Flow Science, TU Dresden, Dresden, Saxony, Germany
T’kindt, Vincent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-60
tkindt@univ-tours.fr
University of Tours, TOURS, France
T. R. Kong, Xiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-23
kongxtr@szu.edu.cn
College of Economics, Shenzhen University, China

660

Taherkhani, Gita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-61
gtaherkhani@luc.edu
Supply Chain Management, Loyola University, Chicago, IL,
United States
Tahery, Mahyar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-50
mahyar.taheri@klu.org
Kuehne Logistics University, Germany
Taisch, Corsin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-19
taischc@student.ethz.ch
D-MAVT, ETH Zurich, Zürich, Switzerland, Switzerland
Takac, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-32
martin.taki@gmail.com
Lehigh University, United States
Takahashi, Natsumi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-55
ntaka@nda.ac.jp
Department of Electrical and Electronic Engineering, National Defense Academy, Kanagawa, Japan
Takashima, Hikaru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-55
0ckr1104@cc.u-tokai.ac.jp
School of Marine Science and Technology, Tokai University,
JapanEURO 2024 - Copenhagen

AUTHOR INDEX

Takkula, Tuomo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-60
tuomo.takkula@ab-ovo.com
APS, Ab Ovo Germany GmbH, Düsseldorf, Germany

Tang, Xinyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-62
x.tang-3@tudelft.nl
Delft University of Technology, Delft, Netherlands

Tal, Gil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-56
gtal@ucdavis.edu
Institute of Transportation Studies, University of California,
Davis, United States

Tanguy, Hervé . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-20
hervet@fr.thalesgroup.com
Thales, Brest, Bretagne, France

Talmor, Irit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-44
iritt@wgalil.ac.il
Sir Harry Solomon School of Management, Western Galilee
College, Kiryat-Mozkin, ISRAEL, Israel
Tamburini, Alberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-29
albetam@dtu.dk
Management Engineering, DTU, Italy
Tamm, Erik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-04
etamm@kth.se
Department of Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden
Tamssaouet, Karim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-60
karim.tamssaouet@bi.no
Accounting and Operations Management, BI NorwegianBusiness School, Norway
Tan, Lijia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-11
l.tan1@tue.nl
IE&IS, Eindhoven University of Technology, Netherlands
Tan, Nuoya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-13
n.tan2@exeter.ac.uk
University of Exeter, United Kingdom
Tan, Tarkan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-24
t.tan@tue.nl
Industrial Engineering Department, Eindhoven University ofTechnology, Eindhoven, Netherlands
Tan, Tarkan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-24
tarkan.tan@business.uzh.ch
Sustainable Operations Management, University of Zurich,
Zurich, Switzerland
Tanaka, Ken-ichi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-61
ken1tnk@ae.keio.ac.jp
Keio University, Yokohama, Japan
Tanaka, Shun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-28
shuntianzhong23@gmail.com
Information Science and Technology, Osaka University, Toyonaka, Osaka, Japan
Tancrez, Jean-Sébastien . . . . . . . . . . . . . . . . . . . . . WC-55, TB-56
js.tancrez@uclouvain.be
CORE - Louvain School of Management, Université
catholique de Louvain, Mons, Belgium
Tang, Junqi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-32
j.tang.2@bham.ac.uk
School of Mathematics, University of Birmingham, United
KingdomTanneau, Mathieu. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-03
mathieu.tanneau@polymtl.ca
Polytechnique Montreal, Canada
Tansitpong, Praowpan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-54
praowpan.tan@nida.ac.th
NIDA Business School, National Institute of Development
Administration, Bangkok, Thailand
Tao, Hanqiao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-60
taohanqiao@126.com
National University of Defense Technology, Changsha, China
Tarafdar, Anirban . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-16
anirban.ganit@gmail.com
Mathematics, NIT Agartala, West Tripura, Tripura, India
Taranto, Eugenia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-16
eugenia.taranto@unikore.it
Department of Humanities, Foreign Language and Education, Kore University of Enna, Enna, Italy
Tarhan, İstenç . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-13
istenctarhan@gmail.com
School of Business, UCD, Ireland
Tarhan, Şeyhmus. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-29
seyhmus.tarhan0647@gmail.com
Selçuklu, Konya, Turkey
Tarhini, Hussein . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-50
ht27@aub.edu.lb
American University of Beirut, Lebanon
Tarim, Armagan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-49
armagan.tarim@hacettepe.edu.tr
University College Cork, Cork, Ireland
Tariq, Syed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-21
stariq@lincoln.ac.uk
Lincoln International Business School, University of Lincoln,
Lincoln, Lincolnshire, United Kingdom
Tariyal, Amit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-08
amittariyal1980@gmail.com
School Of Hospitality Management, Ims Unison University,
DEHRADUN, UTTARAKHAND, India
Tas, Duygu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-60
duygu.tas@sabanciuniv.edu
Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
Taskin, Alev . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-04
ataskin@yildiz.edu.tr
Industrial Engineering, Yildiz Technical University, Turkey

661

AUTHOR INDEXEURO 2024 - Copenhagen

Tasso, Vladimir Yuri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-15
tasso.yuri@gmail.com
ICMC/USP, Osasco, Sao Paulo, Brazil
Tat, Roya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-23
tat.roya@esc-clermont.fr
ESC Clermont Business School & CleRMa, France
Tavana, Madjid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-59
tavana@lasalle.edu
Management, La Salle University, Philadelphia, Pennsylvania, United States
Tavares, Roberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-29
tavares@dep.ufscar.br
UFSCar, São Paulo, Brazil
Tavin, Bertrand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-57, TB-57
tavin@em-lyon.com
EMLYON Business School, Lyon, France
Tavoni, Massimo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-11
massimo.tavoni@polimi.it
Politecnico di Milano and CMCC Foundation - EuroMediterranean Center on Climate Change, Italy and RFFCMCC European Institute on Economics and the Environment, Italy, Italy
Téllez-Macías, Ángel David . . . . . . . . . . . . . . . . . . . . . . . . . MB-19
angel.david.tellez@upc.edu
Polytechnic University of Catalonia, Mexico
Taylor, Adrien . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-32
adrien.taylor@inria.fr
Inria/ENS, France
Tchemisova, Tatiana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-38
tatiana@ua.pt
Mathematics Department, University of Aveiro, Aveiro, Portugal
Teck, Sander . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-04
sander.teck@kuleuven.beCentre for Industrial Management / Traffic & Infrastructure,
KU Leuven, Leuven, Belgium
Tegtmeier, Lars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-51
Lars.Tegtmeier@HS-Merseburg.de
University of Applied Sciences Merseburg, Merseburg, Germany
Teimourzadeh, Saeed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-22
saeed@epra.com.tr
R and D, EPRA Energy, Ankara, Turkey
Teixeira, Ana Paula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-16
ateixeir@utad.pt
Mathematics, Utad / Cio, Portugal
Tejada, Diego . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-09
diego.tejadaarango@tno.nl
Energy Transition Studies, TNO, Amsterdam, Netherlands
Tekiner Mogulkoc, Hatice . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-22
tekinermogulkoc@itu.edu.tr

662

Industrial Engineering, Istanbul Technical University, Turkey
Temprano Garcia, Francisco . . . . . . . . . . . . . . . . . . . . . . . . TC-29
ftgarcia@us.es
Stats and OR, Universidad de Sevilla, Sevilla, Sevilla, Spain
Teng, Fei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-35
f.teng@imperial.ac.uk
Electrical and Electronic Engineering, Imperial College London, London, England, United Kingdom
Tenni, Salma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-16
selmatenni@gmail.com
McGill, Canada
Terán Viadero, Paula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-25
pauteran@ucm.es
Universidad Complutense de Madrid, Madrid, Madrid, Spain
Tercan, Hasan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-03
tercan@uni-wuppertal.deInstitute for Technologies and Management of Digital Transformation, University of Wuppertal, Wuppertal, Germany
Terfasse, Karim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-54
kterfasse@gmail.com
Department of Manufacturing Sciences and Logistics, Ecoledes Mines de Saint-Etienne - LIMOS, Gardanne, France
Terho, Topias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-34
topias.terho@aalto.fi
Department of mathemathics and systems analysis, Aalto
University, Finland
Teruyama, Junichi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-25
junichi.teruyama@gmail.com
University of Hyogo, Japan
Tessitore, Marta Leonina . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-51
marta.tessitore@uniroma3.it
Roma Tre University, Rome, Italy
Teuerle, Marek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-02
marek.teuerle@pwr.edu.pl
Faculty of Pure and Applied Mathematics, Wroclaw University of Science and Technology, Wroclaw, Poland
Teunter, Ruud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-08, WB-39
r.h.teunter@rug.nl
Operations, University of Groningen, Groningen, Netherlands
Teytelboym, Alexander . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-43
alexander.teytelboym@economics.ox.ac.uk
University of Oxford, United Kingdom
Thanassoulis, Emmanuel . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-48
e.thanassoulis@aston.ac.uk
Aston Business School, Aston University, Birmingham,
United Kingdom
Themeli, Natasa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-45
natashathem@gmail.com
University of Athens, Greece

EURO 2024 - Copenhagen
Themelis, Andreas. . . . . . . . . . . . . . . . . . . . . . . . . . . TA-32, WA-41
andreas.themelis@ees.kyushu-u.ac.jp
Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan
Theona, Konstantina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-47
konnith@yahoo.gr
Business Administration, University of West Attica, Athens,
GreeceAUTHOR INDEX

arthur.thuy@ugent.be
Data Analytics, Ghent University, Ghent, Belgium
Tian, Bin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-29
bin01.tian@connect.polyu.hk
The Hong Kong Polytechnic University, Hong Kong
Tian, Jun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-21
tianjun@mail.xjtu.edu.cn
School of Management, Xi’an Jiaotong University, China

Theurich, Franziska . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-54
franziska.theurich@tu-dresden.de
Chair of Traffic Flow Science, TU Dresden, Dresden, Germany

Tian, Yuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-57
tian@econ.ryukoku.ac.jp
Ryukoku University, Japan

Thielen, Clemens . . . . . . . . . . . . . . . . . . . MD-02, WB-37, WC-58
clemens.thielen@tum.de
TUM Campus Straubing for Biotechnology and Sustainability, Technical University of Munich, Straubing, GermanyTichý, Tomáš . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-51
tomas.tichy@vsb.cz
Department of Finance, Faculty of Economics, VSBTechnical University Ostrava, Ostrava, Czech RepublicThies, Christian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-24
christian.thies@tuhh.de
Resilient and Sustainable Operations and Supply Chain Management Group, Hamburg University of Technology, Hamburg, Hamburg, Germany

Tierney, Kevin . . . . . . . . . . . . . . . MC-03, MD-03, TC-03, WC-58
kevin.tierney@uni-bielefeld.de
Decision and Operation Technologies, Bielefeld University,
Bielefeld, Germany

Thieyre, Jordan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-27
yzx9z8iw@duck.com
FranceThijssen, Jacco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-33, MC-33
jacco.thijssen@york.ac.uk
Management School & Department of Mathematics, University of York, York, United Kingdom
Thomas, Doreen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-25
doreen.thomas@unimelb.edu.au
Mechanical Engineering, University of Melbourne, Melbourne, Victoria, Australia
Thompson, Nathan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-43
nathan.thompson@strath.ac.uk
Strathclyde Business School, Management Science Department, United Kingdom
Thomsen, Oliver Rise. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-62
oliver@orthomsen.dk
Department of Technology, Management and Economics,
DTU, Vanløse, Denmark
Thormann, Marah-Lisanne . . . . . . . . . . . . . . . . . . . . . . . . . WB-42
m.-l.thormann@soton.ac.uk
Mathematical Sciences, University of Southampton,
Southampton, Hampshire, United Kingdom
Thorsen, Andreas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-61
andreas.thorsen@montana.edu
Jake Jabs College of Business and Entrepreneurship, Montana State University, Bozeman, Montana, United States
Thuillier, Owein . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-20
owein.thuillier1@gmail.com
Université Bretagne-Sud, Thales, France

Tilk, Christian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-60
christain.tilk@univie.ac.at
Vienna University, Austria
Tilson, Vera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-10
vera.tilson@simon.rochester.edu
University of Rochester, Rochester, United States
Timm, Larissa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-62
larissa.timm@hpa.hamburg.de
HPA, Hamburg, Germany
Tindemans, Simon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-22
s.h.tindemans@tudelft.nl
Department of Electrical Sustainable Energy, Delft University of Technology, Delft, Netherlands
Tinessa, Fiore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-55
fiore.tinessa@unina.it
Federico II University of Naples, Italy
Tinios, Platon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-15
ptinios@unipi.gr
University of Piraeus, Greece
Tishler, Asher . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-09
AsherT@tauex.tau.ac.il
Faculty of Management, Tel-Aviv University, Tel-Aviv, Israel
Tiukhova, Elena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-27
elena.tiukhova@kuleuven.be
Faculty of Economics and Business, Research Center for
Information Systems Engineering (LIRIS), KU Leuven, Leuven, Belgium
Tiwari, Saransh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-48, WB-48
saransh@iiml.ac.in
Decision Sciences, Indian Institute of Management (IIM)
Lucknow, Lucknow, Uttar Pradesh, India

Thuy, Arthur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-31

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AUTHOR INDEXEURO 2024 - Copenhagen

Tobar, Cristina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-58
cristina.tobar@oga.ai
oga, Spaintoensfeuerborn@efi.rwth-aachen.de
Chair of Decision Theory and Financial Services, RWTH
Aachen University, Aachen, Germany

Todella, Elena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-13, WC-23
elena.todella@polito.it
DIST, Politecnico di Torino, Italy

Topaloglou, Nikolas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-63
nikolas@aueb.gr
International and European Economic Studies, Athens University of Economics and Business, Athens, Greece

Tolga, ÇaĞri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-47
ctolga@gsu.edu.tr
Galatasaray University, Turkey
Tolkmitt, Frederik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-43
frederik.tolkmitt@uni-bielefeld.de
Department of Business Administration and Economics,
Bielefeld University, Germany
Tollenaere, Jonas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-07
jonas.tollenaere@kuleuven.be
NUMA, KU Leuven, Belgium
Toloo, Mehdi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-48
m.toloo@surrey.ac.uk
Department of Business Analytics & Operations, Universityof Surrey, Guildford, Surrey, United Kingdom
Toma, Pierluigi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-12, WA-53
pierluigi.toma@unisalento.it
Department of Management and Economics, University ofSalento, Lecce, Italy
Tomar, Vivek. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-50
Vivek.Tomar@walmart.com
CENTROID, Walmart, United States

Toparlak, Umut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-58
umut.toparlak@trendyol.com
Technology, Trendyol, Turkey
Tor, Osman Bulent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-22
osman.tor@epra.com.tr
Epra Energy, Ankara, Turkey
Torabi, Sara . . . . . . . . . . . . . . . . . . . . . . . . MA-18, MD-18, MD-47
sara.torabi@polito.it
Interuniversity Department of Regional and Urban Studies
and Planning (DIST), Politecnico di Torino, Turin, Piedmont,
Italy
Torres Delgado, Jose Fidel. . . . . . . . . . . . . . . . . . . . . . . . . . . TD-51
ftorres@uniandes.edu.co
Universidad de los Andes, Bogotá, Bogotá, Colombia
Torres Escobar, Rafael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-25
rafael.torrese@anahuac.mx
Facultad de Ingeniería, Universidad Anáhuac Mexico,
Huixquilucan, Estado de México, Mexico
Torres, Fabian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-59
fabian.torres@epfl.ch
Epfl Enac Iic Transp-or, Lausanne, Switzerland

Tomasella, Maurizio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-55
Maurizio.Tomasella@ed.ac.uk
Business School, University of Edinburgh, Edinburgh, United
Kingdom

Torres, José Luis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-22
jltorres@ual.es
University of Almería, Spain

Tomasetti, Lorenzo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-26
lorenzo.tomasetti@unibs.it
Department of Information Engineering, University of Brescia, Brescia, Italy

Torri, Gabriele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-51
gabriele.torri@unibg.it
Management, Economics and Quantitative Methods, University of Bergamo, Bergamo, Italy

Tomasgard, Asgeir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-19
asgeir.tomasgard@sintef.no
Department of industrial economics and technology management, NTNU, Trondheim, Norway

Torrieri, Francesca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-47
frtorrie@unina.it
Department of Industrial Engeneering, University of Naples
Federico II, Naples, Italy, Italy

Tomazella, Caio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-15
caio.tomazella@usp.br
ICMC, University of São Paulo, São Carlos, São Paulo, Brazil

Tosunlu, Halil Berkay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-13
berkaytosunlu@gmail.com
LAMSADE, Universite Paris Dauphine, Paris, France,
FranceTomczyk, Michał . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-44
michal.tomczyk@cs.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poland
Tönissen, Denise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-35
d.d.tonissen@vu.nl
Supply Chain Analytics, Vrije Universiteit Amsterdam,
Netherlands
Tönsfeuerborn, Mendy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-45

664

Toth, Paolo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-26, WD-56
paolo.toth@unibo.it
DEI, University of Bologna, Bologna, Italy
Toubeau, Jean-François . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-03
Jean-Francois.TOUBEAU@umons.ac.be
University of Mons, Hennuyères, Hainaut, Belgium
Toufani, Parinaz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-22
parinaz.toufani@insead.edu

EURO 2024 - Copenhagen

AUTHOR INDEXINSEAD, France
Townsend, Adam. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-13
adam@vuit.online
Stoke on Trent, Staffordshire, United Kingdom

Triki, Anis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-18
trikianis@uri.edu
Business - Accounting, University of Rhode Island, UnitedStatesToyasaki, Fuminori . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-23
toyasaki@yorku.ca
York University, Toronto, Canada

Tripathi, Divyansh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-62
divyansh.2022phd@iimu.ac.in
IIM Udaipur, India

Tragler, Gernot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-33
gernot.tragler@tuwien.ac.at
Institute for Statistics and Mathematical Methods in Economics, Vienna University of Technology, Vienna, Austria

Tripathi, Muktak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-59
muktak.tripathi@ie.edu
Accounting and Management Control, IE University, Spain

Tran, Dung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-61
d.h.tran@sms.ed.ac.uk
University of Edinburgh, United Kingdom
Tran, Tri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-45
tri.tran@rug.nl
Department of Operations, University of Groningen, Groningen, Netherlands
Tran, Trung Hieu . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-02, WA-46
t.h.tran@cranfield.ac.uk
School of Water, Energy and Environment, Cranfield University, Cranfield, United Kingdom
Tran, Viet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-42, TA-42
tran@ml.jku.at
Institute for Integrated Circuits, Johannes Kepler University,
Linz, Austria
Transchel, Sandra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-50
sandra.transchel@the-klu.org
Kuehne Logistics University, Hamburg, Germany
Tratta, Diana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-02
diana.tratta@unibs.it
Department of Economics and Management, University of
Brescia, Brescia, Italy
Trautmann, Norbert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-60
norbert.trautmann@unibe.ch
Department of Business Administration, University of Bern,
Bern, BE, Switzerland
Trépanier, Martin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-14
mtrepanier@polymtl.ca
Polytechnique Montréal, Canada
Treigys, Povilas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-28
povilas.treigys@mif.vu.lt
Institute of Data Science and Digital Technologies, Vilnius
University, Vilnius, Lithuania
Trejo, Francisco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-25
francisco_trejo@anahuac.mx
Engineering, Universidad Anahuac, Mexico
Tricoire, Fabien . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-37
fabien.tricoire@wu.ac.at
Institute for Transport and Logistics Management, Vienna
University of Economics and Business, Wien, Austria

Trivella, Alessio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-22
a.trivella@utwente.nl
Industrial Engineering and Business Information Systems,
University of Twente, Enschede, Netherlands
Trivyza, Nikoletta L . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-23
nikoletta.trivyza@outlook.com
United Kingdom
Trnovska, Maria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-48
trnovska@fmph.uniba.sk
Dpt. of applied mathematics and statistics, Faculty of Mathematics, Physics and Informatics, Comenius University in
Bratislava, Slovakia
Trombini, Ilaria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-34
ilaria.trombini@unife.it
Università di Ferrara, Italy
Troncoso, Fredy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-31
ftroncos@ubiobio.cl
Universidad del Bio Bio, Concepcion, Chile
Trono, John . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-16
jtrono@smcvt.edu
Computer Science, Saint Michael’s College, Colchester, Vermont, United States
Trovato, Maria Rosa . . . . . . . . . . . . . . . . . . . . . . . . MD-47, TD-47
mrtrovato@dica.unict.it
Department of Civil Engineering and Architecture, University of Catania, Catania, Italy, Italy
Truden, Christian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-55
christian.truden@aau.at
Department of Operations, Energy, and Environmental Management, University of Klagenfurt, Klagenfurt, Austria
Trutnevyte, Evelina. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-53
evelina.trutnevyte@unige.ch
Institute for Environmental Sciences (ISE), Section of Earthand Environmental Sciences, University of Geneva, Geneva,
Switzerland
Tsadikovich, Dmitry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-33
dmitrytsadikovich@gmail.com
Management, Bar-Ilan University, Ramat-Gan, Israel
Tsai, Ching-Kuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-40
r11546029@ntu.edu.tw
National Taiwan University, Taiwan

665

AUTHOR INDEXEURO 2024 - Copenhagen
NorwayTsai, Jung-Fa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-60
jftsai@ntut.edu.tw
Business Management, National Taipei University of Technology, Taipei, Taiwan
Tsakoumis, Dimitrios . . . . . . . . . . . . . . . . . . . . . . . WC-08, WD-29
jimtsk98@gmail.com
Industrial Management and Technology, University of Piraeus, Athens, Greece
Tsaples, George . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-48
gtsaples@uom.edu.gr
University of Macedonia, Thessaloniki, Greece
Tsay, Calvin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-19
c.tsay@imperial.ac.uk
Department of Computing, Imperial College London, London, United Kingdom
Tsekrekos, Andrianos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-57
tsekrekos@aueb.gr
Athens University of Economics and Business, Athens,
GreeceTshuva, Moshe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-02
MosheT@afeka.ac.il
Afeka College of Engineering, Tel-Aviv, Israel
Tsiligkaridis, Ioannis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-06
itsili13@gmail.com
Heritage University, United States
Tsiourvas, Asterios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-20
atsiour@mit.edu
Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, United States
Tsipinakis, Nick . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-34
nikolaos.tsipinakis@unidistance.ch
Faculty of Mathematics and Computer Science, Uni DistanceSuisse, Brig, Switzerland
Tsoka, Sophia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-27
sophia.tsoka@kcl.ac.uk
Department of Informatics, King’s College London, United
Kingdom
Tsotsolas, Nikos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-44
ntsotsol@uniwa.gr
Business Administration, Univeristy of West Attica, Egaleo,
Attica, Greece
Tsoukias, Alexis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-13, TB-47
tsoukias@lamsade.dauphine.fr
Cnrs - Lamsade, Paris Cedex 16, France
Tuffin, Bruno . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-06
Bruno.Tuffin@inria.fr
Inria, Rennes, Bretagne, France
Tufte-Johnsen, Kristoffer . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-55
kristotu@stud.ntnu.no
Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology,

666

Tuljak-Suban, Danijela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-48
danijela.tuljak@fpp.uni-lj.si
Faculty Of Maritime Studies And Transport, University Of
Ljubljana, PORTOROŽ, Slovenia, Slovenia
Tuma, Axel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-24, WC-60
axel.tuma@wiwi.uni-augsburg.de
Faculty of Business Administration, University of Augsburg,
Augsburg, Germany
Tuma, Niklas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-58
niklas.tuma@tum.deTechnical University of Munich, Munich, Bavaria, Germany
Tunaboylu, Bahadir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-22
bahadir.tunaboylu@marmara.edu.tr
Materials Eng, Marmara University, maltepe, istanbul,
TurkeyTunay, Orkun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-29
tunay@rsm.nl
Erasmus University, Rotterdam, Netherlands
Tuncel, Levent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-38
ltuncel@uwaterloo.ca
Dept. of Combinatorics and Optimization, Faculty of Mathematics, University of Waterloo, Waterloo, Ontario, Canada
Tuncer, Basak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-35
basak.tuncer@socar.com.tr
SOCAR TUrkey R&D and Innovation Co., Turkey
Tundulyasaree, Krissada . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-58
k.tundulyasaree@tue.nl
Operations, Planning, Accounting and Control, Eindhoven
University of Technology, Eindhoven, Netherlands
Tuni, Andrea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-23
andrea.tuni@polito.it
Department of Management and Production Engineering, Politecnico di Torino, Torino, Italy
Tuo, Jiawei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-03
815239009@qq.com
School of Management, Northwestern Polytechnical University, Xi’an, Shaanxi, China
Tural, Mustafa Kemal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-22
tural@metu.edu.tr
Industrial Engineering Department, Middle East Technical
University, Ankara, Turkey
Tureci Isik, Hannan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-21
ht631@bath.ac.uk
Management, University of Bath, Bath, United Kingdom
Turkalj, Ivica . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-37
ivica.turkalj@itwm.fraunhofer.deFraunhofer Institute for Industrial Mathematics ITWM, Germany
Turkensteen, Marcel . . . . . . . . . . . . . . . . . . . . . . . WD-25, WD-56
matu@econ.au.dk

EURO 2024 - Copenhagen
CORAL, Economics, Aarhus University, Aarhus V, Denmark
Turner, Christopher . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-23
christopher.turner@surrey.ac.uk
Department of Business Transformation, University of Surrey, Guildford, United Kingdom
Tuson, Mark . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-18
tusonm@cardiff.ac.uk
School of Mathematics, Cardiff University, Cardiff, Cardiff,
United Kingdom
Tyburec, Marek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-38
marek.tyburec@fsv.cvut.cz
Department of Mechanics, Czech Technical University in
Prague, Faculty of Civil Engineering, Prague 6, Czech Republic
Tzeng, Gwo-Hshiung . . . . . . . . . . . . . . . . . . . . . . . . TB-07, MC-53
ghtzeng@gm.ntpu.edu.tw
Graduate Institute of Urban Planning, National Taipei University, San Shia District, New Taipei City, Taiwan
Tzeremes, Nickolaos G. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-48
bus9nt@econ.uth.gr
Department of Economics, University of Thessaly, Greece
Uberti, Mariacristina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-63
mariacristina.uberti@unito.it
Management, University of Torino, Torino, Italy
Uberti, Pierpaolo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-63
uberti@economia.unige.it
Università di Genova, Genova, Italy
Ucar, Iñaki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-27
inaki.ucar@uc3m.es
UC3M-Santander Big Data Institute, Universidad Carlos III
de Madrid, Spain
Uchoa, Eduardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-58
eduardo.uchoa@gmail.com
Engenharia de Producao, UFF, Niteroi, RJ, Brazil
Uchoa, Eduardo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-58
uchoa@producao.uff.br
Engenharia de Produção, Universidade Federal Fluminense,
Niterói, Rio de Janeiro, BrazilÜçüncü, Duygu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-04
duygu.ucuncu@bau.edu.tr
Bahcesehir University, Istanbul, Turkey
Udovč, Andrej . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-45
andrej.udovc@bf.uni-lj.si
agronomy, University of Ljubljana, Biotechnical Faculty,
Ljubljana, Slovenia
Uhrmeister, Anna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-59
anna.uhrmeister@uni-bielefeld.de
Accounting, University of Bielefeld, Bielefeld, NRW, Germany
Ulmer, Marlin Wolf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-64
marlin.ulmer@ovgu.de

AUTHOR INDEXManagement Science, Otto von Guericke Universität Magdeburg, Magdeburg, Germany
Ulukus, Mehmet Yasin . . . . . . . . . . . . . . . . . . . . . . MB-09, TB-22
ulukus@itu.edu.tr
Industrial Engineering, Istanbul Technical University, Turkey
Ulus, Firdevs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-37
firdevs@bilkent.edu.tr
Industrial Engineering, Bilkent University, Ankara, Turkey
Ulusoy, Aly-Joy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-37
aly-joy.ulusoy15@imperial.ac.uk
Civil and Environmental Engineering, Imperial College London, London, - None -, United Kingdom
Umman, Bahar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-45
baharuludagg@gmail.com
Operational Excellence,
Trendyol,kahramankazan,
ANKARA, Turkey
Unal, Ali Tamer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-24
unaltam@boun.edu.tr
Industrial Engineering, Bogazici University, Istanbul, Turkey
Unzueta, Aitziber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-35
aitziber.unzueta@ehu.eus
Applied Mathematics, UPV/EHU, Bilbao, Spain
Upadhyaya, Manu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-32
manu.upadhyaya@control.lth.se
Lund University, Sweden
Uratani, Tadashi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-57
uratani@hosei.ac.jp
Industrial and System Engineerig, Hosei University, Tokyo,
Japan
Urquhart, María E. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-55
maritaurq@gmail.com
Universidad de la República, Uruguay
Urrutia, Sebastian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-25
sebastian.a.urrutia@himolde.no
University Molde, Molde, Norway
Uster, Halit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-56
uster@smu.edu
Southern Methodist University, United States
Usul, Hamdi Burak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-30
busul@gams.com
Germany
Uzunoglu, Aykut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-60
aykut.uzunoglu@wiwi.uni-augsburg.de
Chair of Production & Supply Chain Management, Augsburg
University, Germany
V. Murthy, Rajluxmi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-54
rajluxmi.vmurthy@iimb.ac.in
Decision Sciences, Indian Institute of Management Bangalore, Bengaluru, Karnataka, India
V.Gernaey, Krist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-24

667

AUTHOR INDEXEURO 2024 - Copenhagen

kvg@dtu.dk
Chemical and Biochemical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark
Vairetti, Carla . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-31, WA-31
cvairetti@uandes.cl
Universidad de los Andes, Santiago, Región Metropolitana,
Chile
Vaiter, Samuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-32
samuel.vaiter@cnrs.fr
FranceVaitiekuniene, Raminta . . . . . . . . . . . . . . . . . . . . . . TA-51, WC-59
raminta.vaitiekuniene@ktu.lt
Department of Mathematical Modeling, Kaunas Universityof Technology, Lithuania
Valadares Siqueira, Hugo . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-28
hugovaladares@hotmail.com
Instituto Brasileiro de Informação em Ciência e Tecnologia,
Brasília, DF, Brazil
Valencia Lucero, Diana Marcela . . . . . . . . . . . . . . . . . . . . WA-22
valencialucero@gmail.com
Brandenburg University of Technology Cottbus-Senftenberg,
Germany

Business Informatics and Operations Management, Ghent
University, Ghent, Belgium
van Dalen, Jan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-24
jdalen@rsm.nl
Technology and Operations Management, RSM Erasmus
University, Rotterdam, Netherlands
van de Bijl, Etienne . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-04
evdb@cwi.nl
CWI, Netherlands
Van de Klundert, Joel Joris . . . . . . . . . . . . . . . . . . TD-10, WB-20
joris.vandeklundert@uai.cl
School of Business, Universidad Adolfo Ibanez, Santiago,
Chile
van den Akker, Marjan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-54
j.m.vandenakker@uu.nl
Information and Computing Sciences, Utrecht University,
Utrecht, Nederland, Netherlands
van den Berg, Pieter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-29
pljvdberg@gmail.com
Rotterdam School of Management, Erasmus University,
Netherlands

Valente, Christian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-30
ccv@ampl.com
AMPL Optimization, United States

van den Berg, Pieter . . . . . . . . . . . . . . . . . . . . . . . . MB-10, MD-18
vandenberg@rsm.nl
Rotterdam School of Management, Erasmus University, Rotterdam, Netherlands

Vallada, Eva . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-62
evallada@eio.upv.es
Estadística e Investigación Operativa Aplicadas y Calidad,
Universidad Politécnica de Valencia, Valencia, Spain

Van den bergh, Ona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-56
ona.van.den.bergh@vub.be
Business Technology and Operations, Vrije Universiteit
Brussel, Brussel, Belgium

Valverde, Carlos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-26
cvalverde@us.es
University of Seville, Seville, Spain

Van den Broeke, Maud TD-07, TA-11, TD-23, WA-23, MA-33
m.vandenbroeke@ieseg.fr
Operations and Supply Chain Management, Ieseg School ofManagement, Lille, France

van Ackere, Ann . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-19
ann.vanackere@unil.ch
HEC, University of Lausanne, Switzerland
Van Aken, Sander . . . . . . . . . . . . MC-46, MD-46, TB-46, TD-46
sander.van-aken@flixbus.com
Flix SE, Germany
Van Belle, Jente . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-31
jente.vanbelle@kuleuven.be
LIRIS, KU Leuven, Leuven, Belgium
van Berkum, Eric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-56
e.c.vanberkum@utwente.nl
Civil Engineering, University of Twente, Enschede, Netherlands
van Beurden, Paul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-19
paul.v.beurden@tatasteeleurope.com
Ceramics Research Centre, Tata Steel Nederland, Netherlands
Van Bulck, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-16, WA-16
david.vanbulck@ugent.be668

van den Heuvel, Wilco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-28
wvandenheuvel@ese.eur.nl
Econometric Institute, Erasmus University Rotterdam, Rotterdam, Netherlands
Van den Poel, Dirk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-31
dirk.vandenpoel@ugent.be
MIO/Data Analytics, Ghent University, Gent, Belgium
van der Hagen, Liana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-29
vanderhagen@rsm.nl
Technology and Operations Management, Rotterdam Schoolof Management, Erasmus University Rotterdam, Netherlands
van der Hurk, Evelien . . . . . . . . . . . . . . . . . . . . . . MA-54, WA-54
evdh@dtu.dk
Management Science, Management Engineering, DTU Technical University of Denmark, Copenhagen, Denmark
van der Hurk, Evelien . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-51
evdh@transport.dtu.dk
Transport, Technical University of Denmark, Kgs. Lyngby,
Denmark

EURO 2024 - Copenhagenvan der Knaap, Renate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-54
r.j.h.vanderknaap@tudelft.nl
Transport & Planning, Delft University of Technology, Delft,
Netherlands
van der Mei, Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-22
agevdmei@mit.edu
Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Netherlands
van der Mei, Rob . . . . MB-04, MB-10, MD-10, TB-23, TD-28,
WD-35, TA-39
mei@cwi.nl
CWI, Netherlands
van Dijkum, Cor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-20
c.j.vandijkum@nosmo.nl
Amsterdam University, NOSMO, Sokrates Consultancy, Den
Helder, Noordhollland, Netherlands
van Donselaar, Karel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-50
k.h.v.donselaar@tm.tue.nl
OPC, TU Eindhoven, Eindhoven, -, Netherlands
van Doorn, Jasper . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-12, TA-64
j.m.h.van.doorn@vu.nl
Mathematics, Vrije Universiteit, Netherlands
van Doornmalen, Jasper . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-30
m.j.v.doornmalen@tue.nl
Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Noord-Brabant,
Netherlands
van Ee, Martijn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-25
secretariaat@ngb-online.nl
Faculty of Military Sciences, Netherlands Defence Academy,Den Helder, Netherlands
van Essen, Theresia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-10
j.t.vanessen@tudelft.nl
Delft University of Technology, Netherlands
van Heeswijk, Wouter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-23
w.j.a.vanheeswijk@utwente.nl
University of Twente, Twente, Netherlands
Van Hentenryck, Pascal . . . . . . . . . . . . . . . . . . . . . . TE-01, TB-03
pascal.vanhentenryck@isye.gatech.edu
Georgia Institute of Technology, Atlanta, United States
Van Hoeck, Inneke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-51
inneke.vanhoeck@kuleuven.be
KU Leuven, Belgium
van Jaarsveld, Willem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-39
W.L.v.Jaarsveld@tue.nl
Eindhoven University of Technology, Netherlands
van Leeuwaarden, Johan . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-39
j.s.h.vanleeuwaarden@tilburguniversity.edu
Tilburg University, Tilburg, Netherlands
van Leeuwen, Britt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-28

AUTHOR INDEX

bevl@cwi.nl
Stochastics, Centrum Wiskunde & Informatica, Amstelveen,
Netherlands
Van Lieshout, Rolf . . . . . . . . . . . . . . . . . . . WC-13, TC-51, TB-58
r.n.v.lieshout@tue.nl
Operations, Planning, Accounting and Control, Eindhoven
University of Technology, Eindhoven, Netherlands
Van Mieghem, Jan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-39
vanmieghem@northwestern.edu
Kellogg School of Management, Northwestern University,
Evanston, IL, United States
van Montfort, Loek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-15
l.g.a.j.van.montfort2@vu.nl
Operations Analytics, Vrije Universiteit Amsterdam, Amstelveen, Netherlands
Van Mulders, Dylan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-31
dylan.vanmulders@ugent.beMarketing, Innovation and Organisation, Ghent University,
Ghent, Belgium
van Oers, Joris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-31
j.j.v.oers@tue.nl
School of Industrial Engineering and Innovation Sciences,
Eindhoven University of Technology, Eindhoven, Netherlands
van Ommeren, Jan-Kees . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-62
j.c.w.vanommeren@utwente.nl
Applied Mathematics, University of Twente, Enschede,
Netherlands
van Oort, Niels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-54
n.vanoort@tudelft.nl
TU Delft, Delft, Netherlands
van Oosterom, Simon. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-55
s.j.m.vanoosterom@tudelft.nl
Faculty of Aerospace Engineering, Delft University of Technology, Delft, Zuid-Holland, Netherlands
van Oudenhoven, Bas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-11
b.v.oudenhoven@tue.nl
School of Industrial Engineering, Eindhoven University ofTechnology, Netherlands
van Rijn, Lisanne . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-21, WA-21
l.vanrijn@rsm.nl
Rotterdam School of Management, Rotterdam, Netherlands
van Rooijen, Maike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-13
maike.vanrooijen@wur.nl
Operations Research and Logistics, Wageningen University,
Wageningen, Gelderland, Netherlands
van Rossum, Bart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-54
vanrossum@ese.eur.nl
Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, Zuid-Holland, Netherlands
van Schuppen, Hans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-31
j.l.vanschuppen@amsterdamumc.nl

669

AUTHOR INDEXEURO 2024 - Copenhagen

Amsterdam UMC, Amsterdam, Netherlands
Van Steelandt, Senne . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-31
svanstee@vols.utk.edu
Thalamus, United States
van Straaten, Dirk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-40
dirk.van.straaten@gmail.com
Department Management, Paderborn University, Paderborn,
Germany
van Treek, Kira . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-38
k.vantreek@tilburguniversity.edu
Econometrics and Operations Research, Tilburg University,
Tilburg, Netherlands
van Twiller, Jaike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-03
jaiv@itu.dk
Computer Science, IT University of Copenhagen, Copenhagen, Sealand, Denmark
van Veggel, Britt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-18
b.w.j.r.vanveggel@uva.nl
Business Analytics, University of Amsterdam, Nijmegen,
Gelderland, Netherlands
van Vemde, Gina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-10
g.van.vemde@isala.nl
Isala, Netherlands
Van Wassenhove, Luk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-21
luk.van-wassenhove@insead.edu
Technology and Operations Management Area, INSEAD,
Fontainebleau cedex, France
van Weerd, Carolina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-11
carolina.vanweerd@tno.nl
Netherlands
van Woensel, Tom . . . WA-07, WC-13, MA-26, TB-28, MD-52,
MA-56, TB-58, WB-58, MA-60, WC-61, MD-64
t.v.woensel@tue.nl
Technische Universiteit Eindhoven, EINDHOVEN, Netherlands
Vanberkel, Peter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-15
peter.vanberkel@dal.ca
5269 Morris Street, Room 201, Dalhousie University, Halifax, Nova Scotia, Canada
Vanden Berghe, Greet . . . . . . . . . . . . . . . MB-07, TC-26, MA-58
greet.vandenberghe@cs.kuleuven.be
Computer Science, KU Leuven, Gent, Belgium
vanden Broucke, Seppe . . . . . . . . . . . . . . . . . . . . . MD-08, WA-31
seppe.vandenbroucke@kuleuven.be
Department of Decision Sciences and Information Management, KU Leuven, Leuven, Belgium
Vanderpooten, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-37
daniel.vanderpooten@lamsade.dauphine.fr
LAMSADE, Université Paris Dauphine, Paris, France
Vanderschueren, Toon . . . . . . . . . . . . . . . . . . . . . . MB-31, MC-31
toon.vanderschueren@gmail.com

670

Research Centre for Information Systems Engineering(LIRIS), KU Leuven, Leuven, Vlaams Brabant, Belgium
Vandomme, Elise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-29
elise.vandomme@uliege.be
HEC - Management School, University of Liège, Liege, Belgium
Vanhaverbeke, Lieselot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-56
lieselot.vanhaverbeke@vub.ac.be
MOSI, Vrije Universiteit Brussel, Brussels, Belgium
Vanhoucke, Mario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-06
mario.vanhoucke@ugent.beFaculty of Economics and Business Administration, Ghent
University, Vlerick Business School, University College London, Ghent, Belgium
Vansteenwegen, Pieter . . . . . . . MB-04, TC-51, WA-51, WB-54
pieter.vansteenwegen@kuleuven.be
Institute for Mobility - CIB, KU Leuven, Leuven, Belgium
Vanswijgenhoven, Frederic . . . . . . . . . . . . . . . . . . . . . . . . . MB-17
frederic.vanswijgenhoven@ugent.be
University of Ghent, Belgium
Varga, Anita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-38
anita.varga@uni-corvinus.hu
Corvinus Centre for Operations Research, Corvinus University of Budapest, Budapest, Hungary
Vargas, Luis Felipe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-38
luis.vargas@idsia.ch
Usi - Supsi, Istituto Dalle Molle Studi sull’Intelligenza Artificiale (IDSIA), Switzerland
Varvitsiotis, Antonios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-40
antonios@sutd.edu.sg
Engineering Systems and Design, Singapore University ofTechnology and Design, Singapore, Singapore
Vasconcellos, André Luiz Carneiro de . . . . . . . . . . . . . . . WD-44
andreluizvasc@hotmail.com
Companhia Carioca de Parcerias e Investimentos - CCPAR,
Rio De Janeiro, RJ, Brazil
Vasegaard, Alex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-44
aev@mp.aau.dk
Materials and Production, Aalborg university, aalborg, Denmark
Vasilakis, Christos . . . . . . . . . . . . . . . . . . MC-15, WB-15, MA-17
c.vasilakis@bath.ac.uk
School of Management, University of Bath, Bath, United
Kingdom
Vasilakis, Chrysovalantis . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-45
c.vasilakis@bangor.ac.uk
Bangor University, United Kingdom
Vasques da Silva, Heloisa . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-49
heloisa.vasques@unesp.br
FEB, São Paulo State University (UNESP), Bauru, São Paulo,
BrazilEURO 2024 - Copenhagen
Vásquez, Jeremías . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-23
jeremias.vasquez@usach.cl
Universidad de Santiago de Chile, Chile
Vásquez, Óscar C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-23
oscar.vasquez@usach.cl
Universidad de Santiago de Chile, Santiago, Santiago, Chile
Vassos, George . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-06
gvas@dtu.dk
Department of Technology, Management and Economics
Operations Research, Technical University of Denmark, Denmark
Vasudeva, Vivek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-51
vivekvasudev@iisc.ac.in
CiSTUP, Indian Institute of Science, India
Vasudevan, Rama . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-31
vasudevanrk@ornl.gov
Oak Ridge National Laboratory, United States
Vatankhah Ghadim, Hadi. . . . . . . . . . . . . . . . . . . . . . . . . . . WA-19
hadi.vatankhahghadim@pg.canterbury.ac.nz
Civil and Natural Resources Engineering, University of Canterbury, Christchurch, New Zealand
Vatikiotis, Stavros . . . . . . . . . . . . . . . . . . WA-25, MD-49, WB-49
stvatikiotis@aueb.gr
Department of Management Science and Technology, Athens
University of Economics and Business, KALLITHEA, Attica, Greece
Vaucher, Timoté . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-57
vaucht@amazon.com
Amazon, Spain
Vaz, Clara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-51
clvaz@ipb.pt
Research Centre in Digitalization and Intelligent Robotics,
Instituto Politécnico de Bragança, Bragança, Bragança, Portugal
Vaze, Vikrant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-25
vikrant.s.vaze@dartmouth.edu
Department of Engineering Sciences, Thayer School of Engineering, Dartmouth, Hanover, NH, United States
Vazquez-Mendez, Miguel E. . . . . . . . . . . . . . . . . . . TA-33, TB-33
miguelernesto.vazquez@usc.es
Applied Mathematics, University of Santiago de Compostela,
Lugo, Spain
Veelenturf, Lucas . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-21, WD-54
LVeelenturf@rsm.nl
Department of Technology & Operations Management, Rotterdam School of Management, Erasmus University, Rotterdam, Netherlands
Vega, Maria Isabel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-62
maria.vega.c@pucv.cl
Escuela de Ingeniería de Transporte, Pontificia Universidad
Católica de Valparaíso, Valparaíso, Valparaíso, Chile
Vegh, Laszlo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-26, MB-38

AUTHOR INDEX

l.vegh@lse.ac.uk
Mathematics, LSE, London, United Kingdom
Velev, Georg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-31
georg.velev.1@hu-berlin.de
School of Business and Economics, Humboldt-Universität zu
Berlin, Germany
Veliov, Vladimir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-33
vladimir.veliov@tuwien.ac.at
Institute of Statistics and Mathematical Methods in Economics, Vienna University of Technology, Vienna, Austria
Vellucci, Pierluigi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-63
pierluigi.vellucci@gmail.com
Dept. of Economics, Rome Tre University, Rome, Latium,
Italy
Veloso, Vânia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-24
vania.veloso@tecnico.ulisboa.pt
Instituto Superior Técnico, Universidade de Lisboa, Lisboa,
Portugal
Vendé, Pierre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-51
pierre.vende@hotmail.fr
Operation management and Logistics, HEC Montréal, France
Venghaus, Sandra . . . . . . . . . . . . TC-19, WA-19, WB-47, TB-53
venghaus@socecon.rwth-aachen.de
Decision Analysis and Socio-Economic Assessment, RWTH
Aachen, Aachen, Germany
Venkatesh, Anand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-48
anand@irma.ac.in
Economics Area, Institute of Rural Management Anand,
Anand, UP, India
Venkitasubramony, Rakesh . . . . . . . . . . . . . . . . . WB-50, WD-61
rakesh.v@iiml.ac.in
Operations Management, Indian Institute of Management
Lucknow, Lucknow, Uttar Pradesh, India
Venkova, Milena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-06
milena.venkova@tudublin.ie
School of Mathematical Sciences, Technological University
Dublin, Dublin, Ireland
Ventura, Vittoria. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-47
vittoriaventura01@gmail.com
Department of Civil Engineering and Architecture, Siracusa,
Italy
Venturi, Beatrice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-33
venturi@unica.it
Economics and Business, University of Cagliari, cagliari,
Italy, Italy
Vera, Juan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-38
j.c.veralizcano@tilburguniversity.edu
Etrie and OR, Tilburg University, Netherlands
Vera, Juan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-38
j.c.veralizcano@uvt.nl
Econometrics and Operations Research, Tilburg University,
Netherlands

671

AUTHOR INDEXEURO 2024 - Copenhagen
University of Alcalá, Spain

Vera-Morales, Alma Elia . . . . . . . . . . . . . . . . . . . . . TC-12, TA-53
alma.vera.m@outlook.com
Systems Planning, National Autonomous University of Mexico, Estado De MÉxico, Estado de México, Mexico

Vieira Conceição, Samuel . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-44
svieira@dep.ufmg.br
Universidade Federal de Minas Gerais, Brazil

Verbeke, Wouter . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-31, MC-31
wouter.verbeke@kuleuven.be
Faculty of Economics and Business, KU Leuven, Leuven,
Belgium

Vieira Marto, Marco Henrique . . . . . . . . . . . . . . . . . . . . . . MB-12
marcovmarto@gmail.com
ALGORITMI Research Center / LASI, University of Minho,
Portugal, Fatima, Santarém, Portugal

Verbruggen, Charlotte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-27
charlotte.verbruggen@kuleuven.be
KU Leuven, Belgium

Vieira, Ana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-07, MA-17
ana.lopes.vieira@tecnico.ulisboa.pt
Centre for Management Studies of Instituto Superior Técnico, Universidade de Lisboa, Portugal, Lisboa, Portugal

Verdonck, Tim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-31, MC-31
tim.verdonck@kuleuven.be
University of Antwerp, Antwerp, Belgium
Vergalli, Sergio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-33
sergio.vergalli@unibs.it
Economics and Management, University of Brescia and Fondazione Eni Enrico Mattei, Brescia, Italy

Vieira, Carolina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-51
carolina.vieira@aluno.ufop.edu.br
Production enginnering, Universidade Federal de Ouro Preto,
Ouro Preto, Minas Gerais, Brazil
Vieira, Douglas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-36
douglas.vieira@enacom.com.br
ENACOM, Brazil

Verschelde, Marijn . . . . . . . . . . . . . . . . . . . TD-07, TA-11, MA-48
m.verschelde@ieseg.fr
Department of Economics and Quantitative Methods, IÉSEGSchool of Management, Lille, France

Vigo, Daniele . . . . . . . . . . . . . . . . . TC-25, TB-26, MA-35, TB-58
daniele.vigo@unibo.it
DEI, University of Bologna, Bologna, (BO), Italy

Versluis, Nina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-51
n.d.versluis@tudelft.nl
Delft University of Technology, Netherlands

Vijayvargy, Lokesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-45
lokesh.vijayvargy@jaipuria.ac.in
Jaipuria Institute of Management, Jaipur, Jaipur, Rajasthan,
India

Verter, Vedat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-15
vedat.verter@queensu.ca
Smith School of Business, Queen’s University, Canada
Vetschera, Rudolf . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-07, MD-11
rudolf.vetschera@univie.ac.atDept. of Business Decisions and Analytics, University of
Vienna, Vienna, Austria
Vezzali, Dario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-29
dario.vezzali@unimore.it
Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Reggio Emilia, EmiliaRomagna, Italy
Viana, Ana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-10, WA-64
aviana@inesctec.pt
INESC TEC and ISEP – School of Engineering, Polytechnicof Porto, Porto, Portugal

Vilalta-Perdomo, Eliseo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-31
e.vilaltaperdomo@aston.ac.uk
Operations and Information Management, Aston University,
Birmingham, West Midlands, United Kingdom
Vilím, Petr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-60
petr.vilim@scheduleopt.com
ScheduleOpt, Czech RepublicVilhelm Barrett Levinsen, Magnus . . . . . . . . . . . . . . . . . . MA-62
mvbl@ruc.dk
Department of People and Technology, Roskilde University,
Roskilde, Denmark
Vilkkumaa, Eeva . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-11, MC-11
eeva.vilkkumaa@aalto.fi
Department of Information and Service Management, Aalto
University, School of Business, Helsinki, Finland

Vicente-Pérez, José . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-42
jose.vicente@ua.es
University of Alicante, Alicante, Spain

Villa, Fulgencia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-62
mfuvilju@eio.upv.es
Department of Applied Statistics, Operations Research andQuality, Universitat Politecnica de Valencia, Valencia, Spain

Vidal, Thibaut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-35
thibaut.vidal@polymtl.ca
Mathematics and Industrial Engineering, PolytechniqueMontréal, Montréal, QC, Canada

Villagra, Matias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-04
mjv2153@columbia.edu
Industrial Engineering and Operations Research, Columbia
University, New York City, New York, United States

Vidal-Nunez, Jose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-42
j.vidal@uah.es

Villena, Javier. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-10
direccionocio@hotmail.com

672

EURO 2024 - Copenhagen
Universidad de Valencia, Spain

AUTHOR INDEX

Centro Ricerche Fiat, Italy

Vimala Rani, M . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-44, MD-55
vimala@vgsom.iitkgp.ac.in
VGSoM, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India

Vivo-Molina, Juana-Maria. . . . . . . . . . . . . . . . . . . . . . . . . . WB-39
jmvivomo@um.es
Statistics and Operations Research, University of Murcia,
Spain

Vinje, Jacob . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-57
jacob.vinje@nbim.no
New York, New York, United States

Vizvari, Bela . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-21
vizvaribela@gmail.com
Industrial Engineering, Eastern Mediterranean University,
Mersin 10, Turkey

Vinot, Marina. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-25
marina.vinot@univ-tours.fr
Université de Tours, France
Vinte, Claudiu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-63
claudiu.vinte@ie.ase.ro
Economic Informatics and Cybernetics Department,
Bucharest University of Economic Studies, Bucures, ti, Romania
Viola, Marco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-32, MA-34
marco.viola@ucd.ie
School of Mathematics and Statistics, University College
Dublin, Dublin, Ireland
Virtanen, Kai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-11
kai.virtanen@aalto.fi
Department of Mathematics and Systems Analysis, Aalto
University, School of Science, Finland
Visagie, Stephan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-55
svisagie@sun.ac.za
Department of Logistics, University of Stellenbosch, Stellenbosch, South Africa
Visintin, Filippo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-64
filippo.visintin@unifi.it
Department of Industrial Engineering, Università degli Studi
di Firenze, Firenze, Italy
Vismara, Silvio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-63
silvio.vismara@unibg.it
Università degli studi di Bergamo, Italy
Vitali, Sebastiano . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-34, MD-51
sebastiano.vitali@unibg.it
Department of Economics, University of Bergamo, Italy
Viterbo, Francisco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-07
francisco.viterbo@tecnico.ulisboa.pt
Instituto Superior Técnico (IST), CEGIST, Lisboa, Portugal
Viti, Francesco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-56
francesco.viti@uni.lu
University of Luxembourg, Luxembourg city, Luxembourg
Vitoriano, Begoña . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-12
bvitoriano@mat.ucm.es
Dept. of Statistics and Operational Research, Interdisciplinary Mathematics Institute, Universidad Complutense de
Madrid, Madrid, Spain
Vivo, Giulio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-29
giulio.vivo@crf.it

Vlasiou, Maria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-39
m.vlasiou@utwente.nl
University of Twente, Netherlands
Vlontzos, George . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-48
gvlontzos@agr.uth.gr
University of Thessaly, Volos, Greece
Vocaturo, Francesca . . . . . . . . . . . . . . . . . MA-35, MC-35, TC-58
vocaturo@unical.it
Department of Economics, Statistics and Finance, Universityof Calabria, Arcavacata di Rende (CS), Italy
Vogel, Jannik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-39
j.vogel@bwl.uni-mannheim.de
SDB Science-driven Business Ltd, Baden-Württemberg, Germany
Vogel, Salome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-45
vogel@verkehr.tu-darmstadt.de
Department of Civil and Environmental Engineering, Institute of Railway Engineering, Darmstadt, Hesse, Germany
Voigt, Stefan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-64
stefan.voigt@ku.de
Department of Supply Chain Management & Operations,
Catholic University of Eichstätt-Ingolstadt, Ingolstadt, Germany
Volk, Rebekka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-61
rebekka.volk@kit.edu
Institute of Industrial Production (IIP), Karlsruhe Institute ofTechnology (KIT), Karlsruhe, Germany
Vollebergh, Maaike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-36
m.a.j.vollebergh@vu.nl
Vrije Universiteit Amsterdam, Netherlands
von der Gracht, Heiko . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-11
vondergracht@steinbeis-sibe.de
Steinbeis University, Germany
von Nitzsch, Rüdiger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-45
nitzsch@efi.rwth-aachen.de
Chair of Decision Theory and Financial Services, RWTH
Aachen University, Aachen, Germany
von Winterfeldt, Detlof . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-11
winterfe@usc.edu
Industrial and Systems Engineering, University of Southern
California, Los Angeles, California, United States
Vong, Eric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-18

673

AUTHOR INDEXEURO 2024 - Copenhagens222594@dtu.dk
DTU Management, Department of Technology, Managementand Economics, Technical University of Denmark, Kongens
Lyngby, Capital Region of Denmark, Denmark
Voorberg, Simon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-45
s.voorberg@tue.nl
SISCAD, NEOMA business school, Rouen, Normandie,
FranceVorobets, Anton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-63
antonvorobets@fortitudo.tech
Fortitudo Technologies, Denmark
Voyvoda, Ebru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-22
voyvoda@metu.edu.tr
Department of Economics, Middle East Technical University,
Ankara, Turkey
Vozzella, Pietro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-63
p.vozzella@staff.univpm.it
Management, Università politecnica delle Marche, Ancona,
Italy
Vretta, Eleni-Maria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-48
emvretta@uom.edu.gr
Thessaloniki, Greece
Vukovic, Manojlo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-34
manojlo.vukovic@uns.ac.rs
University of Novi Sad, Faculty of Technical Sciences, Faculty of Sciences, Serbia
Vuran, Mehmet C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-39
mcv@unl.edu
School of Computing, University of Nebraska-Lincoln,
Linoln, NE, United States
Wagemann, Kelly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-09
kelly@niclabs.cl
NIC Chile Research Labs, Chile
Wagenaar, Joris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-13
j.c.wagenaar_1@uvt.nl
Econometrics and Operations Research, Tilburg University,
Netherlands
Wagenvoort, Mette . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-21
wagenvoort@ese.eur.nl
Econometric Institute, Erasmus University Rotterdam, Rotterdam, Netherlands
Wagner, Stephan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-21
stwagner@ethz.ch
Department of Management, Technology, and Economics,
Swiss Federal Institute of Technology Zurich (ETH Zurich),
Zurich, Switzerland
Waitz, Martin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-23, TB-50
mwaitz@wu.ac.at
Institute for Production Management, WU Vienna Universityof Economics and Business, Austria, Austria
Walde, Maximilian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-09
maximilian.walde@ewi.uni-koeln.de

674

Energiewirtschaftliches Institut an der Universität zu Köln
gGmbH, Köln, Germany
Waldherr, Stefan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-52
s.m.g.waldherr@vu.nl
Vrije Universiteit Amsterdam, Amsterdam, Netherlands
Wall, Friederike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-43
friederike.wall@uni-klu.ac.atDept. for Controlling and Strategic Management, AlpenAdria-Universitaet Klagenfurt, Klagenfurt, Austria
Wall, Graham . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-55
graham.wall@port.ac.uk
Department of Mathematics, University of Portsmouth,
Portsmouth, United Kingdom
Wallace, Stein W. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-18
stein.wallace@nhh.no
Business and Management Science, Norwegian School of
Economics, Bergen, Norway
Wallenius, Jyrki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-11
jyrki.wallenius@aalto.fi
Information and Service Management, Aalto University
School of Business, Helsinki, Uusimaa, Finland
Walls, Lesley . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-39
lesley.walls@strath.ac.uk
Management Science, University of Strathclyde, Glasgow,
United Kingdom
Walraevens, Joris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-40
Joris.Walraevens@ugent.beGhent University, Ghent, Belgium
Walraven, Erwin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-56
erwin.walraven@tno.nl
TNO, Netherlands
Walther, Grit . . . . . . . . . . . . . . . WA-19, MA-24, MC-24, WB-47
walther@om.rwth-aachen.de
School of Business and Economics, Chair of Operations
Management, RWTH Aachen University, Aachen, Germany
Walukiewicz, Stanislaw . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-65
Stanislaw.Walukiewicz@ibspan.waw.pl
Systems Research Institute, Polish Academy of Sciences,
Warsaw, Poland
Walunj, Tushar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-40
tusharwalunj@iitb.ac.inIndustrial Engineering and Operations Research, IIT Bombay, Mumbai, Maharashtra, India
Wan, Meng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-15
23s010043@stu.hit.edu.cn
School of Management, Harbin Institute of Technology,
Harbin City, Heilongjiang Province, China
Wan, Yi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-09
yi.wan@psi.ch
Paul Scherrer Institut, Zurich, Switzerland
Wang, Chenyang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-03

EURO 2024 - Copenhagen
haiwangcy@hotmail.com
School of Management, Northwestern Polytechnical University, Xi’an, Shaanxi, China
Wang, Chien-Hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-15
chienhwang@o365.fcu.edu.tw
TaiwanWang, Chih-Han . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-49
windchwang92626@gmail.com
Transportation and Logistics Management, National Yang
Ming Chiao Tung University, Hsinchu City, Taiwan
Wang, Chong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-29
jet_wang@hust.edu.cn
Huazhong University of Science and Technology, China
Wang, Chun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-43
chun.wang@concordia.ca
Concordia university, Montreal, Canada
Wang, Haibo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-25
hwang@tamiu.edu
Texas A&M International University, United States
Wang, Haoxiang. . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-49, WB-64
whx@kcl.ac.uk
Department of Informatics, King’s College London, London,
United Kingdom
Wang, Hao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-41
haw309@gmail.com
Information Science and Technology, ShanghaiTech University, Shanghai, China
Wang, Hongtao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-62
hwang8@doctor.upv.es
Departamento de Estadistica e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, Valencia, Valencia, Spain
Wang, Jiaqi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-44
jqwang@umd.edu
Department of Mathematics, University of Maryland, China
Wang, Jiazheng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-29
roscoeloveyou@gmail.com
Huawei Hong Kong Research Centre, Hong Kong
Wang, Juanru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-29
wjuanru@nwpu.edu.cn
Northwestern Polytechnical University, Xian, China
Wang, Kuo-Hsiung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-52
khwang1516@asia.edu.tw
Business Administration, Asia University, Wufeng,
Taichung, Taiwan
Wang, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-56
liwang@bjtu.edu.cn
School of Traffic and Transportation, Beijing Jiaotong University, China
Wang, Rui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-03, WC-19
ruiwangnudt@gmail.com

AUTHOR INDEX

College of System Engineering, National University of Defense Technology, China
Wang, Ruting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-31
wangruti@hu-berlin.de
Business School, Sun Yat-sen University, Shenzhen, Guangdong, China
Wang, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-56
2540943797@qq.com
Southeast University, China
Wang, Xihui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-21
wxihui@ustc.edu.cn
University of Science and Technology of China, China
Wang, Xingyi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-25
xingyi.wang@tum.de
TUM School of Management, TU München, Heilbronn, Germany
Wang, Xin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-61
wangxin@sribd.cn
Big Data Intelligent Application Technology Research Institute, Research Institute of Big Data, Shenzhen, ShenZhen,
China
Wang, Yadong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-62
wang_yd@njust.edu.cn
School of Economics & Management, Nanjing University of
Science & Technology, Nanjing, China
Wang, Yanfei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-42
yfwang@mail.iggcas.ac.cn
Institute of Geology and Geophysics, Chinese Academy of
Sciences, Beijing, China
Wang, Yang . . . . . . . . . . . . . . . . . WD-10, WA-15, TA-29, MB-58
sparkle.wy@gmail.com
School of Management, Northwestern Polytechnical University, China
Wang, Yedong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-59
yedongwang@hkaift.com
The Lab of AI Powered Fi nancial Technologies, Hong Kong,
Hong Kong
Wang, Yihui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-54
yihui.wang@bjtu.edu.cn
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, Beijing, China
Wang, Yilin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-55
muqiuxishui@outlook.com
Industrial Engineering, Tsinghua University, Beijing, China
Wang, Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-58
yu.wang1803@student.xjtlu.edu.cn
Department of Mathematical Sciences, University of Liverpool, Suzhou, Jiangsu, China
Warnow, Leo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-37
leo.warnow@tu-ilmenau.deInstitute of Mathematics, Technische Universität Ilmenau,
Ilmenau, Germany

675

AUTHOR INDEXEURO 2024 - Copenhagen

Watanabe, Shiho . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-28
shiho.watanabe@daikin.co.jp
TIC, Daikin Industries, Ltd., Japan
Watling, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-54, TA-54
D.P.Watling@its.leeds.ac.uk
Institute for Transport Studies, University of Leeds, United
Kingdom
Waubert de Puiseau, Constantin . . . . . . . . . . . . . . . . . . . . . TB-03
waubert@uni-wuppertal.deInstitute for Technologies and Management of Digital Transformation, University of Wuppertal, Wuppertal, NordrheinWestfalen, Germany

Institut für Produktionswirtschaft, Leibniz Universität Hannover, Hannover, Niedersachsen, Germany
Wegener, Christoph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-63
christoph.wegener@leuphana.de
Leuphana University Lüneburg, Germany
Wei, Chenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-25
cg_wei@mail.nwpu.edu.cn
School of Management, Northwestern Polytechnical University, China
Wei, Yifu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-07
yw1r20@gmail.com
School of Chemical and Process Engineering, University ofLeeds, Manchester, United Kingdom

Wauters, Tony . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-07, MD-07
tony.wauters@cs.kuleuven.be
Computer Science, KU Leuven, Gent, Oost-Vlaanderen, Belgium

Weibel, Marc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-08
wmar@zhaw.ch
ZHAW School of Management and Law, Zurich, Switzerland

Wawrzyniak, Jakub . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-26
Jakub.Wawrzyniak@cs.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poznan, Poland

Weibezahn, Jens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-09
jew.eco@cbs.dk
Copenhagen School of Energy Infrastructure (CSEI), Copenhagen Business School, Copenhagen, Denmark

Wøhlk, Sanne . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-13, WD-56
sanw@econ.au.dk
Department of Economics and Business Economics, Aarhus
University, Aarhus V, Denmark

Weidinger, Felix . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-50, MA-64
felix.weidinger@tu-darmstadt.deTechnical University of Darmstadt, Germany

Weber, Christoph . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-09, TC-09
christoph.weber@uni-duisburg-essen.de
University Duisburg-Essen, Essen, Germany
Weber, Christoph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-09
Christoph_Weber@uni-duisburg-essen.de
Universität Essen, Essen, Germany
Weber, Gerhard-Wilhelm . . . . MD-16, TC-18, WB-18, MD-65
gerhard-wilhelm.weber@put.poznan.pl
Faculty of Engineering Management, Poznan University ofTechnology, Poznan, Poland

Weiger, Welf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-13
wweiger@alfaisal.edu
Alfaisal University, Saudi Arabia
Weigle, Sofia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-40
SWeigle1@its.jnj.com
Johnson and Johnson Pharmaceutical Research, New Jersey,
United States
Weik, Norman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-54, WD-54
norman.weik@tum.deProfessorship for Design and Operation of Public Rail Transport Systems (RTS), Technical University of Munich, Garching, Germany

Weber, Richard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-31
richard.weber@uchile.cl
Department of Industrial Engineering, FCFM, University ofChile, Santiago, Chile

Weinhardt, Justin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-07
justin.weinhardt@haskayne.ucalgary.ca
Haskayne School of Business, University of Calgary, Canada

Weber, Ryan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-63
ryanweber@fico.com
FICO, Xpress Optimization, US, United States

Weintraub, Gabriel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-43
gweintra@stanford.edu
Stanford GSB, United States

Weber, Tim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-39
tim.weber@uni-mannheim.de
Business Administration, University of Mannheim,
Mannheim, Baden-Württemberg, Germany

Weitzman, Ron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-49
ronwe@jce.ac.il
Industrial Engineering & Management, Azrieli Academic
College of Engineering, Givatayim, Israel, Israel

Weekx, Simon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-56
simon.weekx@vub.be
Business Technology and Operations, Vrije Universiteit
Brussel, Elsene, Brussels, Belgium

Wemelsfelder, Merel L. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-15
m.l.wemelsfelder@uva.nl
Business Analytics, University of Amsterdam, Amsterdam,
Netherlands

Wegel, Sebastian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-49
sebastian.wegel@prod.uni-hannover.deWen, Min . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-58
min.wen@xjtlu.edu.cn

676

EURO 2024 - Copenhagen
Department of Applied Mathematics, Xi’an JiaotongLiverpool University, Suzhou, Jiangxi, China
Wen, Tao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-06, WA-22
tao.wen@postgrad.manchester.ac.uk
Alliance MBS Business School, The University of Manchester, United Kingdom
Wen, Xingang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-33
xingang.wen@uni-bielefeld.de
Business Administration and Economics, Bielefeld University, Bielefeld, Duitsland, Germany
Wendt, Oliver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-52
wendt@wiwi.uni-kl.de
Business Research & Economics, University of Kaiserslautern, Kaiserslautern, Germany
Wens, Maarten . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-51
maarten.wens@kuleuven.be
KU Leuven, Leuven, Belgium
Wenzel, Peter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-62
p.a.wenzel@tudelft.nl
Delft, Netherlands
Westerlund, Andreas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-46
andreas.westerlund@jeppesen.com
Jeppesen, Gothenburg, Sweden
Westgaard, Sjur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-57
sjur.westgaard@iot.ntnu.no
Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology,
Trondheim, Norway
Weston, Jorge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-53
weston.jorge@gmail.com
Friedrich-Alexander-Universtät Erlangen-Nürnberg, Germany
Wetzel, Daniel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-07, WC-58
daniel.wetzel@uni-bielefeld.de
Decision and Operation Technologies, Bielefeld University,
Bielefeld, Germany
Wetzel, Manuel . . . . . . . . . . . . . . . . . . . . . . TB-19, TD-19, WA-53
manuel.wetzel@dlr.de
Energy Systems Analysis, German Aerospace Center (DLR),
Germany
White, Leroy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-13, TC-13
l.white2@exeter.ac.uk
University of Exeter Business School, Exeter, United Kingdom
Wichmann, Matthias Gerhard . . . . . . . . . . . . . . . . . . . . . . WA-45
matthias.wichmann@wiwi.tu-chemnitz.de
BWL - Produktionsmanagement, Technische Universität
Chemnitz, Chemnitz, Germany
Widhalm, Peter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-45
peter.widhalm@ait.ac.at
Center for Energy, AIT Austrian Institute of Technology,
Wien, Wien, Austria

AUTHOR INDEX

Widmann, Philipp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-54
philipp.widmann@dlr.deInstitute of Transportation Systems, German Aerospace Center (DLR), Braunschweig, Germany
Widmer, Marino . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-28
marino.widmer@unifr.ch
Department of Informatics, University of Fribourg, Fribourg,
Switzerland
Widow, Mariapaz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-23
mariapaz.widow.n@mail.pucv.cl
Pontificia Universidad Católica de Valparaíso, Chile
Wiebe, Kirsten S. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-23
kswiebe@gmail.com
Ntnu / Sintef, Trondheim, Norway
Wiedmann, Philipp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-37
philipp.wiedmann@tum.de
Department of Mathematics, Technical University of Munich, Germany
Wiemer, Isabel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-10
isabel.wiemer@uni-due.de
Chair of Business Administration and Production Management, University Duisburg-Essen, Duisburg, Germany
Wiertz, Ann-Kathrin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-19
ann-kathrin.wiertz@fau.deIndustrial Organization and Energy Markets, FriedrichAlexander-Universität Erlangen-Nürnberg, Nürnberg, Germany
Wiese, Andreas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-25, WB-37
andreas.wiese@tum.deMathematics, Technical University of Munich, Garching bei
München, Germany
Wiget, Milena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-11
milena.wiget@eawag.ch
Environmental Social Sciences, Eawag: Swiss Federal Institute of Aquatic Science and Technology, Switzerland
Wild, Stefan M. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-32
wild@lbl.gov
Applied Mathematics and Computational Research Division,
Lawrence Berkeley National Laboratory, Berkeley, California, United States
Wildt, Constantin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-64
constantin.wildt@tu-darmstadt.deTechnical University of Darmstadt, Germany
Willamowski, Felix J. L. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-54
willamowski@or.rwth-aachen.de
Lehrstuhl für Operations Research, RWTH Aachen University, Germany
Willcocks, Ben . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-63
benwillcocks@fico.com
FICO House, FICO, Xpress Optimization, United Kingdom
Willemsen, Rick . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-61

677

AUTHOR INDEX
willemsen@ese.eur.nl
Econometric Institute,
Netherlands

EURO 2024 - Copenhagen

Erasmus University Rotterdam,

Williams, Aled . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-52
a.e.williams1@lse.ac.uk
Mathematics, London School of Economics and Political
Science, London, London, United Kingdom
Williams, Elin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-17
williamseh6@cardiff.ac.uk
Mathematics, Cardiff University, United Kingdom
Wilschewski, Fabian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-62
fabian.wilschewski@uni-due.de
Mercator School of Management, University of DuisburgEssen, Duisburg, Germany
Wilson, John . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-16
jwilson@ivey.ca
Management Science, Ivey Business School, London, Ontario, Canada

sabine.wittevrongel@ugent.be
TELIN, Ghent University, Gent, Belgium
Wittke, Marius . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-07
m.wittke@con.uni-saarland.de
Germany
Wogrin, Sonja . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-09, WA-09
wogrin@tugraz.at
Graz University of Technology, Graz, Austria
Wójcik, Michał . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-44
michal.wojcik@cs.put.poznan.pl
Institute of Computing Science, Poznan University of Technology, Poznań, Poland
Wolf, Laura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-45
laura.wolf@kit.edu
Institute for Operations Research (IOR), Karlsruhe Instituteof Technology (KIT), Karlsruhe, Baden-Württemberg, Germany

Winer, Zvi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-40
zvikav@wgalil.ac.il
Economics, Western Galilee College, Yokneam, Israel

Wolfinger, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-64
david.wolfinger@univie.ac.at
Department of Business Decisions and Analytics, Universityof Vienna, Vienna, Austria

Winheller, Kai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-59
kai.winheller@uni-due.de
Universität Duisburg-Essen, Germany

Woller, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-58
wolledav@cvut.cz
Czech Technical University in Prague, Czech Republic

Winkelmann, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-16
david.winkelmann@uni-bielefeld.de
Business Administration and Economics, Bielefeld University, Bielefeld, Germany

Wolters, Nicolas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-42
nicolas.wolters.external@banque-france.fr
Banque de France, France

Winkenbach, Matthias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-50
mwinkenb@mit.edu
Massachusetts Institute of Technology, Cambridge, UnitedStatesWinkler, Michael . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-30
winkler@gurobi.com
Gurobi GmbH, Germany
Winkler, Tobias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-50
t.winkler@tum.de
Supply and Value Chain Management, Technical Universityof Munich, Hamburg, Germany
Wischik, Damon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-38
damon.wischik@cl.cam.ac.uk
Computer Science and Technology, University of Cambridge,
United Kingdom
Wiszniewska-Matyszkiel, Agnieszka . . MD-33, TA-33, WC-41
agnese@mimuw.edu.pl
Institute of Applied Mathematics and Mechanics, Universityof Warsaw, Warsaw, Poland
Witt, Jonas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-46
jonas.witt@rwth-aachen.deOperations Research, RWTH Aachen University, Germany
Wittevrongel, Sabine . . . . . . . . . . . . . . . . . . . . . . . . WA-39, MB-40

678

Wong, King Tong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-58
s2101679@ed.ac.uk
Business School, University of Edinburgh, Edinburgh, United
Kingdom
Woo, C.k. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-09
chiwoo@eduhk.hk
Education University of Hong Kong, Tai Po, Hong Kong
Wood, Lincoln . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-10
lincoln.wood@otago.ac.nz
Department of Management, University of Otago, Dunedin,
Otago, New Zealand
Wood, Richard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-15, MA-17
richard.wood16@nhs.net
BNSSG Clinical Commissioning Group, Bristol, United
Kingdom
Wouda, Niels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-64
n.a.wouda@rug.nl
University of Groningen, Netherlands
Wozabal, David . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-35
d.wozabal@vu.nl
Vrije Universiteit Amsterdam, Netherlands
Wrede, Sophia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-61
wrede@combi.rwth-aachen.de
RWTH Aachen University, Germany

EURO 2024 - Copenhagen

Wright, George . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-11
george.wright@strath.ac.uk
Business School, Strathclyde University, DUNBLANE,
Perthshire, United Kingdom
Wrzaczek, Stefan . . . . . . . . . . . . . . . . . . . . MB-33, MD-33, TC-33
wrzaczek@iiasa.ac.at
Economic Frontiers, Institute for Applied Systems Analysis(IIASA), Laxenburg, Austria

AUTHOR INDEX

zwu12@syr.edu
Syracuse University, Syracuse, NY, United States
Wulf, Lasse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-04
wulf@math.tugraz.at
Institute of discrete mathematics, TU Graz, Graz, Austria
Wunderlich, Ralf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-57
ralf.wunderlich@b-tu.de
Institute of Mathematics, Brandenburg University of Technology Cottbus-Senftenberg, Germany

Wu, Cassandra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-57
cassandra.wu@outlook.com
Industrial Economics and Technology Management, Norwegian University of Science and Technology, Norway

Wuyts, Brecht . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-27
brecht.wuyts@kuleuven.be
LIRIS, KU Leuven, Belgium

Wu, Gen-han . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-07
genhanwu@saturn.yzu.edu.tw
Dept. of Industrial Engineering and Management, Yuan Ze
University, Taoyuan, Taiwan

Xambre, Ana Raquel . . . . . . . . . . . . . . . . . . . . . . . . MB-16, TA-31
raquelx@ua.pt
DEGEIT/CIDMA, University of Aveiro, Aveiro, Aveiro, Portugal

Wu, Meng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-07
wumeng@scu.edu.cn
Sichuan University, China

Xavier, Adilson Elias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-27
adilson.xavier@gmail.com
Department System Eng and Computer Sciences, Federal
University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil

Wu, Min-Jun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-47
min50403103@gmail.com
Department of Civil Engineering and Hazard Mitigation,
China University of Technology, Taipei, Taiwan
Wu, Qihao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-06
qihaowu@connect.hku.hk
Department of of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, Hong
Kong, Hong Kong
Wu, Ting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-22
t.wu@mmu.ac.uk
Department of Operations, Technology, Events and Hospitality Management, Manchester Metropolitan University,
Manchester, United Kingdom
Wu, Ting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-15
tingwu@nju.edu.cn
Department of Mathematics, Nanjing University, China
Wu, Xinzhu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-23
zanker@mail.nwpu.edu.cn
China
Wu, Yaoxin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-03
y.wu2@tue.nl
Eindhoven University of Technology, Netherlands
Wu, Yating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-56
yatwu@seu.edu.cn
School of Economics and Management, Southeast University, Nanjing, Jiangning, China

Xavier, António . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-44, TB-47
amxav@sapo.pt
University of Évora, CEFAGE-UE (Center for Advanced
Studies in Management and Economics), Portugal
Xavier, Vinicius Layter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-27
viniciuslx@ime.uerj.br
Institute of Mathematics and Statistics, State Universit. of
Rio de Janeiro, Rio de Janeiro, RJ, Brazil
Xepapadeas, Petros . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-40
peterxp@aueb.gr
Management Science & Technology, Athens University of
Economics & Business, Athens, Greece
Xia, Dongyang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-54
20114020@bjtu.edu.cn
Transport & Planning, TU Delft, Netherlands
Xia, Jian-hua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-07, MC-53
m1250216@mail.hfu.edu.tw
Department of Intelligent Life Technology, Huafan University, Taiwan
Xiang, Jingyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-61
jingyu.xiang@ucdconnect.ie
Smurfit Business School, University College Dublin, Dublin,
Ireland, Ireland
Xiang, Qikun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-03
qikun.xiang@ntu.edu.sg
School of Physical and Mathematical Sciences, Nanyang
Technological University, Singapore, Singapore, Singapore

Wu, Yuqia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-41
yuqia.wu@connect.polyu.hk
Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong

Xiao, Junqing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-26
junqing.xjq@alibaba-inc.com
Alibaba Group, China

Wu, Zhengping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-33

Xiao, Shiya. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-10

679

AUTHOR INDEXEURO 2024 - Copenhagensxiao4@u.rochester.edu
University of Rochester, United States
Xiao, Siwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-48
xiaosiwei1006@gmail.com
University of Bristol, Bristol, United Kingdom
Xie, Fengfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-24
fengfeng.xie@nottingham.edu.cn
Department of Entrepreneurship, Marketing and Management Systems, University of Nottingham Ningbo China,
Ningbo, China
Xie, Jingui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-10
jingui.xie@tum.deTechnical University of Munich, Heilbronn, BadenWuerttemberg, Germany
Xie, Lin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-03
lin.xie@utwente.nl
High-Tech Business and Entrepreneurship, University ofTwente, Enschede, Netherlands

Xu, Mengwei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-41
xumengw@hotmail.com
Institute of Mathematics, Hebei University of Technology,
Tianjin, Tianjin, China
Xu, Nan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-28
lynnxn@hotmail.com
China Southern Airlines Company Limited, China
Xu, Peng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-11, TC-45
peng.xu@essex.ac.uk
Essex Business School (EBS), University of Essex,
Southend-on-Sea, United Kingdom
Xu, Yan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-23
yanxu@nwpu.edu.cn
School of Management, Northwestern Polytechnical University, China
Xu, Yinfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-25
yfxu@xjtu.edu.cn
School of management, Xi’anjiaotong University, Xi’an,
Shaanxi, China

Xie, Yangyang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-40
xie-089@163.com
Automation/Management Sci., Tsinghua Univ./CityU of HK,
China

Xu, Yuanfeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-62
Y.Xu-44@student.tudelft.nl
TU Delft, Netherlands

Xie, Yifei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-28
xieyifei95@outlook.com
School of Informatics, University of Edinburgh, United Kingdom

Xu, Zhou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-29, TD-58
zhou.xu@polyu.edu.hk
Department of Logistics and Maritime Studies, The Hong
Kong Polytechnic University, Hong Kong, Hong Kong

Xie, Ying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-03, WA-31
ying.xie@cranfield.ac.uk
School of Management, Cranfield University, Cranfield,
United Kingdom

Xue, Jianwu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-03
18629000350@163.com
School of Management, Northwestern Polytechnical University, Xi’an, Shaanxi, China

Xie, Zhuojun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-28
xie.zhuojun@centralesupelec.fr
Laboratoire Génie Industriel, CentraleSupélec, Université
Paris-Saclay, Gif-sur-Yvette, Île-de-France, France
Xu, Bing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-11
b.xu@hw.ac.uk
Heriot Watt University, United Kingdom
Xu, Jianjun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-28
xujianjun@dufe.edu.cn
Dongbei University of Finance and Economics, Dalian,
Liaoning, China
Xu, Jie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-26
J.Xu@leeds.ac.uk
University of Leeds, United Kingdom
Xu, Liding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-04
lidingx.zz@gmail.com
LIX, École Polytechnique, PALAISEAU, France
Xu, Luze . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-04
luzexu@math.ucdavis.edu
UC Davis, United States

680

Xue, Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-25, MB-56
xueli@nwpu.edu.cn
Northwestern Polytechnical University, Xian, China
Xue, Zhaojie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-62
zjxue@szu.edu.cn
Transportation Engineering, Shenzhen University, China
Yagnik, Vineet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-16
vyagnik@uwo.ivey.ca
Ivey Business School, Canada
Yahi, Zahra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-25
zahra.yahi@univ-bejaia.dz
Faculté SEGC, Université de béjaia, Béjaia, Béjaia, Algeria
Yahyaoui, Hiba . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-52
hiba.yahyawi@gmail.com
Manufacturing Sciences and Logistics, Ecole des Mines deSaint-Etienne - LIMOS, Marseille 12eme, France
Yalcin, Damla . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-35
damlayalcin@iyte.edu.tr
Izmir Institute of Technology, Turkey
Yamada, Felipe Tetsuo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-20
felipeyamada@gmail.com

EURO 2024 - Copenhagen
INESC TEC, Faculdade de Engenharia, Universidade do
Porto, Porto, Porto, Portugal
Yamada, Luciana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-19
luciana.o.yamada@inesctec.pt
INESC TEC, Faculdade de Engenharia, Universidade do
Porto, Porto, Porto, Portugal
Yamada, Syuuji . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-41
yamada@math.sc.niigata-u.ac.jp
Faculty of Science, Niigata University, Niigata, Japan
Yamamoto, Azusa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-25
221007ay@gmail.com
Graduate School of Information Science, University of
Hyogo, JapanAUTHOR INDEX

Yang, Qingqing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-21
qingqingyang@nudt.edu.cn
National University of Defense Technology, China
Yang, Qirui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-03
evan.yang@my.cityu.edu.hk
Department of Management Sciences, City University ofHong Kong, Hong Kong
Yang, Renyu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-26
renyuyang@buaa.edu.cn
Beihang University, China
Yang, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-58
weiyang1905@gmail.com
School of management, Northwestern Polytechnical University, Xi’an, Shaanxi, China

Yamamoto, Megumi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-31
yamamoto@nufs.ac.jp
School of Contemporary International Studies, Nagoya University of Foreign Studies, Nisshin, Aichi, Japan

Yang, Xianjin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-42
yxjmath@caltech.edu
CalTech, United States

Yaman, Hande . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-52
hande.yaman@kuleuven.be
ORSTAT, KU Leuven, Leuven, Belgium

Yang, Xianyan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-29
yangxy19961121@163.com
Hong Kong Polytechnic University, Hong Kong

Yamasaki, Junya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-18
j.yamasaki@up.t.u-tokyo.ac.jp
The University of Tokyo, Japan

Yang, Xian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-06
xian.yang@manchester.ac.uk
Alliance Manchester Business School, University of Manchester, United Kingdom

Yamasawa, Nariyasu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-31
yamasawa@atomi.ac.jp
Faculty of Management, Atomi University, Niiza, Saitama,Japan
Yan, Houmin . . . . . . . . . . . . . . . . . . . . . . . MB-03, WC-40, MD-59
houminyan@cityu.edu.hk
City University of Hong Kong, Hong Kong, Hong Kong
Yan, Nina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-36, MB-57
yannina@cufe.edu.cn
Central University of Finance and Economics, Beijing, China
Yan, Wenjin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-29
wenjin.yan@essec.edu
IDS, ESSEC Business School of Paris, France
Yang, Kang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-04
youngclover23@outlook.com
College of Systems Engineering, National University of Defense Technology, China

Yang, Xiaoqi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-41
mayangxq@polyu.edu.hk
Department of Applied Mathematics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Yang, Xiaoqi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-41
xiao.qi.yang@polyu.edu.hk
The Hong Kong Polytechnic University, Hong Kong, Hong
Kong
Yang, Yingying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-03
yingying.yang1@postgrad.curtin.edu.auMathematics and Statistics, Curtin University, Bentley, WA,
Australia
Yang, Yun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-07
yxy334@student.bham.ac.uk
Management, The University of Birmingham, Birmingham,
United Kingdom

Yang, Kewei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-21
kayyang27@163.com
National University of Defense Technology, China

Yang, Zijing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-36
1369087550@qq.com
School of Management, Harbin Institute of Technology,
Harbin, China

Yang, Lixing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-51, WA-51
lxyang@bjtu.edu.cn
Beijing Jiaotong University, China

Yanikoglu, Ihsan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-49
ihsan.yanikoglu@ozyegin.edu.tr
Ozyegin University, Istanbul, Turkey

Yang, Nan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-10
n.yang@tum.de
School of Management, Technical University of Munich,
Heilbronn, Baden-Württemberg, Germany

Yanık, Seda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-03
sedayanik@itu.edu.tr
Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

681

AUTHOR INDEXEURO 2024 - Copenhagen

Yannacopoulos, Athanasios . . . . . . . . . . . . . . . . . . . . . . . . . . TA-57
ayannaco@aueb.gr
Athens University of Economics and Business, Athens,
GreeceYao, Ming-Jong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-49
myao@nycu.edu.tw
Transportation and Logistics Management, National Yang
Ming Chiao Tung University, Hsinchu City, Taiwan
Yapicioglu, Haluk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-24
hyapicio@eskisehir.edu.tr
Industrial Engineering, Eskisehir Technical University, Eskisehir, Turkey
Yardley, Elizabeth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-15
egmy20@bath.ac.uk
University of Bath, Bath, United Kingdom
Yasarcan, Hakan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-43
yasarcanh@yahoo.com
Industrial Engineering, Bogazici University, Istanbul, Turkey
Yawar, Sadaat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-23
sadaat.yawar@northumbria.ac.uk
Northumbria University, United Kingdom
Yazan, Devrim Murat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-22
d.m.yazan@utwente.nl
Department of Industrial Engineering and Business Information Systems, University of Twente, Netherlands
Yazici, Ahmet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-25
ayazici@ogu.edu.tr
Department of Computer Engineering, Eskisehir Osmangazi
University, Eskisehir, Turkey
Yazıcı, Gözde . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-22
gozde.yazici@bilkent.edu.tr
Industrial Engineering, Bilkent University, Ankara, Turkey
Ye, Xinyi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-49, WB-64
xinyi.ye@kcl.ac.uk
Department of Informatics, King’s College London, United
Kingdom
Yearworth, Mike . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-08, MC-13
m.yearworth@exeter.ac.uk
Business School, University of Exeter, Exeter, Devon, United
Kingdom
Yeh, Li-Ting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-48
dow0623@gmail.com
Cooperative Economics and Social Entrepreneurship, Feng
Chia University, Taichung City, Taiwan
Yeldan, Alp Erinç . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-22
erinc.yeldan@khas.edu.tr
Economics, Kadir Has Univ, Istanbul, Turkey
Yepes Borrero, Juan Camilo . . . . . . . . . . . . . . . . WB-49, WA-60
juancamiloyepes@gmail.com
Universidad Miguel Hernández, Elche, Spain
Yi, Xiajie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-15

682

yixiajie93@gmail.com
Lille, France
Yildirim, Sinan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-56
sinanyildirim@sabanciuniv.edu
Faculty of Engineering and Natural Sciences, Sabancı University, İstanbul, Turkey
Yildiz, Gazi Bilal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-41
bilalyildiz@hitit.edu.tr
TurkeyYilmaz, Atakan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-50
atakan.yilmaz@trendyol.com
Data Science, Trendyol, İstanbul, İstanbul, Turkey
Yilmaz, Gorkem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-60
gorkem.yilmaz@ieu.edu.tr
Industrial Engineering, Izmir University of Economics, Izmir,
TurkeyYin, Chuhan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-54
ml202cy@leeds.ac.uk
Institute for Transport Studies, University of Leeds, Leeds,
United Kingdom
Yin, Daiqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-29
yindaiqiang@mail.nwpu.edu.cn
School of Management, Northwestern Polytechnical University, China
Yin, Jiateng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-51
jtyin@bjtu.edu.cn
State Key Lab of Rail Traffic Control and Safety, Beijing
Jiaotong University, Beijing, China
Yin, Qing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-06
qing.yin-2@postgrad.manchester.ac.uk
Alliance Manchester Business School, University of Manchester, Manchester, United Kingdom
Yıldız, Barış . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-28, WD-52
byildiz@ku.edu.tr
Industrial Engineering, Koç Üniversity, Istanbul, Turkey
Yorke-Smith, Neil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-26
n.yorke-smith@tudelft.nl
Algorithmics, Delft University of Technology, Delft, Netherlands
Yoshida, Takahiro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-18
yoshida@csis.u-tokyo.ac.jp
The University of Tokyo, Japan
Yoshimura, Masafumi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-16
msyoshi@juntendo.ac.jp
Juntendo University, Japan
You, Jintao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-62
youjintao@163.com
Shenzhen Research Institute of Big Data, China
Yu, Carisa K.W. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-41
carisayu@hsu.edu.hk
Department of Mathematics, Statistics and Insurance, The

EURO 2024 - Copenhagen
Hang Seng University of Hong Kong, Hong Kong, HongKong

AUTHOR INDEX

alp.yurtsever@umu.seUmeå University, Sweden

Yu, Huan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-31
huan.yu@soton.ac.uk
University of southampton, United Kingdom

Yushimito, Wilfredo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-48
wilfredo.yushimito@uai.cl
Universidad Adolfo Ibáñez (UAI), Viña del Mar, Chile

Yu, Liyun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-56
liyun.yu@liu.se
Department of Science and Technology, Linköping University, Norrköping, Sweden

Zaarour, Nizar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-50
n.zaarour@northeastern.edu
Supply Chain And Information Management, Northeastern
University, Boston, MA, United States

Yu, Man . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-15
manyu@ust.hk
Hong Kong

Zabala Figueroa, Alejandro . . . . . . . . . . . . . . . . . . . . . . . . . MC-53
alejandro.zabalafigueroa@dlr.deInstitute of Networked Energy Systems, German Aerospace
Center, Oldenburg, Germany

Yu, Renjie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-60
renjie.yu@sydney.edu.au
Business School, The University of Sydney, Chippendale,
NSW, Australia
Yuan, Ya-xiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-42
yyx@lsec.cc.ac.cn
Institute of Computational Mathematics and Sci./Eng. Computing, AMSS, Chinese Academy of Sciences, Beijing, China
Yuan, Zhe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-25
zhe.yuan@devinci.fr
EMLV business school, Courbevoie, France
Yücel, Eda . . . . . . . . . . . . . . . . . . . . . . . . . . WC-15, TA-21, MD-49
edatyucel@gmail.com
Industrial Engineering, TOBB University of Economics andTechnology, Ankara, Turkey
Yucel, Tugce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-45
yuceltugce90@gmail.com
Industrial Engineering, Turkish Aerospace Instution, Turkey
Yuge, Tetsushi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-55
yuge@nda.ac.jp
Dept. of Electrical and Electronics, National Defense
Academy Japan, Yokosuka, Japan
Yugma, Claude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-60
yugma@emse.fr
Centre Microélectronique de Provence- Georges Charpak,
Ecole Nationale Supérieure des Mines de Saint-Etienne, Gardanne, France

Zachariadis, Emmanouil . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-60
ezach@aueb.gr
Department of Management Science & Technology, Athens
University of Economics and Business, Greece
Zacharias, Christos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-15
czacharias@bus.miami.edu
Management Science, University of Miami, Miami, FL,
United States
Zadnik Stirn, Lidija . . . . . . . . . . . . . . . . . . . . . . . . . TB-12, MD-65
lidija.zadnik@bf.uni-lj.si
Biotechnical Faculty, University of Ljubljana, Ljubljana,
Slovenia, Slovenia
Zaffar, Muhammad Adeel . . . . . . . . . . . . . . . . . . . . . . . . . . MC-21
adeel.zaffar@lums.edu.pk
Lahore University of Management Sciences, Lahore, Punjab,
Pakistan
Zajac, Sandra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-50
sandra.zajac@ku.de
Catholic University Eichstätt-Ingolstadt, Germany
Zalinescu, Constantin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-38
zalinesc@uaic.roInstitute of Mathematics, Iasi Branch of Romanian Academy,Iasi, Romania
Zama, Fabiana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-32
fabiana.zama@unibo.it
Department of Mathematics, University of Bologna, Italy

Yuksel, Tugce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-26, TA-56
tyuksel@sabanciuniv.edu
Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey

Zamal, Muchammad Arya . . . . . . . . . . . . . . . . . . . . . . . . . . MA-56
m.a.zamal@tue.nl
Industrial Engineering, TU Eindhoven, Eindhoven, Netherlands

Yurdakul, Ogun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-35
ogunyurdakul@gmail.com
Techniche Universität Berlin, Berlin, Berlin, Germany

Zambara, Marilena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-09
mzambara@mail.ntua.gr
Electric Power, NTUA, Greece

Yurter, Gülin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-22
gulin.yurter@bilkent.edu.tr
Industrial Engineering, Bilkent University, Çankaya, Select,
TurkeyZamorano, Gonzalo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-16
gonzaloazv1982@gmail.com
University of Chile, Santiago, Chile

Yurtsever, Alp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-04

Zand, Fatemeh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-24
fatemeh.zand@hec.ca

683

AUTHOR INDEXEURO 2024 - Copenhagen

Decision Science, HEC Montréal business school, Montreal,
Quebec, Canada
Zander, Anne . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-60
a.b.zander@utwente.nl
Applied Mathematics, University of Twente, Enschede,
Netherlands
Zanetti, Filippo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-38
f.zanetti@sms.ed.ac.uk
School of Mathematics, University of Edinburgh, Edinburgh,
United Kingdom
Zanini, Mariano Angelo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-47
marianoangelo.zanini@unipd.it
University of Padova, Italy
Zanni, Luca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-34
luca.zanni@unimore.it
FIM, University of Modena and Reggio Emilia, Modena,
Italy
Zanocco, Piero . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-43
pzanocco@dii.uchile.cl
Business School, UC Berkeley, Chile
Zanotti, Roberto . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-35, WA-37
roberto.zanotti@unibs.it
Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
Zapata, Asunción . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-36
azapata@us.es
University of Seville, Sevilla, Spain
Zapletal, Frantisek . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-45
frantisek.zapletal@vsb.cz
VSB-Technical University Ostrava, Ostrava, Czech Republic
Zappalà, Silvano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-44
silvano.zappala@phd.unict.it
Department of Economics and Business, University of Catania, Catania, Sicily, Italy
Zarate, Pascale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-01
Pascale.Zarate@ut-capitole.fr
University Toulouse Capitole, Toulouse, France
Zardini, Gioele . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-38
gzardini@mit.edu
Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, United States
Zare Bidaki, Fatemeh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-21
fbidaki19@ku.edu.tr
Business administration, Koc university, Istanbul, Turkey
Zare Hadesh, Ashkan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-53
azarehad@gmu.edu
Civil, Env. & Inf. Engineering, George Mason University,
Fairfax, VA, United States
Zargoush, Manaf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-15
zargousa@mcmaster.ca
McMaster University, Canada

684

Zattoni Scroccaro, Pedro . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-62
P.ZattoniScroccaro@tudelft.nl
TU Delft, Netherlands
Zech, Patricia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-64
zech@europa-uni.de
Chair for Supply Chain Management, European University
Viadrina, Frankfurt (Oder), Germany
Zedda, Stefano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-02
szedda@unica.it
DSEA, Università di Cagliari, Cagliari, CA, Italy
Zehetner, Dominik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-49
dominik.zehetner@aau.at
University of Klagenfurt, Klagenfurt, Austria
Zeimpekis, Vasileios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-45
vzeimp@fme.aegean.gr
Financial and Management Engineering, University of the
Aegean, Chios, North Aegean, Greece
Zeinali, Yasser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-31
yzeinali@ualberta.ca
University of Alberta, Canada
Zemkoho, Alain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-42
a.b.zemkoho@soton.ac.uk
Mathematics, University of Southampton, Southampton,
Hampshire, United Kingdom
Zeng, Yinlian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-36
zengyinlian@sztu.edu.cn
Logistics Management, Shenzhen Technology University,
Shenzhen, Guangdong, China
Zengin, Mehmet Sencer . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-61
mzengin@metu.edu.tr
Industrial Engineering, Middle East Technical University,
Ankara, Turkey
Zenios, Stavros A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-63
zenioss@ucy.ac.cy
Durham University and University of Cyprus, Cyprus
Zeyen, Elisabeth . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-09, TD-30
e.zeyen@tu-berlin.de
Digital Transformation in Energy Systems, Technical University of Berlin, Berlin, Germany
Zhang, Duo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-28
duo.zhang4@mail.mcgill.ca
McGill University, Canada
Zhang, Fan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-29
zhang.fan2@huawei.com
Huawei Hong Kong Research Centre, Hong Kong, Hong
Kong
Zhang, Hao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-28
zhanghaoc@csair.com
China Southern Airlines, China
Zhang, Junhan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-13

EURO 2024 - Copenhagen
junhan.zhang@wur.nl
Operations Research and Logistics, Wageningen University& Research, Netherlands
Zhang, Junlong . . . . . . . . . . . . . . . . . . . . . WC-21, WB-25, TD-55
zhangjunlong@tsinghua.edu.cn
Industrial Engineering, Tsinghua University, Beijing, China
Zhang, Mengxin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-56
213200933@seu.edu.cn
Industrial Engineering, Southeast Univercity, Nanjing,
Jiangsu Province, China
Zhang, Qingkai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-40
qzhang793-c@my.cityu.edu.hk
Management Sciences, City University of Hong Kong, Hong
Kong, China, China
Zhang, Qingpeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-03
qpzhang@hku.hk
The University of Hong Kong, Hong Kong

AUTHOR INDEX

Zhao, Ruidong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-24
myhome8886@126.com
The University of Hong Kong, Hong Kong, Hong Kong
Zhao, Ruyue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-54
21114040@bjtu.edu.cn
Beijing Jiaotong University, Beijing, China
Zhao, Shudian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-04
shudian@kth.se
Department of Mathematics, KTH Royal Institute of Technology, Sweden
Zhao, Wei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-41
szhao_wei@yahoo.com
East China University of Science and Technology, China
Zhao, Xiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-63
xz10n21@soton.ac.uk
School of Mathematics, University of Southampton,
SOUTHAMPTON, UK, United Kingdom

Zhang, Shiqiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-04
s.zhang21@imperial.ac.uk
Department of Computing, Imperial College London, London, London, United Kingdom

Zhao, Xiaobo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-07
xbzhao@tsinghua.edu.cn
Industrial Engineering, Tsinghua University, Beijing, China

Zhang, Weihua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-23
weihua.zhang@northumbria.ac.uk
Newcastle upon Tyne, United Kingdom

Zhao, Yanlu . . . . . . . . . . . . . . . . . . . . . . . . WC-07, MA-52, MB-52
yanlu.zhao@durham.ac.uk
Durham University, United Kingdom

Zhang, Yao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-08
yaozhang_dut@163.com
Dalian, Liaoning, China

Zhao, Yingshuai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-07
yingshuai.zhao@uni-koeln.de
University of Cologne, Germany

Zhang, Yifan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-51
yifan.zhang@ivt.baug.ethz.ch
ETH Zurich, China

Zheng, Rui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-06
rui.zheng-3@postgrad.manchester.ac.uk
Alliance Manchester Business School, University of Manchester, MANCHESTER, Please select a region, state orprovince., United Kingdom

Zhang, Yingqian . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-03, TA-03
yqzhang@tue.nl
Industrial Engineering, TU Eindhoven, Netherlands
Zhang, Yi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-30
yzhang@minlp.com
The Optimization Firm, Hangzhou, Zhejiang, China
Zhang, Yufeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-29
zhangys@bham.ac.uk
Business School, The University of Birmingham, Birmingham, West Midland, United Kingdom
Zhang, Zhengmin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-25
zhangzhengmin@dgut.edu.cn
School of Mechanical Engineering, Dongguan University ofTechnology, Dongguan, Guangdong, China
Zhang, Zibo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-52
zibo.zhang@durham.ac.uk
Durham University, China
Zhao, Jinxian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-31
jinxian.zhao@ed.ac.uk
Business School, University of Edinburgh, Edinburgh, Scotland, United Kingdom

Zheng, Yi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-24
yiazh@sjtu.edu.cn
College of Smart Energy, Shanghai Jiaotong University,
Shanghai, China
Zhivin, Lev . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-02
zhivin@gmail.com
Afeka College of Engineering, Tel Aviv, Israel
Zhong, Hechen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-57
zhonghc@email.cufe.edu.cn
Beijing, China
Zhong, Huaiyang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-17hzhong@vt.edu
Virginia Tech, United States
Zhou, Chenhao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-56, MB-62
zhou_chenhao@u.nus.edu
School of Management, Northwestern Polytechnical University, China
Zhou, Gang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-03, WC-19
liwenhua@nudt.edu.cn

685

AUTHOR INDEXEURO 2024 - Copenhagen

College of Systems Engineering, National University of Defense Technology, China
Zhou, Helper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-18
helperzhou@gmail.com
School of Accounting, Economics and Finance, Universityof KwaZulu Natal, Bulawayo, Zimbabwe
Zhou, Ke (Koko) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-13
ke.zhou@ucl.ac.uk
The Bartlett Faculty, University College London, United
KingdomZhou, Keren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-51
keren.zhou@stud.tu-darmstadt.de
TU Darmstadt, Germany

Zhu, Karl. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-31
karl.zhu@auckland.ac.nz
University of Auckland, New ZealandZhu, Mingyuan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-61
mingyuanzhu@link.cuhk.edu.cn
School of Science and Engineering, The Chinese Universityof Hong Kong, Shenzhen, Shenzhen, Guangdong, China
Zhu, Ning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-34, WD-34
zhuning@tju.edu.cn
Tianjin University, Tianjin, China
Zhu, Shenghui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-62
121107010863@njust.edu.cn
School of Economics & Management, Nanjing University of
Science & Technology, Nanjing, China

Zhou, Qin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-12, WA-23
q.zhou@soton.ac.uk
Decision Analytics and Risk, University of Southampton,
United Kingdom

Zhu, Wanshan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-33
zhuwanshan@rmbs.ruc.edu.cn
Renmin University of China, China

Zhou, Steve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-08
zhoub@uhd.edu
University of Houston-Downtown, Houston, TX, UnitedStatesZhu, Xiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-24
x.zhu@rug.nl
Operations, University of Groningen, Groningen, Groningen,
Netherlands

Zhou, Weihua . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-40
larryzhou@zju.edu.cn
Zhejiang University, Zhejiang, China

Zhu, Zhi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-07
z_zhi@zju.edu.cn
Management, Zhejiang University, Hangzhou, Zhejiang,
China

Zhou, Xun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-48
x.zhou@surrey.ac.uk
Surrey Business School, University of Surrey, Guildford,
United Kingdom
Zhou, Yeqing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-59
y.zhou2@tue.nl
OPAC, Eindhoven University of Technology, Eindhoven,
Netherlands
Zhou, Yuhao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-31
yzho82@uwo.ca
Department of Statistical and Actuarial Sciences, Western
University, London, ON, Canada
Zhu, Anqi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-21
zhuanqi@mail.ustc.edu.cn
ChinaZhu, Emily . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-15
c_z88@txstate.edu
Information Systems & Analytics, Texas State University,
United States
Zhu, Jia Hui . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-54
j.h.zhu@ese.eur.nl
Econometric Institute, Erasmus University Rotterdam, Rotterdam, Netherlands
Zhu, Jinzhen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-41
jxz755@bham.ac.uk
School of Mathematics, University of Birmingham, Birmingham, United Kingdom

Zhurba, Hanna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-51
h.zhurba@studenti.unibg.it
Faculty of Economics, specializing in Economics and Finance, with a curriculum in Quantitative Finance, Universitàdegli Studi di Bergamo, Italy
Ziegler, Timo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-42
timo.ziegler@itp.uni-hannover.deInstitut für theoretische Physik - AG Quanteninformation,
Leibniz Universität Hannover, Germany
Zimmermann, Nici . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-13
n.zimmermann@ucl.ac.uk
The Bartlett Faculty, University College London, London,
United Kingdom
Zizzania, Piero . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TB-13
piero.zizzania@unina.it
DiARC - Dipartimento di Architettura, Università degli Studidi Napoli "Federico II", Castellammare Di Stabia, NAPOLI,
Italy
Zocca, Alessandro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WD-35
azocca@caltech.edu
CMS, California Institute of Technology, Pasadena, California, United States
Zohar, Rotem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MC-54
rotemzohar@mail.tau.ac.il
Industrial engineering, Tel Aviv University, Ramat Hasharon,
Israel, Israel
Zoi, Patrick. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-63

686

EURO 2024 - Copenhagen
patrick.zoi@bancaditalia.it
Banca d Italia, Italy
Zois, Georgios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WA-25, MD-49
georzois@aueb.gr
Management Science and Technology, Athens University of
Economics and Business, Athens, Attica, Greece
Zopounidis, Constantin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-47
kostas@dpem.tuc.gr
Dept. of Production Engineering and Management, Technical University of Crete, Chania, Greece
Zopounidis, Constantin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MA-47
kzopounidis@tuc.gr
Technical University of Crete, Greece
Zormpas, Dimitrios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WC-45
dhzormpas@gmail.com
Financial and Management Engineering, University of the
Aegean, Peristeri, Attiki, Greece
Zotteri, Giulio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-18
giulio.zotteri@polito.it
DIST, Politecnico di Torino, Italy
Zöttl, Gregor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-19
gregor.zoettl@fau.de
VWL, FAU Erlangen-Nuernberg, Nuernberg, Germany
Zouadi, Tarik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-62
tarik.zouadi@uir.ac.ma
BEAR Lab, International University of Rabat, Sala Al Jadida,
MoroccoZschech, Patrick . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TC-31
patrick.zschech@uni-leipzig.deLeipzig University, Germany

AUTHOR INDEX

Zucchi, Giorgio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TD-29
giorgio.zucchi@coopservice.it
Research & Development, Coopservice scpa, Reggio Emilia,
Italy, Italy
Zuidwijk, Rob . . . . . . . . . . . . . . . . . . . . . . MD-24, MA-29, MD-56
rzuidwijk@rsm.nl
Technology Operations Management, RSM Erasmus University, Rotterdam, Netherlands
Zuluaga, Luis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-38
luis.zuluaga@lehigh.edu
Lehigh University, Bethlehem, PA, United States
Zuluaga-Ortiz, Rohemi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-48
rohemialfredo@cue.edu.co
Corporacion Universitaria Empresarial Alexander Von Humboldt, Colombia
Zur, Elisheva . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB-36
elisheva.schwarz@mail.huji.ac.il
Hebrew University of Jerusalem, Jerusalem, Israel
Zwart, Bert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MD-39
bert.zwart@cwi.nl
CWI, Amsterdam, Netherlands
Zychlinski, Noa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MB-20
noazy@technion.ac.il
Data and Decision Sciences, Technion - Israel Institute ofTechnology, Haifa, Israel
Ι. Nikolopoulou, Amalia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TA-56
anikolopoulou@mail.ntua.gr
Civil Enginering, National Technical University of Athens,
Greece687

Session Index
Sunday, 16:30-17:30
SF-01: Opening Session (Sportshallen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Monday, 8:30-10:00
MA-01: Graham Rand (Sportshallen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
MA-02: EDDA (Glassalen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
MA-03: Industrial Optimization (1005 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
MA-04: Recent Methodologies in Explainable AI (XAI) 1 (1001 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
MA-07: Cutting and Packing 1 - 2D rectangular (1019 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
MA-08: Portfolio optimization and sustainability (1020 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
MA-09: Modelling and Economics of Storage Technologies in Energy Markets (10 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . 7
MA-10: EMS logistics (11 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
MA-11: Choice behavior (12 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
MA-12: Optimization in Agriculture (13 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
MA-13: Supporting Planning and Sustainability (15 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
MA-14: Impacts of transitioning to green gases (16 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
MA-15: Machine learning and game theory in healthcare (18 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
MA-16: OR Education I (19 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
MA-17: Decision support in healthcare (40 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
MA-18: Assessment Methods for Shaping the Green, Inclusive, and Digital Cities I (42 (building: 116)) . . . . . . . . . . . . . . . . . . . . 14
MA-19: Learning-assisted Optimization in Energy Problems (44 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
MA-20: Ethics of OR and artificial intelligence (45 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
MA-21: Planning problems in electrical energy systems (49 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
MA-23: Circular Economy, Remanufacturing and Recycling (82 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
MA-24: Sustainable Supply Chain Design (83 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
MA-25: Discrete, continuous or stochastic optimization and control in networks, transportation and design I (011 (building: 208))18
MA-26: Combinatorial optimization topics in transportation (012 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
MA-28: Advancements of OR-analytics in statistics, machine learning and data science 1 (065 (building: 208)) . . . . . . . . . . . . . . 20
MA-29: Combinatorial Optimization models and applications in Logistics and Transportation I (157 (building: 208)) . . . . . . . . . 21
MA-30: MIP Solvers (064 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
MA-31: Recommender systems (046 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
MA-32: Large Scale Constrained Optimization: Algorithms and Applications (41 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . 23
MA-33: Optimal control in supply chain management (42 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
MA-34: Optimization and learning for data science and imaging (Part I) (43 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
MA-35: Robust and Stochastic Routing Problems (44 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
MA-36: Game Theory, Solutions and Structures I (32 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
MA-41: Vector and Set Optimization I (97 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
MA-42: Hybrid Classical-Quantum Algorithms (98 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
MA-43: Simulation of organizations I (99 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
MA-44: Preference Learning 1 (20 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
MA-46: Fast And Furious: Lightning Talks (40 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
MA-47: MCDA applications in Engineering and Management 1 (50 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
MA-48: DEA and its application (60 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
MA-49: Lot-sizing with joint replenishment and routing decisions (M1 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
MA-50: Food Waste (M2 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
MA-51: Risk management in finance (M5 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
MA-52: Combinatorial optimization approaches for freight deliveries and home services (8003 (building: 202)) . . . . . . . . . . . . . . 32
MA-54: Disruption management in passenger railways (S01 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
MA-55: Transportation Network Modelling and Optimization I (S02 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
688

EURO 2024 - Copenhagen

SESSION INDEX

MA-56: Last mile delivery modeling (S04 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
MA-57: Modelling commodity markets dynamics (S06 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
MA-58: Automated Timetabling (S07 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
MA-59: Pricing and applications 3 (S08 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
MA-60: Vehicle routing I (S09 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
MA-61: Advances in Location Analysis (S10 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
MA-62: Seaside Planning I (S12 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
MA-63: Applications in Finance and Economics (S14 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
MA-64: Routing in Warehouses (S16 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Monday, 10:30-12:00
MB-01: Immanuel M. Bomze (Sportshallen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
MB-02: EDDA2 (Glassalen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
MB-03: Optimization in Online Environments (1005 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
MB-04: Recent Methodologies in Explainable AI (XAI) 2 (1001 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
MB-07: Cutting and Packing 2 - 2D irregular (1019 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
MB-08: AI in Eco-Finance: Time, Space, and Networks (1020 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
MB-09: Electricity Market Design (10 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
MB-10: EMS and crisis logistics (11 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
MB-11: Behavioral Decision Analysis I (12 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
MB-12: OR in Forestry I (13 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
MB-13: OR Innovations in Policy Making - A (15 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
MB-14: Flexibility in future energy systems (16 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
MB-15: Machine learning and analytics in healthcare (18 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
MB-16: OR Education II (19 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
MB-17: DEA in healthcare (40 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
MB-18: Assessment Methods for Shaping the Green, Inclusive, and Digital Cities II (42 (building: 116)) . . . . . . . . . . . . . . . . . . . . 50
MB-19: OR in Electricity Markets (44 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
MB-20: Just and ethical sustainability transitions (45 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
MB-21: Energy sector coupling, optimization and equilibrium (49 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
MB-23: Managing product returns (82 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
MB-24: Raw Material Supply Chains (83 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
MB-25: Discrete, continuous or stochastic optimization and control in networks, transportation and design II (011 (building: 208))55
MB-26: Novel topics and recent advances in solution approaches in scheduling (012 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . 56
MB-27: On Mathematical Optimization for Explainable and Fair Machine Learning (047 (building: 208)) . . . . . . . . . . . . . . . . . . . 56
MB-28: Advancements of OR-analytics in statistics, machine learning and data science 2 (065 (building: 208)) . . . . . . . . . . . . . . 57
MB-29: Combinatorial Optimization models and applications in Logistics and Transportation II (157 (building: 208)) . . . . . . . . 57
MB-30: MINLP Solvers (064 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
MB-31: Crime Analytics (046 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
MB-32: Algorithmic Advances in Large Scale Nonconvex Optimization (41 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
MB-33: Dynamics of the Firm I (42 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
MB-34: Optimization and learning for data science and imaging (Part II) (43 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
MB-35: Stochastic Optimization for Energy Transition (44 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
MB-36: Game Theory, Solutions and Structures II (32 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
MB-38: Interior point methods (34 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
MB-40: Advances in Stochastic Modelling and Applied Probability Ι (96 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
MB-41: Vector and Set Optimization II (97 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
MB-42: Decomposition methods for Quantum Optimization (98 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
MB-43: Simulation of organizations II (99 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
MB-44: Preference Learning 2 (20 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
MB-46: Speed Networking (40 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
MB-47: MCDA applications in Engineering and Management 2 (50 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
MB-48: DEA and Machine Learning (60 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
MB-49: Integrated lot-sizing problems (M1 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
MB-50: Retail Inventory Management I (M2 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
689

SESSION INDEXEURO 2024 - Copenhagen

MB-51: Market risk in a volatile world (M5 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
MB-52: Combinatorial optimization approaches for freight deliveries (8003 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
MB-54: Crew Planning in Public Transport (S01 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
MB-55: Transportation Network Modelling and Optimization II (S02 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
MB-56: Freight railway transportation (S04 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
MB-57: Decision making in Insurance and Pensions (S06 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
MB-58: Heuristics for Vehicle Routing 3 (S07 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
MB-59: Revenue Management in Sharing/Platform Economy (S08 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
MB-60: Vehicle routing II (S09 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
MB-61: Location of Alternative Fuel and Charging Stations (S10 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
MB-62: Container Stacking and Yard Planning I (S12 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
MB-63: Insurance Risk Management (S14 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
MB-64: Routing Unmanned Aerial Vehicles 1 (S16 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

Monday, 12:30-14:00
MC-01: Ruben Ruiz (Sportshallen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
MC-02: EEPA (Glassalen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
MC-03: (Deep) Reinforcement Learning for Combinatorial Optimization 1 (1005 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . 79
MC-04: New Trends in Generative Adversarial Networks and Deep Neural Networks (1001 (building: 202)) . . . . . . . . . . . . . . . . . 80
MC-07: Cutting and Packing 3 - 3D loading (1019 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
MC-08: Applications of knowledge and innovation in finance (1020 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
MC-09: New Market Designs & Models for 100% Renewable Power Systems (10 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . 82
MC-10: Mobility and transportation in healthcare (11 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
MC-11: Behavioral Decision Analysis II (12 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
MC-12: Agrifood supply chain decision problems (13 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
MC-13: Approaches for Integrating Quantitative Modelling (15 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
MC-14: Machine Learning for Electricity Market Applications (16 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
MC-15: Healthcare Analytics (18 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
MC-16: Experienced Routes for the Teaching Students’ Problem (19 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
MC-17: Simulation models in healthcare (40 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
MC-18: Enhancement of circularity, inclusivity, and smartness in cities I (42 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
MC-19: Multi-energy systems (44 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
MC-20: Ethics and OR, societal complexity and public service (45 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
MC-21: Disaster Response: Search and Rescue, Resource Allocation and Impactful Prepositioning (49 (building: 116)) . . . . . . 91
MC-22: Advancements in energy system optimization and analysis tools (81 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
MC-23: Sustainable food supply chains (82 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
MC-24: Clean Energy Supply Chains (83 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
MC-25: Discrete, continuous or stochastic optimization and control in networks, transportation and design III (011 (building: 208))
94
MC-26: Combinatorial Optimization for Machine Learning (012 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
MC-27: XAI in Business Processes (047 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
MC-28: Advancements of OR-analytics in statistics, machine learning and data science 3 (065 (building: 208)) . . . . . . . . . . . . . . 96
MC-29: Vehicle routing I (157 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
MC-30: Continuous Solvers (064 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
MC-31: Causal Machine Learning (046 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
MC-32: Advances in Complexity of Convex and Nonconvex Problems (41 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
MC-33: Dynamics of the Firm II (42 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
MC-34: Optimization and learning for data science and imaging (Part III) (43 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
MC-35: Urban Logistics and sustainable TRAnsportation: OPtimization under uncertainTY and MAchine Learning (44 (building:
303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
MC-36: Game Theory, Solutions and Structures III (32 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
MC-38: Applications of polynomial optimization (34 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
MC-40: Advances in Stochastic Modelling and Applied Probability II (96 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
MC-41: Stochastic and Deterministic Global Optimization (97 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
MC-42: Quantum Computing for Discrete and Combinatorial Optimization (98 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . 104
MC-43: Simulation in economics I (99 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
690

EURO 2024 - Copenhagen

SESSION INDEX

MC-44: MCDM for project portfolio problems (20 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
MC-45: Methods and Algorithms of Decision Support (30 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
MC-46: Practical Skills: Knowledge-Sharing Tutorials -1- (40 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
MC-47: How to support complex decisions. Negotiating the trade-off between Social, Environmental and Economic values 1(50 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
MC-48: DEA and stochastic models (60 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
MC-49: Lot-sizing with industrial applications I (M1 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
MC-50: Retail Inventory Management II (M2 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
MC-51: Quantitative methods for systemic and climate risk (M5 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
MC-52: Multi-objective Combinatorial Optimization (8003 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
MC-53: Sustainable Energy (8007 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
MC-54: Ridehailing & Ridepooling (S01 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
MC-55: Airline Applications I (S02 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
MC-56: Traffic flow modeling (S04 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
MC-57: Dynamic portfolio selection: stochastic optimization, filtering, and learning techniques (S06 (building: 101)) . . . . . . . 115
MC-58: Column Generation for Vehicle Routing (S07 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
MC-59: Pricing and learning 2 (S08 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
MC-60: Project scheduling under uncertainty (S09 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
MC-61: Hub Location (S10 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
MC-62: Port-Hinterland Transportation & Corridors (S12 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
MC-63: Natural Resource Management and Commodity Markets (S14 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
MC-64: Routing Unmanned Aerial Vehicles 2 (S16 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

Monday, 14:30-16:00
MD-01: Pedro Amorim (Sportshallen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
MD-02: EPOCG (Glassalen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
MD-03: (Deep) Reinforcement Learning for Combinatorial Optimization 2 (1005 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . 122
MD-04: Hybrid Appraches in Deep Learning and Machine Learning (1001 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
MD-07: Cutting and Packing 4 - 3D irregular (1019 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
MD-08: EJOR: policy, facts and highlights (1020 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
MD-09: Uncertainties in the Energy Transition (10 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
MD-10: Healthcare logistics and routing (11 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
MD-11: Behavioral Decision Analysis III (12 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
MD-12: OR in Agriculture (13 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
MD-13: Methodological Developments in Soft OR and PSMs (15 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
MD-14: Modelling European market coupling (16 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
MD-15: Healthcare services (18 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
MD-16: OR Initiatives for Education, Sustainability, and Developing Countries (19 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . 129
MD-17: Medical decision making (40 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
MD-18: Enhancement of circularity, inclusivity, and smartness in cities II (42 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
MD-19: Electric Vehicles within Electric Power Systems (44 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
MD-20: Military, Defense, and International Security I (45 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
MD-21: Site selection and aid allocation in humanitarian operations (49 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
MD-22: Energy transition and operations (81 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
MD-23: Scheduling for sustainability (82 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
MD-24: Sustainable Operations (83 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
MD-25: Discrete, continuous or stochastic optimization and control in networks, transportation and design IV (011 (building:208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
MD-26: Optimization problems on graphs (012 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
MD-27: Mathematical Optimization for Counterfactual Explanations (047 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
MD-28: Advancements of OR-analytics in statistics, machine learning and data science 4 (065 (building: 208)) . . . . . . . . . . . . . 138
MD-29: Vehicle Routing II (157 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
MD-30: Optimization Solvers (064 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
MD-31: Network Analytics (046 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
MD-32: Algorithms for machine learning and inverse problems: adaptive strategies (41 (building: 303A)) . . . . . . . . . . . . . . . . . . 140
MD-33: Optimal control in organizations (42 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
691

SESSION INDEXEURO 2024 - Copenhagen

MD-34: Preconditioning for Large Scale Nonlinear Optimization (43 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
MD-35: Cross-dock Door Problems (44 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
MD-36: Game Theory, Solutions and Structures IV (32 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
MD-38: Applications of conic optimization (34 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
MD-39: Stochastic Models in Manufacturing (35 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
MD-40: Stochastic Modelling (96 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
MD-41: Optimization on Geodesic Metric Spaces I: Smooth case (97 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
MD-42: Optimization in Quantum Information (98 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
MD-43: Simulation in economics II (99 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
MD-44: Robustness analysis in MCDA 1 (20 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
MD-45: Emerging Trends in Decision Analysis (30 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
MD-46: The Quest For The Right OR Professionals (40 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
MD-47: How to support complex decisions. Negotiating the trade-off between Social, Environmental and Economic values 2(50 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
MD-48: DEA applications in Education and Health I (60 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
MD-49: Lot-sizing with industrial applications II (M1 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
MD-50: Warehouse Management (M2 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
MD-51: Portfolio risk management (M5 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
MD-52: Scheduling and Routing Problems (8003 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
MD-53: Disaster & Emergency Management (8007 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
MD-54: Europe’s Rail MOTIONAL: Algorithms for railway planning (S01 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
MD-55: Airline Applications II (S02 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
MD-56: Optimization in transportation infrastructure design and management (S04 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . 155
MD-57: Methodology in asset allocation and banking (S06 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
MD-58: Real-Life Applications in Routing (S07 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
MD-59: Customer behaviour (S08 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
MD-60: RCPSP and extensions (S09 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
MD-61: Location under uncertainty (S10 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
MD-62: Seaside Planning II (S12 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
MD-63: Optimization Model for Novel Risks in Finance and Climate (S14 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
MD-64: Vehicle Routing Under Uncertainty 1 (S16 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
MD-65: Memorial Session for Jakob Krarup (R021 (building: 358)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

Monday, 16:30-17:30
ME-01: Berit Brouer (Sportshallen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162

Tuesday, 8:30-10:00
TA-01: Pascale Zarate (Sportshallen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
TA-03: (Deep) Reinforcement Learning for Combinatorial Optimization 3 (1005 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . 163
TA-04: Topics in Mixed Integer Programming and Nonconvex Optimization (1001 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . 163
TA-06: Advancements of OR-analytics in statistics, machine learning and data science 12 (1013 (building: 202)) . . . . . . . . . . . . 164
TA-07: Cutting and Packing 5 - related topics (1019 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
TA-09: Decentralized multi-energy markets (10 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
TA-10: Radiotherapy and chemotherapy planning (11 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
TA-11: Behavioral Decision Analysis IV (12 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
TA-12: OR in Livestock farming (13 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
TA-13: OR Innovations in Policy Making - B (15 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
TA-14: Enhanced statistical methods for energy challenges (16 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
TA-15: Location planning in healthcare (18 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
TA-16: Sports scheduling and optimization (19 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
TA-18: Optimization of sustainable urban mobility (42 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
TA-19: OR in Gas Networks (44 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
TA-20: Military, Defense, and International Security II (45 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
692

EURO 2024 - Copenhagen

SESSION INDEX

TA-21: Humanitarian aid provision and disposal (49 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
TA-22: Optimization of energy storage systems (81 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
TA-23: Policy and legislation for a circular economy (82 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
TA-24: Sustainability in Vehicle Routing (83 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
TA-25: Applications of combinatorial optimization I (011 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
TA-26: Applications to Logistics and Transportation (012 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
TA-27: Counterfactual Analysis Across Diverse Domains (047 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
TA-28: Fairness and responsible AI (065 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178
TA-29: Applications of combinatorial optimisation in industry and services I (157 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . 179
TA-30: Parallel Solvers (064 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
TA-31: Analytics and the link with stochastic dynamics I (046 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
TA-32: Nonsmooth optimization and applications, Part I (41 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
TA-33: Heterogeneity in optimal control problems (42 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
TA-34: New Algorithms for Nonlinear Optimization (43 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
TA-35: Optimization under uncertainty: theory and solution algorithms (44 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
TA-36: Game Theory, Solutions and Structures V (32 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
TA-37: Objective Space-Based Approaches in Multiobjective Optimization (33 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . 184
TA-38: Special Classes of Convex Conic Optimization problems (34 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
TA-39: Stochastic Models in Service Operations I (35 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
TA-40: Queueing Models with Strategic Customers (96 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186
TA-41: Optimization on Geodesic Metric Spaces II: Nonsmooth case (97 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186
TA-42: Quantum Computing for Continuous Optimization (98 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
TA-43: Simulation in innovation (99 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
TA-44: Robustness analysis in MCDA 2 (20 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
TA-45: Multiple-Criteria Decision Support (30 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
TA-46: Generative AI For OR People: Disruptor, Enabler Or Distraction? (40 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
TA-47: How to support complex decisions. Negotiating the trade-off between Social, Environmental and Economic values 3(50 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
TA-48: DEA applications in Education and Health II (60 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190
TA-49: Stochastic lot-sizing problems (M1 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190
TA-50: Retail Analytics (M2 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
TA-51: Corporate finance risk management (M5 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
TA-52: Mixed Integer Optimization I (8003 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
TA-53: Charging Infrastructure toward Sustainable Transport (8007 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
TA-54: Resilience in Public Transport Planning (S01 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194
TA-55: ML & OR Applications in Transport Modelling (S02 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194
TA-56: Methods and models for sustainable transport solutions (S04 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
TA-57: Robust decisions in finance and investments (S06 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196
TA-58: Warehouse Operations (S07 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197
TA-59: Learning and pricing (S08 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197
TA-60: Project Management (S09 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198
TA-61: Covering Location Problems (S10 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
TA-62: Seaside Planning III (S12 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
TA-63: Models for Financial Data and Risk Management (S14 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200
TA-64: Vehicle Routing Under Uncertainty 2 (S16 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201

Tuesday, 10:30-12:00
TB-01: Emma Frejinger (Sportshallen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
TB-03: Machine Learning in Applied Optimization (1005 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
TB-04: Topics in Mixed Integer Nonlinear Programming 1 (1001 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
TB-06: Advancements of OR-Analytics in Statistics, Machine Learning and Data Science 13 (1013 (building: 202)) . . . . . . . . . 204
TB-07: Behavioural Studies in Health Care (1019 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204
TB-09: Game Theoretic Market Equilibrium Modelling (10 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
TB-10: Surgery Scheduling and Operating Room Planning (11 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206
TB-11: Behavioral OR general papers (12 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207
TB-12: OR in Forestry II (13 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207
693

SESSION INDEXEURO 2024 - Copenhagen

TB-13: Impact of AI on Soft OR - A (15 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208
TB-15: Appointment planning (18 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208
TB-16: Ranking in sports (19 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
TB-18: Optimization of sustainable urban mobiltiy II (42 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210
TB-19: The role of storage in energy problems (44 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210
TB-21: Demand Forecasting in Humanitarian Operations (49 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
TB-22: Optimization for electric vehicles (81 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212
TB-23: Supply chain design in the circular economy (82 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
TB-24: Sustainable Logistics (83 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
TB-25: Applications of combinatorial optimization II (011 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214
TB-26: Advanced Topics in Combinatorial Optimization (012 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
TB-27: Advances in Process Mining (047 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216
TB-28: Advancements of OR-analytics in statistics, machine learning and data science 5 (065 (building: 208)) . . . . . . . . . . . . . . 216
TB-29: Applications of combinatorial optimisation in industry and services II (157 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . 217
TB-30: Modeling Languages (064 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218
TB-31: Analytics and the link with stochastic dynamics II (046 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
TB-32: Nonsmooth optimization and applications, Part II (41 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220
TB-33: Optimal control in environmental economics (42 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220
TB-35: Risk Averse and Contextual Stochastic Optimization (44 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221
TB-36: Game Theory, Solutions and Structures VI (32 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221
TB-37: Advances in Continuous Multiobjective Optimization (33 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222
TB-38: Advances in Complex and Real Semidefinite Programming (34 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223
TB-39: Stochastic Models in Logistics (35 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223
TB-40: Reinforcement Learning: Methods and Applications (96 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224
TB-41: Optimization on Manifolds (97 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225
TB-42: Variational Methods in Vector Optimization (98 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225
TB-43: Simulation in sustainability (99 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226
TB-44: MCDA applications (20 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227
TB-45: Artificial Intelligence and Machine Learning for Decision Support (30 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . 228
TB-46: Open Space (40 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228
TB-47: MCDA and urban planning 1 (50 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229
TB-48: DEA applications in Environment and Sustainability I (60 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229
TB-49: Lot-sizing with energy aspects (M1 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230
TB-50: Retail Operations and Marketing (M2 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231
TB-51: Electric Busses (M5 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232
TB-52: Mixed Integer Optimization II (8003 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232
TB-53: Toward Climate Neutrality (8007 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233
TB-54: Airplane Boarding (S01 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234
TB-55: Freight transportation and logistic I (S02 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234
TB-56: Crowdsourcing Logistics (S04 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235
TB-57: Risk management and valuation of financial contracts (S06 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236
TB-58: Public Transport (S07 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236
TB-59: Pricing and Capacity Management (S08 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237
TB-60: Projects, risk and law (S09 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237
TB-61: Nonlinear Location Optimization (S10 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238
TB-62: Energy Management in Ports and Shipping I (S12 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239
TB-63: Risk Management and Cryptoassets (S14 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240
TB-64: Arc Routing (S16 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240

Tuesday, 12:30-14:00
TC-01: Milos Kopa (Sportshallen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241
TC-03: Optimization and Machine Learning: Methodological Advances (1005 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241
TC-04: Recent Advances in MINLP (1001 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242
TC-06: Advancements of OR-analytics in statistics, machine learning and data science 14 (1013 (building: 202)) . . . . . . . . . . . . 242
TC-07: Behavioural OR meets Information systems (1019 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243
TC-09: Market-based analyses in long-term energy system models (10 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244
694

EURO 2024 - Copenhagen

SESSION INDEX

TC-10: Kidney Exchange I (11 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245
TC-11: Behavior in group decision-making (12 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245
TC-12: DSS in Agriculture (13 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246
TC-13: Impact of AI on Soft OR - B (15 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247
TC-15: Staffing and workforce planning and scheduling (18 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247
TC-16: Football analytics (19 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248
TC-18: Developing green and sustainable communities (EWG-ORD Workshop 1) (42 (building: 116)) . . . . . . . . . . . . . . . . . . . . 249
TC-19: OR in Heating Systems (44 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250
TC-20: Integrative Approaches in Health and Disease: From Molecular Structures to Clinical Outcomes (45 (building: 116)) 251
TC-21: Efficiency, equity and fairness in humanitarian operations (49 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251
TC-22: Stochastic models in energy systems planning and operations (81 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252
TC-23: Optimization for the Circular Economy (82 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253
TC-24: Agri-Food Supply Chains (83 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254
TC-25: Graph and network optimization (011 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254
TC-26: Sustainability in Distribution and Transportation (012 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
TC-27: Mathematical Optimization for Trustworthy Machine Learning (047 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
TC-28: Advancements of OR-analytics in statistics, machine learning and data science 6 (065 (building: 208)) . . . . . . . . . . . . . . 256
TC-29: Exact Algorithms and Formulations for Network Optimization Problems (157 (building: 208)) . . . . . . . . . . . . . . . . . . . . . 257
TC-30: Modeling tools (064 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258
TC-31: Analytics and the link with stochastic dynamics III (046 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258
TC-32: Algorithms for machine learning and inverse problems: zeroth-order optimisation (41 (building: 303A)) . . . . . . . . . . . . 259
TC-33: Optimal control and resilience (42 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260
TC-34: Portfolio optimization (43 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261
TC-35: Stochastic Optimization with Decision-Dependent Uncertainty (44 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261
TC-36: Game Theory, Solutions and Structures VII (32 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262
TC-37: Multiobjective Mixed-Integer Nonlinear Optimization (33 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263
TC-38: Modern techniques for network optimization (34 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264
TC-39: Inventory Models (35 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264
TC-40: Data mining biomedical applications (96 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264
TC-41: Subgradient-based methods (97 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265
TC-42: Variational Analysis and Subdifferential techniques (98 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266
TC-43: Simulation in transportation and logistics (99 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266
TC-44: Pairwise comparisons and preference relations 1 (20 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267
TC-45: Integer Programming for Decision Support (30 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268
TC-46: From Academia To Practice: Reflections From People Who Have Taken The Leap (40 (building: 324)) . . . . . . . . . . . . . 268
TC-47: MCDA and urban planning 2 (50 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269
TC-48: DEA applications in Environment and Sustainability II (60 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269
TC-49: Lot-sizing with game theory aspects (M1 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270
TC-50: Assortment Management (M2 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271
TC-51: Network Design and Line Planning for Public Transportation 1 (M5 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271
TC-52: Exact methods in combinatorial optimization (Contributed) (8003 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272
TC-53: Sustainability in Consumer Systems & Industry (8007 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273
TC-54: Disruption management and recovery (S01 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274
TC-55: Freight transportation and logistic II (S02 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275
TC-56: Advancing mobility towards sustainable solutions I (S04 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275
TC-57: Market dynamics and implications for portfolio decisions (S06 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276
TC-58: Waste Collection (S07 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277
TC-59: Pricing Strategies (S08 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278
TC-60: Advanced heuristics for machine scheduling (S09 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278
TC-61: Location with Multiple Actors (S10 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279
TC-62: Container Stacking and Yard Planning II (S12 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280
TC-63: Advanced Options Strategies Using O.R. and Machine Learning (S14 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280
TC-64: Dynamic Vehicle Routing 1 (S16 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281

Tuesday, 14:30-16:00
TD-01: Veronica Piccialli (Sportshallen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282
695

SESSION INDEXEURO 2024 - Copenhagen

TD-02: New Tools in Insurance Risk Management (Glassalen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282
TD-03: Data science meets strongly NP-Hard CO (1005 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283
TD-04: Robust and Multi-Level Optimization (1001 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284
TD-06: Advancements of OR-analytics in statistics, machine learning and data science 15 (1013 (building: 202)) . . . . . . . . . . . 284
TD-07: Behaviour and decision support (1019 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285
TD-09: Long-term energy system planning (10 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286
TD-10: Kidney Exchange II (11 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286
TD-11: BOR in public policy and environmental decisions (12 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287
TD-15: Nurse rostering (18 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288
TD-16: Performance and scouting in football (19 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289
TD-18: AI and ESG for the small economy SDG agenda (EWG-ORD Workshop 2) (42 (building: 116)) . . . . . . . . . . . . . . . . . . . 289
TD-19: Decomposition techniques applied to energy problems (44 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290
TD-20: Advancements in AI and Genomics: Bridging Technology and Biology for Future Healthcare Solutions (45 (building:116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291
TD-21: Complex societal problems (49 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291
TD-22: Distributed energy systems (81 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292
TD-23: Remanufacturing and refurbishing operations (82 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293
TD-24: Sustainable Food and Health Care Logistics (83 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294
TD-25: Applications of Machine Learning in Optimization (011 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294
TD-26: Routing for hybrid fleets of vehicles (012 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295
TD-27: Feature attribution and selection for XAI (047 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296
TD-28: Advancements of OR-analytics in statistics, machine learning and data science 7 (065 (building: 208)) . . . . . . . . . . . . . 296
TD-29: Exact Algorithms and Formulations for Combinatorial Optimization Problems (157 (building: 208)) . . . . . . . . . . . . . . . 297
TD-30: Python Modeling Tools (064 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 298
TD-31: Analytics for Combinatorial Problems from Health Care to the Food Industry (046 (building: 208)) . . . . . . . . . . . . . . . . 298
TD-32: Algorithms for machine learning and inverse problems: optimisation for neural networks (41 (building: 303A)) . . . . . . 299
TD-33: Optimal control theory (42 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300
TD-34: Trends and Open Problems in Robust Optimization (43 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301
TD-35: Location and transportation problems under uncertainty (44 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301
TD-36: Game Theory, Solutions and Structures VIII (32 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302
TD-37: Applications of Multiobjective Optimization (33 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302
TD-38: Semidefinite Programming and implementations, Quantum Information Theory and other applications (34 (building: 306))
303
TD-39: Analysis of Stochastic Models I (35 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304
TD-40: Tools and algorithms for equilibrium detection (96 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304
TD-41: Lower-order composite optimization problems (97 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305
TD-42: Variational techniques in conic optimization and mean field games (98 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . 306
TD-43: Market Design 1 (99 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306
TD-44: Pairwise comparisons and preference relations 2 (20 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307
TD-45: Decision Support for Sustainable Operations (30 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307
TD-46: Success, Failure And The Factors That Influence Outcomes (40 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308
TD-47: MCDA and urban planning 3 (50 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308
TD-48: DEA applications in Policy Making and Planning I (60 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309
TD-49: Production planning problems (M1 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310
TD-50: Omni-Channel Retailing (M2 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310
TD-51: Network Design for Public Transport (M5 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311
TD-52: Integer Programming and Combinatorial Optimization: Complexity Questions and Algorithms (8003 (building: 202)) 312
TD-53: Resilient Networks (8007 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312
TD-54: Demand-responsive public transport 3 (S01 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313
TD-55: Freight transportation and logistic III (S02 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314
TD-56: Advancing mobility towards sustainable solutions II (S04 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315
TD-57: Large-scale network optimization and inventory management (S06 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315
TD-58: MILPs for Vehicle Routing 1 (S07 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316
TD-59: Pricing and applications (S08 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317
TD-60: Resource constrained scheduling (S09 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317
TD-61: Location in Logistics and Supply Chain Management (S10 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318
TD-62: Sustainable freight transportation (S12 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319
TD-63: New Challenges for Risk Management (S14 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320
696

EURO 2024 - Copenhagen

SESSION INDEX

TD-64: Dynamic Vehicle Routing 2 (S16 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320

Tuesday, 16:30-17:30
TE-01: Pascal van Henteryck (Sportshallen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322

Wednesday, 8:30-10:00
WA-01: Peter Biro (Sportshallen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323
WA-02: New Advances in Italian Energy Markets (Glassalen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323
WA-03: Data Science and Optimization (1005 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323
WA-04: Algorithms for Mixed-Integer Nonlinear Programming and Nonconvex Optimization (1001 (building: 202)) . . . . . . . . 324
WA-06: Advancements of OR-analytics in statistics, machine learning and data science 16 (1013 (building: 202)) . . . . . . . . . . . 325
WA-07: Behaviour and decision processes (1019 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325
WA-08: Theory of Knowledge, Technology, and Innovation (1020 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326
WA-09: Decarbonized energy systems & markets (10 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327
WA-10: ED logistics (11 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328
WA-11: Scenarios and foresight practices: Behavioural issues I (12 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328
WA-12: YW4OR_1 (13 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329
WA-13: Food Supply Chains (15 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330
WA-15: Home Health Care and Operating Room Scheduling (18 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330
WA-16: Fairness in sports (19 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331
WA-18: OR for Medical Services in Developing Countries (42 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332
WA-19: Towards sustainable development (44 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332
WA-21: Post-Disaster Relief Distribution (49 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333
WA-22: Renewable Energy Challenges (81 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334
WA-23: Game theory for the circular economy (82 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335
WA-24: Sustainable Supply Chain Management (83 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335
WA-25: Topics in Integer Programming I (011 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336
WA-26: Optimization problems in scheduling (012 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337
WA-27: Learning Analytics using Mathematical Optimization and XAI (047 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338
WA-28: Advancements of OR-analytics in statistics, machine learning and data science 8 (065 (building: 208)) . . . . . . . . . . . . . 338
WA-29: Advances in Optimization for Industrial Applications (157 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339
WA-30: Optimization Tools (064 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340
WA-31: Analytics for Decision Making (046 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341
WA-32: Adaptive and Polyak step-size methods (41 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341
WA-34: Decision problems represented as influence diagrams (43 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342
WA-35: Data Valuation from Data-driven Optimization (44 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343
WA-36: Game Theory, Solutions and Structures IX (32 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343
WA-37: Multiobjective Combinatorial Optimization (33 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344
WA-38: Recent advances in LP and SDP for discrete optimization problems (34 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . 345
WA-39: Stochastic Models in Service Operations II (35 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345
WA-40: Equilibrium detection in applications (96 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346
WA-41: Convex optimization algorithms (97 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346
WA-42: Infinite Optimization: stability and duality (98 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347
WA-43: Market Design 2 (99 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347
WA-44: Pairwise comparisons and preference relations 3 (20 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348
WA-45: Decision Support for Operations Management (30 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349
WA-46: Not The Excellence In Practice Award (40 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350
WA-47: Fuzzy sets and systems (50 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350
WA-48: DEA applications in Policy Making and Planning II (60 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350
WA-49: Stochastic inventory systems (M1 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351
WA-50: Retail Distribution I (M2 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352
WA-51: Timetabling 2 (M5 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353
WA-52: Models and algorithms for real-life combinatorial optimization problems (8003 (building: 202)) . . . . . . . . . . . . . . . . . . . 353
697

SESSION INDEXEURO 2024 - Copenhagen

WA-53: Pathways to Climate Resilience (8007 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354
WA-54: Network Design and Line Planning for Public Transportation 2 (S01 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355
WA-55: MOST - MaaS & Innovative Services for Sustainable Mobility (S02 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356
WA-56: Advancing mobility towards sustainable solutions III (S04 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356
WA-57: Large-scale Speed-sensitive Network Optimization (S06 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357
WA-58: MILPs for Vehicle Routing 2 (S07 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358
WA-59: Pricing and applications 2 (S08 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358
WA-60: Manufacturing scheduling with sustainability considerations (S09 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359
WA-61: Applications of Location Methods (S10 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360
WA-62: Machine Learning and Optimization in Ports I (S12 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360
WA-63: Novel Optimization Models in Finance (S14 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361
WA-64: Last-Mile Delivery (S16 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361

Wednesday, 10:30-12:00
WB-01: Javier Alonso-Mora (Sportshallen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363
WB-02: Optimal Portfolio Strategies (Glassalen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363
WB-03: Interpretable Optimization Methods and Applications (1005 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363
WB-04: Mixed Integer Nonlinear Programming and Nonconvex Optimization (1001 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . 364
WB-06: Advancements of OR-analytics in statistics, machine learning and data science 17 (1013 (building: 202)) . . . . . . . . . . . 365
WB-07: Behavioural operations and games (1019 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365
WB-08: Applications of Knowledge and Technology (1020 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366
WB-09: Production Optimization and Supply Chain Management of Green Hydrogen under Uncertainties (10 (building: 116)) 367
WB-10: Integrated planning in healthcare (11 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368
WB-11: Scenarios and foresight practices: Behavioural issues II (12 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368
WB-12: YW4OR_2 (13 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369
WB-13: Humanitarian Aid (15 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 370
WB-15: COVID-19 (18 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371
WB-16: Sports analytics (19 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371
WB-18: Sustainable Economics in Developing Countries (42 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372
WB-19: OR in Energy (44 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372
WB-20: Moments in the history of OR 1 (45 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373
WB-21: Infectious diseases and pandemics (49 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374
WB-22: Capacity expansion planning for energy systems (81 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375
WB-23: Recycling (82 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375
WB-24: Carbon Regulation (83 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376
WB-25: Topics in Integer Programming II (011 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377
WB-26: Combinatorial Optimization in Scheduling (012 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377
WB-27: Unraveling the Black Box: Advances in Model Explainability (047 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378
WB-28: Advancements of OR-analytics in statistics, machine learning and data science 9 (065 (building: 208)) . . . . . . . . . . . . . 379
WB-29: Large Scale Optimization in Air Transportation (157 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380
WB-30: Specialized Optimization Algorithms (064 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380
WB-31: Learning Analytics and other Text Analytics tasks (046 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381
WB-32: Beyond First-Order Optimization Methods (41 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382
WB-34: Stochastic Optimization: Advanced Applications (43 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382
WB-35: Stochastic optimization: theory and applications (44 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383
WB-36: Game Theory, Solutions and Structures X (32 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384
WB-37: Discrete Multiobjective Optimization (33 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384
WB-38: Convex and conic optimization (34 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385
WB-39: Reliability Models (35 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385
WB-40: Experimental economics and game theory 1 (96 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386
WB-41: Structured nonconvex optimization (97 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387
WB-42: Iterative Methods for Feasibility and Optimization Problems (98 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387
WB-43: Recent advances on Variational Inequalities and Equilibrium Problems I (99 (building: 306)) . . . . . . . . . . . . . . . . . . . . . 388
WB-44: Pairwise comparisons and preference relations 4 (20 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 388
WB-45: Decision Support in the Public Sector and Policy Making (30 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389
WB-46: Practical Skills: Knowledge-Sharing Tutorials - 2 (40 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390
698

EURO 2024 - Copenhagen

SESSION INDEXWB-47: MCDA in energy (50 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390
WB-48: DEA applications in transportation (60 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391
WB-49: Flow shop scheduling problems (M1 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391
WB-50: Retail Distribution II (M2 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 392
WB-51: Railway Traffic Management (M5 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393
WB-52: Parallel Optimization and Scalability (8003 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393
WB-53: Emissions and Heating Sector (8007 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394
WB-54: Demand-responsive public transport 1 (S01 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395
WB-55: Public transportation (S02 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395
WB-56: Advancing mobility towards sustainable solutions IV (S04 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396
WB-57: Scheduling and sustainability (S06 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397
WB-58: Logistics 1 (S07 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397
WB-59: OR in Accounting: Planning, Taxation, and Reporting (S08 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398
WB-60: Job shop scheduling (S09 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399
WB-61: Retail Cooperation and Competition (S10 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400
WB-62: Machine Learning and Optimization in Ports II (S12 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400
WB-63: Risk Management in Private and Public Finance (S14 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401
WB-64: Vehicle Routing Problems With Time Windows (S16 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402

Wednesday, 12:30-14:00
WC-01: Bahar Yetis Kara (Sportshallen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403
WC-02: Portfolio Optimization: Models and Methods (Glassalen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403
WC-04: Applications of Mixed-Integer and Nonconvex Optimization 1 (1001 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403
WC-06: Advancements of OR-analytics in statistics, machine learning and data science 18 (1013 (building: 202)) . . . . . . . . . . . 404
WC-07: Behavior in supply chain collaboration (1019 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405
WC-08: Digitization in Knolwedge, Technology, and Innovation (1020 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405
WC-09: Hydrogen and Electricity Modeling and Regulation I (10 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 406
WC-10: Capacity and treatment planning in healthcare (11 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407
WC-11: Scenarios and foresight practices: Behavioural issues III (12 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408
WC-12: YW4OR_3 (13 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409
WC-13: Sustainable Food Supply (15 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409
WC-15: COVID-19 (2) (18 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410
WC-18: Facilities Routing and Planning in Developing Countries (42 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410
WC-19: OR in Energy II (44 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411
WC-20: Moments in the history of OR 2 (45 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411
WC-21: Infectious diseases and pandemics 2 (49 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412
WC-22: Environment and climate change (81 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413
WC-23: New technology for sustainable supply chains (82 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413
WC-24: Game Theory in Sustainable Supply Chains (83 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414
WC-25: Topics in Combinatorial Optimization I (Contributed) (011 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415
WC-26: Topics in scheduling (Contributed) (012 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415
WC-27: Machine Learning and Ensemble Learning with optimization methods (047 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . 416
WC-28: Advancements of OR-analytics in statistics, machine learning and data science 10 (065 (building: 208)) . . . . . . . . . . . . 417
WC-29: Optimization issues on graphs I (Contributed) (157 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 418
WC-30: Optimization Frameworks (064 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 418
WC-31: Innovations in Digital Assets - IDA (046 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419
WC-32: Computer-Assisted Proofs in Optimization (41 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 420
WC-34: Transportation and Logistics under Uncertainty (43 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 420
WC-35: Robust Optimization: Theory and Applications (44 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421
WC-37: Theory of Multiobjective Optimization (33 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 422
WC-38: Advances in algorithms and applications for linear and convex optimization (34 (building: 306)) . . . . . . . . . . . . . . . . . . 422
WC-39: Financial Modelling (35 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423
WC-40: Experimental economics and game theory 2 (96 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424
WC-41: Nonsmooth optimization algorithms I (97 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424
WC-42: Nonlinear optimization algorithms and applications (98 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425
WC-43: Recent advances on Variational Inequalities and Equilibrium Problems II (99 (building: 306)) . . . . . . . . . . . . . . . . . . . . . 425
699

SESSION INDEXEURO 2024 - Copenhagen

WC-44: MCDA and Composite Indicators: Issues, Advances and Applications 1 (20 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . 426
WC-45: Simulation and Modelling for Decision Support (30 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427
WC-46: Which Solver? (40 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427
WC-47: MCDA in medicine (50 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427
WC-48: DEA methodological developments I (60 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 428
WC-49: Machine scheduling problems (M1 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429
WC-50: E-Commerce (M2 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429
WC-51: Timetabling 1 (M5 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 430
WC-52: Heuristic Algorithms for Combinatorial Optimization Problems I (Contributed) (8003 (building: 202)) . . . . . . . . . . . . . 431
WC-53: Sustainability and Equity in Ecosystems, Ecology and Food (8007 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432
WC-54: Demand-responsive public transport 2 (S01 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432
WC-55: Big data analysis and AI in transportation (S02 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433
WC-56: Railway Applications (S04 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434
WC-57: Forecasting for the middle mile (S06 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434
WC-58: Logistics 2 (S07 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435
WC-59: OR in Accounting: Performance and ESG (S08 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 436
WC-60: Machine Learning in Machine Scheduling (S09 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 436
WC-61: Retail Inventory Management III (S10 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437
WC-62: Container Stacking and Yard Planning III (S12 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438
WC-63: Applications to Economics and Finance (S14 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438
WC-64: Heuristics for Vehicle Routing 1 (S16 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439

Wednesday, 14:30-16:00
WD-01: Maria Besiou (Sportshallen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 440
WD-06: Advancements of OR-analytics in statistics, machine learning and data science 19 (1013 (building: 202)) . . . . . . . . . . . 440
WD-07: Modelling social-behavioural phenomena in creative societies (1019 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 440
WD-08: AI in Knowledge, Technology, and Innovation (1020 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441
WD-09: Hydrogen and Electricity Modeling and Regulation II (10 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 442
WD-10: Admission and discharge (11 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443
WD-11: Heuristics in BOR (12 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443
WD-12: YW4OR_4 (13 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444
WD-13: Reducing Food Waste (15 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444
WD-15: Surgery Scheduling and Operating Room Planning (2) (18 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445
WD-18: Developing Countries and Sustainable Humanitarianism (42 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446
WD-19: OR in Energy III (44 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447
WD-22: Empowering Energy Access (81 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447
WD-23: Information sharing in sustainable supply chains (82 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 448
WD-24: Sustainable supply chains (83 (building: 116)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449
WD-25: Topics in Combinatorial Optimization II (Contributed) (011 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 450
WD-26: Combinatorial optimization issues in transportation (Contributed) (012 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . 450
WD-27: Machine Learning for and with Mathematical Optimization (047 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451
WD-28: Advancements of OR-analytics in statistics, machine learning and data science 11 (065 (building: 208)) . . . . . . . . . . . . 452
WD-29: Optimization issues on graphs II (Contributed) (157 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 452
WD-31: AI for Energy Finance (046 (building: 208)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453
WD-32: Distributed and Federated Optimization (41 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454
WD-34: New problems in logistics under uncertainty (43 (building: 303A)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455
WD-35: Optimization under Uncertainty in Manufacturing and Supply Chain Management (44 (building: 303A)) . . . . . . . . . . . 455
WD-37: Multiobjective Mixed-Integer Linear Optimization (33 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456
WD-38: Advances in polynomial optimization and its applications (34 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456
WD-39: Analysis of Stochastic Models II (35 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457
WD-40: Experimental economics and game theory 3 (96 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458
WD-41: Nonsmooth optimization algorithms II (97 (building: 306)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458
WD-43: Recent advances on Variational Inequalities and Equilibrium Problems III (99 (building: 306)) . . . . . . . . . . . . . . . . . . . . 459
WD-44: MCDA and Composite Indicators: Issues, Advances and Applications 2 (20 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . 459
WD-45: Planning Techniques for Decision Support (30 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460
WD-46: Human Centric Analytics (HCA) (40 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461
700

EURO 2024 - Copenhagen

SESSION INDEX

WD-48: DEA methodological developments II (60 (building: 324)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461
WD-49: Mathematical programming for machine scheduling (M1 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 462
WD-50: Retail Optimization (M2 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 462
WD-51: Transit (M5 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463
WD-52: Heuristic Algorithms for Combinatorial Optimization Problems II (Contributed) (8003 (building: 202)) . . . . . . . . . . . . 464
WD-53: Carbon Taxing and Emissions Controls (8007 (building: 202)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464
WD-54: Railway Capacity Management (S01 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465
WD-55: Supply Chain Network Optimization (S02 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465
WD-56: Last mile delivery with drones (S04 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466
WD-57: Real Option Analysis (S06 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467
WD-58: Logistics 3 (S07 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 468
WD-59: OR in Accounting: Wealth and Risk (S08 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 468
WD-60: Flow shop and single machine scheduling (S09 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469
WD-61: Online, Omnichannel, and Pricing (S10 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469
WD-62: Port Performance (S12 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 470
WD-63: Complexity and Financial Patterns (S14 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471
WD-64: Heuristics for Vehicle Routing 2 (S16 (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 472

Wednesday, 16:30-17:30
WE-01: Charles Corbett [IFORS Distinguished Lecture] (Sportshallen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473

Wednesday, 17:30-18:30
WF-01: Closing Session (Sportshallen (building: 101)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473

701