基于改进K-means聚类算法的战区内军事物流基地数量规划  

Quantity Planning of Military Logistics Bases in War Zone Based on Improved K-means Cluster Algorithm

作　　者：张广楠[1] 荀烨[1] 杨祺煊 

机构地区：[1]军事交通学院,天津300161

出　　处：《物流技术》2016年第10期159-161,173,共4页Logistics Technology

摘　　要：以战区军事物流基地数量规划问题为研究对象,考虑战区内军事物流基地同后方仓库的业务关系,利用改进K-means聚类算法,对战区内后方仓库进行空间聚类,并对聚类结果进行分析,以聚类结果中的聚类个数作为战区内军事物流基地数量规划的依据。研究表明,该方法比较科学合理,具有应用价值。In this paper, with the quantity of the military logistics bases in war zone as the objective and considering the business relationship between the bases with rear depots, we used the improved K-means clustering algorithm to have the spatial clustering of the rear depots in the war zone, analyzed the clustering result and proposed to make the number of the cluster in the clustering result as the basis for the planning of the quantity of the military logistics bases in the war zone.

关 键 词：K-MEANS聚类算法 军事物流基地 数量规划 

分 类 号：E234[军事—军事理论] F224[经济管理—国民经济]