Integrating battery-related decisions into truck-drone tandem delivery problem with limited battery resources

IF 7.6 1区 工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY Transportation Research Part C-Emerging Technologies Pub Date : 2025-05-01 Epub Date: 2025-03-17 DOI:10.1016/j.trc.2025.105082
Zhongshan Liu , Bin Yu , Tingting Chen , Li Zhang
{"title":"Integrating battery-related decisions into truck-drone tandem delivery problem with limited battery resources","authors":"Zhongshan Liu ,&nbsp;Bin Yu ,&nbsp;Tingting Chen ,&nbsp;Li Zhang","doi":"10.1016/j.trc.2025.105082","DOIUrl":null,"url":null,"abstract":"<div><div>The truck-drone tandem delivery mode provides a promising application in last-mile package delivery but is limited by the duration of drones. The battery swap strategy is a widely adopted approach to extend the cruising ranges of drones, ensuring that depleted batteries can be swapped for fully charged ones in a matter of minutes. However, most existing studies assume that there are sufficient batteries available at the depot, which is impractical as storing a large number of batteries is expensive. To bridge this gap, this paper considers the truck-drone tandem delivery problem with a battery swap strategy, under the condition of a limited number of batteries. To address the challenges posed by the practical limitation of the number of batteries, we propose a joint optimization problem integrating two types of interdependent decisions, i.e., battery-related decisions and route-related decisions. The battery-related decisions identify which batteries to be installed on drones and establish optimal battery charging schedules at the depot. And the route-related decisions determine the truck-drone tandem delivery routes. The studied joint optimization problem is formulated as a mixed integer linear programming model, and this model is integrated into a well-designed adaptive large neighborhood search algorithm to determine the two types of decisions. Specifically, on the basis of traditional operators, we design a series of depot-related operators tailored to the feature of route-related decisions. Furthermore, regarding the features of battery swapping and charging schedules, we introduce a novel battery operator to determine optimal battery-related decisions. The numerical experiments show that introducing the battery-related decisions can bring flexible battery schedules when the total number of batteries is limited. The effects of battery capacity, charging rate, charging cost, drone speed, and the number of batteries and drones are analyzed to provide practical suggestions for companies.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105082"},"PeriodicalIF":7.6000,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part C-Emerging Technologies","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0968090X25000865","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/3/17 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"TRANSPORTATION SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

The truck-drone tandem delivery mode provides a promising application in last-mile package delivery but is limited by the duration of drones. The battery swap strategy is a widely adopted approach to extend the cruising ranges of drones, ensuring that depleted batteries can be swapped for fully charged ones in a matter of minutes. However, most existing studies assume that there are sufficient batteries available at the depot, which is impractical as storing a large number of batteries is expensive. To bridge this gap, this paper considers the truck-drone tandem delivery problem with a battery swap strategy, under the condition of a limited number of batteries. To address the challenges posed by the practical limitation of the number of batteries, we propose a joint optimization problem integrating two types of interdependent decisions, i.e., battery-related decisions and route-related decisions. The battery-related decisions identify which batteries to be installed on drones and establish optimal battery charging schedules at the depot. And the route-related decisions determine the truck-drone tandem delivery routes. The studied joint optimization problem is formulated as a mixed integer linear programming model, and this model is integrated into a well-designed adaptive large neighborhood search algorithm to determine the two types of decisions. Specifically, on the basis of traditional operators, we design a series of depot-related operators tailored to the feature of route-related decisions. Furthermore, regarding the features of battery swapping and charging schedules, we introduce a novel battery operator to determine optimal battery-related decisions. The numerical experiments show that introducing the battery-related decisions can bring flexible battery schedules when the total number of batteries is limited. The effects of battery capacity, charging rate, charging cost, drone speed, and the number of batteries and drones are analyzed to provide practical suggestions for companies.
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
在电池资源有限的情况下,将电池相关决策整合到卡车-无人机串联运输问题中
卡车-无人机串联递送模式在最后一英里包裹递送中提供了一个有前途的应用,但受到无人机持续时间的限制。电池更换策略是一种广泛采用的方法,以扩大无人机的巡航范围,确保在几分钟内将耗尽的电池更换为充满电的电池。然而,大多数现有的研究都假设车厂有足够的电池可用,这是不切实际的,因为储存大量的电池是昂贵的。为了弥补这一差距,本文考虑了在有限电池数量条件下,采用电池交换策略的卡车-无人机串联运输问题。为了解决电池数量的实际限制所带来的挑战,我们提出了一种集成两种相互依赖决策的联合优化问题,即电池相关决策和路线相关决策。与电池相关的决策确定在无人机上安装哪些电池,并在仓库建立最佳电池充电时间表。与路线相关的决策决定了卡车-无人机串联运输路线。将所研究的联合优化问题表述为一个混合整数线性规划模型,并将该模型集成到设计良好的自适应大邻域搜索算法中,以确定两类决策。具体而言,我们在传统算子的基础上,针对路线相关决策的特点,设计了一系列与仓库相关的算子。此外,针对电池交换和充电计划的特点,我们引入了一种新的电池算子来确定最佳的电池相关决策。数值实验表明,在电池总数有限的情况下,引入与电池相关的决策可以带来灵活的电池计划。分析了电池容量、充电速率、充电成本、无人机速度、电池数量和无人机数量的影响,为企业提供切实可行的建议。
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
CiteScore
15.80
自引率
12.00%
发文量
332
审稿时长
64 days
期刊介绍: Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.
期刊最新文献
GATSim: Urban mobility simulation with generative agents Vision-language model-based scene understanding and decision-making for autonomous vehicles with a tailored augmented reality vehicle-in-the-loop testing platform A temporal-aware conflict risk modeling framework for signalized intersections using the pNEUMA data Cooperative signal and vehicle control in mixed traffic environment using model-guided reinforcement learning Decision evolution and heterogeneity aware pedestrian wayfinding behaviour modelling in VR integrated transportation hub
×
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:604180095
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