Designing an efficient vaccine supply chain network using a two-phase optimization approach: a case study of COVID-19 vaccine

Abstract

The upsurge of the COVID-19 pandemic and its consequences on the world’s population has led to the accelerated production of vaccines across the world. The next challenge in the fight against COVID-19 is to distribute those vaccines to the right person in an optimized way by establishing an efficient vaccine supply network. Consequently, this paper studies an integrated two-phase planning framework for the vaccine distribution network. In the first phase, the population target is classified into several groups to determine their priority for vaccination using a multiple attribute decision making (MADM) technique. The second phase uses a mathematical model to decide on the location of distribution centers, inventory policies, and routing decisions to minimize the total procurement, inventory, and distribution costs. Since the proposed model falls into the category of NP-hard problems, a metaheuristic algorithm entitled population-based simulated annealing (PBSA) is developed to solve the presented model. Besides, the improved grey wolf optimizer (I-GWO) is customized and used as another solution method. A few generated datasets are utilized to evaluate and validate the performance of these solution methods. In addition, this methodology is implemented in a case study of the COVID-19 vaccine supply chain in Iran to demonstrate its practicability.

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