Computers & Operations Research最新文献

{"title":"Great Deluge-based metaheuristic incorporating integer nonlinear programming for modeling and solving dynamic capability-based machine layout problem","authors":"Adil Baykasoğlu ,&nbsp;Kemal Subulan ,&nbsp;Alper Hamzadayı","doi":"10.1016/j.cor.2025.107302","DOIUrl":"10.1016/j.cor.2025.107302","url":null,"abstract":"<div><div>This paper introduces a novel Dynamic Capability-Based Machine Layout (DCB-ML) problem by integrating the Quadratic Assignment Problem (QAP) formulation with a Dynamic Capability-Based Part Flow Assignment (DCB-PFA) problem. This integration enables the simultaneous consideration of machines’ processing capabilities, routing flexibility, dynamic flow assignment, and machine capacity utilization. First, a new Integer Nonlinear Programming (INLP) model is developed. The dynamic part flows are determined via the DCB-PFA sub-problem, while machine–location assignments are obtained by solving QAP. To address the complex nature of this problem, a hybrid solution approach is proposed that combines a Great Deluge Algorithm (GDA) with a Mixed-Integer Linear Programming (MILP) model, complemented by local search procedures. Since the problem has a decomposable structure, the proposed approach allows each sub-problem to be addressed independently, while the overall solution quality is jointly evaluated. Decomposition reduces the size of the resulting MILP model, as several binary variables and assignment constraints are eliminated. The proposed hybrid approach is also compared with the INLP and its linearized equivalent on several test problems. For large-scale problems with medium to high capability overlaps, nonlinear and MIP solvers fail to obtain feasible solutions, whereas the proposed approach can efficiently generate high-quality solutions within reasonable times. Moreover, when the effects of different machine-capability overlaps are investigated, it is observed that the solution of the problem will be more complex in the case of higher machine-capability overlaps. However, considering machine capabilities improves overall layout scores and eliminates the necessity of frequent reconfigurations, which is costly and time-consuming.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"185 ","pages":"Article 107302"},"PeriodicalIF":4.3,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Column generation-based matheuristic for pickup and delivery problem with two-dimensional loading","authors":"Ohhyun Kweon,&nbsp;Byung-In Kim","doi":"10.1016/j.cor.2025.107300","DOIUrl":"10.1016/j.cor.2025.107300","url":null,"abstract":"<div><div>The pickup and delivery problem (PDP) is a well-known NP-complete problem that aims to minimize route length while satisfying all customer requests. While PDP has traditionally considered one-dimensional loading constraints such as weight, this study addresses more realistic loading constraints by incorporating rectangular items. This study restricted item rotation and reloading by assuming that all the items are too heavy to move. A column generation-based matheuristic is proposed to construct the routing sequence. This is followed by a packing algorithm that includes a mixed-integer linear programming model, a constraint programming model, and an open space-based packing heuristics to verify whether the items can be packed based on the route. Finally, logic-based Benders decomposition was applied to refine the routing sequence. When tested against benchmark instances and compared with a state-of-the-art algorithm, the proposed algorithm performed better.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"186 ","pages":"Article 107300"},"PeriodicalIF":4.3,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145322691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A reinforcement learning-improved genetic algorithm for order reorganization-driven energy-efficient flexible job-shop hybrid batch scheduling towards mass personalized manufacturing","authors":"Haiqi Zou ,&nbsp;Kaipu Wang ,&nbsp;Zhuhao Hou ,&nbsp;Yibing Li ,&nbsp;Jun Guo ,&nbsp;Liang Gao","doi":"10.1016/j.cor.2025.107304","DOIUrl":"10.1016/j.cor.2025.107304","url":null,"abstract":"<div><div>Within the mass personalized manufacturing context, diverse requirements from high-volume custom orders impart large-scale discrete characteristics to production systems: proliferating product varieties coupled with drastically reduced batch sizes per process type. This introduces substantial complexities through frequent equipment changeovers and production control challenges. To address this, we propose a collaborative optimization framework integrating order reorganization with hybrid batching. A flexible job-shop scheduling model is formulated with dual minimization objectives of makespan and energy consumption. We subsequently develop a reinforcement learning-improved multi-objective genetic algorithm (RLMOGA) featuring: (1) nine neighborhood search strategies integrated into an adaptive action space, (2) Q-learning-driven dynamic operator selection for enhanced optimization efficiency. Extensive comparative experiments validate RLMOGA’s efficacy. In an industrial case study, the methodology achieves 29.20 % makespan reduction and 29.41 % energy savings, demonstrating simultaneous optimization of production efficiency and sustainable manufacturing objectives.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"185 ","pages":"Article 107304"},"PeriodicalIF":4.3,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-enriched distributionally robust optimization and hybrid decomposition to joint optimization of transportation hub network design and pricing","authors":"Seyed Ashkan Hosseini Shekarabi ,&nbsp;Reza Kiani Mavi ,&nbsp;Flavio Romero Macau ,&nbsp;Ali Papi ,&nbsp;Ashkan Teymouri","doi":"10.1016/j.cor.2025.107291","DOIUrl":"10.1016/j.cor.2025.107291","url":null,"abstract":"<div><div>Freight transportation within the hub-assisted systems enhances economies of scale for carriers by consolidating logistics operations through central hubs, thereby increasing efficiency and reducing costs. Optimally designing such transportation systems creates competitive advantages for carriers, enabling them to reduce shipping prices and, consequently, capture a larger share of the market from shippers’ demand. This research addresses the joint optimization of intermodal transportation hub network design and pricing decisions under price-dependent and uncertain demand, with the objective of maximizing carrier’s profit. Since the uncertain shipping demand follows a nonlinear relation with price, the carrier not only optimizes shipping network design but also seeks optimal pricing strategies to attract shippers and increase market share. In the studied transportation network, each origin–destination (O-D) freight shipping demand is first routed from its origin to the origin hub (OH); The freight is then transported from the OH to the destination hub (DH), and finally, it is delivered from the DH to its destination. Following the problem setting and formulation, we first construct a data-driven ambiguity set using machine learning algorithms and then develop a two-stage distributionally robust optimization (DRO) model to address shipping demand uncertainty. To enhance computational efficiency, we develop a hybrid solution approach combining machine learning and decomposition (ML-Decomposition). The proposed ML-Decomposition method first partitions the problem into sub-problems focusing on hub network design, freight flow assignment, and pricing. Then, it employs second-order nonlinear regression to determine the optimal price and utilizes Benders-style cutting plane decomposition, along with valid inequalities, to jointly optimize hub network design and flow assignment. Finally, to assess the effectiveness of the proposed ML-Decomposition solution method and evaluate the robustness of DRO model, a comprehensive computational study is conducted on a diverse set of instances, demonstrating the superiority of the proposed methodology. Across these benchmarks, the proposed ML-Decomposition attains up to 19.5 % higher profit and reduces computational time by as much as 79 % compared with off-the-shelf Gurobi solver. Furthermore, the benefit of incorporating pricing decisions is analyzed to derive valuable managerial insights.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"186 ","pages":"Article 107291"},"PeriodicalIF":4.3,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145360085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An adaptive and efficient path planning algorithm for UAV navigation in complex environments","authors":"Yonggang Wang ,&nbsp;Guoliang Wang","doi":"10.1016/j.cor.2025.107296","DOIUrl":"10.1016/j.cor.2025.107296","url":null,"abstract":"<div><div>Efficient and reliable path planning is critical for autonomous unmanned aerial vehicle (UAV) navigation in complex environments, including urban obstacle fields and sensor-restricted airspaces. This paper proposes the rapidly converging and greedy-optimized RRT* (RGO-RRT*) algorithm, which integrates four adaptive modules to enhance planning performance. These include: (1) region-based probabilistic sampling that prioritizes high-potential regions to reduce redundant exploration and accelerate convergence; (2) adaptive step-size adjustment based on obstacle density for fine maneuverability in cluttered areas and rapid expansion in open spaces; (3) dynamic goal biasing that gradually increases goal attraction to balance exploration and convergence; and (4) an improved artificial potential field with an adaptive repulsion model to mitigate local minima and ensure smoother trajectories. Additionally, three auxiliary strategies — bidirectional tree expansion, greedy optimization, and feasibility constraints-are employed to further refine path quality and search efficiency. Extensive simulations are conducted in four structurally diverse environments to evaluate performance under various levels of obstacle density and geometric complexity. Results show that RGO-RRT* consistently outperforms five benchmark algorithms (RRT*, Bi-RRT*, APF-RRT*, Bi-APF-RRT*, and Improved Bi-APF-RRT*), achieving up to 83.1% fewer iterations, 11.7% shorter path lengths, and 87.9% reduction in planning time. These findings demonstrate the method’s robustness, efficiency, and applicability to UAV navigation in cluttered scenarios.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"185 ","pages":"Article 107296"},"PeriodicalIF":4.3,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The stochastic and dynamic inventory routing problem with fine time granularity: A reinforcement learning approach","authors":"Felipe Lagos","doi":"10.1016/j.cor.2025.107298","DOIUrl":"10.1016/j.cor.2025.107298","url":null,"abstract":"<div><div>Reinforcement learning (RL) is an approach in which machines solve learning problems effectively, evaluating designs through mathematical analysis or computational experiments. In this work, we apply and extend these RL methods to the inventory routing problem (IRP), a problem that integrates inventory management with vehicle routing and delivery scheduling decisions over several periods. We present a new problem, the stochastic and dynamic IRP with fine time granularity (SDIRPFG), in which the agent must make real-time plan revisions under uncertainty, with time periods representing a fine time discretization. Information on customer inventory levels is provided to the agent, who makes delivery and routing decisions, changing or adjusting the current plan in place to handle events that arise during the operation. We propose benchmark policies for the SDIRPFG, myopic, rollout and cost function approximation (CFA) policies, and we also present online Monte Carlo (MC) control methods with value function approximations (VFAs). For the last method, we propose new learning methodologies and approximations. In particular, we introduce function approximations using Fourier bases, an interpretable approximation barely studied in the literature. In an extensive computational study, including synthetic and real-world instances, we test our policies, methods, and VFAs. We study the convergence of objective functions, provide guidelines on how to use our methods, and report SDIRPFG insights and characteristics. Using cost functions that we can plot, we show that our VFA is interpretable.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"185 ","pages":"Article 107298"},"PeriodicalIF":4.3,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The obnoxious facilities p-center problem with forbidden regions","authors":"Pawel Kalczynski,&nbsp;Zvi Drezner","doi":"10.1016/j.cor.2025.107297","DOIUrl":"10.1016/j.cor.2025.107297","url":null,"abstract":"<div><div>We formulate a planar obnoxious <span><math><mi>p</mi></math></span>-center problem with circular forbidden regions, such as a minimum required distance from some or all the demand points. We use geometric computation to develop a tight lower bound on the optimal value of the objective function and propose solution techniques which yield optimal solutions to several multiple large-scale <em>tsplib</em> instances in less than few hours’ time. We observe that, for larger forbidden regions, the obnoxious <span><math><mi>p</mi></math></span>-center problem reaches the best possible overall objective for a relatively small number of facilities. Our proposed solution approach is particularly useful in instances with multiple overlapping forbidden regions.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"185 ","pages":"Article 107297"},"PeriodicalIF":4.3,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating the physical and digital strategies for supplier: the role of offline showroom and augmented reality experiences","authors":"Zhitang Li ,&nbsp;Benjamin Lev","doi":"10.1016/j.cor.2025.107294","DOIUrl":"10.1016/j.cor.2025.107294","url":null,"abstract":"<div><div>As the retail landscape evolves with the rise of digital technologies, the fusion of online and offline experiences is becoming a key strategy for businesses seeking to enhance customer engagement. This study examines the evolving dynamics of Online-to-Offline (O2O) retail, focusing on the integration of offline showroom experience services and online Augmented Reality (AR) endorsements. Offline showrooms allow consumers to evaluate products physically, while AR services provide immersive online experiences. Our findings highlight several interesting yet challenging insights. First, suppliers may shift from single-channel strategies to integrated interaction-based services when both showroom and AR effects are strong; however, when AR effects are weaker, offline showrooms become more dominant. Second, both offline and AR services stimulate respective service investments and improve supplier profitability, revealing the dual role of these channels as both substitutes and complements. Third, suppliers are more likely to pursue integration strategies when integration costs are low, but intriguingly, they may still integrate even under high costs if the interactive benefits are sufficiently large. Finally, pricing and service quality differentiation across channels illustrate the strategic difficulty of maintaining consistency in omnichannel environments. This research underscores the importance of balancing AR and showroom services, with integration offering strategic advantages for businesses like Apple Inc., especially when backed by strong financial resources to support higher integration costs.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"185 ","pages":"Article 107294"},"PeriodicalIF":4.3,"publicationDate":"2025-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On-demand meal delivery: Drone scheduling with battery replacement optimization","authors":"Wenqian Liu ,&nbsp;Yandong He ,&nbsp;Ginger Y. Ke ,&nbsp;Lianmin Zhang","doi":"10.1016/j.cor.2025.107295","DOIUrl":"10.1016/j.cor.2025.107295","url":null,"abstract":"<div><div>Drones have become a promising solution for on-demand delivery thanks to their ability to travel fast and navigate without road restrictions. In the context of direct meal delivery using drones, it is a common practice to replace the drone’s battery after each round trip between the central launch site and customer locations to prevent power interruptions. This practice can lead to frequent battery replacements, resulting in increased downtime and decreased drone utilization. To enhance the efficiency of drone delivery, we take into account load-dependent energy consumption for the drones and optimize battery replacement along with drone scheduling. A mixed integer programming formulation is constructed to mathematically capture the problem. Additionally, we develop a time-expanded network flow method and a tailored hybrid variable neighborhood search algorithm to solve the problem exactly and heuristically. Computational studies validate the effectiveness and efficiency of the proposed operational model and solution approaches. The results indicate that optimizing battery replacement can induce an increase in on-time deliveries by up to 7.69% compared to replacing batteries after each return, and by 14.29% compared to only replacing them when energy levels are low. Such benefits are particularly significant in scenarios with tighter delivery deadlines and longer battery replacement times.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"185 ","pages":"Article 107295"},"PeriodicalIF":4.3,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mitigating choice model ambiguity: A consensus framework and its application to assortment optimization","authors":"Öykü Naz Attila,&nbsp;Sanjay Dominik Jena,&nbsp;Walter Rei","doi":"10.1016/j.cor.2025.107264","DOIUrl":"10.1016/j.cor.2025.107264","url":null,"abstract":"<div><div>Discrete choice models have become a popular tool to accurately predict complex choice behavior. Due to a variety of possible error sources, estimated choice models tend to be subject to ambiguity, inducing different optimal decisions of highly varying quality. This study aims at mitigating choice model ambiguity associated with a given set of models in terms of their ability to yield optimal decisions. We propose a framework and a set of performance metrics to assess the reliability of choice models and their induced decisions. The use of this framework is then exemplified in the context of rank-based choice models for assortment optimization. Extensive sets of numerical results suggest that our proposed approaches indeed allow decision-makers to identify choice models that are likely to produce high quality decisions, boosting confidence in using choice models in practice. While robust optimization on the original set of choice models tends to be rather conservative, we then use the proposed metrics to reduce the size of the ambiguity set, allowing us to improve the expected assortment quality and the overall downside risk. Given the practical usefulness of robust optimization in this context, we further propose a decomposition algorithm, solving the optimization problem in a fraction of the original time and revealing that only a few among a large set of choice models are determinant in optimal robust solutions.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"185 ","pages":"Article 107264"},"PeriodicalIF":4.3,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}