A multi-echelon dynamic cold chain for managing vaccine distribution

Vijaya Kumar Manupati; Tobias Schoenherr; Nachiappan Subramanian; M Ramkumar; Bhanushree Soni; Suraj Panigrahi

doi:10.1016/j.tre.2021.102542

A multi-echelon dynamic cold chain for managing vaccine distribution

Transp Res E Logist Transp Rev. 2021 Dec:156:102542. doi: 10.1016/j.tre.2021.102542. Epub 2021 Nov 19.

Authors

Vijaya Kumar Manupati¹, Tobias Schoenherr², Nachiappan Subramanian³, M Ramkumar⁴, Bhanushree Soni¹, Suraj Panigrahi⁵

Affiliations

¹ Department of Mechanical Engineering, National Institute of Technology Warangal, Warangal, Telangana 506004, India.
² Department of Supply Chain Management, Broad College of Business, Michigan State University, 632 Bogue St., East Lansing, MI, USA.
³ Science Policy Research Unit, University of Sussex Business School, Brighton, UK.
⁴ Operations and Quantitative Methods Group, Indian Institute of Management Raipur, Atal Nagar, Kurru (Abhanpur), Raipur 493 661.
⁵ Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Gorimedu, Puducherry 605006, India.

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.

Keywords: COVID-19; Cold chain; Decision tree analysis; Supply chain management; Vaccine distribution.