Morvarid Derakhshan Andisheh Journal of Quality Engineering and Management 2322-1305 Articles in Press 2026 06 09 Modeling COVID-19 Vaccine Cold Supply Chain Under Operational and Disruption Risks: A Multi-Criteria Simulation-Optimization Approach Modeling COVID-19 Vaccine Cold Supply Chain Under Operational and Disruption Risks: A Multi-Criteria Simulation-Optimization Approach 245185 10.48313/jqem.2026.568156.1598 FA Mojtaba Akbari 1Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran Ali Tajdin Department of Industrial Engineering Mazandaran University of Science and Technology. Babol. Iran 0000-0002-1117-3666 Iraj Mahdavi Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran Babak Shirazi Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran 0000-0002-0424-5350 Journal Article 2025 12 23 Purpose: The vaccine cold supply chain, as a quality-sensitive service–operational system, plays a critical role in ensuring timely delivery, maintaining vaccine efficacy, and minimizing wastage. The occurrence of operational and disruption risks intensifies process variability, undermines system reliability, and degrades service quality. The objective of this study is to develop a quality-oriented framework for the modeling and optimization of the COVID-19 vaccine cold supply chain, with a particular emphasis on quality-related performance indicators under conditions of uncertainty. Methodology: In this study, a novel multi-period and multi-product simulation–optimization framework is developed to support decision-making in vaccine inventory management, allocation, and distribution under operational and disruption risks. The proposed approach integrates agent-based simulation with optimization techniques. The simulations are configured based on scenarios involving transportation disruptions and vaccine supply disruptions and are benchmarked against a disruption-free case. Findings: The results are evaluated using several key performance indicators, including the expected vaccine delivery time, service level, vaccine wastage due to vehicle failures, and financial metrics. The simulation results indicate that the disruption-free scenario achieves the highest service level (0.82) and greatest degree of performance robustness, whereas transportation disruptions result in the spoilage of 8.9 million vaccine doses, and vaccine supply disruptions lead to the lowest service level (0.76). Statistical validation using the paired sign test further confirms the significance of these differences at the 95% confidence level. Originality/Value: The present study adopts a quality engineering perspective to analyze the vaccine supply chain as a service system sensitive to process variability and proposes a quality-driven risk management framework. The findings provide practical insights for policymakers to enhance system reliability, reduce quality-related costs, and improve the resilience of vaccine distribution systems under crisis conditions. Purpose: The vaccine cold supply chain, as a quality-sensitive service–operational system, plays a critical role in ensuring timely delivery, maintaining vaccine efficacy, and minimizing wastage. The occurrence of operational and disruption risks intensifies process variability, undermines system reliability, and degrades service quality. The objective of this study is to develop a quality-oriented framework for the modeling and optimization of the COVID-19 vaccine cold supply chain, with a particular emphasis on quality-related performance indicators under conditions of uncertainty. Methodology: In this study, a novel multi-period and multi-product simulation–optimization framework is developed to support decision-making in vaccine inventory management, allocation, and distribution under operational and disruption risks. The proposed approach integrates agent-based simulation with optimization techniques. The simulations are configured based on scenarios involving transportation disruptions and vaccine supply disruptions and are benchmarked against a disruption-free case. Findings: The results are evaluated using several key performance indicators, including the expected vaccine delivery time, service level, vaccine wastage due to vehicle failures, and financial metrics. The simulation results indicate that the disruption-free scenario achieves the highest service level (0.82) and greatest degree of performance robustness, whereas transportation disruptions result in the spoilage of 8.9 million vaccine doses, and vaccine supply disruptions lead to the lowest service level (0.76). Statistical validation using the paired sign test further confirms the significance of these differences at the 95% confidence level. Originality/Value: The present study adopts a quality engineering perspective to analyze the vaccine supply chain as a service system sensitive to process variability and proposes a quality-driven risk management framework. The findings provide practical insights for policymakers to enhance system reliability, reduce quality-related costs, and improve the resilience of vaccine distribution systems under crisis conditions.

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