Study on Optimal Allocation of Emergency Resources in Multiple Disaster Sites Under Epidemic Events
WANG Fuyu1, TANG Tao1a, LI Yan1a, WANG Xiaoniu2
1. a.School of Management Science and Engineering, b.Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes, Anhui University of Technology, Maanshan 243002, China; 2. General Surgery Department, Maanshan People's Hospital, Maanshan 243000, China
Abstract：The outbreak of COVID19 has turned many areas into disaster areas. In order to provide timely relief to the disaster areas, accurate supply of post-disaster emergency resources has become the primary factor to ensure the safety of the people in the disaster areas. In this paper, SEIR was used to predict the number of infected people in each disaster area at the decision-making moment, and then the weight of urgency degree and material demand in the disaster area were calculated. Based on the degree of urgency, a multi-objective optimization model of emergency resource scheduling was constructed to maximize the satisfaction of the victims, minimize the total cost and consider the fairness of distribution. A multi-objective artificial bee colony algorithm is proposed. Aiming at the disadvantages of artificial bee colony algorithm such as precocity, the dynamic parameter and Pareto solution set are used to define the new bee colony location updating formula, and the teaching optimization is used to disturb the bee colony location, so as to avoid the algorithm falling into local extremum. The simulation results show that the proposed model and algorithm can effectively solve the problem of optimal allocation of emergency resources at multiple disaster points under epidemic events, and the improved algorithm has better performance.
王付宇, 汤涛, 李艳, 王小牛. 疫情事件下多灾点应急资源最优化配置研究[J]. 复杂系统与复杂性科学, 2021, 18(1): 53-62.
WANG Fuyu, TANG Tao, LI Yan, WANG Xiaoniu. Study on Optimal Allocation of Emergency Resources in Multiple Disaster Sites Under Epidemic Events. Complex Systems and Complexity Science, 2021, 18(1): 53-62.
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