FR 09:40 AM1 003

Cristian Vega cristian.vega@edu.unige.it

University of Genoa

Cesare, Molinari, Istituto Italiano di Tecnologia MaLGa, DIMA, Universitá degli Studi di Genova; Lorenzo, Rosasco, Istituto Italiano di Tecnologia, MaLGa, DIBRIS, Universitá degli Studi di Genova Center for Brains, Minds and Machines, Massachussets Institute of Technology; Silvia, Villa, MaLGa, DIMA, Universitá degli Studi di Genova

*Iterative regularization for convex regularizers with activation onto a priori information*

Inverse problems are often reduced to solve a system of equations in a stable way with respect to noise in the data. A typical approach to en- force stability and select a meaningful solution is to introduce a reg- ularizer, which is minimized under the constraint given by the system of equations. The regularization function is supposed to be convex, but not necessarily smooth neither strongly convex. In this paper, we propose and study two new iter- ative regularization methods, based on primal-dual algorithm, to solve the problem above. Our analysis, in the noise free case, provides convergence rates for the Lagrangian and the feasibility gap. In the noisy case, it provides stabil- ity bounds and practical early-stopping rules with theoretical guarantees. The main novelty is the exploitation of some a priori knowledge about the solution set, i.e. redundant informa- tion. We discuss various approaches to take advan- tage of the redundant information, that are at the same time consistent with our assumptions and flexible in the implementation. Finally, we illustrate our theoretical findings with numerical simulations for robust sparse recovery and image reconstruction through total variation. We confirm the efficiency of the proposed procedures, comparing the results with state-of-the-art methods.

Optimization for machine learning

OML, Optimization for machine learning; Invited Session
Transportation; Contributed Session

FR 10:00 AM1 Auditorium A

Sinia Vilke sinisa.vilke@pfri.uniri.hr

University of Rijeka, Faculty of Maritime Studies

Jasmin, eli, University of Rijeka, Faculty of Maritime Studies; Frane, Tadi, University of Rijeka, Faculty of Maritime Studies; Borna, Deli, University of Rijeka, Faculty of Maritime Studies

*Decision support system in urban traffic management*

Traffic congestion is an unavoidable problem in large cities. Therefore, traffic management centers must respond quickly to traffic disruptions and apply the most appropriate measures to solve problems in the traffic network. The causes leading to traffic congestion are usually related to regular or extraordinary traffic events and traffic condition. Due to a regular traffic event such as a traffic collision on the main traffic corridor, illegal parking and emergency interventions, actions such as detection, closure and speed limits are mostly used. During an extraordinary traffic event such as manifestations or roadwork, applied actions include the partial or complete closure of the road, change of route, change of travel methods, etc. Solving this complex task requires not only a quick response, but also a lot of expertise given the large number of possible actions that need to be considered during the decision-making process. Therefore, simulation models or decision support tools are increasingly used. In this project, the simulations are intended to demonstrate the possibilities of using simulation tools in the decision-making and traffic management process as innovative methods within intelligent solutions for traffic management in cities. The purpose of prototyping is to test innovative scenarios and standard operating procedures that will be used to support decision making. The prototype created in this way can be further developed through validation and calibration procedures to obtain a real traffic situation in an area, based on which different solutions affecting traffic flow can then be tested. As part of the development activities of the CEKOM Connected Traffic scientific research project, an advanced information and communication system is being developed to support decision-making and urban traffic management. The improvement of traffic management within this project will also have a positive impact on the development of mobility and the increase of traffic safety in the city of Rijeka. The innovation of this platform is reflected in its approach to connectivity and Big Data analytics, as well as its real-time traffic management. In this paper, the authors focus on testing innovative scenarios and standard operating procedures for the purpose of decision support to demonstrate the effectiveness of traffic management in urban areas by applying innovative solutions (modeling and simulation). The development of the simulation model in terms of functionality and sus-

Transportation; Contributed Session
tainability covers the narrower and wider urban area of Rijeka and the application of a multi-layer modeling methodology that integrates macro and micro level traffic simulations. Reliable and accurate measurement of vehicle load on individual roads was used to create the simulation. Since the simulation will be validated with some new traffic routes, new realistically calculated traffic load data is expected as one of the outputs of the project. By applying intelligent traffic solutions and information and communication technology, it is possible to integrate large amounts of processed data through the platform, thus achieving the validation of the prototype and the generation of traffic conditions, which increases the quality of traffic management.

* The paper is the result of research activities of the scientific project Connected Traffic implemented within CEKOM for smart cities (CEKOM Center of Competence for smart cities, the city of Rijeka), funded by the EU ESIF fund, started in March 2020, and ends in March 2023.
## FR 10:20 AM1 Auditorium A

Martina Fischetti Martina.FISCHETTI@ec.europa.eu

Joint Research Center

Aycart, Javier, Joint Research Center; Duma, Davide, University of Pavia; Gualandi, Stefano, University of Pavia; Ibáñez, Juan Nicolás, Joint Research Center; Tomasi, Claudio, University of Pavia

*How the European Union uses Operations Research for transport analyses*

One of the main European Union (EU) policy priorities under the European Green Deal is to achieve climate neutrality by 2050. Transport is a key player in that task, as it is a major consumer of energy, and extensive contributor to greenhouse gas emissions. In this context, the EU mandate includes the monitoring of the performances of both public transport and road transport across the EU member states. This is key to be able to inform the relevant policy decisions on the topic. One of elements of analysis that the EU employs is the use of comprehensive data to compute performance and accessibility-to-opportunities metrics associated with different types of transport. Underneath these measures lies a number of operations research (OR) challenges. The road transport analysis, for example, is a routing problem defined on (very) large networks. Moreover, if traffic information is considered, the problem turns into a time-dependent routing problem. In this presentation we will give an overview of some of the OR transport-related challenges faced by the EU and some of possible solution methods proposed by the Joint Research Center (JRC) of the European Commission. The work presented is developed in collaboration with the Department of Mathematics of the University of Pavia.

Transportation; Contributed Session
FR 09:00 AM1 Auditorium A

Maurizio Bruglieri maurizio.bruglieri@polimi.it

Politecnico di Milano

Peruzzini, Roberto, Università Politecnica delle Marche; Pisacane,

Ornella, Università Politecnica delle Marche

*Optimizing a real-time carpooling service*

According to a recent survey, in Europe, the average occupancy rate of most frequent car trips is 1.7 persons/car (including the driver). Moreover, this value has decreased over the last decades. This decreasing trend implies an increase in the number of private cars on the road and then, in the traffic congestion. In addition, more cars on the road also means an increase in the harmful emissions. A possible solution to this problem, more flexible than public transport, is given by carpooling. Carpooling (also known as ridesharing) refers to a mode of transportation in which travelers share a vehicle for a trip with others that have similar itineraries and time schedules, and split travel costs (e.g., those due the fuel consumption). Carpooling is a service that can combine the flexibility and speed of private cars or taxi services with the reduced cost of fixed-line systems. Besides saving travel costs, carpooling also allows reducing the number of circulating cars and thus in mitigating traffic congestion and reducing air pollution. By effectively using new communication technologies, such as smartphone and global positioning system (GPS), there are some attempts to enable real-time ridesharing systems, i.e., a system which supports an automatic ride-matching process among participants on very short notice or even en-route. In this work, we want to investigate the benefit of using autonomous vehicles in a real-time ridesharing service. Indeed, if on one hand, such a service has the clear advantage of not requiring any participant that acts as a driver, on the other hand, it requires that the routes of each vehicle are planned by a central system to optimize simultaneously different objectives such as the total number of passengers served and the total traveled distance. Then, this central system acts as a dispatcher of the requests and provides the users with their assignment to a vehicle that may already have passengers on board. In the assignment process, such a dispatcher has to take into account the level of service and then, to avoid situations in which passengers already on board make a long detour in order to allow the vehicle also serving other users. The resulting decision problem is a challenging Vehicle Routing Problem. To solve it, we propose a mathematical programming based approach in which, in each iteration, a lexicographic Mixed Integer Linear Programming model is solved in order to find partial solutions. Finally, in order to address real-life instances in reasonable computational time, we also develop a metaheuristic in which we combine moves taken from the literature with those ad hoc designed for the problem. Preliminary numerical results on instances derived from a carpooling service in Mel-

Transportation; Contributed Session
bourne show the effectiveness of the proposed metaheuristic compared to
the mathematical programming based approach.

Transportation; Contributed Session
FR 09:20 AM1 Audtorium A

Giusy Macrina giusy.macrina@unical.it

University of Calabria

Archetti, Claudia, ESSEC Business School in Paris; Guerriero, Francesca,

University of Calabria

*Bundles generation and pricing in crowdshipping*

In this work we investigate the crowdshipping paradigm, an innovative
framework of delivering which is gaining more and more success in the last
mile delivery context. The main idea of crowdshipping is to exploit the
capacity of ordinary people, namely occasional drivers (ODs), who offer
their own vehicles and free time for performing shipments, allowing the
delivery companies to provide a more flexible, faster and cheaper delivery
service. Deciding which customer requests should be served by the in-
ternal own fleet of vehicles and which, instead, should be assigned to the
ODs, is a challenging issue, as well as determining the compensation that
should be offered to the ODs to minimize the expected total distribution
cost. Hence, we develop a bundle-based strategy to decide how many, and
which parcels should be assigned to the ODs. We then focus on matching
and pricing problems, with the purpose of maximizing the numbers of
assigned parcels and minimizing the costs. We propose a pricing scheme
to determine the compensation for the ODs. We also assume that each
OD has a willingness to serve function, that is a compensation for which
the OD will accept to deliver a bundle. Hence, we simulate two auction
strategies: a static and a dynamic one. We carry out an exhaustive com-
putational study to compare the pricing strategies and the two auctions,
outlining several managerial insights.
FR 09:40 AM1 Auditorium A

Amparo Mármol amarmol@us.es

Universidad de Sevilla

Diego, Borrero, Universidad de Sevilla; Miguel Angel, Hinojosa,

Universidad Pablo de Olavide de Sevilla

*A pick-up rule for sharing transportation costs in itinerant events*

Fixed route problems model situations in which a company must visit several customers located in different places, traveling along a fixed route that starts and ends at the same point. When the objective of the fixed route is the transportation of material and the installation of equipment for the celebration of itinerant events, there are certain differentiated characteristics that must be taken into account when allocating the corresponding transportation costs. In this paper, we firstly propose some cost-sharing rules for the fixed route problems that represent these situations. Subsequently, we introduce a TU game representing the costs that the different coalitions should bear in these situations, the pick-up game. We prove that pick-up games are convex, thus the existence of stable allocations is assured. Moreover, one the cost-sharing rules previously proposed is highlighted in this context the pick-up rule, since we prove that it coincides with the Shapley value, the nucleolus and the tau-value of the pick-up game.
Continuous and Multiobjective optimization; Contributed Session

FR 10:00 AM1 Auditorium B

Marco Premoli marco.premoli@unimi.it

Department of Computer Science, Università Degli Studi di Milano Ceselli, Alberto, Department of Computer Science, Università Degli Studi di Milano

*Comparing digital and quantum annealing heuristics: the cardinality constrained quadratic knapsack case*

In recent years, several optimization solvers inspired by quantum annealing have been developed, either running on actual quantum hardware or simulating it on classical digital computers. They all provide heuristic solutions for so-called Ising models, which are equivalent to quadratic unconstrained binary optimization problems (QUBO). In this work we benchmark the performance of different solvers for the resolution of the cardinality constrained quadratic knapsack problem (kQKP), which extends QUBO models in a minimal way, including only one inequality and one equality constraint. The solvers we benchmark are of three types: quantum annealing on quantum hardware, quantum-inspired heuristics designed for dedicated digital hardware, probabilistic algorithms for general purpose digital hardware. We test both the use of these solvers to directly provide a primal feasible solution and their use as sub-routine within a heuristic on digital computers. We compare these heuristic solutions with those retrieved by an ad-hoc heuristic and with the dual bound provided by a Dantzig-Wolfe decomposition from literature. We perform extensive computational analyses covering the following aspects: (i) how to obtain effective formulations, mapping the kQKP to a pure QUBO, (ii) which algorithmic steps on digital hardware are useful to enhance the solutions of the solvers, and (iii) how to tune the solvers by parametric analysis. In particular, the reformulation of the problem as a QUBO faces two main choices: how to reformulate the linear constraints and how to encode slack integer variables as a set of binary ones. We explore both the option of reformulating constraints as equalities and penalizing their squared violation in the QUBO, and traditional Lagrangian relaxation with multipliers update. We also explore different encodings of the integer variables. Our results may help to understand strengths and weaknesses of quantum annealers in facing constrained quadratic combinatorial problems.
FR 10:20 AM1 Auditorium B

Annarita De Maio annarita.demaio@unical.it

Department of Economics, Statistics and Finance Giovanni Anania,
University of Calabria (Italy)

Musmanno, Roberto, Department of Mechanical, Energy and Man-
agement Engineering, University of Calabria (Italy); Vocaturo, Francesca,
Department of Economics, Statistics and Finance Giovanni Anania,
University of Calabria (Italy)

*Optimization techniques for the risk mitigation in the hazardous ma-
terial truck transportation*

The transportation of hazardous material (hazmat) involves the move-
ment of freight representing a high risk to health, safety, or environment.
For this reason, this topic has gained great popularity in the last decades
within the scientific community, to preserve the safety of people involved
in the transportation activities, as well as to pay more attention on en-
vironmental issues. Indeed, the incidents involving hazmat vehicles may
cause extensive damages to the affected communities, because of the re-
lease of dangerous substances. Due to its nature, hazmat transportation
is regulated by strict laws and must be treated separately from classi-
cal transportation. From optimization perspective, the risk assessment of
the service plays a central role. The problem under consideration refers
to road transportation of hazmat and can be treated as a variant of the
well-known Hazmat Vehicle Routing and Scheduling Problem with Time
Windows (HVRPTW). It consists in defining the routes performed by a
logistics operator with a given fleet of vehicles for the hazmat transporta-
tion by considering time windows constraints for the customers and the
depot. The risk measurement is modelled as an arc attribute by balancing
different criteria: accident probability, population density, distance from
fire stations, type of road surface, traffic conditions, etc. Therefore, the
time horizon of the service is divided in different time slots and a time-
expanded network is built. The risk attribute can vary within the different
slots. The variant of the HVRPTW is modelled as a three-objective op-
timization problem to be minimized. The three objective functions refer
to the total risk, the total service time, and total vehicle cost. To deter-
mine a Pareto optimal solution of the problem, the ε-constraint method is
applied. In the experimental phase, we consider a road network in North-
ern Italy, where a logistics operator has to carry cylinders of compressed
oxygen in order to serve a group of customers with known demand level.
Managerial insights deriving from the experimental phase are presented.

Continuous and Multiobjective optimization; Contributed Session
FR 09:00 AM1 Auditorium B

Milica Jovanovi-Vujatovi vesna.jankovic-milic@eknfak.ni.
ac.rs

Innovation Centre, University of Ni, Ni, Republic of Serbia
Jankovi-Mili, Vesna, Faculty of Economics, University of Ni, Ni, Re-
public of Serbia; Milanovi, Sandra, Innovation Centre, University of
Ni, Ni, Republic of Serbia; Jovanovi-Vujatovi, Milica, Innovation
Centre, University of Ni, Ni, Republic of Serbia

*Econometrics analysis of multidimensional urban poverty determinants*

Urban poverty differs from rural poverty in terms of demographic,
economic and infrastructural aspects in conditions of accelerated urban-
ization. Poverty can be, also, viewed as an absolute and relative category,
respecting its subjective and objective dimensions. This is an issue that
is closely related to the issue of sustainable development, sustainable eco-
nomic growth and sustainable employment. As a part of the 2030 Agenda
for Sustainable Development adopted in 2015 which includes 17 Sustain-
able Development Goals (SDGs), the United Nations General Assembly
promoted the number one SGD: No Poverty, closely related to the second
SGD: Zero hunger. One of the targeted indicators until 2030 is to mitigate
poverty in every country by increasing a persons daily living budget above
1,25 USD. Additionally, cutting down by half the number of deprived per-
sons in all segments of poverty defined by the national regulation by 2030
is the second most important aim of the SGD1. Having this in mind, the
aim of the paper is to consider the determinants of urban poverty in the
Republic of Serbia (later referred to as Serbia) in order to identify the fac-
tors of poverty reduction. Serbia is on its way to the transformation from
a state-controlled to a market-oriented economy in order to enter Euro-
pean Union (EU). The process of profound social and economic changes
is evolving and the transition caused significant effects on social welfare.
Although the number of people at risk of poverty or social exclusion in
Serbia is declining since 2014 from 42.6 to 29.8 per cent in 2020, it is still
above the EU27 average by 7.8 percentage points to Eurostat. Moreover,
the most threatened categories of poverty and social exclusion are females
(30.6%) compared to men (29%), and people between 55 and 64 years
old (33.6%). Therefore, the profile and dynamic aspect of urban poverty
will be explored. Specifically, the determinants of urban poverty in Serbia
will be examined. The research methodology will include panel data re-
gression analysis. The input of the analysis will be data obtained by the
Statistical Office of the Republic Serbia and collected for the purpose of
the Survey on Income and Living Conditions SILC for the period from
2015 to 2020. The data will cover four domains at the household level: (1)
basic data, (2) housing, (3) material deprivation and (4) income, and five
domains at the personal level: (1) basic/demographic data, (2) education,

Continuous and Multiobjective optimization; Contributed Session
(3) health, (4) labour and (5) income. The paper contributes to defining the concept of urban poverty and measuring it, as well as understanding the differences in relation to rural poverty. The ultimate goal of the analysis is to provide recommendations to policymakers in order to create successful strategies for reducing urban poverty and poverty risk.
FR 09:20 AM1 Audtorium B

Bo Peng bo.peng@univie.ac.at

University of Vienna

Bomze, Immanuel, University of Vienna

Conic formulation of QPCCs applied to truly sparse QPs

We study (nonconvex) quadratic optimization problems with comple-
mentarity constraints, showing an exact completely positive reformulation
of it under — apparently new — mild conditions involving only the con-
straints, not the objective. Moreover, we also give the conditions for strong
conic duality between the obtained completely positive problem and its
dual. Another novelty of our approach is a purely continuous model which
avoids any branching or use of large constants in implementation. An
application on pursuing interpretable sparse solutions of quadratic op-
timization problems is shown to satisfy our settings, and therefore we
link quadratic problems with an exact sparsity term $||x||_0$ to copositive
optimization. The covered problem class includes sparse least-squares re-
gression under linear constraints, for instance. Numerical comparisons
between our method and other approximations are reported from the per-
spective of function value.
FR 09:40 AM1 Auditorium B

Emanuele Pizzari emanuele.pizzari@uniroma2.it

Università degli Studi di Roma Tor Vergata
Caramia, Massimiliano, Università degli Studi di Roma Tor Vergata

Bilevel Formulations for Municipal Waste Management

We propose two bilevel models to cope with problems concerning municipal waste management. The model is a facility location and transportation problem. The upper-level decision maker is a municipality, interested in defining a proper network for managing waste. The municipality needs to locate sorting facilities, and landfills and incinerators. On the other hand, the lower-level decision maker is a company in charge of transporting waste from collection points to sorting centres, and, finally, from sorting centres to either landfills or incinerators. The municipality wants to reduce the overall emissions, as well as the impact of facilities on peoples health and land, while the company wants to reduce the transportation costs.

The first proposed formulation stems from a multi-objective version of the problem present in the literature. We developed a theorem that proves that the aforementioned model works under the hypothesis of homogeneous transportation cost per arc. Taking into account this theoretical finding, we define a second formulation that allows more network design flexibility. For both models we provide the optimistic and the pessimistic versions. We validate these formulations on a case study in the Pathum Thani province in Thailand. Moreover, we compare the results obtained by the bilevel formulation to those obtained by a multi-objective version of the problem.

Finally, we provide our thoughts on future applications in this field.

Continuous and Multiobjective optimization; Contributed Session
# Supply Chains and Inventory; Contributed Session

FR 11:30 AM2 001

Matteo Cosmi matteo.cosmi@uni.lu

University of Luxembourg

Arts, Joachim, University of Luxembourg; Klosterhalfen, Steffen,

BASF SE

*Mathematical programming models for managing the profitability-sustainability trade-off in complex chemical value chains*

Limiting the effects of global warming and climate change is one of the main objectives that the international community has set for the next decades. Therefore, several countries approved laws legally binding them to achieve net-zero targets within the next 25-30 years. One of the major greenhouse gases emitted by human activity is carbon dioxide (CO2) which accounts for more than 75% of the global greenhouse gas emissions. Industry and transport sectors account for more than 35% of the global emissions and there is increasing pressure on industry to pledge net-zero emissions. To remain competitive in their markets while reducing their emissions, companies need to re-optimize their entire supply chain focusing not only on financial key performance indicators (KPIs), such as costs, but also on non-financial KPIs, in particular greenhouse gas emissions. In this work we propose two linear programming models to optimize a deterministic multi-objective supply chain problem aimed at minimizing CO2 emissions and their related costs. We perform an extensive campaign of tests motivated by a real-world industry setting to compare the efficiency of the two model formulations and to analyze the differences with the former supply chain structure when emissions were not a primary concern.
FR 11:50 AM2 001

Giorgio Romanin Jacur giorgio.romaninjacur@unipd.it

Dept of Management and Engineering, University of Padova, Italy
Arturo, Liguori, Dept of Management and Engineering, University
of Padova, Italy

*Generalized Model of a Production System with Fixed Supplies and Deliveries*

Let us consider a generic production system; at a look from the outside, we see that it gets raw materials from its suppliers and delivers finished products to its customers; raw materials may be either stocked in a buffer, to be worked later, or immediately taken as semifinished products and addressed to the workshop; inside the workshop semifinished products are subjected to one or more successive operations, with the scope of creating final products requested by the customers at given delivery times; every operation may require specific technical resources (rooms, work benches, machines, work islands, etc.) and human resources (operators with different skills and tasks). The problem daily faced by managers consists in scheduling all operations in order to minimize total cost by satisfying all customers and respecting constraints imposed by resources and raw materials availability. Such a problem becomes more and more complex if final products are many, some final products can be obtained by different alternative working procedures, and/or from different raw materials, by means of different operations and related resources engagement, some resources can be engaged to work different semifinished products; moreover sometimes we cannot precisely forecast supplies, and sometimes customers requests may vary after the requested products working procedures have already started (such accidents happen in particular in the food field and when the system works on commission). Here we suggest an abstract metamodel which can describe such complex systems and permits to obtain an operation schedule which optimizes their management also in dynamic conditions. We may consider the environment and the following sets: semifinished products set, process set, resources set. Semifinished products are all objects entering the system, being subjected to operations and possible pauses inside it and finally delivered; every semifinished product is uniquely defined according to its physical characteristics (materials, shape, size, weight, etc.) and may be present in the system in lots of any size in different times: every lot is characterized by number of units and arise time, related either to entering from the environment or to production by an operation. Every process is uniquely defined by the performed operation, the input and output semifinished products, the engaged resources, and the related respective times: the possible adjustment time before the operation start, the production time, the amounts of input and output products per production unit time, the amounts of engaged resources during the adjustment and/or during the production; two pro-

Supply Chains and Inventory; Contributed Session
cesses are different if any of the above definition characters is different; the same process may be iteratively activated in different times. The environment supplies semifinished products, either directly through suppliers in the form of raw materials or indirectly through buffers, and requests semifinished products, to be delivered either to customers in the form of finished products or to be stored in buffers. System structural parameters are stored in matrices reporting processes characters; problem data include products forecasted supplies, products requested deliveries and resource availability in time; decision variables include all processes activations start and end time, related to input and output products amount and to resources amounts. The management problem solution shall respect all constraints due to available semifinished products and available resources in time and obtain the minimum total cost. An optimal solution can be obtained by specialized packages for small problem size, while a heuristic method giving an acceptable suboptimal solution, adopting algorithms coming from the project management field is suggested for large problem size. An application in the food production and distribution field is reported.
FR 12:10 AM2 001

Giuseppe Stecca giuseppe.stecca@iasi.cnr.it

Istituto di Analisi dei Sistemi e Informatica "Antonio Ruberti" IASI-CNR.

Massimiliano, Caramia, Dipartimento Ingegneria dell'Impresa "Mario Lucertini", Università di Roma Tor Vergata

*A model and a solution approach for optimal green investment in a two stage supply chain*

In order to contrast climate change, governments established strong chronological paths to reach emission reduction in the next years. The European Commission recently deployed the so called European Green Deal, i.e., a set of policy initiatives aimed at overarching the carbon neutrality in 2050 and achieving a reduction of 55% of net Green House Gas (GHG) within 2030. While clean technologies can help to decrease environmental impact, investment programs must be carefully planned to reach the green targets and maintain or improve industrial efficiency. This is extremely important in supply chains where industrial technologies have a complex impact on efficiency, service level, and emissions of entire production and distribution chains. Moreover, investments in green technologies to increase sustainability in supply chains has become a common practice also because the awareness of consumers on green products is continuously increasing and, therefore, people are more and more willing to buy green products in place of non-green ones. Therefore, even if there are higher costs in producing with green technologies and processes, there is also a higher mark up on the price of products which rewards the former costs. In this work, we propose a multi-period mathematical model for deciding the optimal chronological allocation of a given investment budget on production technologies and on environment protection to meet an aggregate demand in a two layered supply chain. The model has two objectives and non-linear constraints. The main decisional variables are the flow of products from suppliers to facilities, and the environment protection investment for each facility and for each planning period. The objective function measures the total cost of CO2 emission produced by the plants over time plus the cost associated with the investment over time. Other than flow and capacity constraints, a non-linear constraint defines the relation between the investment made until a certain time, for each facility, and the emission for unit of flow at that time, for the same facility. Being the model very difficult to solve by means of a commercial solver, we propose lower bounds and an approximate solution approach. The proposed model and algorithms have been tested on synthetic instances properly generated to evaluate their ability in finding effective schedules of the investments in a given planning horizon.

Supply Chains and Inventory; Contributed Session
FR 12:30 AM2 001

EP Mezatio eric_papain.mezatio@utt

University of Technology of Troyes (UTT), Troyes, France
MM., Aghelinejad, University of Technology of Troyes (UTT), Troyes, France; L., Amodeo, University of Technology of Troyes (UTT), Troyes, France; I., Ferreira, Institut français du textile et de l'habillement(IFTH), Troyes, France

*Design forward and reverse closed-loop supply chain to improve economic and environmental performances*

This paper focuses on modelling and solving the problems related to fourlevel supply chain (SC) management with reverse logistics, from the supplier to the retailers and the recycling center. A new mathematical integer programming model has been developed. This model can be used as a decision support tool for all types of industries that want to improve their economic and environmental performances. In this study, we addressed specially the case of the textile industry. The results show that by recycling and reusing recycled resources, the textile industry can reduce its CO2 emissions up to 42.5%, for an additional investment of 34.19%, with a carbon price of 86 per ton of emissions. While, without recycling and for the same carbon price, industries would invest 68.73% more, for an emissions reduction of 12.74%.
Best AIROYoung Dissertation 2022

FR 11:30 AM2 002

Anna Livia Croella annalivia.croella@uniroma1.it

Sapienza - University of Rome

*Real-time Train Scheduling: Reactive and Proactive Algorithms for Safe and Reliable Railway Networks*

Train scheduling is a critical activity in rail traffic management, both off-line (timetabling) and on-line/in real-time (dispatching). The research aims at the design and development of advanced optimization models and methods for the real-time re-scheduling. The study covers two overlapping areas and defines two different but somewhat complementary research questions:

(i) in case of disturbance, how can we quickly generate a new feasible plan?

(ii) in case of disruption (or partial re-scheduling information), how can we add an additional level of safety to the system?

To answer the first question, we exploited and extended the Dynamic Discretization Discovery (DDD) paradigm, recently introduced by Boland for the continuous time service network design problem. The result is a restricted Time-Indexed formulation for train

traffic re-scheduling problem and an algorithm to solve it with running times comparable with the best alternatives presented in the literature. Furthermore, the implemented procedure does not suffer by any approximation error introduced by a standard time discretization and can be applied to the broader field of job-shop scheduling. This work has been presented at a renowned railway conference (ICROMA).

The second research issue is answered by formulating a Safe Place Assignment Problem. This new model is tested on a set of instances provided by a large Class I U.S. railroad company, demonstrating how it can be used effectively in a real-world context. The relative work has been published on a top tier Operation Research journal whose main focus lays on transport (Transportation Science).

Both research outputs are very innovative and off the beaten track. The achievements regards both the theoretical and methodological point of view and present in addition a practical relevance.

Keywords: Real-time Train Rescheduling; Dynamic Discretization Discovery; Disruption Management.
FR 11:50 AM2 002

Daniel Faccini daniel.faccini@unibg.it

University of Bergamo

*Models and Approximations for Optimization Problems under Uncertainty with Applications to Support Vector Machine and Revenue Management*

In this thesis, we deal with decision making problems plagued by the presence of uncertainty. The first problem we analyze aims at linearly separating two sets of points that have non-disjoint convex closures. To solve this classification task, we formulate robust Support Vector Machine (SVM) models with uncertainty sets in the form of hyperrectangles or hyperellipsoids, and propose a moment-based Distributionally Robust Optimization (DRO) model enforcing limits on first-order deviations along principal directions. The efficiency of the new classifiers is evaluated on real-world databases. Numerical experiments show that considering uncertainty explicitly in the models leads to better solutions with respect to the ones provided by the corresponding deterministic program. Despite the wide applicability of DRO models, this class of problems is notoriously difficult to solve. For this reason, secondly, we propose approximation techniques that provide (lower and upper) bounds on the optimal value for DRO problems. This is done through scenario grouping and using the ambiguity sets associated with -divergences and the Wasserstein distance. Numerical results on a multistage mixed-integer production problem show the efficacy of the novel bounding schemes through different choices of partition strategies, ambiguity sets, and levels of robustness. Thirdly, the last problem we investigate aims at detecting the optimal assortments a retailer shall offer to the market with the aim of maximizing expected profits, when strong preferences among products are observed. This Revenue Management (RM) problem with dominated alternatives is firstly modeled through stochastic dynamic programming, which becomes intractable for instances with a large number of resources. To deal with this curse of dimensionality we recover a compact and tractable deterministic approximation and present preliminary numeric results.

Keywords: Support Vector Machine; Robust Optimization; Distributionally Robust Optimization; Revenue Management.
FR 12:10 AM2 002

Serena Fugaro s.fugaro@iac.cnr.it

Institute for Applications of Calculus "Mauro Picone" - National Research Council of Italy

*Optimizing and Reoptimizing: Tackling Static and Dynamic Combinatorial Problems*

In this thesis both static and dynamic problems of Operations Research are addressed by either designing new procedures or adapting well-known algorithmic schemes. Specifically, three variants of the well-known Shortest Path Problem (SPP) and a Scheduling Problem in 3D printing are studied.

Firstly, we dealt with the Reoptimization of Shortest Paths, in case of multiple and generic cost changes. We implemented an exact primal-dual procedure, and compared its performance with Dijkstra's label setting procedure to detect the approach to prefer, according to the graph features and the type of cost perturbation.

Secondly, the k-Color Shortest Path Problem is tackled: it is a recent problem, defined on an edge-constrained graph, for which we proposed a Dynamic Programming algorithm; its performance is compared with the state of the art solution approach, namely a Branch & Bound procedure.

Finally, the Resource Constrained Clustered Shortest Path Tree Problem is presented. It is a newly defined problem for which we detail both a mathematical model and a Branch & Price procedure; moreover, the performance of this solution approach is compared with that of CPLEX solver.

Furthermore, the first part of the dissertation addresses the Path Planning in Urban Air Mobility, considering the definition of the Free-Space Maps and the computation of the trajectories. For the former purpose, three different but correlated discretization methods are described; as for the latter, a two steps resolution of the resulting shortest path problems is performed, and it is checked if the reoptimization algorithm is suitable for the second step.

The last part of the thesis is devoted to the recently studied Additive Manufacturing Machine Scheduling Problem with not-identical machines. Specifically, a Reinforcement Learning Iterated Local Search meta-heuristic featuring a Q-learning Variable Neighbourhood Search is proposed to solve this problem and its performance is compared with the one of CPLEX solver.

It is worthwhile mentioning that, for each of the proposed approaches, a thorough experimentation is performed and each Chapter is equipped with a detailed analysis of the results in order to appraise the performance of the method and to detect its limits.

Keywords: Constrained Shortest Path Problem; Combinatorial Opti-
mization; Additive Manufacturing Machine Scheduling.
FR 12:30 AM2 002

Matteo Lapucci matteo.lapucci@unifi.it

DINFO, Università di Firenze

*Theory and Algorithms for Sparsity Constrained Optimization Problems*

The dissertation is concerned with mathematical optimization problems where a sparsity constraint appears. The sparsity of the solution is a valuable requirement in many applications of operations research. Several classes of very different approaches have been proposed in the literature for this sort of problems; when the objective function is nonconvex, in presence of difficult additional constraints or in the high-dimensional case, the problem shall be addressed as a continuous optimization task, even though it naturally has an intrinsic combinatorial nature.

Within this setting, we first review the existing knowledge and the
theoretical tools concerning the considered problem; we try to provide a
unified view of parallel streams of research and we propose a new gen-
eral stationarity condition, based on the concept of neighborhood, which
somehow allows to take into account both the continuous and the combi-
natorial aspects of the problem.

Then, after a brief overview of the main algorithmic approaches in the
related literature, we propose suitable variants of some of these schemes
that can be effectively employed in complex settings, such as the noncon-
vex one, the derivative-free one or the multi-objective one. For each of the
proposed algorithms we provide a detailed convergence analysis showing
that these methods enjoy important theoretical guarantees, in line with
the state-of-the-art algorithms.

Afterwards, exploiting the newly introduced concept of stationarity,
we propose a completely novel algorithmic scheme that, combining con-
tinuous local searches and discrete moves, can be proved to guarantee
stronger theoretical properties than most approaches from the literature
and to exhibit strong exploration capabilities in a global optimization
perspective.

All the proposed algorithms have finally been experimentally tested
on a benchmark of relevant problems from machine learning and decision
science applications. The computational results show the actual quality
of the proposed methods when practically employed.

Keywords: Nonlinear Optimization; Sparsity; Convergent Algorithms.
# MLO4EMD, Machine learning-based optimization for extreme metamaterials design; Invited Session

FR 11:30 AM2 003

Amir Darabi amir.adarabi@gmail.com

Georgia Institute of Technology

Leamy Michael, Georgia Institute of Technology

*Designing a broadband GRIN lens through a deep learning based data-driven method*

Controlling flexural waves in thin plates is a challenging task with implications for a variety of applications in acoustic problems. One of the most fascinating problems in such medium is optimizing the amount of focused wave intensity in the focal area for gradient-index (GRIN) acoustic lenses [1], for energy harvesting purposes. Recently, machine learning techniques have been utilized extensively to solve variety of problems in physical sciences and engineering. As such, we use numerically produced data to predict the shape of a discrete lens, formed from a lattice of circular scatterers in a thin aluminum plate, for given focal shapes. For each of these lenses, the inclusion diameters and their locations are varied randomly to obtain different lens shapes. In support of such concepts, twenty one thousand different lens shapes are considered, and the corresponding wave fields are computed numerically using multiple scattering formulation. Next, nineteen thousand of these recorded wavefields (each of them 200 * 200 pixels) are input into a ResNet [2] with twenty layers to train the convolutional neural network to predict the shape of the lens. In addition, the last two thousand samples are used as the test set to evaluate the performance of the network. In order to train the network, ADAM optimizer algorithm with the learning rate of 0.001 is chosen, along with the categorical crossentropy as the loss function, and batch size of thirty two. After running the algorithm for three hundred iterations (each with fifty epochs) the designed network successfully is trained with the loss of 1E-4 and the accuracy of 0.987 percent. Next, the performance of the trained network is tested with the two thousand validation test, to obtain the accuracy of 0.97 with the loss being equal to 2E-3. Finally, three different wave field responses are input to the trained network to predict the shape of the desired lens. The proposed idea herein can be extended to other media such as phononic devices, air, Bulk waves, electromagnetic waves, optical Bulk waves and photonics by changing the phase velocity of the medium.

[1] Darabi, Amir, and Michael J. Leamy. "Analysis and experimental validation of an optimized gradient-index phononic-crystal lens." Physical Review Applied 10.2 (2018): 024045. [2] He, Kaiming, et al. "Deep resid-
ual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
FR 11:50 AM2 003

Danilo Macciò danilo.maccio@cnr.it

Institute of Marine Engineering, National Research Council of Italy
Francesco, Rebora, Institute of Marine Engineering, National Re-
search Council of Italy; Cristiano, Cervellera, Institute of Marine
Engineering, National Research Council of Italy

*Voronoi recursive binary trees for the optimization of nonlinear functionals*

We propose an algorithm for the approximate solution of general non-
linear functional optimization problems through recursive binary Voronoi
tree models. Unlike typical binary tree structures commonly employed
for classification and regression problems, where splits are performed par-
allel to the coordinate axes, here the splits are based on Voronoi cells
defined by a pair of centroids. Models of this kind are particularly suited
to functional optimization, where the optimal solution function can eas-
ily be discontinuous even for very smooth cost functionals. In fact, the
flexible nature of Voronoi recursive trees allows the model to adapt very
well to possible discontinuities. In order to improve efficiency, accuracy
and robustness, the proposed algorithm exploits randomization and the
ensemble paradigm. To this purpose, an ad hoc aggregation scheme is
proposed. Simulation tests involving various test problems, including the
optimal control of a crane-like system, are presented, showing how the
proposed algorithm can cope well with discontinuous optimal solutions
and outperform trees based on the standard split scheme.

MLO4EMD, Machine learning-based optimization for extreme
metamaterials design; Invited Session
FR 12:10 AM2 003

Giorgio Gnecco giorgio.gnecco@imtlucca.it

IMT School for Advanced Studies, Lucca
Andrea, Bacigalupo, Università di Genova; Maria Laura, De Bellis,
Università di Chieti-Pescara; Federico, Nutarelli, IMT School for
Advanced Studies, Lucca

On dispersion curve coloring

A novel automatic technique for smooth curve coloring - i.e., curve identification - in the presence of curve intersections is presented. Formally, the proposed algorithm aims at minimizing the summation of the total variations over a given interval of the first derivatives of all the labeled curves, when these are written as functions of a scalar parameter. The algorithm develops the one proposed in [1]. Specifically, it is based on a first-order finite difference approximation of the curves and a prediction/correction steps sequence. At each step, the predicted points are attributed to the subsequently observed points of the curves by solving a Euclidean bipartite matching subproblem. A comparison with a more computationally expensive dynamic programming formulation is analyzed. The proposed algorithm is applied to mechanical filters made of metamaterials, also known as metafilters [2]. Its output is shown to be in excellent agreement with the desired smoothness and periodicity properties of the metafilter dispersion curves. Possible developments, including those based on machine-learning techniques, are pointed out.

[1] Gnecco, G.: An algorithm for curve identification in the presence of curve intersections. Mathematical Problems in Engineering, vol. 2018, article ID 7243691, 7 pages (2018). [2] Bacigalupo, A., Gnecco, G., Lepidi, M., Gambarotta, L.: Computational design of innovative mechanical metafilters via adaptive surrogate-based optimization. Computer Methods in Applied Mechanics and Engineering, vol. 375, article ID 113623, 22 pages (2021).

MLO4EMD, Machine learning-based optimization for extreme metamaterials design; Invited Session
# Transportation; Contributed Session

FR 11:30 AM2 Auditorium A

Francesca Vocaturo vocaturo@unical.it

Università della Calabria - Dipartimento di Economia, Statistica e Finanza Giovanni Anania

Corvello, Vincenzo, Università della Calabria - Dipartimento di Ingegneria Meccanica, Energetica e Gestionale; Musmanno, Roberto, Università della Calabria - Dipartimento di Ingegneria Meccanica, Energetica e Gestionale; Pavone, Giuseppe, Regione Calabria - Dipartimento Turismo, Marketing Territoriale e Mobilità; Santoro, Francesco, ITACA s.r.l.

## *Graph Partitioning in Public Transport*

The talk addresses the problem of designing optimal lots in Italian transport systems by referring to bus services. In defining lots in terms of number, size, and boundaries, the decision-maker should consider both the service cost (efficiency) and the capacity to meet transport demand, with a special focus on customer satisfaction (effectiveness). Under certain assumptions, this problem can be formulated as a graph partitioning model (GPM) in which the objective function is defined in accordance with a decree-law enacted by the Italian Minister of Transport. In order to individuate high-quality solutions for the GPM, a multi-start approach based on adaptive large neighbourhood search is proposed. In addition, the results of an extensive computational study based on real data from the Calabria Region are presented.
FR 11:50 AM2 Auditorium A

Maksim Lali lamaksim@biosense.rs

Institute BioSense, University of Novi Sad
Nikola, Obrenovi, Institute BioSense, University of Novi Sad; Sanja,
Brdar, Institute BioSense, University of Novi Sad; Ivan, Lukovi,
Faculty of Organizational Sciences, University of Belgrade; Michel,
Bierlaire, Transport and Mobility Laboratory (TRANSP-OR), École
Polytechnique Fédérale de Lausanne

*Assisting Passengers on Rerouted Train Service Using Vehicle Sharing System*

Unexpected blockages of railway sections force trains that usually pass through them to change their route, affecting passengers whose boarding and alighting stations are skipped. In such cases, shared vehicles, whose stations are positioned around railway stations, could be used as an emergency transport option and mitigate the consequences of train rerouting. This paper provides an integer linear program for simultaneous routing of affected passengers and used shared vehicles. Our model assumes that the new train route is fixed, and provides a solution that minimizes the number of dropped passengers and total passenger travel time while keeping additional vehicles engagement as low as possible. Computational experiments are performed on realistic case studies over a part of the Swiss railway network. Behavior characteristics of the analyzed transportation system under low or high passenger demand are recognized and presented.
FR 12:10 AM2 Auditorium A

Gabriella Stecco gabriella.stecco@act-operationsresearch.com

ACT Operations Research
Immacolata, Basile; Arcangelo, DAvino; Luca, Diedolo; Graziano, Galiano; Raffaele, Mariosa; Gabriella, Stecco, ACT Operations Research IT srl, Via Nizza 45, Roma, Italy raffaele.mariosa, gabriella.stecco@act-operationsresearch.com

*Scheduling and Routing of AGVs: solving industrial applications using Constraint Programming*

In this talk we consider the scheduling and conflict-free routing prob-
lems of multiple Automated Guided Vehicles (AGVs). The objective is to
minimize the makespan. These AGVs travel on predetermined guide paths
with capacity constraints and bidirectional paths. In order to compute a
solution in short computing time, we propose a decomposition approach:
the first problem assigns the tasks to the AGVs and the second one routes
the vehicles in order to prevent conflicts and dead-locks [1, 2]. The two
problems are tackled using Constraint Programming (CP) models and
they are solved using the CP-SAT solver of Google OR-Tools. Moreover,
in order to speed up the computation of the second problem, a heuris-
tic based on the Quickest Path Problem with Time Windows (QPPTW)
algorithm [3] is used.

This work is developed under the ongoing H2020 1-SWARM project.
Tests are performed on use cases provided by an industrial partner that
provides hardware and software solutions to apply autonomous navigation
to mobile agents.

[1] Riazi, S., Diding, T., Falkman, P., Bengtsson, K., Lennartson, B.: Scheduling and routing of AGVs for large-scale flexible manufacturing systems. In Proc. IEEE 15th International Conference on Automation Science and Engineering (CASE), pp. 891896, Vancouver, BC, Canada, (2019). [2] Riazi, S., Bengtsson, K., Lennartson, B.: Energy Optimization of Large-Scale AGV Systems. IEEE Transactions on Automation Science and Engineering, 18, 891-896, (2021). [3] Brownlee, A.E.I., Swan, J., Senington, R., Kocsis, Z.A.: Conflict-free routing of multi-stop warehouse trucks, Optimization Letters, 14, 1459-1470, (2020).
FR 12:30 AM2 Auditorium A

Cristiano Cervellera cristiano.cervellera@cnr.it

Institute of Marine Engineering, National Research Council of Italy
Francesco, Rebora, Institute of Marine Engineering, National Re-
search Council of Italy; Danilo, Macciò, Institute of Marine Engi-
neering, National Research Council of Italy

*Receding-horizon dynamic optimization of port-city traffic interactions over shared urban infrastructure*

We introduce a receding-horizon dynamic optimization approach for the real-time efficient management of conflicting traffic flows from a port terminal and urban mobility, in contexts where the port is located within a city center. This common situation typically entails a negative impact on the urban transport network, where the need to share the infrastructure causes issues both to the logistic and mobility flows. For the purpose of mitigating the negative impact, we formalize a model-predictive control scheme based on an integrated model of the port-city environment, focusing on the import and export chains in proximity of the terminal where trucks and general mobility vehicles interact. By exploiting available forecasts on mobility demand and container traffic, together with the coordinated action of perimetral and gate control, our proposed scheme aims at optimizing a desired performance index reflecting the interests of the involved stakeholders. Simulation results are presented regarding a context based on the port of Genova in north-west Italy, showcasing the ability of the proposed receding-horizon scheme to manage complex situations such as bottlenecks and transitions to mobility rush hours.

Transportation; Contributed Session
Continuous and Multiobjective optimization; Contributed Session

FR 11:30 AM2 Auditorium B

Serena Fugaro s.fugaro@iac.cnr.it

Institute for Applications of Calculus "Mauro Picone" - National Research Council of Italy

Sgalambro, Antonino, Sheffield University Management School and
Institute for Applications of Calculus "Mauro Picone" - National Research Council of Italy

*Multi-Objective Covering Location Problems with advanced connectivity features and zonal requirements: Exact and Matheuristic approaches*

Real-world facility location problems often demand to tackle simultaneously zonal requirements and facility interconnection issues; these may arise from administrative, managerial and operational needs, aiming to ensure an equal distribution of services, while concurrently securing an efficient flow of goods, people or information among the located facilities. As the literature appears rather limited at addressing this challenge, in this work we bridge such a gap by exploring the integration of the Maximal Covering Location Problem with spatial-related requirements and advanced connectivity features.

We adopt a broad modeling perspective, accounting for structural and economic aspects of connectivity attributes in facility location, while allowing for the choice of one or more depots to serve and feed the networks of located facilities, and containing the maximal distance between any located facility and such depots. The resulting mathematical models find straightforward applications, among other fields, in Healthcare Management, allowing for instance to optimally design large scale vaccination campaigns or to efficiently set-up mass screening procedures, e.g. during the response to a pandemic. Under the above mentioned modeling assumptions, the objectives to be fulfilled result multiple and inherently conflicting in nature, thus reflecting the increased managerial complexity of the related problems. In order to enhance decision-making in this scenario, we introduce a novel class of Multi-objective Covering Location problems, prove their NP-hard computational complexity, and devise original Mixed Integer Linear Programming models for their mathematical formulations.

As a first contribution to the solution process we adapt the robust version of the AUGMEnted epsilon-CONstraint generation method (AUGMECON-R) [1] as an efficient framework to explore the corresponding Pareto Sets.
As a second contribution we exploit the mathematical properties of the introduced problems to design a Matheuristic algorithm which is integrated
within the AUGMECON-R scheme to allow scalability of the solution method, with particular reference to the case of multiple depots.

We conduct a comprehensive computational study on benchmark instances adapted from the extant literature on Location Problems with interconnected facilities and Clustered Shortest Path Problems. The numerical experiments provide a proof of concept of the proposed models and highlight the challenging nature of the advanced connectivity features, particularly in presence of multiple depots. Both the Exact and the Matheuristic approaches provide a rich and articulate approximation of the relevant Pareto Sets for medium sized problems, while the Matheuristic shows a highly scalable performance when tackling large sized instances and multiple depot configurations.

[1] Nikas, A., Fountoulakis, A., Forouli, A., & Doukas, H. (2020). A robust augmented $\varepsilon$-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems. Operational Research, 1-42
FR 11:50 AM2 Auditorium B

Aly-Joy Ulusoy aly-joy.ulusoy15@imperial.ac.uk

Imperial College London

Pecci, Filippo, Imperial College London; Stoianov, Ivan, Imperial College London

*Multi-objective design-for-control of water networks with global bounds*

The optimal design and operation of water distribution networks (WDN) requires the consideration of conflicting operational objectives. In particular, this study investigates the problem of simultaneously optimizing the placement of new links (pipes or pressure control valves) and valve control settings, for the joint maximization of WDN resilience and the minimization of pressure-induced leakage. We investigate global methods for the solution of the resulting design-for-control problem, which belongs to the challenging class of bi-objective mixed-integer non-linear programs (MINLP) with non-convex constraints. Recently, multi-objective branch-and-bound methods have been proposed to compute outer approximations of the efficient and/or non-dominated sets of a wide range of multi-objective optimization problems, including convex MINLPs (De Santis et al., 2019), continuous NLPs with non-convex objective functions (Niebling & Eichfelder, 2019) and non-convex multi-objective MINLPs (Eichfelder et al., 2022). Following the general framework presented by (Eichfelder et al., 2022), we develop a bi-objective branch-and-bound algorithm based on tailored branching rules, discarding tests and upper and lower bounding procedures to approximate the non-dominated set of the WDN design-for-control problem with guarantees of global non-dominance. We evaluate the proposed bi-objective branch-and-bound algorithm on case study networks with different sizes and levels of connectivity and show that it outperforms previously investigated scalarization-based methods (Ususoy et al., 2021).

[1] De Santis, M., Eichfelder, G., Niebling, J., & Rockt, S. (2019). Solving Multiobjective Mixed Integer Convex Optimization Problems. PreprintSeries of the Institute for Mathematics, Technische Universität Ilmenau, Germany, 5, 10. http://www.optimization-online.org/DB_HTML/%0A2019/05/7229.html
[2] Eichfelder, G., Stein, O., & Warnow, L. (2022). A deterministic solver for multiobjective mixed-integer convex and nonconvex optimization. Optimization Online, Preprint ID, 128. [3] Niebling, J., & Eichfelder, G. (2019). A branch-and-bound-based algorithm for nonconvex multi-objective optimization. SIAM Journal on Optimization, 29(1), 794821. https://doi.org/10.1137/18M1169680 [4] Ulusoy, A.-J., Pecci, F., & Stoianov, I. (2021). Bi-objective design-for-control of water distribution networks with global bounds. Optimization and Engineering. https://doi.org/10.1007/s11081-021-09598-z
FR 12:10 AM2 Auditorium B

Andrea Brilli brilli@diag.uniroma1.it

Sapienza University of Rome Department of Computer Control and Management Engineering A. Ruberti

Liuzzi, Giampaolo, Sapienza University of Rome Department of Computer Control and Management Engineering A. Ruberti; Lucidi, Stefano, Sapienza University of Rome, Department of Computer Control and Management Engineering A. Ruberti

*An interior point method for nonlinear constrained derivative-free Optimization*

In this paper we consider constrained optimization problems where both the objective and constraint functions are of the black-box type. Furthermore, we assume that the nonlinear inequality constraints are non-relaxable, i.e. their values and that of the objective function cannot be computed outside of the feasible region. This situation happens frequently in practice especially in the black-box setting where function values are typically computed by means of complex simulation programs which may fail to execute if the considered point is outside of the feasible region. For such problems, we propose a new derivative-free optimization method which is based on the use of a merit function that handles inequality constraints by means of a log-barrier approach and equality constraints by means of a quadratic penalty approach. We prove convergence of the proposed method to KKT stationary points of the problem under quite mild assumptions. Furthermore, we also carry out a preliminary numerical experience on standard test problems and comparison with a state-of-the-art solver which shows efficiency of the proposed method.
Keynote

FR 14:30 PM0 Auditorium A

Maria Paola Scaparra m.p.scaparra@kent.ac.uk

Kent Business School, University of Kent (U.K.)

*Leveraging OR to build more sustainable, resilient, and equitable communities in Southeast Asia*

The United Nations Sustainable Development Goals (SDGs) are a uni-
versal call to action to end poverty, protect the planet, and improve the
lives and prospects of everyone, everywhere. Operational Research has a
major role to play in finding solutions to sustainable development chal-
lenges and helping the achievement of the SDGs. This talk discusses two
projects, funded by the UK Global Challenges Research Fund (GCRF),
to build more resilient, egalitarian, and inclusive communities in South-
east Asia. The first project (GCRF-OSIRIS) used OR tools integrated
with models and output from the fields of meteorology, hydrology, and
social science to minimise the impacts of severe flooding in urban areas of
Vietnam. Because flooding disproportionally impacts the lives of women
and poor communities, the views and concerns of these vulnerable groups
were elicited and taken into account during the model building phase
to identify community-responsive flood mitigation solutions. Building on
the success of GCRF-OSIRIS, a recent follow-up project (CREST-OR)
focused on fostering inclusive OR communities in Cambodia, Indonesia,
Laos, Myanmar, and Vietnam by equipping future generations (including
women) with the relevant OR expertise needed to tackle a range of sus-
tainable development challenges. The talk will conclude by highlighting
additional opportunities for OR to help realise an inclusive and sustain-
able future for Southeast Asian communities and accelerate progress of
the SDG agenda.

Keynote
# Supply Chains and Inventory; Contributed Session

FR 15:40 PM1 001

Saba Siadati s.siadati@tue.nl

Eindhoven University of Technology

## A Two-echelon Time-dependent Green Location-Routing Problem

In a location-routing problem, the interdependency of facility location and vehicle routing is considered, and consequently, it avoids sub-optimal solutions to the logistics and

transportation design and planning. In this study, we formulate and solve a two-echelon time-dependent green location-routing problem (2E-TDGLRP) in which the optimal location of

intermediate hubs within a set of candidate locations, as well as the routing and scheduling of vehicles, are to be determined simultaneously by minimizing the economic and environmental costs. We consider the economic cost as the investment cost of facilities and vehicles and the drivers wage in this problem. The environmental cost is deemed based on the GHGs emitted by the delivery vehicles, which is calculated using a comprehensive modal emission model (CMEM). Another novelty of our model lies in explicitly considering the effect of traffic

congestion on travel times by assuming time-dependent speed functions. We develop a mixed-integer linear programming formulation of 2E-TDGLRP. We solve the problem exactly using

the Gurobi solver for several small instances. We then present an efficient heuristic algorithm to solve larger instances. Our results show the validity of our mathematical model and allow us to analyze the effect of considering time-dependent travel times in the green location-routing problem.
FR 16:00 PM1 001

Tatiana Grimard tatiana.grimard.1@ulaval.ca

Universite Laval, 2325, rue de lUniversite, Quebec, Canada
Nadia, Lehoux, Universite Laval, 2325, rue de lUniversite, Quebec,
Canada; Luc, Lebel, Universite Laval, 2325, rue de lUniversite, Que-
bec, Canada

*Supply chain design and cost allocation in a collaborative three-
echelon supply network: A literature review*

Supply chains have been the focus of many studies. When warehouses
or distribution centers must be located and allocated to customers and
suppliers, a few key parameters can greatly affect the optimisation of
the supply chain, for example, its flexibility, the inclusion of inventory
management and the consideration for uncertainty. We first provide an
overview of the impacts of these parameters on the network design pro-
cess. The project that motivated this review concerns a supply chain that
must be designed that will be used in a coalition. Therefore, the second
part of our review concerns the different cost allocation methods used in
collaborative networks. We then discuss the advantages of designing sup-
ply chain with a future collaboration in mind and how this consideration
can affect the optimisation.
# Discrete Optimization; Contributed Session

FR 15:40 PM1 002

Martina Cerulli *cerulli@essec.edu*

ESSEC Business School of Paris
Archetti, Claudia, ESSEC Business School of Paris; Ljubic, Ivana, ESSEC Business School of Paris; Serra, Domenico, Università degli Studi di Salerno; Sorgente, Carmine, Università degli Studi di Salerno

*The collapsed k-core problem*

In social networks, the departure of critical users can have a substantial impact on network participation, causing a huge number of additional users to quit. To identify such critical users, the Collapsed *k*-Core Problem, presented for the first time in [1], may be used. Given an undirected graph *G* = (*V*, *E*), and a positive integer *k*, a *k*-core of *G* is a maximal subgraph of *G* in which all the nodes have degree at least *k*. Having a budget *b*, the Collapsed *k*-Core Problem aims to find the set of *b* nodes the deletion of which leads to the smallest *k*-core (i.e., the *k*-core with minimal cardinality), obtained from *G* by iteratively removing nodes with degree strictly lower than *k*. We propose two new formulations of the problem: the first one considers different time slots to model the progressive deletion rounds, while the second one is a bilevel formulation. The leader aims to minimize the cardinality of the *k*-core obtained by removing exactly *b* nodes. The follower wants to detect the *k*-core obtained after the decision of the leader on the *b* nodes to eliminate, i.e., to find the maximal subgraph of the new graph where all the nodes have degree at least *k*. To deal with this bilevel formulation we propose a single-level value function reformulation that we solve through a cutting-plane procedure.

In order to evaluate the proposed formulations, we successfully test different existing instances.

[1] Fan Zhang, Conggai Li, Ying Zhang, Lu Qin, Wenjie Zhang: Finding Critical Users for Social Network Engagement: The Collapsed Core and Truss Problems, IEEE Transactions on Knowledge and Data Engineering, 32(1) 78-91 (2020)
FR 16:00 PM1 002

Federico Della Croce federico.dellacroce@polito.it

DIGEP, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy.

Grosso, Andrea, D.I. Università di Torino, Corso Svizzera 185, 10149 Torino Italy; Tkindt, Vincent, Université de Tours, Laboratoire d'Informatique Fondamentale et Appliquée (EA 6300), ERL CNRS 7002 ROOT, Tours, France

*Operating rooms scheduling with a shared resource: a red-blue knapsack modeling approach*

Operating rooms scheduling has been widely investigated in the last 50 years. We mention here four main reference surveys on the topic published between 2010 and 2019, namely [1], [2], [3] and [4]. We denote this problem as the Shared Resource Operating Rooms scheduling (SRORS) problem. The SRORS problem can be formally expressed as follows. We have *n* surgical interventions (hereafter also denoted as jobs) 1, ..., *n* and *m* operating rooms OR(1), ..., OR(*m*). Each job *j* belongs to a specific surgical specialty that is univocally assigned to a given operating room OR(*i*). Then, for each operating room OR(*i*), there are *n*(*i*) jobs to schedule in the considered day, and we have *n* = sum *n*(*i*) for *i* = 1, ..., *m*. We denote by J(*i*) the set of jobs schedulable in operating room OR(*i*). Each operating room OR(*i*) is available for a common duration *W* for surgical interventions. Each job *j* may or may not require the use of a shared resource. We will denote as red jobs those jobs requiring the shared resource and as blue jobs the other jobs. Also, for each OR(*i*), we will denote as R(*i*) (B(*i*) ) the set of relevant red (blue) jobs. Each job *j* schedulable in OR(*i*) has a nominal duration *w*(*i*,*j*) and a profit *p*(*i*,*j*) directly derived from the job priority. The goal is to maximize the total profit of the scheduled jobs. Besides, to avoid unnecessary moves of the shared resource from one operating room to another, it is assumed that every day this resource can spend at most one single time interval in each operating room. Correspondingly, for each OR(*i*) w.l.o.g. a blue-red-blue sequence with a first subset of blue jobs, then a subset of red jobs and finally a second subset of blue jobs holds. Notice that all these subsets may be empty. Being a generalization of the 0/1 Knapsack Problem, see for instance [5], the SRORS problem is NP-Hard, but it is of interest to determine whether it is in the weak or in the strong sense. We have the following results. The case when *m* is fixed is weakly NP-hard, while the case of an arbitrary *m* is strongly NP-hard (this last result is proved by reduction from 3-Partition. To solve to optimality the SORS problem, it is possible to derive a knapsack-based formulation exploiting the blue-red-blue sequence. This formulation is sufficient enough (by means of an ILP solver) to well solve in practice realistic instances of the considered problem. We also propose another ILP formulation exploiting a genuine combinatorial generation of all relevant

Discrete Optimization; Contributed Session
schedules for each operating room OR(i): let S(i) be the set of schedules of the form red-blue-red. we define boolean variables x(I,k) to determine whether the k-th schedule in S(i) is selected or not. Then, the ILP reduces to determine the selection of one schedule per operating room such that the total profit is maximum and the constraints are satisfied. Clearly, this approach requires to generate all sets S(i) which is doable by means of dynamic programming. Experimental results will show the efficiency of the latter ILP formulation with respect to the first one.

[1] Cardoen B., Demeulemeester E., Belien J. 2010, Operating room planning and scheduling: a literature review, European Journal of Operational Research, Vol. 201 (3), pp. 921-932. [2] Guerriero F., Guido R. 2011, Operational research in the management of the operating theatre: a survey, Health Care Management Science, Vol. 14 (1), pp. 89-114. [3] Samudra M., Van Riet C., Demeulemeester E., Cardoen B., Vansteenkiste N., Rade- makers F.E. 2016, Scheduling operating rooms: achievements, challenges and pit-falls, Journal of Scheduling, Vol. 19, pp.493-525. [4] Zhu S., Fan W., Yang S., Pei J., Pardalos P.M. 2019, Operating room planning and surgical case scheduling: a review of literature, Journal of Combinatorial Optimization, Vol. 37, pp. 757-805. [5] Pferschy U., Pisinger D, Kellerer H. 2004, Knapsack problems, Springer-Verlag.
# Machine Learning; Contributed Session

FR 15:40 PM1 003

Federico D'Onofrio federico.donofrio@uniroma1.it

Dipartimento di Ingegneria dell'Informazione, Automatica e Gestionale "Antonio Ruberti" (DIAG), Sapienza Università di Roma Grani, Giorgio, Dipartimento di Scienze Statistiche, Sapienza Università di Roma; Monaci, Marta, Dipartimento di Ingegneria dell'Informazione, Automatica e Gestionale "Antonio Ruberti" (DIAG), Sapienza Università di Roma; Palagi, Laura, Dipartimento di Ingegneria dell'Informazione, Automatica e Gestionale "Antonio Ruberti" (DIAG), Sapienza Università di Roma

## Maximum Margin Optimal Classification Trees

In recent years there has been a growing attention to interpretable machine learning models which are able to give explanatory insights on the decisions made by the algorithm. Especially in healthcare applications, it is of great interest to find a hierarchy on the subsets of features which mostly affect the outcome of a classification algorithm. Thanks to their interpretability, decision trees have been intensively studied for classification tasks, and, due to the remarkable advances in mixed-integer programming (MIP), various approaches have been proposed to formulate the Optimal Classification Tree (OCT) problem as a MIP model (Bertsimas and Dunn, 2017). We present a novel MIP formulation for the OCT problem which exploits the generalization capabilities of support vector machines for binary classification. The Maximum MARGin Optimal Classification Tree (MARGOT) selects at each node of the decision tree a maximum margin separating hyperplane using an $l_2$-norm linear support vector machine. The resulting model combines such multivariate hyperplanes minimizing the global misclassification error. In contrast to other similar approaches (Bennet et al, 1997, and Zhu et al, 2020), both the objective function and the classification constraints of the standard SVM primal formulation appear in our model. The key idea is that, given a node *t* of the tree and a sample *i*, if the hyperplane at *t* does not need to be trained to classify *i*, classification constraints related to *t* and *i* are deactivated through a special big-M constraint on the misclassification variable $\xi_{t,i}$. This way, each separating hyperplane can be trained on just a subset of samples. The model can also include feature selection constraints which improve the interpretability of our approach. MARGOT has been tested on non-linearly separable synthetic datasets in a 2-features space in order to provide a graphical representation of the optimal hyperplanes. Finally, we evaluate our model with the feature selection constraints on well-known datasets from the UCI repository.

## Machine Learning; Contributed Session
FR 16:00 PM1 003

Luca Bravi luca.bravi@verizonconnect.com

Verizon Connect
Andrew, Harbourne-Thomas, Verizon Connect; Alessandro, Lori, Verizon Connect; Peter, Mitchell, Verizon Connect; Samuele, Salti, Verizon Connect; Leonardo, Taccari, Verizon Connect; Francesco, Sambo, Verizon Connect

*GPS data mining to infer fleet operations for personalised product upselling*

Selecting the correct targets for a marketing campaign and personal-
ising the content of the marketing material requires a deep understanding
of the operations of potential customers. We present a system that mines
raw GPS data from a fleet of vehicles with the aim of inferring fleet oper-
ations. The inference proceeds in subsequent steps of increasingly deeper
understanding, from fleet hotspots to vehicle routes and daily work shifts
and work stops. We present an application of our system where such
information is exploited to select the best suited customers for vehicle
routing software and to create personalised marketing material, based on
the estimated savings for the fleet due to use of the routing software.
Experimental results from an email marketing campaign confirm the ef-
fectiveness of our system and of the personalised marketing material it
delivers.
Transportation; Contributed Session

FR 15:40 PM1 Auditorium A

Antonio Napoletano antonio.napoletano@optit.net

Optit Srl

Andrea, Bettinelli; Andrea, Degiorgis; Claudio, Caremi; Matteo, Pozzi

Vehicle routing at work: a real-world implementation and case studies

Vehicle routing problem (VRP) and its variants are commonly and widely used in many areas of logistics and supply-chain industry to optimize the whole business process and providing significant savings in terms of development time and costs. Optit-logistics is a decision support tool designed for advanced transportation planning, its optimization core is based on fully customizable algorithms for VRP. The software offers multiple visualization tools for KPI analysis and solution validation, as well as full editing functionalities to update the routes generated by the algorithms. In addition to the classic capacitated VRP (C-VRP), the model can be enriched with several different features and constraints relevant for specific applications, such as: homogeneous and heterogeneous fleets (limited or not), multiple-depots and intermediate facilities, hard and soft multiple time windows, pickup & delivery, decision-making cross-docking, split-deliveries and many others. The algorithmic framework behind the Optit-logistics solver is the well-known Ruin and Recreate approach (Schrimpf et al. 2000). This method, combined with Threshold acceptance of the solutions, allows the exploration of very-large neighborhoods and combines implementation simplicity, which is fundamental in industrial deployments, and excellent effectiveness, as widely established in the scientific literature on VRP (see, e.g. Pisinger and Ropke 2006, 2019) The main construction operators implemented and integrated into the solver range from simple greedy insertion heuristics, to the well-known Clarke and Wright savings method, to route-first-cluster-second algorithms based on giant-tour approach. This general-purpose solver was used in several real-world applications ranging from pickup & delivery for retail distribution, to home delivery, closed-loops full-truckload daily transportation and on-demand waste collection. To guarantee the robustness and to validate the reliability of the implemented algorithms, an extensive experimentation phase was carried out taking into account a large data set of VRP benchmark instances, selected with the aim of covering the best-known variants of the VRP in literature. In this session, we focus on a recently addressed new variant of the VRP, for which we introduce a mathematical formulation and the adopted solution strategy. In addition, we discuss an extensive analysis of real-world instances of the problem. The problem under consideration contains considerable

Transportation; Contributed Session
complexities that cannot be trivially modeled and which, to the best of
our knowledge, their combination was not previously treated in the exist-
ing literature. In our problem it is possible to perform both pickup and
delivery operations at customers, thus it belongs to the general family
of Pickup and Delivery VRPs (PD-VRP). Furthermore, the other main
characteristics of the problem are the presence of heterogeneous fleet,
multiple-depots and multiple-time-windows. Finally, orders can be split
over different trips, and at the same time the algorithm should be able
to decide if each order can be shipped in direct-mode from pickup point
to the delivery point or alternatively in cross-docked-mode appropriately
selecting the so-called transit-point where the order is consolidated with
others for the last-mile delivery. The overall solution approach is based on
two phases. In a first one a min-cost-flow over the distribution network
is solved to define the distribution strategy, while in a second step the
detailed routing is determined through the above-described solver. Such,
combined approach is extensively validated on real-world instances and
proved able to determine very good results compared to the previously
implemented solutions.

Transportation; Contributed Session
FR 16:00 PM1 Auditorium A

Xuan Ren renxuan@mail.nwpu.edu.cn

School of Management, Northwestern Polytechnical University
Jabali, Ola, Department of Electronics, Information and Bioengineering, Politecnico di Milano

*The Truck and Drone Routing with Delivery and Optional Pickups*

Using drones for delivery services has been drawing much attention in the logistics industry. In this work, we study a new variant of the vehicle routing problem with drones (VRPD), where a logistics service provider (LSP) must serve customers having delivery requests, and may choose to serve pickup requests. The latter represents customers returning products. We assume that if such a request is served by the LSP, they generate profit. Both trucks and drones may be used to serve both delivery and pickup requests. We call the resulting problem the truck and drone routing problem with delivery and optional pickups (TDRP-OP). We assume that each truck is collaborating with a single drone. Since drones have limitations on payloads and battery capacities, they can only serve one customer each time. Different from most of the literature on the VRPD, this work considers both truck capacity and drone capacity. The objective is to minimize total operational costs, which include the transportation costs of both trucks and drones minus the total profit from picking up returning products. We present a MILP formulation for the TDRP-OP and introduce a variable neighborhood search (VNS) heuristic for it. A dynamic program is proposed to find the best drone sorties for a given sequence of customers. We generate new instances for the TDRP-OP. Computational results show that our method can find optimal solutions for small instances compared to the MILP solutions, and high-quality solutions within a reasonable time for large instances. Furthermore, we compare our heuristic on VRPD instances from the literature. In this respect, our heuristic yields competitive results and identifies new best-known solutions to several instances.
FR 16:20 PM1 Auditorium A

Jean-Paul M. Arnaout arnaout.j@gust.edu.kw

Lebanese American University, Byblos, Lebanon

John, Khoury, Lebanese American University; Jean-Paul, Arnaout,

Gulf University for Science & Technology, Mishref, Kuwait; Maria,

Bassil

*A Preliminary Assessment of the Traffic Operations of Connected and/or Autonomous Vehicles*

Autonomous vehicles are projected to eliminate human errors in the driving task, which means an increase in safety and a reduction in the number of crashes. In addition, autonomous vehicles can reduce traffic congestion and traffic delays leading to improved operational efficiency and smoother traffic flow. This research attempts to evaluate the improvement in the level of service and capacity of freeways and signalized intersections when their operation shifts from conventional vehicles (CVs) to fully autonomous vehicles (AVs). The increased efficiency and capacity of roadways and intersections operated by AVs is due to their computerization, their ability to travel at a steady desired speed, in addition to their ability to keep reduced headways between each other in the traffic stream. For this purpose, the effect of fully autonomous vehicles on the lane width, number of lanes and car following model parameters has been thoroughly examined based on the new vehicle setup. For a fully autonomous vehicle fleet, the driver related parameters are no longer a concern. Narrower lane widths and revised car following behavior were modeled using a well-known microscopic simulation software to simulate the fully autonomous vehicles environment. The results of the simulation scenarios show that the expected benefits of autonomous vehicles in reducing delay, increasing capacity, and improving the level of service are most evident when 100% of the traffic flow consists of autonomous vehicles. In particular, the results of the performed simulations on the freeway section has showed that low levels of AVs penetration can have an adverse effect on the traffic operations. Despite the ambiguity and unpredictability of the freeway transition phase from CVs to AVs, the intended benefits of AVs are expected to be fully achieved with the complete deployment of AVs. For the signalized intersection, AVs lead to a lower vehicle delay, lower queue length, and higher capacity. The results of the microscopic simulations have confirmed that when fully deployed, AVs are expected to improve the capacity, efficiency and Level of Service of freeways and signalized intersections.

Transportation; Contributed Session
Applications of OR; Contributed Session

FR 15:40 PM1 Auditorium B

Stanislav Fedorov stanislav.fedorov@polito.it

DAUIN & CARS@Polito Politecnico di Torino, Torino, Italy
Fadda, Edoardo, DISMA & ICELab@Polito Politecnico di Torino,
Torino, Italy; Perboli, Guido, ICELab@Polito & CARS@Polito Po-
litecnico di Torino Turin, Italy

*Machine Learning heuristic for Variable Cost and Size Bin Packing Problem with Stochastic Items*

Third-party logistics becomes an essential component of efficient delivery, enabling companies to rent transportation services instead of keeping an expensive fleet of vehicles. However, the contracts with the carriers usually have to be booked beforehand when the delivery demand is unknown. This decision process is strongly affected by uncertainty, provided with a long (tactical) planning horizon, and can be expressed as choosing an appropriate set of bins (fleet contracts). Formally, it can be modeled as the Variable Cost and Size Bin Packing Problem with Stochastic Items [1]. It consists of packing the set of items (goods) with uncertain volumes and quantities into containers (bins) of different fixed costs and capacities. This problem is described via a two-stage stochastic programming approach, where the cost of the bins of the second stage is significantly higher. Since it cannot be solved for large realistic instances by means of exact solvers for a reasonable time and memory consumption, this paper introduces a Machine Learning heuristic to approximate the first stage decision variables. Several numerical experiments are outlined to show the effectiveness of the proposed approach to deal with realistic instances of up to 3000 items. Further, the proposed heuristic is compared to the recent Progressive Hedging-based heuristic and showed a significant computational time reduction. Finally, different classification approaches are compared, and the feature selection process is explained to gain insight into heuristic performance to deal with the outlined problem.

[1] Crainic, T. G., Gobbato, L., Perboli, G., Rei, W., Watson, J. P., & Woodruff, D. L. (2014). Bin packing problems with uncertainty on item characteristics: An application to capacity planning in logistics. Procedia-Social and Behavioral Sciences, 111, 654-662.
FR 16:00 PM1 Auditorium B

Mikele Gajda mikele.gajda@unil.ch

HEC Lausanne (Faculty of Business and Economics), University of
Lausanne
Boysen, Nils, Wirtschaftswissenschaftliche Fakultät, Friedrich-Schiller-
Universität Jena; Gallay, Olivier, HEC Lausanne (Faculty of Busi-
ness and Economics), University of Lausanne

*Last-mile delivery with vans and autonomous robots: an analysis on the impact of the return policy*

In recent years, technological advancements have shaped innovative and promising last-mile delivery solutions. One of these approaches involves the employment of autonomous delivery robots (ADRs) in conjunction with vans from which the robots can be picked up and released. These robots operate at pedestrian pace on the sidewalk, providing a reliable operator for the last delivery step. In our research, we study a multi-modal transportation system in which vans convey ADRs from depots to prospective drop-off places, with the final portion of the delivery being handled autonomously by the robots, which then return to one of the available depots. The goal of this contribution is to investigate and compare different policy approaches for the return of the robots to the depots. Indeed, the choice of the return policy has a direct impact on the number of robots required to perform a given delivery plan. Our main objective is to minimize the number of used robots to complete a certain amount of delivery jobs. We define and analyze three possible robot return policies in detail (dedicated-station policy, closest-station policy, most-suitable-station policy), and we propose suitable optimization algorithms for each of them in order to find optimal solutions in polynomial time. Our findings, obtained from computational experiments performed on an extensive set of realistic instances, demonstrate that the robot return policy should be carefully chosen in order to achieve the targeted delivery plan with a minimum number of robots.
FR 16:20 PM1 Audtorium B

Roberto Musmanno roberto.musmanno@unical.it

Department of Mechanical, Energy and Management Engineering
Annarita, De Maio, Department of Economics, Statistics and Finance Giovanni Anania; Aurora, Skrame, roberto.musmanno@unical.it; Francesca, Vocaturo, Department of Economics, Statistics and Finance Giovanni Anania

Selection of Cultural Sites via Optimization

We focus on a combinatorial optimization problem arising in regional tourism management. The problem consists in selecting cultural sites to be promoted among others distributed in a regional geographic area. Specifically, when a cultural site is selected, the potential visitors who decide to travel by train have the opportunity to take advantage of a free-of-charge shuttle dispatched from the closest railway station to the site. For the solution of this problem we propose an optimization framework based on the formulation of a bi-objective mathematical model. The computational results are presented by considering a case study derived from a regional project.

Applications of OR; Contributed Session
Closing Session

FR 17:00 PM2 Auditorium A

Paola Cappanera

Fabio Schoen

*Closing session*

** NO ABSTRACT PROVIDED **

Closing Session
# Index

AMUO, Advances in Multiobjective Optimization; Invited Session, 53
Global Optimization; Invited Session, 77

Applications of OR; Contributed Session, 19, 44, 129, 226, 291
HCM, Health Care Management; Invited Session, 11, 39

Applications: Health Care; Contributed Session, 232
Industrial & Startup Session, 125, 141

Applications: Sustainability, Health Care; Contributed Session, 196
Keynote, 57, 106, 201, 274
KNAP, Knapsack Problems; Invited Session, 63

Applications; Contributed Session, 111
Machine Learning; Contributed Session, 116, 284

Assemblea AIRO - AIRO Council meeting, 225
MLO4EMD, Machine learning-based optimization for extreme metamaterials design; Invited Session, 262

Best AIRO Young Dissertation 2022, 258
MUOS, Multicriteria Optimization for sustainability; Invited Session, 32

Closing Session, 294
Continuous and Multiobjective optimization; Contributed Session, 171, 249, 270
NODAI, Numerical optimization for data analysis and imaging; Invited Session, 68, 89

Discrete Optimization; Contributed Session, 152, 181, 206, 280
NOML, Nonlinear Optimization and Machine Learning; Invited Session, 136, 160, 187

EQVA, Equilibria, variational models and applications; Invited Session, 58
OML, Optimization for machine learning; Invited Session,

Finance; Contributed Session, 86
211, 237

Optimization on Graphs; Contributed Session, 139, 164, 215

OPTSM, Optimization in Public Transport and Shared Mobility; Invited Session, 25, 49, 73, 94, 120

OUUTA, Optimization under Uncertainty: Theory and Applications; Invited Session, 4, 36

PRPI, Path and routing problems in industry; Invited Session, 191

RAVIEP, Recent Advances in Variational Inequalities and Equilibrium Problems; Invited Session, 82, 107

Stochastic Optimization; Contributed Session, 145

Stochastics, Equilibria, Applications; Contributed Session, 202

Supply Chains and Inventory; Contributed Session, 255, 275

Sustainability; Contributed session, 220

Teaching Session, 99

Transportation; Contributed Session, 242, 266, 287

Variational Inequalities and equilibrium problems; Contributed Session, 176