COVID-19疫情下定点收治医院动态选址-分配优化
作者:
作者单位:

1.青岛大学;2.北京交通大学;3.加拿大温莎大学

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中图分类号:

TP273

基金项目:

国家自然科学基金((71942006); 加拿大自然科学基金(RGPIN-2019-07115); WE-SPARK 基金; 青岛大学人才引进科研启动基金(DC2100003542)


Dynamic Location-Allocation Optimization for Designated Hospitals under the COVID-19 Epidemic
Author:
Affiliation:

1.Qingdao University;2.Beijing Jiaotong University;3.Windsor University

Fund Project:

National Natural Science Foundation of China; NSERC Discovery Grant; WE-SPARK Grant; Qingdao University start-up grant for talent researchers

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    摘要:

    针对医疗资源匮乏和经济不发达的国家或地区, 如何选择定点收治医院、分配患者有效控制新型冠状病毒肺炎(COVID-19) 扩散是亟待解决的问题. 首先, 考虑疫情患者数量和症状等级动态变化特征, 以最大化患者的收治率和最小化医院总费用为目标, 构建有限医疗资源约束下定点收治医院动态选址-分配双目标优化模型. 其次, 分析所构建模型的结构特征, 设计基于Epsilon 约束方法的求解框架, 得到Pareto最优解集. 最后, 基于北京市卫生健康委发布的疫情数据进行数值实验, 验证模型的可行性与方法的有效性. 实验结果表明,双目标优化模型可以有效地权衡定点医院的总费用和患者的收治率, 对于COVID-19疫情下医疗资源的合理配置具有重要的指导意义.

    Abstract:

    For the countries or regions with insufficient medical resources and underdeveloped economies, it is an urgent issue to select hospitals and allocate patients to control the spread of COVID-19 (Corona Virus Disease, 2019). Considering the dynamic characteristics of the number and the severity of COVID-19 patients, this paper first presents a bi-objective dynamic location-allocation model for the designated hospitals with the limited medical resources, which simultaneously minimizes the total cost of designated hospitals and maximizes the treatment rate of patients. Then, this paper analyses the characteristics of the proposed model and designs a solution framework based on the Epsilon constraint approach to obtain Pareto optimal solutions. Finally, a series of numerical experiments are conducted to demonstrate the feasibility of the proposed model and effectiveness of the developed method on the basis of the epidemic data provided by Beijing Municipal Health Commission. The experimental results show that the bi-objective model can effectively trade-off the total cost of the designated hospitals and the treatment rate of patients, which can provide valuable guidance for the rational deployment of medical resources under the COVID-19 epidemic.

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历史
  • 收稿日期:2021-10-08
  • 最后修改日期:2022-01-17
  • 录用日期:2022-01-28
  • 在线发布日期: 2022-03-01
  • 出版日期: