摘要
为解决重汽维修服务站的选址问题,结合车辆的历史维修数据、行驶轨迹等信息,对选址问题展开研究。首先采用K-means聚类分析方法对车辆的分布状态进行大致的区域划分;其次选择车辆到达较多的区域进行选址,采用集合覆盖模型进行求解,寻找最佳的一组维修站备选点;最后在其他区域以此类推,得到最终的选址结果。
In this paper,we solved the site selection problem of a heavy truck maintenance service station in connection with the historical maintenance data of the station and the driving trajectory of the vehicles.Firstly,we used the K-means clustering analysis method to divide the distribution region of the vehicles.Secondly,we selected the location of the more-visited regions,and used the set coverage model to find the optimal site for the maintenance station.Finally,we repeated the process for other regions until the final location was arrived at.
作者
张守京
李梦丹
Zhang Shoujing;Li Mengdan(School of Mechanical&Electrical Engineering,Xi'an Polytechnic University,Xi'an 710048,China)
出处
《物流技术》
2019年第11期69-74,共6页
Logistics Technology
基金
陕西省教育厅科研计划项目（17JK0321）
中国纺织工业联合会项目（2017100）
关键词
维修站选址
K-MEANS算法
集合覆盖模型
需求点
地理坐标
maintenance station location selection
K-means algorithm
set coverage model
demand point
geographic coordinates
