{"title":"A flow shop scheduling problem with machine-dependent speeds: An ensemble approach with worst-case analysis","authors":"Insoo Park, Kangbok Lee, Michael Pinedo","doi":"10.1016/j.ejor.2025.09.018","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.09.018","url":null,"abstract":"We consider a flow shop scheduling problem to minimize the total weighted completion time with any given job having the same processing requirement at all stages and each machine operating at its own speed. This problem is known to be NP-hard even for just two machines. In this paper, we propose a simple yet efficient approximation algorithm that leverages the complementary strengths of existing approaches. We first establish that our algorithm achieves a strictly better approximation ratio than previously known methods for the two-machine case. We then extend our analysis to settings with more than two machines, focusing on scenarios where the number of distinct machine speeds is fixed. Finally, we use these insights to derive an approximation ratio for the general case of arbitrary machine speeds.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"18 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094096","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":"On the transition from make-to-stock to make-to-order","authors":"Yogendra Singh, Stephen M. Disney","doi":"10.1016/j.ejor.2025.08.053","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.08.053","url":null,"abstract":"Value stream mapping reveals there are two replenishment decisions and three classes of lead times in the manufacturing echelon of a supply chain. The first replenishment decision releases production orders to maintain the finished goods inventory (FGI), and the second replenishment decision issues supplier orders to replenish the raw materials inventory (RMI). The three different classes of lead times are (a) customer lead time, (b) production lead time, and (c) supplier lead time. We develop a z-transform transfer function model of this system to investigate its dynamic performance. We find that FGI costs increase in the production lead time and decrease in the customer lead time, whereas RMI costs increase in both the production lead time and the supplier lead time. We capture the dynamics of, and transition between, make-to-stock (MTS) and make-to-order (MTO) settings in a single unified model. We show that when transitioning from an MTS to an MTO supply chain, RMI costs decrease in the customer lead time for positively correlated demand and exhibit an odd-even oscillation for negatively correlated demand.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"87 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094098","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}
Nannan Wu, Yejun Xu, Zaiwu Gong, D. Marc Kilgour, Liping Fang
{"title":"Graph model for multiple composite decision makers with large-scale groups: Probability-hesitant fuzzy preference modeling and application","authors":"Nannan Wu, Yejun Xu, Zaiwu Gong, D. Marc Kilgour, Liping Fang","doi":"10.1016/j.ejor.2025.09.014","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.09.014","url":null,"abstract":"Whenever humans interact with others, conflict inevitably arises. Sometimes, multiple composite decision makers (CDMs) are involved, some of which may be large-scale groups. When making a decision or strategy selection, a CDM needs to consider the interests of the group and the wishes of individual decision makers (IDMs). For example, a CDM may judge a move to be an improvement only if a certain fraction of IDMs consider it so – in other words, only when the IDMs reach a certain degree of consensus. This paper proposes an index of group consensus on more preferred (IGCMP) and an index of group consensus on less preferred (IGCLP), and uses them to determine whether a CDM more or less prefers the current state to another and reflect the heterogeneous characteristics of CDMs, including conservative, aggressive, and eclectic. Accordingly, the conflict for multiple CDMs with large-scale groups is investigated in this paper from the perspective of group consensus within the framework of the Graph Model for Conflict Resolution (GMCR). At first, CDMs’ preferences are represented by probability-hesitant fuzzy preference relations, which can reflect the heterogeneity of IDMs and preference uncertainty of CDMs. Then, the new forms of the unilateral improvement list for CDMs and coalitions are developed based on IGCMP and IGCLP. Subsequently, five extended stability definitions and their relationships are studied. Finally, to demonstrate the effectiveness of the new method, it is applied to model a water pollution conflict in the Yangtze River Delta, China.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"60 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094100","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}
Ruozhen Qiu, Xuge Li, Minghe Sun, Yue Sun, Zhi-Ping Fan
{"title":"Online platform demand information sharing with upstream suppliers under the imbalanced market power structure","authors":"Ruozhen Qiu, Xuge Li, Minghe Sun, Yue Sun, Zhi-Ping Fan","doi":"10.1016/j.ejor.2025.09.009","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.09.009","url":null,"abstract":"This work investigates online platform information sharing strategies in a supply chain consisting of an online platform and two, one leading (Supplier A) and the other following (Supplier B), suppliers with an imbalanced market power structure under the agency mode. The online platform has demand information, and the two suppliers engage in horizontal retail price competition. Four information sharing scenarios are examined, including no information sharing (S1), full information sharing (S2), information sharing with only Supplier A (S3), and information sharing with only Supplier B (S4). Game models, involving a signaling game for scenario S3, are formulated. The equilibrium solutions are further examined, the supply chain members’ preferences are analyzed among the four demand information sharing scenarios, and the equilibrium information sharing outcomes are obtained. The findings indicate that Supplier A prefers scenario S2, Supplier B prefers scenario S3, and the online platform prefers scenario S3 when the demand variability is high or low and prefers scenario S2 when the demand variability is moderate. The equilibrium information sharing outcome can be any of the scenarios depending on the demand variability and the supplier acceptance probabilities of the platform’s information sharing agreements. Extensions of the main model are explored. The equilibrium results of the main model are robust under asymmetric market potentials, intuitive criterion and divinity criterion. Under the resale mode, Supplier B prefers scenario S2 or S3, while other results remain unchanged. When the two suppliers are in a coopetition relationship, the theoretical outcomes are significantly different from those of the main model.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"16 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094101","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 vessel traffic scheduling and berth allocation with restricted channel widths","authors":"Hongda Duan, Lixin Miao, Shuai Jia, Canrong Zhang, Jasmine Siu Lee Lam","doi":"10.1016/j.ejor.2025.09.016","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.09.016","url":null,"abstract":"Due to the surging volume of seaborne trade and high frequencies of port calls by vessels, seaports worldwide have been experiencing various levels of traffic congestion in the past few years. The incoming and outgoing vessel traffic in port areas is bottlenecked by the traffic infrastructure (e.g., navigation channels and inner anchorages) and the hydrological conditions (e.g., tidal effects) of a port, which can lead to significant congestion when vessel traffic and vessel service are not effectively planned. In this study, we investigate an integrated vessel traffic scheduling and berth allocation problem for congestion mitigation in a port. The problem encompasses the decision-making process of scheduling incoming and outgoing vessels in the navigation channels and anchorage areas of a port, and allocating berth space to vessels for service, so as to minimize the overall berthing and departure delay of vessels. In particular, we consider a practical scenario where the width of each channel may vary at different segments. This channel width restriction can render the problem much more difficult compared to a traditional setting with identical channel widths. We develop a binary integer programming model for the problem, and present a novel machine-learning-enhanced column generation algorithm for addressing this complex problem. Our method applies machine learning models to restrict vessels’ port stay times within limited time ranges, so that the search space for column generation can be reduced, leading to a trade-off between solution quality and computation efficiency. We validate the effectiveness of the proposed solution method utilizing both a case study of the Port of Shanghai and a computational study on synthetic large-scale instances.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"39 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094103","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}
Yufeng Shen, Xueling Ma, Yukun Bao, Gang Kou, Jianming Zhan
{"title":"A consensus method based on reinforcement learning for group decision-making","authors":"Yufeng Shen, Xueling Ma, Yukun Bao, Gang Kou, Jianming Zhan","doi":"10.1016/j.ejor.2025.08.054","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.08.054","url":null,"abstract":"In group decision-making (GDM), the consensus-reaching process (CRP) is essential for aligning the diverse opinions of decision-makers (DMs) to achieve collective agreements. However, the process often faces obstacles due to the uncertainty of DMs in terms of unit cost and willingness to adjust opinions. To this end, this study constructs a new GDM framework by introducing reinforcement learning (RL) to the CRP. In this framework, we design a unit cost learning algorithm based on RL. The algorithm introduces an action space based on linguistic expressions, and therefore exhibits strong interpretability. On this basis, a weight reward–penalty mechanism based on asymmetric Nash bargaining is further proposed. The mechanism takes marginal and adjustment contributions as objective criteria, which provides a reasonable basis for improving consensus outcomes and managing non-cooperative behaviors. The proposed model incorporates both interactive and automatic strategies: the former is able to accurately capture individuals’ willingness to cooperate with the help of RL, and the latter relies on optimization models to effectively reduce the time and cost spent on reaching consensus. Finally, we provide an example to illustrate the proposed approach and experimentally verify its feasibility and the potential of the RL framework.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"39 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094102","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":"An efficient algorithm for large-scale dynamic assortment planning problems","authors":"Lijue Lu, Hamed Jalali, Mozart B.C. Menezes","doi":"10.1016/j.ejor.2025.09.017","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.09.017","url":null,"abstract":"Single-period dynamic assortment planning involves the retailer’s selection of a set of products to offer and the determination of their initial inventory levels, considering stochastic demand and dynamic substitution. The objective is to maximize the expected revenue, subject to a capacity constraint. While existing heuristics are better suited to brick-and-mortar retailers with limited capacity, we introduce a novel heuristic designed to efficiently address the large-scale problems encountered by online retailers with high customer arrivals, a capacity of thousands of units, and extensive product variety. Through extensive simulation experiments across a range of customer types and demand scenarios, our method consistently delivers high-quality solutions while being significantly faster than existing approaches. We further validate our approach with a numerical example calibrated with real-world data from Wayfair, a major online home goods retailer. In this setting, our algorithm captures 90.16% of the expected revenue upper bound and delivers solutions in under 80 s. In contrast, existing approaches are unable to return solutions within a reasonable amount of time, highlighting the scalability and practical relevance of our method for large dynamic assortment planning problems.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"77 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094099","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":"Blockchain-enabled quality transparency and invoice tokenization in deep-tier supply chains","authors":"Penghui Guo, Gengzhong Feng, Kai Wang, Liqun Wei","doi":"10.1016/j.ejor.2025.09.010","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.09.010","url":null,"abstract":"In hierarchical deep-tier supply chains, private quality information in the headstream’s raw materials and financial constraints in the midstream’s product procurement significantly restrict the downstream’s sales and market supply. To address these issues, downstream retailers can implement blockchain technology to trace and transparentize the headstream’s quality information across the chain and finance the midstream by digitizing accounts payable, i.e., invoice tokenization. To investigate how quality information and financing strategies interact, we formulate a multilevel Stackelberg game to analyze a deep-tier supply chain involving a retailer who may adopt blockchain, a tier-1 capital-constrained supplier, and a tier-2 supplier who privately owns quality information and can provide trade credit financing to the tier-1 supplier. Intuitively, blockchain benefits the retailer since transparentizing quality information can attract more purchases. However, we find that this may not be true, especially when the expected quality is relatively low. Interestingly, we find that merely using blockchain-enabled transparency decreases suppliers’ profits, but further incorporating invoice tokenization can benefit them, potentially achieving a triple-win result, although two suppliers’ interests may not always align. Finally, as the expected quality rises, equilibrium results move from trade credit under no blockchain (NT) to trade credit under blockchain-enabled transparency (BT) and then to blockchain-enabled transparency and invoice tokenization (BI) if the price of raw materials is high; otherwise, BI is more likely to be the equilibrium result. Our findings uncover how to utilize blockchain-driven traceability and invoice tokenization strategically.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"87 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094106","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":"An efficient hybridization of Graph Representation Learning and metaheuristics for the Constrained Incremental Graph Drawing Problem","authors":"Bruna Cristina Braga Charytitsch, Mariá Cristina Vasconcelos Nascimento","doi":"10.1016/j.ejor.2025.08.034","DOIUrl":"https://doi.org/10.1016/j.ejor.2025.08.034","url":null,"abstract":"Hybridizing machine learning techniques with metaheuristics has attracted significant attention in recent years. Many attempts employ supervised or reinforcement learning to support the decision-making of heuristic methods. However, in some cases, these techniques are deemed too time-consuming and not competitive with hand-crafted heuristics. This paper proposes a hybridization between metaheuristics and a less expensive learning strategy to extract the latent structure of graphs, known as Graph Representation Learning (GRL). For such, we approach the Constrained Incremental Graph Drawing Problem (C-IGDP), a hierarchical graph visualization problem. There is limited literature on methods for this problem, for which Greedy Randomized Search Procedures (GRASP) heuristics have shown promising results. In line with this, this paper investigates the gains of incorporating GRL into the construction phase of GRASP, which we refer to as Graph Learning GRASP (GL-GRASP). In computational experiments, we first analyze the results achieved considering different node embedding techniques, where deep learning-based strategies stood out. The evaluation considered the primal integral measure that assesses the quality of the solutions according to the required time for such. According to this measure, the best GL-GRASP heuristics demonstrated superior performance than state-of-the-art literature GRASP heuristics for the problem. A scalability test on newly generated denser instances under a fixed time limit further confirmed the robustness of the GL-GRASP heuristics.","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"16 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181245","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}
Daan Caljon , Jente Van Belle , Jeroen Berrevoets , Wouter Verbeke
{"title":"Optimizing treatment allocation in the presence of interference","authors":"Daan Caljon , Jente Van Belle , Jeroen Berrevoets , Wouter Verbeke","doi":"10.1016/j.ejor.2025.09.015","DOIUrl":"10.1016/j.ejor.2025.09.015","url":null,"abstract":"<div><div>In Influence Maximization (IM), the objective is to — given a budget — select the optimal set of entities in a network to target with a treatment so as to maximize the total effect. For instance, in marketing, the objective is to target the set of customers that maximizes the total response rate, resulting from both direct treatment effects on targeted customers and indirect, spillover, effects that follow from targeting these customers. Recently, new methods to estimate treatment effects in the presence of network interference have been proposed. However, the issue of how to leverage these models to make better treatment allocation decisions has been largely overlooked. Traditionally, in Uplift Modeling (UM), entities are ranked according to estimated treatment effect, and the top entities are allocated treatment. Since, in a network context, entities influence each other, the UM ranking approach will be suboptimal. The problem of finding the optimal treatment allocation in a network setting is NP-hard, and generally has to be solved heuristically. To fill the gap between IM and UM, we propose OTAPI: Optimizing Treatment Allocation in the Presence of Interference to find solutions to the IM problem using treatment effect estimates. OTAPI consists of two steps. First, a causal estimator is trained to predict treatment effects in a network setting. Second, this estimator is leveraged to identify an optimal treatment allocation by integrating it into classic IM algorithms. We demonstrate that this novel method outperforms classic IM and UM approaches on both synthetic and semi-synthetic datasets.</div></div>","PeriodicalId":55161,"journal":{"name":"European Journal of Operational Research","volume":"328 2","pages":"Pages 620-632"},"PeriodicalIF":6.0,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094129","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}