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
{"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":null,"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":4.1000,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Operations Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0305054824004313","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
求助全文
约1分钟内获得全文 去求助
来源期刊
Computers & Operations Research
Computers & Operations Research 工程技术-工程:工业
CiteScore
8.60
自引率
8.70%
发文量
292
审稿时长
8.5 months
期刊介绍: Operations research and computers meet in a large number of scientific fields, many of which are of vital current concern to our troubled society. These include, among others, ecology, transportation, safety, reliability, urban planning, economics, inventory control, investment strategy and logistics (including reverse logistics). Computers & Operations Research provides an international forum for the application of computers and operations research techniques to problems in these and related fields.
期刊最新文献
An interval two-stage robust stochastic programming under a bi-level multi-objective framework toward river basin water resources allocation Ship emission monitoring with a joint mode of motherships and unmanned aerial vehicles Logic-based Benders decomposition methods for the distributed permutation flow shop scheduling problem with production and transportation cost Scheduling moldable tasks on homogeneous multi-cluster platforms with GPUs Online order acceptance and scheduling in a single machine environment
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1