Optimal allocation and route design for station-based drone inspection of large-scale facilities

IF 6.7 2区 管理学 Q1 MANAGEMENT Omega-international Journal of Management Science Pub Date : 2024-08-10 DOI:10.1016/j.omega.2024.103172
Lei Cai , Jiliu Li , Kai Wang , Zhixing Luo , Hu Qin
{"title":"Optimal allocation and route design for station-based drone inspection of large-scale facilities","authors":"Lei Cai ,&nbsp;Jiliu Li ,&nbsp;Kai Wang ,&nbsp;Zhixing Luo ,&nbsp;Hu Qin","doi":"10.1016/j.omega.2024.103172","DOIUrl":null,"url":null,"abstract":"<div><p>The utilization of drones to conduct inspections on industrial electricity facilities, including large-sized wind turbines and power transmission towers, has recently received significant attention, mainly due to its potential to enhance inspection efficiency and save maintenance costs. Motivated by the advantages of drones for facility inspection, we present a novel station-based drone inspection problem (SDIP) for large-scale facilities. The objective of SDIP is to determine the locations of multiple homogeneous automatic battery swap stations (ABSSs) equipped with drones, assign facility inspection tasks to the ABSSs with operation duration constraints, and design drone inspection routes with battery capacity constraints, such that minimize the sum of fixed ABSS costs and drone travel costs. The SDIP can be regarded as a variant of the location-routing problem, which is NP-hard and difficult to solve optimally. To obtain the optimal solution of SDIP efficiently, we firstly formulate this problem into an arc based formulation and a route based formulation, and then develop a logic-based Benders decomposition (LBBD) algorithm to solve it. The SDIP is decomposed into a master problem (MP) and a set of subproblems (SPs). The MP is solved by a branch-and-cut (BC) procedure. Once a feasible integer solution is found, the linear relaxation of SPs are solved by a stabilized column generation to generate Benders cuts. If the cost of all the SPs’ optimal LP solutions plus the cost of the MP’s solution is less that current best cost, the SPs are exactly solved by a Branch-and-Price (BP) algorithm to generate the logic cuts. The numerical results on five scales of randomly generated instances validate the effectiveness of the LBBD algorithm. Specifically, the LBBD can solve all small- and middle-sized instances, and seven out of ten large-sized instances in 1000 s. Furthermore, we conduct a sensitivity analysis by varying the attributes of ABSSs and drones, and provide valuable managerial insights for large-scale facility inspection.</p></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":"130 ","pages":"Article 103172"},"PeriodicalIF":6.7000,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Omega-international Journal of Management Science","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0305048324001373","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0

Abstract

The utilization of drones to conduct inspections on industrial electricity facilities, including large-sized wind turbines and power transmission towers, has recently received significant attention, mainly due to its potential to enhance inspection efficiency and save maintenance costs. Motivated by the advantages of drones for facility inspection, we present a novel station-based drone inspection problem (SDIP) for large-scale facilities. The objective of SDIP is to determine the locations of multiple homogeneous automatic battery swap stations (ABSSs) equipped with drones, assign facility inspection tasks to the ABSSs with operation duration constraints, and design drone inspection routes with battery capacity constraints, such that minimize the sum of fixed ABSS costs and drone travel costs. The SDIP can be regarded as a variant of the location-routing problem, which is NP-hard and difficult to solve optimally. To obtain the optimal solution of SDIP efficiently, we firstly formulate this problem into an arc based formulation and a route based formulation, and then develop a logic-based Benders decomposition (LBBD) algorithm to solve it. The SDIP is decomposed into a master problem (MP) and a set of subproblems (SPs). The MP is solved by a branch-and-cut (BC) procedure. Once a feasible integer solution is found, the linear relaxation of SPs are solved by a stabilized column generation to generate Benders cuts. If the cost of all the SPs’ optimal LP solutions plus the cost of the MP’s solution is less that current best cost, the SPs are exactly solved by a Branch-and-Price (BP) algorithm to generate the logic cuts. The numerical results on five scales of randomly generated instances validate the effectiveness of the LBBD algorithm. Specifically, the LBBD can solve all small- and middle-sized instances, and seven out of ten large-sized instances in 1000 s. Furthermore, we conduct a sensitivity analysis by varying the attributes of ABSSs and drones, and provide valuable managerial insights for large-scale facility inspection.

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
基于站点的大型设施无人机巡检的优化分配和路线设计
最近,利用无人机对大型风力涡轮机和输电塔等工业电力设施进行巡检受到了广泛关注,这主要是因为无人机具有提高巡检效率和节约维护成本的潜力。基于无人机在设施巡检方面的优势,我们提出了一个针对大型设施的新型基于站点的无人机巡检问题(SDIP)。SDIP 的目标是确定多个配备无人机的同质自动电池交换站(ABSS)的位置,将设施巡检任务分配给有运行时间限制的 ABSS,并设计有电池容量限制的无人机巡检路线,从而使固定 ABSS 成本和无人机旅行成本之和最小。SDIP 可以看作是定位路由问题的一个变种,而定位路由问题是 NP 难问题,很难得到最优解。为了高效地获得 SDIP 的最优解,我们首先将该问题表述为基于弧的表述和基于路径的表述,然后开发了一种基于逻辑的 Benders 分解(LBBD)算法来求解该问题。SDIP 被分解成一个主问题 (MP) 和一组子问题 (SP)。主问题通过分支切割(BC)程序求解。一旦找到可行的整数解,SP 的线性松弛问题就会通过稳定列生成来解决,从而生成 Benders 剪切。如果所有 SP 的最优 LP 解的成本加上 MP 解的成本小于当前最佳成本,则通过分支加价算法(BP)精确求解 SP,生成逻辑切分。随机生成的五种规模实例的数值结果验证了 LBBD 算法的有效性。此外,我们还通过改变 ABSS 和无人机的属性进行了灵敏度分析,为大规模设施检测提供了有价值的管理见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Omega-international Journal of Management Science
Omega-international Journal of Management Science 管理科学-运筹学与管理科学
CiteScore
13.80
自引率
11.60%
发文量
130
审稿时长
56 days
期刊介绍: Omega reports on developments in management, including the latest research results and applications. Original contributions and review articles describe the state of the art in specific fields or functions of management, while there are shorter critical assessments of particular management techniques. Other features of the journal are the "Memoranda" section for short communications and "Feedback", a correspondence column. Omega is both stimulating reading and an important source for practising managers, specialists in management services, operational research workers and management scientists, management consultants, academics, students and research personnel throughout the world. The material published is of high quality and relevance, written in a manner which makes it accessible to all of this wide-ranging readership. Preference will be given to papers with implications to the practice of management. Submissions of purely theoretical papers are discouraged. The review of material for publication in the journal reflects this aim.
期刊最新文献
Can feature removal benefit the automotive manufacturers amid supply shortages? An analytical investigation Pre-occurrence location-allocation-configuration of maritime emergency resources considering shipborne unmanned aerial vehicle (UAV) Economically viable reshoring of supply chains under ripple effect The role of hubs and economies of scale in network expansion Evolutive multi-attribute decision making with online consumer reviews
×
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