基于混合蚁群的多温区冷链物流配送路径优化算法  

Distribution route optimization algorithm based on hybrid ant colony for multi-temperature zone cold chain logistics

作　　者：解永亮 XIE Yong-liang(School of Aeronautics,Inner Mongolia University of Technology,Hohhot 010051,China)

机构地区：[1]内蒙古工业大学航空学院,呼和浩特010051

出　　处：《沈阳工业大学学报》2022年第5期552-557,共6页Journal of Shenyang University of Technology

基　　金：内蒙古自治区科技计划项目(20111303);内蒙古自治区大学生创新计划项目(202010128020).

摘　　要：针对冷链物流配送过程同时取货、送货车辆路径规划问题,提出了基于混合蚁群算法多温区冷链物流配送路径优化算法.通过分析影响同时取、送货车辆路径成本的因素,构建了针对多温区冷链物流的带时间窗、同时取送货配送路径优化模型.利用粒子群算法来优化蚁群算法参数,将各个蚂蚁子群的信息素进行交换,再采用基于插入的启发式方法和交叉、反转操作进行路径优化.经过对照实验,结果表明:基于混合蚁群的车辆路径规划算法收敛速度相对于基于改进遗传算法的车辆路径规划算法和基于禁忌搜索算法的车辆路径优化算法,分别提高了24.3%和18.6%.In order to solve the problem of simultaneous pickup and delivery for the vehicle route planning in cold chain logistics distribution process,a distribution route optimization algorithm for multi-temperature zone cold chain logistics based on the hybrid ant colony algorithm was proposed.By analyzing the cost factors that affect the simultaneous pickup and delivery vehicle route,a simultaneous pickup and delivery routes optimization model with time windows for cold chain logistics in the multi-temperature zones was constructed.The particle swarm algorithm was used to optimize the parameters of ant colony algorithm;the pheromone among ant subgroups was exchanged;the heuristic method based on insertion and the crossover and reverse operations was used to optimize the route.Results of comparison experiments show that the convergence speed of the vehicle route planning algorithm based on hybrid ant colony increases by 24.3%and 18.6%,compared with the vehicle route planning algorithms based on improved genetic algorithm and Tabu search algorithm,respectively.

关 键 词：混合蚁群 多温区冷链物流 粒子群 蚁群算法 信息素 插入 弱可行解 交叉 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]