关键词: 应急设施选址, 多周期, 鲁棒优化, 数据驱动, 不确定性

Abstract: The existing medical services cannot meet the current treatment needs when major infectious diseases break out on a large scale. Whether the Fangcang shelter hospital can be built in time and reasonably affects directly the safety of people’s lives and the effect of disease control. However, the number of disease infections fluctuates with time and is highly uncertain, which increases the difficulty of decision-making. To solve this problem, a robust tracking location model of multi-period Fangcang shelter hospital with the goal that minimum service capacity meets the maximum expected treatment demand and minimum expected total weighted distance based on data-driven is established. Firstly, the sigmoid function is used to learn the cumulative diagnosis data and predict the number and trend of infections. Secondly, the box uncertainty set is introduced to describe the uncertainty of the number of infected people, and the robust optimization is used to control the accuracy of prediction data. Thirdly, the model is transformed into mixed integer programming by robust counterpart theory, and a solution method is designed based on Python GUROBIPY and SCIPY modules. Finally, a case study on the location of Wuhan emergency Fangcang shelter hospital is proposed to verify the feasibility and effectiveness of the model. The results show that the robust tracking location has strong robustness. When resources are insufficient, treatment needs can be met in most periods, and when resources are sufficient, treatment needs in all cycles can be met without wasting resources.

Key words: emergency facilities location, multi-period, robust optimization, data-driven, uncertainty