European Journal of Operational Research最新文献

{"title":"Prelim p. 2; First issue - Editorial Board","authors":"","doi":"10.1016/S0377-2217(25)00740-4","DOIUrl":"10.1016/S0377-2217(25)00740-4","url":null,"abstract":"","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"328 1","pages":"Page ii"},"PeriodicalIF":6.0,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Order advancement in a multi-item two-echelon system: Theory and case study","authors":"Q. ten Hagen, M.C. van der Heijden, D.R.J. Prak","doi":"10.1016/j.ejor.2025.09.033","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.09.033","url":null,"abstract":"Motivated by a case at a large retail chain in the Netherlands, we study short-term order advancement in a multi-item, two-echelon system consisting of a distribution centre (DC) and many stores. Application of traditional inventory replenishment rules may lead to a strongly fluctuating workload for order picking, and thus capacity problems, at the DC. By advancing replenishments we can balance the workload at the DC without negative impact on the service levels towards the final customer, trading off backroom usage and handling of incoming orders under limited shelf space at the individual stores. We develop a performance evaluation method and various heuristics to find good order advancement solutions. We select the heuristic with the best trade-off between cost performance and computation time for large problem instances (i.e., hundreds of stores and thousands of stock keeping units). Application of this heuristic to case data from the retail chain shows around 10 % overall cost reduction. In particular, the capacity shortage at the DC – a major issue in our case study – is reduced by almost 67 % at the expense of more peaks in handling workload at the stores (42 % increase in order lines exceeding store capacity), and an increase of 7.6 % in backroom usage. Sensitivity analysis shows that a planning horizon of about two weeks performs best, and that the cost reduction potential is heavily influenced by the available shelf space.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"7 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145228833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated estimate-and-optimize decision trees learning for two-stage linear decision-making problems","authors":"Rafaela Ribeiro, Bruno Fanzeres","doi":"10.1016/j.ejor.2025.08.048","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.08.048","url":null,"abstract":"Several decision-making under uncertainty problems found in industry and the scientific community can be framed as stochastic programs. Traditionally, these problems are addressed using a sequential two-step process, referred to as predict/estimate-then-optimize, in which a predictive distribution of the uncertain parameters is firstly estimated and then used to prescribe a decision. However, most predictive methods focus on minimizing forecast error, without accounting for its impact on decision quality. Moreover, practitioners often emphasize that their main goal is to obtain near-optimal solutions with minimum decision error, rather than least-error predictions. Therefore, in this work, we discuss a new framework for integrating prediction and prescription into the predictive distribution estimation process to be subsequently used to devise a decision. We particularly focus on decision trees and study decision-making problems representable as contextual two-stage linear programs. Firstly, we propose a workable framework along with a non-convex optimization model to account for the impact of the underlying decision-making problem on the predictive distribution estimation process. Then, we recast the non-convex model as a Mixed-Integer Programming (MIP) problem. Acknowledging the difficulty of the MIP reformulation to scale to large-scale instances, we devise a computationally efficient Heuristic strategy for the estimation problem leveraging the structure intrinsic to decision trees. A key feature of the proposed decision-making framework is its ability to instantly assess decisions by mapping new contexts to a leaf and retrieving the precomputed solution of the corresponding two-stage problem. A set of numerical experiments is conducted to illustrate the capability and effectiveness of the proposed framework using three distinct two-stage decision-making problems. We benchmark the proposed approach against prescriptions devised by various alternative frameworks. Five predict/estimate-then-optimize benchmarks that rely on commonly used predictive and distribution estimation methods and three benchmarks based on integrated predict-and-optimize decision-making processes are considered. We focus on evaluating solution quality and the computational performance of the MIP reformulation.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"113 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145228801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-cycle production network design under supply uncertainty","authors":"Nadia Jaoui, Walid Klibi, Nizar El Hachemi, Tarik Aouam, Michel Fender","doi":"10.1016/j.ejor.2025.09.008","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.09.008","url":null,"abstract":"This study addresses a novel production network design problem with an expanded scope inspired by a real-world business context. The problem involves strategic decisions for a long-term horizon regarding production sites’ location and capacity, suppliers’ selection, and transportation modes choice, given in-house vs external service providers’ options. To support these decisions, we integrate key tactical decisions for a large set of planning periods, involving flows between origin–destination pairs, production levels based on bill of materials, and inventory levels. Additionally, we consider uncertainty in raw material availability at suppliers, disruption in production capacities, and perturbation in transportation flows. First, we develop a multi-stage stochastic program that re-optimizes strategic decisions at each design period. Then, this program is reformulated into a multi-cycle two-stage stochastic model. Uncertainty is modeled through a finite set of scenarios generated using the Latin hypercube sampling technique, and the sample average approximation method is used to calibrate the sample size. Given the challenging solvability of the model, we proposed an advanced solution approach that builds on the recently introduced Partial Benders Decomposition (PBD) technique with new scenario creation strategies. Our experiments highlight the superiority of the proposed PBD’s variant in terms of solution quality and time reaching the best solution, compared to classical approaches. Furthermore, we demonstrate the benefits of enlarging the scope of the production network design problem by integrating all strategic decisions, which can yield gains of up to 36% compared to addressing them separately. Finally, we underscore the importance of stochastic modeling, contributing to cost reductions of over 3% compared to the deterministic counterpart.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"98 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145228926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Markov decision processes: Monotonicity of optimal policy in exponential and quasi-hyperbolic discounting parameters","authors":"Hakan Kılıç ,&nbsp;Pelin Gülşah Canbolat ,&nbsp;Evrim Didem Güneş","doi":"10.1016/j.ejor.2025.09.013","DOIUrl":"10.1016/j.ejor.2025.09.013","url":null,"abstract":"<div><div>Intertemporal preferences of decision makers, i.e., the way they discount delayed utilities, impact their decisions. Empirical evidence suggests that individuals commonly have hyperbolic discounting preferences. This can result in time-inconsistent behavior, e.g., procrastination, which may be a barrier to adopting preventive behavior such as machine maintenance and patient adherence to treatment. In this paper, we theoretically compare the actions of individuals based on their discounting characteristics. We consider the Hyperbolic Discounting (HD) model, which is more representative of individual behavior than Exponential Discounting (ED). We formulate a discrete-time finite-horizon Markov decision process with Quasi-Hyperbolic Discounting (QHD), an analytically tractable function representing HD and present sufficient conditions that ensure the monotonicity of the optimal policy in the discounting parameters. We consider submodular maximization or supermodular maximization problems. Our paper is the first to investigate the monotonicity of the optimal policy in QHD parameters for these problems. Moreover, we compare the optimal actions under ED and QHD. We apply our results to the settings of machine maintenance, individual health behavior and inventory control. We provide numerical examples that show there might not be monotonicity if our sufficient conditions are not met. Also, we explore the discrepancy between the expected total exponentially-discounted rewards of the actions obtained from QHD and of the actions that are optimal under ED, and observe that this discrepancy is affected mainly by the present bias.</div></div>","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"328 3","pages":"Pages 877-893"},"PeriodicalIF":6.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement learning algorithm for reusable resource allocation with unknown rental time distribution","authors":"Ziwei Wang, Jie Song, Yixuan Liu, Jingtong Zhao","doi":"10.1016/j.ejor.2025.09.012","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.09.012","url":null,"abstract":"We explore a scenario where a platform must decide on the price and type of reusable resources for sequentially arriving customers. The product is rented for a random period, during which the platform also extracts rewards based on a prearranged agreement. The expected reward varies during the usage time, and the platform aims to maximize revenue over a finite horizon. Two primary challenges are encountered: the stochastic usage time introduces uncertainty, affecting product availability, and the platform lacks initial knowledge about reward and usage time distributions. In contrast to conventional online learning, where usage time distributions are parametric, our problem allows for unknown distribution types. To overcome these challenges, we formulate the problem as a Markov decision process and model the usage time distribution using a hazard rate. We first introduce a greedy policy in the full-information setting with a provable 1/2-approximation ratio. We then develop a reinforcement learning algorithm to implement this policy when the parameters are unknown, allowing for non-parametric distributions and time-varying rewards. We further prove that the algorithm achieves sublinear regret against the greedy policy. Numerical experiments on synthetic data as well as a real dataset from TikTok demonstrate the effectiveness of our method.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"326 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The optimal repair policy for an unreliable production system with limited available spare parts","authors":"Ludwig Brieditis, Gudrun P. Kiesmüller, Filip Malmberg","doi":"10.1016/j.ejor.2025.09.007","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.09.007","url":null,"abstract":"In this study, a serial production system consisting of two machines and an intermediate finite buffer is considered. The machines have random processing times, and each machine contains one unit of a common critical component that is subject to random breakdown. Broken components must be replaced from an inventory of ready-for-use spare parts to restore machine functionality. This inventory is replenished according to a one-for-one replenishment policy with an externally given base stock level. Due to the limited availability of spare parts, the sequence in which the machines are to be repaired and whether they should be repaired immediately must be decided. The objective of our study is the maximization of the expected total discounted revenue per time unit over an infinite planning horizon.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"28 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A stochastic location-inventory problem in multi-period closed-loop leased pallet pooling systems with palletized transport","authors":"Xiangling Hu, Ying Dai, Fei Yang, Zujun Ma","doi":"10.1016/j.ejor.2025.09.029","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.09.029","url":null,"abstract":"Leased pallet pooling is widely adopted by companies using palletized transport to avoid supply chain complexity. This study investigates the integrated location-inventory problem with stochastic demand and returns in multi-period closed-loop leased pallet pooling systems with palletized transport (LPPS-PT), considering hierarchical operation centres (OCs), cyclic inventory in both forward and reverse flows, cross-regional and cross-enterprise characteristics of palletized transport, and initializing inventory configuration. The incorporated factors particularly complicate the distribution and return of leased pallets, leading to highly interdependent and complex location and inventory decisions. The problem is formulated as a two-stage stochastic programming model and solved by a tailored sample average approximation framework embedded with a progressive hedging algorithm with scenario bundles. Numerical experiments are performed on instances generated from real-world data to prove the efficiency of the proposed method. We observe that the LPPS-PT with hierarchical OCs enhances cost-effectiveness and enjoys economies of scale, the indicators of the palletized transport network and the quality of returned pallets significantly affect LPPS-PT design, and the type of stochastic demand has little effect on strategic decisions but considerably affects tactical decisions. Some managerial insights are drawn from the results.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"18 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A unified and computationally efficient framework for procurement portfolio optimization with option contracts","authors":"Xinyu Wang, Jiafu Tang, Bo Chen, Bin Hou","doi":"10.1016/j.ejor.2025.09.003","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.09.003","url":null,"abstract":"Efficient procurement decisions are crucial for maintaining stable material flows in competitive supply chains. This paper introduces a unified and computationally efficient framework for the procurement portfolio problem with option contracts (PPP-OC) in a two-echelon supply chain, consisting of multiple suppliers and one buyer. The buyer, facing uncertain demand, must reserve capacity from suppliers who offer a two-part fee structure: option price and strike price. We equivalently transform the PPP-OC into a transportation problem (TP) that can be solved efficiently. We propose three dominance rules among option contracts to filter out invalid options, simplifying the problem. Additionally, we present a binary-search-based (BSB) algorithm, offering significantly lower computational complexity than existing methods. We also address five PPP-OC variants, namely, procurement settings with fixed costs and a reserving quantity window, stockout allowances, spot market integration, multi-channel procurement with supply risks, and block reservations. These variants are solved efficiently using our TP-based framework, demonstrating the flexibility and robustness of our framework across various procurement settings. An extensive numerical study evaluates the efficiency of our proposed solution methods in various procurement scenarios, verifies the dominance rules and identifies active options, and examines the sensitivity of the optimal solution to varying stockout cost, demand variability, and levels of supply risk.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"77 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145228800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Continuous-time optimal investment with portfolio constraints: A reinforcement learning approach","authors":"Huy Chau ,&nbsp;Duy Nguyen ,&nbsp;Thai Nguyen","doi":"10.1016/j.ejor.2025.08.032","DOIUrl":"10.1016/j.ejor.2025.08.032","url":null,"abstract":"<div><div>In a reinforcement learning (RL) framework, we study the exploratory version of the continuous time expected utility (EU) maximization problem with a portfolio constraint that includes widely-used financial regulations such as short-selling constraints and borrowing prohibition. The optimal feedback policy of the exploratory unconstrained classical EU problem is shown to be Gaussian. In the case where the portfolio weight is constrained to a given interval, the corresponding constrained optimal exploratory policy follows a truncated Gaussian distribution. We verify that the closed form optimal solution obtained for logarithmic utility and quadratic utility for both unconstrained and constrained situations converge to the non-exploratory expected utility counterpart when the exploration weight goes to zero. Finally, we establish a policy improvement theorem and devise an implementable reinforcement learning algorithm by casting the optimal problem in a martingale framework. Our numerical examples show that exploration leads to an optimal wealth process that is more dispersedly distributed with heavier tail compared to that of the case without exploration. This effect becomes less significant as the exploration parameter is smaller. Moreover, the numerical implementation also confirms the intuitive understanding that a broader domain of investment opportunities necessitates a higher exploration cost. Notably, when subjected to both short-selling and money borrowing constraints, the exploration cost becomes negligible compared to the unconstrained case.</div></div>","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"328 3","pages":"Pages 1068-1092"},"PeriodicalIF":6.0,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}