基于改进蚁群算法的物流路径优化研究——以河南省物流网络为例

A STUDY OF LOGISTICS ROUTING OPTIMIZATION BASED ON IMPROVED ANT COLONY ALGORITHM:AN EXAMPLE OF THE LOGISTICS NETWORK IN HENAN PROVINCE

研究物流路径优化问题,目的是有效降低物流配送距离,实现配送任务完成的高效率与低成本。通过对基本蚁群算法启发函数的优化提出改进蚁群算法,并且以现代物流技术作为理论支撑,利用改进的蚁群算法解决现代物流运输路径规划方面的车辆调度问题,追求运输系统的最大效益,找到物流运输的最优路径。以河南省物流网络为例,利用2000国家大地坐标系高斯大地坐标转换为3°投影带平面直角坐标(X、Y)的方法处理收集的数据,发现经过优化后的运输距离较传统蚁群算法降低了2.2%,最后通过仿真实验表明:改进的蚁群算法较同类研究中的遗传算法和粒子群算法更具有优势。

The study of logistics routing optimization is aimed at the distance reduction of logistics distribution to achieve high efficiency and low cost.In terms of the pheromone update of the traditional ant colony algorithm,the improvement strategy is proposed to alter the transfer rule,and optimize the heuristic function.With the modern logistics technology as theoretical support,the improved ant colony algorithm is used to solve the vehicle scheduling problem in logistics transportation routing optimization,for the maximum benefits of transportation system and the optimal route of logistics transportation.The logistics network in Henan province is taken as an example to collect data and convert the plane coordinates by Gauss-Kruger projection calculation of CGCS 2000.The results show that the optimized distance of transportation is reduced by 3.8%,compared to the traditional ant colony algorithm.And the simulation experiment indicates that the improved ant colony algorithm has more advantages compared to the genetic algorithm or the particle swarm optimization algorithm.