基于改进蚁群算法的物流配送路径优化研究  

Logistics distribution path optimization research based on improved ant colony algorithm

作　　者：邓波[1] 蒲保兴[1] 

机构地区：[1]邵阳学院信息工程学院,湖南邵阳422000

出　　处：《邵阳学院学报（自然科学版）》2017年第4期69-75,共7页Journal of Shaoyang University：Natural Science Edition

基　　金：湖南省科技计划项目(2012FJ3108);湖南省教育厅科学研究项目(13C845)

摘　　要：物流配送行业不但要求所有货物能及时进行配送,而且也要求尽可能降低整个物流运输成本。所以物流配送车辆路径优化问题是重点亟待解决的关键问题,由于传统的优化方法搜索时间较长,且难以找到全局最优路径,从而造成配送成本高,效率低。为了降低成本,提高车辆路径优化率,本文以蚁群算法为基础,并加以改进,首先建立优化物流配送路径的全局数学模型,然后采用改进信息素更新规则、改进启发信息更新策略获取最优物流路径,通过优选算法参数,改进蚁群算法对全局数学模型进行求解。从而有效避免只有局部优化解的出现。仿真实验结果表明,改进后的算法效率提高较大,算法在实验环境下收敛性好,是解决物流配送路径优化问题的有效算法。Logistics industry can not only requires that all the goods in a timely manner and distribution, but also the requirement as far as possible to reduce the logistics cost. So the logistics distribution vehicle routing op-timization problem is focused on the key problems to be solved, due to the traditional optimization method to search for a long time, and hard to find the global opt imal path, result ing in the distribut ion cost is high, the eff iciency is low. In order to reduce costs, increase the rate of vehicle routing optimizat ion, in this paper, based on the ant colony algorithm, and improved it, the first to establish the mathematical model of the logistics distribut ion path of global optimization and then with the improved pheromone update rule, improvement of heuristic informat ion update strategy, obtain the opt imal logistics path, through the parameter opt imization algorithm, ant colony algorithm for solving global mathematical model. Thus ef fect ively avoid the occurrence of only local optimization solution. The simulation experimental results show that the improved algorithm eff iciency is larger,convergence algorithm in experiment environment is good, and it is effective to solve the problem of logistics distribut ion route optimization algorithm.

关 键 词：物流配送 蚁群算法 路径优化 仿真 VRP 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]