摘要
为提高不同城市、不同企业之间的物流配送效率,改善当前物流发展现状,在兼顾配送距离的基础上,引入外部影响因子对Mean Shift聚类算法进行改进,得到现有物流网点的类簇集合,再以传统灰狼优化算法(GWO)为基础,结合分段线性映射(PWLCM)与黄金正弦算法(Gold-SA)对其进行改进,有效解决了传统灰狼优化(GWO)算法搜索速度慢、全局搜索能力差,易陷入局部最优解等问题。实验结果表明,上述模型不仅在收敛速度和迭代次数上有明显优势,且包含定址区域的实际信息,可具有针对性的解决物流网点辐射中心定址问题,有效缩短网点与辐射中心的平均距离,对节省物流资源,提升配送效率有一定的帮助。
In order to improve the logistics d istribution efficiency between different ci ties and enterprises and improve the current logistics development situ ation,on the basis of considering the distri bution distance,the external influence factor is introduced to improve the mean shift clustering algorithm to obtain the cluster set of existing logistics outlets.Then,based on the traditional gray wolf optimization algorithm(GWO),it is impr oved by combining the piecewise linear mapping(pwlcm)and the golden sine algorithm(gold SA),it effectively solves the problems of traditional grey wolf optimization(GWO)algori thm,such as slow search speed,poor global se arch ability and easy to fall into local optimal solution.The experimental results show that the model not only has obvious advantages in convergence speed and iteration times,but also con tains the actual information of the location area.It can solve the problem of the location of the radiation center of the logistics network,effectively shorten the average distance between the network and the radiation center,and help to save material resources and improve distribution efficiency.
作者
刁艳茹
张仰森
段瑞雪
冉紫涵
DIAO Yan-ru;ZHANG Yang-sen;DUAN Rui-xue;RAN Zi-han(School of Information Management,Beijing Information Science&Technology University,Beijing 100192,China;Beijing Laboratory of National Economic Security Early Warning Project,Beijing 100044,China)
出处
《计算机仿真》
2024年第7期178-183,共6页
Computer Simulation
基金
国家自然科学基金面上项目(62176023)。
关键词
辐射中心
选址
聚类
灰狼算法
分段线性映射
黄金正弦算法
Radial center
Site selection
Clu stering
Gray wolf algorithm
PWLCM
Golden sine algorithm
