Optimization for vaccination demand allocation and distribution routes in pandemics based on a hierarchy decision model

Abstract

Vaccination plays a significant role in interrupting the spread of pandemics, the process of moving the vaccine from the distribution center (DC) through the vaccination station and ultimately to the consumer involves many decisions. Which vaccination stations are open each day, how to allocate demand residents to the open stations, how many medical personnel to arrange at the open stations and the route of refrigerated vehicles to deliver vaccines to the open stations are issues that need to be addressed in this process. To improve the efficiency of vaccination and vaccine delivery during the actual vaccination scheduling process and to reduce the total cost, this study constructs two levels to describe the above problems and proposes a hierarchical decision mixed-integer linear program (MILP) that combines vaccine delivery and vaccination that jointly addresses the problems involved in both hierarchies. A case study of the COVID-19 vaccine administered at the Center for Disease Control and Prevention (CDC) in Hongshan District, Wuhan City, China, is then given to demonstrate the feasibility of the MILP and to highlight the managerial implications. It was found that opening more vaccination stations could reduce vaccination travel costs for the demand communities, opening a reasonable number of stations and rationally allocating the demand communities to these stations could reduce the costs of the whole system. This model also provides guidance for the CDC to address similar problems and has the potential to assist in the scheduling of public medical materials.