A robust optimization approach model for a multi-vaccine multi-echelon supply chain

TL;DR

This paper develops a robust optimization model for a complex multi-vaccine supply chain under uncertainty, aiming to minimize total costs while accounting for demand fluctuations, inspection errors, and waste management.

Contribution

It introduces a novel robust optimization framework for multi-product, multi-echelon vaccine supply chains considering multiple uncertainties and decision risk attitudes.

Findings

Model effectively captures various uncertainties impacting costs.

Different uncertainty sets influence the robustness of solutions.

The approach provides managerial insights for vaccine supply chain resilience.

Abstract

This research investigates a multi-product, multi-echelon, and multi-period vaccine supply chain network model under uncertainty and quality inspection errors. The objective function seeks optimizing the total cost of the supply chain. Moreover, the proposed model is formulated as a mixed integer linear programming problem under multiple sources of uncertain parameters including demand, inspection errors, vaccine waste generated in healthcare centers, and defective treatment rate of vaccine waste. To provide meaningful solutions that are robust against future fluctuation of parameters, the robust optimization approach is utilized to incorporate the decision maker risk attitude under different type of uncertainty sets. Namely, box, polyhedral and combination of interval polyhedral. The performance of the proposed model is demonstrated through an illustrative example. The results show the effect of different types of uncertainties on the overall objective function. Managerial insights and research implications in terms of vaccine supply chain is advised and future research directions are proposed.