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Keywords Data envelopment analysis; Capacity utilization; Iron and steel enterprises; Sustainable development Highlights * A novel indicator is defined for measuring CU of iron and steel enterprises. * An unrestricted DDF considering directly linked good and bad outputs is used. * We define the status of DMUs having excess capacity. * We propose the policy suggestions based on empirical findings. Abstract This paper aims to investigate the capacity utilization (CU) of selected large Chinese iron and steel enterprises by using the data envelopment analysis (DEA) approach. In this paper, we first propose a DEA model with an unrestricted capacity directional output distance function by incorporating two joint cost disposability relations and differentiating between non-emission-causing inputs and emission-causing inputs. Second, we define the status of decision-making units (DMUs) with excess capacity and design a method to identify insufficient variable inputs. We use a group of 48 largest Chinese iron and steel enterprises as our sample to investigate their performance in terms of CU indicator. The main findings are summarised and can be used to support the corresponding policymaking for the managers of both government and enterprises. Author Affiliation: (a) Faculty of Commerce, Fukuoka University, Fukuoka 814-0180, Japan (b) Institutes of Science and Development, Chinese Academy of Sciences, P.O. Box 8712, Beijing 100190, China (c) University of Chinese Academy of Sciences, Beijing 100049, China (d) School of Economics, Capital University of Economics and Business, Beijing 100070, China * Corresponding author at: Institutes of Science and Development, Chinese Academy of Sciences, P.O. Box 8712, Beijing 100190, China. Article History: Received 28 June 2019; Accepted 10 June 2020 Byline: Hirofumi Fukuyama (a), Hui-hui Liu (b,c), Yao-yao Song (d), Guo-liang Yang [glyang@casipm.ac.cn] (b,c,*)

Keywords Logistics; First mile; Dual channel; Customers loyalty; Game theory Highlights * Suggest a new business model with a common platform for first-mile logistics. * Investigate strategic decisions of two courier companies under competition. * Adopt a game theoretical approach with analytical analysis. * Present joint effects of internal competition and cost savings on profitability. Abstract Booming parcel shipping business has raised wide attention. The trunk line is easier to manage, since parcels are consolidated to fully utilize vehicle capacities. However, the first mile handled by individual companies is beset by high empty haul rates and low efficiency. This paper takes the lead in exploring a new business model and possibilities of establishing a common service platform for the first mile. We investigate strategic decisions of two courier logistics companies competing with partially substitutable collection service. They can choose to cooperate with the cost-efficient platform to enjoy a saving, while the market share of their original channel has to be eroded by the outsourcing channel. We propose three scenarios: (i) Both companies have only one direct channel. (ii) Only one company has two channels. (iii) Both companies have two. This paper comprehensively presents the joint effect of internal competition and the cost saving on companies' welfare and customers' consuming experience. The findings show that in most cases, cooperating with the platform will help the companies gain a profit increment and customers will benefit from a price decrease compared to the current practice. However, when customers loyalty to the direct channel is moderate, they will suffer a price increase, though companies are better off. Additionally, given the cost advantage of outsourcing is slighter, and customers show less difference to two channels, the internal competition will be the most intensified and overtake the effect of cost sparing. The company running two channels will suffer a profit loss. Author Affiliation: Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong * Corresponding author. Article History: Received 6 January 2020; Accepted 25 May 2020 Byline: Xin Wang [mandywx@hku.hk], George Q. Huang [gqhuang@hku.hk] (*)

Keywords heterogeneous quasi-hyperbolic preferences; life insurance; time-consistency Highlights * A framework accommodates present bias and heterogeneity. * A Hamilton--Jacobi--Bellman equation is derived. * Discount function & Hamilton--Jacobi--Bellman equation reduce to those in many papers. * Sophisticated, precommitted and naive paradigms are solved under three utilities. * Graphical description produces novel results. Abstract This paper establishes a reusable framework, including a stochastic heterogeneous quasi-hyperbolic (SHQH) discount function, its non-standard Hamilton--Jacobi--Bellman equation (HJB) and its naive and precommitted solutions. To gurantee the broad generalities of the framework, we adopt a game theoretic approach in the sense of refraining from imposing functional specifications. This framework is the first which attains sub-game equilibrium in the presence of heterogeneous preferences (e.g. coexistence of present bias and age-related increases in self-control). As an example, this framework is used to optimize an insurance policy-holder's asset allocation. The results show: (i) the sophisticated paradigm (formulated via the SHQH HJB) yields higher life insurance investment than the precommitted and naive paradigms (formulated via conventional optimization); (ii) the very instantaneous gratification that composes the resistance to delayed rewards also necessitates the insurance consumption to circumvent this resistance. Author Affiliation: (a) School of Management, Guizhou University, China (b) Department of Mathematics, University of Connecticut, USA (c) Institution of Mathematics, Goethe Universitat Frankfurt am Main, Germany * Corresponding author. Article History: Received 17 December 2018; Revised 28 May 2020; Accepted 29 May 2020 Byline: Ling Peng [anny@whu.edu.cn] (*,a,b), Peter E. Kloeden (c)

Keywords Routing; Time windows; Time-dependent travel times; Road network; Tabu search Highlights * A vehicle routing problem with time-dependent travel times is addressed. * Time dependency is accounted for in each road segment of a network. * The path to go from one customer to the next in the road network is time-dependent. * A tabu search is proposed to solve the problem. * A dominant shortest path structure is used to speed up the computations. Abstract Travel times inside cities often vary quite a lot during a day and significantly impact the duration of commercial delivery routes. Several authors have suggested time-dependent variants of the most commonly encountered vehicle routing problems. In these papers, however, time-dependency is usually defined on customer-based graphs. Thus, one major impact of travel time variations is missed: in an urban environment, not only do travel times change, but also the paths used to travel from one customer to another. In fact, during a day, different paths may be used at different points in time. To address this issue, one possible approach is to work directly with the road network and consider travel time (or travel speed) variations on each road segment. In this paper, we propose a solution approach for a time-dependent vehicle routing problem with time windows in which travel speeds are associated with road segments in the road network. This solution approach involves a tabu search heuristic that considers different shortest paths between any two customers at different times of the day. A major contribution of this work is the development of techniques to evaluate the feasibility and the approximate cost of a solution in constant time, which allows the solution approach to handle problem instances with up to 200 nodes and 580 arcs in very reasonable computing times. The performance of our algorithm is assessed by comparing it to an exact method on a set of benchmark instances. The results show that solutions of high quality are produced. Author Affiliation: (a) Departement de mathematiques et de genie industriel, Polytechnique Montreal, C.P. 6079, succ. Centre-Ville, Montreal H3C 3A7, Quebec, Canada (b) Departement d'informatique et de recherche operationnelle, Universite de Montreal, C.P. 6128, succ. Centre-Ville, Montreal H3C 3J7, Quebec, Canada (c) Chaire d'excellence en recherche du Canada sur la science des donnees pour la prise de dcision en temps reel, Polytechnique Montreal, C.P. 6079, succ. Centre-Ville, Montreal H3C 3A7, Quebec, Canada (d) Centre interuniversitaire de recherche sur les reseaux d'entreprise, la logistique et le transport, Universite de Montreal, C.P. 6128, succ. Centre-Ville, Montreal H3C 3J7, Quebec, Canada * Corresponding author at: Departement d'informatique et de recherche operationnelle Universite de Montreal, C.P. 6128, succ. Centre-Ville, Montreal H3C 3J7, Quebec, Canada. Article History: Received 13 January 2020; Revised 4 May 2020; Accepted 20 May 2020 Byline: Maha Gmira (a,c,d), Michel Gendreau (a,d), Andrea Lodi (a,c,d), Jean-Yves Potvin [potvin@iro.umontreal.ca] (*,b,d)

Keywords Portfolio selection model; Trapezoidal fuzzy number; Possibilistic covariance Highlights * The corrections of all the mistakes in papers should be published. * The mistake has brought no problems in the considered paper and/or in other ones. * The mistake can put in difficulties researchers developing new models based on it. * References have been updated. * Careful revision of the paper, correcting typos and inaccuracies. Abstract In a paper published in this journal -- Zhang W.-G., Zhang X.-L., Xiao W.-L. (2009), Portfolio selection under possibilistic mean-variance utility and a SMO algorithm. European Journal of Operational Research, 197(2), 693-700 --, the Authors investigate a fuzzy approach to the portfolio selection problem in which the stock returns are represented in terms of trapezoidal fuzzy numbers. In this note, we show that the expression provided for the possibilistic covariance is not consistent with the definition of possibilistic covariance given in the paper itself, and we derive the right expression for such a covariance. Author Affiliation: Department of Economics, Ca' Foscari University of Venice, Sestiere Cannaregio 873, 30121 Venice, Italy * Corresponding author. Article History: Received 18 June 2018; Revised 9 April 2020; Accepted 18 May 2020 Byline: Marco Corazza [corazza@unive.it] (*)

Key Words DEA; Balanced Benchmarking; Disadvantaged Decision-Making Units; Fairness in Performance Evaluation Highlights * Conventional DEA assumes homogeneity of DMUs * The current paper considers DEA in the presence of identifiable groups of DMUs * We consider case of two groups of automobiles, conventional and hybrid drives * Differing average scores in the groups point to need for an adjustment methodology * A DEA type model is presented for creating a level playing field for the two groups Abstract Balancing fairness and efficiency has become an emerging issue in today's society. In this paper we propose a balanced benchmarking methodology to address the fairness issue in performance evaluation. The methodology used to create performance measures is data envelopment analysis (DEA), a tool designed to evaluate the relative efficiencies of comparable decision-making units (DMUs); i.e. all DMUs use the same inputs and outputs and experience the same general operating conditions. In many applications, however, the DMUs may experience non-homogenous operating conditions or environments. An example might be a set of manufacturing plants where some have been upgraded and others not. Such settings can necessitate modifying the DEA structure such as to make allowance for different environmental conditions. Such a model is developed herein to create a level playing field for performance evaluation in two different settings: a setting involving hybrid and conventional (non-hybrid) vehicles; and another setting involving bank branches located in poverty and non-poverty regions. Our model and empirical tests contribute not only to the advance of balanced benchmarking methodologies, but also to the practice of incorporating fairness in performance evaluation across multiple products and organizations. Author Affiliation: (a) College of Management, National Taiwan University, Taipei 10617, Taiwan, ROC (b) Schulich School of Business, York University, Toronto, Ontario, Canada, M3J 1P3 (c) School of Business, Harvard University, Soldiers Field, Boston, Massachusetts, USA (d) School of Business, Worcester Polytechnic Institute, Worcester, Massachusetts, USA, 01609 * Corresponding author. Article History: Received 12 April 2019; Accepted 11 May 2020 Byline: Chailin Chen [cchen026@ntu.edu.tw] (a), Wade D. Cook [wcook@schulich.yorku.ca] (b,*), Raha Imanirad [rimanirad@hbs.edu] (c), Joe Zhu [jzhu@wpi.edu] (d)

Keywords OR in environment and climate change; Extended Producer Responsibility (EPR); Recycling coalition; Farsighted stability; product heterogeneity Highlights * Adoption of Extended Producer Responsibility (EPR) increases social welfare when the recycling task is more challenging. * Under EPR, IPR should be chosen when the fixed recycling cost is low and/or products are heterogeneous, and CPR otherwise. * Without EPR, the government should tax the producers to improve the environmental impact by reducing the product quantity. Abstract A recently emerging concept, Extended Producer Responsibility (EPR), is being adopted by the government in more and more countries and regions. It shifts the burden of proper disposal of end-of-life consumer products from (local) governments to the producers that bring the products to the market. To comply with governments' EPR-type legislation, producers form coalitions to have their products recycled in a more efficient way. In this paper, we study how two important determinants of recycling costs, fixed recycling costs and material-stream heterogeneity, influence producers' recycling network (structure of producers' recycling coalitions). On one hand, large fixed recycling costs make the coalitions typically larger, due to the economies of scale. On the other hand, large coalitions generate typically more heterogeneous material streams, which increase the variable recycling costs due to additional separation and disassembly efforts. This paper discusses two currently existing scenarios: one exists prior to EPR-type legislation (referred to as the Disparate Problem, or DP) and the other is motivated by EPR-type legislation (referred to as the Endogenous Problem, or EP). In DP, the recycling of end-of-life products is not the responsibility of any producer but the government. Therefore, the recycling network is determined to minimize the total recycling cost, which also maximizes the social welfare, while the outputs are determined by producers without concerns about recycling. In EP, producers who compete in a horizontally differentiated primary market may also collaborate at the same time to organize proper disposal of their products. As each producer is only interested in maximizing its own payoff and there may exist conflict of interests, we use the game-theoretical methodology to analyze the endogenous process of coalition-formation. We find structural differences in these two scenarios and conclude by discussing implications for social welfare of imposing tax or subsidy. Author Affiliation: (a) Business Administration Division, Pepperdine University, Malibu, CA 90263, United States (b) Marshall School of Business, University of Southern California, Los Angeles, CA 90089, United States (c) Tuck School of Business, Dartmouth College, Hanover, NH 03755, United States * Corresponding author. Article History: Received 4 September 2019; Revised 29 April 2020; Accepted 1 May 2020 Byline: Fang Tian [Fang.Tian@pepperdine.edu] (a), Greys Sosic [sosic@marshall.usc.edu] (*,b), Laurens Debo [Laurens.G.Debo@tuck.dartmouth.edu] (c)

Keywords Global Optimization; Simulation-Optimization; Two-Stage Sequential Optimization; eTSSO; Convergence Highlights * eTSSO solves optimization problems extending the previous TSSO algorithm eliminating the need to select the budget per iteration. * A theoretical adaptive budget allocation is provided. * Four algorithmic variants were implemented. * eTSSO asymptotic properties are theoretically analyzed. * eTSSO in its implementations improves on TSSO under different classes of functions, noise levels and total budget. Abstract This paper looks into nonlinear non convex stochastic unconstrained optimization with finite simulation budget. Our work builds upon the Two-Stage Sequential Optimization (TSSO) algorithm that addresses the class of problems of interest by using the modified nugget effect kriging (MNEK) meta-model and proposing a budget allocation followed by a two-stage sequential procedure. Despite its efficiency and performance, we have observed that, given a finite budget, the choice of the number of replications per iteration, currently left to the user, is particularly critical for the algorithm performance. A fixed a-priori assignment can affect the ability to control the algorithm making it particularly sensitive to the initial settings. In this paper, we propose the extended TSSO (eTSSO). Specifically, a general simulation budget allocation scheme is proposed with the objective to balance the need of accurate function estimations to improve the selection in the search stage, with the need to explore the solution space. The new scheme adaptively, and recursively, increases the simulation budget based upon information iteratively returned by the optimizer itself. We analyze the asymptotic properties of eTSSO. Subsequently, we propose four alternative variants of the general allocation that we empirically analyze by comparing the quality of the estimated optimum input combination and the corresponding estimated optimum output against TSSO and other state of the art algorithms. Author Affiliation: (a) School of Computing, Informatics, and Decision Systems Engineering Arizona State University, United States (b) Department of Industrial Systems Engineering and Management, National University of Singapore, Singapore * Corresponding author. Article History: Received 18 October 2018; Revised 20 April 2020; Accepted 23 April 2020 Byline: Giulia Pedrielli (*,a), Songhao Wang (b), Szu Hui Ng (b)

Keywords Stochastic programming; Progressive hedging; Parallel computing; Maintenance scheduling; Natural gas trading Highlights * An integration of generation maintenance scheduling and natural gas trading. * A multistage decision-making process considering uncertain natural gas and electricity prices. * The implementation of progressive hedging algorithm with parallel computing technique in a distributed HPC system. * Studied the impact of natural gas generator assets and natural gas contract clauses on the optimal profit for GENCOs. Abstract A preventive maintenance scheduling problem is studied on behalf of generation companies (GENCOs) with natural gas power plants, while taking into account their signed natural gas contracts and the opportunities of purchasing and selling natural gas in the spot market. This paper considers the uncertain prices of both natural gas and electricity in the spot market, and proposes a multistage stochastic mixed integer programming (MSMIP) model seeking the optimal operations regarding maintenance outage scheduling and natural gas trading. Large-scale MSMIP problems suffer not only the curse of dimensionality, but also computational difficulties with both discrete and continuous variables at each stage. To this respect, this paper leverages the progressive hedging algorithm based on scenario-based decomposition to solve large MSMIP problems. The solutions obtained from the algorithm exhibit promising quality under our numerical studies. Due to the independence among all the subproblems after the decomposition, the algorithm is amenable to parallel computing, which leads to faster convergence as demonstrated in the numerical results. Computational experiments also show that it is beneficial to use MSMIP while considering both maintenance planning and natural gas contracting. In addition, the results also indicate the GENCOs with a larger number of small generators perform better than those with a smaller number of big generators. Author Affiliation: (a) College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China (b) Department of Industrial Engineering and Management Systems, University of Central Florida, Orland, FL, USA * Corresponding author. Article History: Received 7 April 2019; Revised 11 March 2020; Accepted 11 March 2020 Byline: Zhouchun Huang (a), Qipeng Phil Zheng [qipeng.zheng@ucf.edu] (*,b)

Keywords Productivity and competitiveness; Endogeneity; Technical efficiency; Allocative efficiency; Incidental parameters problem Highlights * Alternative formulations and estimation of technical and allocative inefficiency. * Panel stochastic frontier model which includes time-invariant firm effects (heterogeneity). * Flexibility in estimating the model parameters as well as both technical and allocative inefficiency and costs therefrom. * The incidental parameter problem can be avoided by parameterizing in terms of exogenous variables. Abstract In this paper we propose some alternative formulations and estimation of technical and allocative inefficiency in the presence of some exogenous variables in the context of a panel stochastic frontier model which includes time-invariant firm effects (heterogeneity) along with time-varying technical inefficiency and random noise. These exogenous variables are used to explain technical and allocative inefficiency as well as firm heterogeneity. The presence of these exogenous variables allows us to relax some of the assumptions made in a recent paper by . These variables also allow to add flexibility in estimating the model parameters as well as both technical and allocative inefficiency and costs therefrom. More specifically, the incidental parameters problem associated with firm heterogeneity in the production function as well in the first-order conditions of cost minimization can be avoided by parameterizing them in terms of the exogenous variables. We propose and implement model comparison based on Bayes factors and marginal likelihood. Author Affiliation: (a) State University of New York, Binghamton, NY 13902, USA (b) Lancaster University Management School, LA1 4YX, UK * Corresponding author. Article History: Received 4 June 2019; Revised 28 January 2020; Accepted 23 April 2020 Byline: Subal C. Kumbhakar [kkar@binghamton.edu] (a), Mike G. Tsionas [m.tsionas@lancaster.ac.uk] (*,b)

Keywords Conditional autoregressive model; Survival model; Spatial contagion; Mortgage defaults Highlights * This paper proposes a spatial discrete survival model to estimate time to default. * The model includes a flexible parametric link function and a dynamic spatially varying baseline hazard function to capture neighbourhood. * We incorporate time and space varying variables into the model. * We analyse 74,081 mortgage loans issued in England and Wales from 2006 to 2015. * We improve the forecasting accuracy of a spatial-temporal probit. Abstract This paper proposes a spatial discrete survival model to estimate the time to default for UK mortgages. The model includes a flexible parametric link function given by the Generalised Extreme Value Distribution and a dynamic spatially varying baseline hazard function to capture neighbourhood effects over time. We incorporate time and space varying variables into the model. The gains of the proposed model are illustrated through the analysis of a dataset on around 74,000 mortgage loans issued in England and Wales from 2006 to 2015. Author Affiliation: Credit Research Centre, University of Edinburgh Business School, United Kingdom * Corresponding author. Article History: Received 19 February 2019; Revised 16 April 2020; Accepted 17 April 2020 Byline: Raffaella Calabrese [raffaella.calabrese@ed.ac.uk] (*), Jonathan Crook [jonathan.crook@ed.ac.uk]

Keywords E-commerce; Attended home delivery; Revenue management; Dynamic pricing; Approximate dynamic programming Highlights * Attended home delivery describes the delivery of goods to customers. * E-grocers try to influence customers' bookings by dynamic pricing. * We solve this optimization problem with approximate dynamic programming. * Our approach learns value function approximations using route-based features. * It is evaluated and compared to state-of-the-art methods and industry practices. Abstract Attended home delivery describes the delivery of goods by e-grocers or e-tailers to customers within an agreed time window. Because customers expect narrow time windows, offering such services may lead to expensive fulfillment operations. This has led to research on how to influence customers' bookings using time window pricing or slotting. In this paper, we reconsider the problem of demand management through dynamic pricing for attended home delivery services. This problem is usually modeled as a stochastic dynamic program, but even small instances cannot be solved to optimality due to the curses of dimensionality. The major challenges consist of finding feasible time windows for an incoming customer, estimating the opportunity costs, i.e., the future monetary loss due to accepting a booking, and optimizing the time window prices in real time. In this paper, we propose a route-based approximate dynamic programming approach to tackle these challenges. The approach carefully combines and partially extends state-of-the-art methods in attended home delivery, dynamic pricing, and dynamic vehicle routing. In an extensive simulation study, we compare its performance with state-of-the-art benchmark heuristics. The results indicate a superior performance of our approach in terms of both profit and number of customers served. Author Affiliation: Chair of Analytics and Optimization, University of Augsburg, Universitatsstra[sz]e 16, D-86159 Augsburg, Germany * Corresponding author. Article History: Received 27 August 2018; Revised 31 March 2020; Accepted 2 April 2020 Byline: Sebastian Koch [sebastian-koch-1986@outlook.de], Robert Klein [robert.klein@wiwi.uni-augsburg.de] (*)

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2017.03.082 Byline: Roland Pongou (a), Bertrand Tchantcho (b), Narcisse Tedjeugang (b) Abstract: * We propose a model of a ladder tournament in organizations. * We study the ordinal properties of the ladder rule. * We fully characterize organizations for which the ladder rule is rational. * We study the relationship between tournament rank and pivotability. * We show that ladder tournaments discriminate more effectively between workers than do mechanisms based on pivotability or marginal productivity. Author Affiliation: (a) University of Ottawa, Department of Economics, Ottawa, ON, Canada (b) Ecole Normale Superieure (UYI), Department of Mathematics, Yaounde, Cameroon Article History: Received 10 March 2016; Revised 17 March 2017; Accepted 18 March 2017 Article Note: (footnote) This paper previously circulated under the title ``A Ladder Tournament''. We thank the Editor and three anonymous referees for very useful comments and suggestions that have helped improve the paper.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2017.04.019 Byline: Dmitriy Belyi (a), Elmira Popova (a), David P. Morton (b), Paul Damien (c) Abstract: * The interface between stochastic optimization and modern Bayesian methods using Markov chain Monte Carlo is gaining momentum in recent years, and our paper advances this research agenda. * The relabeled in the paper holds for any bathtub hazard rate model is for these reasons that our industry sponsor (South Texas Nuclear Power Operating Company) saw fit to implement these ideas in real time. * The adaptation of the extended gamma process to better model monotone failure rates within the operations framework is a new contribution. * All models are exemplified using actual data from industry. The cost savings and the increased efficiency are thus illustrated. Author Affiliation: (a) Department of Operations Research and Industrial Engineering, The University of Texas at Austin, Austin, TX 78712, USA (b) Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL 60208, USA (c) Department of Information, Risk and Operations Management, McCombs School of Business, The University of Texas at Austin, Austin, TX 78703, USA Article History: Received 12 July 2016; Revised 30 January 2017; Accepted 7 April 2017

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2017.04.022 Byline: Min Ouyang (a)(b) Abstract: This paper defines the hazards that can cause direct localized damage or interruption as Spatially Localized Attacks (SLAs). The SLAs-induced impact on a critical infrastructure system (CIS) is modeled as the failure of system components that exist within some localized area while those outside this area remain operating. Instead of identifying and analyzing each type of the SLAs-related hazards, this paper makes a worst-case analysis and proposes a mathematical framework to support resilience optimization of interdependent CISs under the worst SLA. For illustrative purposes, this paper mainly considers two types of strategies to enhance CIS resilience, including protecting weak components, and building new components to increase redundancy. The problem is mathematically formulated as a tri-level defender-attacker-defender model, which is exactly solved by a proposed decomposition algorithm. The case study on interdependent power and water systems demonstrates how the proposed approach can not only identify the optimum resilience enhancement strategy as well as the worst-case SLA, but also analyze the importance of considering interdependencies from both the attacker's and the defender's perspectives. Author Affiliation: (a) Department of Systems Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China (b) Key Lab. for Image Processing and Intelligent Control, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, PR China Article History: Received 6 October 2016; Revised 13 February 2017; Accepted 7 April 2017

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2017.03.016 Byline: Chiang Kao Abstract: * This paper illustrates the problems in efficiency measurement for network systems. * This paper develops a model to measure efficiencies for general two-stage systems. * This paper measures the innovation efficiency for thirty-five countries. Author Affiliation: Department of Industrial and Information Management, National Cheng Kung University, Tainan, Taiwan, ROC Article History: Received 30 September 2016; Revised 10 January 2017; Accepted 7 March 2017

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2017.01.018 Byline: Juan Pablo Torres (a), Martin Kunc (b), Frances O'Brien (b) Abstract: * The paper presents protocol for supporting strategy development via system dynamics (SD) modeling. * The protocol generates scenarios of alternative strategic situations organizations face. * SD modeling improves performance when a facilitation process with the CEO generates strategic options. * The paper reports on the effectiveness of the protocol one year after the protocol was used with 5 CEOs. Author Affiliation: (a) Department of Business, School of Economics and Business, Universidad de Chile, 257 Diagonal Paraguay, Santiago, Chile (b) Warwick Business School, The University of Warwick, CV4 7AL Coventry, United Kingdom Article History: Received 9 October 2015; Revised 6 January 2017; Accepted 10 January 2017

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2016.12.043 Byline: Daniel E. O'Leary Abstract: * Finds crowd out predicts experts. * Finds one crowdsourcing approach generates better predictions. * Finds one crowd out predicts another crowd. * Uses statistical tests to evaluate forecasts. * Uses Brier Score to evaluate forecasts. Author Affiliation: University of Southern California Article History: Received 27 April 2016; Revised 5 December 2016; Accepted 27 December 2016 Article Note: (footnote) [star] The author would like to acknowledge the comments of the editor and the extensive comments of two referees on four earlier versions of this paper. In addition, the author would like to acknowledge the discussion with Professor Gareth James, of the University of Southern California on an earlier version of the paper.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2017.01.009 Byline: Louis de Mesnard Abstract: * Counting an article as one paper for each coauthor is too generous. * Simply dividing the credit by the number of coauthors is too harsh. * So, we take into account the productivity gains of parallelization. * We propose to credit each coauthor with a fraction of a single paper. * The credit varies from 2/3 for two coauthors to 1/3 for a large number of coauthors. Author Affiliation: Univ. de Bourgogne Franche-Comte, CREGO (EA 7317), 2 Bd Gabriel, Dijon 21000, France Article History: Received 17 June 2015; Revised 3 January 2017; Accepted 4 January 2017 Article Note: (footnote) [star] The author thanks the anonymous reviewers and the editor of the journal who greatly helped to improve this paper.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2016.10.029 Byline: Ran Etgar (a), Roy Gelbard (a), Yuval Cohen (b) Abstract: * We introduce a new practical problem optimizing version release dates and their feature contents. * We define a novel optimization model for adequately setting: release contents and release dates. * We offer a benchmark set of these new problems and their solutions for further research. * We validated our results against greedy heuristics and provided the results. * This is a seminal paper presenting a new practical problem to be tackled by future papers. Author Affiliation: (a) Bar-Ilan University, Ramat-Gan 52900, Israel (b) Tel-Aviv Academic College of Engineering, Tel-Aviv 69988, Israel Article History: Received 31 August 2015; Revised 19 July 2016; Accepted 15 October 2016

Keywords Productivity; Spillover; Agglomeration; Human capital; Nonparametric econometrics; Spatial econometrics, Highlights * Asses productivity growth and technology spillovers among clustered firms in China. * Propose a semiparametric spatial autoregressive production function where the coefficients are smooth unknown functions of human capital. * Spillovers affect productivity growth. * Human capital increases the spillover effects. Abstract This paper examines whether there are productivity spillover effects among clustered firms and, if so, whether a firm's human capital affects its absorption of spillovers. To analyze these questions, we use a semiparametric spatial autoregressive production function in which the coefficients are smooth unknown functions of firms' human capital. The varying coefficients not only allow flexible interactions between human capital and other inputs, but also permit heterogeneous spatial dependence and spillover effects among firms. Although the commonly used spatial matrix captures the possible learning opportunities among firms, we hypothesize that a firm with more human capital is better at seizing these opportunities. We address the simultaneity issue arising from the endogeneity of inputs by using the proxy variable method. Finally, we apply the model to analyze the productivity spillovers and the human capital effect of China's computer and peripheral equipment industry from 1998 to 2007. Author Affiliation: (a) Department of Economics, State University of New York at Binghamton, NY, USA (b) Department of Economics, Oakland University, MI, USA (c) Inland Norway University of Applied Sciences, Hedmark, Norway * Corresponding author at: Department of Economics, State University of New York at Binghamton, NY, USA. Article History: Received 12 May 2019; Revised 14 April 2020; Accepted 20 April 2020 (footnote)[white star] We would like to thank the editor, Professor Robert Dyson, and three anonymous referees, whose comments substantially improved the paper. We are also grateful to Professor Shunan Zhao for his unconditional support in this project. We, alone, are responsible for any remaining errors. This research was partially supported by the 2019 School of Business Administration Spring/Summer Research Fellowship from Oakland University. Byline: Zhezhi Hou [zhou3@binghamton.edu] (a), Man Jin [mjin@oakland.edu] (b), Subal C. Kumbhakar [kkar@binghamton.edu] (*,a,c)

Keywords Literature review; Circular economy; Reverse logistics; Operations research; Production planning; Lot-sizing Highlights * We offer an overview of research towards circular economy(CE) in production planning. * We provide an unequivocal taxonomy of inherent CE-oriented industrial processes. * We derive new avenues for both academics and practitioners. Abstract In the actual era of the international trade, global warming and depletion of Earth's natural resources, the willingness to generate sustainable and competitive benefits determines us to stop thinking linearly (produce, consume and dispose) and to shift towards a circular approach by closing material loops. The latter falls within the concept of circular economy that, in turn, derives from reverse logistics. This paper proposes a comprehensive state-of-the-art review around the topic of circular economy and reverse logistics with a particular emphasis on mid-term production planning under discrete time settings. The broad spectrum of reviewed publications is categorized and discussed with respect to the main recovery operations, namely: (i) disassembly for recycling, (ii) from product to raw material recycling, and (iii) by-products and co-production. For each of the aforementioned recovery options, this paper elucidates the related definitions, reviews the mathematical formulations jointly with a structured overview of the solution methods, and discusses their industrial implications. Given the legislative pressure to mitigate environmental impacts caused by production processes, a special attention is paid to the greenhouse gas emissions and energy consumption. A cross-cutting analysis of the reviewed literature brought forward a number of research gaps and revealed multiple research opportunities to support the development of the circular economy. The key findings show an ever-growing interest in making sustainable the traditional linear industrial processes within a circular economy context. Author Affiliation: Department of Manufacturing Sciences and Logistics, Mines Saint-Etienne, University Clermont Auvergne, CNRS, UMR 6158 LIMOS CMP, Gardanne, France * Corresponding author. Article History: Received 8 April 2019; Revised 9 March 2020; Accepted 22 April 2020 Byline: Elodie Suzanne, Nabil Absi [absi@emse.fr] (*), Valeria Borodin

Keywords OR in maritime industry; Constraint programming; Container terminal operations; Scheduling Highlights * First Formulation of the Integrated Port Container Terminal Problem (IPCTP) under realistic constraints. * The IPCTP integrates the joint assignment and scheduling of quay cranes, yard cranes, and yard trucks. * Constraint Programming models are proposed to solve the IPCTP optimally or near optimally. * A two-stage optimization approach is developed to increase solution quality for large IPCTP instances. * Performances of mixed-integer and constraint programming models are compared. Abstract Although operations in container terminals are highly interdependent, they are traditionally optimized by decomposing the overall problem into a sequence of smaller sub-problems, each focusing on a single operation. Recent studies, however, have demonstrated the need and potential of optimizing these interdependent operations jointly. This paper proposes the Integrated Port Container Terminal Problem (IPCTP) that considers the joint optimization of quay crane assignment and scheduling, yard crane assignment and scheduling, yard location assignments, and yard truck assignment and scheduling. The IPCTP aims at minimizing the turnover times of the vessels and maximize terminal throughput. It also considers inbound and outbound containers simultaneously and models the safety distance and the interference constraints for the quay cranes. To solve the IPCTP, the paper proposes several constraint programming (CP) models. Computational results show that CP provides exact solutions in acceptable time to IPCTP instances derived from an actual (small) container terminal in Turkey. For hard IPCTP instances, the CP model can be generalized in a two-stage optimization approach to produce high-quality solutions in reasonable times. Author Affiliation: (a) Department of Industrial Engineering, Izmir Democracy University, Izmir 35140, Turkey (b) Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, USA (c) Department of Industrial Engineering, Izmir Bakircay University, Izmir 35665, Turkey * Corresponding author. Article History: Received 25 January 2019; Revised 10 April 2020; Accepted 14 April 2020 (footnote)[white star] A Preliminary version of this paper appeared in CPAIOR'18 (Kizilay, Eliiyi, and HentenryckKizilay et al., 2018). Byline: Damla Kizilay [damla.kizilay@idu.edu.tr] (*,a), Pascal Van Hentenryck [pascal.vanhentenryck@isye.gatech.edu] (b), Deniz T. Eliiyi [deniz.eliiyi@bakircay.edu.tr] (c)

Keywords Finance; Gaussian dynamic term structure Models; HJM; Finite dimensional realizations; Interest rate derivatives Highlights * We develop a unified Heath--Jarrow--Morton-based framework. * We show that most existing Gaussian dynamic term structure models can be nested as special cases. * Euro area forward rates are used to calibrate the models. * The traditional models fail to capture key empirical features due to restrictive constraints. * We develop a fast MLE estimation for structured covariance matrix. Abstract In this paper, we show that most existing Gaussian dynamic term structure models (GDTSMs) can be nested as special cases under a unified Heath--Jarrow--Morton (HJM)-based framework of GDTSM construction. Our study provides not only a systematic way to examine the commonality of many seemingly distinct GDTSMs, but also a novel and convenient approach to constructing GDTSMs that are otherwise unavailable or intractable under the traditional approach. In our empirical study using the Euro area forward rates, we conduct a specification analysis based on this novel approach. The analysis reveals that the traditional models impose restrictive constraints limiting their flexibility in capturing key features of the correlations and volatilities of the forward rates. Author Affiliation: (a) Cheung Kong Graduate School of Business (CKGSB), 1 E Chang'an Ave, Wangfujing, Dongcheng District, Beijing, China (b) University of Liverpool Management School, Chatham St, Liverpool L69 7ZH, United Kingdom (c) Claremont McKenna College, 888 Columbia Avenue, Claremont, CA 91711, United States * Corresponding author. Article History: Received 2 July 2019; Revised 7 April 2020; Accepted 8 April 2020 (footnote)[white star] We are grateful to the editor Emanuele Borgonovo and anonymous referees for their suggestions and comments which substantially improved the paper. We also thank Areski Cousin, Jin-Chuan Duan, Koji Inui, Masaaki Kijima, Shaoyu Li, Linlin Niu, Haoxi Yang, and participants at the Young Researchers Workshop in Finance at Tokyo Metropolitan University and the Workshop in Fixed Income and Bond Markets at Xiamen University for their helpful comments. All remaining errors are our own. Byline: Haitao Li [htli@ckgsb.edu.cn] (a), Xiaoxia Ye [xiaoxia.ye@liverpool.ac.uk] (*,b), Fan Yu [fyu@cmc.edu] (c)

Keywords Robustness and sensitivity analysis; Ordered weighted averaging; Combinatorial optimization; Approximation algorithms Highlights * Combinatorial optimization problems with the OWA criterion are discussed. * Approximation algorithms for the problem are proposed. * Results of computational tests are presented. Abstract This paper deals with combinatorial optimization problems with K cost scenarios, inducing K linear objectives. The popular Ordered Weighted Averaging (OWA) criterion is used to aggregate the objectives and compute a solution. It is well-known that minimizing OWA for most basic combinatorial problems is weakly NP-hard even if the number of scenarios K equals two, and strongly NP-hard when K is a part of the input. In this paper, the problem with nonincreasing weights in the OWA criterion and a large K is first considered. A method of reducing the number of scenarios, by appropriately aggregating the costs before solving the problem, is proposed. It is shown that an optimal solution to the reduced problem has a guaranteed worst-case approximation ratio. Some new approximation results for the Hurwicz criterion, which is a special case of OWA, are also presented. Author Affiliation: (a) Data Analytics Center of Excellence, Deutsche Post DHL Group, Bonn, Germany (b) Network and Data Science Management, University of Siegen, Germany (c) Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Poland (d) Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Poland * Corresponding author. Article History: Received 30 July 2019; Revised 16 January 2020; Accepted 11 April 2020 Byline: Andre Chassein [andre-chassein@web.de] (a), Marc Goerigk [marc.goerigk@uni-siegen.de] (b), Adam Kasperski [adam.kasperski@pwr.edu.pl] (*,c), Pawel Zielinski [pawel.zielinski@pwr.edu.pl] (d)

Keywords Data envelopment analysis; Composite indicator; Weight restrictions; Undesirable indicators; Local governments Highlights * Innovative application to OR by measuring service level provision of municipalities. * The tool is flexible to account for political preferences and undesirable features. * A robust and conditional approach is used to encompass the operating context. * We show the applicability for 307 Flemish municipalities over the years 2006--2011. Abstract With increasing decentralization of public activities to the municipalities, it has become imperative to deploy an enhanced service provision analysis at the local level. This paper suggests the innovative use of a composite indicator to measure the multidimensional aspects of the local public provision comprising of several commonly administered municipal tasks. We propose a robust conditional version of a directional distance composite indicator with weight restrictions based on the municipal expenditure composition. Specifically, we deal with the presence of 'undesirable' municipal service indicators and with the heterogeneity among the municipalities in their political preferences, priority public activities and operating environment characteristics. To illustrate the applicability of the suggested method, we show the construction of the municipal service provision composite indicator for 307 Flemish municipalities over the year 2006--2011. Author Affiliation: (a) KU Leuven, Faculty of Business and Economics (FEB), Leuven, Belgium (b) Maastricht University, UNU--MERIT, Maastricht, the Netherlands * Corresponding author. Article History: Received 13 December 2018; Revised 24 January 2020; Accepted 7 April 2020 (footnote)[white star] We are grateful to Wim Moesen, Peter Bogetoft, Tom Van Puyenbroeck, Benny Geys, Ana Camanho, Massimo Riccaboni, Laura Carosi, Riccardo Cambini, three anonymous referees and the participants at the 6th Workshop on Efficiency and Productivity Analysis (Porto, Portugal) and at the 15th European Workshop on Efficiency and Productivity Analysis (London, UK) for their valuable comments and suggestions on a previous version of the paper. Giovanna D'Inverno gratefully acknowledges financial support from Research Foundation -- Flanders, FWO (Postdoctoral Fellowship 12U0219N). Kristof De Witte acknowledges financial support from Research Foundation -- Flanders, FWO (Grant G068518N). Byline: Giovanna D'Inverno [giovanna.dinverno@kuleuven.be] (*,a), Kristof De Witte [kristof.dewitte@kuleuven.be] (a,b)

Keywords Supply chain management; Quality; Optimal control; Pricing; Contract design Highlights * The paper examines contract design for product quality. * Product quality is co-created by the platform firm and the service provider. * Unlike standard models, the platform firm can disintermediate the service provider. * An ideal platform firm is neither too efficient nor inefficient in its quality. * The optimal contract is designed to exclude low-type service providers. Abstract This paper investigates contract design by a firm in a supply chain where the quality of the product delivered to consumers is co-created by the quality decisions of the contract designer (platform firm) and the agent (the service provider) whose inputs need to be coordinated. Revenue is a function of the price charged to consumers, the product quality, and a market parameter which may be private information to the service provider. We focus on a contract with payment terms commonly used by large platforms such as Amazon. The platform firm adopts a menu-of-contracts approach to get the service provider to reveal its private information, resulting in optimal quality effort and price decisions that maximize the expected profit of the platform firm. We examine cases where the platform firm should disintermediate the service provider and make and sell the product itself. To study the coordination ability of the proposed contract, we compare the results with those obtained in the case of complete information via various benchmark scenarios. Author Affiliation: (a) Department of Management, Bar-Ilan University, Ramat-Gan, 5290002, Israel (b) Graduate School of Business Administration, Bar-Ilan University, Ramat-Gan, 5290002, Israel (c) Anderson Graduate School of Management, University of California, Riverside, CA, 92521, United States * Corresponding author. Article History: Received 28 March 2019; Revised 22 March 2020; Accepted 26 March 2020 (footnote) Authors are listed alphabetically. This research did not receive any specific grant from funding agencies. The authors thank the Editor and anonymous review team for their helpful comments. Byline: Tal Avinadav [tal.avinadav@biu.ac.il] (a), Tatyana Chernonog [Tatyana.Chernonog@biu.ac.il] (a), Gila E. Fruchter [Gila.Fruchter@biu.ac.il] (b), Ashutosh Prasad [aprasad@ucr.edu] (c,*)

Keywords Routing; Stochasticity; Dynamism; Familiarity; Cost function approximation Highlights * This paper studies the effects of familiarity between drivers and customers. * A Markov decision process is presented to model the stochastic and dynamic problem. * The presented policy allows targeted investments in familiarity. * A systematical analysis shows the value of both policy and familiarity. Abstract With a growing number of services provided at or to a customer's home, the familiarity of the service provider, or driver, with the customer's location is increasingly important. One prominent example is retail distribution, where familiarity with the delivery location can save the driver time. In contrast to other kinds of familiarity (e.g., tasks, customer needs) that continually increase with a larger number of repetitions, location familiarity is primarily established with a driver's first visit. Thus, familiarity results from operational routing decisions. However, as we show in this paper, there is potential value in considering the tactical value of familiarity and its development over a longer horizon. To this end, we develop and solve a tactical model to specify the long-term implications of improved driver-customer familiarity, introducing a solution methodology for the stochastic and dynamic multi-period routing problem with driver-customer familiarity. Our methodology utilizes a policy that explicitly invests in the familiarity between selected driver-customer pairs, encouraging the development of pairs that are tactically beneficial. We determine the appropriate investment dimensions for each pair, considering which locations a driver has visited and how many drivers have visited a location. We show that under the problem conditions tested this investment policy leads to a reduction in cost compared to a short term, myopic policy, while increasing the overall level of familiarity between drivers and customers and hedging against driver or customer turnover. We also find that focusing only on routing or on exploiting existing familiarity leads to substantial increases in cost. Author Affiliation: (a) Technische Universitat Braunschweig, Braunschweig 38106, Germany (b) Loyola University Chicago, Chicago 60611, USA (c) SGH Warsaw School of Economics, Warsaw 02--554, Poland * Corresponding author. Article History: Received 14 April 2019; Revised 5 March 2020; Accepted 11 March 2020 Byline: Marlin Ulmer [m.ulmer@tu-braunschweig.de] (*,a), Maciek Nowak [mnowak4@luc.edu] (b), Dirk Mattfeld [d.mattfeld@tu-braunschweig.de] (a), Bogumil Kaminski [bogumil.kaminski@sgh.waw.pl] (c)

Keywords Rough sets; Dominance-based rough set approach; Relative importance; Decision rules; Attribute reducts; Crowdfunding; Lendwithcare Highlights * New parameterised measures for calculating the relative importance of condition attributes. * Measures are based on the characteristics of decision rules and attribute reducts. * Measures are extended to compute the relative importance of a collection of condition attributes. * Rule-based measures are extended to deduce the importance of attributes' specific values. * Proposed measures have been applied and validated using real-world data from Lendwithcare. Abstract Crowdfunding is the practice of funding a project or venture by raising monetary contributions from a large number of people, typically via the Internet. Lendwithcare is amongst the first crowdfunding platforms specifically dedicated to support individual and group entrepreneurs in developing countries through partner microfinance institutions. A key objective of Lendwithcare is to identify the attributes (i.e., the characteristics of crowdfunding projects in their online descriptions) that affect investors/potential investors when taking their investment decision. This paper proposes a decision rule-based approach to address this issue. This approach relies on the Dominance-Based Rough Approach (DRSA), which is a well-known multicriteria sorting method. The outputs of DRSA are a collection of if-then decision rules and a collection of attribute reducts. In this paper, new measures are proposed for calculating the relative importance of condition attributes based on the characteristics of decision rules and of attribute reducts. Decision rule-based measures are parameterised in order to consider the characteristics of decision rules using both learning and testing datasets. The proposed measures can be aggregated into a comprehensive measure indicating the overall importance of each condition attribute. Furthermore, the proposed measures are extended in order to compute the relative importance of a collection of condition attributes taken together. In addition, decision rule-based measures are extended to evaluate the relative importance of specific values of condition attributes. The proposed approach has been applied and validated using real-world data from Lendwithcare. Author Affiliation: (a) Portsmouth Business School, University of Portsmouth, Portsmouth, UK (b) Centre for Operational Research and Logistics, University of Portsmouth, Portsmouth, UK (c) NEOMA Business school, Mont-Saint-Aignan, France (d) Faculty of Business, Athabasca University, Athabasca, Canada (e) School of Economics, Finance and Accounting, Faculty of Business & Law, Coventry University, Coventry, UK * Corresponding author. Article History: Received 22 March 2019; Accepted 11 March 2020 Byline: Salem Chakhar (a,b), Alessio Ishizaka (c), Andy Thorpe [andy.thorpe@port.ac.uk] (*,a), Joe Cox (a,d), Thang Nguyen (e), Liz Ford (a)

Keywords Operations research in health services; Scheduling; Uncertainty modeling; Heuristics; Simulation Highlights * Block scheduling of patient surgeries under uncertainty. * Random surgery times and lengths of stay are incorporated as constraints. * Multiobjective function consisting of leveling variability at the wards, reducing waiting lists, and maximizing the number of surgeries. * Two-stage solution approach consisting of an approximate integer linear program, followed by Monte Carlo simulation and tabu search. * Numerical experiments applied to a Dutch hospital. Abstract In this paper we consider the problem of scheduling patients in allocated surgery blocks in a Master Surgical Schedule. We pay attention to both the available surgery blocks and the bed occupancy in the hospital wards. More specifically, large probabilities of overtime in each surgery block are undesirable and costly, while large fluctuations in the number of used beds requires extra buffer capacity and makes the staff planning more challenging. The stochastic nature of surgery durations and length of stay on a ward hinders the use of classical techniques. Transforming the stochastic problem into a deterministic problem does not result into practically feasible solutions. In this paper we develop a technique to solve the stochastic scheduling problem, whose primary objective it to minimize variation in the necessary bed capacity, while maximizing the number of patients operated, and minimizing the maximum waiting time, and guaranteeing a small probability of overtime in surgery blocks. The method starts with solving an Integer Linear Programming (ILP) formulation of the problem, and then simulation and local search techniques are applied to guarantee small probabilities of overtime and to improve upon the ILP solution. Numerical experiments applied to a Dutch hospital show promising results. Author Affiliation: (a) Eerste Atjehstraat 57E, KC Amsterdam 1094, the Netherlands (b) Department of Econometrics and Operations Research, Vrije Universiteit Amsterdam, de Boelelaan 1105 HV, Amsterdam 1081, the Netherlands (c) HOTflo Company, Bisonspoor 5000 - B404, LT Maarssen 3605, the Netherlands (d) Centrum voor Wiskunde en Informatica, Science Park 123, Netherlands & Erable XG Amsterdam 1098, France * Corresponding author. Article History: Received 4 December 2018; Revised 20 February 2020; Accepted 9 March 2020 Byline: Anne van den Broek d'Obrenan (a,b), Ad Ridder [ad.ridder@vu.nl] (*,b), Dennis Roubos [dennis@hotflo.net] (c), Leen Stougie [stougie@cwi.nl] (b,d)

Keywords Location; Support vector machine; Ramp loss model; Mixed integer programming; Indicator constraints Highlights * Support vector machines with ramp loss models are analyzed. * Strategies for tightening the values of big M parameters of the model are proposed. * The strategies are focused on l.sub.1-norm and l.sub.2-norm cases. * Linear, quadratic and Lagragian relaxation models are solved in the strategies. * The performance of these strategies are tested in simulated and real-life datasets. Abstract This paper considers various models of support vector machines with ramp loss, these being an efficient and robust tool in supervised classification for the detection of outliers. The exact solution approaches for the resulting optimization problem are of high demand for large datasets. Hence, the goal of this paper is to develop algorithms that provide efficient methodologies to exactly solve these optimization problems. These approaches are based on three strategies for obtaining tightened values of the big M parameters included in the formulation of the problem. Two of them require solving a sequence of continuous problems, while the third uses the Lagrangian relaxation to tighten the bounds. The proposed resolution methods are valid for the l.sub.1-norm and l.sub.2-norm ramp loss formulations. They were tested and compared with existing solution methods in simulated and real-life datasets, showing the efficiency of the developed methodology. Author Affiliation: Departamento de Estadistica e Investigacion Operativa, Universidad de Cadiz, Cadiz, Spain * Corresponding author. Article History: Received 28 June 2019; Revised 1 March 2020; Accepted 4 March 2020 Byline: Marta Baldomero-Naranjo [marta.baldomero@uca.es] (*), Luisa I. Martinez-Merino [luisa.martinez@uca.es], Antonio M. Rodriguez-Chia [antonio.rodriguezchia@uca.es]

Keywords OR in maritime industry; Speed optimization; CO2; Robustness; Bi-objective Highlights * Describes an applicable decision support model for speed optimization. * Contributes to the 2050 agreement of 50% reduction of CO2. * The issues such as feeder services frequencies, transit time delay, port work shifts are modelled. * Deals with robustness in terms of bi-objective modelling. * The model and real-life problem data has been developed with a liner shipping company. Abstract In 2018 the International Maritime Organization (IMO) agreed to cut the shipping sector's overall CO2 output by 50% by 2050. One of the key methods in reaching this goal is to improve operations to limit fuel consumption. However, it is difficult to optimize speed for a complete liner shipping network as routes interact with each other, and several business constraints must be respected. This paper presents a unified model for speed optimization of a liner shipping network, satisfying numerous real-life business constraints. The speed optimization is in this research achieved by rescheduling the port call times of a network, thus, the network is not changed. The business constraints are among others related to transit times, port work shifts and emission control areas. Other restrictions are fixed times for canal crossing, speed restrictions in the piracy areas and desire for robust solutions. Vessel sharing agreements and other collaboration between companies must also be included. The modeling of the different restrictions is described in detail and tested on real-life data. The scientific contribution of this paper is threefold: We present a unified model for speed optimization together with numerous business constraints. We present a general framework for handling routes with different frequencies. Moreover, we present a bi-objective model for balancing robustness of schedules against fuel consumption. The tests show that the real-life requirements can be handled by mixed integer programming and that the model finds significant reductions of bunker consumption and cost for large-scale real-life instances. Author Affiliation: (a) Department of People and Technology, Roskilde University, Universitetsvej 1, Roskilde 4000, Denmark (b) DTU Management, Technical University of Denmark, Akademivej 358, 2800 Kongens Lyngby, Denmark (c) Maersk Line, Esplanaden 50, Copenhagen K. 1098, Denmark * Corresponding Author. Article History: Received 6 December 2019; Accepted 22 February 2020 Byline: Line Blander Reinhardt [liner@ruc.dk] (*,a), David Pisinger [pisinger@man.dtu.dk] (b), Mikkel M. Sigurd [Mikkel.Muhldorff.Sigurd@maersk.com] (c), Jonas Ahmt [Jonas.Ahmt@maersk.com] (c)

Keywords (O) Combinatorial optimization; Rainbow spanning forest problem; Problem-specific heuristic; Artificial bee colony algorithm; Neighborhood strategies Highlights * Develop a novel and fast problem-specific heuristic for the problem. * Develop an artificial bee colony algorithm for the problem. * Propose grouping-based neighborhood strategies. * Uses of problem specific heuristic in artificial bee colony algorithm. * Artificial bee colony algorithm outperforms the existing approaches. Abstract A rainbow spanning forest (say RF) of a given connected, undirected and edge-colored graph (G) is a spanning forest of a set of rainbow components, where a rainbow component is a tree whose edges have different colors. The rainbow spanning forest problem (RSFP) of G aims to find a RF of G with the minimum number of rainbow components. The RSFP which is recently introduced is NP-hard on general graphs and trees, but finds its applications in many situations in which one requires to distinguish between different types of connections in network. To the best of our knowledge, there exists only one paper (Carrabs et al. 2018b) that presents a problem-specific heuristic called greedy algorithm (say GH) and a multi-start scheme embedding this GH (say MSGH) in order to further improve the results. This paper presents a novel and fast problem-specific heuristic Heu_RSF. With the intent of improving solution quality at the cost of higher computational time, we present an artificial bee colony algorithm (ABC_RSFP) for the RSFP. Main components of ABC_RSFP such as randomized version of Heu_RSF in solution initialization and grouping-based neighborhood strategies in conjunction with the use of Heu_RSF as a repair operator help in making ABC_RSFP more effective framework. On a set of benchmark instance scenarios, experimental results suggest that Heu_RSF is effective particularly on instances of larger scenarios with higher density and is computationally much faster than GH and MSGH. ABC_RSFP outperforms MSGH on almost all instance scenarios and shows its effectiveness in terms of computational time. Author Affiliation: Department of Computer Applications, National Institute of Technology Raipur, Raipur 492010, India * Corresponding author Article History: Received 7 April 2019; Revised 2 January 2020; Accepted 25 February 2020 Byline: Sudishna Ghoshal [sghoshal.phd2017.mca@nitrr.ac.in], Shyam Sundar [ssundar.mca@nitrr.ac.in] (*)

Keywords Productivity and competitiveness; Cost variation; Decomposition; Efficiency change; Technological change Highlights * Symmetric decompositions of cost difference. * Symmetric decompositions of cost ratio. * Empirical comparison of decompositions on real-life data. Abstract In this paper a number of symmetric, empirically implementable decompositions of the cost variation (in difference and ratio form) of a production unit are developed. The components distinguished are price level change, technical efficiency change, allocative efficiency change, technological change, scale of activity change, and price structure change. Given data from a (balanced) panel of production units, all the necessary ingredients for the computation of the various decompositions can be obtained by using linear programming techniques (DEA). An application is provided. Author Affiliation: (a) Rotterdam School of Management, Erasmus University, the Netherlands (b) Department of Economics, Universidad Autonoma de Madrid, Spain (c) Erasmus Research Institute of Management, Erasmus University, the Netherlands * Corresponding author. Article History: Received 15 June 2019; Revised 20 December 2019; Accepted 19 February 2020 (footnote)[white star] This paper has been presented at the XVI European Workshop on Efficiency and Productivity Analysis, London, 10--13 June 2019. A preliminary version of the theoretical part was presented at the North American Productivity Workshop at Union College, Schenectady NY, 15--17 June 2000. We thank participants of the research seminar at the Center for Operations Research, Elche (Alicante), Spain, 7 November 2019, for comments and suggestions. We are also grateful to three referees. Jose L. Zofio acknowledges support from the Spanish Ministry for Economy and Competitiveness under grant MTM2016-79765-P. Byline: Bert M. Balk [bbalk@rsm.nl] (*,a), Jose L. Zofio [jose.zofio@uam.es] (b,c)

Keywords Productivity and competitiveness; Stochastic frontier analysis; Semiparametric smooth coefficient model; Dairy farms; Lithuania Highlights * This paper presents a four-component stochastic frontier model. * The frontier function is an unknown smooth input distance function. * Inefficiency and total factor productivity growth are decomposed. * A semiparametric smooth coefficient framework is proposed. Abstract This paper presents a four-component stochastic frontier model in which the frontier function is represented by an unknown smooth input distance function, and inefficiency is decomposed into persistent and transient inefficiencies. Furthermore, the pre-truncation mean and variance of the transient inefficiency are functions of the environmental variables. By differentiating the four-component input distance frontier with respect to the time trend, total factor productivity (TFP) growth is estimated under the semiparametric smooth coefficient framework, and is decomposed into six components, i.e., technical change, scale component, allocative component, external component, efficiency change, and residual component. The empirical example focuses on the Lithuanian dairy sector with multiple outputs. Our results show that there are some persistent and transient inefficiencies in Lithuanian dairy farms. However, these farms maintained TFP growth of 2% per annum on average during 2004--2016, and much of it is attributed to the technical change and scale components. Author Affiliation: (a) Lithuanian Institute of Agrarian Economics, A. Vivulskio Str. 4A-13, 03220 Vilnius, Lithuania (b) School of Economics, Shanghai University, 99 Shangda Rd., Shanghai 200444, China * Corresponding author. Article History: Received 25 April 2019; Revised 22 December 2019; Accepted 19 February 2020 Byline: Tomas Balezentis (a), Kai Sun [ksun1@shu.edu.cn] (*,b)

Keywords Productivity and competitiveness; Bennet; Luenberger-Hicks-Moorsteen; Superlative; Italian food and beverages sector Highlights * Bennet indicator is superlative indicator for Luenberger-Hicks-Moorsteen (LHM) indicator. * It is also a superlative indicator for the Luenberger indicator. * We further show when both Bennet indicators coincide. * We empirically check the extent to which theoretical equivalence holds in practice. * Bennet indicator is a close empirical alternative to LHM indicator for the sample. Abstract Consisting of the difference between an output indicator and an input indicator, the Luenberger-Hicks-Moorsteen (LHM) productivity indicator allows straightforward interpretation. However, its computation requires estimating distance functions that are inherently unknown. This paper shows that a computationally simple Bennet indicator is a superlative indicator for the LHM indicator when one can assume profit-maximizing behavior and the input and output directional distance functions can be represented up to the second order by a quadratic functional form. We also show that the Luenberger- and LHM-approximating Bennet indicators coincide for an appropriate choice of directional vectors. Focusing on a large sample of Italian food and beverages companies for the years 1995-2007, we empirically investigate the extent to which this theoretical equivalence translates into similar estimates. We find that the Bennet indicator is a close empirical alternative to the LHM indicator for the sample. Author Affiliation: (a) Business Economics Group, Wageningen University, P.O. Box 8130, 6700 EW Wageningen, The Netherlands (b) Department of Food and Resource Economics (IFRO), University of Copenhagen, Rolighedsvej 25, Frederiksberg C DK-1958, Denmark * Corresponding author. Article History: Received 1 November 2018; Revised 17 February 2020; Accepted 19 February 2020 (footnote)[white star] We thank Bert Balk and Hideyuki Mizobuchi for comments on an earlier working paper without application (). The authors gratefully acknowledge four anonymous referees for their valuable comments and suggestions. Both authors have contributed equally to this paper. Email pjk@ifro.ku.dk for correspondence. The usual disclaimer applies. Byline: Frederic Ang (a), Pieter Jan Kerstens [pjk@ifro.ku.dk] (*,b)

Keywords Maintenance; Replacement; Repair Highlights * A review of papers on maintenance optimization in the period 2001--2018. * A distinction between single-unit and multi-unit systems. * Future research avenues in the area of maintenance optimization. Abstract To this day, continuous developments of technical systems and increasing reliance on equipment have resulted in a growing importance of effective maintenance activities. During the last couple of decades, a substantial amount of research has been carried out on this topic. In this study, we review more than two hundred papers on maintenance modeling and optimization that have appeared in the period 2001 to 2018. We begin by describing terms commonly used in the modeling process. Then, in our classification, we first distinguish single-unit and multi-unit systems. Further sub-classification follows, based on the state space of the deterioration process modeled. Other features that we discuss in this review are discrete and continuous condition monitoring, inspection, replacement, repair, and the various types of dependencies that may exist between units within systems. We end with the main developments during the review period and with potential future research directions. Author Affiliation: (a) Department of Operations, Faculty of Economics and Business, University of Groningen, P.O. Box 800, Groningen 9700 AV, the Netherlands (b) Salford Business School, University of Salford, Lady Hale Building, Salford M5 4WT, United Kingdom * Corresponding author. Article History: Received 1 May 2018; Revised 20 September 2019; Accepted 23 September 2019 Byline: Bram de Jonge [b.de.jonge@rug.nl] (*,a), Philip A. Scarf [p.a.scarf@salford.ac.uk] (b)

Keywords 3D printing; Supply chain management; New technologies Highlights * We consider the impact of 3D printing on a simple supply chain. * 3D printers can be utilized by either the manufacturer or the retailer. * We characterize the conditions where either firm adopts 3D printing. Abstract In this paper we consider the impact of 3D printing, or additive manufacturing, on a simple supply chain, consisting of a manufacturer and retailer, that serves stochastic customer demand. 3D printing is a relatively new manufacturing technology that is attracting attention from many firms. However, the impact of 3D printing on operations and firm relationships in a supply chain is relatively unexplored in academic research. A unique aspect of 3D printers is that they can be installed at the retailer in a supply chain, a characteristic that we highlight in our paper since it enables the supply chain to be more responsive to demand. Consequently, 3D printers can be adopted by either the manufacturer or, in a more novel situation, the retailer; we analyze the equilibrium of Stackelberg games in both cases. We characterize the economic and competitive conditions where either firm adopts 3D printing, and show that under either scenario, it is possible for both firms to earn more profit than a benchmark system without 3D printing. We identify and quantify the positive benefits associated with 3D printing, for both firms in a simple supply chain, when either firm adopts this new manufacturing technology. In many cases, the scenario where the manufacturer adopts 3D printing and installs 3D printers at the retailer results in the best profit outcomes for the manufacturer. The retailer's preference, however, depends on problem parameters. Therefore, supply chain managers should carefully consider the possibility of 3D printing products in their supply chain. Author Affiliation: (a) Pamplin School of Business, University of Portland, USA (b) Michael G. Foster School of Business, University of Washington, USA * Corresponding author. Article History: Received 1 February 2019; Revised 29 January 2020; Accepted 30 January 2020 Byline: Mohammad E. Arbabian [arbabian@up.edu] (a), Michael R. Wagner [mrwagner@uw.edu] (*,b)

Keywords Business failure prediction; Cost-sensitive learning; Ensemble selection; Cost uncertainty; Multicriteria optimization; NSGA-II Highlights * CSMES is an ensemble selection method for accommodating cost uncertainty. * Ensemble selection involves optimization of cost space through NSGA-II. * The ensemble nomination curve is introduced mapping model competence regions. * Experiments on 21 datasets compare CSMES to 3 sets of benchmarks. * Results demonstrate the validity of CSMES in 3 cost uncertainty evaluation scenarios. Abstract In order to assess risks associated with establishing relationships with corporate partners such as clients, suppliers, debtors or contractors, decision makers often turn to business failure prediction models. While a large body of literature has focused on optimizing and evaluating novel methods in terms of classification accuracy, recent research has acknowledged the existence of asymmetric misclassification costs associated with prediction errors and thus, advocates the usage of alternative evaluation metrics. However, these papers often assume a misclassification cost matrix to be known and fixed for both the training and the evaluation of models, whereas in reality these costs are often uncertain. This paper presents a methodological framework based upon heterogeneous ensemble selection and multi-objective optimization for cost-sensitive business failure prediction that accommodates uncertainty at the level of misclassification costs. The framework assumes unknown costs during model training and accommodates varying degrees of uncertainty during model deployment. Specifically, NSGA-II is deployed to optimize cost space resulting in a set of pareto-optimal ensemble classifiers where every learner minimizes expected misclassification cost for a specific range of cost ratios. An extensive set of experiments evaluates the method on multiple data sets and for different scenarios that reflect the extent to which cost ratios are known during model deployment. Results clearly demonstrate the ability of our method to minimize cost under the absence of exact knowledge of misclassification costs. Author Affiliation: (a) Audencia Business School, 8 Route de la Joneliere, F-44312 Nantes, France (b) IESEG School of Management, 3 Rue de la Digue, F-59000 Lille, France (c) LEM-CNRS 9221, 3 Rue de la Digue, Lille, France (d) School of Business and Economics, Humboldt-University of Berlin, Unter den Linden 6, D-10099 Berlin, Germany * Corresponding author. Article History: Received 24 January 2019; Accepted 23 January 2020 Byline: Koen W. De Bock [kdebock@audencia.com] (a,*), Kristof Coussement [k.coussement@ieseg.fr] (b,c), Stefan Lessmann [stefan.lessmann@hu-berlin.de] (d)

Keywords Decision support systems; Artificial intelligence; Machine learning; Metaheuristics; Heuristics Highlights * A review of contemporary literature of selection hyper-heuristics is given. * The existing classification of selection hyper-heuristics is extended. * Frameworks designed for hyper-heuristic development are discussed. * Critical discussion, current trends and directions for future research are included. Abstract Hyper-heuristics have emerged as a way to raise the level of generality of search techniques for computational search problems. This is in contrast to many approaches, which represent customised methods for a single problem domain or a narrow class of problem instances. The term hyper-heuristic was defined in the early 2000s as a heuristic to choose heuristics, but the idea of designing high-level heuristic methodologies can be traced back to the early 1960s. The current state-of-the-art in hyper-heuristic research comprises a set of methods that are broadly concerned with intelligently selecting or generating a suitable heuristic for a given situation. Hyper-heuristics can be considered as search methods that operate on lower-level heuristics or heuristic components, and can be categorised into two main classes: heuristic selection and heuristic generation. Here we will focus on the first of these two categories, selection hyper-heuristics. This paper gives a brief history of this emerging area, reviews contemporary selection hyper-heuristic literature, and discusses recent selection hyper-heuristic frameworks. In addition, the existing classification of selection hyper-heuristics is extended, in order to reflect the nature of the challenges faced in contemporary research. Unlike the survey on hyper-heuristics published in 2013, this paper focuses only on selection hyper-heuristics and presents critical discussion, current research trends and directions for future research. Author Affiliation: (a) School of Informatics, University of Leicester, University Road, Leicester LE1 7RH, UK (b) Department of Management Science, Lancaster University, Lancaster LA1 4YX, UK (c) School of Computer Science, University of Nottingham, Wollaton Road, Nottingham NG8 1BB, UK * Corresponding author. Article History: Received 16 February 2018; Accepted 31 July 2019 Byline: John H. Drake [john.drake@leicester.ac.uk] (*,a), Ahmed Kheiri [a.kheiri@lancaster.ac.uk] (b), Ender Ozcan [ender.ozcan@nottingham.ac.uk] (c), Edmund K. Burke [edmund.burke@leicester.ac.uk] (a)

Keywords Supply chain contracts; Nash bargaining; Multiple products; Supply chain management; Coordination Highlights * A comprehensive study on the zero wholesale price (ZWP) contracts. * Performances of ZWP contracts are examined under Nash bargaining models. * Comparing ZWP contracts with the greedy wholesale price (GWP) contract using mean-variance domination. * Uncovering the psychological perception aspects of ZWP and non-ZWP contracts. * The findings are robust to the number of product and channel leadership. Abstract In the real world, some manufacturers supply their products to retailers at a zero wholesale price (ZWP), and receive compensation by sharing the revenue or receiving some side payment from the retailers. In this paper, we analyse this kind of ZWP-based supply-chain contract. In the basic model, ordering is done either by the manufacturer or the retailer. For both of these cases, we explore ZWP-revenue-sharing (ZR) contracts, ZWP-side-payment (ZS) contracts, and ZWP-revenue-sharing-plus-side-payment (ZRS) contracts. We prove that, irrespective of the ordering scenario being retailer-led or manufacturer-led, only a ZRS contract can achieve win-win coordination. In the extended models, we first study the scenario with multiple products and discuss how a generalised ZRS contract can coordinate the supply chain efficiently. We then investigate greedy wholesale price (GWP)--based contracts, in which the manufacturer charges the retailer a wholesale price equal to the retail price. We find that a GWP-based revenue-sharing-plus-side-payment (GRS) contract and a ZRS contract can both achieve win-win coordination. However, the ZRS contract mean-variance dominates the GRS contract in bringing a lower level of risk to both the retailer and manufacturer if the unit production cost is sufficiently small. We further discuss cases of differences in the perception of ZWP versus non-ZWP contracts. Important managerial insights are derived from the findings. Author Affiliation: (a) Business Division, Institute of Textiles & Clothing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (b) School of Management, University of Liverpool, Chatham Street, Liverpool, United Kingdom * Corresponding author. Article History: Received 15 June 2019; Accepted 22 January 2020 (footnote) We sincerely thank Xuting Sun, Yingjia Wang, and Xiaoyan Xu for their helpful comments on an earlier version of this paper. Tsan-Ming Choi's research is supported by Research Grants Council (Hong Kong) -- General Research Fund (Grant No. PolyU 152294/16E). Byline: Tsan-Ming Choi [jason.choi@polyu.edu.hk] (a), Shu Guo [eunice.guo@liverpool.ac.uk] (b,*)

Keywords Supply chain management; Newsvendor model; Order-dependent lead times; Capacity expansion; Coordination mechanism Highlights * Proposes a two-stage newsvendor model with controllable lead time. * Links lead time to production capacity and order quantity decisions. * Investigates the supply chain actor's decisions under coordination and non-coordination. * Proposes a contract to coordinate the supply chain. Abstract This paper considers a supply chain consisting of a retailer for short life cycle products facing stochastic customer demand and a manufacturer that initiates production upon receipt of retail orders. Departing from the common view of the newsvendor problem, we assume that the delivery lead time is not fixed, but that both the retailer and the manufacturer have the option to shorten it. Shorter lead times enable the retailer to place orders closer to the start of the selling season where additional information on customer preferences has become available, reducing demand uncertainty. In the work at hand, lead time is assumed to depend on the order quantity, on the supplier's production capacity, and a fixed transportation delay. This paper proposes a model for determining the optimal order quantity and production capacity in centralized and decentralized settings. For the uncoordinated case, we show that if the retailer's ability to gather and analyze additional demand information is revealed to the manufacturer, the arising information asymmetry between the two parties can aggravate the double marginalization effect and, in turn, erode supply chain efficiency. In a coordinated supply chain, however, both parties have an incentive to align both order quantity and investments in lead time reduction. To coordinate the decentralized supply chain, we propose a buy-back contract that helps to leverage supply chain profitability. We conclude with an outlook on future research opportunities. Author Affiliation: (a) Department of Law and Economics, Technische Universitat Darmstadt, Hochschulstra[sz]e 1, 64289 Darmstadt, Germany (b) EMLYON Business School, DISP Lab, 23 Avenue Guy de Collongue, 69134 Ecully, France (c) Cass Business School, City University London, 106 Bunhill Row, EC1Y 8TZ London, United Kingdom * Corresponding author. Article History: Received 12 December 2018; Accepted 21 January 2020 Byline: Christoph H. Glock [glock@pscm.tu-darmstadt.de] (a,*), Yacine Rekik [rekik@em-lyon.com] (b), Jorg M. Ries [joerg.ries@city.ac.uk] (c)

Keywords Stochastic optimisation; Stochastic processes; Real options portfolio; Endogenous uncertainty; Decision/state-dependent uncertainty Highlights * Introduces an approach for valuing option portfolios under exogenous and endogenous uncertainties. * Studies a portfolio of real options to defer, to stage, to mothball, and to abandon. * Considers endogenous, decision-dependent cost to completion and state-dependent salvage value. * Illustrates applicability of approach by valuing expansion of a district heating network. * Demonstrates that ignoring endogenous uncertainty can lead to substantial misvaluation. Abstract Although the value of portfolios of real options is often affected by both exogenous and endogenous sources of uncertainty, most existing valuation approaches consider only the former and neglect the latter. In this paper, we introduce an approach for valuing portfolios of interdependent real options under both types of uncertainty. In particular, we study a large portfolio of options (deferment, staging, mothballing, abandonment) under conditions of four underlying uncertainties. Two of the uncertainties, decision-dependent cost to completion and state-dependent salvage value, are endogenous, the other two, operating revenues and their growth rate, are exogenous. Assuming that endogenous uncertainties can be exogenised, we formulate the valuation problem as a discrete stochastic dynamic program. To approximate the value of this optimisation problem, we apply a simulation-and-regression-based approach and present an efficient valuation algorithm. The key feature of our algorithm is that it exploits the problem structure to explicitly account for reachability -- that is the sample paths in which resource states can be reached. The applicability of the approach is illustrated by valuing an urban infrastructure investment. We conduct a reachability analysis and show that the presence of the decision-dependent uncertainty has adverse computational effects as it increases algorithmic complexity and reduces simulation efficiency. We investigate the way in which the value of the portfolio and its individual options are affected by the initial operating revenues, and by the degrees of exogenous and endogenous uncertainty. The results demonstrate that ignoring endogenous, decision- and state-dependent uncertainty can lead to substantial over- and under-valuation, respectively. Author Affiliation: (a) Department of Civil and Environmental Engineering, Imperial College London, London, UK (b) Risk and Resilience Program, International Institute for Applied Systems Analysis, Laxenburg, Austria (c) Department of Statistics and Operations Research, University of Vienna, Vienna, Austria (d) Centre for Transport Studies and Urban Systems Laboratory, Imperial College London, London, UK * Corresponding author at: Department of Civil and Environmental Engineering, Imperial College London, London, UK. Article History: Received 1 March 2018; Revised 20 December 2018; Accepted 22 January 2019 (footnote)[white star] This paper is a significantly expanded version of a paper first presented at the 21st Annual International Real Options Conference in Boston, MA (USA), in June/July 2017. Byline: Sebastian Maier [sebastian.maier@imperial.ac.uk] (*,a,b), Georg C. Pflug (c,b), John W. Polak (d)

Keywords Behavioral OR; Pricing; Platform operations; Mean-risk analysis; Blockchain Highlights * Explore on-demand platform operations with hired agents. * Consider service pricing with consumers having different risk attitudes. * Uncover the role played by the blockchain technology. * Compare the common pricing policy and the customized pricing policy. * Determine the value of blockchain technology mediated customized service pricing strategy. Abstract On-demand service platforms are popular nowadays. Many platforms hire agents to serve customers who are risk sensitive towards the waiting-time. In this paper, we apply the mean-risk theory to analytically explore how the risk attitude of customers affects the optimal service pricing decision of the on-demand platform, consumer surplus (CS) of customers, the expected profit (EP) and profit risk (PR) of the platform (and the hired service agents). In the basic model, assuming consumers are homogeneous, we find that if the customers are more risk averse (risk seeking), the optimal service price will drop (increase). Comparing among the three different risk attitudes of customers, we find that when the customers are risk seeking, the CS and the platform's EP are highest, even though the platform's PR is also highest. While the opposite happens when the customers are risk averse. In the extended model with a market including customers with different risk attitudes, the blockchain technology helps the platform assess the proportion of risk seeking, risk neutral and risk averse customers accurately. We explore the optimal service prices under both the common pricing policy and the customized pricing policy (with-respect-to customer's risk attitude), and derive the value of blockchain technology mediated customized service pricing strategy. We conclude by highlighting that the risk attitudes of customers play a critical role in determining the optimal on-demand service pricing, and the blockchain technology is a valuable technological tool to help. Author Affiliation: (a) Business Division, ITC, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (b) School of Management, University of Liverpool, Chatham Street, Liverpool, United Kingdom (c) Business School, Beijing Institute of Fashion Technology, Beijing, China (d) School of Economics and Management, Nanjing University of Science and Technology, Nanjing, China * Corresponding author. Article History: Received 8 June 2019; Accepted 22 January 2020 (footnote) We sincerely thank Professor Robert Graham Dyson (the editor), and anonymous reviewers for their insightful, critical and constructive comments on this paper. This paper is partially supported by The Hong Kong Polytechnic University (Project ID: P0009613), National Natural Science Foundation of China (Project account: 71501097), and Beijing Municipal Commission of Education in China (Project account: CIT&TCD201804043). Byline: Tsan-Ming Choi [jason.choi@polyu.edu.hk] (a), Shu Guo [eunice.guo@liverpool.ac.uk] (b), Na Liu [sxyln@bift.edu.cn] (c), Xiutian Shi [xtshi@njust.edu.cn] (d,*)

Keywords Vehicle routing problem; Multi-pickup and delivery problem; Sequential ordering problem; Branch-and-cut Highlights * Different mathmetical formulations are proposed for the multi-pickup and delivery problem with with time windows. * Several valid inequalities are presented. * A new type of formulation that removes symmetry is proposed. * Results indicate that the new type of formulation outperforms the other ones. Abstract In the multi-pickup and delivery problem with time windows (MPDPTW) a set of vehicles must be routed to satisfy a set of client requests between given origins and destinations. A request is composed of several pickups of different items, followed by a single delivery at the client location. This paper introduces two new formulations for the MPDPTW, the 2-index formulation, and the asymmetric representatives formulation. In addition, we also present an existing 3-index formulation for this problem and improve it by means of several preprocessing and valid inequalities. We solve the problem exactly via a branch-and-cut algorithm. We introduce several families of valid inequalities to strengthen the LP relaxations of the proposed formulations. Computational results are reported on different types of instances to firstly highlight the advantage of adding different families of valid inequalities then to compare the performance of the different formulations presented in this paper. While the heuristic and exact algorithms of the literature prove optimality for 16 instances containing up to 50 nodes, we prove optimality for 41 instances for cases containing up to 100 nodes from the existing benchmark set. Author Affiliation: (a) CIRRELT and Universite Laval, Quebec, Canada (b) Canada Research Chair in Integrated Logistics, Canada * Corresponding author. Article History: Received 30 May 2019; Accepted 20 January 2020 Byline: Imadeddine Aziez (a), Jean-Francois Cote [jean-francois.cote@fsa.ulaval.ca] (*,a), Leandro C. Coelho (a,b)

Keywords Transportation; Iterated local search; Memory; Multi-depot; Vehicle routing Highlights * The iterated local search algorithm (MBILSA) which is presented to solve the multi-depot open vehicle routing problem (MDOVRP) is based on the history of the search. * The MBILSA is effective and efficient, outperforming all existing algorithms for the MDOVRP. * The perturbation procedures are based on the memory of the search and are simple and effective. * We propose a fast and effective heuristic method to improve the open routes, which is based on their structure. Abstract The problem studied in this paper is the multi-depot open vehicle routing problem, which has the following two differences in relation to the classical vehicle routing problem: there are several depots; the vehicles do not return to the depot after delivering the goods to the customers, i.e., the end of the route is not the starting point. There are many practical applications for this problem, however the great majority of the studies have only addressed the open vehicle routing problem with a single depot. In this paper, we present an iterated local search algorithm, in which the moves performed during the local search are recalled and this historical search information is then used to define the moves executed inside the perturbation procedures. Therefore, it is recorded the number of times that each customer is moved during the local search. Since this information is continuously updated and changes in each iteration, the search is driven to potentially better regions of the solution space, and increases the chance of avoiding cycling, even when using deterministic perturbations. The performance of this algorithm was tested using a large set of benchmark problems and was compared with other algorithms, and the results show that it is very competitive. Author Affiliation: (a) Departamento de Gestao, Escola de Economia e Gestao, Universidade do Minho, Largo do Paco, 4704 -- 553 Braga, Portugal (b) CEMAPRE, ISEG, University of Lisbon, Portugal Article History: Received 24 April 2019; Accepted 7 January 2020 Byline: Jose Brandao [sbrandao@eeg.uminho.pt] (a,b)