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Data-driven modeling for designing a sustainable and efficient vaccine supply chain: A COVID-19 case study

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  • Kargar, Bahareh
  • MohajerAnsari, Pedram
  • Esra Büyüktahtakın, İ.
  • Jahani, Hamed
  • Talluri, Sri

Abstract

We present an agent-based simulation-optimization modeling framework to determine the optimal location of warehouses for the distribution of vaccines. We first extend an agent-based epidemiological simulation model of COVID-19 to capture disease transmission and forecast the number of susceptible individuals and infections. We then develop a sustainable VSC considering the impact of greenhouse gases and integrate the simulation model into the VSC model to minimize total costs and environmental impacts. We validate our proposed model using a real-world COVID-19 VSC in the US. Our findings underscore the importance of strategically managing vaccine supplies to control COVID-19 and other infectious outbreaks.

Suggested Citation

  • Kargar, Bahareh & MohajerAnsari, Pedram & Esra Büyüktahtakın, İ. & Jahani, Hamed & Talluri, Sri, 2024. "Data-driven modeling for designing a sustainable and efficient vaccine supply chain: A COVID-19 case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).
    • Handle: RePEc:eee:transe:v:184:y:2024:i:c:s1366554524000851
    DOI: 10.1016/j.tre.2024.103494
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    References listed on IDEAS

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    Cited by:

    1. Tang, Lianhua & Li, Yantong & Zhang, Shuai & Wang, Zheng & Coelho, Leandro C., 2025. "Mobile COVID-19 vaccination scheduling with capacity selection," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 193(C).
    2. Li, Lixu & Zhu, Wenwen & Chen, Lujie & Liu, Yaoqi, 2024. "Generative AI usage and sustainable supply chain performance: A practice-based view," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 192(C).

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