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Keywords Metaheuristics; Biological analogue; Candidate solutions; Evolutionary algorithms; Individuals Highlights * The existing studies assume identical behavior for all candidate solutions. * In nature, we know that individuals do not react similarly to the same stimulus. * This is called character and it is lacking from existing implementations. * In this paper, we emulate the corresponding human social analogue. * We implement the different behaviors with the assistance of a novel mutation operator. Abstract The fundamental unit of each evolutionary algorithm is the individual. Each individual represents a potential solution to the problem at hand. Despite the importance of individual solution for multi-objective algorithms' performance the majority of the existing implementations select a simplistic approach by assuming identical behavior for all candidate solutions of a population. However, from the biological analogue we know that individuals do not react similarly to the same stimulus. This is called character and it is lacking from existing implementations. In this paper, we emulate the corresponding human social analogue by generating individuals that exhibit different behavior when are subject to the same stimulus. The implementation of different behaviors is facilitated through a novel mutation operator. The experimental results favor the proposed approach when compared with other state-of-the-art algorithms for a number of test instances. Author Affiliation: Decision Support Systems Laboratory, Department of Informatics, University of Piraeus, 80, Karaoli & Dimitriou Str., 18534 Piraeus, Greece * Corresponding author. Article History: Received 7 February 2020; Accepted 15 November 2020 Byline: Konstantinos Liagkouras [kliagk@unipi.gr] (*), Konstantinos Metaxiotis [kmetax@unipi.gr]

Keywords Portfolio optimization; Higher moments; Downside risk; Kullback--Leibler divergence Highlights * We capture investors' preferences via a target-return distribution. * The optimal portfolio has a return distribution as close as possible to the target. * The framework can accommodate a variety of investment objectives. * We study the framework theoretically for two flexible parametric distributions. * Empirically, the approach trades off mean-variance efficiency and higher moments. Abstract We introduce a novel framework for the portfolio selection problem in which investors aim to target a return distribution, and the optimal portfolio has a return distribution as close as possible to the targeted one. The proposed framework can be applied to a variety of investment objectives. In this paper, we focus on improving the higher moments of mean-variance-efficient portfolios by designing the target so that its first two moments match those of the chosen efficient portfolio but has more desirable higher moments. We show theoretically that the optimal portfolio is in general different from the mean-variance portfolio, but remains mean-variance efficient when asset returns are Gaussian. Otherwise, it can move away from the efficient frontier to better match the higher moments of the target distribution. An extensive empirical analysis using three characteristic-sorted datasets and a dataset of 100 individual stocks indicates that the proposed framework delivers a satisfying compromise between mean-variance efficiency and improved higher moments. Author Affiliation: (a) LFIN/LIDAM, UCLouvain, Belgium (b) HEC Montréal, Department of Decision Sciences, Canada * Corresponding author. Article History: Received 13 April 2022; Accepted 13 February 2023 (footnote)[white star] We gratefully acknowledge Kris Boudt, Victor DeMiguel, Roberto Renò, Guofu Zhou, and conference participants at 9th General AMaMeF Conference, 2019 Paris Financial Management Conference, and 13th Actuarial and Financial Mathematics Conference for comments and discussions. This work is supported by the Fonds de la Recherche Scientifique (F.R.S.-FNRS) under Grant number T.0221.22 (F. Vrins) and J.0115.22 (N. Lassance) and the Belgian Federal Science Policy Office under Grant number ARC 18-23/089 (F. Vrins). Byline: Nathan Lassance [nathan.lassance@uclouvain.be] (*,a), Frédéric Vrins [frederic.vrins@uclouvain.be] (a,b)

Keywords Control; Markov decision processes (MDP); Risk-sensitive; Constrained MDP; Exponential utility Highlights * We consider the problem to find solution of infinite-horizon, constrained risk-sensitive Markov decision processes. * Objective and constraints are standard and risk-sensitive discounted cost functions over finite & infinite horizons. * Two methods are given for finding an ultimately stationary randomized policy by time-truncating the cost functions. * These two methods essentially solve two finite-horizon risk-sensitive Markov decision processes. * We discuss two applications and illustrate an inventory control problem. Abstract This paper considers the problem of finding near-optimal Markovian randomized (MR) policies for finite-state-action, infinite-horizon, constrained risk-sensitive Markov decision processes (CRSMDPs). Constraints are in the form of standard expected discounted cost functions as well as expected risk-sensitive discounted cost functions over finite and infinite horizons. We first show that the aforementioned CRSMDP optimization problem possesses a solution if it is feasible (that is, if there exists a policy which satisfies all the constraints). Secondly, we provide two methods for finding an approximate solution in the form of an ultimately stationary (US) MR policy. The latter is achieved through two approximating finite-horizon CRSMDPs constructed from the original CRSMDP by time-truncating the original objective and constraint cost functions, and suitably perturbing the constraint upper bounds. The first approximation gives a US policy which is -optimal and feasible for the original problem, while the second approximation gives a near-optimal US policy whose violation of the original constraints is bounded above by a specified tolerance value . A key step in the proofs is an appropriate choice of a metric that makes the set of infinite-horizon MR policies and the feasible regions of the three CRSMDPs compact, and the objective and constraint functions continuous. We also discuss two applications and use an infinite-horizon risk-sensitive inventory control problem as an example to illustrate how existing solution techniques may be used to solve the two approximate finite-horizon problems mentioned above. Author Affiliation: (a) TCS Research, Hyderabad, India (b) Indian Institute of Technology Bombay, Mumbai, India * Corresponding author. Article History: Received 26 November 2021; Accepted 27 February 2023 Byline: Uday M Kumar [udaykumar1.m@tcs.com] (*,a), Sanjay P. Bhat [sanjay.bhat.m@tcs.com] (a), Veeraruna Kavitha [vkavitha@iitb.ac.in] (b), Nandyala Hemachandra [nh@iitb.ac.in] (b)

Keywords Supply chain management; Proprietary parts; Remanufacturing; Scavenging; Closed-loop supply chains Highlights * We analyze the impact of part failure rate asymmetry on remanufacturing decisions. * We counter conventional wisdom suggesting IRs always prefer a lower PFRA. * We show that proprietary parts reduce remanufacturing but may increase collection. * IRs preferences towards PFRA can differ when parts are generic versus proprietary. Abstract Sourcing affordable replacement parts is a major issue in the secondary market. Independent remanufacturers (IRs) typically use two sources. They harvest collected products for useable spare parts and procure new spare parts. The first option is complicated by having products with specific parts showing high relative failure rates, e.g., a washing machine that, when collected for remanufacturing, almost always contains a defective PCB board. Thus harvesting for such PCB boards is a challenging option and arguably more complicated when the part failure rate asymmetry (PFRA) increases. The second option is potentially expensive when the failed parts are proprietary, i.e., sold by a monopolist manufacturer at a high price. In this paper, we examine both of those challenges. Using a stylized model and starting with a setting without proprietary parts, we observe that a high PFRA does not necessarily lower IR profitability but might increase both collection and remanufacturing. We extend our results to examine the impact of PFRA on remanufacturing decisions for products containing proprietary parts, and our results are structurally identical. Interestingly, an increase in the proprietary content of a product can benefit the IR in specific settings. Yet, proprietary parts always lead to less remanufacturing, while the collection may increase. For policymakers invested in promoting remanufacturing, our results show important trade-offs on the interplay between part proprietariness and the PFRA. Author Affiliation: (a) Faculty of Economics and Management, Otto-von-Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany (b) Alliance Manchester Business School, University of Manchester, Booth St W, Manchester M15 6PB, UK (c) Department of Production and Operations Management, University of Graz, Universitätsstrasse 15/E3, 8010 Graz, Austria (d) Newcastle Business School, Northumbria University, City Campus East, Newcastle upon Tyne NE1 8ST, UK * Corresponding author. Article History: Received 29 March 2022; Accepted 1 March 2023 Byline: Rainer Kleber [rainer.kleber@ovgu.de] (a), João Quariguasi Frota Neto [joao.quariguasifrotanet@manchester.ac.uk] (b), Marc Reimann [marc.reimann@uni-graz.at] (*,c,d)

Keywords Routing; Multi-period; Demand management; Touting; Metaheuristics Highlights * Introducing 'touting', a new demand management technique in dynamic VRPs. * Touting encourages customers to place order earlier. * Proposing several strategies to determine the most relevant customers to tout. * Touting strategies are combined with a rolling-time horizon vehicle routing algorithm. * Empirical evaluation shows that touting allows for more efficient routing. Abstract This paper introduces a dynamic multi-period vehicle routing problem with touting as demand management technique, where customers that have not yet placed an order can be actively encouraged to order a service sooner. Touting the right customers, such as those located nearby customers who already placed orders, allows for more efficient routes over time. However, it also increases the frequency of visits at such touted customers as they are serviced before they would normally require, which leads to smaller demand volumes per visit. To tackle this trade-off, we propose several strategies to decide which customers to tout and when, using the characteristics of the customers as well as the current plan at the time of touting. Specifically, using the demand and the location information, we approach the ones which are close to the current tour, relatively far from the depot and not likely to easily be covered in the near future. This information is then used as a part of different touting strategies, which are further embedded in a rolling-time horizon vehicle routing algorithm to address the multi-period nature of the problem. These different strategies are empirically compared in a simulation based on a real-world waste collection problem. We demonstrate that touting indeed allows to significantly reduce the travel distance in a dynamic vehicle routing problem. Author Affiliation: (a) Sheffield University Management School, Sheffield, S10 1FL, United Kingdom (b) Warwick Business School, University of Warwick, Coventry, CV4 7AL, United Kingdom (c) Mercator Endowed Chair of Demand Management & Sustainable Transport, WHU - Otto Beisheim School of Management, Vallendar 56179, Germany * Corresponding author. Article History: Received 29 October 2021; Accepted 25 February 2023 Byline: Merve Keskin [m.keskin@sheffield.ac.uk] (*,a), Juergen Branke [juergen.branke@wbs.ac.uk] (b), Vladimir Deineko [vladimir.deineko@wbs.ac.uk] (b), Arne K. Strauss [arne.strauss@whu.edu] (c)

Keywords Production; Lot-sizing and scheduling problem; Sequence-dependent setup; Valid inequality; Extended formulation Highlights * Lot-sizing and scheduling problem with sequence dependent setups is studied. * Polyhedral study on the single-period substructure of the problem is conducted. * Facet defining inequalities which can be separated in polynomial time are derived. * New extended formulations are proposed and compared with the existing formulations. * Proposed inequalities and formulations are shown to facilitate tightening bounds. Abstract In this paper, we propose new valid inequalities and extended formulations for the lot-sizing and scheduling problem with sequence-dependent setups, which are derived by investigating the single-period substructure of the problem. Specifically, we derive two new families of valid inequalities and identify their facet-defining conditions. Additionally, we demonstrate that these inequalities can be separated in polynomial time. After introducing the existing extended formulations for the problem, we provide new extended formulations adapting decision variables representing the time-flow and compare the theoretical strengths of the various formulations and valid inequalities, including the proposed ones. Finally, we conduct computational experiments to demonstrate the effectiveness of the proposed inequalities and formulations. The test results indicate that the proposed inequalities and extended formulations facilitate tightening the linear programming relaxation bounds. Author Affiliation: Department of Industrial Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea * Corresponding author. Article History: Received 8 March 2022; Accepted 3 March 2023 Byline: Younsoo Lee [fifays@snu.ac.kr], Kyungsik Lee [optima@snu.ac.kr] (*)

Keywords Education; Stochastic frontier; Persistent and transient technical inefficiency; Impact evaluation; Causal inference Highlights * Methodology for handling endogeneity issues in the evaluation of policy performance. * Estimation of causal impact of a policy intervention on the outcome variable. * Distinguishes direct effect of policy, efficiency-enhancing effect, and combination. * Application estimates the effect of providing additional resources to schools. * Effect on the outcome originates from the efficiency-enhancing channel. Abstract This paper proposes an innovative methodology for handling endogeneity issues in the evaluation of policy performance. By estimating a regression discontinuity design with a four-component stochastic frontier panel data model, we estimate the causal impact of a policy intervention on the outcome variable, whenever the treatment status depends on an exogenous threshold. We distinguish between (i) the direct effect of the intervention, (ii) the efficiency-enhancing effect, or (iii) their combination. Moreover, we distinguish between persistent (time-invariant) and transient (time-varying) inefficiency components while accounting for unobserved heterogeneity, which is important for policy implications. We showcase the practical usefulness of the proposed approach by estimating the effect of providing additional resources on schools that exceed an exogenously set share of disadvantaged students in secondary schools in Flanders, Belgium. We also demonstrate the trade-off between balance of the covariates in the treated and control group and statistical power. Thus, despite insignificant effects in a balanced but smaller sample close to the discontinuity, the results become significant in the unbalanced sample with more statistical power. In both samples, we observe that the policy had an effect on the outcome mostly through the efficiency-enhancing channel. To this extent, we show that the model specification including both direct and indirect effects outperforms the other two specifications and it offers a more exhaustive perspective from a policy view point. Author Affiliation: (a) Department of Economics and Finance, College of Business, Arts and Social Sciences, Brunel University London, Uxbridge, UB8 3PH, UK (b) Department of Economics and Management, University of Pisa, Via C. Ridolfi 10, Pisa 56124, Italy (c) Leuven Economics of Education Research, Faculty of Economics and Business, KU Leuven, Naamsestraat 69, Leuven 3000, Belgium (d) UNU-Merit, Maastricht University, Boschstraat 24, AX Maastricht 6211, Netherlands * Corresponding author. Article History: Received 17 February 2022; Accepted 24 March 2023 Byline: Oleg Badunenko [oleg.badunenko@brunel.ac.uk] (a), Giovanna D'Inverno [giovanna.dinverno@unipi.it] (*,b,c), Kristof De Witte [kristof.dewitte@kuleuven.be] (c,d)

Keywords Scheduling under uncertainty; Robust scheduling; Multi-scenario; Multi-criteria optimization; Complexity Highlights * We provide a survey on multi-scenario scheduling problems. * Scheduling decisions are to be made prior knowing the scenario realization. * We suggest a unified framework and provide some new results. * We list a large set of open questions for future research. Abstract In classical deterministic scheduling models, it is common to assume that each of the parameters has only a single predefined value. However, in many applications this is not the case, and there is a certain level of uncertainty in the values of the parameters. The multi-scenario approach is one of the most common approaches to model uncertainty. In this approach, each scenario defines a different possible realization of the parameters and the scheduler has to decide upon the exact schedule prior to knowing the exact scenario realization. Different scenarios may arise for a large number of reasons, such as: (i) the assignment of different workers to the processing operations, where the identity of the worker is beyond the scheduler's control; (ii) unpredictable changes in weather conditions; or (iii) a possible strike at the port, which may affect job release dates. The purpose of this paper is to offer a unified framework for scheduling in a multi-scenario environment by presenting an up-to-date survey of results in this field. We also included some new results, presented here for the first time, and suggest many challenges for future research. Author Affiliation: Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel * Corresponding author. Article History: Received 28 March 2022; Accepted 10 November 2022 Byline: Dvir Shabtay [dvirs@bgu.ac.il] (*), Miri Gilenson [gilenson@post.bgu.ac.il]

Keywords Multiple objective programming; Nonlinear programming; Mixed-integer programming; Enclosure; Warm start strategies Highlights * Presents a framework to solve multi-objective optimization problems. * Suitable for convex and mixed-integer quadratic multi-objective optimization problems. * Generalizes the concept of Local Upper Bounds. * Demonstrates how to warm start computations of enclosures. * Includes computational results of algorithmic framework for selected test instances. Abstract A central goal for multi-objective optimization problems is to compute their nondominated sets. In most cases these sets consist of infinitely many points and it is not a practical approach to compute them exactly. One solution to overcome this problem is to compute an enclosure, a special kind of coverage, of the nondominated set. For that computation one often makes use of so-called local upper bounds. In this paper we present a generalization of this concept. For the first time, this allows to apply a warm start strategy to the computation of an enclosure. We also show how this generalized concept allows to remove empty areas of an enclosure by deleting certain parts of the lower and upper bound sets which has not been possible in the past. We demonstrate how to apply our ideas to the box approximation algorithm, a general framework to compute an enclosure, as recently used in the solver called BAMOP. We show how that framework can be simplified and improved significantly, especially concerning its practical numerical use. In fact, we show for selected numerical instances that our new approach is up to eight times faster than the original one. Hence, our new framework is not only of theoretical but also of practical use, for instance for continuous convex or mixed-integer quadratic optimization problems. Author Affiliation: Institute of Mathematics, Technische Universität Ilmenau, Po 10 05 65, Ilmenau D-98684, Germany * Corresponding author. Article History: Received 13 April 2022; Accepted 19 February 2023 Byline: Gabriele Eichfelder [gabriele.eichfelder@tu-ilmenau.de], Leo Warnow [leo.warnow@tu-ilmenau.de] (*)

Keywords Project scheduling; Optimisation model; Tabu search; Dedicated resource; Maximum cash flow gap Highlights * We propose a multiproject scheduling problem to minimize the maximum cash flow gap. * We construct an optimisation model for the problem which is composed of two submodels. * We design a tabu search algorithm consisting of two modules. * We conduct a computational experiment to evaluate the performance of the algorithm. * We analyse the effects of some key parameters on the maximal cash flow gap. Abstract Maintaining a positive balance between cash outflow and inflow is very important for contractors to smoothly implement projects. Based on this fact, with the objective of minimising the maximal cash flow gap, this paper investigates a dedicated resource-constrained multiproject scheduling problem with stochastic activity duration where renewable resources are dedicated to individual projects in advance, and the schedule of each project is then arranged independently. First, we construct an optimisation model for the problem and propose properties for it. Second, for the NP-hardness of the problem, we design a tabu search algorithm consisting of two modules and develop the measures according to the proposed properties to improve the searching efficiency of the algorithm. Finally, we conduct a computational experiment on a randomly generated dataset to evaluate the performance of the developed algorithm and analyse the effects of key parameters on the objective function value. The conclusions are as follows. The developed tabu search algorithm with the improvement measures is the most promising algorithm for solving the studied problem, and the contribution of the improvement measures may vary as the project parameters change. The maximal cash flow gap increases along with the resource factor, required robustness of project schedule, and discount rate per period of cash flows, and decreases with the increase in the project deadline, resource strength, number of milestone activities, advance payment proportion, or progress payment proportion. The interactions among the resource strength, resource factor, and project deadline may also generate an effect on the maximal cash flow gap. Author Affiliation: School of management, Xi'an Jiaotong University, Xi'an 710049, China * Corresponding author. Article History: Received 12 February 2022; Accepted 17 February 2023 Byline: Yukang He [HeYukang@stu.xjtu.edu.cn], Tao Jia [jiatao@mail.xjtu.edu.cn] (*), Weibo Zheng [zhengweb@mail.xjtu.edu.cn]

Keywords Heuristics; Time-dependent; Green vehicle routing problem; Adaptive large neighborhood search Abstract In recent years, reducing emissions has been important for mitigating global warming and the effects of traffic congestion. As a variant of the green vehicle routing problem (GVRP), the time-dependent GVRP with time windows (TDGVRPTW) accounts for both time-dependent travel times and time window constraints and integrates the minimization of carbon emissions. Therefore, the TDGVRPTW is of great practical interest. In this paper, we design an effective adaptive large neighborhood search (ALNS) algorithm for solving the TDGVRPTW. The proposed algorithm uses a time discretization search (TDS) method to determine the departure time from each customer node, together with efficient feasibility checking procedures. The ALNS algorithm has been extensively tested on benchmark instances derived from the literature. The results show that, for small-size instances for which optimal solutions are known, the proposed algorithm can solve several instances to optimality and that high-quality solutions can be obtained for the instances that are not solved to optimality. For large-size instances involving up to 1000 customers, ALNS is particularly effective in computing solutions using a very limited amount of computing time. Author Affiliation: (a) State Key Laboratory of Synthetic Automation for Process Industries, Institute of Intelligent Data and Systems Engineering, Northeastern University, Shenyang 110819, PR China (b) Division of Engineering Management and Decision Sciences, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, P.O. Box 34110, Doha, Qatar (c) School of Business Administration, Southwestern University of Finance and Economics, Chengdu 611130, PR China (d) Business School, Liaoning University, Shenyang 110316, PR China (e) Dalian University of Technology, School of Economics and Management, 116024, Dalian, Liaoning, People's Republic of China * Corresponding author. Article History: Received 5 May 2022; Accepted 17 February 2023 Byline: Yiming Liu (a), Baldacci Roberto (b), Jianwen Zhou (a), Yang Yu (e), Yu Zhang (c), Wei Sun [lnusunwei@163.com] (*,d)

Keywords Inventory; Multi-echelon; Delivery policy; Stochastic; Sustainable Highlights * Quantity restricted deliveries in divergent multi-echelon inventory systems. * Method for exact evaluation of the stochastic inventory level distributions. * Continuous review batch ordering and compound Poisson demand. * Enables inclusion of fixed costs and emissions for different load carriers. * Optimization of inventory and shipment decisions. Abstract Joint consideration and coordination of inventory and shipment decisions is an important challenge in the quest for sustainable distribution systems. This paper addresses this issue by presenting a method for exact analysis of the inventory level distributions in a centralized One-Warehouse-Multiple-Retailer (OWMR) inventory system with quantity restricted deliveries from the warehouse. The system is characterized by continuous review (R,nQ) policies, complete backordering, and compound Poisson demand. The class of quantity restricted delivery policies we consider is motivated by a common desire in practice to ship full load carriers. It generalizes the assumption of unrestricted partial deliveries often used in the literature, and allows the warehouse to only ship retailer specific delivery batch quantities to the different retailers. The delivery batches typically represent package or pallet sizes, containers, or full truckloads. An attractive feature of our approach is that it does not add to the computational effort of analyzing the special case of unrestricted partial deliveries available in the existing literature. A numerical study shows that accounting for quantitative delivery restrictions used in transportation and handling operations when optimizing the reorder points in multi-echelon systems can be very important. Failure to do so can lead to high backorder costs and inadequate customer service. Author Affiliation: Division of Production Management, Lund University, Box 118 SE-22100 Lund, Sweden * Corresponding author. Article History: Received 25 June 2022; Accepted 17 February 2023 Byline: Jonas Andersson [jonas.tuomaala.jat@gmail.com], Filip Malmberg [filip.malmberg@iml.lth.se], Johan Marklund [johan.marklund@iml.lth.se] (*)

Keywords Multi-agent system; Cooperative game theory; Externalities; Ranking method; Covert network Highlights * The members of covert networks usually cooperate to achieve a common goal. * The organization of agents outside a merger influences its relative effectiveness. * We introduce covert network games under the presence of externalities. * We rank the members of covert networks using specific solutions. * As an application, we rank the hijackers supporting 9/11 attacks. Abstract The identification of the most potentially hazardous agents in a terrorist organisation helps to prevent further attacks by effectively allocating surveillance resources and destabilising the covert network to which they belong. In this paper, several mechanisms for the overall ranking of covert networks members in a general framework are addressed based on their contribution to the overall relative effectiveness in the event of a merger. In addition, the possible organisation of agents outside of each possible merger naturally influences their relative effectiveness score, which motivates the innovative use of games in partition function form and specific ranking indices for individuals. Finally, we apply these methods to analyse the effectiveness of the hijackers of the covert network supporting the 9/11 attacks. Author Affiliation: (a) MODESTYA Research Group, Department of Statistics, Mathematical Analysis and Optimisation, Faculty of Mathematics, University of Santiago de Compostela, Campus Vida, Santiago de Compostela 15782, Spain (b) CITMAga, MODESTYA Research Group, Department of Statistics, Mathematical Analysis and Optimisation, Faculty of Mathematics, University of Santiago de Compostela, Campus Vida, Santiago de Compostela 15782, Spain * Corresponding author. Article History: Received 3 March 2022; Accepted 16 February 2023 Byline: Alejandro Saavedra--Nieves [alejandro.saavedra.nieves@usc.es] (*,a), Balbina Casas--Méndez (b)

Keywords Reliability; Complex systems; Degradation; Stochastic processes; Survival signature Highlights * The model combines the survival signature and stochastic processes for the first time. * The reliability of various complex systems can be calculated without failure data. * The components can degrade with different stochastic degradation processes. * The system reliability could be evaluated with different amounts of degradation data. Abstract For systems with complicated structures, reliability analysis based on survival signature has been carried out by modelling time-to-failure data with specific distributions. However, for highly reliable systems, only little or no failure data may be available. To enable reliability analysis without failure data, a new generalised reliability method is proposed for complex systems, based on the survival signature and using stochastic processes to model degradation. The combination of the survival signature and stochastic processes enables the proposed method to be applied to complex systems with different structures and stochastically degrading components. First, system reliability is analysed based on the survival signature and a generalised stochastic process. Then, component reliability analysis based on the generalised stochastic process is introduced using Wiener and Gamma processes. Finally, the approach presented in this paper is illustrated using two numerical examples, and the estimation results are compared with those calculated using failure time distribution functions. Author Affiliation: (a) School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China (b) Department of Mathematical Sciences, Durham University, Durham DH1 3LE, United Kingdom (c) Key Laboratory of Vibration and Control of Aero Propulsion Systems Ministry of Education of China, Northeastern University, Shenyang 110819, China * Corresponding author at: School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China. Article History: Received 2 March 2022; Accepted 17 February 2023 Byline: Miaoxin Chang [changmiaoxin@stumail.neu.edu.cn] (a,b), Xianzhen Huang [xzhhuang@mail.neu.edu.cn] (a,c,*), Frank PA Coolen [frank.coolen@durham.ac.uk] (b), Tahani Coolen-Maturi [tahani.maturi@durham.ac.uk] (b)

Keywords Transportation; Last-mile delivery; Drone; Robust optimization; Branch-and-price Abstract In this paper, we show the potential of a drone-truck logistics system to provide fast last-mile delivery services. In the system, a truck and a drone work in tandem to serve customers within pre-specified delivery time windows. Since the uncertainty in the ground traffic network can not only fail a service promise but also expose the drone to danger, we focus on mitigating such risks when designing the routing plan. In particular, we investigate the robust drone-truck delivery problem (RDTDP) that seeks a robust joint route for the truck-and-drone tandem to maximize the profit. We develop an exact branch-and-price (B&P) solution approach that can solve RDTDP instances, both randomly generated and collected from real-life data, with up to 40 service requests. In a numerical study, we demonstrate that the solution obtained with our proposed B&P approach is significantly more robust than the one obtained without considering any uncertainty. In particular, while maintaining a comparable mean value in the solution quality measures, the robust solution features a variance up to 58% smaller and a feasibility ratio (i.e., on-time performance) up to 90% higher than the deterministic solution. These insights suggest that the robust route can be carried out much more frequently in practical usage. Author Affiliation: (a) Department of Industrial and Systems Engineering, University of Florida, Weil Hall 401C, Gainesville FL 32611, USA (b) Department of Industrial and Systems Engineering, University of Washington, Box 352650, Seattle WA 98195, USA (c) Department of Management Science, Antai College of Economics and Management, Shanghai Jiao Tong University, 1954 Huashan Road, Antai #1308, Shanghai 200030, China (d) Department of Information Engineering, University of Padova, Via Gradenigo 6/b, Padova 35131, Italy * Corresponding author. Article History: Received 23 May 2022; Accepted 17 February 2023 Byline: Yu Yang [yu.yang@ise.ufl.edu] (*,a), Chiwei Yan [chiwei@uw.edu] (b), Yufeng Cao [yufeng.cao@sjtu.edu.cn] (c), Roberto Roberti [roberto.roberti@unipd.it] (d)

Keywords Combinatorial optimization; Disjunctively constrained knapsack problem; Heuristics; Strategic oscillation Highlights * The disjunctively constrained knapsack problem is a general model for a number of applications. * We propose an effective heuristic algorithm for solving this computationally challenging problem. * The algorithm relies on an original combination of feasible search and infeasible search. * The algorithm shows excellent results on 6361 benchmark instances of the literature. * The algorithm discovers 39 improved best results (new lower bounds). Abstract This paper presents a responsive strategic oscillation algorithm for the NP-hard disjunctively constrained knapsack problem, which has a variety of applications. The algorithm uses an effective feasible local search to find high-quality local optimal solutions and employs a strategic oscillation search with a responsive filtering strategy to seek still better solutions by searching along the boundary of feasible and infeasible regions. The algorithm additionally relies on a frequency-based perturbation to escape deep local optimal traps. Extensive evaluations on two sets of 6340 benchmark instances show that the algorithm is able to discover 39 new lower bounds and match all the remaining best-known results. Additional experiments are performed on 21 real-world instances of a daily photograph scheduling problem. The critical components of the algorithm are experimentally assessed. Author Affiliation: (a) School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China (b) LERIA, Université d'Angers, 2 Boulevard Lavoisier, Angers 49045, France (c) Business School, Hohai University, Nanjing 210098, China (d) Entanglement, Inc., Boulder, Colorado 80305, USA * Corresponding author. Article History: Received 4 October 2022; Accepted 7 February 2023 Byline: Zequn Wei (a), Jin-Kao Hao [jin-kao.hao@univ-angers.fr] (*,b), Jintong Ren (c), Fred Glover (d)

Keywords Data envelopment analysis; Technical efficiency; Allocative efficiency; Hospitals Highlights * Variable cost efficiency measures that account for demand uncertainty and environmental change. * DEA models estimated using data on both observed outputs and output targets. * Poor input mix found to be the main driver of inefficiency in some regional and remote hospital and health services. Abstract Public service managers generally make input choices in the face of uncertainty about future demands for service. This is generally not taken into account when estimating cost efficiency. In the operations research literature, for example, the standard approach to estimating cost efficiency is based on the assumption that managers choose inputs to minimise the cost of producing realised (i.e., observed) outputs. However, when outputs are unknown at the time input decisions are made, most managers will instead choose inputs to minimise the cost of producing output targets (e.g., minimum service levels, predicted maximum demands). In this paper, we explain how data envelopment analysis (DEA) estimators can be used to estimate cost, technical and allocative efficiency in these situations. The methodology is applied to Australian data on hospital and health service providers. We obtain estimates of efficiency that are quite different from estimates obtained using a standard approach that ignores uncertainty. Our work has important implications for performance evaluation and improvement programs in many public service settings. Author Affiliation: School of Economics, University of Queensland, St Lucia, QLD 4072, Australia * Corresponding author. Article History: Received 27 November 2021; Accepted 15 February 2023 Byline: Hong Ngoc Nguyen [hongngoc.nguyen@uq.edu.au] (*), Christopher O'Donnell [c.odonnell@economics.uq.edu.au]

Keywords Game theory; Green product development; Government subsidies; Research and development cost; Social welfare Highlights * A monopolist firm with two potential marketing strategies is considered. * The government provides subsidies to encourage the development of green products. * A two-stage Stackelberg game is utilized to assess the role of government intervention. * We show how an optimal subsidy level should be selected to maximize social welfare. * The results illustrate that providing subsidies may increase the price of products. Abstract This paper tackles the problem of a monopolist firm that is considering designing products with environmental qualities while facing significant research and development costs. A mathematical formulation is adopted to model the impact of government subsidy on the firm's choice between mass marketing, where only one standard product serves the entire market, and market segmentation, in which the firm develops ordinary and green products for two market segments. The firm's behavior in reaction to the subsidies is analyzed through a two-stage Stackelberg game. The obtained results reveal that the subsidy level does not affect the relationships between the environmental qualities of the manufactured products under different marketing strategies when the green market is not strong. Our analyses also demonstrate how an optimal subsidy level should be selected to maximize the social welfare, and how this optimal subsidy is impacted by various parameters such as the magnitude of the development cost. Author Affiliation: (a) Ted Rogers School of Management, Toronto Metropolitan University, 575 Bay Street, Toronto, ON M5G 2C5, Canada (b) Pilon School of Business, Sheridan College, 1430 Trafalgar Rd, Oakville, Ontario, Canada * Corresponding author. Article History: Received 25 May 2021; Accepted 10 February 2023 Byline: Hossein Zolfagharinia [h.zolfagharinia@torontomu.ca] (a,*), Maryam Zangiabadi (a), Maryam Hafezi (b)

Keywords Scheduling; Uncertain job types; Predictive information; Learning; Mismatch and rescheduling Highlights * A scheduling problem with uncertain job types. * Learning and rescheduling if jobs are assigned to the wrong machine after a period of detection time. * A near-optimal policy to minimize the completion time with two types of jobs. * Heuristic algorithms to balance the trade-off between machine idling and job mismatch. Abstract Uncertain job types can arise as a result of predictive or diagnostic inaccuracy in healthcare or repair service systems and unknown preferences in matching service systems. In this paper, we study systems with multiple types of jobs, in which type information is imperfect and will be updated dynamically. Each job has a prior probability of belonging to a certain type which may be predicted by data, models, or experts. A job can only be processed by the right machine, and a job assigned to the wrong machine must be rescheduled. More information is learned from the mismatch, and job type probabilities are updated. The question is how to dynamically schedule all jobs so that they can be processed in a timely fashion. We use a novel coupling and inductive method to conduct optimality analysis. We obtain the near-optimal policy regarding completion time, named the less-uncertainty-first policy, when there are two types of jobs; the insights it yields are used to develop heuristic algorithms for more general cases. We also consider other objectives, including the number of mismatches and the total amount of time jobs spend in the system. In our numerical study, we examine the performance of the proposed heuristics when there are more than two types of jobs under two learning schemes: dedicated learning and exclusive learning. In the extension, we also analyze an online version of the problem in which jobs arrive sequentially to the system and must be assigned immediately and irrevocably without any knowledge of future jobs. We analyze the competitive ratios of different scheduling policies and find similar insights. It is essential that managers dynamically schedule services by leveraging predictive information and knowledge learned from mismatches. Our proposed less-uncertainty-first policy, which accounts for system dynamics to avoid mismatches and resource idling, can be used to improve system efficiency in various contexts. Author Affiliation: (a) Department of Industrial Engineering and Operations Research, University of California, Berkeley, CA 94720-1777, United States (b) School of Management, Technical University of Munich, Heilbronn 74072, Germany (c) Lee Kong Chian School of Business, Singapore Management University, Singapore 178899, Singapore * Corresponding author. Article History: Received 24 December 2021; Accepted 13 February 2023 Byline: Zuo-Jun Max Shen [maxshen@berkeley.edu] (a), Jingui Xie [jingui.xie@tum.de] (b), Zhichao Zheng [danielzheng@smu.edu.sg] (c), Han Zhou [han.zhou@tum.de] (*,b)

Keywords Transportation; Electric aircraft; Network optimization; Regional routes; Kernel search Highlights * Optimization method to locate focal charging hubs in regional air transport networks. * Bi-objective formulation trading-off charging hubs and regional connectivity. * A kernel-search heuristic is proposed to handle large-scale real-world-like instances. * Computational experiments demonstrate the benefits of the proposed algorithm. * Real-world case study of Sweden demonstrates the strategic insights of the model. Abstract The uptake of electric aircraft appears faster today than predicted. Given the prominent electric aircraft technologies, short- and medium-haul routes are the ones that will benefit first, with the promise to revolutionize regional aviation at short notice. This paper proposes an optimization model to support the strategic design of charging networks for electric aircraft as a key enabling factor to prepare for and take full advantage of aviation electrification. The model, named Electric Aircraft Charging Network for Regional Routes, defines a network of airports and flight paths to optimally trade-off the number of charging bases (and associated investment costs) with connectivity and population coverage targets typical of regional routes serving remote regions. Due to computational challenges in large problem instances, we propose a Kernel Search heuristic and illustrate how it can deliver high quality solutions for large cases in a shorter computational time than the branch-and-cut algorithms. A real-world application to Sweden then demonstrates the practical insights of the proposed approach. We find that leveraging the many currently under-utilized regional airports has connectivity and investment benefits (on average +12.1% in number of origins covered, +5.8% in population coverage, and -7.3% reduction of travel times). Furthermore, increasing the maximum aircraft range on a single charge implies significantly fewer charging bases and more feasible travel options, thus favoring network resilience and granting higher flexibility for later planning stages. Author Affiliation: (a) Div. Communication and transportation Systems, ITN, Linköping University, Bredgatan 33, Norrköping 601 74, Sweden (b) Department of Management, Information and Production Engineering, University of Bergamo, via 7 Pasubio 7b, Dalmine, BG 24044, Italy * Corresponding author. Article History: Received 21 November 2021; Accepted 7 February 2023 Byline: Alan Kinene [alan.kinene@liu.se] (a), Sebastian Birolini [sebastian.birolini@unibg.it] (*,b), Mattia Cattaneo [mattia.cattaneo@unibg.it] (b), Tobias Andersson Granberg [tobias.andersson.granberg@liu.se] (a)

Keywords Metaheuristic; Dial-a-ride problem; Electric autonomous vehicles; Deterministic annealing Highlights * We propose a new approach that efficiently computes minimum excess user ride time by introducing a fragment-based representation of paths. * We propose an efficient DA algorithm and provide the first heuristic results for the static version of the E-ADARP. Our DA algorithm allows an exact route evaluation of linear time complexity and computes minimum excess user ride time for a feasible route in linear time after preprocessing. * We provide the best solutions for 70 out of 84 existing E-ADARP instances, among which 25 are the new best solutions. On newly-constructed instances, we provide 19 new solutions. * We investigate the effects of allowing unlimited visits to recharging stations and provide the first results under this setting. Abstract This paper investigates the Electric Autonomous Dial-A-Ride Problem (E-ADARP), which consists in designing a set of minimum-cost routes that accommodates all customer requests for a fleet of Electric Autonomous Vehicles (EAVs). Problem-specific features of the E-ADARP include: (i) the employment of EAVs and a partial recharging policy; (ii) the weighted-sum objective function that minimizes the total travel time and the total excess user ride time. In this work, we propose a Deterministic Annealing (DA) algorithm and provide the first heuristic results for the static E-ADARP. Partial recharging (i) is handled by an exact route evaluation scheme of linear time complexity. To tackle (ii), we propose a new method that allows effective computations of minimum excess user ride time by introducing a fragment-based representation of paths. These two methods compose an exact and efficient optimization of excess user ride time for a generated E-ADARP route. To validate the performance of the DA algorithm, we compare our algorithm results to the best-reported Branch-and-Cut (B&C) algorithm results on existing instances. Our algorithm provides 25 new best solutions and 45 equal solutions on 84 existing instances. To test the algorithm performance on larger-sized instances, we establish new instances with up to 8 vehicles and 96 requests, and we provide 19 new solutions for these instances. Our final investigation extends the state-of-the-art model and explores the effect of allowing multiple visits to recharging stations. This relaxation can efficiently improve the solution's feasibility and quality. Author Affiliation: (a) Université Paris-Saclay, CentraleSupélec, Laboratoire Génie Industriel, Gif-sur-Yvette, 91190, France (b) Univ Angers, LERIA, SFR MATHSTIC, Angers F-49000, France (c) EM Normandie Business School, Métis Lab, Clichy, 92110, France * Corresponding author. Article History: Received 24 March 2022; Accepted 10 February 2023 Byline: Yue Su [yue.su@centralesupelec.fr] (*,a), Nicolas Dupin [nicolas.dupin@univ-angers.fr] (b), Jakob Puchinger [jpuchinger@em-normandie.fr] (c,a)

Keywords Portfolio optimization; Mixed-integer semidefinite optimization; Distributionally robust optimization; Cutting-plane algorithm; Matrix completion Highlights * Distributionally robust portfolio optimization model with a cardinality constraint. * Mixed-integer semidefinite optimization reformulation. * Specialized cutting-plane algorithm with guaranteed global optimality. * Positive semidefinite matrix completion technique to accelerate the algorithm. * Good computational efficiency and out-of-sample investment performance. Abstract This paper studies a distributionally robust portfolio optimization model with a cardinality constraint for limiting the number of invested assets. We formulate this model as a mixed-integer semidefinite optimization (MISDO) problem by means of the moment-based ambiguity set of probability distributions of asset returns. To exactly solve large-scale problems, we propose a specialized cutting-plane algorithm that is based on bilevel optimization reformulation. We prove the finite convergence of the algorithm. We also apply a matrix completion technique to lower-level SDO problems to make their problem sizes much smaller. Numerical experiments demonstrate that our cutting-plane algorithm is significantly faster than the state-of-the-art MISDO solver SCIP-SDP. We also show that our portfolio optimization model can achieve good investment performance compared with the conventional robust optimization model based on the ellipsoidal uncertainty set. Author Affiliation: (a) Department of Industrial Engineering and Economics, School of Engineering, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152--8552, Japan (b) Institute of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba-shi, Ibaraki 305--8573, Japan * Corresponding author. Article History: Received 22 December 2021; Accepted 20 January 2023 Byline: Ken Kobayashi [kobayashi.k.ar@m.titech.ac.jp] (*,a), Yuichi Takano (b), Kazuhide Nakata (a)

Keywords Logistics; Reselling mode; Marketplace mode; Remanufacturing; Blockchain; Cap-and-trade Highlights * The optimal decisions considering the platform-enabled power and remanufacturing are explored. * Marketplace mode and reselling mode are considered in our work. * Whether to adopt blockchain for the manufacturer is analyzed. * The quantity and social welfare coordination challenges are discussed. Abstract Blockchain technology has been widely used in many industries. One current application is in remanufacturing. In this paper we consider the combination of remanufacturing and blockchain, and model a supply chain composed of a manufacturer, a third-party firm, and an online platform. Among them, the manufacturer faces the cap-and-trade regulation and adopts blockchain to record the information on the used products and then remanufactures products. The platform has the power to expand the potential market size and can operate in the marketplace or reselling mode. The third-party firm collects used products for the manufacturer. We conduct a Stackelberg game analysis and obtain the following major findings: First, the optimal production quantities and optimal collection rates with and without blockchain in the marketplace and reselling modes increase with the allocated cap and platform-enabled power. Second, in the reselling or marketplace mode, the manufacturer should not adopt blockchain if the emissions intensity is low; otherwise, it should adopt blockchain. Third, selecting the reselling (marketplace) mode is more profitable for the manufacturer if the platform-enabled power is low (high). Fourth, for quantity coordination, the reselling mode under the wholesale price contract can always coordinate the manufacturer and platform, and the manufacturer, online platform, and third-party firm. However, the marketplace mode with a commission rate can only coordinate the manufacturer, online platform, and third-party firm. For social welfare coordination, the manufacturer, online platform, and third-party firm can achieve coordination in the marketplace or reselling mode. Finally, extending the work to consider the cross-channel effect, we find that the major findings for both quantity coordination and social welfare coordination in the reselling and marketplace modes still hold. Author Affiliation: (a) School of Business, Anhui University, Hefei, China (b) Centre for Supply Chain Research, University of Liverpool Management School, Chatham Building, Liverpool L69 7ZH, United Kingdom (c) Department of Logistics and Maritime Studies, Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong * Corresponding author. Article History: Received 6 November 2021; Accepted 31 January 2023 Byline: Xiaoping Xu (a), Luling Yan (a), Tsan-Ming Choi [tsanmingchoi@link.cuhk.edu.hk] (b,*), T.C.E. Cheng (c)

Keywords Global optimization; Concave minimization; Mixed integer non-linear programming; Knapsack problem; Production-transportation problem Highlights * We present an exact method for solving concave minimization problems. * We use a bilevel program as an approximate representation of the problem. * Solving the bilevel program iteratively guarantees convergence to global optimal. * Concave knapsack and production-transportation problems are solved. * The method is more than an order of magnitude faster than contemporary methods. Abstract In this article, we discuss an exact algorithm for solving mixed integer concave minimization problems. A piecewise inner-approximation of the concave function is achieved using an auxiliary linear program that leads to a bilevel program, which provides a lower bound to the original problem. The bilevel program is reduced to a single level formulation with the help of Karush--Kuhn--Tucker (KKT) conditions. Incorporating the KKT conditions lead to complementary slackness conditions that are linearized using BigM, for which we identify a tight value for general problems. Multiple bilevel programs, when solved over iterations, guarantee convergence to the exact optimum of the original problem. Though the algorithm is general and can be applied to any optimization problem with concave function(s), in this paper, we solve two common classes of operations and supply chain problems; namely, the concave knapsack problem, and the concave production-transportation problem. The computational experiments indicate that our proposed approach outperforms the customized methods that have been used in the literature to solve the two classes of problems by an order of magnitude in most of the test cases. Author Affiliation: (a) Production and Quantitative Methods, Indian Institute of Management Ahmedabad, Gujarat 380015, India (b) Production and Operations Management, Indian Institute of Management Visakhapatnam, Andhra Pradesh 530003, India * Corresponding author. Article History: Received 9 July 2021; Accepted 5 February 2023 Byline: Ankur Sinha [asinha@iima.ac.in] (a), Arka Das [phd17arkadas@iima.ac.in] (*,a), Guneshwar Anand [guneshwar.anand-phd19@iimv.ac.in] (b), Sachin Jayaswal [sachin@iima.ac.in] (a)

Keywords Project scheduling; Resource constrained; Multi project; Project management Highlights * We review the resource-constrained multi-project scheduling problem from its inception. * We propose a taxonomy to classify related literature based on problem features. * We classify and analyze solution methods and proposed benchmarks for this problem. * Related applications in real environments are discussed. Abstract Project Management is becoming increasingly crucial in competitive environments such as manufacturing and the service industries. The Resource-Constrained Multi-Project Scheduling Problem (RCMPSP) consists of assigning start times to jobs corresponding to two or more projects that must be executed simultaneously while respecting the precedence between jobs and limited resources. The existing rise in the study of the RCMPSP resulted in numerous works on the topic while proposing different problem features. This research analyzes different variants of the problem based on aspects related to jobs, projects, relationships, resources, and time management. Moreover, based on the problem variants considered in the collected works, a taxonomy allowing (i) the identification and positioning of each RCMPSP variant and (ii) the analysis of the current state-of-the-art of the problem is proposed. In addition, the solution methods proposed to solve RCMPSPs are classified and analyzed, together with the benchmarks used to validate such approaches. Finally, this review paper discusses the RCMPSP connection to practice and provides future research opportunities in light of recent advances. Author Affiliation: (a) Departamento de Informática, Universidad Técnica Federico Santa María, Av. España 1680, Valparaíso, 2390123 Chile (b) Departament of Business Information Systems and Industrial Engineering, University of Twente, Hallenweg 17, Enschede, 7522NH The Netherlands (c) Institute of Information Systems, University of Hamburg, Von-Melle-Park 5, Hamburg,20146 Germany * Corresponding author. Article History: Received 1 September 2021; Accepted 25 September 2022 Byline: Mariam Gómez Sánchez [mggomez@jp.inf.utfsm.cl] (*,a), Eduardo Lalla-Ruiz (b), Alejandro Fernández Gil (a), Carlos Castro (a), Stefan Voß (c)

Keywords Graph theory; Matching; Augmenting paths; Complexity Highlights * Problem of assignment of landing slots to aircraft. * Modelled as a constraint matching problem in graphs. * Polynomial case: when the augmenting paths have length at most k=3. * Hardness: when augmenting paths have length at most k for k at least 5. * Simulations of the algorithm for k=3 on real data. Abstract When an aircraft is approaching an airport, it gets a short time interval called a slot by Air Traffic Control that it can use to land. If the landing of the aircraft is delayed for some reason, it loses its slot and Airline Operation Controllers have to assign it a new one. However, landing slots are a scarce resource of the airports and, in order to avoid that an aircraft waits too long, Airline Operation Controllers have to regularly modify the assignment of slots to the aircraft. Due to the system implemented to exchange slots, controllers can modify the slot-assignment using only two kinds of operations: either assign a slot that was free to aircraft A, or give the slot of another aircraft B to A and assign a free slot to B. The problem can then be modelled as follows. Let k[greater than or equal to]1 be an odd integer, let G be a graph and M be a matching in G, i.e., a set of pairwise disjoint edges. A k-maximum matching is a largest possible matching that can be obtained from M by using only augmenting paths of length at most k. This problem has already been studied in the context of wireless networks, mainly because it provides a simple approximation for the maximum matching problem. This paper provides a polynomial-time algorithm when k[less than or equal to]3. We then prove that the problem is NP-hard in planar bipartite graphs with maximum degree at most 3 for any odd integer k[greater than or equal to]5. Author Affiliation: (a) Université Côte d'Azur, Inria, CNRS, I3S, France (b) Innovation & Research, Amadeus IT Group SA, France (c) Innovation & Research, Amadeus IT Pacific, Australia * Corresponding author. Article History: Received 31 August 2017; Accepted 18 December 2017 (footnote)[white star] This work was partly funded by the ANR project STINT, and promoted by the Inria associate-team AlDyNet. This work is the extended version of half of the results presented in LAGOS 2017 . Byline: Nicolas Nisse [nicolas.nisse@inria.fr] (*,a), Alexandre Salch (b), Valentin Weber (c)

Keywords OR in sports; Snooker; Monte Carlo simulation Highlights * A Monte-Carlo simulation of the progression of a snooker frame is developed. * The development of dynamic measures of player performance is advocated. * Analysis of how a frame's progression and outcome depend on player ability. * Assessment of the influence of the break-off shot on the progression of a frame. * Indictive strategies are conceived when snookered or faced with a risky pot. Abstract This paper is the first to develop a Monte Carlo simulation model of a snooker frame, using post-match video analysis of over 30,000 shots to inform the probability that a top professional will pot a ball on each shot depending on the number of balls remaining on the table and the status of their current visit to the table. We demonstrate that this accurately reflects the progression of a frame at this level, advocating new approaches in viewing a frame as a succession of scoring visits and in using dynamic measures to evaluate player performance. The simulation model also produces a realistic expectation of the probability that the frame will be won from any given position. The model is then used to analyse the effectiveness of the break-off shot and the influence that this has on the outcome of the frame. We also use simulated outcomes to evaluate the choice between taking on a risky pot or playing safe and the merits of alternative approaches to escaping from a snooker. Author Affiliation: Management Science, Lancaster University Management School, LA1 4YX, UK * Corresponding author. Article History: Received 5 February 2021; Accepted 9 November 2022 Byline: James A.P. Collingwood [jacollingwood@yahoo.com] (*), Michael Wright, Roger J. Brooks

Keywords Or in disaster relief; Simulation optimization; Rapid-screening algorithm; Adaptive particle global and hyperbox local search; Mass casualty incident Highlights * Stochastic casualty collection point location and resource allocation problem was studied. * A hybrid simulation--optimization approach was presented to solve this problem. * An ORSA-APGHLS algorithm was developed to efficiently search optimal solutions. * A sensitivity analysis was conducted and bring some managerial insights. Abstract After a severe disaster strikes, a large number of casualties in need of urgent treatment, known as a mass casualty incident (MCI) event, arise from multiple disaster areas in a very short period of time and overwhelm available resources of the local healthcare system. This paper focuses on the effective design of casualty collection point (CCP) locations and the efficient allocation of limited emergency medical service (EMS) resources to transport casualties quickly to appropriate hospitals and increase the survival rate of casualties. A hybrid simulation--optimization approach to optimize the CCP location and EMS resource allocation problem over mixed binary and integer feasible domains for the minimization of expected complete delivery time of all casualties from disaster region to hospitals is presented in this work due to the stochastic and dynamic nature of the MCI logistics environment. A high-resolution stochastic discrete event simulation model considering time-varying stochastic casualty arrivals, random triage service times, and stochastic travel times caused by road network vulnerability is first constructed to describe a more detailed modeling of comprehensive MCI humanitarian logistics from the disaster regions to the hospitals. Then, a novel two-stage sequential algorithm, namely a combination of optimal computing budget allocation-based rapid-screening algorithm and adaptive particle global and hyperbox local search (ORSA-APGHLS), is developed to speed up convergence to near-optimal solutions compared to three common existing algorithms (Genetic Algorithm, Tabu Search and Stochastic Nelder-Mead Algorithm) under a limited simulation budget. We collaborate with the National Science and Technology Center for Disaster Reduction (NCDR) in Taiwan to conduct computational experiments to demonstrate the efficiency and efficacy of the proposed two-stage ORSA-APGHLS algorithm according to a potential earthquake scenario occurring in Tainan City, Taiwan. Through sensitivity analysis, the influence of different levels of scarce emergency medical resources and degrees of road damage on the expected complete delivery time of all casualties and the location-allocation decisions are investigated. Author Affiliation: (a) Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Taiwan (b) Graduate Institute of Intelligent Manufacturing Technology, National Taiwan University of Science and Technology, Taiwan (c) National Science and Technology Center for Disaster Reduction, Taiwan * Corresponding author. Article History: Received 5 March 2022; Accepted 30 January 2023 Byline: Kuo-Hao Chang (a,c), Tzu-Li Chen [chentzuli@gmail.com] (b,*), Fu-Hao Yang (a), Tzu-Yin Chang (c)

Keywords Risk analysis; Interdependent critical infrastructures; Coupling interface; Random failures; Distributionally robust optimization Highlights * The topology of the coupling interface between interdependent critical infrastructures is optimized to enhance their combined resilience * A distributionally robust optimization approach is used to avoid excessively conservative solution. * The results are compared with traditional stochastic and robust solutions. * The results highlight the potential of optimal coupling interfaces in terms of resilience enhancement. Abstract Critical infrastructures (CIs), such as energy systems, transportation networks and telecommunications networks, are the backbone of any advanced society, and ensuring their resilience is a fundamental task. CIs are often interconnected to, and interdependent on, each other through complex coupling interfaces. Failures can propagate among different CIs through these coupling interfaces, causing multi-sectoral disruption. The design of the coupling interface can strongly impact the cascading effect between different CIs. In this paper, we propose a data-driven distributionally robust approach for the optimal coupling of interdependent CIs. Our model obtains an optimal coupling interface that maximizes the expected combined performance of interdependent CIs under random failure scenarios with ambiguous probability distributions. We demonstrate the validity of the proposed approach using an ambiguity set built upon a synthetic data set of historical contingency scenarios. Interdependent power and gas networks (IPGNs) are used as an illustrative case study. We show that our proposed approach leads to better coupling interfaces with higher expected performance under disruptive scenarios. Author Affiliation: (a) Thales Research & Technology, Palaiseau, France (b) Chair on Risk and Resilience of Complex Systems, Laboratoire Génie Industriel, CentraleSupélec, Université Paris-Saclay, 3 Rue Joliot Curie, Gif-sur-Yvette 91190, France (c) Laboratoire Génie Industriel, CentraleSupélec, Université Paris-Saclay, 3 Rue Joliot Curie, Gif-sur-Yvette 91190, France * Corresponding author. Article History: Received 19 January 2022; Accepted 27 January 2023 Byline: Andrea Bellè [andrea.belle@thalesgroup.com] (*,a,b), Adam F. Abdin [adam.abdin@centralesupelec.fr] (c), Yi-Ping Fang [yiping.fang@centralesupelec.fr] (b), Zhiguo Zeng [zhiguo.zeng@centralesupelec.fr] (b), Anne Barros [anne.barros@centralesupelec.fr] (b)

Keywords Finance; Energy; Real options; Operations Highlights * Categories to review research on real options in energy. * Categorization of papers in the operations literature. * Discussion of potential research directions. Abstract Real option models maximize the estimated market value of operational assets, exploiting the flexibility that decision makers have in managing these assets. Inspired by the valuation of financial options, their natural domains of application feature high levels of market or operational risk. The existence of financial markets that trade instruments associated with the inputs or outputs of the underlying processes facilitates the use of these models. The energy industry has thus received a substantial amount of attention within the real option and operations (management/research) communities. The extant work lacks a review of the operations literature on energy real options. We present a synthesis of this literature considering review categories that pertain to the phenomena it studies and the tools it uses to conduct its analysis. Further, we outline potential research directions. Author Affiliation: (a) Department of Information and Decision Sciences, College of Business, University of Illinois at Chicago, Chicago, IL, USA (b) Jones Graduate School of Business, Rice University, Houston, TX, USA (c) Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA, USA * Corresponding author. Article History: Received 30 June 2021; Revised 6 August 2022; Accepted 18 September 2022 Byline: Selvaprabu Nadarajah [selvan@uic.edu] (*,a), Nicola Secomandi (b,c)

Keywords Humanitarian logistics; OR in disaster relief; Inventory; Multi-echelon logistics network Highlights * We investigate relief inventory-distribution strategies in a multi-echelon network. * A dynamic stochastic optimization problem is formulated. * Deterministic approximation & decomposition analytically derive an approximate solution. * In most practical cases, the conventional network structure proved unfavorable. * A theoretical foundation for empirical decisions about road restoration is provided. Abstract The quick distribution of relief goods is vital in alleviating human suffering in affected areas. A relief strategy that delivers (pushes) goods to the affected population depending on the predicted demand is critical, especially during the early post-disaster period when accurate demand information is lacking. Several inventory-distribution strategies based on multi-echelon humanitarian logistics networks have been previously investigated to facilitate a quick response to demand information. However, natural disasters have raised doubts about the performance of these networks. This paper presents the disaster conditions under which conventional multi-echelon networks are conducive to push-mode strategies that deliver relief goods depending on the predicted demand. Specifically, an approximate solution to the optimal inventory-distribution strategy is analytically derived from a dynamic stochastic optimization problem using deterministic approximation and decomposition. Several numerical validations ensure the optimality and practical applicability of the strategy. The approximate strategy identifies disaster conditions that do not contribute to push-mode strategies in conventional multi-echelon networks and proposes an alternative network structure. Author Affiliation: (a) Department of Civil and Environmental Engineering, Tokyo Institute of Technology, 2-12-1-M1-13 O-okayama, Meguro-ku, Tokyo Japan (b) Graduate School of Information Science, Tohoku University, 6-6-06 Aramaki aza aoba, Aoba-ku, Sendai, Japan * Corresponding author. Article History: Received 10 December 2021; Accepted 25 January 2023 Byline: Riki Kawase [kawase.r.ac@m.titech.ac.jp] (*,a), Takamasa Iryo [iryo@tohoku.ac.jp] (b)

Keywords Location; Hub networks; Service level; Priority queue; Cutting plane method Highlights * We study hub location models with service level constraints for two shipment classes. * Hub network is modeled as spatially distributed priority queues. * Service level of low priority consignments is computed using matrix geometric method. * We present a cutting plane based exact solution method. * Incorporating service levels impact the design of hub network significantly. Abstract In this paper, we study a hub network design problem arising in the context of a third-party logistics (3PL) service provider, which acts as an intermediary between shippers and carriers. A 3PL service provider usually caters to different classes of shipments that require different levels of service, e.g. two-day delivery, next-day delivery etc. We, therefore, study the problem under stochastic demand from two classes of shipments, with one class receiving priority over the other in service at the hubs to maintain the different service levels required by them. To this end, we present two models for designing a capacitated hub network with a service level constraint, defined using the distribution of time spent at hubs, for each shipment class. The models seek to design the hub network at the minimum total cost, which includes the total fixed cost of equipping open hubs with sufficient processing capacity and the variable transportation costs. The network of hubs, given their locations, is thus modeled as spatially distributed priority queues. The resulting model is challenging to solve, for which we propose a cutting plane-based exact solution method. Author Affiliation: (a) Indian Institute of Management, Vastrapur, Ahmedabad, Gujarat 380 015, India (b) Department of Supply Chain and Business Technology Management, John Molson School of Business and CIRRELT, Concordia University, Montreal, QC H3E 2A3, Canada * Corresponding author. Article History: Received 27 December 2021; Accepted 31 January 2023 Byline: Sachin Jayaswal [sachin@iima.ac.in] (a), Navneet Vidyarthi [n.vidyarthi@concordia.ca] (*,b)

Keywords Decision support systems; Dynamic pricing; Forecast orders; Attended home delivery; Demand management Highlights * Incorporating forecast orders without time windows into vehicle routing system to tackle dynamic pricing problem. * A simple-to-implement dynamic opportunity cost approximation for marginal delivery cost and potential revenue loss. * Capturing the influence of new order commitments and facilitating opportunity cost approximation. * Find a cost-effective route map for delivery with forecast orders to guide the delivery system. * Better scheduling for the overall route map and more slot availability for incoming customers. Abstract In this paper, we propose a partially time-windowed dynamic routing approach with forecast orders to tackle the dynamic pricing problem of attended home delivery, one of the challenging problems in last-mile logistics. The purpose of forecast orders is to find a cost-effective route map for delivery with forecast orders to guide the delivery system to accept real orders in potentially better time slots for servicing. Initially, we build and optimise virtual routes using forecast orders without time windows. Upon the arrival of real orders, we replace the forecast orders with incoming real orders to help the delivery system estimate the opportunity cost of making the delivery in each time slot. Dynamic pricing is then used to influence the customer's choices of servicing slots, and based on what the customer selects, the real order replaces the forecast order, and the route map is re-optimised. This strategy leads to better scheduling for the overall route map and more slot availability for incoming customers. Through computational study, we demonstrate the benefit of our approach compared to static pricing and previous dynamic pricing policies, with or without forecast orders. In particular, by employing forecast orders without time windows, we witness a decrease in delivery costs and an increase in the number of accepted orders, leading to higher profits. Author Affiliation: (a) Department of Mathematics, University of Essex, Colchester CO4 3SQ, United Kingdom (b) Ocado Technology, Hatfield AL10 9UL, United Kingdom (c) Department of Computer Science and Electrical Engineering, University of Essex, Colchester CO4 3SQ, United Kingdom * Corresponding author. Article History: Received 30 June 2022; Accepted 13 January 2023 Byline: Mohammad Abdollahi [ma20139@essex.ac.uk] (*,a), Xinan Yang [xyangk@essex.ac.uk] (a), Moncef Ilies Nasri [moncef.nasri@ocado.com] (b), Michael Fairbank [m.fairbank@essex.ac.uk] (c)

Keywords Heuristics; Container terminal; Yard crane scheduling; Integer models; Local search Highlights * Two mathematical models for the yard crane scheduling problem. * Heuristics and local search methods for the yard crane scheduling problem. * Input/Output assignment. * Input/Output congestion is considered. * A benchmark of instances is proposed. * The proposed methods are efficient and effective. Abstract This paper addresses the scheduling of a yard crane in an automated container block, considering its relationships with the other terminal subsystems. Containers coming into the block to be stored have release times, indicating the moment at which they arrive from ships or trucks. Containers being retrieved have due times, indicating when they will be required by other subsystems. The problem can be seen as a pickup routing problem and also as a one-machine scheduling problem. As a starting point, integer linear models were developed for both approaches. Then, to obtain good solutions for large instances, several heuristic algorithms, coupled with a local search, were proposed. An extensive computational study, based on a newly generated benchmark, shows the limits of the exact methods and the quality of the solutions obtained by the heuristics. Author Affiliation: (a) Grupo de Sistemas de Optimización Aplicada, Instituto Tecnológico de Informática, Ciudad Politécnica de la Innovación, Edifico 8G, Acc. B. Universitat Politècnica de València, Camino de Vera s/n, València, 46021, Spain (b) University of Valencia, Department of Statistics and Operations Research, Doctor Moliner 50, Burjassot, 46100, València, Spain * Corresponding author. Article History: Received 1 April 2022; Accepted 25 January 2023 Byline: Eva Vallada [evallada@eio.upv.es] (*,a), Jose Manuel Belenguer (b), Fulgencia Villa (a), Ramon Alvarez-Valdes (b)

Keywords Supply chain management; Manufacturing; Recycled content; Supply uncertainty; Stochastic dynamic programming Highlights * Manufacturers declare recycled content claims that are specific or average. * We study a manufacturer's recycled content claim decision under supply uncertainty. * We develop a multi-period recycled input inventory and production model. * Our model is calibrated to European data from the fiberglass insulation industry. Abstract Manufacturers often declare recycled content claims that are specific (e.g., to each production period) or average (e.g., across many production periods). Such claims are manifested through the choice of certification and generate demand for the manufacturer's recycled content product. However, since the amount of usable recycled input available from local sources is often limited and uncertain, and external (non-local) sources are expensive, a manufacturer needs to balance the demand-side benefits of a recycled content claim with the associated sourcing and inventory costs. In this paper, we develop a multi-period model over a planning horizon, where a manufacturer makes a product's source mix decision (recycled input vs. virgin content) and recycled input carryover decision each period in the presence of a stochastic supply and external sourcing cost. We solve for the manufacturer's optimal recycled content claim decision over a planning horizon and calculate the manufacturer's profit, recycled input, and virgin raw material usage at the optimal claim. Our key contributions are as follows. We show that (i) a shorter (longer) planning horizon may sometimes increase (decrease) the recycled content claim, (ii) demand stimulation for recycled products may sometimes decrease the optimal recycled content claim, and (iii) supply stabilization (i.e., lowering variability in the municipal stream) may decrease the recycled content claim. We provide numerical results for parameters driven by European data from the fiberglass insulation industry and use our analysis to quantify the impact of several European policies relevant to the glasswool insulation industry on a manufacturer's incentive to incorporate recycled content. Author Affiliation: (a) School of Management, State University of New York at Buffalo, 160 Jacobs Management Center, University at Buffalo, Buffalo, NY, USA (b) Department of Operations Management and Strategy, State University of New York at Buffalo, 326G Jacobs Management Center, University at Buffalo, Buffalo, NY, USA (c) Alba Graduate Business School, 6-8 Xenias Street, Athens, Greece * Corresponding author. Article History: Received 22 November 2021; Accepted 7 January 2023 Byline: Ananth V. Iyer (a), Aditya Vedantam [adityave@buffalo.edu] (*,b), Paul Lacourbe (c)

Keywords OR In energy; Multi-stage robust optimization; Dynamic programming; Economic dispatch; Energy storage Highlights * A multi-stage robust optimization method for an economic dispatch problem. * The system involves renewable generation sources and storage units. * Day-ahead plan is computed under nominal conditions to alleviate conservativeness. * The feasibility of real-time operation is guaranteed for the day-ahead plan. * A new approach for preventing simultaneous charge/discharge problem is proposed. Abstract In this paper, an economic dispatch problem for power grids involving renewable generation and storage units is studied. A multi-stage robust optimization algorithm for computing base dispatches and re-dispatches is proposed. Unlike the other approaches in the literature, the proposed method finds an exact solution to the multi-stage robust optimization problem considered when there are no storage devices or storage devices are ideal (i.e. charging/discharging inefficiencies are neglectable). In the presence of non-ideal storage devices, the algorithm can produce upper-bounding approximations. The devised approximation scheme has two distinguishing features. Firstly, the base dispatches and, especially, multi-stage re-dispatch policies computed are guaranteed to be free of simultaneous charges/discharges. Secondly, it produces lower bounds allowing one to assess the quality of solutions. Optimality and convergence properties of the proposed method are proven and it is compared with other alternatives in the literature through numerical experiments. Results show that significant economic benefits can be achieved compared to the other approaches and solutions could be obtained within acceptable time limits for large-scale problems. Author Affiliation: Yildiz Technical University, Control and Automation Engineering Department, Davutpasa Campus, 34320, Istanbul, Turkey Article History: Received 4 August 2022; Accepted 23 January 2023 Byline: Ugur Yildiran [uyildiran@yildiz.edu.tr]

Keywords Scheduling; Seru; On-line operation; Resource conflicts; Competitive ratio Highlights * A proactive waiting strategy is proposed for SPSs with on-line vision. * A seru may proactively wait even it is ready to be physically constructed. * A nested algorithm is designed for on-line seru scheduling with resource conflicts. * For two seru families, the nested algorithm is proved to be 2[rho]-competitive. * For more than two seru families, 7,290,000 instances are randomly generated. Abstract Seru production systems (SPSs) with quick responsiveness and high flexibility have been applied in many Asian electronic enterprises. An SPS can quickly respond to market demands with changing varieties and fluctuated volumes. Resource conflicts are used to indicate the requirements for a same worker by more than one seru. This paper studies the on-line seru scheduling problem with resource conflicts in limited work space, which decides serus' built-up sequence over time and aims at minimizing the makespan. To keep a balance between quick responsiveness and production efficiency in an SPS, a proactive waiting strategy is proposed and a nested algorithm is structured with an external framework and an internal sequencing policy. The external framework handles resource conflicts among serus and divides serus into groups in which resource conflicts do not exist. The internal sequencing policy acts on each group and decides the built-up sequence for serus. The nested algorithm is proved to be 2[rho]-competitive with two seru families. Performance of the nested algorithm can be enhanced by improving the internal sequencing policy without changing the external framework, which is conformed by computational experiments. With more seru families, experimental results show that the nested-LPT algorithm can adapt to frequent arrivals of orders, complicated resource conflicts, and fluctuating market demands. Author Affiliation: (a) School of Computer Science, Beijing Institute of Technology, Beijing, China (b) Southeast Academy of Information Technology, Beijing Institute of Technology, Putian, China (c) School of Automation, Beijing Institute of Technology, Beijing, China (d) Guangdong Shenling Environmental Systems Co., Ltd., Foshan, China (e) Graduate School of Business, Doshisha University, Kyoto, Japan * Corresponding author. Article History: Received 30 March 2022; Accepted 13 January 2023 Byline: Dongni Li [ldn@bit.edu.cn] (*,a,b), Yuzhou Jiang [jiang_yuzhou@163.com] (a), Jinhui Zhang [zhangjinh@bit.edu.cn] (c), Zihua Cui [zihua.cui@shenling.com] (d), Yong Yin [yyin@mail.doshisha.ac.jp] (e)

Keywords Dynamic programming; Ensemble models; Value function approximation Highlights * Ensemble learning strategies for value function approximation in dynamic programming. * Optimized output aggregation improves the dynamic programming performance. * Use of ensembles of models for the active selection of state sampling points. * Simulation experiments show the advantages of ensembles in dynamic programming. Abstract Approximate dynamic programming (ADP) is the standard tool for the solution of multistage dynamic optimization problems under general conditions, such as nonlinear state equation and cost, and continuous state and control spaces. In the typical ADP implementation, the value function is approximated by means of a single model trained over a suitable sampling of the state space. In this paper we investigate the ensemble learning paradigm in the ADP context, which consists in exploiting the outputs of many models trained for the value function approximation. To this purpose, we introduce an optimization scheme for the aggregation of the ensemble outputs, related to the supremum norm error on which the ADP accuracy depends. Furthermore, we show that the ensemble of value function approximations can be used to identify a-priori good state points used to train the approximating models, exploiting an ambiguity-like term tailored to the proposed ensemble optimization scheme. The advantages of ensembles in ADP are showcased both through error analysis and a simulation campaign involving various test problems. Our results show how ensembles obtained through the proposed output weights optimization scheme yield more accurate and robust value function approximations with respect to single elements. At the same time, we show how the ensembles can successfully be employed to select good state samples to be employed as training set for the value function approximations. Author Affiliation: Institute of Marine Engineering, National Research Council of Italy, Via De Marini 6, Genova, 16149, Italy Article History: Received 30 August 2022; Accepted 25 January 2023 Byline: Cristiano Cervellera [cristiano.cervellera@cnr.it]

Keywords Travelling salesman; Integer programming; Branch-and-cut algorithm; Facet-inducing inequalities; Polyhedral structure Highlights * We address the Precedence Constrained Generalized Traveling Salesman Problem (PCGTSP). * We propose novel families of valid inequalities for the PCGTSP. * We prove the first facet-inducing inequalities for the PCGTSP. * We introduce MILP-models (formulations) and Branch-and-Cut algorithm for the PCGTSP. * We prove high performance of proposed models and algorithm by numerical evaluation. Abstract The Precedence Constrained Generalized Traveling Salesman Problem (PCGTSP) is an extension of two well-known combinatorial optimization problems -- the Generalized Traveling Salesman Problem (GTSP) and the Precedence Constrained Asymmetric Traveling Salesman Problem (PCATSP), whose path version is known as the Sequential Ordering Problem (SOP). Similarly to the classic GTSP, the goal of the PCGTSP, for a given input digraph and partition of its node set into clusters, is to find a minimum cost cyclic route (tour) visiting each cluster in a single node. In addition, as in the PCATSP, feasible tours are restricted to visit the clusters with respect to the given partial order. Unlike the GTSP and SOP, to the best of our knowledge, the PCGTSP still remain to be weakly studied both in terms of polyhedral theory and algorithms. In this paper, for the first time for the PCGTSP, we propose several families of valid inequalities, establish dimension of the PCGTS polytope and prove sufficient conditions ensuring that the extended Balas' [pi]- and [sigma]-inequalities become facet-inducing. Relying on these theoretical results and evolving the state-of-the-art algorithmic approaches for the PCATSP and SOP, we introduce a family of MILP-models (formulations) and several variants of the branch-and-cut algorithm for the PCGTSP. We prove their high performance in a competitive numerical evaluation against the public benchmark library PCGTSPLIB, a known adaptation of the classic SOPLIB to the problem in question. Author Affiliation: (a) Kedge Business School, 680 Cours de la Liberation, Talence, France (b) Inria Centre at the University of Bordeaux, 200 avenue de la Vieille Tour, Talence, France (c) Ivannikov Institute for System Programming of RAS, Moscow, Russia * Corresponding author. Article History: Received 18 April 2022; Accepted 21 January 2023 (footnote)[white star] Polyhedral study, formulations, and Branch-and-Cut for PCGTSP. Byline: Daniil Khachai [daniil.khachai@kedgebs.com] (*,a), Ruslan Sadykov [ruslan.sadykov@inria.fr] (b), Olga Battaia [olga.battaia@kedgebs.com] (a), Michael Khachay [mkhachay@imm.uran.ru] (c)

Keywords Pricing; Return policy; Wardrobing; Store credit and gift card refund Highlights * We develop a model for optimizing price, quantity, and terms of the return policy. * We incorporate consumer opportunistic behavior (wardrobing) in a newsvendor setting. * We examine the implications of using store credit or gift card (SC/GC) refunds. * SC/GC refunds increase the profits of the e-tailer. * SC/GC refunds benefit consumers by increasing quantity available and lowering price. Abstract Product return rates are much higher for online retailers than brick-and-mortar retailers. In this paper, we develop a model for optimizing the retailer's price, quantity, and terms of the product return policy in the presence of consumer opportunistic behavior (wardrobing). We consider a seasonal product and formulate and solve a newsvendor (e-tailer) model where an in-season price markdown may be used before any remaining inventory is salvaged at the end of the season. We compare a return policy in which refunds for returned products are in cash to one where refunds are in store credit or gift cards (SC/GC). Products sold at the clearance (salvage) price cannot be returned or exchanged. We find that with both cash and SC/GC refunds, consumer product match uncertainty, measured by the probability of the product matching consumers' expectations, benefits consumers who keep the product more than consumers who return it. Moreover, under realistic conditions, using SC/GC refund increases the profits of the e-tailer and benefits consumers who keep the product by lowering the price. We also find that even when consumers redeem all SC/GC, using SC/GC refunds can still be beneficial to the retailer because SC/GC refunds deter proportionally more wardrobers from buying than consumers from buying. As a result, costly returns are reduced and the e-tailer benefits. Author Affiliation: (a) The Belk College of Business, The University of North Carolina at Charlotte, Charlotte 28213, USA (b) Rennes School of Business, 2 Rue Robert d'Arbrissel, Rennes 35065, France * Corresponding author. Article History: Received 21 December 2021; Accepted 19 January 2023 Byline: Moutaz Khouja [mjkhouja@uncc.edu] (*,a), Ramzi Hammami [ramzi.hammami@rennes-sb.com] (b)

Keywords Reactive scheduling; Uncertainty modelling; Surgery management; Approximate dynamic programming Highlights * A real-time surgery management problem is studied. * We formulate the problem using stochastic dynamic programming. * Uncertain non-elective arrivals and surgery durations are modelled. * An approximate dynamic programming approach is introduced. Abstract Real-time surgery management involves a complex and dynamic decision-making process. The duration of surgeries in many cases cannot be known until the surgery has actually been completed. Furthermore, disruptions such as equipment failure or the arrival of a non-elective surgery can occur simultaneously. Thus, the assignment of surgeries needs to be updated, as and when disruptions occur, to minimize their effects. In this paper, we present a stochastic dynamic programming approach to the surgery allocation problem with multiple operating rooms under uncertainty. Given an elective list for the day, the dynamic optimization model minimizes the number of surgeries not carried out by the end of the shift and the total waiting times of patients during the day weighted according to their urgency level. Due to the curse of dimensionality, we apply an approximate dynamic programming algorithm to solve the stochastic dynamic surgery management model. Computational experiments are designed to demonstrate the performance of the proposed algorithm and its applicability to practical settings. The results show that the approximate dynamic programming algorithm provides a good approximation to the optimum policy and leads to some managerial insights. Author Affiliation: (a) School of Management, University of Bath, Bath BA2 7AY, UK (b) Warwick Business School, The University of Warwick, Coventry CV4 7AL, UK * Corresponding author. Article History: Received 18 May 2021; Accepted 2 December 2022 Byline: E. Gökalp [elvan.gokalp@warwick.ac.uk] (a), N. Gülpinar [Nalan.Gulpinar@wbs.ac.uk] (*,b), X.V. Doan [Xuan.Doan@wbs.ac.uk] (b)

Keywords OR in government; Local government performance; Balanced budget rules; Cost efficiency; Difference-in-differences Abstract During the past few decades, countries have experienced a remarkable increase in local expenditure levels to address rising local needs. However, the limited availability of financial resources, exacerbated first by the 2008 financial crisis and then by Covid19 crisis, has called for budget restrictions usually imposed by higher levels of government. In this paper, we evaluate the impact of a balance budget rule enforcement, exploring its effect on the local government cost efficiency and, in particular, considering the complex trade-off between efficiency and equity. Specifically, our identification strategy considers the exogenous introduction of a new budget balance rule that requires local governments to respect both an annual and a longer-term equilibrium criterion. The difference-in-differences analysis builds on a rich panel dataset covering all the public functions. We find that, on average, the budget rule enforcement exerted a positive effect on local government efficiency. Author Affiliation: (a) Department of Economics and Management, University of Pisa, Via Ridolfi 10, Pisa 56124, Italy (b) Faculty of Economics and Business, KU Leuven, Naamsestraat 69, Leuven 3000, Belgium (c) Faculty of Economics, University of Urbino 'Carlo Bo', Via Saffi 42, Urbino 61029, Italy (d) UNU-Merit, Maastricht University, Boschstraat 24, AX Maastricht 6211, The Netherlands * Corresponding author. Article History: Received 22 February 2022; Accepted 18 January 2023 Byline: Giovanna D'Inverno [giovanna.dinverno@unipi.it] (*,a,b), Francesco Vidoli [francesco.vidoli@uniurb.it] (c), Kristof De Witte [kristof.dewitte@kuleuven.be] (b,d)

Keywords Inventory; Lot-sizing; Systematic literature review; Variable production rate; Controllable production rate Highlights * Presents a systematic review of inventory models with controllable production rates. * Develops a conceptual framework to facilitate structuring research. * Classifies and discusses the sampled works in light of the conceptual framework. * Controlling production rates may lower cost and make the system more robust against stockouts. * Only a few works considered flexible control and inventory systems with a complex structure. Abstract The lot sizing problem has attracted the attention of researchers for more than a century, and it still belongs to the most relevant decision problems in many manufacturing companies. During the evolution of research on lot sizing, the seminal economic order quantity (EOQ) model proposed by Harris [1913. How many parts to make at once. Factory, the Magazine of Management, 10 (2), 135-136.] has remained the most popular model, despite its limitations. To support lot sizing decisions in practice, researchers have frequently extended Harris' basic EOQ model to better reflect the characteristics of real production processes. One of these extensions is the consideration of controllable (variable) production rates, which gives production planners more flexibility in managing the build-up and depletion of inventory and in controlling costs. The aim of this paper is to provide a comprehensive and systematic overview of EPQ-type lot sizing models that consider controllable production rates. First, the paper proposes a conceptual framework that captures the characteristics of controllable production rates including the planning horizon (short vs. long term), the number of potential interventions per production run (one vs. multiple), the effect of controllable production rates on the performance of the inventory system (e.g., unit production costs, energy consumption, product quality), and the type of lot sizing model considered (e.g., two-stage models, multi-stage models, multi-item models). Secondly, the paper presents the results of a systematic literature review and evaluates the state-of-research of lot sizing models with controllable production rates. Based on the analysis of the literature, key trends are summarized and promising research opportunities are discussed. Author Affiliation: (a) Institute of Production and Supply Chain Management, Technical University of Darmstadt, Hochschulstr. 1, 64289 Darmstadt, Germany (b) Juniorprofessorship of Digital Transformation in Operations Management, Saarland University, 66041 Saarbrucken, Germany * Corresponding author: Christoph H. Glock Article History: Received 26 April 2019; Accepted 17 May 2020 Byline: Christoph H. Glock [glock@pscm.tu-darmstadt.de] (*,a), Eric H. Grosse [grosse@pscm.tu-darmstadt.de] (a,b)

Keywords Analytics; Employee online reviews; Topic modeling; Big data; Decision processes Highlights * We study the informational value of structured and unstructured big data. * Online employee review narratives offer benefits in unveiling satisfaction factors. * Our analysis lends support to the existence of 'motivators' and 'hygienes' factors. * We show information gains in predicting employee turnover and firm performance. * We provide managerial implications of coupling numerical and textual information. Abstract This paper investigates the informational value of online reviews posted by employees for their employer, a rather untapped source of online information from employees, using a sample of 349,550 reviews from 40,915 UK firms. We explore this novel form of electronic Word-of-Mouth (e-WOM) from different perspectives, namely: (i) its information content as a tool to identify the drivers of job satisfaction/dissatisfaction, (ii) its predictive ability on firm financial performance and (iii) its operational and managerial value. Our approach considers both the rating score as well as the review text through a probabilistic topic modeling method, providing also a roadmap to quantify and exploit employee big data analytics. The novelty of this study lies in the coupling of structured and unstructured data for deriving managerial insights through a battery of econometric, financial and operational research methodologies. Our empirical analyses reveal that employee online reviews have informational value and incremental predictability gains for a firm's internal and external stakeholders. The results indicate that when models integrate structured and unstructured big data there are leveraged opportunities for firms and managers to enhance the informativeness of decision support systems and in turn, gain competitive advantage. Author Affiliation: (a) Leeds University Business School, University of Leeds, LS2 9JT Leeds, United Kingdom (b) Norwich Business School, University of East Anglia, Norwich Research Park, NR4 7TJ Norwich, United Kingdom (c) MBS College of Business & Entrepreneurship, King Abdullah Economic City 23965-2609, Kingdom of Saudi Arabia * Corresponding author. Article History: Received 31 December 2018; Accepted 2 June 2020 (footnote) We thank the Associate Editor, Robert Dyson, and three anonymous referees for their comments. We are also grateful to Glassdoor's data science team for providing us the data used in this study. An earlier part of this paper was circulated as 'Employee Satisfaction and Corporate Performance in the UK'. Byline: Efthymia Symitsi (a), Panagiotis Stamolampros [p.stamolampros@leeds.ac.uk] (a,*), George Daskalakis (b,c), Nikolaos Korfiatis (b)

Highlights * In participatory too many projects generates choice overload. * An utilitarian or a Rawlsian approach can be adopted in selecting projects. * The costs and benifits of projects should be included in the voting stage. Abstract In participatory budgeting, citizens are invited to vote on different projects. This paper sets out to study the voting stage of participatory budgeting from theoretical and practical perspectives. At theoretical level, the potential objectives of voting procedures are examined. From the practical point of view it should be easy to select projects, even when the number of projects is large. This paper proposes three algorithms which are theoretically justified and can be easily applied. Participatory budgeting in the town of Portugalete (Spain) provides a test for them. The results obtained with 2018 voting data suggest that the algorithm that includes the costs of the projects at the selection stage performs better than the others. KEYWORDS: decision processes; participatory budgeting; collective decision-making; cost-benefit analysis. Author Affiliation: (a) BRiDGE, Foundation of Economic Analysis I, University of the Basque Country (UPV/EHU) Avenida Lehendakari Aguirre, 83, Bilbao E-48015, Spain (b) IKERBASQUE, Basque Foundation of Science, Bilbao 48011, Spain * Correspondence to: BRiDGE, Foundation of Economic Analysis I, University of the Basque Country (UPV/EHU) Avenida Lehendakari Aguirre, 83, Bilbao E-48015, Spain. Article History: Received 3 May 2019; Revised 25 March 2020; Accepted 31 May 2020 (footnote)[white star] The author is grateful to the three anonymous referees for their helpful comments and suggestions. She acknowledges the support of the Departamento de Educacion, Politica Linguistica y Cultura from the Basque Government (Research Group IT1367-19). She is grateful to the city of Portugalete and its mayor, M. Torres for providing the data file. She benefitted from discussion with the staff at city hall, in particular R. Parreno. Byline: Annick Laruelle [annick.laruelle@ehu.eus] (*,a,b)