A multi-echelon dynamic cold chain for managing vaccine distribution

Manupati, Vijaya Kumar, Schoenherr, Tobias, Subramanian, Nachiappan, Ramkumar, M, Soni, Bhanushree and Panigrahi, Suraj (2021) A multi-echelon dynamic cold chain for managing vaccine distribution. Transportation Research Part E: Logistics and Transportation Review, 156. 102542 1-19. ISSN 1366-5545

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Abstract

While cold chain management has been part of healthcare systems, enabling the efficient administration of vaccines in both urban and rural areas, the COVID-19 virus has created entirely new challenges for vaccine distributions. With virtually every individual worldwide being impacted, strategies are needed to devise best vaccine distribution scenarios, ensuring proper storage, transportation and cost considerations. Current models do not consider the magnitude of distribution efforts needed in our current pandemic, in particular the objective that entire populations need to be vaccinated. We expand on existing models and devise an approach that considers the needed extensive distribution capabilities and special storage requirements of vaccines, while at the same time being cognizant of costs. As such, we provide decision support on how to distribute the vaccine to an entire population based on priority. We do so by conducting predictive analysis for three different scenarios and dividing the distribution chain into three phases. As the available vaccine doses are limited in quantity at first, we apply decision tree analysis to find the best vaccination scenario, followed by a synthetic control analysis to predict the impact of the vaccination programme to forecast future vaccine production. We then formulate a mixed-integer linear programming (MILP) model for locating and allocating cold storage facilities for bulk vaccine production, followed by the proposition of a heuristic algorithm to solve the associated objective functions. The application of the proposed model is evaluated by implementing it in a real-world case study. The optimized numerical results provide valuable decision support for healthcare authorities.

Item Type: Article
Schools and Departments: University of Sussex Business School > SPRU - Science Policy Research Unit
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 19 Nov 2021 08:19
Last Modified: 19 Nov 2021 08:30
URI: http://sro.sussex.ac.uk/id/eprint/103001

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