Computers & Operations Research最新文献

{"title":"Stochastic casualty response planning with multiple classes of patients","authors":"Pedram Farghadani-Chaharsooghi ,&nbsp;Hossein Hashemi Doulabi ,&nbsp;Walter Rei ,&nbsp;Michel Gendreau","doi":"10.1016/j.cor.2025.107165","DOIUrl":"10.1016/j.cor.2025.107165","url":null,"abstract":"<div><div>In this paper, we study the stochastic casualty response planning problem (CRP) in the context of providing treatments to multiple classes of patients with different types of injuries. In this general setting, both patients’ demands and hospitals’ treatment capacity are considered uncertain. To the best of our knowledge, this is the first time that this problem is solved. We propose a novel two-stage stochastic mixed-integer programming model which, in the first stage, determines the location of the Alternative Care Facilities (ACFs) and allocates different resources, such as rescue vehicles, medical equipment, and physicians, to them. In the second stage, this model helps decide how to allocate patients with multiple injuries to either ACFs or hospitals, considering their care itineraries and available resources. Moreover, it recommends potential patient transfers between ACFs and hospitals when required. Furthermore, we introduce an alternative two-stage stochastic model that is more compact than the first. This formulation significantly reduces solution times. We also provide an equivalency proof between the two formulations. As the solution method, we develop both the L-shaped algorithm, a pure cutting-plane method tailored to our stochastic mathematical model, and the branch-and-Benders-cut (B&amp;BC) algorithm. To further enhance the efficiency of these algorithms, we develop a wide range of acceleration techniques, including Benders dual decomposition, Lagrangian dual decomposition, a multi-cut reformulation, Pareto-optimal cuts, and the inclusion of lower bounding functional valid inequalities. We carry out extensive computational experiments demonstrating that these algorithmic enhancements dramatically improve the performance of the B&amp;BC algorithm, reducing the average optimality gap from 7898% in the standard B&amp;BC algorithm to just 0.92% in the enhanced version. Additionally, we benchmark our approach against the progressive hedging algorithm (PHA), a widely used decomposition method in disaster response operations, to further assess its effectiveness. Finally, we present a case study from the 2011 Van earthquake in Turkey, demonstrating the applicability and efficiency of our optimization methods.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"183 ","pages":"Article 107165"},"PeriodicalIF":4.1,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524296","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 synchronized vessel and autonomous vehicle model for environmental monitoring: Mixed integer linear programming model and adaptive matheuristic","authors":"Parisa Torabi ,&nbsp;Ahmad Hemmati","doi":"10.1016/j.cor.2025.107188","DOIUrl":"10.1016/j.cor.2025.107188","url":null,"abstract":"<div><div>In offshore environmental monitoring projects, ocean currents enable the detection of chemical signals from a distance, with longer observation times at each point increasing the area that can be monitored. Leveraging this principle, the covering tour problem with varying coverage was recently introduced for environmental monitoring. In this paper, we introduce a generalization of this problem, where we utilize a main vessel and a fleet of autonomous underwater vehicles (AUVs), and the properties of time-varying coverage, which refers to the dynamic changes in the area that can be monitored based on the duration spent at each location, to minimize the required time to visit or cover a set of pre-specified locations in an area of interest. This problem can be presented as a rich covering salesperson problem, namely the multi-visit multi-drone covering salesperson problem with varying coverage (mCSP-VC). We present a mixed integer linear programming (MILP) model of mCSP-VC, and considering the complexity of the problem, design and implement an adaptive matheuristic algorithm and showcase its effectiveness. Moreover, we investigate the effects of altering different parameters on the solutions and provide managerial insights into optimizing monitoring operations and resource allocation.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"183 ","pages":"Article 107188"},"PeriodicalIF":4.1,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517046","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 hazardous materials vehicle routing problem with time-dependent arc capacity","authors":"Hanxing Cui ,&nbsp;Qilan Zhao ,&nbsp;Guowei Hua ,&nbsp;Shiwei He ,&nbsp;JingXin Dong","doi":"10.1016/j.cor.2025.107187","DOIUrl":"10.1016/j.cor.2025.107187","url":null,"abstract":"<div><div>The transportation of vehicles fully loaded with hazardous materials on road segments with high population density, or the simultaneous presence of multiple hazardous materials vehicles on the same road, significantly increases transportation risk. To ensure the safe operation of vehicles carrying hazardous materials on the road, we develop a mathematical framework based on the time-dependent vehicle routing problem with time windows (TDVRPTW). This framework treats each road segment as an independent entity and incorporates the variation of its external environment over time. Specifically, it integrates the arc capacity with time-dependent attributes, forming the time-dependent arc capacity (TDAC). The TDAC controls the risks in each road segment by implementing restrictions on the total quantity of hazardous materials allowed to enter. The restriction is based on the specific external environments during different time periods of the day. To address these time-dependent factors in path searching, we develop an algorithm called the Time-Dependent Variant of Dijkstra’s Algorithm with Time-Dependent Weight. We then integrate this algorithm into a bi-objective tabu search approach to solve the TDVRPTW. We utilized the augmented <span><math><mi>ɛ</mi></math></span>-constraint method to solve small-scale problems and compared the results with those obtained from heuristic algorithms. Subsequently, by conducting computational experiments on road networks of various scales, we validate the efficiency of our heuristic algorithm. The results show that our method can achieve a more reasonable distribution of risks, enable staggered utilization of roads, and effectively control the overall risk of the system and the risk of each road in the network at a relatively low level. Moreover, these benefits are achieved without significantly increasing the total cost.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"183 ","pages":"Article 107187"},"PeriodicalIF":4.1,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517005","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":"Generalized variable neighborhood search algorithm for vehicle routing problem with time windows and synchronization","authors":"Malek Masmoudi ,&nbsp;Rahma Borchani ,&nbsp;Bassem Jarboui","doi":"10.1016/j.cor.2025.107193","DOIUrl":"10.1016/j.cor.2025.107193","url":null,"abstract":"<div><div>The problem addressed in this paper is the Vehicle Routing Problem with Time Windows and Synchronization (VRPTW-S), a variant of the Vehicle Routing Problem where each customer must be served within a specific time window, and some customers must be visited by more than one vehicle at the same time. A Generalized Variable Neighborhood Search (GVNS) algorithm is provided and composed of Random-Insertion heuristic, neighborhood structures, shaking procedure, Basic sequential Variable Neighborhood Descent (B-VND), and augmented evaluation function with dynamic penalties that are specifically tailored to the characteristics of the VRPTW-S. The parameters of our GVNS are tuned through a Design of Experiments (DoE) approach on randomly generated instances. The experimentation is conducted on two benchmark datasets with a total of 84 instances. Numerical results show that the GVNS outperforms the existing best-performing solving approaches in terms of effectiveness, efficiency, and robustness. Among the 84 benchmark instances, the GVNS successfully attains 52 best-known solutions, including all 20 proven optimal solutions, and introduces 18 new best solutions.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"183 ","pages":"Article 107193"},"PeriodicalIF":4.1,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491704","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":"Distributed train timetable synchronization in metro network: An ADMM-based decomposition framework","authors":"Renming Liu ,&nbsp;Shukai Li ,&nbsp;Lixing Yang ,&nbsp;Ronghui Liu","doi":"10.1016/j.cor.2025.107180","DOIUrl":"10.1016/j.cor.2025.107180","url":null,"abstract":"<div><div>The increasing spatial or temporal scales of metro networks generate an important research challenge in developing fast and efficient optimization methods for handling the train timetable synchronization problem (TTSP). This paper develops a distributed optimization algorithm for the TTSP of complex metro networks, with the objective of minimizing both the waiting time of inbound and transferring passengers in the whole network. We construct explicit dynamic equations of train passenger loads throughout the network and quantify the transferring passengers at transfer stations. These equations encapsulate the dynamic passenger transfer behavior within the metro system. To deal with the computationally expensive large-scale MINP problem, an alternating direction method of multipliers (ADMM) based decomposition approach is proposed to split the original TTSP into a set of single-line timetabling subproblems that can be solved in a decentralized manner. Furthermore, a novel heuristic two-level ADMM-based approach, where the upper level decides the connections among trains of different lines and the lower level applies standard ADMM with fixed binary variables to optimize the timetable, is designed to deal with the nonconvexity issue. We demonstrate its ability to conveniently obtain a high-quality solution to the network timetable synchronization problem numerically.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"183 ","pages":"Article 107180"},"PeriodicalIF":4.1,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501660","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":"Three-machine flowshop scheduling with outsourcing lead-time","authors":"Eun-Seok Kim ,&nbsp;Kangbae Lee ,&nbsp;Ik Sun Lee","doi":"10.1016/j.cor.2025.107192","DOIUrl":"10.1016/j.cor.2025.107192","url":null,"abstract":"<div><div>This paper addresses a scheduling issue in a three-machine flowshop with incorporating the outsourcing lead-time. In this problem, the first and second operations of jobs can either be handled in-house or outsourced to subcontractors, whereas the third operation must be processed in-house. Outsourcing a job not only incurs costs depending on each operation but also introduces a lead-time for the outsourced operation, meaning that the subsequent operation can only commence after this lead-time has elapsed. The goal of this paper is to minimize the weighted sum of both outsourcing and scheduling costs, which may be defined as either makespan or total completion time. The study explores the properties of the optimal solution and introduces two approaches: the Biased Random-Key Genetic Algorithm and the Branch-and-Bound algorithm. Computational experiments demonstrate that the proposed algorithms yield efficient and effective solutions.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"183 ","pages":"Article 107192"},"PeriodicalIF":4.1,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517004","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 mixed-integer optimization formulation for buyer formation","authors":"Sebastián Dála-Gálvez ,&nbsp;Martine Labbé ,&nbsp;Vladimir Marianov ,&nbsp;Fernando Ordónẽz ,&nbsp;Frédéric Semet","doi":"10.1016/j.cor.2025.107181","DOIUrl":"10.1016/j.cor.2025.107181","url":null,"abstract":"<div><div>Companies frequently offer wholesale prices for their products that decrease with the number of items purchased. However, individual buyers may not be willing or able to purchase large quantities of a single item. To address this consumers can form groups to purchase at wholesale prices and gain bargaining power. This practice can be extended from single products to product bundles. This paper proposes a <span>combinatorial coalition formation</span> problem to create groups of buyers who wish to optimally purchase product bundles. We propose a generic mathematical model and present mixed-integer programming formulations for nonincreasing price and step price functions. To handle large instances, a Benders decomposition method is proposed for step price functions. Computational experiments conducted on a large set of synthetic instances illustrate the performance of the method.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"183 ","pages":"Article 107181"},"PeriodicalIF":4.1,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524297","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":"Smart Automated Guided Vehicle scheduling with flexible battery management: A new formulation and an exact approach","authors":"Yantong Li ,&nbsp;Xin Wen ,&nbsp;Shanshan Zhou ,&nbsp;Sai-Ho Chung","doi":"10.1016/j.cor.2025.107156","DOIUrl":"10.1016/j.cor.2025.107156","url":null,"abstract":"<div><div>Automated Guided Vehicles (AGVs) have gained widespread application within modern smart transportation or industrial systems. The AGV scheduling problem, particularly considering battery management, holds a pivotal role in enhancing system efficiency, cost-effectiveness, and safety. Existing research on the AGV scheduling problem predominantly assumes fixed charging or battery swapping strategies, wherein the duration of each energy replenishment operation remains constant and predetermined. However, allowing AGVs to undergo partial charging durations offers increased flexibility and potential efficiency gains by minimizing downtime. The incorporation of flexible charging introduces additional complexity to the AGV scheduling problem, as it necessitates determining the duration for each charging operation. In this study, we investigate an AGV scheduling problem with flexible charging and charging setup time (ASP-FLC-ST). Initially, we propose a novel mixed-integer linear programming model tailored to address the ASP-FLC-ST. Subsequently, we conduct a structural analysis of the problem, demonstrating its strong NP-hardness and deriving a valid lower bound. To tackle the complexity of the ASP-FLC-ST, we develop a customized exact logic-based Benders decomposition algorithm (LBBD) and introduce an “alternating cut” generation scheme to enhance its performance. Computational experiments conducted on 360 random instances of the ASP-FLC-ST showcase the superiority of our approach over state-of-the-art commercial solvers. Moreover, the devised LBBD method effectively addresses benchmark instances of a reduced counterpart, yielding 173 new best solutions and establishing optimality in 161 instances with open solutions.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"183 ","pages":"Article 107156"},"PeriodicalIF":4.1,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470828","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":"Uncapacitated multi-item lot sizing with shipment minimization","authors":"Marcel Turkensteen ,&nbsp;Wilco van den Heuvel","doi":"10.1016/j.cor.2025.107169","DOIUrl":"10.1016/j.cor.2025.107169","url":null,"abstract":"<div><div>Companies often order multiple items from suppliers. There can be environmental and economic benefits from combining these orders. Based on a company case, we formulate the problem of determining in which periods orders for multiple items should be placed to minimize the sum of the order and inventory costs. As a second objective, the goal is to limit the number of shipment periods and we measure the impact of varying this number. We call this problem the Bi-objective Lot Sizing Problem with Shipment Minimization (BLSPSM). This problem combines an overarching problem, namely the determination of shipment periods, with subproblems for each item, namely the determination of order periods and order quantities. We develop so-called bi-level dynamic programming heuristics for the BLSPSM, where the bi-level nature of the problem is exploited. We find efficient frontiers very close to the optimal ones for all instances in our test set. Moreover, we show that our heuristic performs well on the Dynamic Joint Replenishment Problem, a special case of BLSPSM and a known problem in the literature.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"183 ","pages":"Article 107169"},"PeriodicalIF":4.1,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492145","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 deep reinforcement learning based multi-agent simulation optimization approach for IGV bidirectional task allocation and charging joint scheduling in automated container terminals","authors":"Caiyun Yang,&nbsp;Yu Zhang,&nbsp;Junjie Wang,&nbsp;Lijun He,&nbsp;Han Wu","doi":"10.1016/j.cor.2025.107189","DOIUrl":"10.1016/j.cor.2025.107189","url":null,"abstract":"<div><div>Intelligent guided vehicle (IGV) task allocation and charging scheduling at automated container terminal (ACT) are two important operational links that interact with each other. The joint scheduling problem of IGV task allocation and charging aims to improve the operational coherence and efficiency of the transportation system. In the parallelly arranged ACT, since IGV needs to enter the yard for side loading operations, the task allocation will greatly affect the empty travel distance and power consumption of IGV. In addition, the dual-cycling mode and the changes in power consumption rate under different IGV operation states make the above joint scheduling problem more complicated. In order to solve this problem, this paper uses the Markov decision process to characterize the IGV bidirectional task allocation and charging joint scheduling problem, and designs a multi-agent simulation optimization method based on deep reinforcement learning to generate a real-time adaptive scheduling solution. Considering the comprehensive impact of the agent’s long-term and short-term goals, a reward function is designed, and the deep neural network training is used to guide the IGV agent to choose the action with the largest expected cumulative reward. In addition, an adaptive double threshold charging strategy is designed, under which IGV can flexibly select the minimum charging threshold according to the system status. Finally, a multi-agent fine-grained simulation model is constructed to verify the effectiveness of the proposed method. Through comparative experiments with three single heuristic scheduling rules, different reinforcement learning algorithms and a fixed single-threshold charging strategy, it is proved that the new method can improve the operating efficiency of the system and the battery utilization of the IGV, and reduce the empty travel time of the IGV. Simulation experiments show that flexible task allocation and charging strategies can better adapt to the complex and dynamic operating environment of the ACT.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"183 ","pages":"Article 107189"},"PeriodicalIF":4.1,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470826","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}