文献类型：期刊文献

中文题名：基于改进蚁群算法的物流配送车辆路径优化

英文题名：Vehicle Routing Optimization in Logistics Distribution Based on Improved Ant Colony Algorithm

作者：胡立栓[1];王育平[1];亓呈明[1]

第一作者：胡立栓

机构：[1]北京联合大学城市轨道交通与物流学院

第一机构：北京联合大学城市轨道交通与物流学院

年份：2017

卷号：0

期号：6

起止页码：60-62

中文期刊名：智能建筑

外文期刊名：Intelligent Building

基金：基金项目"2016年中国物流学会;中国物流与采购联合会研究课题计划";项目编号:2016CSLKT3-173

语种：中文

中文关键词：蚁群算法;迭代局部搜索;车辆路径优化

外文关键词：Ant Colony Algorithm, Iterative Local Search, Vehicle Routing Problem

摘要：车辆路径问题是物流系统优化中的关键内容之一,是现代物流管理研究中的重要内容。为了克服基本蚁群算法搜索时间过长、易陷于局部最优等缺点,提出了一种改进的蚁群算法——IACA,在算法中引入迭代局部搜索算法,该算法能保持解的多样性,跳出局部最优,增强全局搜索的能力。实验在VRP基准测试集上进行,并与基本蚁群算法进行对比分析,验证了改进蚁群算法的有效性和可行性。
The Vehicle Routing Problem （VRP） is an important management problem in the field of physical distribution and logistics. Good vehicle routing can not only increase the profit of logistics but also make logistics management more scientific. The Capacitated Vehicle Routing Problem （CVRP） constrained by the capacity of a vehicle is the extension of VRP. In order to solve costly procedure of search and premature convergence for VRP, Iterative Local Search （ILS） method is employed to seeking the close-to-optimal solution in local scope based on the capacity of the vehicle. It can enhance the ability of global search by increasing diversity of solutions. Experimental results on benchmark problems show that our algorithm is superior to original ant colony algorithm and can efficiently find better solutions.