Computers & Operations Research最新文献

英文中文

Seru scheduling problem with lot streaming and worker transfers: A multi-objective approach

IF 4.1 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-01-09 DOI: 10.1016/j.cor.2024.106967

Beren Gürsoy Yılmaz , Ömer Faruk Yılmaz , Elif Akçalı , Emre Çevikcan

Seru production system (SPS) offers the flexibility of job shop production environments with the efficiency of traditional assembly lines. The SPSs are particularly attractive to industries characterized by high product variety and micro production volumes, and effective utilization of production and workforce resources is a critical challenge for SPSs. This paper addresses the seru scheduling problem with lot streaming and worker transfers for a SPS using a multi-objective approach. To this end, first, a multi-objective mixed-integer linear programming (MILP) model is developed for the minimization of makespan, average flow time, and maximum workload imbalance. Six different algorithms based on non-dominating sorting genetic algorithm II (NSGA-II) are developed, each corresponding to an operational setting dictated by the lot streaming and worker transfers strategies in effect. A design of experiment (DoE) framework is utilized to generate realistic problem instances based on the several controllable factors and their levels. Analysis of comprehensive computational results demonstrates the effectiveness of the proposed algorithm (NS2) in finding high-quality and diversified solutions by simultaneous utilization of lot streaming with variable-sized sublots and worker transfers. The results indicate that the performance improvement achieved by the NS2 ranges between 10% and 20% compared to other algorithms. Furthermore, Analysis of Variance (ANOVA) confirms the significance of the number of workers and number of serus as critical parameters for the design or redesign of SPSs. Drawing on these findings, managerial insights are provided regarding the impact of lot streaming and worker transfers on SPS performance. This study offers practical and theoretical insights for decision-makers seeking to enhance SPS performance and bridge the gap between the conceptual analysis and practical implementation of SPSs.

{"title":"Seru scheduling problem with lot streaming and worker transfers: A multi-objective approach","authors":"Beren Gürsoy Yılmaz , Ömer Faruk Yılmaz , Elif Akçalı , Emre Çevikcan","doi":"10.1016/j.cor.2024.106967","DOIUrl":"10.1016/j.cor.2024.106967","url":null,"abstract":"<div><div>Seru production system (SPS) offers the flexibility of job shop production environments with the efficiency of traditional assembly lines. The SPSs are particularly attractive to industries characterized by high product variety and micro production volumes, and effective utilization of production and workforce resources is a critical challenge for SPSs. This paper addresses the seru scheduling problem with lot streaming and worker transfers for a SPS using a multi-objective approach. To this end, first, a multi-objective mixed-integer linear programming (MILP) model is developed for the minimization of makespan, average flow time, and maximum workload imbalance. Six different algorithms based on non-dominating sorting genetic algorithm II (NSGA-II) are developed<em>,</em> each corresponding to an operational setting dictated by the lot streaming and worker transfers strategies in effect. A design of experiment (DoE) framework is utilized to generate realistic problem instances based on the several controllable factors and their levels. Analysis of comprehensive computational results demonstrates the effectiveness of the proposed algorithm (NS2) in finding high-quality and diversified solutions by simultaneous utilization of lot streaming with variable-sized sublots and worker transfers. The results indicate that the performance improvement achieved by the NS2 ranges between 10% and 20% compared to other algorithms. Furthermore, Analysis of Variance (ANOVA) confirms the significance of the number of workers and number of serus as critical parameters for the design or redesign of SPSs. Drawing on these findings, managerial insights are provided regarding the impact of lot streaming and worker transfers on SPS performance. This study offers practical and theoretical insights for decision-makers seeking to enhance SPS performance and bridge the gap between the conceptual analysis and practical implementation of SPSs.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"177 ","pages":"Article 106967"},"PeriodicalIF":4.1,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143132569","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}

引用次数: 0

Improved linear programming relaxations for flow shop problems with makespan minimization

IF 4.1 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-01-08 DOI: 10.1016/j.cor.2024.106970

Roderich Wallrath , Meik Franke , Matthias Walter

Machine scheduling problems with makespan minimization have been addressed in various academic and industrial fields using mixed-integer programming (MIP). In most MIP models, however, the makespan variable is poorly linked to the natural date variables of jobs. To address this, we propose novel, strengthening inequalities, derived from the single-machine scheduling polyhedron augmented by a makespan variable. While the associated optimization problem for a single machine is trivial, these inequalities can be applied as cutting planes to more complicated scheduling problems. In this work, we demonstrate their use for non-permutation flow shops. Using the Taillard benchmark set, we analyze the effect of the inequalities on the linear programming relaxations and mixed-integer programs of three commonly used MIP models. The experiments show that the inequalities significantly improve the ability of linear-ordering and time-indexed models to bound the optimum. The positive effect also extends to linear-ordering models with changeover times, demonstrating the potential of these inequalities to improve more general, application-oriented flow shop problems.

{"title":"Improved linear programming relaxations for flow shop problems with makespan minimization","authors":"Roderich Wallrath , Meik Franke , Matthias Walter","doi":"10.1016/j.cor.2024.106970","DOIUrl":"10.1016/j.cor.2024.106970","url":null,"abstract":"<div><div>Machine scheduling problems with makespan minimization have been addressed in various academic and industrial fields using mixed-integer programming (MIP). In most MIP models, however, the makespan variable is poorly linked to the natural date variables of jobs. To address this, we propose novel, strengthening inequalities, derived from the single-machine scheduling polyhedron augmented by a makespan variable. While the associated optimization problem for a single machine is trivial, these inequalities can be applied as cutting planes to more complicated scheduling problems. In this work, we demonstrate their use for non-permutation flow shops. Using the Taillard benchmark set, we analyze the effect of the inequalities on the linear programming relaxations and mixed-integer programs of three commonly used MIP models. The experiments show that the inequalities significantly improve the ability of linear-ordering and time-indexed models to bound the optimum. The positive effect also extends to linear-ordering models with changeover times, demonstrating the potential of these inequalities to improve more general, application-oriented flow shop problems.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"177 ","pages":"Article 106970"},"PeriodicalIF":4.1,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143132633","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}

引用次数: 0

Designing visually and operationally attractive routes to improve driver acceptance in road cleaning vehicle routing problem

IF 4.1 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-01-07 DOI: 10.1016/j.cor.2025.106973

Mingwei Chen, Xiaofang Wang, Linning Cai, Liang Ma

As urbanization accelerates, the planning of effective operating routes for urban road cleaning fleets has become a significant concern for municipalities. However, traditional vehicle routing algorithms often overlook drivers’ route preferences, resulting in poor driver acceptance. This study aims to design routes that are simultaneously cost-effective and attractive to drivers, thereby facilitating the implementation of efficient algorithms. Based on an analysis of road cleaning tasks and driver behavior, we introduce the concept of ”operational attractiveness” to complement the notion of visual attractiveness in road cleaning contexts. Two strategies are proposed for improving driver acceptance: imitating drivers’ routing behavior and reducing route overlap. The first strategy enhances the operational attractiveness of the route, while the second focuses on its visual attractiveness. We have integrated these two strategies into Randomized-Merge (RM), which is known for its remarkable cost-optimization performance. Numerical experiments on one real-world instance and ten new randomly generated instances show that the proposed heuristics can generate routes with lower cost than RM. Moreover, these routes are more attractive based on operational and visual metrics.

{"title":"Designing visually and operationally attractive routes to improve driver acceptance in road cleaning vehicle routing problem","authors":"Mingwei Chen, Xiaofang Wang, Linning Cai, Liang Ma","doi":"10.1016/j.cor.2025.106973","DOIUrl":"10.1016/j.cor.2025.106973","url":null,"abstract":"<div><div>As urbanization accelerates, the planning of effective operating routes for urban road cleaning fleets has become a significant concern for municipalities. However, traditional vehicle routing algorithms often overlook drivers’ route preferences, resulting in poor driver acceptance. This study aims to design routes that are simultaneously cost-effective and attractive to drivers, thereby facilitating the implementation of efficient algorithms. Based on an analysis of road cleaning tasks and driver behavior, we introduce the concept of ”operational attractiveness” to complement the notion of visual attractiveness in road cleaning contexts. Two strategies are proposed for improving driver acceptance: imitating drivers’ routing behavior and reducing route overlap. The first strategy enhances the operational attractiveness of the route, while the second focuses on its visual attractiveness. We have integrated these two strategies into Randomized-Merge (RM), which is known for its remarkable cost-optimization performance. Numerical experiments on one real-world instance and ten new randomly generated instances show that the proposed heuristics can generate routes with lower cost than RM. Moreover, these routes are more attractive based on operational and visual metrics.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"177 ","pages":"Article 106973"},"PeriodicalIF":4.1,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143132570","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}

引用次数: 0

An efficient solution methodology for the airport slot allocation problem with preprocessing and column-and-row generation

IF 4.1 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-01-06 DOI: 10.1016/j.cor.2024.106972

Paula Fermín Cueto, Sergio García, Miguel F. Anjos

Airport coordination is a demand control mechanism that maximizes the use of existing infrastructure at congested airports. Aircraft operators submit a list of regular flights that they wish to operate over a five to seven-month period and a designated coordinator is responsible for allocating the available airport slots, which represent the permission to operate a flight at a specific date and time. From an optimization perspective, this problem is a special class of the Resource Constrained Project Scheduling Problem where the objective is to minimize the difference between the allocated and requested slots subject to airport capacity constraints and other operational restrictions. Most studies on the topic focus on developing complex models and fast heuristics. Little attention has been paid to exact methods despite their potential to obtain higher quality solutions with better airline acceptability and fewer slot rejections. In this paper, we present Caracal, an efficient column-and-row generation algorithm to solve the single airport slot allocation problem. We also present a problem-specific preprocessing scheme that can identify more redundant constraints and variables than a commercial solver in a fraction of the time. We propose a novel formulation to model historic overages in Level 3 airports, and we find optimal or near optimal solutions to instances originating from practical slot allocation models and real data from UK airports coordinated by Airport Coordination Limited significantly faster than the best exact method in the literature to date. We also conduct experiments on a set of synthetic, realistic instances that we include in this paper, along with the code to generate them, to facilitate benchmarking of slot allocation software.

{"title":"An efficient solution methodology for the airport slot allocation problem with preprocessing and column-and-row generation","authors":"Paula Fermín Cueto, Sergio García, Miguel F. Anjos","doi":"10.1016/j.cor.2024.106972","DOIUrl":"10.1016/j.cor.2024.106972","url":null,"abstract":"<div><div>Airport coordination is a demand control mechanism that maximizes the use of existing infrastructure at congested airports. Aircraft operators submit a list of regular flights that they wish to operate over a five to seven-month period and a designated coordinator is responsible for allocating the available airport slots, which represent the permission to operate a flight at a specific date and time. From an optimization perspective, this problem is a special class of the Resource Constrained Project Scheduling Problem where the objective is to minimize the difference between the allocated and requested slots subject to airport capacity constraints and other operational restrictions. Most studies on the topic focus on developing complex models and fast heuristics. Little attention has been paid to exact methods despite their potential to obtain higher quality solutions with better airline acceptability and fewer slot rejections. In this paper, we present Caracal, an efficient column-and-row generation algorithm to solve the single airport slot allocation problem. We also present a problem-specific preprocessing scheme that can identify more redundant constraints and variables than a commercial solver in a fraction of the time. We propose a novel formulation to model historic overages in Level 3 airports, and we find optimal or near optimal solutions to instances originating from practical slot allocation models and real data from UK airports coordinated by Airport Coordination Limited significantly faster than the best exact method in the literature to date. We also conduct experiments on a set of synthetic, realistic instances that we include in this paper, along with the code to generate them, to facilitate benchmarking of slot allocation software.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"177 ","pages":"Article 106972"},"PeriodicalIF":4.1,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143132565","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}

引用次数: 0

Transportation and carbon emissions costs minimization for time-dependent vehicle routing problem with drones

IF 4.1 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-01-03 DOI: 10.1016/j.cor.2024.106963

Yong Peng , Zhi Ren , Dennis Z. Yu , Yonghui Zhang

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.

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引用次数: 0

Towards green manufacturing: Co-optimizing capacity expansion planning of production and renewable energy generation with endogenous uncertainty

IF 4.1 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2025-01-03 DOI: 10.1016/j.cor.2024.106971

Xin Zhou , Bo Zeng , Feng Cui , Na Geng

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&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.

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引用次数: 0

Capacity planning to cope with demand surges in fourth-party logistics networks under chance-constrained service levels

IF 4.1 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2024-12-30 DOI: 10.1016/j.cor.2024.106956

Songchen Jiang , Min Huang , Yunan Liu , Yuxin Zhang , Xingwei Wang

In this paper, we study a capacity planning problem for a fourth-party logistics network (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&B) algorithm designed for solving the SAA-based model. Compared to the standard C&B method, our G-C&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 Local Experimentation for Global Optimization strategy based cell-and-bound (LEGO-C&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 (i) the algorithm’s hyperparameters such as the sample size and training ratio, and (ii) 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.

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引用次数: 0

A robust optimal control problem with moment constraints on distribution: Theoretical analysis and an algorithm

IF 4.1 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2024-12-30 DOI: 10.1016/j.cor.2024.106966

Jianxiong Ye , Lei Wang , Changzhi Wu , Jie Sun , Kok Lay Teo , Xiangyu Wang

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.

{"title":"A robust optimal control problem with moment constraints on distribution: Theoretical analysis and an algorithm","authors":"Jianxiong Ye , Lei Wang , Changzhi Wu , Jie Sun , Kok Lay Teo , 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}

引用次数: 0

Deep learning based high accuracy heuristic approach for knapsack interdiction problem

IF 4.1 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2024-12-28 DOI: 10.1016/j.cor.2024.106965

Sunhyeon Kwon, Hwayong Choi, Sungsoo Park

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.

引用次数: 0

An adaptive large neighborhood search method for the drone–truck arc routing problem

IF 4.1 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Operations Research

Pub Date : 2024-12-27 DOI: 10.1016/j.cor.2024.106959

Xufei Liu , Sung Hoon Chung , Changhyun Kwon

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.

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