基于改进的K-means聚类的多区域物流中心选址算法

针对当前多区域物流中心选址需建立配送中心个数不定、位置、覆盖范围不明的问题，本文提出了一种改进的k-means聚类算法，以城市经济引力模型为基础，将城市运输距离与居民消费能力的指标相结合，重新定义对象之间相似性度量的距离因子.并将密度思想引入k-means算法，提出类内差分均值的概念确定最优聚类数.实现分区后，分别在这些区域中利用重心法对配送中心进行最终的确定.最后实例分析了在西部地区37个城市创建物流配送中心的选址过程，并通过和传统的k-means聚类的选址结果对比，说明改进后的算法不仅可以节省配送时间，而且大大降低了运输成本，有很好的经济利用价值.

Multi-Regional Logistics Distribution Center Location Method Based on Improved K-means Algorithm

Focusing on the issues that the number, location, and coverage of multi-regional logistics centers of distribution centers are unknown, an improved k-means clustering algorithm is proposed. Based on the urban economic gravity model, this algorithm combines the urban transportation distance with the indicators of household consumption capacity, redefines the distance factor of the similarity measure between objects. The idea of density is introduced into the k-means algorithm, and the concept of intra-class difference mean is raised to determine the optimal number of clusters. After the partition is implemented, the centroid method is used to determine the final distribution center in these areas. Finally, in case study, we analyze the location process of constructing logistics distribution centers in 37 cities in the western region, and compares them with the traditional k-means clustering results. The comparing result shows that the improved algorithm not only saves the delivery time, but also greatly reduces the transportation cost and has sound economic value.