# A study on the siting of emergency medical facilities under uncertain demand—A case study of Wuhan country parks

## TL;DR

This paper proposes a model for locating emergency medical facilities in urban parks to handle public health emergencies, using Wuhan as a case study.

## Contribution

The novel contribution is the use of robust optimization with polyhedral uncertainty sets to model emergency medical facility siting under uncertain demand.

## Key findings

- The model determines optimal locations and patient allocation for emergency medical facilities in Wuhan's suburban parks.
- The approach effectively handles uncertainty in patient numbers during public health emergencies.
- The model provides feasible solutions for decision-makers during sudden outbreaks.

## Abstract

At the beginning of 2020, the novel coronavirus broke out as a sudden public health emergency worldwide, with the number of confirmed patients constantly rising, which brought huge pressure to the medical system. Many countries and regions have noticed the positive role of emergency medical facilities in combating the COVID-19 pandemic. Therefore, the analysis of the location and construction of emergency medical facilities for public health emergencies has practical significance.

This paper mainly discusses the use of urban suburban parks as the construction sites for emergency medical facilities and builds a maximum service quality level model for emergency medical facilities in response to public health emergencies.

Considering the suddenness and unpredictability of public health emergencies, this study introduces polyhedral uncertainty sets to describe the uncertainty of the number of confirmed patients and transforms the model into an easily solvable mixed-integer programming model through the Bertsimas and Sim robust optimization method. The GAMS software is used for programming and the CPLEX solver is called to solve the model. Taking 13 urban suburban parks in Wuhan as an example, the optimal location plan and patient allocation of emergency medical facilities are determined, verifying the feasibility and effectiveness of the model.

The results show that the model effectively promotes the determination of location plans and patient transfer routes. It is expected that in the event of a sudden public health emergency in a city, it can provide reference and basis for decision-makers to deal with public health emergencies.