Mass Vaccination Scheduling: Trading Off Infections, Throughput, and Overtime
研究如何安排大规模疫苗接种中心的到达时间，以最小化等待期间感染数、吞吐量和加班的三目标函数，发现标准调度方案远离帕累托前沿的拐点，而拐点策略可减少约38%的预期感染。
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. This paper was accepted by Carri Chan, healthcare management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.02958 .