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Mass Vaccination Scheduling: Trading Off Infections, Throughput, and Overtime

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  • Shanshan Luo

    (Sauder School of Business, University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada)

  • Steven M. Shechter

    (Sauder School of Business, University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada)

Abstract

Mass vaccination is essential for epidemic control, but long queues can increase infection risk. We study how to schedule arrivals at a mass vaccination center to minimize a tri-objective function of (a) the expected number of infections acquired while waiting, (b) throughput, and (c) overtime. Leveraging multimodularity results of a related optimization problem, we construct a solution algorithm and apply it to a case study of COVID-19. We find that although the standard equally distributed, equally spaced schedule sits near the Pareto-optimal frontier, it is located away from a sharp elbow in the tradeoff between infections and overtime. Specifically, the “elbow policy” achieves approximately 38% fewer expected infections for nearly the same expected overtime. We also discuss managerial insights around the structure of the optimal schedule and compare it to the well-known “dome-shaped” policies found in other appointment scheduling settings.

Suggested Citation

  • Shanshan Luo & Steven M. Shechter, 2026. "Mass Vaccination Scheduling: Trading Off Infections, Throughput, and Overtime," Management Science, INFORMS, vol. 72(4), pages 3435-3453, April.
    • Handle: RePEc:inm:ormnsc:v:72:y:2026:i:4:p:3435-3453
    DOI: 10.1287/mnsc.2023.02958
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