Multi-objective location-allocation optimization of temporary delivery points for medical supplies during large-scale epidemic outbreaks

Abstract

The surge in the demand for medical supplies and the lack of service delivery points in the early stages of an epidemic are likely to exacerbate the spread of the epidemic and create serious inequities in distribution. To more effectively allocate medical supplies during large-scale epidemic outbreaks, we develop a multi-objective mixed integer nonlinear programming model incorporating appointment allocation rules to optimize the locations of delivery points, the flow of medical supplies among different nodes, and the appointment allocation rules. The model aims to minimize the number of infected individuals in the study area, infection risks at delivery points, and total cost and service level deviation at the demand points. To achieve such objectives, we adopt the hierarchy optimization method and ε-constraint method encompassing model complexity and applicability. The practicality of our model is demonstrated through a numerical case for mask allocation in accordance with appointment allocation rules during COVID-19 in 2020 based on real population distribution data of Xipu Subdistrict in Pidu District, Chengdu, China. The results reveal trade-offs between different objectives. In addition, related suggestions are provided for variations in demand satisfaction rate, the number of individuals at each delivery point and appointment allocation rules.