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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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    1. Kenan Arifoglu & Sarang Deo & Seyed M. R. Iravani, 2012. "Consumption Externality and Yield Uncertainty in the Influenza Vaccine Supply Chain: Interventions in Demand and Supply Sides," Management Science, INFORMS, vol. 58(6), pages 1072-1091, June.
    2. Ochoa-Barragán, Rogelio & Munguía-López, Aurora del Carmen & Ponce-Ortega, José María, 2023. "Strategic planning for the optimal distribution of COVID-19 vaccines," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    3. Jahani, Hamed & Chaleshtori, Amir Eshaghi & Khaksar, Seyed Mohammad Sadegh & Aghaie, Abdollah & Sheu, Jiuh-Biing, 2022. "COVID-19 vaccine distribution planning using a congested queuing system—A real case from Australia," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    4. Hamed Mamani & Stephen E. Chick & David Simchi-Levi, 2013. "A Game-Theoretic Model of International Influenza Vaccination Coordination," Management Science, INFORMS, vol. 59(7), pages 1650-1670, July.
    5. Sebastian A Müller & Michael Balmer & William Charlton & Ricardo Ewert & Andreas Neumann & Christian Rakow & Tilmann Schlenther & Kai Nagel, 2021. "Predicting the effects of COVID-19 related interventions in urban settings by combining activity-based modelling, agent-based simulation, and mobile phone data," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-32, October.
    6. Tinglong Dai & Soo-Haeng Cho & Fuqiang Zhang, 2016. "Contracting for On-Time Delivery in the U.S. Influenza Vaccine Supply Chain," Manufacturing & Service Operations Management, INFORMS, vol. 18(3), pages 332-346, July.
    7. Laura J. Kornish & Ralph L. Keeney, 2008. "Repeated Commit-or-Defer Decisions with a Deadline: The Influenza Vaccine Composition," Operations Research, INFORMS, vol. 56(3), pages 527-541, June.
    8. Pimentel, Bruno S. & Mateus, Geraldo R. & Almeida, Franklin A., 2013. "Stochastic capacity planning and dynamic network design," International Journal of Production Economics, Elsevier, vol. 145(1), pages 139-149.
    9. Soo-Haeng Cho, 2010. "The Optimal Composition of Influenza Vaccines Subject to Random Production Yields," Manufacturing & Service Operations Management, INFORMS, vol. 12(2), pages 256-277, November.
    10. Manupati, Vijaya Kumar & Schoenherr, Tobias & Subramanian, Nachiappan & Ramkumar, M. & Soni, Bhanushree & Panigrahi, Suraj, 2021. "A multi-echelon dynamic cold chain for managing vaccine distribution," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    11. Babus, Ana & Das, Sanmay & Lee, SangMok, 2023. "The optimal allocation of Covid-19 vaccines," Economics Letters, Elsevier, vol. 224(C).
    12. Kundu, Tanmoy & Sheu, Jiuh-Biing & Kuo, Hsin-Tsz, 2022. "Emergency logistics management—Review and propositions for future research," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    13. Saif, Ahmed & Elhedhli, Samir, 2016. "Cold supply chain design with environmental considerations: A simulation-optimization approach," European Journal of Operational Research, Elsevier, vol. 251(1), pages 274-287.
    14. Zhang, Jianghua & Long, Daniel Zhuoyu & Li, Yuchen, 2023. "A reliable emergency logistics network for COVID-19 considering the uncertain time-varying demands," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 172(C).
    15. Awi Federgruen & Nan Yang, 2009. "Competition Under Generalized Attraction Models: Applications to Quality Competition Under Yield Uncertainty," Management Science, INFORMS, vol. 55(12), pages 2028-2043, December.
    16. Mehdi Alizadeh & Mir Saman Pishvaee & Hamed Jahani & Mohammad Mahdi Paydar & Ahmad Makui, 2023. "Viable healthcare supply chain network design for a pandemic," Annals of Operations Research, Springer, vol. 328(1), pages 35-73, September.
    17. Peng Sun & Liu Yang & Francis de Véricourt, 2009. "Selfish Drug Allocation for Containing an International Influenza Pandemic at the Onset," Operations Research, INFORMS, vol. 57(6), pages 1320-1332, December.
    18. Thanh, Phuong Nga & Bostel, Nathalie & Péton, Olivier, 2008. "A dynamic model for facility location in the design of complex supply chains," International Journal of Production Economics, Elsevier, vol. 113(2), pages 678-693, June.
    19. Lotty E. Duijzer & Willem L. van Jaarsveld & Jacco Wallinga & Rommert Dekker, 2018. "Dose†Optimal Vaccine Allocation over Multiple Populations," Production and Operations Management, Production and Operations Management Society, vol. 27(1), pages 143-159, January.
    20. Kargar, Bahareh & Pishvaee, Mir Saman & Jahani, Hamed & Sheu, Jiuh-Biing, 2020. "Organ transportation and allocation problem under medical uncertainty: A real case study of liver transplantation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 134(C).
    21. Abazari, Seyed Reza & Aghsami, Amir & Rabbani, Masoud, 2021. "Prepositioning and distributing relief items in humanitarian logistics with uncertain parameters," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    22. Vahdani, Behnam & Mohammadi, Mehrdad & Thevenin, Simon & Gendreau, Michel & Dolgui, Alexandre & Meyer, Patrick, 2023. "Fair-split distribution of multi-dose vaccines with prioritized age groups and dynamic demand: The case study of COVID-19," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1249-1272.
    23. Fattahi, Mohammad & Mahootchi, Masoud & Govindan, Kannan & Moattar Husseini, Seyed Mohammad, 2015. "Dynamic supply chain network design with capacity planning and multi-period pricing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 81(C), pages 169-202.
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