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. 2022 Dec:113:102725.
doi: 10.1016/j.omega.2022.102725. Epub 2022 Jul 28.

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

Mehrdad Mohammadi  1 , Milad Dehghan  2 , Amir Pirayesh  3 , Alexandre Dolgui  4
Affiliations

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

Mehrdad Mohammadi et al. Omega. 2022 Dec.

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: Bi-objective mathematical optimization model; COVID-19; Disruption; Robust-stochastic optimization; Uncertainty; Vaccine distribution network.

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Figures

Fig. 1
Fig. 1
Vaccine distribution network (VDN).
Fig. 2
Fig. 2
Mutual infection among three categories of individuals.
Fig. 3
Fig. 3
Congestion of individuals with different classes of risk at vaccination centers.
Fig. 4
Fig. 4
The case study information on each province.
Fig. 5
Fig. 5
Optimal cost of each scenario vs. Actual cost.
Fig. 6
Fig. 6
Number of vaccination centers opened in different scenarios (Z1*1,Z1*2,…,Z1*8,Z2*).
Fig. 7
Fig. 7
Total proportional order of vaccines at different scenarios.
Fig. 8
Fig. 8
Quantity of vaccines (%) transferred to each region in each scenario (see scenarios separately). The percentage of the population in each region to the total national population has been provided in parentheses in the legends.
Fig. 9
Fig. 9
Quantity of vaccines (%) transshipped in each scenario.
Fig. 10
Fig. 10
Vaccinated individuals (%) with different risk classes (proportional to their population) in each scenario.
Fig. 11
Fig. 11
Share of different vaccines (%) at the first-dose vaccination of each risk class of individuals.
Fig. 12
Fig. 12
Sensitivity of the total number of deaths to the vaccination campaign’s parameters.
Fig. 13
Fig. 13
Sensitivity of the total number of deaths to pandemic parameters.
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References

    1. Johns Hopkins University and Medicine. Coronavirus COVID-19 global cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). https://coronavirus.jhu.edu/map.html; 2021.
    1. Mullard A. COVID-19 vaccine development pipeline gears up. Lancet. 2020;395(10239):1751–1752. - PMC - PubMed
    1. Abbasi B., Fadaki M., Kokshagina O., Saeed N., Chhetri P.. Modeling vaccine allocations in the COVID-19 pandemic: a case study in Australia. Available at SSRN 37445202020;.
    1. Chen X., Li M., Simchi-Levi D., Zhao T.. Allocation of COVID-19 vaccines under limited supply. Available at SSRN 36789862020;.
    1. Ivanov D. Predicting the impacts of epidemic outbreaks on global supply chains: a simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transp Rese Part E. 2020;136:101922. - PMC - PubMed

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