Computers & Operations Research最新文献

{"title":"A robust optimal control problem with moment constraints on distribution: Theoretical analysis and an algorithm","authors":"Jianxiong Ye ,&nbsp;Lei Wang ,&nbsp;Changzhi Wu ,&nbsp;Jie Sun ,&nbsp;Kok Lay Teo ,&nbsp;Xiangyu Wang","doi":"10.1016/j.cor.2024.106966","DOIUrl":"10.1016/j.cor.2024.106966","url":null,"abstract":"<div><div>We study an optimal control problem in which both the objective function and the dynamic constraint contain an uncertain parameter. Since the distribution of this uncertain parameter is not exactly known, the objective function is taken as the worst-case expectation over a set of possible distributions of the uncertain parameter. This ambiguity set of distributions is, in turn, defined by the first two moments of the random variables involved. The optimal control is found by minimizing the worst-case expectation over all possible distributions in this set. If the distributions are discrete, the stochastic minimax optimal control problem can be converted into a conventional optimal control problem via duality, which is then approximated as a finite-dimensional optimization problem via the control parametrization. We derive necessary conditions of optimality and propose an algorithm to solve the approximation optimization problem. The results of discrete probability distribution are then extended to the case with one dimensional continuous stochastic variable by applying the control parametrization methodology on the continuous stochastic variable, and the convergence results are derived. A numerical example is present to illustrate the potential application of the proposed model and the effectiveness of the algorithm.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"176 ","pages":"Article 106966"},"PeriodicalIF":4.1,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166418","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":"Deep learning based high accuracy heuristic approach for knapsack interdiction problem","authors":"Sunhyeon Kwon,&nbsp;Hwayong Choi,&nbsp;Sungsoo Park","doi":"10.1016/j.cor.2024.106965","DOIUrl":"10.1016/j.cor.2024.106965","url":null,"abstract":"<div><div>Interdiction problems are a subfamily of bilevel optimization problems, characterized by a hierarchical structure involving two agents: a leader and a follower. In these problems, the objective functions of the leader and the follower are identical but are optimized in opposite directions. In this paper, we focus on the knapsack interdiction problem, where the leader and the follower compete for a shared set of items. While exact algorithms exist to solve this problem, they may not be suitable for slightly larger instances. As an alternative to exact algorithms, we propose a heuristic approach based on deep learning. Our method involves training three types of neural networks: a core network that aggregates information about the problem, a classification network that directly identifies solutions, and an identification network that assesses the reliability of the classification network’s results. Our algorithm successfully finds optimal or near-optimal solutions up to 21 times faster than the exact algorithm for both the training data sizes and larger problem instances.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"176 ","pages":"Article 106965"},"PeriodicalIF":4.1,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An adaptive large neighborhood search method for the drone–truck arc routing problem","authors":"Xufei Liu ,&nbsp;Sung Hoon Chung ,&nbsp;Changhyun Kwon","doi":"10.1016/j.cor.2024.106959","DOIUrl":"10.1016/j.cor.2024.106959","url":null,"abstract":"<div><div>For applications such as traffic monitoring, infrastructure inspection, and security, ground vehicles (trucks) and unmanned aerial vehicles (drones) may collaborate to finish the task more efficiently. This paper considers an Arc Routing Problem (ARP) with a mixed fleet of a single truck and multiple homogeneous drones, called a Drone–Truck Arc Routing Problem (DT-ARP). While the truck must follow a road network, the drone can fly off of it. With a limited battery capacity, however, the drone has a length constraint, i.e., the maximum flight range. A truck driver can replace a battery for the drone after each flight trip. We first transform the DT-ARP into a node routing problem, for which we present a MIP formulation for the case with a truck and a drone. To solve large-size instances with multiple drones, a heuristic method based on Adaptive Large Neighborhood Search is proposed. The performance of ALNS is evaluated on small-size randomly generated instances and large-size undirected rural postman problem benchmark instances. In addition, an analysis is provided on the relationship between truck/drone speeds and the drone’s flight range, which affects the difficulty level to solve. The robustness of ALNS is shown via numerical experiments.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"176 ","pages":"Article 106959"},"PeriodicalIF":4.1,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166431","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 column generation heuristic for simultaneous lot-sizing and scheduling problems with secondary resources and setup carryovers","authors":"Cevdet Utku Şafak ,&nbsp;Erinç Albey ,&nbsp;Görkem Yılmaz","doi":"10.1016/j.cor.2024.106962","DOIUrl":"10.1016/j.cor.2024.106962","url":null,"abstract":"<div><div>This study introduces an innovative approach to address the Capacitated Lot-Sizing and Scheduling Problem with Sequence-Dependent Setups (CLSD), considering both the sequence-dependent setups and costs. Facing the challenge of large-scale instances, a Column Generation-based Neighbourhood Search (CGNS) algorithm is proposed, efficiently handling real-life CLSD scenarios with extensions like secondary resources and setup carryover and crossovers. The algorithm demonstrates superior performance compared to commercial solvers and fix and relax-based benchmark algorithms, producing high-quality solutions within specified time limits on large data sets. The study’s contributions include a distinctive pattern and column structure in the proposed formulation, effectively managing the exponential increase in decision variables. Test instances and a real-life case study validate the algorithm’s applicability to production systems under the CLSD and Capacitated Lot-Sizing Problem (CLSP) frameworks, making it a valuable tool for optimising simultaneous lot-sizing and scheduling challenges in practical settings.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"176 ","pages":"Article 106962"},"PeriodicalIF":4.1,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166429","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":"Topology reconstruction in telecommunication networks: Embedding operations research within deep learning","authors":"Tobias Engelhardt Rasmussen ,&nbsp;Siv Sørensen ,&nbsp;David Pisinger ,&nbsp;Thomas Martini Jørgensen ,&nbsp;Andreas Baum","doi":"10.1016/j.cor.2024.106960","DOIUrl":"10.1016/j.cor.2024.106960","url":null,"abstract":"<div><div>We consider the task of reconstructing the cabling arrangements of <em>last-mile</em> telecommunication networks using customer modem data. In such networks, downstream data traverses from a source node down through the branches of the tree network to a set of customer leaf nodes. Each modem monitors the quality of received data using a series of continuous data metrics. The state of the data, when it reaches a modem, is contingent upon the path it traverses through the network and can be affected by, e.g., corroded cable connectors.</div><div>We train an encoder to identify irregular inherited <em>events</em> in modem quality data, such as network faults, and encode them as discrete data sequences for each modem. Specifically, the encoding scheme is obtained by using unsupervised contrastive learning, where a Siamese neural network is trained on a positive (true) topology, its modem data, and a set of negative (false) topologies. The weights of the Siamese network are continuously updated based on a new modified version of the Maximum Parsimony optimality criterion. This approach essentially integrates an optimization problem directly into a deep learning loss function.</div><div>We evaluate the encoder’s performance on simulated data instances with randomly added events. The performance of the encoder is tested both on its ability to extract and encode events as well as whether the encoded data sequences lead to accurate topology reconstructions under the modified version of the Maximum Parsimony optimality criterion.</div><div>Promising computational results are reported for trees with a varying number of internal nodes, up to a maximum of 20. The encoder identifies a high percentage of simulated events, leading to nearly perfect topology reconstruction. Overall, these results affirm the potential of embedding an optimization problem into a deep learning loss function, unveiling many interesting topics for further research.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"176 ","pages":"Article 106960"},"PeriodicalIF":4.1,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166424","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":"A Large Neighborhood Search-based approach to tackle the very large scale Team Orienteering Problem in industrial context","authors":"Charly Chaigneau ,&nbsp;Nathalie Bostel ,&nbsp;Axel Grimault","doi":"10.1016/j.cor.2024.106954","DOIUrl":"10.1016/j.cor.2024.106954","url":null,"abstract":"<div><div>The Team Orienteering Problem (TOP) is an optimization problem belonging to the class of Vehicle Routing Problem with Profits in which the objective is to maximize the total profit collected by visiting customers while being limited to a time limit. This paper deals with the very large scale TOP in an industrial context. In this context, computing time is decisive and classical methods may fail to provide good solutions in a reasonable computational time. To do so, we propose a Large Neighborhood Search (LNS) combined with various mechanisms in order to reduce the computational time of the method. It is applied on classical sets of instances from the literature and on a new set of very large scale instances ranging from 1001 to 5395 customers that we adapted from Kobeaga et al. (2017). On the small scale set of instances, most best-known solutions are found. On the large scale set of instances, three new best-known solutions are found while the algorithm quickly gets more than half of the other best-known solutions.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"176 ","pages":"Article 106954"},"PeriodicalIF":4.1,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166426","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":"Minimum cost consensus model considering dual behavior preference","authors":"Yingying Liang ,&nbsp;Jindong Qin ,&nbsp;Witold Pedrycz","doi":"10.1016/j.cor.2024.106961","DOIUrl":"10.1016/j.cor.2024.106961","url":null,"abstract":"<div><div>In actual consensus-reaching problems, decision makers (DMs) may exhibit non-unique behaviors originating from comparisons between themselves and expectations and reality, such as fairness concern and overconfidence behaviors, which may result in solution recommendation deviation when using the existing minimum cost consensus models (MCCMs). In order to handle consensus issues when DMs show fairness concern behavior, a behavior between DMs, the MCCM considering fairness concern (MCCM-FC) is established. Moreover, DMs may exhibit overconfidence regarding their own opinions, which is managed by the MCCM considering overconfidence (MCCM-O) to offset the actual difference between expectations and reality. To cope with the scenario that incorporates both behaviors simultaneously, the integrated fairness concern and overconfidence MCCM (MCCM-FC-O) is constructed and the relationships of the three MCCMs are discussed. The proposed models are justified through an illustrated application, and further sensitivity and comparative analyses are conducted to illustrate their practicability.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"176 ","pages":"Article 106961"},"PeriodicalIF":4.1,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166423","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 the Transport of Organs for Transplantation","authors":"Isaac Balster ,&nbsp;Joyce Azevedo Caetano ,&nbsp;Glaydston Mattos Ribeiro ,&nbsp;Laura Bahiense","doi":"10.1016/j.cor.2024.106934","DOIUrl":"10.1016/j.cor.2024.106934","url":null,"abstract":"<div><div>As an organ becomes available for transplantation, a recipient must be selected. Usually, donor and recipient are geographically apart. Therefore, the transport of the organ must be planned and executed within the time window imposed by the maximum preservation time of the organ, which can impact recipient selection. The Cold Ischemia Time - CIT, that is the time elapsed between the surgical removal of the organ and its transplantation, must be the minimum possible to improve the transplantation success. In this sense, the air transport becomes the best option and, sometimes, it is the only way to deliver the organ before perishing. The planning of an organ transportation means choosing, among thousands of possible sequences of flights, the option that delivers the organ faster to its destination. This problem can be modeled as a resource constrained shortest path. Given the urgency and importance of this task, which is solved manually in Brazil, we present a labeling algorithm to find the optimal sequence of flights. Computational tests performed on 25 Brazilian real cases showed a reduction, on average, of 37,46% for the CITs and 44,17% for the transport times.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"176 ","pages":"Article 106934"},"PeriodicalIF":4.1,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165818","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":"New models for close enough facility location problems","authors":"Alejandro Moya-Martínez ,&nbsp;Mercedes Landete ,&nbsp;Juan F. Monge ,&nbsp;Sergio García","doi":"10.1016/j.cor.2024.106957","DOIUrl":"10.1016/j.cor.2024.106957","url":null,"abstract":"<div><div>Two integer programming problems are introduced and formulated in this paper, both based on the concepts of <em>close enough</em> and facility location. Location problems using the notion of <em>close enough</em> allow customers to pick up their demand at pickup points different from the facilities but that are still not too far from the latter.</div><div>Given a discrete set of customers, a discrete set of potential facility locations, and a maximum distance that each customer is willing to travel free of charge to pick up their order, the Close Enough Facility Location Problem consists in determining which facilities to open among the candidates, on which points on the plane to install pickup points, and how to assign customers to both facilities and pickup points, in an optimal way taking into account different costs. In this work we propose two generalizations of this problem. The first is to consider that the pickup points have capacities. The second is to consider that the communications network is restricted to a graph, and that therefore the pickup points cannot be installed on any point on the plane but only on the network. These problems are named the Capacitated Close-Enough Facility Location Problem and the Network Capacitated Close-Enough Facility Location Problem, respectively. We propose a column generation algorithm for the two introduced problems that allows us to obtain better results for large-scale problems than the CPLEX solver.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"176 ","pages":"Article 106957"},"PeriodicalIF":4.1,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166432","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":"Genetic algorithm-based selection of optimal Monte Carlo simulations","authors":"Francesco Strati ,&nbsp;Luca G. Trussoni","doi":"10.1016/j.cor.2024.106958","DOIUrl":"10.1016/j.cor.2024.106958","url":null,"abstract":"<div><div>The aim of this work is to propose the use of a genetic algorithm to solve the problem of the optimal subsampling of Monte Carlo simulations to obtain desired statistical properties. It is designed to optimally select the best <span><math><mi>m</mi></math></span> Monte Carlo simulations from a larger pool of <span><math><mrow><mi>N</mi><mo>&gt;</mo><mi>m</mi></mrow></math></span> simulations. The concept of an “optimal selection” is defined through a target metric, in this work the first and second moments of the distribution, from the set of <span><math><mi>N</mi></math></span> simulations, to which the subset of <span><math><mi>m</mi></math></span> simulations should closely converge. The implementation employs an objective function, allowing the algorithm to balance computational efficiency and optimization performance, achieving fast and precise selection of the <span><math><mi>m</mi></math></span> simulations.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"176 ","pages":"Article 106958"},"PeriodicalIF":4.1,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165819","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}