基于HACO-ALNS的车辆与无人机协同配送问题研究  

Research on Collaborative Delivery of Vehicles and Drones Based on HACO-ALNS

作　　者：辜勇[1] 柴子艺 刘迪[1] 李文锋[1] GU Yong;CHAI Zi-yi;LIU Di;LI Wen-feng(School of Transportation and Logistics Engineering,Wuhan University of Technology,Wuhan 430063,China)

机构地区：[1]武汉理工大学交通与物流工程学院,武汉430063

出　　处：《武汉理工大学学报》2024年第2期69-79,共11页Journal of Wuhan University of Technology

基　　金：国家自然科学基金(62173263).

摘　　要：针对物流配送需求增大、“最后一公里”交付困难、车辆或无人机配送均具有一定局限性等问题,作者提出了带有动态能耗约束的车辆与无人机协同配送问题,以最小化总配送成本为目标建立了混合整数规划模型,在约束中考虑了无人机一次起飞可完成多点配送、客户点差异等限制。设计了一种基于自适应大邻域搜索的混合蚁群算法进行求解,在蚁群算法中融入遗传算法,设计新的启发式因子。实验结果表明,该算法在不同规模算例上均具有良好的求解精度和运行速度。与不同配送模式的对比表明,多点配送的无人机装载率比单点配送高22.1%,动态能耗模式的成本与固定能耗相比平均降幅为3.31%。Based on the problems of increasing demand for logistics delivery and limitations of vehicle and drone delivery,this paper proposed collaborative delivery of vehicles and drones with dynamic energy consumption(CDVD-DEC).To minimize delivery cost,a mixed integer linear programming model was established.The limitations such as multi-point delivery by drones and customer point differences were considered in the constraints.A hybrid ant colony optimization based on adaptive large neighborhood search(HACO-ALNS)was designed to solve the model,genetic algorithm was incorporated in the ant colony optimization,a new heuristic factor was designed.The experimental results show that HACOALNS has good solution accuracy and running speed on different scale cases.The comparison with different delivery modes shows that the drone loading rate for multi-point delivery is 22.1%higher than for single-point delivery,and the average cost reduction for dynamic energy consumption mode compared to fixed energy consumption is 3.31%.

关 键 词：车辆无人机 协同配送 动态能耗 蚁群算法 自适应大邻域搜索算法 

分 类 号：U492.3[交通运输工程—交通运输规划与管理]