varunr14/newsummary huggingface dataset:
The characterization of a technology from an economic point of view often uses the first derivatives of either the transformation or the production function . In a parametric setting these quantities are readily available as they can be easily deduced from the first derivatives of the specified function . In the standard framework of data envelopment analysis models these quantities are not so easily obtained . The difficulty resides in the fact that marginal changes of inputs and outputs might affect the position of the frontier itself while the calculation of first derivatives for economic purposes assumes that the frontier is held constant . We develop here a procedure to recover first derivatives of transformation functions in DEA models and we show how we can evacuate the problem of the shift of the frontier . We show how the knowledge of the first derivatives of the frontier estimated by DEA can be used to deduce and compute marginal products marginal rates of substitution and returns to scale for each decision making unit in the sample . 


Summary: This paper raises the problem of measuring partial derivatives of transformation functions in DEA models. The derivatives of the DEA frontier imply a displacement on the frontier and a shift of the frontier. We reconcile the theoretical partial derivatives and the empirical DEA version of these derivatives. We show the invariance of the marginal rate of substitution to the partial derivatives definition. We show the invariance of the returns to scale to the partial derivatives definition.
A characteristic aspect of risks in a complex modern society is the nature and degree of the public response sometimes significantly at variance with objective assessments of risk . A large part of the risk management task involves anticipating explaining and reacting to this response . One of the main approaches we have for analysing the emergent public response the social amplification of risk framework has been the subject of little modelling . The purpose of this paper is to explore how social risk amplification can be represented and simulated . The importance of heterogeneity among risk perceivers and the role of their social networks in shaping risk perceptions makes it natural to take an agent based approach . We look in particular at how to model some central aspects of many risk events the way actors come to observe other actors more than external events in forming their risk perceptions the way in which behaviour both follows risk perception and shapes it and the way risk communications are fashioned in the light of responses to previous communications . We show how such aspects can be represented by availability cascades but also how this creates further problems of how to represent the contrasting effects of informational and reputational elements and the differentiation of private and public risk beliefs . Simulation of the resulting model shows how certain qualitative aspects of risk response time series found empirically such as endogenously produced peaks in risk concern can be explained by this model . 


Summary: Managing major societal risks involves the need to understand public risk responses. The social amplification of risk framework has been our main theoretical approach. We explore how to model endogenised risk observation behaviour and communication. Agent simulation shows characteristic outcomes like peaks and drift in risk beliefs. The model indicates the key areas where further empirical research is needed.
We consider robust one way trading with limited information on price fluctuations . Our analysis finds the best guarantee of difference from the optimal offline performance . We provide closed form solution and reveal for the first time all possible worst case scenarios . Numerical experiments show that our policy is more tolerant of information inaccuracy than Bayesian policies and can earn higher average revenue than other robust policies while keeping a lower standard deviation . 


Summary: We provide a competitive difference analysis of the online one way trading problem with limited information on prices. We formulate the problem recursively and obtain closed form solutions via backward induction. Our competitive difference analysis reveals for the first time all possible worst case scenarios. Simulations show that our method exhibits robustness against information inaccuracy. Our method can outperform trading policies by competitive ratio analysis in simulations.
Discontinuities are common in the pricing of financial derivatives and have a tremendous impact on the accuracy of quasi Monte Carlo method . While if the discontinuities are parallel to the axes good efficiency of the QMC method can still be expected . By realigning the discontinuities to be axes parallel succeeded in recovering the high efficiency of the QMC method for a special class of functions . Motivated by this work we propose an auto realignment method to deal with more general discontinuous functions . The k means clustering algorithm a classical algorithm of machine learning is used to select the most representative normal vectors of the discontinuity surface . By applying this new method the discontinuities of the resulting function are realigned to be friendly for the QMC method . Numerical experiments demonstrate that the proposed method significantly improves the performance of the QMC method . 


Summary: The idea of machine learning is introduced to the QMC area. A new path generation method called the auto realignment method is proposed. The k means clustering algorithm is used to select representative normal vectors. Experiments demonstrate the efficiency and robustness of the proposed method.
Demand based pricing is often used to moderate demand fluctuations so as to level resource utilization and increase profitability . However such pricing policies may not be effective when customers purchase decisions are influenced by social interactions . This paper investigates the demand dynamics under a demand based pricing policy of a frequently purchased service when social interactions are at work . Customers are heterogeneous and adaptively forward looking . Existing customers re purchase decisions are based on adaptively formed price expectations and reservation prices . Potential customers are attracted through social interactions with existing customers . The demand process is characterized by a two dimensional dynamical system . It is shown that the equilibrium demand can be unstable . For a given reservation price distribution we first analyze the stability of the equilibrium demand under various scenarios of social interactions and customers adaptively forward looking behavior and then characterize their dynamics using the bifurcation plots Lyapunov exponents and return maps . The results indicate that the demand process can be stable periodic or chaotic . The study shows that the intended effect of a demand based pricing policy may be offset by customers adaptively forward looking behavior under the influence of social interactions . In fact the interplay of these factors may even lead to chaotic demand dynamics . The result highlights the complex dynamics produced by a simple demand price mechanism under social interactions . For a demand based pricing strategy to be effective companies must take social interactions into account . 


Summary: The effectiveness of demand based pricing under social interactions in moderating demand fluctuations is studied. Results show that the demand dynamics can exhibit chaos when social interactions are at work. Social interactions and customers forward looking behavior may offset the intended effect of a demand based pricing. Operational policies must be examined in the appropriate context e.g. under the influence of social interactions.
We consider storage loading problems where items with uncertain weights have to be loaded into a storage area taking into account stacking and payload constraints . Following the robust optimization paradigm we propose strict and adjustable optimization models for finite and interval based uncertainties . To solve these problems exact decomposition and heuristic solution algorithms are developed . For strict robustness we also propose a compact formulation based on a characterization of worst case scenarios . Computational results for randomly generated data with up to 300 items are presented showing that the robustness concepts have different potential depending on the type of data being used . 


Summary: We present a new model for stacking problems with payload constraints. Due to uncertain item weights two robust optimization approaches are developed. Problems are solved using exact decomposition and heuristic algorithms. Using a characterization of worst case scenarios a compact model is presented.
We consider a Cournot duopoly under general demand and cost functions where an incumbent patentee has a cost reducing technology that it can license to its rival by using combinations of royalties and upfront fees . We show that for drastic technologies licensing occurs and both firms stay active if the cost function is superadditive and licensing does not occur and the patentee monopolizes the market if the cost function is additive or subadditive . For non drastic technologies licensing takes place provided the average efficiency gain from the cost reducing technology is higher than the marginal gain computed at the licensee s reservation output . Optimal licensing policies have both royalties and fees for significantly superior technologies if the cost function is superadditive . By contrast for additive and certain subadditive cost functions optimal licensing policies have only royalties and no fees . 


Summary: We analyze the licensing of a cost reducing innovation in a Cournot duopoly. We focus on two part tariff policies for both drastic and non drastic innovations. A drastic innovation is licensed not licensed if cost is super additive sub additive . A non drastic innovation is licensed if technology s average gain exceeds marginal gain. Royalties are positive fees could be zero.
This study addresses the optimal pipe sizing problem of a tree shaped gas distribution network with a single supply source . An algorithm was developed with the aim of minimizing the investment for constructing a gas distribution network with a tree shaped layout in which demands are fixed . The construction cost is known to depend on the pipe diameters used for each arc in the network . However under the assumption that pipe diameters are continuous we prove that it is possible to obtain the minimum construction cost directly and analytically by an iterating procedure that converts the original tree into a single equivalent arc . In addition we also show that expanding the converted single arc inversely to the original tree computes the optimal continuous pipe diameter for each arc . Following this we present an additional heuristic to convert optimal continuous pipe diameters into approximate discrete pipe diameters . The algorithms were evaluated by applying them to sample networks . The numerical results obtained by comparing the approximate discrete diameters with the optimal discrete diameters confirm the efficiency of our algorithms thereby demonstrating their suitability for designing real gas distribution networks . 


Summary: Algorithm to minimize investment cost for tree shaped gas distribution networks. Minimum cost obtained by converting the original tree into a single equivalent arc. Optimal continuous pipe diameters are converted to discrete approximate diameters. The algorithms are suitable for designing real gas distribution networks.
Given the evolution in the agricultural sector and the new challenges it faces managing agricultural supply chains efficiently has become an attractive topic for researchers and practitioners . Against this background the integration of uncertain aspects has continuously gained importance for managerial decision making since it can lead to an increase in efficiency responsiveness business integration and ultimately in market competitiveness . In order to capture appropriately the uncertain conjuncture of most agricultural real life applications an increasing amount of research effort is especially dedicated to treating uncertainty . In particular quantitative modeling approaches have found extensive use in agricultural supply chain management . This paper provides an overview of the latest advances and developments in the application of operations research methodologies to handling uncertainty occurring in the agricultural supply chain management problems . It seeks to offer a representative overview of the predominant research topics highlight the most pertinent and widely used frameworks and discuss the emergence of new operations research advances in the agricultural sector . The broad spectrum of reviewed contributions is classified and presented with respect to three most relevant discerned features uncertainty modeling types programming approaches and functional application areas . Ultimately main review findings are pointed out and future research directions which emerge are suggested . 


Summary: This study provides a state of the art of OR use in agriculture under uncertainty. It offers an overview of the commonly used OR approaches for handling uncertainty. Main review findings are pointed out. Future research directions are derived and suggested.
We study the regulation of one way station based vehicle sharing systems through parking reservation policies . We measure the performance of these systems in terms of the total excess travel time of all users caused as a result of vehicle or parking space shortages . We devise mathematical programming based bounds on the total excess travel time of vehicle sharing systems under any passive regulation and in particular under any parking space reservation policy . These bounds are compared to the performance of several partial parking reservation policies a parking space overbooking policy and to the complete parking reservation and no reservation policies introduced in a previous paper . A detailed user behavior model for each policy is presented and a discrete event simulation is used to evaluate the performance of the system under various settings . The analysis of two case studies of real world systems shows the following a significant improvement of what can theoretically be achieved is obtained via the CPR policy the performances of the proposed partial reservation policies monotonically improve as more reservations are required and parking space overbooking is not likely to be beneficial . In conclusion our results reinforce the effectiveness of the CPR policy and suggest that parking space reservations should be used in practice even if only a small share of users are required to place reservations . 


Summary: The exertion of parking reservation policies in vehicle sharing systems is studied. Partial parking reservations are introduced and analyzed. Mathematical programming based lower bounds on the quality of service are devised. The complete parking reservation policy is shown to be both simple and effective. Parking overbooking policies are demonstrated not to be worthwhile.
In this paper we present an approach to generate cyclic production schemes for multiple products with stochastic demand to be produced on a single production unit . This scheme specifies a fixed and periodic production sequence called a cycle where each product may occur more than once in the sequence . In order to stabilize the cycle length alternative control strategies are introduced . These strategies keep the cycle length close to a target length by means of adding idle time to the cycle cutting production short or overproduction . On basis of the selected control strategy as well as a base stock policy target inventory levels are determined for each production run in the cycle . Demand can be backlogged but a certain service level is required . Setup times are sequence dependent and the storage is capacitated . Employing the developed strategies we investigate the tradeoff between stability of cycle length and total cost induced by the production schedule in a computational study based on both real world data and random test instances . 


Summary: We present a cyclic production scheme for multiple products with stochastic demand. We impose bounds for the cycle length in order to achieve a regular schedule. We consider sequence dependent setup times service levels and storage capacity. The computational study based on real world data compares six strategies that control the cycle length.
This paper addresses an integrated framework for deciding about the supplier selection in the processed food industry under uncertainty . The relevance of including tactical production and distribution planning in this procurement decision is assessed . The contribution of this paper is three fold . Firstly we propose a new two stage stochastic mixed integer programming model for the supplier selection in the process food industry that maximizes profit and minimizes risk of low customer service . Secondly we reiterate the importance of considering main complexities of food supply chain management such as perishability of both raw materials and final products uncertainty at both downstream and upstream parameters and age dependent demand . Thirdly we develop a solution method based on a multi cut Benders decomposition and generalized disjunctive programming . Results indicate that sourcing and branding actions vary significantly between using an integrated and a decoupled approach . The proposed multi cut Benders decomposition algorithm improved the solutions of the larger instances of this problem when compared with a classical Benders decomposition algorithm and with the solution of the monolithic model . 


Summary: We present a stochastic programming model for supplier selection. We incorporate uncertainty in the demand and in the supplier operation. We propose an enhanced Benders decomposition algorithm for this problem. Sourcing actions vary between using an integrated and a decoupled approach. Solving a single master problem in the Benders decomposition is more efficient.
In this paper we consider the Asymmetric Quadratic Traveling Salesman Problem . Given a directed graph and a function that maps every pair of consecutive arcs to a cost the problem consists in finding a cycle that visits every vertex exactly once and such that the sum of the costs is minimal . We propose an extended Linear Programming formulation that has a variable for each cycle in the graph . Since the number of cycles is exponential in the graph size we propose a column generation approach . Moreover we apply a particular reformulation linearization technique on a compact representation of the problem and compute lower bounds based on Lagrangian relaxation . We compare our new bounds with those obtained by some linearization models proposed in the literature . Computational results on some set of benchmarks used in the literature show that our lower bounding procedures are very promising . 


Summary: Present different reformulations for the quadratic traveling salesman problem. Develop new bound based on reformulation linearization technique. Propose a cycle based model and solve it by column generation approach. Develop a stabilized column generation.
We propose a novel meta approach to support collaborative multi objective supplier selection and order allocation decisions by integrating multi criteria decision analysis and linear programming . The proposed model accounts for suppliers performance synergy effects within a hierarchical decision structure . It incorporates both heterogeneous objective data and subjective judgments of the decision makers representing various groups with different voting powers . We maximize the total value of purchasing by optimizing order quantity assignment to suppliers and taking into consideration their synergies encountered in different time horizons . We apply the proposed model to a contractor selection and order quantity assignment problem in an agricultural commodity trading company . We maximize the strategic effectiveness of both the customers and the suppliers minimize risks increase the degree of cooperation between trading partners on all levels of supply chain integration enhance transparent knowledge sharing and aggregation and support collaborative decision making . 


Summary: A meta approach is proposed for collaborative multi criteria supplier selection and order allocation. The model incorporates both heterogeneous objective data and subjective judgments. The model considers synergistic effects within a hierarchical decision structure. The suppliers strategic performance indicators are monitored in the proposed model. The applicability of the model is demonstrated in commodity trading.
The linguistic term set is an applicable and flexible technique in qualitative decision making . To further develop the linguistic term set this paper proposes a generalized asymmetric linguistic term set based on the asymmetric sigmoid semantics which belongs to an asymmetric and non uniform linguistic term set and can be used to address the QDM problems involving risk appetites of the decision maker . Then a value at risk fitting approach is designed for obtaining the risk appetite parameters of the GALTS and six desirable properties of the GALTS are analyzed i.e . asymmetry non uniformity generality variability range consistency and diminishing utility . Based on the above approaches and the generalized asymmetric linguistic preference relations a QDM process involving risk appetites of the DM is designed . Because the GALPRs consist of subjective information provided by the DM the process is not perfectly consistent and is usually difficult to change or repeat . Thus a transitivity improvement approach is investigated and the corresponding calculation steps are provided . Finally an example dealing with the problem of investment decision making is provided and the results fully demonstrate the validity of the proposed methods for QDM involving risk appetites . 


Summary: We develop a generalized asymmetric linguistic term set GALTS with six properties. The value at risk fitting method is designed to obtain the risk appetite parameters. The transitivity improvement approach of the GALTS is provided. A new QDM process involving risk appetites of the decision maker is proposed.