ALBERT

Description: 〈p〉Publication date: Available online 8 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): K.T. Huynh〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We are interested in the stochastic modeling of a condition-based maintained system subject to continuous deterioration and maintenance actions such as inspection, partial repair and replacement. The partial repair is assumed dependent on the past in the sense that it cannot bring the system back into a deterioration state better than the one reached at the last repair. Such a past-dependency can affect (〈em〉i〈/em〉) the selection of a type of maintenance actions, (〈em〉ii〈/em〉) the maintenance duration, (〈em〉iii〈/em〉) the deterioration level after a maintenance, and (〈em〉iv〈/em〉) the restarting system deterioration behavior. In this paper, all these effects are jointly considered in an unifying condition-based maintenance model on the basis of restarting deterioration states randomly sampled from a probability distribution truncated by the deterioration levels just before a current repair and just after the last repair/replacement. Using results from the semi-regenerative theory, the long-run maintenance cost rate is analytically derived. Numerous sensitivity studies illustrate the impacts of past-dependent partial repairs on the economic performance of the considered condition-based maintained system.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 5 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Shichen Zhang, Jianxiong Zhang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Nowadays, some suppliers are looking for offline expansion in addition to their preexisting online channels relying on e-tailers. This study focuses on the e-tailer’s demand information sharing strategy with the supplier who may build upon brick-and-mortar stores. Both prevailing agreements between the supplier and the e-tailer are investigated: agency selling and reselling. The equilibrium results are quite different under these two agreements. Specifically, when the supplier’s offline entry cost is very small or large, the e-tailer shares information under agency selling while keeps information private under reselling. When the entry cost is intermediate, channel substitution rate is large and information uncertainty is small, the e-tailer withholds the demand information under agency selling while shares information under reselling to deter the supplier from entering an offline channel. Furthermore, two extensions about consumer behavior in multichannel selection are discussed: showrooming and webrooming. With showrooming or webrooming, the e-tailer’s information sharing decisions qualitatively hold, while with showrooming the drive factor behind may change; that is, withholding information under agency selling and sharing information under reselling may also serve as measures to encourage supplier offline entry when the effect of showrooming is strong.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 2 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Kieran Conboy, Patrick Mikalef, Denis Dennehy, John Krogstie〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉While the topic of analytics is rapidly growing in popularity across various domains, there is still a relatively low amount of empirical work in the field of operations research (OR). While studies of various technical and business aspects of analytics are emerging in OR, little has been done to address how the OR community can leverage business analytics in dynamic and uncertain environments – the very place where OR is supposed to play a key role. To address this gap, this study draws on the dynamic capabilities view of the firm and builds on eight selected case studies of operations research activity in large organisations, each of which have invested significantly in analytics technology and implementation. The study identifies fourteen analytics-enabled micro-foundations of dynamic capabilities, essentially highlighting how organisations can use analytics to manage and enhance their OR activities in dynamic and uncertain environments. This study also identifies six key cross-cutting propositions emerging from the data and develops a roadmap for future OR researchers to address these issues and improve the use and value of analytics as enablers of organisational dynamic capabilities.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 279, Issue 2〈/p〉 〈p〉Author(s): 〈/p〉

Description: 〈p〉Publication date: Available online 29 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Claudio Arbib, Mustafa Ç. Pınar, Matteo Tonelli〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Consider a three-level non-capacitated location/pricing problem: a firm first decides which facilities to open, out of a finite set of candidate sites, and sets service prices with the aim of revenue maximization; then a second firm makes the same decisions after checking competing offers; finally, customers make individual decisions trying to minimize costs that include both purchase and transportation. A restricted two-level problem can be defined to model an optimal reaction of the second firm to known decision of the first.〈/p〉 〈p〉For non-metric costs, the two-level problem corresponds to 〈span〉Envy-free Pricing〈/span〉 or to a special 〈span〉Network Pricing〈/span〉 problem, and is 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si65.svg"〉〈mi mathvariant="bold-script"〉APX〈/mi〉〈/math〉-complete even if facilities can be opened at no fixed cost. Our focus is on the metric 1-dimensional case, a model where customers are distributed on a main communication road and transportation cost is proportional to distance. We describe polynomial-time algorithms that solve two- and three-level problems with opening costs and single 1〈sup〉〈em〉st〈/em〉〈/sup〉 level facility. Quite surprisingly, however, even the two-level problem with no opening costs becomes 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si66.svg"〉〈mi mathvariant="bold-script"〉NP〈/mi〉〈/math〉-hard when two 1〈sup〉〈em〉st〈/em〉〈/sup〉 level facilities are considered.〈/p〉 〈/div〉

Description: 〈p〉Publication date: Available online 29 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Zachary Feinstein〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We construct a continuous time model for price-mediated contagion precipitated by a common exogenous stress to the banking book of all firms in the financial system. In this setting, firms are constrained so as to satisfy a risk-weight based capital ratio requirement. We use this model to find analytical bounds on the risk-weights for assets as a function of the market liquidity. Under these appropriate risk-weights, we find existence and uniqueness for the joint system of firm behavior and the asset prices. We further consider an analytical bound on the firm liquidations, which allows us to construct exact formulas for stress testing the financial system with deterministic or random stresses. Numerical case studies are provided to demonstrate various implications of this model and analytical bounds.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 29 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Bo Jin〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In a recent paper, Galle, Barnhart, and Jaillet [Galle, V., Barnhart, C., & Jaillet, P. (2018). A new binary formulation of the restricted container relocation problem based on a binary encoding of configurations. 〈em〉European Journal of Operational Research, 267〈/em〉(2), 467–477] introduced a new variant of the container relocation problem (CRP), named the relaxed restricted CRP, where every container can be relocated at most once for retrieving each target container. The authors also proposed a binary integer programming model for formulating the relaxed restricted CRP. In this paper, it is first shown that the proposed model contains two deficiencies in formulating the “last in, first out” (LIFO) policy. These deficiencies will cause the solutions obtained by the model to correspond to infeasible configurations or infeasible relocation sequences. Then, the LIFO policy is analyzed in detail and reformulated as linear constraints correctly. Lastly, the corrected integer programming formulation is presented. Computational experiments show that the corrected model dramatically reduces complexity and improves performance.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 28 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Mike G. Tsionas〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Mitropoulos et al. (Mitropoulos P., M. A. Talias, and I. Mitropoulos, 2015, Combining stochastic DEA with Bayesian analysis to obtain statistical properties of the efficiency scores: An application to Greek public hospitals. European Journal of Operational Research 243, 302-311) suggested the use of a Bayesian approach in Data Envelopment Analysis (DEA) which can be used to obtain posterior distributions of efficiency scores. In this paper, we avoid their assumption that alternative data sets are simulated from the predictive distribution obtained from their simple data generating process of a normal distribution for the data. The new approach has two significant advantages. First, the posterior proposed in this paper is coherent or principled in the sense that it is consistent with the DEA formulation. Second, and perhaps surprisingly, it is not necessary to solve linear programming problems for each observation in the sample. Bayesian inference is organized around Markov Chain Monte Carlo techniques that can be implemented quite easily. We conduct extensive Monte Carlo experiments to investigate the finite-sample properties of the new approach. We also provide an application to a large U.S banking data set. The sample is an unbalanced panel of US banks with 2,397 bank–year observations for 285 banks. The main purpose of the analysis is to compare distributions of efficiency scores. Relative to DEA, Bayes DEA provides different efficiency scores and their sample distribution has significantly less probability concentration around unity. The comparison with bootstrap-DEA shows that results from Bayes DEA are in broad agreement.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 21 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Zelin Zhang, Minghui Ma, Peter T.L. Popkowski Leszczyc, Hejun Zhuang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉This research proposes an analytical model of the joint optimization of coupon face value and duration together with the product price, and determines the impact of coupon design on consumers’ redemption behavior. A model of rational forward-looking consumers’ redemption behavior is derived that incorporates forgetting (to redeem) and stochastic redemption costs.〈/p〉 〈p〉Results show that when product price is exogenous, long-duration coupons may result in increased seller profits and always increase consumer surplus. Moreover, a one-period coupon is never optimal when (1) the difference in valuations for high-value (loyal) and low-value (non-loyal) consumers or (2) the coupon face value is larger than the redemption costs of high-value consumers. Long-duration coupons tend to be optimal when the level of recall of high-value consumers is sufficiently low, which reduces redemption by high-value consumers.〈/p〉 〈p〉Coupon duration together with face value plays an important role in coupons’ ability to price discriminate between different consumer segments and to avoid head-on competition with other sellers. Results can replicate empirically observed redemptions patterns, which has important implications for the strategic targeting of coupons to different consumer segments.〈/p〉 〈p〉A coupon may result in an increase or decrease in price. When the difference in valuation between high-value and low-value consumers is high (relative to the redemption costs), a seller can either reduce price and lower face value or increase coupon duration for the purpose of avoiding redemption by high-value consumers.〈/p〉 〈/div〉

Description: 〈p〉Publication date: Available online 21 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Sturla F. Kvamsdal, José M. Maroto, Manuel Morán, Leif K. Sandal〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Seasonality or periodicity–biological, environmental, or economic–are fundamental properties of most marine fisheries. We propose a generic infinite horizon discrete time fisheries management model by modifying existing models and frameworks of analysis to reflect seasonal variation or more general multiperiodicity in parameters, variables, or functional forms. Our model captures such variations via repeated cycles of multiple intervals with differing characteristics. The framework offers a simple and intuitive set up of arbitrary periodicity and seasonality in any feature, which significantly increases model realism. Further, it distances itself from continuous time modeling approaches where uniqueness and solvability of periodic models generally are difficult to assert. In our setting, the governing equations for the time-dependent value function of the management optimization problem are shown to be equivalent to a high-dimensional contraction and hence ensure uniqueness and a feasible solution algorithm. We illustrate our approach using a simple example to demonstrate that accounting for seasonality in fisheries management can improve outcomes considerably. Our framework also provides for analysis of seasonal regulatory measures. Ultimately, our approach applies to renewable resource management more generally and to many infinite-horizon, discrete time optimization problems with periodic features.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 20 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Chandra Ade Irawan, Majid Eskandarpour, Djamila Ouelhadj, Dylan Jones〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Scheduling maintenance routing for an offshore wind farm is a challenging and complex task. The problem is to find the best routes for the Crew Transfer Vessels to maintain the turbines in order to minimise the total cost. This paper primarily proposes an efficient solution method to solve the deterministic maintenance routing problem in an offshore wind farm. The proposed solution method is based on the Large Neighbourhood Search metaheuristic. The efficiency of the proposed metaheuristic is validated against state of the art algorithms. The results obtained from the computational experiments validate the effectiveness of the proposed method. In addition, as the maintenance activities are affected by uncertain conditions, a simulation-based optimisation algorithm is developed to tackle these uncertainties. This algorithm benefits from the fast computational time and solution quality of the proposed metaheuristic, combined with Monte Carlo simulation. The uncertain factors considered include the travel time for a vessel to visit turbines, the required time to maintain a turbine, and the transfer time for technicians and equipment to a turbine. Moreover, the proposed simulation-based optimisation algorithm is devised to tackle unpredictable broken-down turbines. The performance of this algorithm is evaluated using a case study based on a reference wind farm scenario developed in the EU FP7 LEANWIND project.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 20 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Simon J. Höller, Raik Özsen, Ulrich W. Thonemann〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Order expediting is an inventory control practice that allows companies to reduce inventory while maintaining service levels. However, expediting outstanding orders is costly and a trade-off must be made between expediting orders and holding inventory. We model the problem as a periodic-review inventory system with the option to move outstanding units forward in the replenishment pipeline. The objective is to minimize the sum of expected inventory holding and expediting costs per period subject to a minimum expected service level constraint. We consider a generalized base-stock policy where outstanding units are expedited when the inventory level drops below a certain threshold. We develop structural properties and present an efficient procedure to determine the optimal policy parameters. In a numerical study with real-world data, we show that our expediting policy offers substantial savings compared to other policies. We also provide managerial insights by numerically analyzing how the model parameters impact the savings.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 20 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): M. Fampa, D. Lubke, F. Wang, H. Wolkowicz〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We consider a parametric convex quadratic programming (CQP) relaxation for the quadratic knapsack problem (QKP). This relaxation maintains partial quadratic information from the original QKP by perturbing the objective function to obtain a concave quadratic term. The nonconcave part generated by the perturbation is then linearized by a standard approach that lifts the problem to matrix space. We present a primal-dual interior point method to optimize the perturbation of the quadratic function, in a search for the tightest upper bound for the QKP. We prove that the same perturbation approach, when applied in the context of semidefinite programming (SDP) relaxations of the QKP, cannot improve the upper bound given by the corresponding linear SDP relaxation. The result also applies to more general integer quadratic problems. Finally, we propose new valid inequalities on the lifted matrix variable, derived from cover and knapsack inequalities for the QKP, and present separation problems to generate cuts for the current solution of the CQP relaxation. Our best bounds are obtained alternating between optimizing the parametric quadratic relaxation over the perturbation and applying cutting planes generated by the valid inequalities proposed.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 20 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Yusuke Zennyo〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper examines strategic contracting between a monopoly platform and suppliers that sell their goods through the platform. I consider two competing suppliers: a high-volume supplier with the larger potential demand and a low-volume supplier with the smaller one. Each supplier chooses one of two contracts: wholesale or agency. The platform has to strategically determine the royalty rate for the agency contract by taking into account which contracts the suppliers will choose. I show that the platform offers a low (high) royalty rate to induce the suppliers to adopt the agency (wholesale) contract when product substitutability is low (high) enough. More interestingly, when the degree of substitution is at an intermediate level, asymmetric contracting, in which only the low-volume supplier adopts the agency contract, can arise in equilibrium. This result is related to the fact that many long-tail and niche products with lower potential market sizes are traded on platform-based marketplaces, such as Amazon Marketplace and Walmart Marketplace.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 31 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Shu Guo, Tsan-Ming Choi, Bin Shen〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Motivated by the observed industrial issues, we analytically develop a fashion supply chain consisting of one manufacturer and two competing retailers and investigate how retail competition and consumer returns affect green product development in fashion apparel. In the basic model, that is, the pure “product greenness level” game, we find that the optimal greenness level of the fashion product decreases along with the level of market competition. This finding implies that a more competitive market leads to a lower optimal greenness level. We also identify that when the consumer return rate increases, the optimal product greenness level is substantially reduced. In the extended model with joint decisions on greenness and pricing, we find that the optimal product greenness level for the whole channel is always higher in the scenario when both retailers charge a higher retail price than in the case with a lower retail price. As such, the underdevelopment of green fashion products is a result of fashion industry features, such as an extremely competitive environment for green product development, relatively low retail prices for fashion products, and high consumer return rates. Therefore, fashion companies should join a co-opetition game for the green product market and simultaneously enhance their efficiency in managing consumer returns. To support our analytical findings, we conduct extensive industrial interviews with various representative companies. Based on this multi-methodological approach (MMA), this paper generates practice-relevant managerial insights that not only contribute to the literature, but also act as valuable references for industrialists.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 31 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Daqiang Chen, Joshua Ignatius, Danzhi Sun, Mark Goh, Shalei Zhan〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper addresses the problem of the firms operating on cross-border or inter-regional platforms that are subject to the enforcement of each local government's carbon emissions regulatory policy, thus causing an imbalance in the sharing of the burden of the greening of the total supply chain. We introduce the concept of equity as the incentive mechanism to coordinate this green supply chain which is a function of the carbon emission permits and the revenue generated by the firms. Due to the complexity and imbalance in the original incentive mechanism to this problem, we provide a new equivalent supply chain network equilibrium model under elastic demand based on user equilibrium theory. We state the user equilibrium conditions and provide the equivalent formulation. We show the trade-offs under various carbon emissions regulatory policies. A product with higher price elasticity and carbon emission intensity not only hampers the firm from gaining a higher revenue, but it also reduces the equity of the system under an invariant emission regulatory policy.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 28 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Noemie Balouka, Izack Cohen〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper suggests a robust optimization approach for the multi-mode resource-constrained project scheduling problem with uncertain activity durations. The objective is to minimize the worst-case project duration by deciding on activity modes, resource allocations and a schedule baseline. The problem is solved by a Benders decomposition approach with specialized cuts. We consider polyhedral uncertainty sets in which the level of conservatism can be adjusted. Using a computational study in which various problem instances are explored under varying levels of uncertainty, conservatism and several types of duration distributions, we provide insights about the price of robustness and the performance of the approach. The hope is that these insights can guide future multi-mode project scheduling implementations when there is partial information about the distribution of activity durations.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 13 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Gilberto Montibeller, Pratik Patel, Victor J. del Rio Vilas〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Multi-criteria assessments are increasingly being employed in the prioritisation of health threats, supporting decision processes related to health risk management. The use of multi-criteria analysis in this context is welcome, as it facilitates the consideration of multiple impacts of health threats, it can encompass the use of expert judgment to complement and amalgamate the evidence available, and it permits the modelling of policy makers’ priorities. However, these assessments often lack a clear multi-criteria conceptual framework, in terms of both axiomatic rigour and adequate procedures for preference modelling. Such assessments are 〈em〉ad hoc〈/em〉 from a multi-criteria decision analysis perspective, despite the strong health expertise used in constructing these models. In this paper we critically examine some key assumptions and modelling choices made in these assessments, comparing them with the best practices of multi-attribute value analysis. Furthermore, we suggest a set of guidelines on how simulation studies might be employed to assess the impact of these modelling choices. We apply these guidelines to two relevant studies available in the health threat prioritisation domain. We identify severe variability in our simulations due to poor modelling choices, which could cause changes in the ranking of threats being assessed and thus lead to alternative policy recommendations than those suggested in their reports. Our results confirm the importance of carefully designing multi-criteria evaluation models for the prioritisation of health threats.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 13 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Antoine Lesage-Landry, Joshua A. Taylor〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We construct a model for transmission planning with both alternating and direct current lines, the latter of which can be interfaced via either line-commutated converters or voltage-source converters. The transmission expansion problem is nonlinear and nonconvex. Thus, nonlinear solvers cannot guarantee their convergence to the global optimum of the problem. We use relaxations and approximations to formulate a mixed-integer second-order cone transmission expansion model, which can be solved to optimality by current industrial solvers. We base our formulation on the branch flow relaxation. We include losses and reactive power placement, and consider direct current lines connected by both line-commutated converters and voltage-sourced converters. We show that our approach lowers the expansion cost on 6-bus and 24-bus system examples. We evaluate the feasibility of our formulation using a semidefinite relaxation of optimal power flow and find that the resulting plan admits feasible or close to feasible power flows.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 12 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Valentin Zelenyuk〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The main goal of this paper is to explore the possible solutions to a ‘big data’ problem related to the very large dimensions of input–output data. In particular, we focus on the cases of severe ‘curse of dimensionality’ problem that require dimension-reduction prior to using Data Envelopment Analysis. To achieve this goal, we have presented some theoretical grounds and performed a new to the literature simulation study where we explored the price-based aggregation as a solution to address the problem of very large dimensions.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 12 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): M. Densing〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉A stored commodity is sold under a capacity constraint depending on an exogenous random market price, and, alternatively, a service contract can be provided in which the selling amount must be held constant over time independent of price changes. The seller of the commodity is assumed to optimize the trade-off between the received payment for the provision of the service and the loss of flexibility by the reduced selling on the market. The chosen setup allows for closed-form solutions, such that the analysis is of theoretical interest. A potential application is hydropower storage optimization against exogenous electricity prices with the option to enter contracts for providing spinning reserve; spinning reserve is needed to stabilize large-scale power systems. A single-period model is considered, and the storage level of the commodity is bounded from below in expectation. These simplifications allow a closed-form solution of bang-bang type, even under our assumption of an infinite probability space.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 8 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Landir Saviniec, Maristela O. Santos, Alysson M. Costa, Lana M.R. dos Santos〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉High school timetabling problems consist in building periodic timetables for class-teacher meetings considering compulsory and non-compulsory requirements. This family of problems has been widely studied since the 1950s, mostly via mixed-integer programming and metaheuristic techniques. However, the efficient search of optimal or near-optimal solutions is still a challenge for many problems of practical size. In this paper, we investigate mixed-integer programming formulations and a parallel metaheuristic based algorithm for solving high school timetabling problems with compactness and balancing requirements. We propose two pattern-based formulations and a solution algorithm that simultaneously exploits column generation and a team of metaheuristics to build and improve solutions. Extensive computational experiments conducted with real-world instances demonstrate that our formulations are competitive with the best existing high school timetabling formulations, while our parallel algorithm presents superior performance to alternative methods available in the literature.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 5 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Kai A. Konrad〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper studies the attack-and-defence game between a web user and a whole set of players over this user’s ‘valuable secrets.’ The number and type of these valuable secrets are the user’s private information. Attempts to tap information as well as privacy protection are costly. The multiplicity of secrets is of strategic value for the holders of these secrets. Users with few secrets keep their secrets private with some probability, even though they do not protect them. Users with many secrets protect their secrets at a cost that is smaller than the value of the secrets protected. The analysis also accounts for multiple redundant information channels with cost asymmetries, relating the analysis to attack-and-defence games with a weakest link.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 2 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): C. Archetti, D. Feillet, A. Mor, M.G. Speranza〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The dynamic traveling salesman problem with stochastic release dates (DTSP-srd) is a problem in which a supplier has to deliver parcels to its customers. These parcels are delivered to its depot while the distribution is taking place. The arrival time of a parcel to the depot is called its release date. In the DTSP-srd, release dates are stochastic and dynamically updated as the distribution takes place. The objective of the problem is the minimization of the total time needed to serve all customers, given by the sum of the traveling time and the waiting time at the depot. The problem is represented as a Markov Decision Process and is solved through a reoptimization approach. Two models are proposed for the problem to be solved at each stage. The first model is stochastic and exploits the entire probabilistic information available for the release dates. The second model is deterministic and uses an estimation of the release dates. An instance generation procedure is proposed to simulate the evolution of the information to perform computational tests. The results show that a more frequent reoptimization provides better results across all tested instances and that the stochastic model performs better than the deterministic model. The main drawback of the stochastic model lies in the computational time required to evaluate a solution, which makes an iteration of the heuristic substantially more time-consuming than in the case where the deterministic model is used.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 28 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): María Isabel Hartillo-Hermoso, Haydee Jiménez-Tafur, José María Ucha-Enríquez〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉A new exact algorithm for bi-objective linear integer problems is presented, based on the classic ϵ-constraint method and algebraic test sets for single-objective linear integer problems. Our method provides the complete Pareto frontier 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si19.svg"〉〈mi mathvariant="bold-script"〉N〈/mi〉〈/math〉 of non-dominated points and, for this purpose, it considers exactly 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si5.svg"〉〈mrow〉〈mo〉|〈/mo〉〈mi mathvariant="bold-script"〉N〈/mi〉〈mo〉|〈/mo〉〈/mrow〉〈/math〉 single-objective problems by using reduction with test sets instead of solving with an optimizer. Although we use Gröbner bases for the computation of test sets, which may provoke a bottleneck in principle, the computational results are shown to be promising, especially for unbounded knapsack problems, for which any usual branch-and-cut strategy could be much more expensive. Nevertheless, this algorithm can be considered as a potentially faster alternative to IP-based methods when test sets are available.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 16 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Qichen Deng, Bruno F. Santos, Richard Curran〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper presents a practical dynamic programming based methodology to optimize the long-term maintenance check schedule for a fleet of heterogeneous aircraft. It is the first time that the long-term aircraft maintenance check schedule is optimized, integrating different check types in a single schedule solution. The proposed methodology aims at minimizing the wasted interval between checks. By achieving this goal, one is also reducing the number of checks over time, increasing aircraft availability and, therefore, reducing maintenance costs, while respecting safety regulations. The model formulation takes aircraft type, status, maintenance capacity, and other operational constraints into consideration. We also validate and demonstrate the proposed methodology using fleet maintenance data from a European airline. The outcomes show that, when compared with the current practice, the number of maintenance checks can be reduced by around 7% over a period of 4 years, while computation time is less than 15 minutes. This could result in saving worth $1.1M–$3.4M in maintenance costs for a fleet of about 40 aircraft and generating more than $9.8M of revenue due to higher aircraft availability.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 14 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Urtats Etxegarai, Eva Portillo, Jon Irazusta, Lucien Koefoed, Nikola Kasabov〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this work, a heuristic as operational tool to estimate the lactate threshold and to facilitate its integration into the training process of recreational runners is proposed. To do so, we formalize the principles for the lactate threshold estimation from empirical data and an iterative methodology that enables experience based learning. This strategy arises as a robust and adaptive approach to solve data analysis problems. We compare the results of the heuristic with the most commonly used protocol by making a first quantitative error analysis to show its reliability. Additionally, we provide a computational algorithm so that this quantitative analysis can be easily performed in other lactate threshold protocols. With this work, we have shown that a heuristic (%60 of 〈em〉endurance running speed reserve〈/em〉), serves for the same purpose of the most commonly used protocol in recreational runners, but improving its operational limitations of accessibility and consistent use.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 13 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): P. Goos, U. Syafitri, B. Sartono, A.R. Vazquez〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Mixture experiments usually involve various constraints on the proportions of the ingredients of the mixture under study. In this paper, inspired by the fact that the available stock of certain ingredients is often limited, we focus on a new type of constraint, which we refer to as an ingredient availability constraint. This type of constraint substantially complicates the search for optimal designs for mixture experiments. One difficulty, for instance, is that the optimal number of experimental runs is not known a priori. The resulting optimal experimental design problem belongs to the class of nonlinear nonseparable multidimensional knapsack problems. We present a variable neighborhood search algorithm as well as a mixed integer nonlinear programming approach to tackle the problem to identify D- and I-optimal designs for mixture experiments when there is a limited stock of certain ingredients, and we show that the variable neighborhood descent algorithm is highly competitive in terms of solution quality and computing time.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 14 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Truong Van Nguyen, Li Zhou, Alain Yee Loong Chong, Boying Li, Xiaodie Pu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Remanufacturing has received increasing attention from researchers over the last decade. While many associated operational issues have been extensively studied, research into the prediction customer demand for, and the market development of, remanufactured products is still in its infancy. The majority of the existing research into remanufactured product demand is largely based on conventional statistical models that fail to capture the non-linear behaviour of customer demand and market factors in real-world business environments, in particular e-marketplaces. Therefore, this paper aims to develop a comprehensible data-mining prediction approach, in order to achieve two objectives: (1) to provide a highly accurate and robust demand prediction model of remanufactured products; and (2) to shed light on the non-linear effect of online market factors as predictors of customer demand. Based on the real-world Amazon dataset, the results suggest that predicting remanufactured product demand is a complex, non-linear problem, and that, by using advanced machine-learning techniques, our proposed approach can predict the product demand with high accuracy. In terms of practical implications, the importance of market factors is ranked according to their predictive powers of demand, while their effects on demand are analysed through their partial dependence plots. Several insights for management are revealed by a thorough comparison of the sales impact of these market factors on remanufactured and new products.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 9 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): N.H. Chieu, V. Jeyakumar, G. Li〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper we show that a convexifiability property of nonconvex quadratic programs with nonnegative variables and quadratic constraints guarantees zero duality gap between the quadratic programs and their semi-Lagrangian duals. More importantly, we establish that this convexifiability is hidden in classes of nonnegative homogeneous quadratic programs and discrete quadratic programs, such as mixed integer quadratic programs, revealing zero duality gaps. As an application, we prove that robust counterparts of uncertain mixed integer quadratic programs with objective data uncertainty enjoy zero duality gaps under suitable conditions. Various sufficient conditions for convexifiability are also given.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 9 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): C.J. Jagtenberg, A.J. Mason〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Most literature on the ambulance location problem aims to maximize coverage, i.e., the fraction of people that can be reached within a certain response time threshold. Such a problem often has one optimum, but several near-optimal solutions may exist. These may have a similar overall performance but provide different coverage for different regions. This raises the question: are we making ‘arbitrary’ choices in terms of who gets coverage and who does not? In this paper we propose to share time between several good ambulance configurations in the interest of fairness. We argue that the Bernoulli–Nash social welfare measure should be used to evaluate the fairness of the system. Therefore, we formulate a nonlinear optimization model that determines the fraction of time spent in each configuration to maximize the Bernoulli–Nash social welfare. We solve this model in a case study for an ambulance provider in the Netherlands, using a combination of simulation and optimization. Furthermore, we analyze how the Bernoulli–Nash optimal solution compares to the maximum-coverage solution by formulating and solving a multi-objective optimization model.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 9 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Jian Luo, Xin Yan, Ye Tian〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Unsupervised classification is a highly important task of machine learning methods. Although achieving great success in supervised classification, support vector machine (SVM) is much less utilized to classify unlabeled data points, which also induces many drawbacks including sensitive to nonlinear kernels and random initializations, high computational cost, unsuitable for imbalanced datasets. In this paper, to utilize the advantages of SVM and overcome the drawbacks of SVM-based clustering methods, we propose a completely new two-stage unsupervised classification method with no initialization: a new unsupervised kernel-free quadratic surface SVM (QSSVM) model is proposed to avoid selecting kernels and related kernel parameters, then a golden-section algorithm is designed to generate the appropriate classifier for balanced and imbalanced data. By studying certain properties of proposed model, a convergent decomposition algorithm is developed to implement this non-covex QSSVM model effectively and efficiently (in terms of computational cost). Numerical tests on artificial and public benchmark data indicate that the proposed unsupervised QSSVM method outperforms well-known clustering methods (including SVM-based and other state-of-the-art methods), particularly in terms of classification accuracy. Moreover, we extend and apply the proposed method to credit risk assessment by incorporating the T-test based feature weights. The promising numerical results on benchmark personal credit data and real-world corporate credit data strongly demonstrate the effectiveness, efficiency and interpretability of proposed method, as well as indicate its significant potential in certain real-world applications.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 9 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Célia Paquay, Yves Crama, Thierry Pironet〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The problem considered in this work stems from a non-profit organization in charge of door-to-door passenger transportation for medical appointments. Patients are picked up at home by a driver and are then dropped at their appointment location. They may also be driven back home at the end of their appointment. Some patients have specific requirements, e.g., they may require an accompanying person or a wheelchair. Planning such activities gives rise to a so-called dial-a-ride problem. In the present work, it is assumed that the requests assigned to the drivers have been selected, and the transportation plan has been established for the next day. However, in practice, appointment durations may vary due to unforeseen circumstances, and some transportation requests may be modified, delayed or canceled during the day. The aim of this work is to propose a reactive algorithm which can adapt the initial plan in order to manage the disruptions and to take care of as many patients as possible in real-time. The plan should be modified quickly when a perturbation is observed, without resorting to major changes which may confuse the drivers and the patients. Several recourse procedures are defined for this purpose. They allow the dispatcher to temporarily delete a request, to insert a previously deleted request, or to permanently cancel a request. Simulation techniques are used to test the approach on randomly generated scenarios. Several key performance indicators are introduced in order to measure the impact of the disruptions and the quality of the solutions.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 8 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Noémie Le Carrer, Scott Ferson, Peter L. Green〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper describes a framework that combines decision theory and stochastic optimisation techniques to address tide routing (i.e. optimisation of cargo loading and ship scheduling decisions in tidal ports and shallow seas). Unlike weather routing, tidal routing has been little investigated so far, especially from the perspective of risk analysis. Considering the journey of a bulk carrier between 〈em〉N〈/em〉 ports, a shipping decision model is designed to compute cargo loading and scheduling decisions, given the time series of the sea level point forecasts in these ports. Two procedures based on particle swarm optimisation and Monte Carlo simulations are used to solve the shipping net benefit constrained optimisation problem. The outputs of probabilistic risk minimisation are compared with those of net benefit maximisation, the latter including the possibility of a ‘rule-of-the-thumb’ safety margin. Distributional robustness is discussed as well, with respect to the modelling of sea level residuals. Our technique is assessed on two realistic case studies in British ports. Results show that the decision taking into account the stochastic dimension of sea levels is not only robust in real port and weather conditions, but also closer to optimality than standard practices using a fixed safety margin. Furthermore, it is shown that the proposed technique remains more interesting when sea level variations are artificially increased beyond the extremes of the current residual models.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 8 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Ju Zhao, Yong-Wu Zhou, Zong-Hong Cao, Jie Min〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Consider a two-echelon supply chain consisting of two manufacturers and a dominant retailer, such as big supermarkets like Walmart. Under a consignment contract with revenue sharing, the two manufacturers sell through the retailer two substitutable products whose demands are dependent on their shelf space and sales prices. The two manufacturers may compete horizontally for shelf space and pricing by three scenarios: Nash game, Stackelberg game, and collusion, and play vertically the retailer-Stackelberg game with the retailer. For each of these horizontal scenarios, we present all participators’ equilibrium strategies and their corresponding profits, based on which the impacts of manufacturers’ cost difference and moving sequence are investigated. Additionally, we discuss whether a horizontal collusion among manufacturers occurs when they choose their scenarios and whether centralization is always beneficial for the entire chain under the considered consignment contract. The study reveals the following results: (i) When the manufacturers compete horizontally, the high-cost manufacturer always sets a high-price and less shelf space strategy, while the low-cost manufacturer always adopts a low-price and more shelf space strategy, which is not affected by their moving sequence. If they collude horizontally, it is just reverse. (ii) When the two manufacturers compete horizontally, all participators’ equilibrium strategies and their corresponding profits are significantly influenced by manufacturers’ moving sequence. (iii) A horizontal collusion between the manufacturers can occur only when their cost difference is relatively small; this finding supplements existing literature. (iv) When the cost difference between manufacturers is relatively big, then centralization may be detrimental to the entire chain, which can explain why several supply chains adopt vertical competition strategies in practice. In addition, we find that these results still hold for the limited shelf space scenario and shelf-space limitation enhances the horizontal and vertical competition intensity by increasing shelf space fee.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 7 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): John H. Drake, Ahmed Kheiri, Ender Özcan, Edmund K. Burke〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Hyper-heuristics have emerged as a way to raise the level of generality of search techniques for computational search problems. This is in contrast to many approaches, which represent customised methods for a single problem domain or a narrow class of problem instances. The current state-of-the-art in hyper-heuristic research comprises a set of methods that are broadly concerned with intelligently 〈em〉selecting〈/em〉 or 〈em〉generating〈/em〉 a suitable heuristic in a given situation. Hyper-heuristics can be considered as search methods that operate on lower-level heuristics or heuristic components, and can be categorised into two main classes: heuristic selection and heuristic generation. The term hyper-heuristic was defined in the early 2000s as a 〈em〉heuristic to choose heuristics〈/em〉, but the idea of designing high-level heuristic methodologies can be traced back to the early 1960s. This paper gives a brief history of this emerging area, reviews contemporary hyper-heuristic literature, and discusses recent hyper-heuristic frameworks. In addition, the existing classification of selection hyper-heuristics is extended, in order to reflect the nature of the challenges faced in contemporary research. Unlike the survey on hyper-heuristics published in 2013, this paper focuses only on selection hyper-heuristics and presents critical discussion, current research trends and directions for future research.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 6 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Sana Dahmen, Monia Rekik, François Soumis, Guy Desaulniers〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper, we address a personalized multi-department multi-day shift scheduling problem with a multi-skill heterogeneous workforce where employees can be transferred between departments under some restrictions. The objective is to construct a schedule that minimizes under-coverage, over-coverage, transfer and labor costs. We propose a novel two-stage approach to solve it: the first stage considers an approximate and smaller problem based on data aggregation and produces approximate transfers. The second stage constructs personalized schedules based on the information deduced from the first stage. An exhaustive experimental study is conducted and proves the efficiency of the proposed approach in terms of solution quality and computing times.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 5 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Peter Nystrup, Erik Lindström, Pierre Pinson, Henrik Madsen〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We propose four different estimators that take into account the autocorrelation structure when reconciling forecasts in a temporal hierarchy. Combining forecasts from multiple temporal aggregation levels exploits information differences and mitigates model uncertainty, while reconciliation ensures a unified prediction that supports aligned decisions at different horizons. In previous studies, weights assigned to the forecasts were given by the structure of the hierarchy or the forecast error variances without considering potential autocorrelation in the forecast errors. Our first estimator considers the autocovariance matrix within each aggregation level. Since this can be difficult to estimate, we propose a second estimator that blends autocorrelation and variance information, but only requires estimation of the first-order autocorrelation coefficient at each aggregation level. Our third and fourth estimators facilitate information sharing between aggregation levels using robust estimates of the cross-correlation matrix and its inverse. We compare the proposed estimators in a simulation study and demonstrate their usefulness through an application to short-term electricity load forecasting in four price areas in Sweden. We find that by taking account of auto- and cross-covariances when reconciling forecasts, accuracy can be significantly improved uniformly across all frequencies and areas.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 5 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Bowei Chen, Jingmin Huang, Yufei Huang, Stefanos Kollias, Shigang Yue〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉While page views are often sold instantly through real-time auctions when users visit websites, they can also be sold in advance via guaranteed contracts. In this paper, we present a dynamic programming model to study how an online publisher should optimally allocate and price page views between guaranteed and spot markets. The problem is challenging because the allocation and pricing of guaranteed contracts affect how advertisers split their purchases between the two markets, and the terminal value of the model is endogenously determined by the updated dual force of supply and demand in auctions. We take the advertisers’ purchasing behaviour into consideration, i.e., risk aversion and stochastic demand arrivals, and present a scalable and efficient algorithm for the optimal solution. The model is also empirically validated with a commercial dataset. The experimental results show that selling page views via both channels can increase the publisher’s expected total revenue, and the optimal pricing and allocation strategies are robust to different market and advertiser types.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 6 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): M. Van Den Eeckhout, M. Vanhoucke, B. Maenhout〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The personnel staffing problem calculates the required workforce size and is determined by constructing a baseline personnel roster that assigns personnel members to duties in order to cover certain staffing requirements. In this research, we incorporate the planning of the duty demand in the staff scheduling problem in order to lower the staffing costs. More specifically, the demand originates from a project scheduling problem with discrete time/resource trade-offs, which embodies additional flexibility as activities can be executed in different modes. In order to tackle this integrated problem, we propose a decomposed branch-and-price procedure. A tight lower and upper bound are calculated using a problem formulation that models the project scheduling constraints and the time-related resource scheduling constraints implicitly in the decision variables. Based upon these bounds, the strategic problem is decomposed into multiple tactical subproblems with a fixed workforce size and an optimal solution is searched for each subproblem via branch-and-price. Fixing the workforce size in a subproblem facilitates the definition of resource capacity cuts, which limit the set of eligible project schedules, decreasing the size of the branching tree. In addition, in order to find the optimal integer solution, we propose a specific search strategy based upon the lower bound and dedicated rules to branch upon the workload generated by a project schedule. The computational results show that applying the proposed search space decomposition and the inclusion of resource capacity cuts lead to a well-performing procedure outperforming different other heuristic and exact methodologies.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 28 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Javier Duran-Micco, Evert Vermeir, Pieter Vansteenwegen〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Urban transportation contributes significantly to CO〈sub〉2〈/sub〉 emissions. Public transport systems are a good strategy to reduce these, but the emissions generated by public transport vehicles should not be neglected during the design of the service. The Transit Network Design and Frequency Setting Problem (TNDFSP) has usually been addressed considering only the passengers’ and the operator's point of view. However, we show it is worthwhile to consider also the emissions already during this planning phase.〈/p〉 〈p〉This paper proposes a memetic algorithm to address the bi-objective TNDFSP where both the total travel time and the CO〈sub〉2〈/sub〉 emissions are minimized. The analysis considers a heterogeneous fleet, meaning that buses of different sizes and technologies can be assigned under a budget constraint. The results on benchmark instances show that the proposed memetic algorithm performs as well as state-of-the-art algorithms where CO〈sub〉2〈/sub〉 emissions are not considered. In addition, several experiments are carried out to observe the effect of incorporating emissions and heterogeneous fleet into the model. The heterogeneous fleet allows reducing travel times and emissions at the same time, compared to solutions without a heterogeneous fleet. Moreover, the explicit minimization of CO〈sub〉2〈/sub〉 emissions within a bi-objective framework allows illustrating the trade-off between both objectives. Reductions of about 30% in the emissions can be achieved by increasing the travel time only 1%, while the costs for the operator remain the same. This clearly demonstrates the benefits of considering both the CO〈sub〉2〈/sub〉 emissions and a heterogeneous fleet during the design stage of public transport systems.〈/p〉 〈/div〉

Description: 〈p〉Publication date: Available online 14 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Xingmei Li, Yao-Huei Huang, Shu-Cherng Fang, Youzhong Zhang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Project portfolio selection problem (PPSP) is usually formulated as a mixed integer polynomial program with cross-product terms. The problem is hard to solve due to the non-convex cross-product terms involved. To find an exact optimal solution, currently available methods adopt different linearization techniques to handle the cross-product terms and then utilize a branch-and-bound scheme for computations. This study proposes an alternative efficient representation for PPSP using fewer continuous variables than the current methods to achieve global optimum. Numerical experiments are presented to demonstrate the effectiveness and efficiency of the proposed method. In addition, the proposed method is integrated with a general binary cut scheme for identifying all alternative solutions for decision makers to consider better options.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 14 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Jesus T. Pastor, C.A. Knox Lovell, Juan Aparicio〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉A natural multiplicative efficiency measure for the Constant Returns to Scale proportional directional distance function (pDDF) is derived, relating its associated linear program to that of the well-known output-oriented radial efficiency measurement model. Based on this relationship, a traditional CCD (Caves, Christensen and Diewert) Malmquist index is introduced to show that, when it is based on the new efficiency measure associated with the pDDF, rather than on a radial efficiency measure associated with an oriented distance function, it becomes a Total Factor Productivity (TFP) index. This constitutes a new result, because heretofore the traditional CCD Malmquist index has not been considered a TFP index. Additionally, a new decomposition of the CCD Malmquist index is proposed that expresses productivity change as the ratio of two components, productivity change due to output change in the numerator and productivity change due to input change in the denominator. In an Appendix the efficiency measure is extended to include any returns to scale pDDF.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 January 2020〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 280, Issue 1〈/p〉 〈p〉Author(s): 〈/p〉

Description: 〈p〉Publication date: Available online 12 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Jianming Dong, Ruyan Jin, Taibo Luo, Weitian Tong〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We investigate the approximability of the 〈em〉m〈/em〉 parallel two-stage flow-shop (mP2FS) problem, where a set of jobs is scheduled on the multiple identical two-stage flow-shops to minimize the 〈em〉makespan〈/em〉, i.e., the finishing time of the last job. Each job needs to be processed non-preemptively on one flow-shop without switching to the other flow-shops. This problem is a hybrid of the classic parallel machine scheduling and two-stage flow-shop scheduling problems. Its strong NP-hardness follows from the parallel machine scheduling problem when the number of machines is part of the input. Our main contribution is a polynomial-time approximation scheme (PTAS) for the mP2FS problem when the number of shops is part of the input, which improves the previous best approximation algorithm of a ratio 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si6.svg"〉〈mrow〉〈mo〉(〈/mo〉〈mn〉2〈/mn〉〈mo linebreak="goodbreak"〉+〈/mo〉〈mi〉ϵ〈/mi〉〈mo〉)〈/mo〉〈/mrow〉〈/math〉. Owing to the strong NP-hardness, our PTAS achieves the best possible approximation ratio.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 12 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Luca E. Schäfer, Tobias Dietz, Nicolas Fröhlich, Stefan Ruzika, José R. Figueira〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We investigate the single-source-single-destination “shortest” path problem in directed, acyclic graphs with ordinal weighted arc costs. We define the concepts of ordinal dominance and efficiency for paths and their associated ordinal levels, respectively. Further, we show that the number of ordinally non-dominated path vectors from the source node to every other node in the graph is polynomially bounded and we propose a polynomial time labeling algorithm for solving the problem of finding the set of ordinally non-dominated path vectors from source to sink.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 9 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Sally Giuseppe Arcidiacono, Salvatore Corrente, Salvatore Greco〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The level dependent Choquet integral has been proposed to handle decision making problems in which the importance and the interaction of criteria may depend on the level of the alternatives’ evaluations. This integral is based on a level dependent capacity, which is a family of single capacities associated to each level of evaluation for the considered criteria. We present two possible formulations of the level dependent capacity where importance and interaction of criteria are constant inside each one of the subintervals in which the interval of evaluations for considered criteria is split or vary with continuity inside the whole interval of evaluations. Since, in general, there is not only one but many level dependent capacities compatible with the preference information provided by the Decision Maker, we propose to take into account all of them by using the Robust Ordinal Regression (ROR) and the Stochastic Multicriteria Acceptability Analysis (SMAA). On one hand, ROR defines a necessary preference relation (if an alternative 〈em〉a〈/em〉 is at least as good as an alternative 〈em〉b〈/em〉 for all compatible level dependent capacities), and a possible preference relation (if 〈em〉a〈/em〉 is at least as good as 〈em〉b〈/em〉 for at least one compatible level dependent capacity). On the other hand, considering a random sampling of compatible level dependent capacities, SMAA gives the probability that each alternative reaches a certain ranking position as well as the probability that an alternative is preferred to another. A real-world decision problem on rankings of universities is provided to illustrate the proposed methodology.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 11 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Eberhard Feess, Christian Grund, Markus Walzl, Ansgar Wohlschlegel〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We investigate the choice between posted prices and auctions of competing sellers with private valuations. Assuming that buyers face higher hassle costs in auctions, we show the existence of monotone pure strategy equilibria where sellers offer posted prices rather than auctions if and only if they have a sufficiently high reservation value. Posted prices sell with lower probability but yield a larger revenue in case of trade. Using an empirical strategy to compare revenues of posted prices and auctions that takes selling probabilities explicitly into account, we find our theoretical predictions supported by data from eBay auctions on ticket sales for the EURO 2008 European Football Championship.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 8 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Sven Boge, Sigrid Knust〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper, we consider the parallel stack loading problem (PSLP) with the objective to minimize the number of reshuffles in the retrieval stage. Since in the PSLP the incoming items have to be stored according to a fixed arrival sequence, some reshuffles cannot be avoided later on. We study two surrogate objective functions (number of unordered stackings, number of badly placed items) to estimate the number of reshuffles and compare them theoretically as well as in a computational study. For this purpose, MIP formulations and a simulated annealing algorithm are proposed.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 6 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Milena Bieniek〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Inventories are ubiquitous in nature and inventory control is a crucial activity undertaken in the supply chain (SC) by a company’s management. The Vendor Managed Inventory (VMI) contract has become a common technique for supply chain management (SCM) since the 1980’s. In this technique, the decision about how much inventory to hold is made by the vendor. In the paper, we consider VMI with consignment (VMCI). Consignment is a frequently used form of business arrangement, in which the vendor retains the ownership of the inventory and gets paid by the retailer on actual units sold. Under VMCI, decisions are made in two steps. In the first step, the vendor specifies a consignment price and an order quantity with the objective to maximize the vendor’s expected profit. In the second step, the retailer chooses a retail price which maximizes the retailer’s expected profit. The customer demand is assumed to be stochastic, additive and price–sensitive. Additive uncertainty can produce negative demand realizations, which may occur in adverse market conditions. We prove that in this case an optimal and possibly non–unique solution to VMCI exists. We calculate closed–form formulas for optimal quantities for uniformly distributed demand. Finally, we demonstrate our approach through a numerical example and we show that the imposition of a non–negativity constraint can cause a higher vendor’s expected profit.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 7 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): M.N. López-García, J.E. Trinidad-Segovia, M.A. Sánchez-Granero, I. Pouchkarev〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper, a new long-term memory factor for extending the well-known Fama and French model is proposed and discussed thoroughly. The new long-term memory factor is based on the Hurst exponent and is calculated using the fractal dimension (FD) algorithm. The relevance of the new factor is illustrated using a sample of 1500 largest U.S. companies from different sectors.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 6 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Jinjiang Yuan, C.T. Ng, T.C.E. Cheng〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper, we study multi-agent scheduling with release dates and preemption on a single machine, where the scheduling objective function of each agent to be minimized is regular and of the maximum form (max-form). The multi-agent aspect has three versions, namely ND-agent (multiple agents with non-disjoint job sets), ID-agent (multiple agents with an identical job set), and CO-agent (multiple competing agents with mutually disjoint job sets). We consider three types of problems: The first type (type-1) is the constrained scheduling problem, in which one objective function is to be minimized, subject to the restriction that the values of the other objective functions are upper bounded. The second type (type-2) is the weighted-sum scheduling problem, in which a positive combination of the objective functions is to be minimized. The third type (type-3) is the Pareto scheduling problem, for which we aim to find all the Pareto-optimal points and their corresponding Pareto-optimal schedules. We show that the type-1 problems are polynomially solvable, and the type-2 and type-3 problems are strongly 〈em〉NP〈/em〉-hard even when all jobs’ release dates are zero and processing times are one. When the number of the scheduling criteria is fixed and they are all lateness-like, such as minimizing 〈em〉C〈/em〉〈sub〉max〈/sub〉, 〈em〉F〈/em〉〈sub〉max〈/sub〉, 〈em〉L〈/em〉〈sub〉max〈/sub〉, 〈em〉T〈/em〉〈sub〉max〈/sub〉, and 〈em〉WC〈/em〉〈sub〉max〈/sub〉, where 〈em〉WC〈/em〉〈sub〉max〈/sub〉 is the maximum weighted completion time of the jobs, the type-2 and type-3 problems are polynomially solvable. To address the type-3 problems, we develop a new solution technique that guesses the Pareto-optimal points through some elaborately constructed schedule-configurations.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 6 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Zhuo Jin, Guo Liu, Hailiang Yang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper, we consider the optimal consumption and investment strategies for households throughout their lifetime. Risks such as the illiquidity of assets, abrupt changes of market states, and lifetime uncertainty are considered. Taking the effects of heritage into account, investors are willing to limit their current consumption in exchange for greater wealth at their death, because they can take advantage of the higher expected returns of illiquid assets. Further, we model the liquidity risks in an illiquid market state by introducing frozen periods with uncertain lengths, during which investors cannot continuously rebalance their portfolios between different types of assets. In liquid market, investors can continuously remix their investment portfolios. In addition, a Markov regime-switching process is introduced to describe the changes in the market’s states. Jumps, classified as either moderate or severe, are jointly investigated with liquidity risks. Explicit forms of the optimal consumption and investment strategies are developed using the dynamic programming principle. Markov chain approximation methods are adopted to obtain the value function. Numerical examples demonstrate that the liquidity of assets and market states have significant effects on optimal consumption and investment strategies in various scenarios.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 2 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Bruno S. Vieira, Sérgio F. Mayerle, Lucila M.S. Campos, Leandro C. Coelho〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The operation of Water Distribution Systems (WDS) is often complex, especially when considering the changes in tariffs throughout the day. The cost of energy in these systems can reach 30% of total operating costs and its careful management can represent increased efficiency. The optimization of WDS scheduling operation appears as an effective method to reduce operating costs while ensuring a good service level to the population. In this paper we propose a new linear relaxation for a non-linear integer programming formulation for WDS in order to optimize its operation costs. This study makes five main contributions. First, our formulation includes new aspects related to the state of the system when the tanks are full, that were not considered before in mathematical programming models. Second, our linearization technique includes a variable number of breakpoints, resulting in significantly fewer binary variables for a given error level. Third, our relaxation reduces the search space of the solutions. Fourth, we have outperformed the best results for three benchmark instances from the literature. Lastly, we also provide a larger new real-life instance with specific conditions of energy tariffs, obtained from the WDS from the city of Florianópolis, southern Brazil, significantly outperforming the current solution employed by the utility provider.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 2 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Antoine Mandel, Xavier Venel〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We propose an analytical approach to the problem of influence maximization in a social network where two players compete by means of dynamic targeting strategies. We formulate the problem as a two-player zero-sum stochastic game. We prove the existence of the uniform value: if the players are sufficiently patient, both can guarantee the same mean-average opinion without knowing the exact length of the game. Furthermore, we put forward some elements for the characterization of equilibrium strategies. In general, players must implement a trade-off between a forward-looking perspective, according to which they aim to maximize the future spread of their opinion in the network, and a backward-looking perspective, according to which they aim to counteract their opponent’s previous actions. When the influence potential of players is small, we describe an equilibrium through a one-shot game based on eigenvector centrality.〈/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): Mauro Dell’Amico, Maxence Delorme, Manuel Iori, Silvano Martello〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We consider the multiple knapsack problem, that calls for the optimal assignment of a set of items, each having a profit and a weight, to a set of knapsacks, each having a maximum capacity. The problem has relevant managerial implications and is known to be very difficult to solve in practice for instances of realistic size. We review the main results from the literature, including a classical mathematical model and a number of improvement techniques. We then present two new pseudo-polynomial formulations, together with specifically tailored decomposition algorithms to tackle the practical difficulty of the problem. Extensive computational experiments show the effectiveness of the proposed approaches.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 31 October 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Alexandre M. Florio, Richard F. Hartl, Stefan Minner〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We present a model for the single-vehicle routing problem with stochastic demands (SVRPSD) with optimal restocking. The model is derived from a characterization of the SVRPSD as a Markov decision process (MDP) controlled by a certain class of policies, and is valid for general discrete demand probability distributions. We transform this MDP into an equivalent mixed-integer linear model, which is then used to solve small instances to optimality. By doing so, we are able to quantify the drawbacks associated with the detour-to-depot restocking policy, an assumption of many exact approaches for the (multivehicle) VRPSD. We also examine the tradeoff between the deterministic a priori cost and the stochastic restocking cost for varying route load scenarios. Finally, a wait-and-see model for the SVRPSD is proposed, and is used within a parallel heuristic to solve larger literature instances with up to 150 nodes and Poisson distributed demands. Computational experiments demonstrate the effectiveness of the heuristic approach, and also indicate under which circumstances near-optimal solutions can be obtained by the myopic strategy of a priori route cost minimization.〈/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): Dariush Khezrimotlagh, Joe Zhu, Wade D. Cook, Mehdi Toloo〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In the traditional data envelopment analysis (DEA) approach for a set of 〈em〉n〈/em〉 Decision Making Units (DMUs), a standard DEA model is solved 〈em〉n〈/em〉 times, one for each DMU. As the number of DMUs increases, the running-time to solve the standard model sharply rises. In this study, a new framework is proposed to significantly decrease the required DEA calculation time in comparison with the existing methodologies when a large set of DMUs (e.g., 20,000 DMUs or more) is present. The framework includes five steps: (i) selecting a subsample of DMUs using a proposed algorithm, (ii) finding the best-practice DMUs in the selected subsample, (iii) finding the exterior DMUs to the hull of the selected subsample, (iv) identifying the set of all efficient DMUs, and (v) measuring the performance scores of DMUs as those arising from the traditional DEA approach. The variable returns to scale technology is assumed and several simulation experiments are designed to estimate the running-time for applying the proposed method for big data. The obtained results in this study point out that the running-time is decreased up to 99.9% in comparison with the existing techniques. In addition, we illustrate the essential computation time for applying the proposed method as a function of the number of DMUs (cardinality), number of inputs and outputs (dimension), and the proportion of efficient DMUs (density). The methods are also compared on a real data set consisting of 30,099 electric power plants in the United States from 1996 to 2016.〈/p〉〈/div〉

Description: 〈p〉Publication date: 16 April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 274, Issue 2〈/p〉 〈p〉Author(s): Damiano Brigo, Marco Francischello, Andrea Pallavicini〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Since the 2008 global financial crisis, the banking industry has been using valuation adjustments to account for default risk and funding costs. These adjustments are computed separately and added together by practitioners as if the valuation equations were linear. This assumption is too strong and does not allow to model market features such as different borrowing and lending rates and replacement default closeout. Hence we argue that the full valuation equations are nonlinear, and this paper is devoted to studying the nonlinear valuation equations introduced in Pallavicini et al (2011).〈/p〉 〈p〉We illustrate all the cash flows exchanged by the parties involved in a derivative contract, in presence of default risk, collateralisation with re-hypothecation and funding costs. Then we show how to obtain semi-linear PDEs or Forward Backward Stochastic Differential Equations (FBSDEs) from present-valuing said cash flows in an arbitrage-free setup, and we study the well-posedness of these PDEs and FBSDEs in a viscosity and classical sense.〈/p〉 〈p〉Moreover, from a financial perspective, we discuss cases where classical valuation adjustments (XVA) can be disentangled. We show how funding costs are offset by treasury valuation adjustments when one takes a whole-bank perspective in the valuation, while the same costs are not offset by such adjustments when taking a shareholder perspective. We show that although we use a risk-neutral valuation framework based on a locally risk-free bank account, our final valuation equations do not depend on the risk-free rate. Finally, we show how to consistently derive a netting set valuation from a portfolio level one.〈/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): Arnab Bhattacharya, Simon P. Wilson, Refik Soyer〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper we present a Bayesian competing risk proportional hazards model to describe mortgage defaults and prepayments. We develop Bayesian inference for the model using Markov chain Monte Carlo methods. Implementation of the model is illustrated using actual default/prepayment data and additional insights that can be obtained from the Bayesian analysis are discussed.〈/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): Xinjie Xing, Paul R. Drake, Dongping Song, Yang Zhou〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Tank Container Operators (TCOs) are striving to maximize profit through the integration of their global Tank Container (TC) operations with the job quotation-booking process. However, TCOs face a set of unique challenges not faced by general shipping container operators, including the process uncertainties arising from TC cleaning and the use of Freight Forwarders (FFs). In this paper, a simulation-based two-stage optimization model is developed to address these challenges. The first stage focuses on tactical decisions of setting inventory levels and control policy for empty container repositioning. The second stage integrates the dynamic job acceptance/rejection decisions in the quotation-booking processes with container operations decisions in the planning and execution processes, such as job fulfillment, container leasing terms, choice of FFs considering cost and reliability, and empty tank container repositioning. The solution procedure is based on the simulation model combined with heuristic algorithms including an adjusted Genetic Algorithm, mathematical programming, and heuristic rules. Numerical examples based on a real case study are provided to illustrate the effectiveness of the model.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 273, Issue 2〈/p〉 〈p〉Author(s): Zhenyu Cui, J. Lars Kirkby, Duy Nguyen〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper, we propose a general approximation framework for the valuation of (path-dependent) options under time-changed Markov processes. The underlying background process is assumed to be a general Markov process, and we consider the case when the stochastic time change is constructed from either discrete or continuous additive functionals of another independent Markov process. We first approximate the underlying Markov process by a continuous time Markov chain (CTMC), and derive the functional equation characterizing the double transforms of the transition matrix of the resulting time-changed CTMC. Then we develop a two-layer approximation scheme by further approximating the driving process in constructing the time change using an independent CTMC. We obtain a single Laplace transform expression. Our framework incorporates existing time-changed Markov models in the literature as special cases, such as the time-changed diffusion process and the time-changed Lévy process. Numerical experiments illustrate the accuracy of our method.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 273, Issue 2〈/p〉 〈p〉Author(s): Xiaole Wu, Yu Zhou〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Managers broadly agree that the entry of third-party remanufacturers (TPRs) is detrimental to original equipment manufacturers (OEMs), and social planners broadly agree that a nondiscriminatory uniform pricing policy is more desirable than a buyer-specific pricing policy. In this study, we develop a game theoretical model to revisit the effects of these policies in a closed-loop supply chain in which one supplier sells a component that cannot be remanufactured to one OEM and one TPR. The supplier must charge the same wholesale price in the uniform pricing model. Our analysis shows that regardless of the pricing policy, third-party remanufacturing could lead to a triple win to the supplier, the OEM, and the TPR. When compared with a buyer-specific pricing policy, a uniform pricing policy may result in a fourfold loss to the supplier, the OEM, the TPR, and the consumers, and hence an absolute reduction in social welfare. The key intuition driving the findings is that under certain conditions, the buyer-specific pricing policy facilitates third-party remanufacturing, which may increase the total profit of the supply chain and improve social welfare. Therefore, the OEM should not always attempt to deter the entry of TPRs. In addition, the government should allow the supplier to charge buyer-specific wholesale prices to foster the remanufacturing sector, particularly in its infancy.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 273, Issue 2〈/p〉 〈p〉Author(s): Joydeep Paul, Niels Agatz, Remy Spliet, René De Koster〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉More and more retailers allow customers to order goods online and then pick them up in a store. In this setting, these orders are typically served from a dedicated warehouse. This often means that the stores are visited by different vehicles to replenish the store inventory and to supply the pick-up points. Motivated by a collaboration with an omni-channel grocery retailer in the Netherlands, we study how to best share capacity between the routes associated with these different sales channels. As operational constraints prevent jointly planning the routes, we consider the replenishment routes as fixed when planning the routes to serve the pick-up orders. An order can be transferred to the replenishment route, if capacity allows. We consider the problem of deciding which customer orders to transfer and which to deliver directly such that the total costs are minimized. We present an exact and a heuristic approach to solve this problem. Computational experiments on both real-world and artificial instances show that substantial savings can be achieved by sharing vehicle capacity across different channels.〈/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): Hao Hua, Ludger Hovestadt, Peng Tang, Biao Li〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We present an integer program (IP) for urban design that (1) maximizes the floor area; (2) fills building volume with room templates; (3) encodes translational symmetry in urban layout; and (4) constructs economical urban routes. Regardless that integer programming is intensively studied in operational research (OR), its role in solving geometrical and topological problems in urban design was overlooked. Based on a regular grid, our 0–1 IP formulates the sunlight-gain rules, which give urban sites their shapes, especially for residential projects. With predefined plot templates, the gross floor area (volume) within a given site can be maximized under various sunlight requirements. Subsequently, the IP fills each building volume with 2D/3D room templates. Finally, an IP-based algorithm constructs routes that connect all plots and the site’s entrances to public transportation. Both the classical Steiner tree model and the latest coverage network model are extended to create reasonable routes. In addition, this work extends the concept of special ordered sets (SOS) to encode translational symmetry in urban layouts. Encoding layout symmetry can benefit from the solvers’ SOS2 mechanism in the Branch-and-Bound search algorithm. The results indicate that traditional decision making for cities could be partially automated by IP and an abundance of valid solutions are available for designers.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 273, Issue 2〈/p〉 〈p〉Author(s): Xinchang Wang, Qiang Meng〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The 〈em〉congestion effect〈/em〉 refers to the phenomenon that more customers choosing to use the same facility reduces the facility’s utility. This work addresses the optimal pricing problem for a firm operating a joint-venture terminal under the congestion effect. The firm is formed between a port terminal operator and a shipping line, thus being able to provide a bundle of ocean transportation and port terminal solutions to cargo suppliers. The objective is to determine the optimal prices charged to cargo suppliers to maximize the total profit of the firm. First, we develop a tractable flow-based optimization model that uses a fixed-point equation to capture the interaction between the congestion effect and cargo suppliers’ choice. Second, we characterize the optimal solution for a variety of cases, including the single origin-destination case, partially homogeneous case that includes the fully homogeneous case as a special case, and heterogeneous case. Third, we evaluate the profit loss incurred by ignoring the congestion effect with numerical studies. Moreover, we propose one-variable and two-variable search methods for the partially homogeneous and heterogeneous cases, respectively. We learn that the firm should quote the same price to all cargo suppliers under the fully homogeneous case. However, this is not necessarily optimal under the partially homogeneous or heterogeneous cases. The profit loss incurred by neglecting the congestion effect can be significant and increases as cargo suppliers become less tolerant of congestion.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 3 November 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Matthieu Guillot, Gautier Stauffer〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper, we give a new framework for the stochastic shortest path problem in finite state and action spaces. Our framework generalizes both the frameworks proposed by Bertsekas and Tsitsiklis 〈em〉(An analysis of stochastic shortest path problems. Math. Oper. Res., 16(3):580-595, Aug. 1991)〈/em〉and by Bertsekas and Yu 〈em〉(Stochastic shortest path problems under weak conditions, to appear in Math. Oper. Res., 2016)〈/em〉. We prove that the problem is well-defined and (weakly) polynomial when (i) there is a way to reach the target state from any initial state and (ii) there is no transition cycle of negative costs (a generalization of negative cost cycles). These assumptions generalize the standard assumptions for the deterministic shortest path problem and our framework encapsulates the latter problem (in contrast with prior works). In this new setting, we can show that (a) one can restrict to deterministic and stationary policies, (b) the problem is still (weakly) polynomial through linear programming, (c) Value Iteration and Policy Iteration converge, and (d) we can extend Dijkstra’s algorithm.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 273, Issue 2〈/p〉 〈p〉Author(s): Marco Botte, Anita Schöbel〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉In robust optimization, the parameters of an optimization problem are not deterministic but uncertain. Their values depend on the scenarios which may occur. 〈em〉Single-objective〈/em〉 robust optimization has been studied extensively. Since 2012, researchers have been looking at robustness concepts for 〈em〉multi-objective〈/em〉 optimization problems as well.〈/p〉 〈p〉In another line of research, single-objective uncertain optimization problems are transformed to deterministic multi-objective problems by treating every scenario as an objective function. In this paper we combine these two points of view. We treat every scenario as an objective function also in uncertain 〈em〉multi-objective〈/em〉optimization, and we define a corresponding concept of dominance which we call 〈em〉multi-scenario efficiency〈/em〉. We sketch this idea for finite uncertainty sets and extend it to the general case of infinite uncertainty sets. We then investigate the relation between this dominance and the concepts of highly, locally highly, flimsily, and different versions of minmax robust efficiency. For all these concepts we prove that every 〈em〉strictly〈/em〉 robust efficient solution is multi-scenario efficient. On the other hand, under a compactness condition, the set of multi-scenario efficient solutions contains a robust efficient solution for all these concepts which generalizes the 〈em〉Pareto robustly optimal〈/em〉(PRO) solutions from single-objective optimization to 〈em〉Pareto robust efficient〈/em〉 (PRE) solutions in the multi-objective case. We furthermore present two results on reducing an infinite uncertainty set to a finite one which are a basis for computing multi-scenario efficient solutions.〈/p〉 〈/div〉

Description: 〈p〉Publication date: 1 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 273, Issue 2〈/p〉 〈p〉Author(s): Defeng Sun, Lixin Tang, Roberto Baldacci〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper, we study the Quay Crane Scheduling Problem (QCSP) in container terminals. We describe a new mathematical formulation for the QCSP and by addressing the structure of workload assignments we develop an easier way to handle non-crossing constraints. The proposed mathematical formulation is used in an exact solution framework based on logic-based Benders decomposition. The proposed approach decomposes the problem into a workload-assignment master problem and operation-sequence slave subproblems. Logic-based cuts are proposed to ensure the convergence of the approach. Computational results show the effectiveness of the proposed solution approach.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 273, Issue 2〈/p〉 〈p〉Author(s): Pamela J. Palomo-Martínez, M. Angélica Salazar-Aguilar〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this work we introduce a variant of the well-known Traveling Purchaser Problem in which the purchased products must be delivered to a set of customers. The objective is to minimize the total cost (purchasing plus traveling costs) and the waiting time of the customers, simultaneously, while satisfying the total demand. This problem is called the bi-objective Traveling Purchaser Problem with Deliveries. In order to approximate Pareto fronts for this problem, a relinked variable neighborhood search is proposed and tested over a large set of artificial instances. Our results show that our algorithm is highly competitive compared to the ϵ-constraint method in small instances. On the other hand, experiments carried out over large instances show that our algorithm is able to find Pareto front approximations with more points in a shorter running time for uncapacitated instances than for capacitated ones. Also, computational results show that the performance of some local searches used in our algorithm depends on the characteristics of the instances, this underlines the importance of designing a metaheuristic based on multiple local searches.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 273, Issue 2〈/p〉 〈p〉Author(s): Inmaculada Rodríguez-Martín, Juan-José Salazar-González, Hande Yaman〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The Periodic Vehicle Routing Problem is a generalization of the classical capacitated vehicle routing problem in which routes are determined for a planning horizon of several days. Each customer has an associated set of allowable visit schedules, and the objective of the problem is to design a set of minimum cost routes that give service to all the customers respecting their visit requirements. In this paper we study a new variant of this problem in which we impose that each customer should be served by the same vehicle/driver at all visits. We call this problem the Periodic Vehicle Routing Problem with Driver Consistency. We present an integer linear programming formulation for the problem and derive several families of valid inequalities. We solve it using an exact branch-and-cut algorithm, and show computational results on a wide range of randomly generated instances.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 273, Issue 2〈/p〉 〈p〉Author(s): Spiridon Penev, Pavel V. Shevchenko, Wei Wu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We quantify model risk of a financial portfolio whereby a multi-period mean-standard-deviation criterion is used as a selection criterion. In this work, model risk is defined as the loss due to uncertainty of the underlying distribution of the returns of the assets in the portfolio. The uncertainty is measured by the Kullback–Leibler divergence, i.e., the relative entropy. In the worst case scenario, the optimal robust strategy can be obtained in a semi-analytical form as a solution of a system of nonlinear equations. Several numerical results are presented which allow us to compare the performance of this robust strategy with the optimal non-robust strategy. For illustration, we also quantify the model risk associated with an empirical dataset.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 273, Issue 2〈/p〉 〈p〉Author(s): Teresa Cardoso-Grilo, Marta Monteiro, Mónica Duarte Oliveira, Mário Amorim-Lopes, Ana Barbosa-Póvoa〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Medical training is an intricate and long process, which is compulsory to medical practice and often lasts up to twelve years for some specialties. Health stakeholders recognise that an adequate planning is crucial for health systems to deliver necessary care services. However, proper planning needs to account for complexity related with the setting of medical school vacancies and of residency programs, which are highly influenced by multiple stakeholders with diverse perspectives and views, as well as by the specificities of medical training. Aiming at building comprehensive models with a potential to assist health decision-makers, this article develops a multi-methodological framework to assist the planning of medical training under such a complex environment. It combines the structuring of the objectives and specificities of the medical training problem with a Soft Systems Methodology through the CATWOE (Customer, Actor, Transformation, 〈em〉Weltanschauung〈/em〉, Owner, Environment) approach, and the formulation of a Mixed Integer Linear Programming model that considers all relevant aspects. Considering the specificities of countries based on a National Health Service structure, a multi-objective planning model emerges, informing on how many vacancies should be opened/closed per year in medical schools and in each specialty. This model aims at (i) minimizing imbalances between medical demand and supply; (ii) minimizing costs; and (iii) maximizing equity across medical specialties. A case study in Portugal is explored so as to illustrate the applicability of the proposed multi-methodology, showing the relevance of proper structuring for planning models having the potential to inform health decision-makers and planners in practice.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 273, Issue 2〈/p〉 〈p〉Author(s): Chi Seng Pun, Hoi Ying Wong〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper studies the mean-variance (MV) portfolio problems under static and dynamic settings, particularly for the case in which the number of assets (〈em〉p〈/em〉) is larger than the number of observations (〈em〉n〈/em〉). We prove that the classical plug-in estimation seriously distorts the optimal MV portfolio in the sense that the probability of the plug-in portfolio outperforming the bank deposit tends to 50% for 〈em〉p〈/em〉 ≫ 〈em〉n〈/em〉 and a large 〈em〉n〈/em〉. We investigate a constrained ℓ〈sub〉1〈/sub〉 minimization approach to directly estimate effective parameters that appear in the optimal portfolio solution. The proposed estimator is implemented efficiently with linear programming, and the resulting portfolio is called the linear programming optimal (LPO) portfolio. We derive the consistency and the rate of convergence for LPO portfolios. The LPO procedure essentially filters out unfavorable assets based on the MV criterion, resulting in a sparse portfolio. The advantages of the LPO portfolio include its computational superiority and its applicability for dynamic settings and non-Gaussian distributions of asset returns. Simulation studies validate the theory and illustrate its finite-sample properties. Empirical studies show that the LPO portfolios outperform the equally weighted portfolio and the estimated optimal portfolios using shrinkage and other competitive estimators.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 273, Issue 2〈/p〉 〈p〉Author(s): Phil Scarf, Rishikesh Parma, Ian McHale〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper we investigate the relationship between outcome uncertainty and scoring rates in the framework of a “Poisson match”. We argue that increasing scoring rates in the hope of increasing entertainment may have a detrimental impact on the popularity of sport. The basis of our argument is that higher scoring-rates decrease outcome uncertainty. We use international rugby to demonstrate our findings and show that scoring rates have indeed increased significantly over the previous half-century in this sport. Therefore, administrators should recognise our general point and we suggest that rugby union administrators in particular ought to consider the introduction of new laws to reduce scoring-rates. Scenarios in which the scoring-rate is radically reduced are illustrated through a simulation of the Rugby World Cup tournament.〈/p〉〈/div〉

Description: 〈p〉Publication date: 1 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 273, Issue 2〈/p〉 〈p〉Author(s): Nils Löhndorf, Alexander Shapiro〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We consider the multistage stochastic programming problem where uncertainty enters the right-hand sides of the problem. Stochastic Dual Dynamic Programming (SDDP) is a popular method to solve such problems under the assumption that the random data process is stagewise independent. There exist two approaches to incorporate dependence into SDDP. One approach is to model the data process as an autoregressive time series and to reformulate the problem in stagewise independent terms by adding state variables to the model (TS-SDDP). The other approach is to use Markov Chain discretization of the random data process (MC-SDDP). While MC-SDDP can handle any Markovian data process, some advantages of statistical analysis of the policy under the true process are lost. In this work, we compare both approaches based on a computational study using the long-term operational planning problem of the Brazilian interconnected power systems. We found that for the considered problem the optimality bounds computed by the MC-SDDP method close faster than its TS-SDDP counterpart, and the MC-SDDP policy dominates the TS-SDDP policy. When implementing the optimized policies on real data, we observe that not only the method but also the quality of the stochastic model has an impact on policy performance and that using an 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.gif" overflow="scroll"〉〈mrow〉〈mi mathvariant="sans-serif"〉AV〈/mi〉〈mo〉@〈/mo〉〈mi mathvariant="sans-serif"〉R〈/mi〉〈/mrow〉〈/math〉 formulation is effective in making the policy robust against a misspecified stochastic model.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 28 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Moshe Levy〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Almost Stochastic Dominance implies that stocks are preferred over bonds in the long-run by all preferences with bounded marginal utility. Unfortunately, this analysis excludes the very central Constant Relative Risk Aversion (CRRA) preferences, which have unbounded marginal utility. We derive a simple closed-form solution for the portfolio-choice problem of CRRA investors which, with the empirical parameters, implies that stocks are preferred over bonds for all CRRA investors with relative risk aversion smaller than 3.9. As a by-product, the analysis offers an alternative derivation of the continuous-time CAPM, that does not rely on dynamic programming, and allows for ambiguous investment horizons.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 30 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Bahareh Mansouri, Elkafi Hassini〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In a combinatorial procurement auction with large demand volumes, bidders can find it challenging to carefully combine, evaluate and price different units of items in a package while at the same time considering their internal capacity and production costs. As opposed to static combinatorial procurement auctions, flexible procurement combinatorial auctions provide the bidders with the ability to reveal their price functions and therefore more efficiently communicate numerous variations of pricing for multiple units of items via concise bids. In this paper we propose a fully expressive bidding language for flexible combinatorial auctions. We define its associated winner determination problem, show that it is NP-hard and propose a Lagrangian-based approach to solve it. Optimal Lagrangian multipliers are used as proxies for auction item prices. The multipliers are also used in formulating the suppliers’ profit maximization problem to find their optimal bid quantities and prices while taking into account various levels of suppliers’ risk-taking attitudes. We analytically show the convergence of the iterative flexible auction. Our numerical experiments show that on average flexible iterative auctions generate lower market prices, require less computational effort and converge faster than their static counterpart. At termination, while the auctioneer secures lower procurement costs in a flexible auction, the total winning-suppliers’ profit slightly reduces due to price reduction in the auction.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 28 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Jozsef Voros〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper we develop a dynamic model in which demand depends on both price and quality. The objective function maximizes the profit and the salvage value of the firm. In part, quality can be increased by investing into development processes and the value of the accumulated quality knowledge is incorporated into the model. It is pointed out that this salvage value may have large impact on the solution and fundamentally may influence the dynamics of the quality improvement process. The quality dynamics may have both increasing and decreasing impact on price, and besides known factors the influence of the Hamiltonian is identified. We can observe also, that improving operational efficiency softens price increase while quality increases.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 28 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Arthur Kramer, Eduardo Lalla-Ruiz, Manuel Iori, Stefan Voß〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper addresses the well-known dynamic berth allocation problem (DBAP), which finds numerous applications at container terminals aiming to allocate and schedule incoming container vessels into berthing positions along the quay. Due to its impact on ports’ performance, having efficient DBAP formulations is of great importance, especially for determining optimal schedules in quick time as well as aiding managers and developers in the assessment of solution strategies and approximate approaches. In this work, we propose two novel formulations, a time-indexed formulation and an arc-flow one, to efficiently tackle the DBAP. Additionally, to improve computational performance, we propose problem-based modeling enhancements and a variable-fixing procedure that allows to discard some variables by considering their reduced costs. By means of these contributions, we improve the models’ performance in those instances where the optimal solutions were already known, and we solve to optimality for the first time other instances from the literature.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 29 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Dursun Delen, Kazim Topuz, Enes Eryarsoy〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Student attrition – the departure from an institution of higher learning prior to the achievement of a degree or earning due educational credentials – is an administratively important, scientifically interesting and yet practically challenging problem for decision makers and researchers. This study aims to find the prominent variables and their conditional dependencies/interrelations that affect student attrition in college settings. Specifically, using a large and feature-rich dataset, proposed methodology successfully captures the probabilistic interactions between attrition (the dependent variable) and related factors (the independent variables) to reveal the underlying, potentially complex/non-linear relationships. The proposed methodology successfully predicts the individual students' attrition risk through a Bayesian Belief Network-driven probabilistic model. The findings suggest that the proposed probabilistic graphical/network method is capable of predicting student attrition with 84% in AUC – Area Under the Receiver Operating Characteristics Curve. Using a 2-by-2 investigational design framework, this body of research also compares the impact and contribution of data balancing and feature selection to the resultant prediction models. The results show that (1) the imbalanced dataset produces similar predictive results in detecting the at-risk students, and (2) the feature selection, which is the process of identifying and eliminating unnecessary/unimportant predictors, results in simpler, more understandable, interpretable, and actionable results without compromising on the accuracy of the prediction task.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 29 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Julie A Niederhoff, Panos Kouvelis〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We consider under what conditions a revenue sharing contract is most effective at improving system efficiency relative to a simple wholesale price contract. We find that a complex coordinating contract is not always necessary because the wholesale price contract may perform sufficiently well; and coordinating contracts are not always effective if decision makers are strongly risk averse. Using a behavioral laboratory approach, we investigate how a supplier's risk aversion and fairness concerns influence how they set the pricing parameter(s) of a contract. We find that risk-neutral self-interested suppliers were able to improve the system and their own profits significantly under revenue sharing compared to wholesale pricing. However, individual behavioral factors of risk aversion or fairness preferences often made the more complicated revenue sharing contract either ineffective or unnecessary. Specifically, given a fairness-seeking supplier, the simple wholesale price contract is comparably efficient to the revenue sharing contract set by a risk-neutral supplier and we find the coordinating contract is unnecessary. Conversely, given a strongly risk-averse supplier with no concerns for fairness a revenue sharing contract is ineffective in overcoming double marginalization.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 28 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Mike G. Tsionas, Michael L. Polemis〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper provides an alternative general empirical method for the estimation of Total Factor Productivity (TFP). We use a decomposition which allows non-parametric estimation and at the same time addresses the issue of endogeneity of inputs. In this way, we also deal with the unavailability of input prices which is common in the TFP literature. We apply the new techniques to U.S four-digit manufacturing data using a novel Bayesian nonparametric model based on local likelihood. We use Markov Chain Monte Carlo (MCMC) techniques organized around the method of Girolami and Calderhead (2011). We compare and contrast the estimates from the proposed new method with standard parametric methods such as the translog, the Generalized Leontief and the Normalized Quadratic and we also propose novel diagnostic tests for correct specification and validity of instruments. We show that parametric methods lead to biased estimation of TFP growth. Our empirical findings show that the new model passes successfully a battery of robustness checks including diagnostic testing and tests for weak identification as well as weak instruments. Finally policy implications relating to the nature of TFP growth are also provided.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 27 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): E. Topan, A.S. Eruguz, W. Ma, M.C. van der Heijden, R. Dekker〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper, we provide an overview of operational spare parts planning in service control towers. We conduct company surveys to identify the issues, the challenges, and the needs in practice. We propose a classification framework to reveal the key aspects of operational planning decisions, and to provide an overview of the scientific literature. We identify promising research directions. These include incorporating service level agreements in operational decision making, managing exception messages, integrating tactical and operational plans, using advance supply and demand information for operational interventions, and developing a unified and holistic approach for selecting interventions in large scale systems.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 28 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Elmar Plischke, Emanuele Borgonovo〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Copula theory is concerned with defining dependence structures given appropriate marginal distributions. Probabilistic sensitivity analysis is concerned with quantifying the strength of the dependence among the output of a simulator and the uncertain simulator inputs. In this work, we investigate the connection between these two families of methods. We define four classes of sensitivity measures based on the distance between the empirical copula and the product copula. We discuss the new classes in the light of transformation invariance and Rényi’s postulate D of dependence measures. The connection is constructive: the new classes extend the current definition of sensitivity measures and one gains an of understanding which sensitivity measures in use are, in fact, copula-based. Also a set of new visualization tools can be obtained. These tools ease the communication of results to the modeler and provide insights not only on statistical dependence but also on the partial behaviour of the output as a function of the inputs. Application to the benchmark simulator for sensitivity analysis concludes the work.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 27 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Oualid Guemri, Placide Nduwayo, Raca Todosijević, Saïd Hanafi, Fred Glover〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The Cross-Docking Assignment Problem (CDAP) is a challenging optimization problem in supply chain management with important practical applications in the trucking industry. The goal is to assign incoming trucks (outgoing trucks) to inbound (outbound) doors to minimize the material handling cost within a cross-docking platform while respecting the capacity and assignment constraints. A capacity constraint is imposed on each inbound/outbound door and an associated assignment constraint is imposed on each incoming/outgoing truck requiring it to be assigned to only one inbound/outbound door. To solve this NP-hard optimization problem, we develop two novel heuristics based on Probabilistic Tabu Search utilizing a new neighborhood structure applicable both to CDAP and related problems. The proposed heuristics are evaluated on 99 benchmark instances from the literature, disclosing that our approaches outperform recent state-of-the-art approaches by reaching 45 previous best-known solutions and discovering 53 new best-known solutions while consuming significantly less CPU time.〈/p〉〈/div〉

Description: 〈p〉Publication date: 16 August 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 277, Issue 1〈/p〉 〈p〉Author(s): 〈/p〉

Description: 〈p〉Publication date: Available online 27 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Stein-Erik Fleten, Erik Haugom, Alois Pichler, Carl J. Ullrich〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We estimate costs associated with mothballing, restarting, abandoning and maintaining peaking power plants. We develop a real options model to explain switching and maintenance behavior of plant managers. The constrained optimization approach to estimate crucial costs accommodates non-parametric dynamics for the expectations of the plant managers regarding future profitability. The empirical analysis is based on the status of power plants reported annually to the United States Energy Information Administration (EIA) during 2001–2009. We arrive at economically meaningful estimates of maintenance costs and switching costs, and discuss these in light of rates used in the Pennsylvania-New Jersey-Maryland capacity market.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 27 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Jiao Wang, Zhibing Liu, Ruiqing Zhao〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Demand information is critical information for manufacturers when contracting, whereas very often, both the demand signal and its accuracy are privately known by the retailer. To better understand the impact of these two types of asymmetric information and further the information structure preferences of both the retailer and the manufacturer, this paper first establishes a multi-dimensional screening model, and then discusses the contracting problem under several simpler but commonly investigated information structures. It demonstrates that for all the cases, the optimal contract has a threshold structure: the manufacturer offers a pooling contract when the unit production cost is lower or higher than a certain level, but offers a separating one when the unit production cost is intermediate. Further, it shows that the private demand signal and forecast accuracy information complement each other under certain circumstances, whereas under other circumstances, one dimensional asymmetric information might not have an impact on the other and even be detrimental to the retailer, which is quite interesting. Meanwhile, given that the forecast accuracy is asymmetric information, it reveals that retailer possessing more dimensional private information might benefit the manufacturer; however, if the demand signal is asymmetric information, obtaining more information on the forecast accuracy becomes important. Finally, the value of one particular type of information under one dimensional asymmetric information is not necessarily the same as that under two dimensional asymmetric information due to the interaction effect.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 28 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research〈/p〉 〈p〉Author(s): Yeming Gong, Jiawen Liu, Joe Zhu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Retailers increasingly incorporate sustainable operations to improve their efficiency, which raises questions: Is it always beneficial to increase firms’ sustainable operations for operational efficiency? Under which conditions should a retailer increase its socially-responsible and environmentally-friendly operations to improve efficiency? Our research addresses inconsistent viewpoints in relation to sustainable activities and performance at an operational level, and fills in research gaps in measuring the efficiency of, and identifying the operational mechanisms active in, sustainable retail operations. By collecting data from 124 retailers, we integrate the DEA (data envelopment analysis) model with empirical methods. We first apply DEA models to evaluate the efficiency of retailers. Using efficiency values provided by DEA, we conduct hierarchical regression analysis to examine the influence of socially-responsible and environmentally-friendly operations, and understand the role of sustainable operations in the supply chain. Finally, we use nonlinear analysis to identify the conditions required to increase the efficiency of sustainable operations. Supply chain integration can improve efficiency with higher levels of socio-economic integration and environmental-economic integration. Firms in an internal operational environment with a higher level of financial flow integration and a lower level of physical flow integration are more likely to achieve high retail efficiency.We find two conditions for implementation with managerial insights. When these conditions, characterized by financial flow and physical flow integration, are satisfied, a retailer can increase sustainable operations to increase efficiency. We have a surprising but reasonable finding: The interaction of sustainable operations and physical flow integration is negatively correlated to efficiency.〈/p〉〈/div〉

Description: 〈p〉Publication date: 16 May 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 275, Issue 1〈/p〉 〈p〉Author(s): Roberto Rossi, Maurizio Tomasella, Belen Martin-Barragan, Tim Embley, Christopher Walsh, Matthew Langston〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We investigate opportunities offered by telematics and analytics to enable better informed, and more integrated, collaborative management decisions across construction sites. We focus on efficient refuelling of assets across construction sites. More specifically, we develop decision support models that, by leveraging data supplied by different assets, schedule refuelling operations by minimising the distance travelled by the refuelling truck — the so-called “bowser” — as well as fuel shortages. Motivated by a practical case study elicited in the context of a project we recently conducted at Crossrail, we introduce the Dynamic Bowser Routing Problem. In this problem the decision maker aims to dynamically refuel, by dispatching a bowser truck, a set of assets which consume fuel and whose location changes over time; the goal is to ensure that assets do not run out of fuel and that the bowser covers the minimum possible distance. We investigate deterministic and stochastic variants of this problem and introduce effective and scalable mathematical programming models to tackle these cases. We demonstrate the effectiveness of our approaches in the context of an extensive computational study designed around data collected on site.〈/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): Hailong Cui, Greys Sošić〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉A growing amount of municipal solid waste (MSW) is generated worldwide, and common materials (paper, plastic, metals, and glass), which account for more than half of MSW, exhibit low recovery rates. In this paper, we aim to investigate some key questions about recycling across three dimensions: greenhouse gas emissions, operational costs, and aggregate costs (social costs of emissions plus operational costs.) First, we build supply chain models for cradle-to-grave and cradle-to-cradle supply chains to derive an analytical condition for recycling effectiveness, and use US emissions and cost data to empirically validate that recycling is effective in reducing emissions for all the abovementioned materials. Furthermore, our analysis shows that recycling is effective for all materials, with the exception of glass, with respect to both operational and aggregate costs. Second, we study optimal recycling decisions in terms of collection and yield rates in a socially optimal case, as well as in scenarios in which recycling decisions are made by a local government, a product manufacturer, and an independent recycling firm. Unlike some of existing findings, we show that there are instances in which a product manufacturer or an independent firm might be the best choice for organizing recycling operations. Finally, we discuss and analyze incentives that a social planner should offer to recyclers to bring their efforts closer to the socially optimal choice. We obtain a novel result, which shows that a deposit/refund scheme implemented by a social planner with a refund to local governments might lead to a socially optimal collection rate.〈/p〉〈/div〉

Description: 〈p〉Publication date: 16 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 273, Issue 3〈/p〉 〈p〉Author(s): Ivo Adan〈/p〉

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): J.K. Budd, P.G. Taylor〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Motivated by a desire to calculate the optimal credit limit for a credit card account in terms of the card-holder’s purchasing behaviour, we consider a single-period inventory model in which the total value of all attempted purchases increases in jumps throughout the period. If a purchase does not cause the limit to be exceeded, then that purchase is approved and the total value of approved purchases is increased by its value. On the other hand, if the limit is exceeded, then the purchase is rejected and the total value of approved purchases remains at its previous level.〈/p〉 〈p〉We derive an equation for the Laplace–Stieltjes transform of the expected total value of approved purchases at the end of the period. Unfortunately this equation appears to be intractable, a situation that we address by providing upper and lower bounds. We provide numerical examples that show that these upper and lower bounds can be close, which leads to good upper and lower approximations for the optimal limit.〈/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): Sandra Ulrich Ngueveu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Various optimization problems result from the introduction of nonlinear terms into combinatorial optimization problems. In the context of energy optimization for example, energy sources can have very different characteristics in terms of power range and energy demand/cost function, also known as efficiency function or energy conversion function. Introducing these energy sources characteristics in combinatorial optimization problems, such as energy resource allocation problems or energy-consuming activity scheduling problems may result into mixed integer nonlinear problems neither convex nor concave. Approximations via piecewise linear functions have been proposed in the literature. Non-convex optimization models and heuristics exist to compute optimal breakpoint positions under a bounded absolute error-tolerance. We present an alternative solution method based on the upper and lower bounding of nonlinear terms using non necessarily continuous piecewise linear functions with a relative epsilon-tolerance. Conditions under which such approach yields a pair of mixed integer linear programs with a performance guarantee are analyzed. Models and algorithms to compute the non necessarily continuous piecewise linear functions with absolute and relative tolerances are also presented. Computational evaluations performed on energy optimization problems for hybrid electric vehicles show the efficiency of the method with regards to the state of the art.〈/p〉〈/div〉

Description: 〈p〉Publication date: 16 May 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 275, Issue 1〈/p〉 〈p〉Author(s): Kenji Matsui〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Recently, there have been several cases in which a large-scale retailer has demanded a margin for a consumer product from a supplier before the supplier has determined the margin or the wholesale price, reflecting a power shift from upstream suppliers to downstream retailers in supply chains. Given the recent change of power structures in supply chains, we investigate a practical decision-making problem of when a supply chain member should set its margin in the presence of uncertainty based on a stochastic game-theoretic supply chain model. We assume a typical two-echelon supply chain that consists of a manufacturer and a retailer, each of which determines the margin of a product based on private information of its marginal cost. Hence, the cost structure of a firm is uncertain and is known only to the firm. We construct an incomplete information game model with this setting, drawing the following clear-cut managerial implication: a supply chain member should set its margin later if the other member's cost is highly uncertain. By delaying decision-making, the late-moving member can make a more precise inference on the cost of the early-moving member by observing the margin demanded by the early-mover, thereby choosing a more desirable margin. Despite the current power shift from manufacturers to large-scale retailers in various consumer product categories, our result warns a retailer that if it assumes leadership to demand a margin from a manufacturer in an uncertain environment simply because it has power, it may cut its own throat.〈/p〉〈/div〉

Description: 〈p〉Publication date: 16 May 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 275, Issue 1〈/p〉 〈p〉Author(s): Florian Grenouilleau, Antoine Legrain, Nadia Lahrichi, Louis-Martin Rousseau〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The home health care routing and scheduling problem comprises the assignment and routing of a set of home care visits over the duration of a week. These services allow patients to remain in their own homes, thereby reducing governmental costs by decentralizing the care. In this work, we present a set partitioning heuristic which takes into account most of the industry’s practical constraints. The developed method is based on a set partitioning formulation and a large neighborhood search (LNS) framework. The algorithm solves a linear relaxation of a set partitioning model using the columns generated by the large neighborhood search. A constructive heuristic is then called to build an integer solution. This project is joint work with Alayacare, a start-up sited in Montral (Canada) developing an operations management platform for home health care agencies. They provide their clients with a flexible optimization module that solves real-life instances in no more than 10 minutes. Based on their real instances, the proposed method is able to provide a reduction in travel time by 37% and an increase by more than 16% in continuity of care. We also provide a public benchmark for this problem.〈/p〉〈/div〉

Description: 〈p〉Publication date: 16 May 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 European Journal of Operational Research, Volume 275, Issue 1〈/p〉 〈p〉Author(s): Maaike Hoogeboom, Wout Dullaert〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Unpredictable routes may be generated by varying the arrival time at each customer over successive visits. Inspired by a real-life case in cash distribution, this study presents an efficient solution approach for the vehicle routing problem with arrival time diversification by formulating it as a vehicle routing problem with multiple time windows in a rolling horizon framework. Because waiting times are not allowed, a novel algorithm is developed to efficiently determine whether routes or local search operations are time window feasible. To allow infeasible solutions during the heuristic search, four different penalty methods are proposed. The proposed algorithm and penalty methods are evaluated in a simple iterated granular tabu search that obtains new best-known solutions for all benchmark instances from the literature, decreasing average distance by 29% and reducing computation time by 93%. A case study is conducted to illustrate the practical relevance of the proposed model and to examine the trade-off between arrival time diversification and transportation cost.〈/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): Man Jin, Shunan Zhao, Subal C. Kumbhakar〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper explores the link between financial constraints and productivity using firm-level data from Chinese manufacturing industries. Based on a dynamic optimization model which provides a direct measure of the degree of firms’ external financial constraints, we endogenize the relationship between firms’ financial constraints and productivity through the research and development (R&D) investment channel. We resolve the simultaneity issues in the estimation of productivity with the proxy variable method. Using an unbalanced panel including 312,534 Chinese manufacturing firms over the period 1998–2007, we find that about 90% of firms face external financial constraints and the state firms are much less constrained than the non-state firms. We hypothesize that financial constraints have two opposite effects: (i) they raise productivity by reducing “sub-optimal” investment, and (ii) they reduce productivity because the paucity of funds reduces productivity-enhancing investment. Consistent with our hypothesis, we find an inverted U-shaped relationship between financial constraints and firm productivity. If the level of financial constraints for a typical unconstrained firm is adjusted to the productivity-maximizing level, its productivity will increase by 11% in the short run and 16% in the long run after controlling for firm specific characteristics. The empirical results are robust when we control for the sample selection issue in the estimation of productivity.〈/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): Christian Komusiewicz, André Nichterlein, Rolf Niedermeier, Marten Picker〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Finding large “cliquish” subgraphs is a central topic in graph mining and community detection. A popular clique relaxation are 2-clubs: instead of asking for subgraphs of diameter one (these are cliques), one asks for subgraphs of diameter at most two (these are 2-clubs). A drawback of the 2-club model is that it produces star-like hub-and-spoke structures as maximum-cardinality solutions. Hence, we study 2-clubs with the additional constraint to be well-connected. More specifically, we investigate the algorithmic complexity for three variants of well-connected 2-clubs, all established in the literature: robust, hereditary, and “connected” 2-clubs. Finding these more cohesive 2-clubs is NP-hard; nevertheless, we develop an exact combinatorial algorithm, extensively using efficient data reduction rules. Besides several theoretical insights we provide a number of empirical results based on an engineered implementation of our exact algorithm. In particular, the algorithm significantly outperforms existing algorithms on almost all (sparse) real-world graphs we considered.〈/p〉〈/div〉