Bi‐objective optimization of a stochastic resilient vaccine distribution network in the context of the COVID‐19 pandemic

Mehrdad Mohammadi, Milad Dehghan, Amir Pirayesh and Alexandre Dolgui

Omega, 2022, vol. 113, issue C

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.

Keywords: Vaccine distribution network; Bi-objective mathematical optimization model; Disruption; Uncertainty; Robust-stochastic optimization; COVID-19 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.omega.2022.102725

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