A multi-objective robust optimization model with defective vaccine and reverse supply chain under uncertainty

Purpose: This study introduces a multi-vaccine multi-echelon supply chain (MVMS) framework designed to ensure sustainable vaccine distribution during outbreaks. The framework aims to minimize the total costs of vaccine distribution and reduce greenhouse gas (GHG) emissions to mitigate environmental impacts while maximizing job opportunities within the network. Design/methodology/approach: Our proposed appraoch employs a multi-objective mixed-integer linear programming model. Findings: The findings indicate that incorporating uncertainties related to demand and inspection errors significantly facilitates timely responses to unexpected shortages, fulfills the requirements of healthcare facilities, and enhances the supply chain’s resilience against future uncertainties. This study also explores managerial implications and suggests avenues for future research to further advance this field. Originality/value: Existing literature on MVMS often relies on simplifying assumptions of perfect vaccines and primarily focuses on demand uncertainty. However, real-world supply chains are typically marked by imperfections, disruptions, and a variety of uncertainties beyond demand. In this work, we address several sources of parameter uncertainty, including demand variability, inspection errors, vaccine waste, and defective treatments rates to enhance the robustness of our model.