On the mass COVID-19 vaccination scheduling problem.

Area of Science:

• Operations Research
• Public Health Policy
• Epidemiology

Background:

• The COVID-19 pandemic necessitates efficient mass vaccination strategies.
• Mass vaccination programs face significant logistical challenges in scheduling and resource allocation.
• Optimizing vaccination site selection and appointment management is crucial for pandemic control.

Purpose of the Study:

• To develop an optimization model for mass COVID-19 vaccination site selection, appointment acceptance, assignment, and scheduling.
• To minimize operational costs, recipient travel distances, appointment rejections, and vaccination delays.
• To provide decision-makers with effective tools for managing large-scale vaccination efforts.

Main Methods:

• Formulation of the problem as a mixed-integer linear program (MILP).
• Development of an exact logic-based Benders decomposition (LBBD) method.
• Implementation of a matheuristic (MH) method for solving large-scale instances.
• Numerical experiments on instances ranging from small to large sizes.

Main Results:

• The proposed MILP model and solution methods effectively address the vaccination scheduling problem.
• Optimal solutions were found for small instances, and near-optimal solutions for large instances.
• The matheuristic method demonstrated efficiency in solving practical-sized problems (up to 500 appointments, 50 sites).

Conclusions:

• The developed optimization approaches provide valuable insights for mass COVID-19 vaccination planning.
• The matheuristic offers an efficient solution for real-world, large-scale vaccination scheduling challenges.
• Managerial implications support decision-makers in optimizing vaccination rollout and resource allocation.