Computers & Operations Research最新文献

{"title":"AI automatic decision in newsvendor model with Nash bargaining fairness concern","authors":"Rui Hou,&nbsp;Yishen Cen,&nbsp;Jianxin Chen","doi":"10.1016/j.cor.2025.107227","DOIUrl":"10.1016/j.cor.2025.107227","url":null,"abstract":"<div><div>This paper investigates the impact of artificial intelligence (AI) automatic ordering and producing decisions on fairness-concerned supply chains under the newsvendor model. We develop a dyadic supply chain model in which the manufacturer acts as the Stackelberg leader while the retailer serves as the follower in a push supply chain. In contrast, their roles are switched in a pull supply chain. We assume that only human decision-making leads to decision regret behavior, whereas AI-automated decision-making does not. Without adopting AI, our results show that fairness concern does not necessarily lead to a decreasing quantity in ordering or producing, which is different from most previous studies. Different from the prior findings, our work reveals that in binding equilibrium, if fairness concerns are considered, the order quantity will decrease, while in non-binding equilibrium, the order quantity may not necessarily be less than the previous results. Interestingly, when decision regret bias is considered for fairness-concerned decision-makers, we can obtain quantity coordination solutions for supply chains under specific conditions. With adopting AI, our results show that increasing fairness concerns are beneficial for improving the follower’s profit while at the expense of sacrificing the leader’s profit margins, while the leader can only benefit from AI adoption when the decision regret bias of the follower is relatively high. It is noteworthy that under certain conditions, AI automation may negatively impact the profits of both push and pull decentralized supply chains. For instance, in low-margin profit scenarios where decision-makers exhibit moderate regret bias and fairness concerns, such effects can emerge. This indicates that under specific circumstances, the human behavioral factors — regret bias and fairness concerns — may sometimes enhance the performance of decentralized supply chain members. Our research findings provide significant practical implications for the adoption of AI-automated decision-making in real-world supply chains.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"185 ","pages":"Article 107227"},"PeriodicalIF":4.3,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989455","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":"Fair and efficient multi-agent routing for cooperative and autonomous agricultural fleets with implements","authors":"Aitor López-Sánchez ,&nbsp;Marin Lujak ,&nbsp;Frédéric Semet ,&nbsp;Holger Billhardt","doi":"10.1016/j.cor.2025.107252","DOIUrl":"10.1016/j.cor.2025.107252","url":null,"abstract":"<div><div>The growing use of autonomous tractor fleets with detachable implements presents complex logistical challenges in agriculture. Current systems often rely on simple heuristics and avoid implement swapping, limiting efficiency. A central challenge is to dynamically coordinate vehicle routing and implement exchanges to enable efficient, low-intervention task execution. Due to high costs, such fleets are owned mainly by large enterprises or cooperatives, where fair task allocation and profit sharing are critical. Addressing both coordination and fairness, in this paper, we introduce the Agricultural Fleet Vehicle Routing Problem with Implements (AFVRPI). We propose a distributed model derived from a centralized formulation also presented in this paper. This model is embedded within a Distributed Multi-Agent System Architecture (DIMASA), where autonomous vehicle agents manage routing and implement use under limited fuel autonomy, while implement agents ensure compatibility and sufficient capacity to meet task demands. Our solution applies systematic egalitarian social welfare optimization to iteratively maximize the profit of the worst-off vehicle, balancing fairness with system efficiency. To enhance scalability, we use column generation in the distributed model, achieving solution quality comparable to the centralized model while significantly reducing computing time. Simulation results on new benchmark instances demonstrate that our distributed multi-agent AFVRPI approach is scalable, efficient, and fair.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"185 ","pages":"Article 107252"},"PeriodicalIF":4.3,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925994","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":"Green horizons: Sustainable global logistics in dynamic supply chain management","authors":"Mahsa Mohammadi,&nbsp;Babak Mohamadpour Tosarkani","doi":"10.1016/j.cor.2025.107226","DOIUrl":"10.1016/j.cor.2025.107226","url":null,"abstract":"<div><div>Supply chain management in a global scale involves addressing numerous uncertainties, from demand fluctuations to unforeseen disruptions. Developing advanced solution approaches is critical to manage such complexities and ensure resilience. This study presents a multi-stage stochastic–dynamic model for the global supply chain, incorporating hedging policies. The aim is to identify optimal order scheduling for bill of materials, production planning, and inventory management across warehouses (i.e., materials and finished products). Due to the dynamic nature of the global supply chain (e.g., demand fluctuations, disruptions, and lead time), a multi-stage stochastic model is developed for the stochastic–dynamic supply chain network. To address dynamic factors of real-world global supply chain, an accelerated parallel stochastic dual dynamic integer programming <strong><em>(SDDiP)</em></strong> approach is proposed to deal with disruptions (e.g., political unrest, natural disasters, and pandemics), enhancing supply chain resiliency. To validate the proposed parallel <strong><em>SDDiP</em></strong>, various scenarios with different sizes are generated using the case study and compared to the <strong><em>SDDiP</em></strong> with Benders cuts and integrated stage-wise Lagrangian dual cut (<strong><em>SWLDC</em></strong>) (i.e., <strong><em>SDDiP-SWLDC</em></strong>). According to the obtained results, the proposed parallel node strategy for accelerated <strong><em>SDDiP</em></strong> consistently outperforms the basic stochastic dual dynamic programming <strong><em>(SDDP)</em></strong> and demonstrated robust CPU scalability. Evaluation across various scenario sizes shows stochastic dual dynamic integer programming-mixed integer rounding cuts (<strong><em>SDDiP-MIR</em></strong>) achieving faster computation and a smaller 7% optimality gap compared to <strong><em>SDDiP-SWLDC</em></strong> and <strong><em>SDDiP</em></strong> in large-size instances, highlighting its superior performance in complex supply chain settings.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"185 ","pages":"Article 107226"},"PeriodicalIF":4.3,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926580","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":"Solving Type-I unpaced synchronous mixed-model two-sided assembly line balancing problem using a genetic algorithm","authors":"Shi-Gen Liao ,&nbsp;Chun-Yan Sang ,&nbsp;Ai-Wei Liu ,&nbsp;Hui Liu","doi":"10.1016/j.cor.2025.107257","DOIUrl":"10.1016/j.cor.2025.107257","url":null,"abstract":"<div><div>This research aims to propose a genetic algorithm for the Type-I unpaced synchronous mixed-model two-sided assembly line balancing problem. A major challenge in addressing this problem is that the assembly line has the characteristic of variable output, requiring the solution approach to generate feasible solutions where the process time of certain workstations may exceed the given cycle time, while the average output time of the assembly line must not exceed this cycle time. To address this, the proposed algorithm incorporates a mechanism referred to as pre-allocation and formal allocation. It first assigns tasks to the currently active workstation, then evaluates whether the task assignment results meet the given cycle time, and adjusts them accordingly. This paper first models and analyzes the problem, then introduces the proposed genetic algorithm and illustrates its key steps through an example. Finally, experimental research is performed to demonstrate the effectiveness of the algorithm. The experimental results show that the proposed algorithm can construct solutions that match the characteristics of the problem.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"184 ","pages":"Article 107257"},"PeriodicalIF":4.3,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907772","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":"Optimizing a partial vertically integrated supply chain with hierarchical internal coordination: Bilevel model and solution algorithms","authors":"Dámaris Dávila ,&nbsp;Marta Kadlubek ,&nbsp;José-Fernando Camacho-Vallejo","doi":"10.1016/j.cor.2025.107250","DOIUrl":"10.1016/j.cor.2025.107250","url":null,"abstract":"<div><div>Partial vertically integrated supply chains (PVI-SCs) involve multiple stakeholders, with some controlling specific stages of the supply chain and others managing the remaining ones, all working collaboratively toward a common objective. While stakeholders are often modeled as single entities, certain contexts require recognizing their internal structure, where distinct departments operate under separate decision makers. This paper analyzes a PVI-SC in which a company owns the distribution centers and assembly plants, has access to customer demand information and outsources the procurement of raw materials to a set of independent suppliers. Coordination is assumed within the company, from which two departments are considered: sales and production. A hierarchical relationship exists between them, with the sales department holding a higher decision-making level than the production department. Consequently, the sales manager’s decisions serve as inputs for designing the production plan. The production manager’s decisions affect total service time, as they must account for the lead time of components provided by suppliers and the duration of the assembly process. To address this problem, we propose a mixed-integer nonlinear bilevel programming model and its equivalent linear formulation. To solve the linearized model optimally, we develop a customized Branch &amp; Bound algorithm, employing two bounding strategies: one based on the high point relaxation and the other on an infeasibility criterion. To further reduce computational time for solving the bilevel problem, we design an effective and efficient nested iterated local search algorithm. A realistic case study validates our modeling approach, while numerical experiments on synthetic instances with realistic parameters confirm the strong performance of the proposed algorithms. The results highlight the importance of analyzing potential decision-making conflicts between departments as a critical step in effective supply chain management, ultimately reflected in the fulfillment of customer requirements.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"184 ","pages":"Article 107250"},"PeriodicalIF":4.3,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144892984","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":"Flexible scheduling of customized bus for green mega-events: A distributionally robust optimization approach","authors":"Xiaojie An ,&nbsp;Xiang Li ,&nbsp;Bowen Zhang","doi":"10.1016/j.cor.2025.107249","DOIUrl":"10.1016/j.cor.2025.107249","url":null,"abstract":"<div><div>Mega-events such as the Olympics and the World Championships face significant challenges in evacuating large numbers of attendees after the events conclude, which consume substantial transportation resources. Under the global pressure to reduce carbon emissions, energy conservation and emission reduction are increasingly becoming top priorities. This paper focuses on the efficient scheduling of customized buses (CB) after green mega-events, incorporating skip-stop operations and coordinated bus services to minimize energy consumption, fixed and transportation costs, and facilitate the evacuation of attendees, while accounting for practical constraints such as the availability of customized buses, vehicle capacity, time windows, and flow balance. A distributionally robust optimization (DRO) model is developed, using a novel ambiguity set to model uncertain demand via parametric interval-valued fuzzy variables. To ensure computational tractability, the model is reformulated as an integer linear programming model. To address the computational challenges of large-scale instances, an improved variable neighborhood search heuristic is designed by incorporating the reinforcement learning techniques, including the KL-UCB algorithm and a sliding window mechanism. Extensive numerical experiments are conducted to verify the performance of the proposed heuristic. Computational results demonstrate that the proposed DRO model effectively handles uncertainty, offering robust and adaptable solutions. Compared to existing heuristics, the proposed heuristic improves performance by 6.51% on average, and incorporating reinforcement learning into VNS enhances computational efficiency by 4.88% on average. A real-life case study further validates the model, demonstrating that the skip-stop strategy significantly reduces vehicle travel time and enhances overall operational efficiency.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"185 ","pages":"Article 107249"},"PeriodicalIF":4.3,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934004","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":"Hierarchical compromise optimization of ambulance locations under stochastic travel times","authors":"Imanol Gago-Carro ,&nbsp;Unai Aldasoro ,&nbsp;Dae-Jin Lee ,&nbsp;María Merino","doi":"10.1016/j.cor.2025.107208","DOIUrl":"10.1016/j.cor.2025.107208","url":null,"abstract":"<div><div>The location of ambulances is a crucial strategic decision for Emergency Medical Services (EMS). The base stations must achieve efficient dispatching under the inherent uncertainty of emergency locations and travel times. Additionally, managers need decision-support models that incorporate the multi-objective nature of such an efficient system. This paper bridges the gap between these requirements by developing a multi-objective hierarchical compromise optimization framework under stochastic travel times. Our hierarchical compromise optimization approach leverages quasi-optimal coverage solutions to provide EMS managers with flexibility in balancing (a) minimal average response time, (b) maximal resource adequacy, and (c) minimal worst-case response times. The stochasticity of travel times is incorporated into the models using a methodology to estimate continuous probability distributions for available and non-available historical data. The proposed modeling induces cross-scenario constraints, which are computationally challenging as the problem size increases. We tackle this issue by presenting an ad-hoc extension of a primal scenario-decomposition algorithm that deals with such constraints. This extension achieves superior performance over state-of-the-art optimization software. Finally, we use real-world data from the Basque Public Healthcare System to test the framework and prove the managerial interest of the obtained results.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"184 ","pages":"Article 107208"},"PeriodicalIF":4.3,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904411","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":"Carousel greedy algorithms for the minimum stretch spanning tree problem","authors":"Jiaqi Wang ,&nbsp;Carmine Cerrone ,&nbsp;Bruce Golden","doi":"10.1016/j.cor.2025.107229","DOIUrl":"10.1016/j.cor.2025.107229","url":null,"abstract":"<div><div>The minimum stretch spanning tree problem aims to find a spanning tree that minimizes the maximum ratio of the distance in the spanning tree to that in the original graph between each possible pair of vertices. Existing heuristic algorithms for this problem are either computationally expensive or they often produce solutions with significant optimality gaps. In this paper, we introduce a straightforward and promising carousel greedy algorithm to tackle this challenging combinatorial optimization problem. By investigating the properties of the problem, we further enhance the algorithm’s performance. Our algorithm significantly outperforms the best-known algorithms in the literature for both unweighted and weighted graphs, demonstrating superior solution quality with efficient running time.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"184 ","pages":"Article 107229"},"PeriodicalIF":4.3,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144892985","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":"Transportation infrastructure maintenance planning: An exact column enumeration approach","authors":"Daniel Yamín ,&nbsp;Andrés L. Medaglia ,&nbsp;Juan F. Correal","doi":"10.1016/j.cor.2025.107246","DOIUrl":"10.1016/j.cor.2025.107246","url":null,"abstract":"<div><div>Transportation infrastructure assets deteriorate over time due to natural hazards, heavy traffic, and aging, increasing their risk of failure. National transportation agencies must strategically invest in maintenance to avoid significant social and economic impacts. We address the infrastructure maintenance planning problem, in which a maintenance plan must be designed for each asset within a budget limit to maximize the weighted average asset condition over a planning horizon. We derive a knapsack-type mathematical formulation and propose an exact column enumeration algorithm to solve it. First, a column-and-cut generation algorithm computes a (dual) upper bound on the optimal value. The master problem selects a maintenance plan for each asset and is strengthened with extended <span><math><mi>q</mi></math></span>-cover inequalities. By representing maintenance plans as paths over a directed acyclic multigraph that captures asset deterioration and maintenance decisions, the pricing problems unveil feasible plans through a specialized labeling algorithm. Second, a relaxation-enforced neighborhood search finds a (primal) lower bound. Finally, using these bounds, we enumerate sufficient columns to find an optimal solution via a commercial MILP solver. Computational results on generated instances spanning a 10-year planning horizon demonstrate that our algorithm delivers optimal solutions for instances with up to 50 assets and near-optimal solutions (gap <span><math><mo>&lt;</mo></math></span> 0.18%) for instances with up to 100 assets within five hours.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"184 ","pages":"Article 107246"},"PeriodicalIF":4.3,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895676","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":"Minimal and stable feedback arc sets and graph centrality measures","authors":"Claudia Cavallaro,&nbsp;Vincenzo Cutello,&nbsp;Mario Pavone","doi":"10.1016/j.cor.2025.107247","DOIUrl":"10.1016/j.cor.2025.107247","url":null,"abstract":"<div><div>In this paper we tackle one of the most famous problems in graph theory and, in general, in the area of discrete optimization, namely the Minimum Feedback Arc Set Problem for a directed graph. In particular, we study the problem using the methodology of the linear arrangements of the vertices to find feedback arc sets, and an optimization heuristic to reduce their size. We test the efficacy of the heuristic against several linear arrangements of the vertices obtained by using some well known centrality metrics. We experimentally show that, independently from the linear arrangement used, our heuristic methodology obtains feedback arc sets with an average approximation ratio not greater than <span><math><mrow><mfrac><mrow><mn>1</mn></mrow><mrow><mn>4</mn></mrow></mfrac><mo>.</mo></mrow></math></span></div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"184 ","pages":"Article 107247"},"PeriodicalIF":4.3,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865157","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}