ALBERT

Description: 〈p〉Publication date: 16 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 275, Issue 3〈/p〉 〈p〉Author(s): Da Lu, Fatma Gzara〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We study the vehicle routing problem with time windows under demand uncertainty. Such a problem arises in fuel delivery to gas stations, to farms, or to production plants. We suggest a robust formulation where uncertain demand has support defined by cardinality constrained sets. The model ensures the feasibility of each route when demand values on the route change in predefined set. The range is customer specific, does not assume any probability distribution of demand, and may be estimated using historical demand data. We develop a branch-and-price-and-cut algorithm where the pricing problem is a robust resource constrained shortest path problem. We extend the rounded capacity inequalities to the robust case and suggest a separation procedure that solves a robust bin packing problem. The bound is further strengthened using 2-path and subset row inequalities and the full algorithm is tested on an extension of the benchmark Solomon instances. The robust schedules are simulated and compared to schedules obtained by solving the deterministic problem under varying levels and distributions of uncertainty. Simulation results show that the robust model provides superior protection against demand uncertainty. Over all scenarios, only 0.57% of the robust solutions turn out to be infeasible compared to 41.62% of the deterministic ones. Such protection against infeasibility comes with an 11.22% increase in routing costs on average.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 May 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 274, Issue 3〈/p〉 〈p〉Author(s): 〈/p〉

Description: 〈p〉Publication date: 1 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 276, Issue 1〈/p〉 〈p〉Author(s): Elmar Lukas, Sascha Thiergart〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Significant progress has been made toward understanding a levered firm's optimal investment policy under uncertainty. To date, however, not much work has concentrated on the time scale trade-off and collectively held real options. We employ a game-theoretic real option model between a firm and a government to analyze the effect of uncertainty and investment stimulus in the form of cash grants on optimal investment timing, financing and investment scaling. We find that the jointly held real option between the firm and the government leads to underinvestment, regardless of whether the firm has the possibility to issue debt. Subsidies, however, reduce the level of underinvestment. Notably, the results indicate that even though levered firms receive less support, they invest more than unlevered firms. This challenges recent findings that a firm's optimal investment level is not affected by the way it finances a project. Similarly, we find that for realistic parameter constellations the levered firm's optimal investment threshold can be higher than that of its unlevered counterpart. This indicates that the availability of tax shield benefits does not necessarily serve as an incentive to invest earlier. Finally, we show that the effect of cash flow uncertainty on the equilibrium level of grants is ambiguous and triggers the switch from a subsidy to non-subsidy regime.〈/p〉〈/div〉

Description: 〈p〉Publication date: 16 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 275, Issue 3〈/p〉 〈p〉Author(s): Francesca Mariani, Maria Cristina Recchioni, Mariateresa Ciommi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper formulates a portfolio optimization in a friction market as a suitable stochastic control problem with hyperbolic absolute risk aversion utility functions under a specific functional form of the amount of wealth invested in the risky asset inspired by the Merton optimal dynamics. The state variable is the amount of wealth invested in the risky asset, while the control variable is the share rate. This formulation has two advantages. First, it provides a portfolio where the optimality is obtained by minimizing the probability of large changes in the portfolio composition with consequently low frictions, while allowing for an explicit solution to the optimal control problem. Second, with this formulation, it can be proven that this optimal solution is the optimal solution of the frictionless Merton problem for corrected price dynamics. The corrected dynamics shows that the market friction increases the risk premium by an additional term that is an elementary function of the risk associated with the market frictions, as well the price of the risk, which also depends on the risk aversion of the investor. In addition, the corrected dynamics supports the existence of a shadow price in friction markets as already shown in Kallsen and Muhle-Karbe (2010). Finally, an empirical analysis to assess the model performance is illustrated on five-minute data of four major traded stocks, three market indexes, and four exchange rates including the Bitcoin/USD rate.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 276, Issue 1〈/p〉 〈p〉Author(s): Jingyuan Shen, Lirong Cui, Yizhong Ma〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In both engineering practice and theory, system degradation is often modeled by a stochastic process. When the degradation behavior is significantly affected by environments, a single stochastic process is not suitable to describe the system degrading in different environments any more. In this paper, a model is developed for systems degrading in dynamic environments subject to several imperfect maintenance actions before each replacement, in which the degradation of the system within different environments is governed by different stochastic processes. The evolution of the environments is described by a Markov process. To describe the system performance, system availabilities including instantaneous availability and limiting average availability, and some time distributions of interest are some important indexes which are derived in the paper. Then the problem of optimal maintenance policy is formulated by considering constraints of availability and operating times. The objective of the optimal maintenance policy is to find an optimal number of imperfect maintenance actions between two adjacent replacements. The existence of the optimal solution is proved and the algorithm of calculation is also presented. Numerical examples are given to illustrate the results obtained in the paper.〈/p〉〈/div〉

Description: 〈p〉Publication date: 16 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 275, Issue 3〈/p〉 〈p〉Author(s): Amirmohsen Golmohammadi, Elkafi Hassini〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We consider capacitated joint lot-sizing and pricing problems when supply and a price-sensitive demand are uncertain. In cases where there is excess capacity, the decision maker has the option to rent her capacity. We model the case where price and production quantity are determined before the yield is realized (joint decisions) as well as the case where the price is determined after the yield is known (sequential decisions). In the joint decisions case, we introduce the concept of expected demand fill rate elasticity and characterize the conditions in which a one-sided production and pricing policy is optimal. We extend the results to the case when there is a fixed production cost and show that a two-sided production and pricing policy is optimal. We also investigate the conditions under which it is optimal for the decision maker to rent her capacity. In addition, we look at the case in which the decision maker is risk averse and analyze the effect of the risk attitude on the optimal price and production quantity. We extend our model to the multi-period case and the case where there is a lead time to acquire new capacity. We show that a one-sided production, pricing and capacity planning policy is optimal in this situation as well. We apply our model to the agricultural sector and present numerical examples using data from the California almond industry.〈/p〉〈/div〉

Description: 〈p〉Publication date: 16 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 275, Issue 3〈/p〉 〈p〉Author(s): D.I. Singham〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We develop a method for finding approximate solutions to the continuous agent type principal-agent problem when analytical methods are not available. The solution is calculated by solving a discrete agent type version of the problem using sample average approximation and bootstrapping. We show how a solution to the approximate problem can be used to derive a lower bound and expected upper bound for the optimal objective function, and evaluate the error associated with the approximation. Numerical examples illustrate convergence in the approximate solution to the true solution as the number of samples increases. This works yields a method for obtaining some tractability in continuous type principal-agent problems where solutions were previously unavailable.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 276, Issue 1〈/p〉 〈p〉Author(s): Zhuzhu Song, Wansheng Tang, Ruiqing Zhao〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In the shipping market, the transportation of cargo back and forth over two routes is not independent because empty container repositioning (ECR) from a surplus port to a deficit port may occur. To solve canvassing problems with ECR, we study a sea-cargo supply chain in which a liner company and a forwarder provide transportation services between two ports. The forwarder can order capacity from the liner company through one-way or round-trip canvassing. Furthermore, the liner company may sell capacity to both the forwarder and the shipper, which is called liner company encroachment. The results show that round-trip canvassing does not always outperform one-way canvassing for the liner company, although the former can eliminate ECR due to differences in the forwarder’s orders in two directions. Regarding the canvassing strategy, both parties can achieve a win-win situation under certain conditions without encroachment, whereas they can always achieve a win-win situation in the presence of encroachment. Moreover, encroachment always makes the liner company better off but damages the freight forwarder. We also analyze how the forwarder’s order quantity is affected by the canvassing strategy and encroachment.〈/p〉〈/div〉

Description: 〈p〉Publication date: 16 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 275, Issue 3〈/p〉 〈p〉Author(s): Ata Jalili Marand, Ou Tang, Hongyan Li〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Delivery time and delivery reliability are two top-level measures of delivery performance, and they both influence customers’ perception of service value. However, the classic queue-pricing literature emphasizes the former and ignores the latter. In order to bridge the gap between research and practice, this study addresses the interactive impact of price, delivery time, and delivery-reliability level on the equilibrium behaviour of rational customers and the optimal decisions of a revenue-maximizing service provider. We assume that the customers’ sensitivity to the delivery-reliability level is characterized by an increasing concave service value function. We model the operations of the service provider as an 〈em〉M〈/em〉/〈em〉M〈/em〉/1 queue. Two cases are investigated: homogeneous customers and heterogeneous customers. For the homogeneous customers case, we analytically characterize the service provider’s optimal price, delivery time, and delivery-reliability level decisions. We show how the service provider’s decisions on whether to provide faster or more reliable service are affected when different problem parameters are subject to variation. For instance, when customers become more sensitive to the delivery-reliability level, the service provider increases the delivery-reliability level at the expense of a longer delivery time. However, the optimal price may either increase or decrease depending on a benchmark value for the delivery-reliability level. For the heterogeneous customers case, our results suggest that when the potential arrival rate is sufficiently high, the service provider always benefits from markets with higher levels of customer heterogeneity.〈/p〉〈/div〉

Description: 〈p〉Publication date: 16 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 275, Issue 3〈/p〉 〈p〉Author(s): Isabella M. Lami, Elena Tavella〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Applications of Problem Structuring Methods (PSMs) are extensively reported in the literature; however, their evaluation remains challenging. Moreover, scholars’ disagreement on how to evaluate and compare PSMs constrains research that seeks to show whether certain PSMs are more useful than others within particular contexts and ‘better than doing nothing’. Drawing on scholars’ suggestion to evaluate PSMs through a link between action, outcomes and context, we address this gap by adopting an exploratory, quasi-experimental research design with MSc students to evaluate and compare three workshops supported by the use of (i) Strategic Choice Approach, (ii) Soft Systems Methodology and (iii) a non-PSM supported approach, and report on the findings of a quantitative and qualitative analysis. We explore the 〈em〉usefulness〈/em〉 of PSMs for achieving outcomes that can support decision making. Our findings show variation in the usefulness of the three approaches and the different workshop outcomes.〈/p〉〈/div〉

Description: 〈p〉Publication date: 16 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 275, Issue 3〈/p〉 〈p〉Author(s): Guillermo Durán, Santiago Durán, Javier Marenco, Federico Mascialino, Pablo A. Rey〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Operations research methods are applied to design the season schedules of Argentina’s professional basketball leagues using a format adopted in 2014 by the top two divisions. Following the setup used by the National Basketball Association (NBA) in North America, games are played any day of the week and away games are scheduled in road trips of one to four consecutive games. The main scheduling objective is to reduce the teams’ total travel distance compared to previous season formats through the use of predetermined trips submitted by the teams. The mathematical form of the problem is a variation on the well-known Travelling Tournament Problem. The modelling is divided into two successive stages, the first one defining the sequences in which each team plays the other teams and the second one assigning the days on which each game is played. Both stages use integer programming models that incorporate a series of constraints reflecting criteria requested by the Argentine Basketball Club Association. Implementation of the models has generated average travel distance reductions of more than 30% per away game, with consequential benefits in lower travel costs and less player fatigue.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 276, Issue 1〈/p〉 〈p〉Author(s): K Hervé Dakpo, Alfons Oude Lansink〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This article extends the by-production model to the dynamic context of adjustment costs associated with investment. The empirical application focuses on panel data of French suckler cow farms over the period 1978–2014, considering emissions of greenhouse gases as bad output. The paper estimates input and output-specific technical inefficiency scores in the dynamic context and compares them with efficiency measures from the conventional static context. Our results reveal significant differences between inefficiency scores derived from the static and the dynamic frameworks. For all variables except meat production (the good output), the inefficiency score is lower in the dynamic context than in the static context.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 276, Issue 1〈/p〉 〈p〉Author(s): Tolga Bektaş, Luís Gouveia, Daniel Santos〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper studies the asymmetric Hamiltonian p-median problem, which consists of finding 〈em〉p〈/em〉 mutually disjoint circuits of minimum total cost in a directed graph, such that each node of the graph is included in one of the circuits. Earlier formulations view the problem as the intersection of two subproblems, one requiring at most 〈em〉p〈/em〉, and the other requiring at least 〈em〉p〈/em〉 circuits, in a feasible solution. This paper makes an explicit connection between the first subproblem and subtour elimination constraints of the traveling salesman problem, and between the second subproblem and the so-called path elimination constraints that arise in multi-depot/location-routing problems. A new formulation is described that builds on this connection, that uses the concept of an acting depot, resulting in a new set of constraints for the first subproblem, and a strong set of (path elimination) constraints for the second subproblem. The variables of the new model also allow for effective symmetry-breaking constraints to deal with two types of symmetries inherent in the problem. The paper describes a branch-and-cut algorithm that uses the new constraints, for which separation procedures are proposed. Theoretical and computational comparisons between the new formulation and an adaptation of an existing formulation originally proposed for the symmetric Hamiltonian p-median problem are presented. Computational results indicate that the algorithm is able to solve asymmetric instances with up to 171 nodes and symmetric instances with up to 100 nodes.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 276, Issue 1〈/p〉 〈p〉Author(s): Yilin Fang, Quan Liu, Miqing Li, Yuanjun Laili, Duc Truong Pham〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In the remanufacturing industries, automated disassembly becomes one of the most promising solution in achieving economic benefit. Robotic disassembly line balancing is a key problem that enables automated disassembly to be implemented at industrial scale. This paper focuses on evolutionary many-objective optimization for mixed-model disassembly line balancing with multi-robotic workstations. In each workstation, multiple skilled robots perform different tasks belonging to the different end-of-life products or subassemblies simultaneously. Based on the transformed AND/OR graph and parallel disassembly, a mathematical programming model is proposed to minimize the cycle time, the total energy consumption, the peak workstation energy consumption, and the number of robots being used simultaneously. Furthermore, a problem knowledge-leveraging evolutionary algorithm, including encoding/decoding scheme, initialization approach and problem-specific variation operators, is developed to deal with the above problem. Comprehensive experiments are conducted based on 8 product models and 63 problem instances generated in this study. In particular, a comparative study of our proposed algorithm and 5 representative evolutionary algorithms selected from the 3 classes of approaches of dealing with many-objective problems are provided. Then some insights with respect to the design of evolutionary algorithms for our problem are gained from the investigation.〈/p〉〈/div〉 〈h5〉Graphical abstract〈/h5〉 〈div〉〈p〉〈figure〉〈img src="https://ars.els-cdn.com/content/image/1-s2.0-S0377221718311081-fx1.jpg" width="301" alt="Graphical abstract for this article" title=""〉〈/figure〉〈/p〉〈/div〉

Description: 〈p〉Publication date: 16 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 275, Issue 3〈/p〉 〈p〉Author(s): Lei Fan, Kai Pan, Yongpei Guan〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Due to increased utilization of gas-fired combined-cycle units for electric power generation in the U.S., accurate and computationally efficient models for operating these units are crucial to keep an electricity system reliable. The recently proposed edge-based formulation for combined-cycle units helps accurately describe the operations of combined-cycle units by capturing the state transition processes and physical constraints for each turbine. In this paper, we derive tighter constraints and several families of strong valid inequalities to strengthen the edge-based model and improve its computational efficiency by exploring the physical characteristics of combined-cycle units and utilizing the edge-based modeling framework. Meanwhile, we provide the validity and facet-defining proofs for certain inequalities. Finally, the computational results indicate that our derived formulation significantly reduces the computational time, which verifies the effectiveness of proposed tighter constraints and strong valid inequalities.〈/p〉〈/div〉

Description: 〈p〉Publication date: 16 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 275, Issue 3〈/p〉 〈p〉Author(s): David Franz Koza〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Tactical service scheduling and operational cargo allocation are two interdependent problems in liner shipping. The schedules and sailing speeds of individual liner shipping services and the synchronization among all services determine the transit times of containers through a liner shipping network. On the other hand, the market demand in terms of container volume and expected transit times between origin and destination ports drive the schedule design of liner shipping services. We present a graph-based model and a branch-and-price algorithm to solve the combined problem. The goal is to minimize the difference between fuel consumption costs and revenues from transporting containers under consideration of transit time limits. Fuel consumption is modeled as a function of both speed and payload. Results are presented for 12 liner shipping networks and emphasize the importance of explicitly modeling schedules in large networks; transshipment times and thus transit times may be severely miscalculated otherwise. The results further show that neglecting payload in the fuel consumption function can result in suboptimal service schedules and cargo routing decisions.〈/p〉〈/div〉

Description: 〈p〉Publication date: 16 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 275, Issue 3〈/p〉 〈p〉Author(s): Eitan Bachmat〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Airlines have been implementing several policies which partition passengers into groups of fast and slow passengers and placing them in different queue/row combinations. For example, the policy which provides boarding precedence to passengers needing assistance and families traveling with small children, or a policy which provides precedence to passengers with no overhead bin luggage. The idea of separating customers into groups with different service times also appears in the very different setting of express line queues, for example, in the supermarket or in server farms. Such queues have been extensively explored in the last 20 years in the queueing theoretic literature.〈/p〉 〈p〉We show that the two systems, airplane boarding with slow and fast passenger groups and express line queues are intimately related in the asymptotic regime where the ratio between the slowest and fastest customer becomes large. We produce good algorithms for placing different groups of passengers in the airplane boarding setting with competitive guarantees compared to optimal placements. We then show how we can analyze the airplane boarding setting using results from the express line setting. This relation provides a novel bridge between the theory of project management and the critical path method to which airplane boarding belongs and queueing theory to which express line queues belong.〈/p〉 〈p〉The analysis uses very basic notions and arguments from geometry, but in the setting of space-time (Lorentzian) geometry, a geometry which was invented to study relativity theory.〈/p〉 〈/div〉

Description: 〈p〉Publication date: 1 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 275, Issue 2〈/p〉 〈p〉Author(s): Frederic Murphy, Axel Pierru, Yves Smeers〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Government involvement in managing domestic prices of energy and other commodities is a major issue in emerging economies. We examine one aspect of the problem, price controls, when governments set or cap prices. We show how a Mixed Complementarity Problem (MCP) formulation can be used to model and assess the impacts of price controls in multi-sector economic-equilibrium models. Both the gains from deregulation and the consequences of imposing new or altering existing regulations can thus be measured. We present three distinct models that capture different price-control situations: firms have to meet demand and receive an implicit subsidy, demand rationing occurs due to an associated price control constraint, and subsidies limit demand rationing. We present an approach to measuring the effects on the equilibrium in the first case and the levels of disequilibrium induced by price controls in the other cases. We also show how to determine the most efficient allocation program when a government engages in rationing. We illustrate the cases described by these models using markets that have or had price controls.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 275, Issue 2〈/p〉 〈p〉Author(s): Janiele Custodio, Miguel Lejeune, Antonio Zavaleta〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉A recent article by Zaghian et al. (2018) proposed reformulations, proposed reformulations of stochastic chance-constrained programming models for radiation therapy treatment planning. This note questions the validity of the proposed reformulations and shows that they are not equivalent to the original formulations. Two numerical examples illustrate that the approach proposed by Zaghian et al. (2018) provides approximation problems and not reformulations.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 26 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Georgios Manthoulis, Michalis Doumpos, Constantin Zopounidis, Emilios Galariotis〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Bank failure prediction models usually combine financial attributes through binary classification approaches. In this study we extend this standard framework in three main directions. First, we explore the predictive power of attributes that describe the diversification of banking operations. Second, we consider the prediction of failure in a multi-period context. Finally, an enhanced ordinal classification framework is introduced, which considers multiple instances of failed banks prior to failure (up to three years prior to bankruptcy). Various ordinal models are developed using techniques from multiple criteria decision analysis, statistics, and machine learning. Moreover, ensemble models based on error-correcting output codes are examined. The analysis is based on a sample consisting of approximately 60,000 observations for banks in the United States over the period 2006-2015. The results show that diversification attributes improve the predictive power of bank failure prediction models, mainly for mid to long-term prediction horizons. Moreover, ordinal classification models provide a better description of the state of the banks prior to failure and are competitive to standard binary classification models.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 26 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Priyank Sinha, Sameer Kumar, Surya Prakash〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Supply chains operating in informal sector of emerging economies are mostly characterised by inefficiencies, highly price sensitive customers, fragmented markets, frequent operational disturbances, and members with irrational profit seeking behaviour (naïve members). We focus on cost disturbances and measure their effects in terms of demand variation (QV) in muti-echelon informal supply chain. We show how naïve members contribute to amplification and transmission of QV across the supply chain. Strategic members (characterised by rational profit seeking behaviour) on the contrary, dampens the transmission of QV. Efficient reconfiguration solutions are proposed which minimize the QV for a specific cost disturbance scenario. Rational profit seeking behaviour is one of the criteria for selecting members in this reconfiguration. Rapid reconfiguration in these supply chains is possible due to the existence of informal contracts. A robust deviation reconfiguration solution is also proposed which performs satisfactorily over entire disturbance scenario set. Goodness of this solution is evaluated according to two metrics; namely cost penalty and robust efficiency. Lastly, an illustrative case study on apparel supply chain is presented and it is inferred from the discussion that large aggregators are both cost and robust efficient, hence their presence in supply chain improves performance of robust solution along the two metrics (cost penalty, robust efficiency).〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 26 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Matteo Fischetti, Michele Monaci〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper we consider the Mixed-Integer Bilinear Programming problem, a widely-used reformulation of the classical mixed-integer quadratic programming problem. For this problem we describe a branch-and-cut algorithm for its exact solution, based on a new family of intersection cuts derived from bilinear-specific disjunctions. We also introduce a new branching rule that is specifically designed for bilinear problems. We computationally analyze the behavior of the proposed algorithm on a large set of mixed-integer quadratic instances from the MINLPlib problem library. Our results show that our method, even without intersection cuts, is competitive with a state-of-the-art mixed-integer nonlinear solver. As to intersection cuts, their extensive use at each branching node tends to slow down the solver for most problems in our test bed, but they are extremely effective for some specific instances.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 26 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Shuhua Chang, Zhaowei Zhang, Xinyu Wang, Yan Dong〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We propose an optimal customer relationship marketing policy in a duopolistic market, where each firm’s market share depends on the CRM decisions of its own and of its competitors. The evolution process of the number of customers served by each firm is governed by a differential equation in which customer acquisition and retention processes are considered. A differential game model is presented to determine the optimal acquisition and retention expenditures of players. We derive a Nash equilibrium of a duopolistic differential game using the Hamilton-Jacobi-Bellman equations. Our results can be summarized mainly by the following three points. First, the optimal acquisition and retention expenditure strategies depend on each firm’s marginal customer equity, but not on the market share or the number of customers. Second, in response to the variation of the firm’s parameters, if its acquisition effectiveness is greater than its retention effectiveness, the firm would take action by making the same investment decision as its rival’s; instead, if its acquisition effectiveness is lower than its retention effectiveness, its rival would take action by making the different investment decision from the firm’s. Last, besides a mature product market, we also consider a market with remaining potential. In this case, a firm’s marginal customer equity can be decomposed into two components: the marginal customer equity from its own and from its rival’s. We show that the firm’s optimal investments in retention and acquisition are both positively related with a weighted difference between two components of the marginal customer equity.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 24 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Thomas Setzer, Sebastian M. Blanc〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Existing techniques for solving large multidimensional knapsack problems (MKP) aim at reducing the number of variables (items). While these approaches solve problems with many items efficiently, their performance declines with increasing number of constraints. We propose empirical orthogonal constraint generation (EOCG) to reduce the number of constraints. The intuition is that, geometrically, each constraint is a dimension of a hypercube, with capacity as side length, while items correspond to vectors with their weights as coordinates along the dimensions–the basis vectors. MKP problems guarantee the feasibility of a solution by one constraint on the coordinate sum for each dimension. In contrast, EOCG aims at reducing the number of dimensions to be constrained by using new basis vectors to represent the optimal item set with less coordinates. The key challenge is that a concise problem representation has to be formulated so that its solution also solves the original MKP. EOCG allows for this transfer by successively choosing new dimensions so that capacity violations on the next dimension added decline with a steep descent until a valid and optimal solution is found. We evaluate EOCG on established benchmark instances, which EOCG often solves in lower dimensions. EOCG finds the currently best-known solutions to all high-dimensional Chu & Beasley instances, improves the best-known solutions to two Glover & Kochenberger instances, and proves the optimality of a solution to another instance.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 26 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Andreas Bortfeldt, Junmin Yi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The Split Delivery Vehicle Routing Problem with three-dimensional loading constraints (3L-SDVRP) combines vehicle routing and three-dimensional loading with additional packing constraints. In the 3L-SDVRP splitting deliveries of customers is basically possible, i.e. a customer can be visited in two or more tours. We examine essential problem features and introduce two problem variants. In the first variant, called 3L-SDVRP with 〈em〉forced〈/em〉 splitting, a delivery is only split if the demand of a customer cannot be transported by a single vehicle. In the second variant, termed 3L-SDVRP with 〈em〉optional〈/em〉 splitting, splitting customer deliveries can be done any number of times. We propose a hybrid algorithm consisting of a local search algorithm for routing and a genetic algorithm and several construction heuristics for packing. Numerical experiments are conducted using three sets of instances with both industrial and academic origins. One of them was provided by an automotive logistics company in Shanghai; in this case some customers per instance have a total freight volume larger than the loading space of a vehicle. The results prove that splitting deliveries can be beneficial not only in the one-dimensional case but also when goods are modeled as three-dimensional items.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 12 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Maarten Otten, Judith Timmer, Annemieke Witteveen〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Frequency and duration of follow-up for breast cancer patients is still under discussion. Currently, in the Netherlands follow-up consists of annual mammography for the first five years after treatment and does not depend on the personal risk of developing a locoregional recurrence or a second primary tumor. The aim of this study is to gain insight in how to allocate resources for optimal and personalized follow-up. We formulate a discrete-time Partially Observable Markov Decision Process (POMDP) over a finite horizon with both discrete and continuous states, in which the size of the tumor is modeled as a continuous state. Transition probabilities are obtained from data of the Netherlands Cancer Registry. We show that the optimal value function of the POMDP is piecewise linear and convex and provide an alternative representation for it. Under the assumption that the tumor growth follows an exponential distribution and that the model parameters can be described by exponential functions, the optimal value function can be obtained from the parameters of the underlying probability distributions only. Finally, we present results for a stratification of the patients based on their age to show how this model can be applied in practice.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 12 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Rolf Färe, Giannis Karagiannis〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this note we compare and contrast arithmetic and geometric share-weighting aggregations of ratio expressions (i.e., relative scores) and we provide conditions under which they result in approximately the same value of the aggregate measure. We also provide some comparative empirical results about the differences among the suggested alternative aggregate measures.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 12 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Fabien Lehuédé, Olivier Péton, Fabien Tricoire〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Vehicle routing problems generally aim at designing routes that minimize transportation costs. However, in practical settings, many companies also pay attention at how the workload is distributed among its drivers. Accordingly, two main approaches for balancing the workload have been proposed in the literature. They are based on minimizing the duration of the longest route, or the difference between the longest and the shortest routes, respectively. Recently, it has been shown on several occasions that both approaches have some flaws. In order to model equity we investigate the lexicographic minimax approach, which is rooted in social choice theory. We define the leximax vehicle routing problem which considers the bi-objective optimization of transportation costs and of workload balancing. This problem is solved by a heuristic based on the multi-directional local search framework. It involves dedicated large neighborhood search operators. Several LNS operators are proposed and compared in experimentations.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 12 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Jérôme Detemple, Souleymane Laminou Abdou, Franck Moraux〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper examines the valuation of American knock-out and knock-in step options. The structures of the immediate exercise regions of the various contracts are identified. Typical properties of American vanilla calls, such as uniqueness of the optimal exercise boundary, up-connectedness of the exercise region or convexity of its t-section, are shown to fail in some cases. Early exercise premium representations of step option prices, involving the Laplace transforms of the joint laws of Brownian motion and its occupation times, are derived. Systems of coupled integral equations for the components of the exercise boundary are deduced. Numerical implementations document the behavior of the price and the hedging policy. The paper is the first to prove that finite maturity exotic American Options written on a single underlying asset can have multiple disconnected exercise regions described by a triplet of coupled boundaries.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 13 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Tobias Düsterhöft, Alexander Hübner, Kai Schaal〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉This paper addresses the shelf-space and replenishment problem where a retailer must decide how much shelf space to assign to items and how frequently to replenish retail shelves. Since space is limited, these two decisions are interdependent. Existing optimization models approximate shelf sizes by reducing the shelf to a one-dimensional space. However, in practice shelf sizes and their shelf segments vary in width, height and depth. Without considering the actual shelf dimensions, results from optimization models cannot be transferred to actual shelves. To account for these constraints, we propose the first shelf-space model capable of modeling three-dimensional shelf characteristics. Our model determines optimal shelf quantities, an item’s optimal shelf segment, as well as optimal replenishment frequencies. To do this we include a space-elastic function that impacts the customer demand for an item depending on its allocated shelf space. We also consider different shelf segments in different positions.〈/p〉 〈p〉To solve the non-linear integer problem (NLIP), we develop an optimal solution approach based on precalculations to transfer the model into a binary-integer problem that considers logical item-specific bounds to significantly reduce computation time. We conduct various numerical analyses. First, we show that results are generated time-efficiently, even for large-scale data instances. We then analyze the extent to which the stylized assumption of a one-dimensional space results in non-feasible solutions in practice, thus underlining the necessity of our multi-dimensional model. Finally, the results of a field test in which model solutions were actually applied in practice show how one of the largest German grocery retailers was able to increase its profits across different categories and stores by 9.1% on average compared to a control group by using the approach.〈/p〉 〈/div〉

Description: 〈p〉Publication date: Available online 15 October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Pelin G. Canbolat〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper considers a clearing service system where customers arrive according to a Poisson process, and decide to join the system or to balk in a boundedly rational manner. It assumes that all customers in the system are served at once when the server is available and times between consecutive services are independently and identically distributed random variables. Using logistic quantal-response functions to model bounded rationality, it first characterizes customer utility and system revenue for fixed price and degree of rationality, then solves the pricing problem of a revenue-maximizing system administrator. The analysis of the resulting expressions as functions of the degree of rationality yields several insights including: (i) For an individual customer, it is best to be perfectly rational if the price is fixed; however, when customers have the same degree of rationality and the administrator prices the service accordingly, a finite nonzero degree of rationality uniquely maximizes customer utility, (ii) System revenue grows arbitrarily large as customers tend to being irrational, (iii) Social welfare is maximized when customers are perfectly rational, (iv) In all cases, at least 78% of social welfare goes to the administrator. The paper also considers a model where customers are heterogeneous with respect to their degree of rationality, explores the effect of changes in distributional parameters of the degree of rationality for fixed service price, provides a characterization for the revenue-maximizing price, and discusses the analytical difficulties arising from heterogeneity in the degree of bounded rationality.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 10 October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Ming Tang, Huchang Liao, Jiuping Xu, Dalia Streimikiene, Xiaosong Zheng〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Large-scale group decision making, which involves dozens to hundreds of experts, is attracting increasing attention and has become an important topic in the field of decision making. Because of the clustering process, a large-scale group decision making problem can be divided into two levels: inter sub-group and intra sub-group. In existing consensus models under the large-scale group decision making environment, the degree of consensus within the intra sub-group is not truly taken into account. To deal with this issue, this work develops an adaptive consensus model for the sub-groups composed of hybrid strategies, with or without a feedback mechanism, according to the different levels of inter and intra degrees of consensus. These different levels of consensus are divided into four scenarios (high–high, high–low, low–high, low–low), and different feedback suggestions are generated corresponding to different cases. This hybrid mechanism can reduce the cost of supervision for the moderator. The fuzzy c-means clustering algorithm is used to classify experts. A weight-determining method combining the degree of cohesion and the size of a sub-group is introduced. Finally, an illustrative example is offered to verify the practicability of the proposed model. Some discussions and comparisons are provided to reveal the advantages and features of the proposed model.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 10 October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Thibaut Vidal, Gilbert Laporte, Piotr Matl〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Vehicle routing problems have been the focus of extensive research over the past sixty years, driven by their economic importance and their theoretical interest. The diversity of applications has motivated the study of a myriad of problem variants with different attributes. In this article, we provide a concise overview of existing and emerging problem variants. Models are typically refined along three lines: considering more relevant objectives and performance metrics, integrating vehicle routing evaluations with other tactical decisions, and capturing fine-grained yet essential aspects of modern supply chains. We organize the main problem attributes within this structured framework. We discuss recent research directions and pinpoint current shortcomings, recent successes, and emerging challenges.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 9 October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Erik Miehling, Demosthenis Teneketzis〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper investigates monotonicity properties of optimal policies for two-action partially observable Markov decision processes when the underlying (core) state and observation spaces are partially ordered. Motivated by the desirable properties of the monotone likelihood ratio order in imperfect information settings, namely the preservation of belief ordering under conditioning on any new information, we propose a new stochastic order (a generalization of the monotone likelihood ratio order) that is appropriate for when the underlying space is partially ordered. The generalization is non-trivial, requiring one to impose additional conditions on the elements of the beliefs corresponding to incomparable pairs of states. The stricter conditions in the proposed stochastic order reflect a conservation of structure in the problem – the loss of structure from relaxing the total ordering of the state space to a partial order requires stronger conditions with respect to the ordering of beliefs. In addition to the proposed stochastic order, we introduce a class of matrices, termed generalized totally positive of order 2, that are sufficient for preserving the order. Our main result is a set of sufficient conditions that ensures existence of an optimal policy that is monotone on the belief space with respect to the proposed stochastic order.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 11 October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): George Christodoulakis〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The commodity futures curve is viewed as a market-based path forecast, a term structure, optimizing multivariate loss preferences. Based on the forecast decision setting, we apply estimation of flexible multivariate loss functions, which reveal the preference term structure along the futures curve, which can be flat, smoothly sloping or oscillating, rotating among optimism, pessimism and symmetry. Evidence from the thirty main world commodities around the global crisis period, accommodates the futures curve forecast rationality questioned in the literature, suggesting the presence of joint preference asymmetries for longer maturities and symmetries for shorter ones. This reveals joint optimistic preferences for most commodities until 2004, evolving into oscillating preferences rotating within the term structure from symmetry to pessimism and optimism in 2005-2008 and finally back to weaker optimism until 2013.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 7 October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Guilherme Dean Pelegrina, Leonardo Tomazeli Duarte, Michel Grabisch, João Marcos Travassos Romano〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In several multicriteria decision making problems, it is important to consider interactions among criteria in order to satisfy the preference relations provided by the decision maker. This can be achieved by using aggregation functions based on fuzzy measures, such as the Choquet integral and the multilinear model. Although the Choquet integral has been studied in a large number of works, one does not find the same literature with respect to the multilinear model. In this context, the contribution of this work is twofold. We first provide a formulation of the multilinear model by means of a 2-additive capacity. A second contribution lies in the problem of capacity identification. We consider a supervised approach and apply optimization models with and without regularization terms. Results obtained in numerical experiments with both synthetic and real data attest the performance of the considered approaches.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 3 October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Kaoru Tone, Tsung-Sheng Chang, Chen-Hui Wu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper proposes slacks-based measure (SBM) data envelopment analysis (DEA) models that handle negative data. Unlike existing negative data allowable DEA models, the proposed SBM DEA models are consistent with ordinary SBM models and units invariant, they handle various types of returns to scale, and they avoid division by zero. These new SBM DEA models transform original negative inputs and outputs into positive counterparts based on a newly defined “base point”. Hence, these models are referred to as the BP-SBM DEA models. In addition to the basic BP-SBM DEA models, this research further develops data-oriented and application-oriented BP-SBM DEA-type models for different application problems involving negative data. Numerical examples are provided to illustrate various aspects and implementation details of these models.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 2 October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Dimitrina S. Dimitrova, Zvetan G. Ignatov, Vladimir K. Kaishev, Senren Tan〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We develop an efficient method for computing the probability that a non-decreasing, pure jump (compound) stochastic process stays between arbitrary upper and lower boundaries (i.e., deterministic functions, possibly discontinuous) within a finite time period. The compound process is composed of a process modelling the arrivals of certain events (e.g., demands for a product in inventory systems, customers in queuing, or claims/capital gains in insurance/dual risk models), and a sequence of independent and identically distributed random variables modelling the sizes of the events. The events arrival process is assumed to belong to the wide class of point processes with conditional stationary independent increments which includes (non-)homogeneous Poisson, binomial, negative binomial, mixed Poisson and doubly stochastic Poisson (i.e., Cox) processes as special cases. The proposed method is based on expressing the non-exit probability through Chapman–Kolmogorov equations, re-expressing them in terms of a circular convolution of two vectors which is then computed applying fast Fourier transform (FFT). We further demonstrate that our FFT-based method is computationally efficient and can be successfully applied in the context of inventory management (to determine an optimal replenishment policy), ruin theory (to evaluate ruin probabilities and related quantities) and double-barrier option pricing or simply computing non-exit probabilities for Brownian motion with general boundaries.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 March 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 281, Issue 2〈/p〉 〈p〉Author(s): 〈/p〉

Description: 〈p〉Publication date: Available online 11 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Huanhuan Zhang, Gang Kou, Yi Peng〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In a group decision-making (GDM) process, experts reach a consensus after discussion and persuasion, which requires a moderator to spend time and resource to persuade experts to change their original opinions. Since a unanimous agreement is hard to achieve and costly, a consensus degree or soft consensus was used and various approaches have been proposed to measure the level of consensus in GDM. Though cost is an important factor in GDM, few works have calculated consensus cost occurred during the process. Moreover, the degree of consensus was not considered in the minimum cost consensus study. The objective of this paper is to propose consensus models under a certain degree of consensus, which considers both consensus degree and cost in GDM. To do this, we develop a generalized soft cost consensus model under a certain degree of consensus, which is built by defining a consensus level function and a generalized aggregation operator. A soft minimum cost consensus model is constructed based on arithmetic weighted average operator (AWAO), and the maximum return model is constructed through its dual model. The cost (compensation) is studied from both the perspectives of a moderator and individual experts. The relationships between the two soft consensus cost models are analyzed, and the economic significance of the models are also discussed. Numerical examples are used to explain the proposed models. In addition, to show the usability of the proposed models in real-world context, we apply the proposed models to a loan consensus scenario using data from an online peer-to-peer lending platform.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 8 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Martin Quinteros, Monique Guignard, Andres Weintraub, Marc Llambias, Camilo Tapia〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper describes the creation, development, implementation and impact of a system optimizing the distribution of petroleum products by pipeline at Empresa Nacional del Petroleo, the state-owned oil company in Chile. Refined oil products are sent via a pipeline from a refinery in the South to terminals located between the refinery and the center of the country. The sequencing of different products needs to be determined. This scheduling used to be done by hand, based on experience and on the need to satisfy physical constraints in the pipeline, supply constraints in and out of the refinery and demand constraints from clients. The complexity of the problem and the need to cut down on operating costs suggested turning to optimization, specifically integer programming. The positive results of the project owe a lot to the constant interaction between schedulers, decision makers, and the optimization team, and to the insights provided by the schedulers that allowed to limit the model’s complexity. The resulting system is easy to use for schedulers thanks to a graphical user interface (GUI), and its solution requires little computer time. It is used once a month for planning the next month operations and negotiating delivery dates and amounts with the clients based on the solution suggested by the model. In addition, in case of an operational disruption in the middle of a month, the model is run again after updating the parameters accordingly. Operating cost savings are of the order of 10%.〈/p〉〈/div〉

Description: 〈p〉Publication date: 16 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 276, Issue 2〈/p〉 〈p〉Author(s): Gyöngyi Kovacs, Mohammad Moshtari〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Given the substantial costs of natural and man-made disasters (i.e., mortality, morbidity, and financial losses), scholars in operations management and operations research have conducted extensive research in the last decade in a humanitarian setting. A total of 43 studies that reviewed papers on disaster management and humanitarian operation and pointed out the research gaps in this field of study were published from 2006 to 2018. To enhance the rigor and relevance of future studies, this paper focuses on the methodological aspect of studies on humanitarian operations. The study highlights a set of vital items that should be considered when conducting research in a humanitarian setting: including the problem structuring, understanding the contextual factors in a humanitarian setting, acknowledging the uncertainties in humanitarian operations, incorporating uncertainty in the model, enabling technologies in model development and implementation, and selecting appropriate data and research methods. In addition, this study suggests a meta-process for research on humanitarian operations to target a higher level of research quality in this setting. The implications of the study for authors and reviewers of manuscripts and research proposals are discussed in the last section of the paper.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 7 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Zahra Mobini, Wilco van den Heuvel, Albert Wagelmans〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We study a two-echelon supply chain consisting of a supplier and a retailer, where the supplier uses a simple and easily implementable incentive scheme of making a side payment to influence the retailer’s ordering plan. The supplier makes a take-it-or-leave-it offer to the retailer in the form of a menu of contracts, each consisting of a procurement plan plus a side payment. The retailer, who possesses private information about customer demand and his cost parameters, either accepts one of the contracts or imposes his own optimal plan. We formulate the supplier’s problem of designing optimal contracts with the assumption that the retailer’s outside option depends on his private information. Taking into account the retailer’s reaction to the proposed offer, the supplier faces a nested (bi-level) optimization problem, which we transform into a single-level mixed integer programming variant. In our analysis, we use a network interpretation for the set of incentive constraints and show several structural properties of optimal contracts. This enables us to considerably reduce the set of incentive constraints and to find optimal values of side payments. Our findings regarding the possible behavior of the opportunistic retailer deviate from those of previous studies as a result of considering more realistic assumptions.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 7 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Banu Soylu, Hatice Katip〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper presents a biobjective multiple allocation p-hub median problem, discusses the properties of the Pareto frontier and proposes exact and heuristic algorithms for finding the Pareto frontier. Our motivation emanates from airline networks and their new hub investment strategies. The first objective minimizes the total transportation cost of the network, while the second one minimizes 2-stop journeys in order to improve customer satisfaction, which is negatively affected by the multiple-transit routes of airlines. Although using hubs reduces operating costs in networks, a cost-effective hub network may not imply minimum individual travel times for passengers, or happy passengers. It is well-known that airline customers prefer flights with fewer stops. However, reducing 2-stop routes increases the number of arcs, non-stop and 1-stop routes, and thus the total cost in the network. We analyzed the tradeoff between these objective functions. We performed experiments on well-known data sets from the literature. We were able to find the Pareto frontier exactly for small/medium size instances. A variable neighborhood search (VNS) heuristic is presented to approximate the Pareto frontier of large size instances. We also performed an application on the current Turkish aeronautics network. The results are presented and discussed.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 6 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Mohamad Soltani, Michele Samorani, Bora Kolfal〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In many appointment scheduling systems with multiple providers, customers are assigned appointment times but they are not assigned a specific provider in advance – that is, customers can be seen by any available provider. This type of system is common in a variety of service sectors, such as healthcare, banking, and legal counseling. The majority of the existing literature assumes constant service times or does not consider customer no-shows, which are unrealistic assumptions in many situations. In this paper, we overcome this shortcoming by developing an appointment scheduling model that considers stochastic service times along with customer no-shows for multiple-provider systems with identical providers. The objective is to minimize the weighted sum of customers’ waiting time, and providers’ idle time and overtime. We model this problem as a time-inhomogeneous Discrete-Time Markov Chain process. We use analytical results to reduce the space of optimal schedule candidates, and we employ machine learning techniques to detect patterns among optimal or near-optimal schedules. We then develop an effective heuristic method which provides schedules that perform better than the ones generated by existing models. We test our heuristic both on simulated data and a real-world application. As the real-world application, we collaborate with a local counseling center to implement the schedules suggested by our method. Results from this field experiment reveal an average schedule cost reduction of 16% per day, with a maximum reduction of 40% per day.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 6 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Oded Stark, Wiktor Budzinski, Grzegorz Kosiorowski〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We use queuing-related behavior as an instrument for assessing the social appeal of alternative cultural norms. Specifically, we study the behavior of rational and sophisticated individuals who stand in a given queue waiting to be served, and who, in order to speed up the process, consider switching to another queue. We look at two regimes that govern the possible order in which the individuals stand should they switch to the other queue: a regime in which cultural convention, social norms, and basic notions of fairness require that the order in the initial queue is preserved, and a regime without such cultural inhibitions, in which case the order in the other queue is random, with each configuration or sequence being equally likely. We seek to find out whether in these two regimes the aggregate of the behaviors of self-interested individuals adds up to the social optimum defined as the shortest possible total waiting time. To do this, we draw on a Nash Equilibrium setting. We find that in the case of the preserved order, the equilibrium outcomes are always socially optimal. However, in the case of the random order, unless the number of individuals is small, the equilibrium outcomes are not socially optimal.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 6 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Daniel Guhl〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉It is well known that price endogeneity is a severe problem in demand models for market-level data (e.g., aggregate logit models) because it leads to biased estimates and therefore incorrect managerial implications. If the price parameter varies over time, as is usually the case, the relevance of the issue increases because standard methods to correct endogeneity biases (e.g., generalized method of moments) fail. This paper presents a control function approach as a remedy. A comprehensive simulation study demonstrates this method’s suitability, such that addressing endogeneity with the control function approach is the best choice. Moreover, addressing the endogeneity problem incorrectly may be even more harmful than simply ignoring it. To further illustrate the control function approach, we analyze the demand for canned tuna using aggregate retailer-level data. Here, all utility parameters vary over time and price endogeneity is indeed an issue. Effectively addressing price endogeneity correct has positive economic consequences: a normative model analysis reveals that implementing the control function approach yields a 3 % increase in retailer profits.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 6 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Edoardo Fadda, Guido Perboli, Roberto Tadei〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Due to the spread of the social engagement paradigm, several companies are asking people to perform tasks in exchange for a reward. The advantages of this business model are savings in economic and environmental terms. In previous works, it has been proved that the problem of finding the minimum amount of reward such that all tasks are performed is difficult to solve, even for medium-size realistic instances (if more than one type of person is considered). In this paper, we propose a customized version of the progressive hedging algorithm that is able to provide good solutions for large realistic instances. The proposed method reaches the goal of defining a procedure that can be used in real environments.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 6 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): S. Raghavan, Mustafa Sahin, F. Sibel Salman〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The capacitated mobile facility location problem (CMFLP) arises in logistics planning of community outreach programs delivered via mobile facilities. It adds capacity restrictions to the mobile facility location problem introduced previously by Demaine et al. (2009), thereby extending the problem to a practical setting. In the problem, one seeks to relocate (or move) a set of existing facilities and assign clients to these facilities while respecting capacities so that the weighted sum of facility movement costs and the client travel costs (each to its assigned facility) is minimized. We provide two integer programming formulations for the CMFLP. The first is on a layered graph, while the second is a set partitioning formulation. We prove that the linear relaxation of the set partitioning formulation provides a tighter lower bound to the CMFLP than the linear relaxation of the layered graph formulation. We then develop a branch-and-price algorithm on the set partitioning formulation. We find that the branch-and-price procedure is particularly effective both in terms of solution quality and running time, when the ratio of the number of clients to the number of facilities is small and the facility capacities are tight. Finally, we present two heuristic approaches for the CMFLP. One is a linear programming rounding heuristic, and the other is based on a natural decomposition of the problem on the layered graph.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 12 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Daniel F. Otero, Mariana Escallón, Cristina López, Raha Akhavan-Tabatabaei〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Offering promotions has become common practice in the airline industry as a strategy to boost the total revenue. An effective promotion campaign should be adequately priced and timed to attract sufficient extra demand and compensate for the markdown price. Diversion of demand from the regular fare to the markdown price is also a side-effect of offering promotions, which needs to be considered in designing successful campaigns. Demand dilution occurs when customers are attracted to the promotional fare from higher fare families, or from future purchases to the promotional time window. We propose a stochastic dynamic model for the optimal timing of promotions, considering both types of dilution and given fixed prices for the regular and promotional fares. We prove the existence of an optimal policy, and derive structural properties to find the minimum number of unsold seats that justifies the promotion under dilution. We examine the performance of this model on two cases from a Latin American airline and demonstrate considerable savings by applying our proposed optimal policy versus the airline’s current policy.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 13 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Bin Ji, Xiaohui Yuan, Yanbin Yuan, Xiaohui Lei, Tyrone Fernando, Herbert H.C. Iu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper focuses on lock and quay co-scheduling problem (LQCP) so that delay time of ships at a lock and time spent at the quay are minimized. The task can be regarded as a main problem where an alternative mode for a ship is determined by solving two sub-problems of lock scheduling and berth allocation. For the first time, the LQCP considers the discrete berth allocation of container ships and the mooring constraints of lock scheduling. A mixed integer linear programming (MILP) model is formulated for the LQCP and small-scale problems are solved by branch and bound method. In addition, fuzzy logic control based heuristic method is proposed to handle large-scale LQCP. Specifically, a fuzzy-controlled quantum inspired gravitational search algorithm is proposed to search optimal mode combinations for the main problem iteratively. In each iteration, Tabu search based multi-order best fit algorithm is proposed to solve lock scheduling sub-problem and an adaptive large neighborhood search algorithm is applied to solve berth allocation sub-problem. The MILP and heuristic methods are tested on 42 instances, in which the MILP is implemented in Gurobi 7.5.1. Experimental results indicate that the MILP model can handle different traffic situations. The proposed heuristic method shows tiny optimality gap for small-scale instances and outperforms Gurobi on most of medium-large scale instances with respect to solution quality and computation time. Furthermore, comparison between different heuristics on medium and large scale instances confirms that the fuzzy logic control based heuristic outperforms other heuristic methods.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 7 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Tanveer Hossain Bhuiyan, Maxwell C. Moseley, Hugh R. Medal, Eghbal Rashidi, Robert K. Grala〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Reducing the potential damage caused by a wildfire is a problem of significant importance to land and fire managers. Fuel reduction treatment is a well-known method of reducing the risk of fire occurrence and spread on landscapes. However, officials seeking fuel reduction treatments on privately owned lands can only encourage it through incentive programs such as cost-share programs. This research developed a methodology that provides the basis for a decision-making tool to help managers allocate limited cost-share resources among a set of landowners to maximize wildfire risk reduction by implementing a hazardous fuel reduction treatment. A key feature of the methodology is that it incorporates uncertainty in the landowners’ decision of whether or not to implement treatment on their lands. The methodology is based on a stochastic programming model with endogenous uncertainty where the probability that a landowner accepts a cost-share offer to implement a fuel reduction treatment on their land depends on the offer amount. To estimate the probability that a landowner accepts a given cost-share offer amount, we used a predictive modeling technique to analyze landowner survey data. The results provide insight about the effects of different cost-share allocation strategies on the expected damage. Numerical experiments show that the risk-based allocation provides up to 37.3% more reduction in damage compared to other strategies that allocate equal cost-share amounts among landowners. Additionally, the results show that the solution quality is substantially sensitive to changes in the number of resource allocation levels.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 8 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Min Wang, Lindu Zhao, Michael Herty〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We investigate a fresh food supply chain comprising a large-scale supplier and multiple small-scale retailers under a carbon cap-and-trade policy. Retailers' joint replenishment and the carbon trading behavior of supply chain members are studied. We assume that three replenishment strategies are available for the supply chain: (1) separate replenishment; (2) joint replenishment: a leader-follower relationship among retailers; and (3) joint replenishment: the coalition of retailers. Under each strategy, a bargaining framework for supply chain members is set up to maximize their profits, where the price of the refrigerated transportation services provided by the supplier and the retail price of fresh food are optimized. The optimal decisions are analyzed to provide insights into logistics pricing and retail pricing strategies. Through comparing three replenishment strategies, we also identify the optimal replenishment strategies from the perspectives of the supplier, retailers and a carbon emission optimizer. Moreover, we investigate the role of the carbon cap-and-trade policy by comparing the cases with and without a carbon cap-and-trade policy. It is noteworthy that the goals of profit growth and emission reduction are simultaneously achieved under the carbon cap-and-trade policy.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 7 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Sumit Sarkar〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Standard game-theoretic models that apply principal-agent problems to analyse buyer–supplier relations have postulated that in a single-period game, the agent will not choose non-contractible quality. However, evidence exists that suppliers (or service providers) choose costly quality in non-contractible dimensions, even in absence of reputation concern, if they are offered a premium. Such actions violate the axiom of ‘individual rationality’ and are not subgame perfect in standard game theory, wherein only material payoffs are considered. This paper builds three models of supplier behaviour in a single-period Stackelberg-like buyer–supplier game by using the framework of psychological game theory and explains supplier motivations to choose quality on non-contractible dimensions in absence of any reputation effect. If the buyer offers a price higher than the market price, the supplier may be motivated to choose non-contractible quality due to a sense of gratitude, or because of his conscience, or in order to reciprocate kindness. The results show that maximum possible quality is chosen by the supplier only in the presence of a sufficiently strong sense of gratitude or a sufficiently strong conscience. However, the buyer is better-off with a fair supplier who is motivated to reciprocate than a supplier having a very poor sense of gratitude or one having a weak conscience, because the buyer can choose an optimal premium above market price to ensure that a fair and reciprocal supplier chooses an optimal quality.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 7 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Mingyuan Wei, Mingyao Qi, Tao Wu, Canrong Zhang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This study examines the multi-level capacitated lot-sizing problems with a special focus on item replaceability. Two linear programming models are established using different variable definitions and the McCormick envelope. These are further strengthened using some valid inequalities. In large-scale instances, the problem becomes computationally difficult to solve because of its non-deterministic polynomial-time hardness. Therefore, a matching-induced search algorithm to solve the problem is proposed. The algorithm leverages the matching between the rounded relaxation and the incumbent solution values to fix a few binary variables. It also applies the relax-and-fix heuristic to solve the reduced-size problems and progressively improve solution quality. The algorithm is further enhanced by using both the matching information of historical feasible solution values and the distance between the relaxation and incumbent solution values. Extensive computational experiments are conducted to test the efficiency of the models and algorithms. Experimental results show that increasing the number of substitutable items on the bill of materials (BOM) or increasing the overlapping ratios among the BOMs, or both, can effectively reduce the total cost. The proposed enhanced algorithm can perform better than the relax-and-fix and some other heuristics in the literature.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 9 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Jean-Philippe Boussemart, Hervé Leleu, Zhiyang Shen, Michael Vardanyan, Ning Zhu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper proposes a non-parametric approach of a banking production technology that decomposes performance into economic and credit risk efficiencies. The basis of our approach is to separate the production technology into two sub-technologies. The former is the production of non-interest income and loans from a set of traditional inputs. The latter is attached to the production of interest income from loans where an explicit distinction between good and non-performing loans is introduced. Economic efficiency comes from the production of good outputs, namely interest and non-interest income, while credit risk management efficiency is related to the minimization of the non-performing loans that can be considered as an unintended or bad output. The model is applied to Chinese financial data covering 30 banks from 2005 to 2012 and different scenarios are considered. The results indicate that income could be increased by an average rate of 16% while non-performing loans could be decreased by an average rate of 33%. According to our results, banking managers could strike a balance between economic performance and credit risk management and make more appropriate decisions in line with their preferences.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 19 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Ibrahim M. Tavakoli, Amin Mostafaee〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Data Envelopment Analysis (DEA) is a nonparametric method for measuring the performance of a set of decision making units (DMUs). Recently, applications of DEA have attracted particular interest of scholars in circumstances in which two-stage or network structures are involved. This paper is the first systematic attempt to extend the network DEA models to a class of non-convex technologies, i.e., Free Disposal Hull (FDH). The models are provided in both cases of the same and the different optimal peers in both stages. Some closed form formulae are provided to calculate the overall and stage efficiency scores under different RTS assumptions, without solving any mathematical programming problem. Also, the formulations are adapted to include the situations in which some of the data are missing. Furthermore, it is proved that the provided models project the inefficient DMUs onto the efficient frontier. Finally, the case of Bank Keshavarzi Iran is used to validate the reasonableness and acceptability of the proposed approach in FDH models.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 19 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Roman Słowiński, Co-ordinating Editor〈/p〉

Description: 〈p〉Publication date: Available online 18 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Filipe Monnerat, Joana Dias, Maria João Alves〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Many institutions have their own vehicle fleet and hired drivers that can be assigned to working trips to ensure that they all take place within the desired time periods. In these situations, it is important that vehicles and drivers are assigned to planned trips in an optimal and automated way. Trips are planned to occur over a given planning horizon and have a number of characteristics that have to be taken into account: a trip is characterized by a starting time, start and end locations, travelling distance and time, number of drivers needed for each assigned vehicle, number of passengers, and so on. This paper considers the problem of assigning both vehicles and drivers to a set of planned trips, having as objective the minimization of total cost. To properly address this problem it is necessary to consider a number of features, like the possibility of vehicles being shared between different trips if the timeframes and destinations are compatible. The mathematical model developed and presented in this paper takes all these features into account. The problem was motivated by a case study in the context of a public Brazilian university. This case study is also described and computational experiments using a general solver are reported. The general solver is capable of calculating optimal solutions in reasonable computational times, but problems where the fleet is mainly composed of small vehicles are more challenging. A matheuristic has been developed and tested, that seems to be competitive for these type of problems.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 16 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): David Rios Insua, Fabrizio Ruggeri, Refik Soyer, Simon Wilson〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Starting in the late 80s Bayesian methods have gained increasing attention in the reliability literature. The focus of most of the earlier Bayesian work in reliability involved statistical inference and thus the main emphasis was on modeling and analysis. Advances in Bayesian computing after the 90’s have significantly contributed not only to the use of Bayesian inference and prediction but also to the implementation of Bayesian decision-theoretic approaches in reliability problems. In this review we present an overview of Bayesian methods to solve decision problems in reliability some of which involve two or more decision makers with conflicting objectives. We consider problems in areas such as design, life testing, preventive maintenance, reliability certification, or warranty policies. In doing so, we present key aspects of the decision problems, give a brief review of earlier methods and finally discuss recent advances in Bayesian approaches to solve them.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 276, Issue 3〈/p〉 〈p〉Author(s): 〈/p〉

Description: 〈p〉Publication date: Available online 15 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Filipe Rodrigues, Agostinho Agra, Marielle Christiansen, Lars Magnus Hvattum, Cristina Requejo〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Uncertainty is inherent in many planning situations. One example is in maritime transportation, where weather conditions and port occupancy are typically characterized by high levels of uncertainty. This paper considers a maritime inventory routing problem where travel times are uncertain. Taking into account possible delays in the travel times is of main importance to avoid inventory surplus or shortages at the storages located at ports.〈/p〉 〈p〉Several techniques to deal with uncertainty, namely deterministic models with inventory buffers; robust optimization; stochastic programming and models incorporating conditional value-at-risk measures, are considered. The different techniques are tested for their ability to deal with uncertain travel times for a single product maritime inventory routing problem with constant production and consumption rates, a fleet of heterogeneous vessels and multiple ports. At the ports, the product is either produced or consumed and stored in storages with limited capacity. We assume two stages of decisions, where the routing, the visit order of the ports and the quantities to load/unload are first-stage decisions (fixed before the uncertainty is revealed), while the visit time and the inventory levels at ports are second-stage decisions (adjusted to the observed travel times).〈/p〉 〈p〉Several solution approaches resulting from the proposed techniques are considered. A computational comparison of the resulting solution approaches is performed to compare the routing costs, the amount of inventory bounds deviation, the total quantities loaded and unloaded, and the running times. This computational experiment is reported for a set of maritime instances having up to six ports and five ships.〈/p〉 〈/div〉

Description: 〈p〉Publication date: Available online 14 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Binyamin Oz, Ivo Adan, Moshe Haviv〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We consider the M〈sub〉〈em〉n〈/em〉〈/sub〉/G〈sub〉〈em〉n〈/em〉〈/sub〉/1 queue with vacations and exhaustive service in which the server takes (repeated) vacations whenever it becomes idle, the service time distribution is queue length dependent, and the arrival rate varies both with the queue length and with the status of the server, being busy or on vacation. Using a rate balance principle, we derive recursive formulas for the conditional distribution of residual service or vacation time given the number of the customers in the system and the status of the server. We also derive a closed-form expression for the steady-state distribution as a function of the probability of an empty system. As an application of the above, we provide a recursive computation method for Nash equilibrium joining strategies to the observable M/G/1 queue with vacations.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 18 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Juan Peng, Zhili Zhou〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Effective working capital management encourages rapid turnover of inventory and capital from a single firm perspective. This strategy could cause financial pressure on a firm's suppliers or distributors, hurt the supply chain, and even have an adverse effect on the firm itself. We attempt to find out the optimal deploying of working capital in a supply chain perspective. A supply chain with one supplier and one retailer who faces uncertain demand is considered. The payment period of the retailer is determined to maximize the profits, besides the wholesale price and the order quantity. We build three models to capture different cooperation levels of the supply chain: non-cooperative, negotiatory and centralized. The equilibrium solutions show that the payment period should be maximized when the retailer's discount rate is higher than the supplier's and vice versa, regardless of the cooperation levels. In consideration of different claims of profit sharing, we propose the supply chain-oriented solution of working capital management.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 13 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Peiping Shen, Zeyi Zhu, Xiao Chen〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper, a practicable contraction approach is proposed for solving the sum of the generalized polynomial ratios problem (P) with generalized polynomial constraints. Due to the intrinsic difficulty of problem (P), less work has been devoted to solving this problem. The proposed approach is based on reducing the original nonconvex problem (P) as a standard geometric programming (GP) problem by utilizing simple transformation and contraction strategies. The resulting optimization problem can be solved effectively by utilizing the solutions of a series of GP problems. The tractability and effectiveness of the proposed successive contraction approach are demonstrated by several numerical examples, and the performance comparison of the proposed approach and other methods published is also presented in terms of solution quality.〈/p〉〈/div〉 〈h5〉Graphical abstract〈/h5〉 〈div〉〈p〉〈figure〉〈img src="https://ars.els-cdn.com/content/image/1-s2.0-S037722171930253X-fx1.jpg" width="301" alt="Graphical abstract for this article" title=""〉〈/figure〉The sum of the generalized polynomial ratios problem (P) can be reduced as a standard geometric programming (GP) problem by utilizing transformation and contraction strategies, and can be solved effectively by the solutions of a series of (GP) problems.〈/p〉〈/div〉

Description: 〈p〉Publication date: 19 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 277, Issue 3〈/p〉 〈p〉Author(s): Maryam Darvish, Claudia Archetti, Leandro C. Coelho, M. Grazia Speranza〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper deals with an integrated routing problem in which a supplier delivers a commodity to its customers through a two-echelon supply network. Over a planning horizon, the commodity is first sent from a single depot to a set of Distribution Centers (DCs). Then, from the DCs, it is delivered to customers. Two sources of flexibility are analyzed: flexibility in network design and flexibility in due dates. The former is related to the possibility of renting any of the DCs in any period of the planning horizon, whereas the latter is related to the possibility of serving a customer between the period an order is set and a due date. The objective is to minimize the total cost consisting of the sum of the shipping cost from the depot to the DCs, the traveling cost from the DCs to the customers, the renting cost of DCs, and the penalty cost for unmet due dates. A mathematical programming formulation is presented, together with different classes of valid inequalities. Moreover, an exact method is proposed that is based on the interplay between two branch-and-bound algorithms. Computational results on randomly generated instances show the value of each of the two kinds of flexibility. Their combination leads to average savings of up to about 30%.〈/p〉〈/div〉

Description: 〈p〉Publication date: 19 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 277, Issue 3〈/p〉 〈p〉Author(s): Edward Anderson, Marta Monjardino〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In an agricultural setting it is natural to consider yield risk in the context of a three level supply chain: with a small number of suppliers, large numbers of growers, and a small number of buyers. In the cereal growing case that is our focus, there is a supplier of fertiliser, a potentially large number of growers of cereal crops and a buyer, who purchases grain from the growers. The yield depends both on the input level of fertiliser and also on random weather-related factors. We study the impact of a new type of contract structure in which the grower purchases inputs at a discount, but agrees to a reduced price for the crop. The buyer makes a payment to the supplier to compensate for the discount offered. We show how this can coordinate the supply chain and demonstrate the potential advantages of this contract form when producers are risk averse. We look in detail at the implications of the use of these contracts by Australian wheat growers using data generated by APSIM, a growth simulation tool, to understand the connection between yields, fertiliser use and the weather. By using APSIM we can estimate the distribution of yields implied by the grower’s decision on fertiliser application and hence estimate optimal fertiliser use for risk averse growers.〈/p〉〈/div〉

Description: 〈p〉Publication date: 19 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 277, Issue 3〈/p〉 〈p〉Author(s): Weimiao Liu, Tianhu Deng, Jianbin Li〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉China, the world’s largest logistics market, is experiencing insatiable demand for logistics services. The boom in China’s e-commerce has led to massive growth in the need for product shipping and warehouse storage. Some of China’s largest retailers have invested tremendous amounts of money in warehouse development. Two important functions of a warehouse are packing and stacking: packing small products into medium-sized containers and stacking the containers on pallets. Often, the packing problem is solved independently from that of stacking or is ignored. Generally, the stacking problem is solved while treating the packing decisions as given. However, considering these two problems separately may result in suboptimal, i.e., less efficient operations. Therefore, we propose a mixed integer linear programming formulation that addresses these two problems as a whole and two relaxations for actual-size examples. Due to the inherent demand uncertainty, we present a two-stage robust model that employs interval-based and scenario-based uncertainty sets. We numerically verify that the integrated model achieves considerable economic benefits and efficient space utilization while exploiting spatial flexibility. The additional computational experiments suggest that two relaxations yield tiny optimality gaps of 0.3% on average (4.6% at maximum), and considerable computation time improvements of 207.9% on average (347.4% at maximum). Our numerical results also illustrate that the scenario-based robust approach outperforms the deterministic approach and interval-based robust approach. We verify the model with varying variability and sizes of containers. The computational results indicate that compared with single type of containers, two or three types would bring significant economic benefits.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 27 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Lina Johansson, Danja R. Sonntag, Johan Marklund, Gudrun P. Kiesmüller〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We consider a one-warehouse-multiple-retailer inventory system where the retailers face stochastic customer demand, modelled as compound Poisson processes. Deliveries from the central warehouse to groups of retailers are consolidated using a time based shipment consolidation policy. This means that replenishment orders have to wait until a vehicle departures, which increases the lead time for the retailers and therefore also the safety stock. Thus, a trade-off exists between expected shipment costs and holding costs. Our aim is to determine the shipment intervals and the required amount of safety stock for each retailer and the warehouse to minimize total cost, both for backorder costs and fill rate constraints. Previous work has focused on exact solutions which are computationally demanding and not applicable for larger real world problems. The focus of our present work is on the development of computationally attractive heuristics that can be applied in practice. A numerical study shows that the proposed heuristics perform well compared to the exact cost minimizing solutions. We also illustrate that the approaches are appropriate for solving real world problems using data from a large European company.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 26 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): S.S. Panwalkar, Christos Koulamas〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Anomalies in flow shop scheduling are rare; only two anomalies have been reported. We present five new anomalies for the permutation flow shop models with the minimum makespan objective and seven more anomalies for the minimum total flow time objective. These anomalies (including the existing ones) are divided into three types corresponding to an increased processing time of a single operation, the addition of a job and the addition of a machine. We derive two properties which, when satisfied, eliminate the possibility of certain anomalies. We conclude that restrictions such as no-delay schedules, no job waiting or no machine idle time (after it starts processing) often result in anomalies. We also show that anomalies can also occur in non-permutation flow shops (four new anomalies presented).〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 26 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Alexander D. Stead, Phill Wheat〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Multiple Imputation (MI) methods have been widely applied in economic applications as a robust statistical way to incorporate data where some observations have missing values for some variables. However in Stochastic Frontier Analysis (SFA), application of these techniques has been sparse and the case for such models has not received attention in the appropriate academic literature. This paper fills this gap and explores the robust properties of MI within the stochastic frontier context. From a methodological perspective, we depart from the standard MI literature by demonstrating, conceptually and through simulation, that it is not appropriate to use imputations of the dependent variable within the SFA modelling, although they can be useful to predict the values of missing explanatory variables. Fundamentally, this is because efficiency analysis involves decomposing a residual into noise and inefficiency and as a result any imputation of a dependent variable would be imputing efficiency based on some concept of average inefficiency in the sample. A further contribution that we discuss and illustrate for the first time in the SFA literature, is that using auxiliary variables (outside of those contained in the SFA model) can enhance the imputations of missing values. Our empirical example neatly articulates that often the source of missing data is only a sub-set of components comprising a part of a composite (or complex) measure and that the other parts that are observed are very useful in predicting the value.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 27 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): A. Saavedra-Nieves, J. Schouten, P. Borm〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper addresses interactive one-machine sequencing situations in which the costs of processing a job are given by an exponential function of its completion time. The main difference with the standard linear case is that the gain of switching two neighbors in a queue is time-dependent and depends on their exact position. We illustrate that finding an optimal order is complicated in general and we identify specific subclasses, which are tractable from an optimization perspective. More specifically, we show that in these subclasses, all neighbor switches in any path from the initial order to an optimal order lead to a non-negative gain. Moreover, we derive conditions on the time-dependent neighbor switching gains in a general interactive sequencing situation to guarantee convexity of the corresponding cooperative game. These conditions are satisfied within our specific subclasses of exponential interactive sequencing situations.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 22 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Peng Ma, Yeming Gong, Mingzhou Jin〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We consider supply chain (SC) contracts in a new setting, the medical equipment industry, where concern for patient benefits is essential and quality efforts are critical for profits compared with supply chains (SCs) in other industries. It remains unclear how quality efforts and patient concern levels affect SC performance and how medical equipment manufacturers’ quality effort levels are linked to their patient concern levels. This study focuses on the impact of a manufacturer's and a retailer's patient concern levels on optimal pricing and quality decisions in an SC consisting of a manufacturer facing quality effort-dependent demand and a retailer in the medical equipment industry. We use the Stackelberg game to characterize and determine the optimal operational decisions in five scenarios and address the effects of patient concern levels under above five scenarios. A real case is studied and shows that optimized quality efforts can improve SC profits. The parameters settings are derived from the real data. Our findings bridge the gap between SC quality management and patient benefits and help to understand contract design in relation to patient concerns in different SC structures. This paper is among the earliest to investigate quality efforts for SC contract design in relation to patient concerns and to study SC contract design in the medical equipment industry. Our managerial insights are expected to help manufacturers move toward better quality effort decisions considering patient benefits and are also applicable to other SCs with effort-dependent demand and the effect of altruistic preferences.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 19 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Robert Klein, Sebastian Koch, Claudius Steinhardt, Arne K. Strauss〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Originating from passenger air transport, revenue management has evolved into a general and indispensable methodological framework over the last decades, comprising techniques to manage demand actively and to further improve companies’ profits in many different industries. This article is the second and final part of a paper series surveying the scientific developments and achievements in revenue management over the past 15 years. The first part focused on the general methodological advances regarding choice-based theory and methods of availability control over time. In this second part, we discuss some of the most important generalizations of the standard revenue management setting: product innovations (opaque products and flexible products), upgrading, overbooking, personalization, and risk-aversion. Furthermore, to demonstrate the broad use of revenue management, we survey important industry applications beyond passenger air transportation that have received scientific attention over the years, covering air cargo, hotel, car rental, attended home delivery, and manufacturing. We work out the specific revenue management-related challenges of each industry and portray the key contributions from the literature. We conclude the paper with some directions for future research.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 20 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Toshiyuki Sueyoshi, Aijun Li, Xiaohong Liu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This study proposes a decomposition approach based upon data envelopment analysis that identifies various sources of CO〈sub〉2〈/sub〉 emission. In addition to the previously identified seven sources, we propose three new ones. As an empirical application, this study applies the proposed approach to examine ten sources of CO〈sub〉2〈/sub〉 emission across Chinese provinces from 2008 to 2015. In the empirical study, we overcome methodological difficulties related to (a) what methodological merits of technology change indexes are and how to measure them in a separated manner and (b) how to separate effects of various sources and how to identify the annual shift of those sources of CO〈sub〉2〈/sub〉 emission changes. This study finds three empirical implications. First, four sources may increase the amount of CO〈sub〉2〈/sub〉 emission. They include an economic activity, a technology change on a desirable output and a potential energy intensity change. Second, two sources are important in reducing the amount of CO〈sub〉2〈/sub〉 emission. They are an operational efficiency change on a desirable output and a change in energy saving technology. Finally, conflicting results exist in some sources in the manner that they increase CO〈sub〉2〈/sub〉 emission in some provinces but decrease it in the other provinces.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 20 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Vincent Charles, Juan Aparicio, Joe Zhu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Data envelopment analysis (DEA) is a technique for identifying the best practices of a given set of decision-making units (DMUs) whose performance is categorized by multiple performance metrics that are classified as inputs and outputs. Although DEA is regarded as non-parametric, the sample size can be an issue of great importance in determining the efficiency scores for the evaluated units, empirically, when the use of too many inputs and outputs may result in a significant number of DMUs being rated as efficient. In the DEA literature, empirical rules have been established to avoid too many DMUs being rated as efficient. These empirical thresholds relate the number of variables with the number of observations. When the number of DMUs is below the empirical threshold levels, the discriminatory power among the DMUs may weaken, which leads to the data set not being suitable to apply traditional DEA models. In the literature, the lack of discrimination is often referred to as the ”curse of dimensionality”. To overcome this drawback, we provide a simple approach to increase the discriminatory power between efficient and inefficient DMUs using the well-known pure DEA model, which considers either inputs only or outputs only. Three simple real cases have been discussed to illustrate the proposed approach.〈/p〉〈/div〉

Description: 〈p〉Publication date: 16 November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 279, Issue 1〈/p〉 〈p〉Author(s): 〈/p〉

Description: 〈p〉Publication date: Available online 19 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Ping Cao, Nenggui Zhao, Jie Wu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper, we consider a seller selling a single product over a finite horizon, with the objective of maximizing the expected total discounted revenue by dynamically adjusting posted prices. One distinct feature of our problem is that the customers’ arrival rate is unknown to the seller and will be learned in a Bayesian method. Moreover, arriving customer’s purchase behavior is affected by reference price. We formulate this problem as a Bayesian dynamic programming. First, we analyze the structural properties of the optimal revenue function and the optimal pricing policy. We find that the problem can be substantially simplified in the case of sufficient inventory and demand learning can be decoupled from pricing decision. Then, we investigate the value of market size (customers’ arrival rate) and the effect of the reference price. Furthermore, we conduct several numerical examples to justify our theoretical results, examine the influence of demand learning, and find that ignoring the reference price effect will lose substantial revenue.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 18 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Dominik Kress, Sebastian Meiswinkel, Erwin Pesch〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We address an optimization problem that arises at seaports where containers are transported between stacking areas and small buffer areas of restricted capacity that are located within the reach of quay cranes. The containers are transported by straddle carriers that have to be routed such that given unloading and loading sequences of the containers at the quay cranes are respected. The objective is to minimize the turnaround times of the vessels. We analyze the problem’s computational complexity, present an integer program, and propose a heuristic framework that is based on decomposing the problem into its routing component and a component that handles the time variables and buffer capacities. The framework is analyzed in computational tests that are based on real-world data. Based on these tests, we analyze the question of whether or not it pays off to deviate from the approach of permanently assigning a fixed number of straddle carriers to each quay crane, which is the strategy that is currently implemented at the port.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 18 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Paul Göpfert, Stefan Bock〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We consider the facility location problem of installing a refueling and recharging infrastructure for vehicles with a strongly limited driving range. For this purpose, a novel problem formulation is introduced that is based on an analogy to the well-known duality relationship of Max Flow and Min Cut. In order to optimally solve this problem, a decomposition-based Branch&Cut approach is developed that iteratively generates violated inequalities and so-called zero-half-cuts as specific cutting planes. A comprehensive computational study on two real-world road networks reveals that this considerable tightening of partial problems in each node enables an efficient enumeration process whereby even large scale instances are solved to optimality for the first time.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 18 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): J. Cole Smith, Yongjia Song〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper discusses the development of interdiction optimization models and algorithms, with an emphasis on mathematical programming techniques and future research challenges in the field. After presenting basic interdiction concepts and notation, we recount the motivation and models behind founding research in the network interdiction field. Next, we examine some of the most common means of solving interdiction problems, focusing on dualization models and extended formulations solvable by row-generation techniques. We then examine contemporary interdiction problems involving incomplete information, information asymmetry, stochasticity, and dynamic play. We conclude by discussing several emerging applications in the field of network interdiction.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 1 April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Pablo San Segundo, Stefano Coniglio, Fabio Furini, Ivana Ljubić〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We study the maximum edge-weighted clique problem, a problem related to the maximum (vertex-weighted) clique problem which asks for finding a complete subgraph (i.e., a clique) of maximum total weight on its edges. The problem appears in a wide range of applications, including bioinformatics, material science, computer vision, robotics, and many more. In this work, we propose a new combinatorial branch-and-bound algorithm for the problem which relies on a novel bounding procedure capable of pruning a very large amount of nodes of the branch-and-bound tree. Extensive computational experiments on random and structured graphs, encompassing standard benchmarks used in the literature as well as recently introduced real-world large-scale graphs, show that our new algorithm outperforms the state-of-the-art by several orders of magnitude on many instances.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 1 April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Marcus Ang, Yun Fong Lim〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We study a problem of optimizing storage classes in a unit-load warehouse such that the total travel cost is minimized. This is crucial to the operational efficiency of unit-load warehouses, which constitute a critical part of a supply chain. We propose a novel approach called the FB method to solve the problem. The FB method is suitable for general receiving-dock and shipping-dock locations that may not coincide. The FB method first ranks the locations according to the frequencies that they are visited, which are estimated by a linear program based on the warehouse’s layout as well as the products’ arrivals and demands. The method then sequentially groups the locations into a number of classes that is implementable in practice. After forming the classes, we use a policy based on robust optimization to determine the storage and retrieval decisions. We compare the robust policy with the traditional storage-retrieval policies on their respective optimized classes. Our results suggest that if the warehouse utilization is low, different class-formation methods may lead to very different total travel costs, with our approach being the most efficient. We observe the robustness of this result across various parameter settings. A case study based on data from a third-party logistics provider suggests that the robust policy under the FB method outperforms the other storage-retrieval policies by at least 8% on average, which indicates the potential savings by our approach in practice. One of our findings is that the importance of optimizing classes depends on the warehouse utilization.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 19 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Hamed Rahimian, Güzi̇n Bayraksan, Tito Homem-de-Mello〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We use distributionally robust optimization (DRO) to model a general class of newsvendor problems with unknown demand distribution. The goal is to find an order quantity that minimizes the worst-case expected cost among an ambiguity set of distributions. The ambiguity set consists of those distributions that are not far—in the sense of the total variation distance—from a nominal distribution. The maximum distance allowed in the ambiguity set (called 〈em〉level of robustness〈/em〉) places the DRObetween the risk-neutral stochastic programming and robust optimization models. An important problem a decision maker faces is how to determine the level of robustness—or, equivalently, how to find an appropriate level of risk-aversion. We answer this question in two ways. Our first approach relates the level of robustness and risk to the regions of demand that are critical (in a precise sense we introduce) to the optimal cost. Our second approach establishes new quantitative relationships between the DRO model and the corresponding risk-neutral and classical robust optimization models. To achieve these goals, we first focus on a single-product setting and derive explicit formulas and properties of the optimal solution as a function of the level of robustness. Then, we demonstrate the practical and managerial relevance of our results by applying our findings to a healthcare problem to reserve operating room time for cardiovascular surgeries. Finally, we extend some of our results to the multi-product setting and illustrate them numerically.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 19 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Benny Mantin, Jasper Veldman〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In supplier-retailer interactions, the retailer may carry inventories strategically as a bargaining mechanism to induce the supplier to drop the future wholesale price. As per Anand, Anupindi, and Bassok (2008), the introduction of strategic inventories always benefits the supplier and possibly also the retailer if the holding cost is sufficiently low (due to the contract-space-expansion effect). Is such a move beneficial for the supply chain agents in the presence of process improvement efforts? Such efforts—initiated by suppliers—ultimately reduce production cost and may translate into lower wholesale prices as well as lower consumer prices. We find that strategic inventories may stimulate investment in process improvement when the holding cost is high (as it encourages the supplier to further reduce future cost to eliminate the need for strategic inventories), but may suppress such investment when the holding cost is low (as strategic inventories are cheap to stock and hence cannot be eliminated). Our key result, contrary to the existing literature, is that strategic inventories may be harmful to both supply chain agents in the presence of process improvement. In that case, the supplier effectively over-invests in process improvement efforts, inducing the retailer to reduce the stock of strategic inventories, while reversing the benefits of the contract-space-expansion effect. We also consider variations to the model, whereby the supplier may delay his investment decision, the holding cost may be a function of the wholesale price set by the supplier, consumers may behave strategically, and the planning horizon may consist of multiple periods.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 14 June 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Morteza Alizadeh, Mehdi Amiri-Aref, Navonil Mustafee, Sumohon Matilal〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper, a Casualty Collection Points (CCPs) location problem is formulated as a two-stage robust stochastic optimization model in an uncertain environment. In this modelling approach, the network design decisions are integrated with the multi-period response operational decisions where the number of casualties with different levels of injuries coming from the affected areas is uncertain. Furthermore, the transportation capacity for the evacuation of casualties to CCPs and hospitals is also uncertain. To solve this complex problem, a robust sample average approximation method with the feasibility restoration technique is proposed, and its efficiency is examined through a statistical validation procedure. We then evaluate the proposed methodology in the backdrop of a hypothetical case of Bhopal gas tragedy (with the same hazard propagation profile) at the present day. We also report the solution robustness and model robustness of 144 instances of the case-study to show the proficiency of our proposed solution approach. Results analysis reveals that our modelling approach enables the decision makers to design a humanitarian logistic network in which not only the proximity and accessibility to CCPs are improved, but also the number of lives lost is decreased. Moreover, it is shown that the proposed robust stochastic optimization approach converges rapidly and more efficiently. We hope that our methodology will encourage urban city planners to pre-identify CCP locations, and, in the event of a disaster, help them decide on the subset of these CCPs that could be rapidly mobilised for disaster response.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 29 April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): MHR. Khouzani, Zhengliang Liu, Pasquale Malacaria〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We present a framework to efficiently solve a multi-objective optimisation problem for cyber-security defence. Facing an attacker who can mount a multi-stage attack (modelled using attack graphs), the defence problem is to select a portfolio of security controls which minimises the security risk and the (direct and indirect) costs of the portfolio of controls. The main challenges for the optimisation are: (a) the effect of the security controls is in general probabilistic, for example, the effect of staff anti-phishing training; moreover, some controls like taking regular back-ups do not have an attack-preventing effect, but rather, mitigate the losses of a successful attack; (b) each control may affect multiple vulnerabilities; and each vulnerability may be affected by multiple controls; (c) there can be a prohibitively large number of attack paths, each involving exploitation of different vulnerabilities. Our mathematical framework deals with all these problems. In particular, we model the problem as a min-max multi-objective optimisation. Using techniques such as ILP conversion, exact LP relaxation and dualisation, we convert the problem into a very efficient MILP. For instance, it returns the optimal solution for attack graphs with 20,000 nodes in less than four minutes typically.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 26 April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Xiaofeng Nie〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Checked baggage screening, an important layer of an aviation security system, has gained a lot of research interest in the literature. Due to the different characteristics (for example, cost, capacity, and efficiency) of various screening devices, several cost-benefit models have been proposed to help select screening devices to include in a checked baggage screening system. Almost all cost-benefit models involving multiple devices implicitly assume that, given that a bag carries a threat or not, the responses from different screening devices are conditionally independent. This assumption may not hold in real situations because one device’s response may be positively or negatively correlated with another device’s response. In this paper, we bridge this research gap and address the impact of conditional dependence on checked baggage screening based on a more general system configuration and an existing cost-benefit model. The cost-benefit model investigates a two-device screening system with the objective of minimizing the expected cost per bag. Our detailed numerical results suggest that (1) the higher the correlation coefficient, the higher the optimal expected cost per bag, (2) incorporating correlation coefficients may even alter the choice of devices to include in the optimal screening system, and (3) the more the responses are correlated, the higher the relative increase of the optimal expected cost per bag when ignoring conditional dependence.〈/p〉〈/div〉

Description: 〈p〉Publication date: 19 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 277, Issue 3〈/p〉 〈p〉Author(s): İhsan Yanıkoğlu, Bram L. Gorissen, Dick den Hertog〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Static robust optimization (RO) is a methodology to solve mathematical optimization problems with uncertain data. The objective of static RO is to find solutions that are immune to all perturbations of the data in a so-called uncertainty set. RO is popular because it is a computationally tractable methodology and has a wide range of applications in practice. Adjustable robust optimization (ARO), on the other hand, is a branch of RO where some of the decision variables can be adjusted after some portion of the uncertain data reveals itself. ARO generally yields a better objective function value than that in static robust optimization because it gives rise to more flexible adjustable (or wait-and-see) decisions. Additionally, ARO also has many real life applications and is a computationally tractable methodology for many parameterized adjustable decision variables and uncertainty sets. This paper surveys the state-of-the-art literature on applications and theoretical/methodological aspects of ARO. Moreover, it provides a tutorial and a road map to guide researchers and practitioners on how to apply ARO methods, as well as, the advantages and limitations of the associated methods.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 26 April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Yongjian Li, Lipan Feng, Kannan Govindan, Fangchao Xu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉As environmental regulations are becoming increasingly strict, firms are setting up remanufacturing systems and using trade-in programmes that take back used products to stimulate demand. Meanwhile, they are starting to sell remanufactured products to secondary markets to avoid the problem of cannibalization. In this study, we establish a two-period model in which a monopolistic original equipment manufacturer (OEM) offers a trade-in programme to improve sales and collect used products. At the same time, the OEM can elect to remanufacture these used products and resell them to a secondary market. The results for the static pricing case show that the two primary driving factors, customers’ maximum willingness to pay into the secondary market and production cost, produce different outcomes. Depending on the relationship between these two key factors, seven outcomes exist. Specifically, although all used products are collected and the secondary market is available, the OEM may not remanufacture or may partially remanufacture. We study the above problem using a dynamic pricing case in which the product price during the second period is different from that in the first period. We find that the OEM prefers to offer a menu such that all rather than just some holders participate in the trade-in programme. Furthermore, in the dynamic pricing case, all rather than some of these used products are remanufactured, in contrast with the static pricing case. However, the layout of the OEM's trade-in and remanufacturing policies under the static pricing case is similar to that under the dynamic pricing case. We further extend our study to include a competitive situation and find that the results for the core model can essentially be reproduced under competition.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 25 April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Luis C. Dias, Rudolf Vetschera〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Stochastic Multicriteria Acceptability Analysis (SMAA) has become a popular Multi-Criteria Decision Aiding tool when some parameters of the decision problem are uncertain or have not been set. SMAA methods use Monte-Carlo simulation of the uncertain values to obtain indicators that inform decision making. While there is considerable literature on sampling of attribute weights, the problem of generating utility values is addressed less frequently. In this paper, we show that direct assignment of random utility values can lead to a biased sample of utility functions. We then introduce new methods to avoid this bias and discuss desirable properties for generating such utility functions. Then, we present a computational study to compare the different methods with regards to the desired properties, concluding that the new approaches perform much better. We also show how these techniques can be extended to consider only utility functions of a specific shape such as concave, convex or s-shaped. Besides SMAA multi-criteria analyses, the techniques studied in this paper can also be used in other contexts, e.g., decision trees and game theory, whenever utility functions need to be simulated to inform decision makers or to study the behavior of different methods.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 25 April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): A. Gutierrez-Alcoba, E.M.T. Hendrix, G. Ortega, E.E. Halvorsen-Weare, D. Haugland〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Maintenance costs account for a large part of the total cost of an offshore wind farm. Several models have been presented in the literature to optimize the fleet composition of the required vessels to support maintenance tasks. We provide a mixed integer linear programming (MILP) description of such a model, where on the higher level, the fleet composition is decided and on the lower level the maintenance operations are scheduled for a set of weather and breakdown scenarios. A drawback of deciding an a priori information schedule for the coming year is that, the weather outcomes and breakdowns are not known in advance. Consequently, given a fleet composition, its corresponding maintenance costs are underestimated compared to what can be realised in practice under incomplete information. Therefore, we present a heuristic that simulates the practical scheduling and may provide a better cost estimate. The latter method is used to evaluate a fleet composition based on available information and it is compared with the MILP solution based on a priori information.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 25 April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Borja Ponte, Mohamed M. Naim, Aris A. Syntetos〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Several studies have determined that product returns positively impact on the dynamics of hybrid manufacturing-remanufacturing systems, provided that they are perfectly correlated with demand. By considering imperfect correlation, we observe that intrinsic variations of returns may dramatically deteriorate the operational performance of these closed-loop supply chains. To cope with such added complexity, we propose a structure for controlling the reverse flow through the recoverable stock. The developed mechanism, in the form of a prefilter, is designed to leverage the known positive consequences of the deterministic component of the returns and to buffer the harmful impact of their stochastic component. We show that this outperforms both the benchmark push system and a baseline solution consisting of regulating all the returns. Consequently, we demonstrate that the operation of the production system is greatly smoothed and inventory is better managed. By developing a new framework for measuring the dynamics of closed-loop supply chains, we show that a significant reduction in the net stock, manufacturing, and remanufacturing variances can be achieved, which undoubtedly has implications both for stock reduction and production stabilization. Thus, the known benefits of circular economy models are strengthened, both economically and environmentally.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 27 February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Arvind Agarwal, Aparna Gupta, Arun Kumar, Srikanth G. Tamilselvam〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Risk culture is arguably a leading contributor to risk outcomes of a firm. We define risk culture indicators based on unstructured news data to develop a qualitative assessment of risk culture of banks. For US banks participating in an annual stress test program, we conduct a supervised learning ridge regression analysis to identify the most significant features to evaluate banks’ risk culture characteristics. These features are used for unsupervised clustering to determine the high to low quality of risk culture. The distinct groups obtained from clustering define and allow monitoring changes in the quality of risk culture in banks.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 28 February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Yi-Ting Chen, Edward W. Sun, Yi-Bing Lin〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Mobile internet usage has exploded with the mass popularity of smartphones that offer more convenient and efficient ways of doing anything from watching movies, playing games, and streaming music. Understanding the patterns of data usage is thus essential for strategy-focused data-driven business analytics. However, data usage has several unique stylized facts (such as high dimensionality, heteroscedasticity, and sparsity) due to a great variety of user behaviour. To manage these facts, we propose a novel density-based subspace clustering approach (i.e., a three-stage iterative optimization procedure) for intelligent segmentation of consumer data usage/demand. We discuss the characteristics of the proposed method and illustrate its performance in both simulation with synthetic data and business analytics with real data. In a field experiment of wireless mobile telecommunications for data-driven strategic design and managerial implementation, we show that our method is adequate for business analytics and plausible for sustainability in search of business value.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 27 April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Richard Vidgen, Giles Hindle, Ian Randolph〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The ethical aspects of data science and artificial intelligence have become a major issue. Organisations that deploy data scientists and operational researchers (OR) must address the ethical implications of their use of data and algorithms. We review the OR and data science literature on ethics and find that this work is pitched at the level of guiding principles and frameworks and fails to provide a practical and grounded approach that can be used by practitioners as part of the analytics development process. Further, given the advent of the General Data Protection Regulation (GDPR) an ethical dimension is likely to become an increasingly important aspect of analytics development. Drawing on the business analytics methodology (BAM) developed by Hindle and Vidgen (2018) we tackle this challenge through action research with a pseudonymous online travel company, EuroTravel. The method that emerges uses an opportunity canvas and a business ethics canvas to explore value creation and ethical aspects jointly. The business ethics canvas draws on the Markkula Center's five ethical principles (utility, rights, justice, common good, and virtue) to which explicit consideration of stakeholders is added. A contribution of the paper is to show how an ethical dimension can be embedded in the everyday exploration of analytics development opportunities, as distinct from a stand-alone ethical decision-making tool or as an overlay of a general set of guiding principles. We also propose that value and ethics should not be viewed as separate entities, rather they should be seen as inseparable and intertwined.〈/p〉〈/div〉

Description: 〈p〉Publication date: 19 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 277, Issue 3〈/p〉 〈p〉Author(s): 〈/p〉

Description: 〈p〉Publication date: Available online 25 April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Marlin W. Ulmer, Barrett W. Thomas〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The rise of mobile communication, ample computing power, and Amazon’s training of customers has led to last-mile delivery challenges and created struggles for companies seeking to budget their limited delivery resources efficiently to generate enough revenue. In this paper, we examine the capacitated customer acceptance problem with stochastic requests (CAPSR), a problem in which a company seeks to maximize expected revenue by accepting or rejecting requests. Each accepted request generates revenue and must be routed, consuming driver time and vehicle capacity. To solve the problem, we introduce a novel method of value function approximation (VFA). Conventionally, VFAs are either parametric (P-VFAs) or non-parametric (N-VFAs). Both VFAs have advantages and shortcomings and their performances rely significantly on the structure of the underlying problem. To combine the advantages and to alleviate the shortcomings of P-VFA and N-VFA used individually, we present a novel method, meso-parametric value function approximation (M-VFA). The results of computational experiments show that the M-VFA outperforms benchmarks for the CAPSR and show M-VFA offers the advantages of the individual VFAs while alleviating their shortcomings. Most importantly, we demonstrate that simultaneous approximations lead to better outcomes than either N- and P-VFA individually or some ex-post combination.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 22 April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Yoel G. Yera, Rosa E. Lillo, Pepa Ramírez-Cobo〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The Batch Markov Modulated Poisson Process (〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si98.gif" overflow="scroll"〉〈mrow〉〈mi〉B〈/mi〉〈mi〉M〈/mi〉〈mi〉M〈/mi〉〈mi〉P〈/mi〉〈mi〉P〈/mi〉〈/mrow〉〈/math〉) is a subclass of the versatile Batch Markovian Arrival Process (〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si99.gif" overflow="scroll"〉〈mrow〉〈mi〉B〈/mi〉〈mi〉M〈/mi〉〈mi〉A〈/mi〉〈mi〉P〈/mi〉〈/mrow〉〈/math〉) which has been proposed for the modeling of dependent events occurring in batches (such as group arrivals, failures or risk events). This paper focuses on exploring the possibilities of the 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si98.gif" overflow="scroll"〉〈mrow〉〈mi〉B〈/mi〉〈mi〉M〈/mi〉〈mi〉M〈/mi〉〈mi〉P〈/mi〉〈mi〉P〈/mi〉〈/mrow〉〈/math〉 for the modeling of real phenomena involving point processes with group arrivals. The first result in this sense is the characterization of the two-state 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si98.gif" overflow="scroll"〉〈mrow〉〈mi〉B〈/mi〉〈mi〉M〈/mi〉〈mi〉M〈/mi〉〈mi〉P〈/mi〉〈mi〉P〈/mi〉〈/mrow〉〈/math〉 with maximum batch size equal to 〈em〉K〈/em〉, the 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si100.gif" overflow="scroll"〉〈mrow〉〈mi〉B〈/mi〉〈mi〉M〈/mi〉〈mi〉M〈/mi〉〈mi〉P〈/mi〉〈msub〉〈mi〉P〈/mi〉〈mn〉2〈/mn〉〈/msub〉〈mrow〉〈mo〉(〈/mo〉〈mi〉K〈/mi〉〈mo〉)〈/mo〉〈/mrow〉〈mo〉,〈/mo〉〈/mrow〉〈/math〉 by a set of moments related to the inter-event time and batch size distributions. This characterization leads to a sequential fitting approach via a moments matching method. The performance of the novel fitting approach is illustrated on both simulated and a real teletraffic data set, and compared to that of the EM algorithm. In addition, as an extension of the inference approach, the queue length distributions at departures in the queueing system 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si101.gif" overflow="scroll"〉〈mrow〉〈mi〉B〈/mi〉〈mi〉M〈/mi〉〈mi〉M〈/mi〉〈mi〉P〈/mi〉〈mi〉P〈/mi〉〈mo〉/〈/mo〉〈mi〉M〈/mi〉〈mo〉/〈/mo〉〈mn〉1〈/mn〉〈/mrow〉〈/math〉 is also estimated.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 23 April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Gyorgy Dosa, Hans Kellerer, Zsolt Tuza〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉A selfish bin packing game is a variant of the classical bin packing problem in a game theoretic setting. In our model the items have not only a size but also a nonnegative weight. Each item plays the role of a selfish agent, and any agent/item pays some cost for being in a bin. The cost of a bin is 1, and this cost is shared among the items being in the bin, proportionally to their weight. A packing of the items into bins is called a Nash equilibrium if no item can decrease its cost by moving to another bin. In this paper we present two different settings for the weights which provide better values for the price of anarchy (PoA) than previous settings investigated so far. The improved PoA is not bigger than 16/11 ≈ 1.4545. Moreover, we give a general lower bound for the price of anarchy which holds for all possible choices of the weights.〈/p〉〈/div〉