Computers & Operations Research最新文献

{"title":"Transportation and carbon emissions costs minimization for time-dependent vehicle routing problem with drones","authors":"Yong Peng ,&nbsp;Zhi Ren ,&nbsp;Dennis Z. Yu ,&nbsp;Yonghui Zhang","doi":"10.1016/j.cor.2024.106963","DOIUrl":"10.1016/j.cor.2024.106963","url":null,"abstract":"<div><div>Integrating drones in package delivery has emerged as an innovative application of unmanned aerial vehicle (UAV) technology in the logistics and transportation sector. In this context, trucks serve a dual role as delivery vehicles for customers and launch platforms for drones. Drones are deployed for efficient package delivery and can be retrieved from predetermined rendezvous locations using trucks. Our study explicitly targets the collaborative package delivery approach between trucks and drones within urban environments. To optimize this collaboration, we develop a mixed-integer programming (MIP) model for the time-dependent vehicle routing problem with drones (TDVRP-D), which aims to minimize the transportation costs of both trucks and drones, along with the carbon emissions costs associated with trucks. To solve this complex problem efficiently, we propose a highly effective metaheuristic algorithm based on the variable neighborhood search (VNS) technique. Through extensive experimental studies and rigorous comparisons with existing methods, we demonstrate the superiority of our proposed algorithm in terms of solution quality and computational efficiency, particularly for large-scale instances.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"176 ","pages":"Article 106963"},"PeriodicalIF":4.1,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166419","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":"Towards green manufacturing: Co-optimizing capacity expansion planning of production and renewable energy generation with endogenous uncertainty","authors":"Xin Zhou ,&nbsp;Bo Zeng ,&nbsp;Feng Cui ,&nbsp;Na Geng","doi":"10.1016/j.cor.2024.106971","DOIUrl":"10.1016/j.cor.2024.106971","url":null,"abstract":"<div><div>The manufacturing industry stands as a significant consumer of electricity, for which the production of renewable energy through integrated distributed generation systems represents a sustainable alternative. However, uncertainty about customer demand and energy generation poses challenges for capacity planning. In this paper, we aim to address the joint decision-making for production capacity and renewable energy-generation capacity. To this end, we first establish a two-stage robust optimization (TRO) framework that considers uncertain product demand and generation rates, with the objective of minimizing the total costs. The TRO encompasses not only strategic decisions on production and electricity-generation capacity, but also tactical decisions on production planning, inventory, and emission targets. To solve this model, we propose a pre-check parametric column and constraint generation (PP-C&amp;CG) algorithm. Subsequent validation with benchmark data and application to two practical cases demonstrate that our proposed joint-decision approach is more efficient than non-robust decisions. Lastly, despite its additional costs, our approach based on robust decisions offers practical utility in addressing worst-case scenarios characterized by considerable uncertainty.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"176 ","pages":"Article 106971"},"PeriodicalIF":4.1,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166422","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":"Capacity planning to cope with demand surges in fourth-party logistics networks under chance-constrained service levels","authors":"Songchen Jiang ,&nbsp;Min Huang ,&nbsp;Yunan Liu ,&nbsp;Yuxin Zhang ,&nbsp;Xingwei Wang","doi":"10.1016/j.cor.2024.106956","DOIUrl":"10.1016/j.cor.2024.106956","url":null,"abstract":"<div><div>In this paper, we study a capacity planning problem for a <em>fourth-party logistics network</em> (4PLN) in the face of event-triggered demand surges. We aim to solve a stochastic optimization problem in order to minimize the total cost for the 4PLN under chance-constrained service-level targets, where the stochastic demand process is modeled as a summation of random variables with a Bernoulli term of jump processes. At the heart of our solution procedure is a greedy pricing and weighting strategy based cell-and-bound (G-C&amp;B) algorithm designed for solving the SAA-based model. Compared to the standard C&amp;B method, our G-C&amp;B is able to largely reduce the number of non-essential cell enumerations and achieve reduced running time complexity. To mitigate the performance degradation due to large system scale and/or sample instance, we extend our base algorithm to a two-step <em><strong>L</strong>ocal <strong>E</strong>xperimentation for <strong>G</strong>lobal <strong>O</strong>ptimization strategy based cell-and-bound</em> (LEGO-C&amp;B) framework, in which we first solve a small-scale training problem to find the important scenarios (eliminating excessive cell enumerations) and then use the training results to expedite the full optimization problem. We evaluate the performance of our algorithms by conducting a comprehensive series of numerical experiments. Besides, our results also demonstrate how the effectiveness of our methods depends on various factors including (<em>i</em>) the algorithm’s hyperparameters such as the sample size and training ratio, and (<em>ii</em>) the 4PLN’s input parameters such as the network scale, surge demand frequency, and rental price of 3PL resource. Our results exhibit several qualitative insights.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"176 ","pages":"Article 106956"},"PeriodicalIF":4.1,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166425","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 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}