Computers & Operations Research最新文献

{"title":"An effective biogeography-based optimization algorithm for multi-objective green scheduling of distributed assembly permutation flowshop scheduling problem","authors":"Long Cheng ,&nbsp;Lei Wang ,&nbsp;Jingcao Cai ,&nbsp;Kongfu Hu ,&nbsp;Yuan Xiong ,&nbsp;Qiangqiang Xia","doi":"10.1016/j.cor.2025.107158","DOIUrl":"10.1016/j.cor.2025.107158","url":null,"abstract":"<div><div>The distributed assembly permutation flowshop scheduling problem (DAPFSP) plays a crucial role in advancing distributed manufacturing systems (DMS). While much attention has been given to optimizing production scheduling for improved efficiency, energy consumption often remains overlooked. In line with sustainable development strategies, this research focuses on the multi-objective green scheduling of DAPFSP (MO-DAPFSP), introducing a mixed integer linear programming (MILP) model to minimize the maximum completion time and total machine energy consumption. To solve MO-DAPFSP, an effective biogeography-based optimization algorithm (EBBO) is proposed. EBBO incorporates a dual-population heuristic initialization method, specifically designed to generate high-quality initial solutions based on problem characteristics. A hybrid migration operator and an improved mutation operator are employed to enhance both global and local search capabilities. Additionally, a novel perturbation operator is integrated into the migration process, boosting the Pareto quality of partial solutions and accelerating convergence toward the true Pareto frontier. To evaluate the performance of EBBO, 810 instances of varying sizes were designed. The experimental results demonstrate that EBBO algorithm is highly effective in solving complex scheduling problems, providing a promising approach for optimizing multi-objective green scheduling in distributed manufacturing environments.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"183 ","pages":"Article 107158"},"PeriodicalIF":4.1,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366072","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 team orienteering problem with service times and mandatory & incompatible nodes","authors":"Alberto Guastalla,&nbsp;Roberto Aringhieri,&nbsp;Pierre Hosteins","doi":"10.1016/j.cor.2025.107170","DOIUrl":"10.1016/j.cor.2025.107170","url":null,"abstract":"<div><div>The Team Orienteering Problem with Service Times and Mandatory &amp; Incompatible Nodes (TOP-ST-MIN) is a variant of the classic Team Orienteering Problem (TOP), which includes three features that stem from two real-world problems previously studied by the authors: service time at nodes, mandatory nodes and physical or logical incompatibilities between pairs of nodes. We gather all these ingredients for the first time in the same model and prove that even finding a feasible solution to this problem is NP-complete unlike the TOP where finding a feasible solution is straightforward. Two versions of this variant are considered in our study. For such versions, we proposed two alternative mathematical formulations, a route-based and a flow-based formulations. Based on the flow-based formulation, we developed a Cutting-Plane Algorithm (CPA) exploiting five families of valid inequalities, which are either new or have generally not been used as such in the TOP literature and separated by means of new algorithmic methods. Extensive computational experiments showed that the CPA outperforms CPLEX in solving the new benchmark instances, generated in such a way to evaluate the impact of the three novel features that characterise the problem. The CPA is also competitive for the TOP since it is able to solve almost the same number of instances as the state-of-art algorithms.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"183 ","pages":"Article 107170"},"PeriodicalIF":4.1,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366073","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":"Multi-door cross-dock scheduling under flexible doors mode and material handling resource restrictions","authors":"Nikhil A. Eti ,&nbsp;William G. Ferrell Jr. ,&nbsp;Nathan Huynh","doi":"10.1016/j.cor.2025.107183","DOIUrl":"10.1016/j.cor.2025.107183","url":null,"abstract":"<div><div>Cross-docks are used in distribution networks to improve efficiency by minimizing the need for warehouse space and optimizing the supply chain. Cross-docks are primarily implemented for consolidating less-than-truckload shipments to realize gains from economies of scale in transportation. This is accomplished by scheduling inbound truck arrivals to the strip doors where their freight is either moved directly to outbound trucks parked at the stack doors or held for a very short time waiting for the assigned outbound truck to arrive. This research focuses on two important operating dimensions of cross-docks. The first is the decision to preassign each door in the cross-dock to either be used for strip or stack operations exclusively (separate door operations) or dynamically assign doors for strip or stack operations as needed (mixed door operations). The second examines assigning material handling devices (e.g., forklifts) to various tasks to make the cross-dock operate efficiently and achieve the required high throughput. A mathematical programming model of mixed door operation is developed and compared with a separate doors model that exists in the literature. A second mathematical programming model is presented that has finite material handling capacity and explicitly models the unloading and loading tasks. To improve computation times, a convex hull formulation of the separate-door model is developed and compared with the widely used Big-M method. Simulated annealing based algorithms are developed to solve cross-dock problems for medium to largescale instances. An integrated simulated annealing algorithm decodes a genotypic representation for the restricted material handling model’s solution to assign doors and forklifts to trucks simultaneously via a series of operating rules. Simulated annealing is used to improve the resultant forklift assignments from the integrated method to produce an upper bound for the limited material handling problem. Numerical results provide important insights into improved delivery times associated with using a mixed door strategy in certain situations. Additionally, results indicate transition points exist that allow decision makers to determine the minimum number of forklifts required for various cross-dock sizes to avoid bottleneck delays.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"183 ","pages":"Article 107183"},"PeriodicalIF":4.1,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534746","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 effective aggregation heuristic for Capacitated Facility Location Problems with many demand points","authors":"R.J.W. Buijs ,&nbsp;R.D. van der Mei ,&nbsp;E.R. Dugundji ,&nbsp;S. Bhulai","doi":"10.1016/j.cor.2025.107153","DOIUrl":"10.1016/j.cor.2025.107153","url":null,"abstract":"<div><div>In location analysis, the effects of demand aggregation have been the subject of many studies. This body of literature is mainly focused on <span><math><mi>p</mi></math></span>-median and <span><math><mi>p</mi></math></span>-center problems. Relatively few papers in the literature on aggregation explicitly concern the Capacitated Facility Location Problem <strong>(CFLP)</strong>. Our work examines the beneficial use of aggregation in the context of the CFLP. We focus on problems where there are significantly more demand points than potential facility locations, since this is where aggregation is most applicable in reducing complexity. We examine ways to obtain an aggregation at a fixed resolution, that is likely to perform well for a given instance of the problem. These aggregation techniques will form the core of a broader algorithmic framework, which contributes to the literature concerning heuristics for CFLPs. Our core aggregation method is based on applying <span><math><mi>k</mi></math></span>-means clustering in <span><math><msup><mrow><mi>R</mi></mrow><mrow><mi>m</mi></mrow></msup></math></span>, where <span><math><mi>m</mi></math></span> is the number of potential facilities. The space in which we apply the clustering is constructed by applying a transformation to the normalized distance matrix corresponding to the original CFLP problem. The aim of applying the transformation is to magnify differences in distance where relevant, and to compress irrelevant differences in distance. We evaluate our heuristic method on larger instances based on a real-world problem in reverse logistics. The results are encouraging and indicate that our method is capable of outperforming an intuitive benchmark aggregation method. We find that choosing the right hyperparameters and starting with a good initialization help our method perform better.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"183 ","pages":"Article 107153"},"PeriodicalIF":4.1,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491703","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":"Novel valid inequalities and branch-and-price for Stackelberg security games","authors":"Pamela Bustamante-Faúndez ,&nbsp;Víctor Bucarey L. ,&nbsp;Martine Labbé ,&nbsp;Vladimir Marianov ,&nbsp;Fernando Ordoñez","doi":"10.1016/j.cor.2025.107122","DOIUrl":"10.1016/j.cor.2025.107122","url":null,"abstract":"<div><div>Anticipating the strategies of potential attackers is crucial for protecting critical infrastructure. We can represent the challenge of the defenders of such infrastructure as a Stackelberg security game. The defender must decide how to allocate limited resources to protect specific targets, aiming to maximize their expected utility (such as minimizing the extent of damage) and considering that attackers will respond in a way that is most advantageous to them.</div><div>We present novel valid inequalities to find a Strong Stackelberg Equilibrium in both Stackelberg games and Stackelberg security games. We also consider a Stackelberg security game that aims to protect targets with a defined budget. We use branch-and-price in this game to show that our approach outperforms the standard formulation in the literature, in terms of both solution speed and memory usage.</div><div>Additionally, we present an extensive computational study to assess the impact of various parameters in branch-and-price, such as the number of initial columns, the number of columns generated per iteration, and the effects of stabilization techniques. The results show that our approach reduces the solution time of the problem to less than a fifth of the time required by the state-of-the art methods.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"183 ","pages":"Article 107122"},"PeriodicalIF":4.1,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471250","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":"Algorithms for pickup and delivery problems with hours of service constraints","authors":"Lucas Sippel,&nbsp;Michael A. Forbes,&nbsp;Joseph Menesch","doi":"10.1016/j.cor.2025.107123","DOIUrl":"10.1016/j.cor.2025.107123","url":null,"abstract":"<div><div>We propose new exact and heuristic algorithms for solving an extension of the Pickup and Delivery problem with Time Windows that considers numerous constraints encountered in the real world. The problem involves optimally routing a fleet of identical vehicles to service a set of pickup and delivery pairs subject to capacity, time window, pairing, precedence, and last-in-first-out loading constraints as well as complex driver rules. We consider a set partitioning model based on routes, and also introduce a formulation based on fragments which are segments of routes with a particular structure. Computational results on randomly generated instances are used to compare the scalability of the two formulations with respect to the number of requests. A technique for reducing the number of routes or fragments is proposed which relies on a machine learning model to determine those that are likely to be in the optimal solution. When the number of routes or fragments is reduced using the machine learning model, high quality solutions are obtained on the instances solvable by the exact method. Solutions can also be obtained for instances where route or fragment generation is intractable.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"183 ","pages":"Article 107123"},"PeriodicalIF":4.1,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306219","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":"Constraint programming approaches for finding conserved metabolic and genomic patterns","authors":"Mohamed Lemine Ahmed Sidi ,&nbsp;Ronan Bocquillon ,&nbsp;Florent Cabret ,&nbsp;Hafedh Mohamed Babou ,&nbsp;Cheikh Dhib ,&nbsp;Emmanuel Néron ,&nbsp;Ameur Soukhal ,&nbsp;Mohamedade Farouk Nanne","doi":"10.1016/j.cor.2025.107166","DOIUrl":"10.1016/j.cor.2025.107166","url":null,"abstract":"<div><div>Systems biology is a relatively new field of science that studies living organisms as they are found in nature. This approach differs from previous approaches by combining information from different fields (biology, physiology, biochemistry, etc.) to understand the functions of these organisms, requiring the use of specialized and efficient treatment and analysis algorithms. Many approaches for comparing biological networks are based on graph models in which the vertices represent biological components and the edges or arcs represent interactions between components. This paper focuses on an <span><math><mi>NP</mi></math></span>-hard problem related to heterogeneous biological networks. The main objective is to study the relationship between metabolism and genome. The metabolic network is modeled by a directed graph <span><math><mi>D</mi></math></span> and gene proximity is modeled by an undirected graph <span><math><mi>G</mi></math></span> (<span><math><mi>D</mi></math></span> and <span><math><mi>G</mi></math></span> are built on the same set of vertices). The proposed approaches (based on constraint programming) identify paths or trails in <span><math><mi>D</mi></math></span> whose vertices induce a connected component in <span><math><mi>G</mi></math></span>. The paths represent reaction chains in the metabolic network catalyzed by products of neighboring genes in the genome. These biologically significant patterns allow different species to be compared.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"183 ","pages":"Article 107166"},"PeriodicalIF":4.1,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291018","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 k-swap local search for makespan scheduling","authors":"Lars Rohwedder ,&nbsp;Ashkan Safari ,&nbsp;Tjark Vredeveld","doi":"10.1016/j.cor.2025.107168","DOIUrl":"10.1016/j.cor.2025.107168","url":null,"abstract":"<div><div>Local search is a widely used technique for tackling challenging optimization problems, offering significant advantages in terms of computational efficiency and exhibiting strong empirical behavior across a wide range of problem domains. In this paper, we address the problem of scheduling a set of jobs on identical parallel machines with the objective of <em>makespan minimization</em>. For this problem, we consider a local search neighborhood, called <span><math><mi>k</mi></math></span>-<em>swap</em>, which is a generalized version of the widely-used <em>swap</em> and <em>jump</em> neighborhoods. The <span><math><mi>k</mi></math></span>-swap neighborhood is obtained by swapping at most <span><math><mi>k</mi></math></span> jobs between two machines. First, we propose an algorithm for finding an improving neighbor in the <span><math><mi>k</mi></math></span>-swap neighborhood which is faster than the naive approach, and prove an almost matching lower bound on any such an algorithm. Then, we analyze the number of local search steps required to converge to a local optimum with respect to the <span><math><mi>k</mi></math></span>-swap neighborhood. For <span><math><mrow><mi>k</mi><mo>≥</mo><mn>3</mn></mrow></math></span>, we provide an exponential lower bound regardless of the number of machines, and for <span><math><mrow><mi>k</mi><mo>=</mo><mn>2</mn></mrow></math></span> (similar to the swap neighborhood), we provide a polynomial upper bound for the case of having two machines. Finally, we conduct computational experiments on various families of instances.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"183 ","pages":"Article 107168"},"PeriodicalIF":4.1,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298470","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":"Optimal interventions in robust optimization with time-dependent uncertainties","authors":"Shaunak S. Dabadghao ,&nbsp;Ahmadreza Marandi ,&nbsp;Arkajyoti Roy","doi":"10.1016/j.cor.2025.107162","DOIUrl":"10.1016/j.cor.2025.107162","url":null,"abstract":"<div><div>Uncertainties can be time-dependent, particularly in areas such as maintenance scheduling and cancer radiotherapy planning, where the condition of the system (or patient) can change over the course of the operation (or treatment). When solving problems in such areas, it is crucial to intervene and observe the condition of the system prior to adapting the decisions. However, observations can be costly, and the timing of observations is directly impacted by time-dependent uncertainties. We address these challenges by developing optimal intervention policies for robust optimization models that employ time-dependent uncertainty sets where (i) making an observation fully resets the uncertainty yet incurs a cost, and (ii) the solution of the static robust optimization problem without making observation continuously becomes more conservative. Further, we provide heuristic procedures to compute adaptive robust solutions from the models, efficiently. We evaluate the practicality of the developed procedures by applying them to problems in maintenance planning of devices, where our results provide an efficient procedure to obtain an optimal maintenance policy. Moreover, in cancer radiotherapy, we develop optimally-intervened robust treatment plans that reduce dose to healthy organs without affecting tumor dose when compared to naively-intervened robust models and other deterministic approaches.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"183 ","pages":"Article 107162"},"PeriodicalIF":4.1,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306692","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":"Rule generation for classification: Scalability, interpretability, and fairness","authors":"Tabea E. Röber ,&nbsp;Adia C. Lumadjeng ,&nbsp;M. Hakan Akyüz ,&nbsp;Ş. İlker Birbil","doi":"10.1016/j.cor.2025.107163","DOIUrl":"10.1016/j.cor.2025.107163","url":null,"abstract":"<div><div>We introduce a new rule-based optimization method for classification with constraints. The proposed method leverages column generation for linear programming, and hence, is scalable to large datasets. The resulting pricing subproblem is shown to be NP-Hard. We recourse to a decision tree-based heuristic and solve a proxy pricing subproblem for acceleration. The method returns a set of rules along with their optimal weights indicating the importance of each rule for learning. We address interpretability and fairness by assigning cost coefficients to the rules and introducing additional constraints. In particular, we focus on local interpretability and generalize a separation criterion in fairness to multiple sensitive attributes and classes. We test the performance of the proposed methodology on a collection of datasets and present a case study to elaborate on its different aspects. The proposed rule-based learning method exhibits a good compromise between local interpretability and fairness on the one side, and accuracy on the other side.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"183 ","pages":"Article 107163"},"PeriodicalIF":4.1,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312568","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}