基于改进Floyd算法的物流运输路径规划

Logistics transportation path planning based on improved Floyd algorithm

当站点较多时,物流运输路径规划存在困难,传统Floyd算法路径规划的时间复杂度过高.鉴于传统Floyd算法规划时间复杂度高是因节点数量过大导致,提出一种结合改进K-means聚类算法的Floyd算法,该算法在节点数量较大情况下,运用改进K-means聚类算法分割物流区域,降低规划所需考虑节点数量,从而降低Floyd算法的时间复杂度.在复杂环境下进行传统Floyd算法和改进算法的对比实验,仿真分析结果表明,改进算法可以在更少的时间内找到一条较优的路径.

Logistics transportation path planning is difficult when there are many stations,and the traditional Floyd algorithm has a high time complexity for path planning.Considering that the traditional Floyd algorithm has high planning time complexity due to the large number of nodes,a Floyd algorithm combined with an improved K-means clustering algorithm is proposed.This algorithm divides the logistics area by improving the K-means clustering algorithm in the case of a large number of nodes,reduces the number of nodes required for planning consideration,thereby reduces the time complexity of the Floyd algorithm.The comparative experiments between traditional Floyd algorithm and improved algorithm is conduct in complex environments,simulation analysis results show that the improved algorithm can find a better path in less time.