Vaccine supply chains are highly vulnerable to viral mutations and the introduction of new vaccines, which cause sudden demand fluctuations and impose additional constraints on cold and ultra-cold storage capacities. This study addresses these disruptions by developing a two-stage stochastic programming model with scenario analysis to optimize storage capacity decisions under uncertainty. Unlike previous research that primarily focused on production and transportation, this study explicitly examines storage capacity constraints and integrates system dynamics and regression analysis, through which the shortage cost function is estimated, overcoming the limitations of fixed-cost assumptions used in prior studies. Additionally, uncertainty in vaccine demand and shortages in cold and ultra-cold storage capacities are considered through multiple scenarios. To enhance resilience, three strategic interventions are analyzed: (i) proactive initial capacity expansion to accommodate unexpected demand fluctuations, (ii) third-party backup contracts for emergency capacity when cold and ultra-cold storage is strained, and (iii) precautionary inventory strategies to mitigate demand variability caused by viral mutations and new vaccine rollouts. Additionally, post-disruption recovery strategies, such as restoring lost storage capacity and dynamically adjusting supply chain operations to evolving demand patterns, are incorporated. The findings indicate that combining preventive and reactive strategies significantly improves vaccine supply chain performance under uncertainty. This research provides a decision-support framework to mitigate vaccine distribution disruptions. Future studies can explore interdependent disruptions, multi-objective optimization, and integrated transportation-storage strategies to further enhance supply chain resilience.

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