Vaccine supply chain network design by considering viability, robustness and risk

Abstract

This study aims to develop a solution for the Viable Vaccine Supply Chain Network Design (VVSCND) problem, which concurrently addresses multiple factors such as sustainability, resiliency, agility, risk, and robustness. To achieve this, the researchers propose a Mixed-Integer Linear Programming (MILP) model based on a Robust Stochastic Programming (RSP) approach, which minimizes the weighted expected and max cost function. Furthermore, the research considers factors such as limiting CO₂ emissions, introducing flexible capacity, and ensuring the reliability and redundancy of facilities to establish a more agile and environmentally sustainable supply chain. The key decisions in the proposed methodology involve determining facility location and product flow within an efficient healthcare system. According to the findings, the cost function of the VVSCND problem was only marginally higher, by 0.04%, than its counterpart in a VSCND that did not consider any risks or worst-case scenarios. However, increasing the conservatism coefficient or agility coefficient by 50% and 10%, respectively, leads to similar increases in the cost function. Similarly, the resiliency coefficient has a direct relationship with the cost function. Overall, the study demonstrates that an optimal VSCND can be achieved by considering multiple factors through RSP-based MILP modeling.