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
贾兆红 1王少贵 2刘闯2
作者信息
- 1. 安徽大学互联网学院,合肥 230000;安徽大学计算机科学与技术学院,合肥 230000
- 2. 安徽大学互联网学院,合肥 230000
- 折叠
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.
Abstract
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Key words
truck-drone joint distribution/routing optimization/drones/genetic algorithm/last-mile distribution引用本文复制引用
出版年
2024
NETL
NSTL
万方数据