基于遗传算法的电子商务物流配送研究

Logistics Distribution Problem in E-Business Environment

针对电子商务在物流配送中存在的问题,本研究以车辆可行驶最大路程为限制条件,将遗传算法与节约算法相结合,利用节约算法产生遗传算法的初始解,构造节约遗传算法解决电子商务环境下的物流配送路径优化问题。仿真结果表明,节约遗传算法比遗传算法更具有全局最优性,求得最短路径的效果明显高于遗传算法;随着进化代数的增加,两种算法都越来越趋向于最优值,节约遗传算法的进化起点远高于遗传算法的进化起点,其最优值比遗传算法最优值好;节约遗传算法中的进化代数和种群规模对算法的性能有一定的影响;有路程限制与无路程限制所取得的货车运行路线不同,车辆的最大运行距离也不同。该研究可以提高物流配送效率、缩短配送距离,对节约物流成本和提高客户服务水平具有重要意义。

In electronic commerce, there are some problems in logistics distribution. The paper combines genetic algorithm with the saving algorithm to solve the distance constraints of car. It uses the result of saving algorithm as the initial value of genetic algorithm to solve the vehicle routing problem. The result shows that the method can improve the logistics distribution＇s efficiency,the effect of finding the shortest path is better than that result of the genetic algorithm. With the increase of evolution algebra, the two algo- rithms tend to the optimal value. However,the evolutionary of CWGA and population size can influence the performance of the algorithm. Moreover, the vehicle running routes with or without distance limitation are different and they have different maximum running distance. The research can improve the efficiency of lo- gistics distribution, shorten the delivery distance, save the cost of distribution, and improve the service for costomers. Therefore, the study is of great theoretical and practical significance.