面向防疫物资分区配送车机协同路径规划问题  

Vehicle Routing Problem with Drones Considering Zoned Distribution of Epidemic Prevention Materials

作　　者：马华伟[1,2] 闫伯英 Ma Huawei;Yan Boying(School of Management,Hefei University of Technology,Hefei 230009,China;Intelligent Management of Space System Anhui Engineering Research Center,Hefei 230009,China)

机构地区：[1]合肥工业大学管理学院,安徽合肥230009 [2]空天系统智能管理安徽省工程研究中心,安徽合肥230009

出　　处：《系统仿真学报》2025年第1期234-244,共11页Journal of System Simulation

基　　金：科技部重点研发计划(JZ2020ZDYF0514)。

摘　　要：针对目前防疫物资车机协同配送中没有满足疫区无接触配送需求的问题,提出车机协同分区配送问题。以最短配送时间作为优化目标,建立线性规划模型,并提出一种两阶段启发式算法,其中第一阶段通过贪婪算法生成初始解,第二阶段设计了一种混合遗传算法(tabu search algorithm with genetic algorithm,TSGA),将禁忌搜索算法思想与遗传算法相结合进行求解,通过引入禁忌表与节点交换算子和节点变异算子,改进了染色体方式,提升了算法的求解性能。实验结果表明,TSGA与基于遗传思想的自适应算法以及混合禁忌模拟退火算法对比,其解质量与求解时间均优。综上,该两阶段算法能够有效解决VRPD-ZD问题,提升防疫物资车机协同配送效率。To address the shortcomings of current contactless delivery methods in the collaborative distribution of epidemic prevention supplies,we introduce a specialized model called the vehicle routing problem with drones considering zoned distribution(VRPD-ZD).In order to solve the problem,a linear programming model is established with the shortest delivery time as the optimization objective,and a two-stage heuristic algorithm is proposed.The initial solution is generated by greedy algorithm in the first stage.In the second stage,we develop a Tabu search algorithm with genetic algorithm(TSGA)hybrid.This enhanced algorithm integrates a taboo list and employs advanced chromosome encoding techniques to improve performance.The experimental results show that TSGA compares favourably with the adaptive algorithm based on genetic method(AAGM)as well as the simulated-annealing-based two-phase optimization(SATO)in terms of solution quality and solution time.This two-stage algorithm can effectively solve the VRPD-ZD problem and can improve the efficiency of cooperative vehicle-machine distribution of epidemic prevention materials.

关 键 词：车机协同 分区配送 防疫物资配送 两阶段启发式算法 遗传算法 

分 类 号：U116[交通运输工程] TP18[自动化与计算机技术—控制理论与控制工程]