Resilient COVID-19 vaccine supply chain: An optimization and simulation approach for multi-objective management

Saeedeh Khalilpoor, Mehdi A. Kamran and Maghsud Solimanpur

Transportation Research Part E: Logistics and Transportation Review, 2025, vol. 201, issue C

Abstract: This study develops a comprehensive optimization-simulation model to enhance the efficiency and resilience of the COVID-19 Vaccine Supply Chain Network (VSCN). The model integrates multi-objective, multi-product, and multi-period programming to address critical decisions related to location-allocation, inventory levels, safety stock, vaccine flow, and shortage, while considering stringent ultra-cold chain requirements for mRNA vaccines and wastage. Leveraging constrained multi-objective Grey Wolf Optimizer (MOGWO) and Non-Dominated Sorting Genetic Algorithm (NSGA-II), the study provides robust solutions for a wide range of problem sizes, demonstrating the superior performance of constrained MOGWO in solving high-dimensional constraints. The algorithms are fine-tuned using the Taguchi method, and their performance is validated through metrics such as MID, HV, SP, and NS, highlighting their respective strengths. A large-scale case study conducted in Iran using anyLogistix simulation software over 60 days evaluates the resiliency of the SC under disruption scenarios. Strategies like equipping specific hubs with ultra-cold chains and implementing a Min-Max inventory policy with an LTL transportation policy reduce total costs and enhance service levels. The findings emphasize the importance of demand fulfillment, waste reduction, and resource allocation, offering actionable insights for logistics managers and policymakers in the healthcare sector, such as dynamically adjusting vaccine distribution based on real-time demand, optimizing cold chain infrastructure placement, and prioritizing vaccination center locations based on accessibility and fair distribution considerations. This study further highlights the importance of fine-tuning delivery frequency to vaccination centers based on actual demand patterns, reducing operational costs related to storage, transportation, and wastage, improving VSC resilience to respond rapidly to disruptions, and provides strategies for minimizing operational costs, reducing wastage, and improving vaccine accessibility. This work significantly contributes to COVID-19 VSC management by presenting a holistic framework that addresses real-world complexities.

Keywords: COVID-19 vaccine supply chain; Multi-objective mixed-integer programming; Grey wolf optimizer; Non-dominated sorting genetic algorithm; Optimization-simulation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.tre.2025.104168

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