多模式下的车辆和无人机联合配送模型与优化算法  

Vehicle and drones joint distribution model and optimization algorithm in multi-mode

作　　者：贾兆红 王少贵[1] 刘闯 JIA Zhao-hong;WANG Shao-gui;LIU Chuang(School of Internet,Anhui University,Hefei 230000,China;School of Computer Science and Technology,Anhui University,Hefei 230000,China)

机构地区：[1]安徽大学互联网学院,合肥230000 [2]安徽大学计算机科学与技术学院,合肥230000

出　　处：《控制与决策》2024年第7期2125-2132,共8页Control and Decision

基　　金：国家自然科学基金项目(71971002)。

摘　　要：无人机已广泛用于物流配送,具有快速投递和低成本的优势.针对远离仓库中心、交通受限制客户的需求,在车机并行配送模式上引入车载无人机以服务该类客户,提出多模式下的车辆和无人机联合配送模型及其路径优化问题.该模型融合了车机协同配送模型和并行配送模型,包括搭载无人机的卡车和独立的无人机舰队.在此基础上建立以最小化交付时间为优化目标的混合整数规划模型,并设计基于知识学习策略的多算子遗传算法来提高搜索效率.实验结果表明,与传统交付方式相比,车辆与无人机联合配送模型可显著减少交付时间.在大规模数据集上,改进的遗传算法表现出更好的性能.该研究成果可为解决物流配送中的复杂动态的“最后一公里”问题提供指导和参考.Drones have been widely utilized in logistics delivery,offering advantages of fast delivery and low cost.In this study,we propose a multi-mode vehicle-drone joint delivery model and its path optimization problem to address the needs of customers located far from the warehouse center and facing transportation limitations.This model integrates the vehicle-drone collaborative delivery and parallel delivery models,incorporating trucks equipped with drones and independent drone fleets.Building upon this model,we establish a mixed integer programming model with the objective of minimizing delivery time and design a multi-operator genetic algorithm based on knowledge learning strategies to improve search efficiency.Experimental results demonstrate that the vehicle-drone joint delivery model significantly reduces delivery time compared to traditional delivery methods.The improved genetic algorithm exhibits superior performance on large-scale datasets.The findings of this research provide guidance and reference for tackling the complex and dynamic“last-mile”problem in logistics delivery.

关 键 词：卡车无人机联合配送 路径优化 无人机 遗传算法 最后一公里 

分 类 号：U492.3[交通运输工程—交通运输规划与管理]