Mobile COVID-19 vaccination scheduling with capacity selection

Abstract

Massive COVID-19 vaccination can significantly reduce both mild and severe infection rates. Some governments have adopted mobile vaccination vehicles, offering a more convenient and flexible service compared to static walk-in sites. This paper addresses a new scheduling problem arising from the mobile COVID-19 vaccination planning practice. Given a set of communities, each with a specific number of residents to vaccinate, the objective is to assign mobile vaccination vehicles to communities and determine each vehicle’s service capacity and routes, attempting to minimize the total operational cost. To our knowledge, this is the first attempt to tackle the joint challenge of mass vaccination scheduling and routing. We formulate the problem as a mixed-integer nonlinear program model, which we linearize by treating each vehicle with multiple stations as separate units. Given that the problem is NP-hard, we then developed a tailored adaptive large neighborhood search (ALNS) approach that effectively solves practical-sized instances by utilizing the intrinsic structure of the problem. To illustrate the efficiency of the suggested model and solution methodologies, we conduct numerical experiments on instances of varying sizes. The results demonstrate the effectiveness of the developed ALNS algorithm in solving instances with realistic sizes, efficiently handling up to 100 communities and 14 vaccination vehicles. In addition, a case study shows that our method significantly reduces operational expenses compared to some experience-based greedy methods.