Computers & Operations Research最新文献

{"title":"Periodic and aperiodic train timetabling and rolling stock circulation planning using an efficient Lagrangian relaxation decomposition","authors":"Naijie Chai ,&nbsp;Ziyu Chen ,&nbsp;Wenliang Zhou","doi":"10.1016/j.cor.2025.107062","DOIUrl":"10.1016/j.cor.2025.107062","url":null,"abstract":"<div><div>Train timetabling and rolling stock circulation planning are problems of crucial importance for integrated planning. Often, these problems are separately solved in a sequential way without consideration of periodic train schedule. However, a notable drawback of this layered planning process is poor coordination and efficiency between train and rolling stock timetables, as well as lack of regularity. To this end, we explore the joint optimization problem of periodic and aperiodic train timetabling and rolling stock circulation planning in this paper. To address this comprehensive problem, an integer programming model is initially established by incorporating rolling stock circulation into optimizing periodic and aperiodic train timetabling. Due to the model-solving complexity, a three-dimensional space–time-state network is constructed to reformulate this model. Within this three-dimensional network, states are used to represent trains served by rolling stocks. Subsequently, the problem is transformed into a minimum-cost multi-commodity network flow problem with incompatible arcs based on the space–time-state network. And an efficient Lagrangian relaxation decomposition algorithm is proposed to solve this network flow problem. The effectiveness of the algorithm is verified through a series of case studies.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"180 ","pages":"Article 107062"},"PeriodicalIF":4.1,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683216","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 note on “Self-adaptive General Variable Neighborhood Search algorithm for parallel machine scheduling with unrelated servers”","authors":"Andrea Grosso ,&nbsp;Fabio Salassa","doi":"10.1016/j.cor.2025.107055","DOIUrl":"10.1016/j.cor.2025.107055","url":null,"abstract":"<div><div>In a recent paper a two-machines two-servers scheduling problem with identical machines but unrelated servers and makespan objective is studied and solved via sophisticated Variable-Neighbourhood Search procedures, for instances up to 120 jobs in size. We show that the same problem can be transformed to a pretty standard problem with unrelated machines that can be efficiently solved to optimality, up to much larger instances.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"180 ","pages":"Article 107055"},"PeriodicalIF":4.1,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683214","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 interval two-stage robust stochastic programming under a bi-level multi-objective framework toward river basin water resources allocation","authors":"Yan Tu ,&nbsp;Yongzheng Lu ,&nbsp;Benjamin Lev","doi":"10.1016/j.cor.2025.107045","DOIUrl":"10.1016/j.cor.2025.107045","url":null,"abstract":"<div><div>The uncertainty stemming from hydrological variables and socio-economic parameters poses new challenges to river basin water resources allocation (RBWRA). Given the pressing need for efficient, environmentally friendly, and equitable solutions in RBWRA, a bi-level multi-objective interval two-stage robust stochastic programming (BLMOITRSP) model is proposed. This model aims to achieve the optimal balance among efficiency, eco-friendliness, and equity, collectively called the “3E”. A novel hierarchical mixed water allocation mechanism is introduced within this model. The basin authority pursues the “3E” objectives at the macro-control level through administrative water allocation. Conversely, sub-areas as followers prioritize economic interests, striving for economic benefit maximization through water market allocation. Furthermore, uncertain parameters (e.g., water demand) are treated as interval parameters, employing interval two-stage robust stochastic programming (ITRSP) to address uncertainty issues and control systemic risks in the model. To solve the BLMOITRSP model, we present a bi-level interactive global equilibrium optimization algorithm, fusing with the modified particle swarm optimization (PSO) algorithm. The bi-level algorithm provides solutions tailored to the preferences of different decision-makers. The proposed model and method are also applied to the Hanjing River Basin in China to demonstrate its feasibility and effectiveness. The results indicate that the proposed model effectively ensures the “3E” balance. The introduction of the hierarchical mixed water allocation mechanism proves conducive to promoting water distribution and enhancing economic benefits. ITRSP effectively controls the systemic risks of the model’s impact on the basin’s total economic benefit. The economic performance of each sub-area varies in response to different decision preferences under RBWRA schemes. Finally, the conclusions and future research directions are provided.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"180 ","pages":"Article 107045"},"PeriodicalIF":4.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629091","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 heuristic approach for the critical chain project scheduling problem based on resource flows","authors":"Wuliang Peng ,&nbsp;Ziyan Wang ,&nbsp;Fang Xie ,&nbsp;Haitao Li","doi":"10.1016/j.cor.2025.107054","DOIUrl":"10.1016/j.cor.2025.107054","url":null,"abstract":"<div><div>The Critical Chain Project Scheduling Problem (CCPSP) aims to obtain robust baseline schedules by optimizing the size and insertion of buffers for projects with uncertain activity durations. To overcome the challenge of handling new resource conflicts due to insertion of buffers, we develop a novel approach based on resource flow to add additional precedence relationships that resolve resource conflicts. Our priority-rule based heuristic is easy to implement, fast, and effective. A comprehensive computational experiment is conducted to examine the performance of a large set of priority rules and their combinations, which is then estimated using regression analysis with the problem characteristics as independent variables. Our algorithm outperforms the existing benchmark method for the addressed problem in both solution quality and efficiency, and provides project managers an efficient and effective tool to handle large-scale projects under uncertainty.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"180 ","pages":"Article 107054"},"PeriodicalIF":4.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637574","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 novel mathematical modeling approach for integrating a periodic vehicle routing problem and cross-docking system","authors":"Mostafa Asgharyar ,&nbsp;Nima Farmand ,&nbsp;Seyyed Nader Shetab-Boushehri","doi":"10.1016/j.cor.2025.107048","DOIUrl":"10.1016/j.cor.2025.107048","url":null,"abstract":"<div><div>To remain competitive in a globalized market, manufacturers must effectively respond to customer demands in various situations. In parallel, logistics companies have adopted cross-docking systems as a key component of lean supply chain management to handle high transportation volumes. By integrating this pivotal component in the supply chain, goods are efficiently distributed to retailers via cross-dock facilities. This article introduces, for the first time, an integrated framework for the periodic vehicle routing problem with cross-docking (PVRPCD) system between supplier and retailer locations. The goal is to optimize three key decisions: 1. Vehicle scheduling and routing for each period, 2. The loading and unloading quantities of goods at the cross-dock, and 3. The selection of a daily combination from periodic retailer demands to minimize the costs incurred by transportation and cross-docking operations. To formulate the PVRPCD, a novel mixed-integer linear programming (MILP) model is designed. Given the computational complexity of large-scale instances, a heuristic algorithm is designed to produce near-optimal initial solutions, which are then embedded into two metaheuristic algorithms: variable neighborhood search (VNS) and population-based variable neighborhood search (PBVNS). These algorithms incorporate four shaking and four local search operators to enhance solution quality and scalability. To validate the effectiveness of the metaheuristic algorithms, computational experiments are conducted using benchmark instances. The optimal solutions obtained via the CPLEX solver for small-scale instances serve as a baseline for comparison. The computational results illustrate that both algorithms effectively solve small-scale problems. Nevertheless, PBVNS consistently outperforms VNS in terms of solution quality, though it requires more computation time. Despite the increased solution time, the improvement in solution quality justifies the additional computational effort. Finally, sensitivity analyses on key PVRPCD parameters provide managerial insights for decision-makers, offering a profound understanding into the influence of model parameters on solution performance.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"180 ","pages":"Article 107048"},"PeriodicalIF":4.1,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759495","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 causal framework for stochastic local search optimization algorithms","authors":"Alberto Franzin,&nbsp;Thomas Stützle","doi":"10.1016/j.cor.2025.107050","DOIUrl":"10.1016/j.cor.2025.107050","url":null,"abstract":"<div><div>Despite the multitude of optimization algorithms available in the literature and the various approaches that study them, understanding the behaviour of an optimization algorithm and explaining its results are fundamental open questions in artificial intelligence and operations research. We argue that the body of available literature is already very rich, and the main obstacle to advancements towards answering those questions is its fragmentation.</div><div>In this work, we focus on stochastic local search algorithms, a broad class of methods to compute good quality suboptimal solutions in a short time. We propose a causal framework that relates the entities involved in the solution of an optimization problem. We demonstrate how this conceptual framework can be used to relate many approaches aimed at understanding how stochastic local search algorithms work, and how it can be utilized to address open problems, both theoretical and practical.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"180 ","pages":"Article 107050"},"PeriodicalIF":4.1,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637572","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 supervised learning approach to rankability","authors":"Nathan McJames ,&nbsp;David Malone ,&nbsp;Oliver Mason","doi":"10.1016/j.cor.2025.107049","DOIUrl":"10.1016/j.cor.2025.107049","url":null,"abstract":"<div><div>The rankability of data is a novel problem that considers the ability of a dataset, represented as a graph, to produce a <em>meaningful</em> ranking of the items it contains. To study this concept, a number of rankability measures have been proposed, based on comparisons to a complete dominance graph via combinatorial and linear algebraic methods. Interest in this field has been steadily expanding, with a growing appreciation for the significance of evaluating rankability across diverse applications. Consequently, the validation of these rankability methodologies in different scenarios holds paramount importance. In this paper, we review existing measures of rankability and highlight some questions to which they give rise. We go on to introduce a new framework designed to evaluate rankability with a tailored approach, one that allows for efficient estimation in specific problem domains. Finally, we present a comparative analysis of these metrics by applying them to both synthetic and real-life sports data.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"180 ","pages":"Article 107049"},"PeriodicalIF":4.1,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ship emission monitoring with a joint mode of motherships and unmanned aerial vehicles","authors":"Dan Zhuge ,&nbsp;Jianhui Du ,&nbsp;Lu Zhen ,&nbsp;Shuaian Wang ,&nbsp;Peng Wu","doi":"10.1016/j.cor.2025.107012","DOIUrl":"10.1016/j.cor.2025.107012","url":null,"abstract":"<div><div>Ship emission monitoring is crucial for improving compliance with emission control area (ECA) policies. To address the limitations of traditional base station-based monitoring methods, we propose a highly maneuverable mothership-based unmanned aerial vehicle (UAV) monitoring mode. We develop a mixed integer non-linear programming model to maximize the total profit (i.e., the revenues of ship emission monitoring minus the fixed costs of motherships and UAVs, the fuel cost of motherships, and the electricity cost of UAVs). Three types of integer variables are relaxed to continuous variables based on the model properties. We then design a tailored Benders decomposition algorithm to solve the model. Moreover, to improve the performance of the algorithm, we also present a variety of acceleration strategies, including lower bound limit inequalities and knapsack inequalities. Finally, we verify the effectiveness of the proposed algorithm using experimental instances based on the North American ECA. We also find a relationship between the width of emission inspection area and the total monitoring cost.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"179 ","pages":"Article 107012"},"PeriodicalIF":4.1,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610810","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 local search enhanced logic-based Benders decomposition approach for order acceptance and scheduling problem with preemption","authors":"Lin Wang ,&nbsp;Ziqing Zhang ,&nbsp;Sirui Wang","doi":"10.1016/j.cor.2025.107047","DOIUrl":"10.1016/j.cor.2025.107047","url":null,"abstract":"<div><div>This paper addresses an order acceptance and scheduling problem (OAS) that incorporates the allowance for preemption. We introduce a novel continuous-time mixed-integer linear programming (MILP) formulation for the problem. Allowing preemption greatly increases the complexity of the MILP model by requiring a larger number of variables and constraints to sequence order parts, rather than merely orders. Consequently, the performance of the MILP formulation rapidly deteriorates as the problem size grows. To efficiently solve the problem, we propose an approach based on the logic-based Benders decomposition (LBBD). The preemptive Earliest Due Date (EDD) rule is utilized to efficiently solve the subproblem in LBBD. Additionally, a local search heuristic is developed to construct high-quality solutions based on the LBBD master problem solutions. This local search-enhanced LBBD approach (LS-LBBD) is capable of solving instances with up to 200 orders to optimality within 3600 s, achieving an average optimality gap of only 3.02% across 200-order instances with various parameters. The effectiveness of the local search heuristic has been validated by comparative experiments. For instances where the optimal solution was not obtained, the solutions from LS-LBBD were on average 6.57% better than those from the original LBBD</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"180 ","pages":"Article 107047"},"PeriodicalIF":4.1,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767726","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":"Logic-based Benders decomposition methods for the distributed permutation flow shop scheduling problem with production and transportation cost","authors":"Fuli Xiong,&nbsp;Jiangbo Shi,&nbsp;Lin Jing,&nbsp;An Ping","doi":"10.1016/j.cor.2025.107044","DOIUrl":"10.1016/j.cor.2025.107044","url":null,"abstract":"<div><div>Distributed manufacturing mode can significantly enhance production flexibility and efficiency. Considering that factories and customers in distributed manufacturing environments may be geographically dispersed, we address a distributed permutation flow shop scheduling problem (DPFSP) with direct transportation under different cost of production and transportation while the goal is to minimize of weighted sum cost and makespan (DPFSP-PTM). First, we formulate two mixed-integer linear programming (MILP) models and one constraint programming (CP) model to optimize the objective simultaneously. Then, by decomposing DPFSP-PTM into an order assignment master problem (AMP) and a series of scheduling subproblems (SSPs), we develop two exact methods based on logic-based Benders decomposition (LBBD) and Branch-and-Check (BCH). To accelerate convergence, we propose three strong SSP relaxations based on the single-machine bottleneck to enhance the MILP models and AMP. Additionally, we introduce an initial solution generated by the iterated greedy (IG) algorithm to warm-start the LBBD. Finally, we demonstrate the effectiveness of the proposed methods in achieving competitive average optimality gaps and lower bounds across both small-scale and large-scale instances.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"179 ","pages":"Article 107044"},"PeriodicalIF":4.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143593075","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}