Bi‐objective optimization of a stochastic resilient vaccine distribution network in the context of the COVID‐19 pandemic. (December 2022) - Mohammadi, Mehrdad; Dehghan, Milad; Pirayesh, Amir; Dolgui, Alexandre
Highlights: Optimizing a bi-objective stochastic vaccine distribution network under uncertainty and disruption Minimizing the expected number of total deaths among both un-vaccinated and vaccinated people Considering multiple vaccines with different effectiveness at different doses against different variants of the disease Modelling the vaccination centers as a novel queuing system to estimating the capacity of each center Applying the proposed model and solution approach on the COVID-19 vaccination campaign in France Abstract: This paper develops an approach to optimize a vaccine distribution network design through a mixed-integer nonlinear programming model with two objectives: minimizing the total expected number of deaths among the population and minimizing the total distribution cost of the vaccination campaign. Additionally, we assume that a set of input parameters (e.g., death rate, social contacts, vaccine supply, etc.) is uncertain, and the distribution network is exposed to disruptions. We then investigate the resilience of the distribution network through a scenario-based robust-stochastic optimization approach. The proposed model is linearized and finally validated through a real case study of the COVID-19 vaccination campaign in France. We show that the current vaccination strategies are not optimal, and vaccination prioritization among the population and the equity of vaccine distribution depend on other factors than those conceived by health policymakers. Furthermore, we demonstrate that a vaccination strategy mixing the population prioritization and the quarantine restrictions leads to an 8.5% decrease in the total number of deaths.