On the mass COVID-19 vaccination scheduling problem. (May 2022)

• Record Type: Journal Article
• Authors: Zhang, Chuang; Li, Yantong; Cao, Junhai; Wen, Xin
• Is Part Of: Computers & operations research. Volume 141(2022)
• Publication Date: 2022-05
• Subjects: COVID-19 -- Mass vaccination -- Appointment scheduling -- Logic-based Benders decomposition -- Matheuristic; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2022.105704
• Languages: English
• ISSNs: 0305-0548

Abstract

Abstract: The outbreak of COVID-19 dramatically impacts the global economy. Mass COVID-19 vaccination is widely regarded as the most promising way to fight against the pandemic and help return to normal. Many governments have authorized certain types of vaccines for mass vaccination by establishing appointment platforms. Mass vaccination poses a vital challenge to decision-makers responsible for scheduling a large number of appointments. This paper studies a vaccination site selection, appointment acceptance, appointment assignment, and scheduling problem for mass vaccination in response to COVID-19. An optimal solution to the problem determines the open vaccination sites, the set of accepted appointments, the assignment of accepted appointments to open vaccination sites, and the vaccination sequence at each site. The objective is to simultaneously minimize 1) the fixed cost for operating vaccination sites; 2) the traveling distance of vaccine recipients; 3) the appointment rejection cost; and 4) the vaccination tardiness cost. We formulate the problem as a mixed-integer linear program (MILP). Given the NP-hardness of the problem, we then develop an exact logic-based Benders decomposition (LBBD) method and a matheuristic method (MH) to solve practical-sized problem instances. We conduct numerical experiments on small- to large-sized instances to demonstrate the performance of the proposed model and solution methods. Computational results indicate that the proposed methods provide optimal solutions to small-sized instances and near-optimal solutions to large ones. In particular, the developed matheuristic can efficiently solve practical-sized instances with up to 500 appointments and 50 vaccination sites. We discuss managerial implications drawn from our results for the mass COVID-19 vaccination appointment scheduling, which help decision-makers make critical decisions. Highlights: Study a novel scheduling problem arising from the mass COVID-19 vaccination campaign. Formally describe the problem and formulate it as a mixed integer linear program. Develop an efficient exact logic-based Benders decomposition method for the probelm. Design a matheuristic to solve practical-sized instances. Conduct sensitivity analysis to provide insightful managerial implications.