基于应急资源集散中心选址优化的疫情隔离分区策略

 为探索区域最优的疫情隔离方式，以最小化距离为目标，利用遗传算法优化应急资源集散中心位置，计算按行政分区隔离、按经纬度欧式距离k均值聚类隔离、按经纬度网格隔离的全局距离，探讨最便捷、高效的疫情隔离分区方式。研究结果显示，按行政分区进行隔离划分全局距离最长，按k均值聚类进行隔离划分全局距离最短； k均值聚类或网格划分下，分区越多，全局距离越短。在进行疫情隔离时，城市中心与边缘区域应急资源集散中心应区别建设，行政分区边缘的应急需求点应就近划分，隔离区域内可进行二次精细分区以便管理。

A study on epidemic isolation patterns based on optimal locations of emergency resource centers

The purpose of this study is to explore the optimal regional epidemic isolation pattern by optimizing the locations of emergency resource centers. To aim at minimizing the distance, a genetic algorithm is used to obtain the global distances under isolation patterns by administrative division, k-means clustering and grid partition, respectively. The results show that the global distance of the isolation by administrative division is the longest while the global distance of the isolation by k-means clustering is the shortest. Under the isolation by the k-means clustering or the grid partition, the global distance can be shortened if there are more partitions. During the epidemic isolation, emergency resource centers in the central or on the edge of an area should be constructed differently. Emergency demand points on the edge of an administrative district should be divided by the nearest distance. There could be delicacy partitions within the isolated areas for better management.