无人机
计算机科学
妥协
运筹学
遗传算法
软件部署
卡车
分类
旅行商问题
组分(热力学)
最后一英里(运输)
工程类
英里
机器学习
遗传学
生物
社会科学
热力学
物理
算法
天文
社会学
程序设计语言
航空航天工程
操作系统
作者
Kangzhou Wang,Biao Yuan,Mengting Zhao,Yuwei Lu
标识
DOI:10.1080/01605682.2019.1621671
摘要
The deployment of drones to support the last-mile delivery has been initially attempted by several companies such as Amazon and Alibaba. The complementary capabilities of the drone and the truck pose an innovative delivery mode. The relevant optimisation problem associated with this new mode, known as the travelling salesman problem with drone (TSP-D), aims to find the coordinated routes of a drone and a truck to serve a list of customers. In practice, managers sometimes intend to attain a compromise between operational cost and completion time. Therefore, this article addresses a bi-objective TSP-D considering both objectives. An improved non-dominated sorting genetic algorithm (INSGA-II) is proposed to solve the problem. Specifically, the label algorithm-based decoding method, the fast non-dominated sorting approach, the crowding-distance computation procedure, and the local search component are devised to accommodate the features of the problem. Furthermore, the first Pareto front obtained by the INSGA-II is improved by a post-optimisation component. Computational results validate the competitive performance of the proposed algorithm. Meanwhile, the trade-off analysis demonstrates the relationship between operational cost and completion time and provides managerial insights for managers designing reasonable compromise routes.
科研通智能强力驱动
Strongly Powered by AbleSci AI