摘要
为了提高配送效率,需要更好地规划配送路线,通过建立配送路径最优化模型,使用聚类-遗传算法对其求得配送路径的最优解,聚类-遗传算法采用新的染色体编码方式和交叉原则,相较传统遗传算法,迭代收敛速度更快,且呈现出更优的结果,使得区域配送路径路线最短、成本最低。
In order to improve the distribution efficiency,it is necessary to better plan the distribution route,establish the distribution path optimization model,and use the clustering-genetic algorithm to obtain the optimal solution of the distribution path.The clustering-genetic algorithm adopts a new chromosome coding method and crossover principle.Compared with the traditional genetic algorithm,the iterative convergence speed is faster,and the results are better,making the regional distribution route shortest and the cost lowest.
作者
高涵
杨杰
张军
GAO Han;YANG Jie;ZHANG Jun(School of Electromechanic Engineering,Qingdao University,Qingdao 266071,China;Qingdao Zhanye Construction Group Co.,Ltd,Qingdao 266200,China)
出处
《青岛大学学报（自然科学版）》
CAS
2020年第1期47-51,61,共6页
Journal of Qingdao University(Natural Science Edition)
关键词
聚类算法
遗传算法
路径优化
clustering algorithm
genetic algorithm
path optimization
