低碳视角下基于机器学习的医疗救援中心选址研究
Machine Learning-Based Location for Medical Rescue Centers from the Perspective of Low-Carbon
彭舒悦 1刘勤明 1李佳翔1
作者信息
摘要
针对伤员遭受突发灾害又无法获得及时救援这一情形,以医院向救援中心输送物资的距离和时间为研究重点,提出一种低碳视角下的应急医疗救援中心选址模型.首先,以上海市为例,基于机器学习的K-means聚类,将上海市划分成四个子区域,使用直线相连法确定各子区域的初始备选点.其次,使用熵权法,从各子区域的初始备选点中筛选出几个最终备选点.最后,通过考虑运输成本、碳排放成本和晚到惩罚成本,计算各子区域内所有医院到救援中心的总成本,将总成本最小的点确定为各子区域的最优选址点,确保每个医院都能向就近的救援中心提供医疗资源.通过对选址结果进行可行性分析,模型得出的最优选址点可作为上海市后续医疗救援中心选址参考.
Abstract
Since the injured can't get timely rescue when they suffer from sudden disasters,a location model of emergency medical rescue centers from the perspective of low-carbon is proposed,focusing on the distance and time of transporting medical supplies from hospitals to rescue centers.First,taking Shanghai as an example,the K-means clustering algorithm based on machine learning divides Shanghai into four sub-regions,and the straight-line connection method is used to determine the initial alternative sites of each sub-region.Secondly,the entropy weighting method is used to select several final alternative sites from the initial alternative sites in each sub-region.Finally,by considering the transportation cost,carbon emission cost and late arrival penalty cost,the total cost of all hospitals in each sub-region to the rescue center is calculated,and the site with the minimum total cost is determined as the optimal location site of each sub-region,to ensure that each hospital can provide medical resources to the nearest rescue center.Through the feasibility analysis of the optimal location site,the research result can be used as a reference for subsequent medical rescue center location in Shanghai.
关键词
医疗救援中心选址/机器学习/熵权法/碳排放成本/晚到惩罚成本Key words
medical rescue center location/machine learning/entropy method/cost of carbon emissions/late arrival penalty cost引用本文复制引用
基金项目
国家自然科学基金(71632008)
国家自然科学基金(71840003)
上海市自然科学基金(19ZR1435600)
教育部人文社会科学研究规划基金(20YJAZH068)
上海理工大学科技发展项目(2020KJFZ038)
2021年上海市大学生创新创业训练计划项目(SH2021078)
出版年
2024
NETL
NSTL
万方数据