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<p>The pandemic has reshaped how supply chain networks (SCNs) are designed, particularly in the context of vaccine distribution. This study introduces a new solution methodology to address the challenges of managing large-scale vaccine supply chains under uncertainty, focusing on the challenge of distributing vaccines within tight time constraints. It introduces a Robust Model (RM) for vaccine SCN design and proposes a novel heuristic algorithm, derived from the Lagrangian Relaxation Algorithm (LRA), to efficiently handle large-scale scenarios. Results from sensitivity analysis and validation confirm that this heuristic significantly improves the algorithm's ability to solve complex cases within reasonable time and accuracy. Additionally, the framework simplifies the consideration of vaccine immunogenicity during outbreaks such as the COVID-19, and it accounts for unpredictable factors such as fluctuating demand, cost variability, and potential vaccine wastage. The model supports decision-makers in effectively distributing vaccines during epidemics. A case study in the Greater Toronto Area (GTA) illustrates the model’s practicality, demonstrating how it can enhance immunization efforts, reduce hospitalizations, and mitigate the impact of health crises. This research offers a comprehensive solution for large-scale vaccine SCN design in uncertain environments, with proven relevance in real-world scenarios such as the GTA.</p>

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Strategic management of vaccine distribution during pandemics under uncertainty

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • Mahsa Mohammadi,
  • Mohsen Roytvand Ghiasvand,
  • Donya Rahmani,
  • Babak Mohamadpour Tosarkani

摘要

The pandemic has reshaped how supply chain networks (SCNs) are designed, particularly in the context of vaccine distribution. This study introduces a new solution methodology to address the challenges of managing large-scale vaccine supply chains under uncertainty, focusing on the challenge of distributing vaccines within tight time constraints. It introduces a Robust Model (RM) for vaccine SCN design and proposes a novel heuristic algorithm, derived from the Lagrangian Relaxation Algorithm (LRA), to efficiently handle large-scale scenarios. Results from sensitivity analysis and validation confirm that this heuristic significantly improves the algorithm's ability to solve complex cases within reasonable time and accuracy. Additionally, the framework simplifies the consideration of vaccine immunogenicity during outbreaks such as the COVID-19, and it accounts for unpredictable factors such as fluctuating demand, cost variability, and potential vaccine wastage. The model supports decision-makers in effectively distributing vaccines during epidemics. A case study in the Greater Toronto Area (GTA) illustrates the model’s practicality, demonstrating how it can enhance immunization efforts, reduce hospitalizations, and mitigate the impact of health crises. This research offers a comprehensive solution for large-scale vaccine SCN design in uncertain environments, with proven relevance in real-world scenarios such as the GTA.