邓晓衡

教授 博士生导师 硕士生导师

入职时间:2006-01-05

所在单位:电子信息学院

职务:院长

学历:博士研究生毕业

性别:男

联系方式:Email:dxh@csu.edu.cn

学位:博士学位

在职信息:在职

主要任职:计算机学院副院长 湖南省数据传感与交换设备工程中心 主任 IEEE RS Chapter长沙 主席CCF普适计算专委 委员 CCF长沙 执委

毕业院校:中南大学

学科:计算机科学与技术
信息与通信工程

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Yiwen Luo, Xiaoheng Deng, Wendong Zhang, et al. Collaborative Intelligent Delivery With One Truck and Multiple Heterogeneous Drones in COVID-19 Pandemic Environment[J]. IEEE Transactions on Intelligent Transportation Systems,DOI: 10.1109/TITS.2024.3350876, 2024

发布时间:2024-05-23

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关键字:Drones, Payloads, Energy consumption, Logistics, COVID-19, Traveling salesman problems, Costs

摘要:The outbreak of COVID-19 has caused a serious impact on the traditional logistics industry. Considering that the truck-drone collaborative delivery system can both reduce the risk of COVID-19 propagation and deliver supplies in a cost effective and timely manner, this paper introduces the Multiple visits Travelling Salesman Problem with Multiple Heterogeneous Drones (MTSP-MHD). The model allows a truck to carry a fleet of heterogeneous multi-visit drones for cooperative deliveries, where the drones are capable of delivering to multiple customers on a single route and the flight is restricted by energy consumption and payload constraints. To solve MTSP-MHD, we develop an approach that combines K-Means ++ clustering, Nearest neighbor search and Greedy strategies (KNG) to construct feasible solutions. Meanwhile, an Improved Artificial Bee Colony algorithm combining Metropolis acceptance criterion of Simulated Annealing, Tabu list of Tabu Search, and Elite selection strategies (IABC-MTE) is proposed to enhance the quality of solutions. Particularly, three problem-specific neighborhood operators are adopted to search for new solutions. The massive experimental results indicate that IABC-MTE achieves significant improvements over other competitors, with average objective value reductions ranging from 1.81% to 29.16% and standard deviations reduced by 0.04 to 26.44. Finally, the influencing factors of the drone fleet, the performance of different drone fleets and delivery modes are evaluated in detail.

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