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A flexible robust model for blood supply chain network design problem

Author

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  • Soheyl Khalilpourazari

    (Concordia University
    Logistics and Transportation (CIRRELT))

  • Hossein Hashemi Doulabi

    (Concordia University
    Logistics and Transportation (CIRRELT))

Abstract

World Health Organization (WHO) declared COVID-19 as a pandemic On March 12, 2020. Up to January 13, 2022, 320,944,953 cases of infection and 5,539,160 deaths have been reported worldwide. COVID-19 has negatively impacted the blood supply chain by drastically reducing blood donation. Therefore, developing models to design effective blood supply chains in emergencies is essential. This research offers a novel multi-objective Transportation-Location-Inventory-Routing (TLIR) formulation for an emergency blood supply chain network design problem. We answer questions regarding strategic, operational, and tactical decisions considering disruption in the network and blood shelf-life. Since, in real-world applications, the parameters of the proposed mathematical formulation are uncertain, two flexible uncertain models are proposed to provide risk-averse and robust solutions for the problem. We applied the proposed formulations in a case study. Under various scenarios and realizations, we show that the offered robust model handles uncertainties more efficiently and finds solutions that have significantly lower costs and delivery time. To make a reliable conclusion, we performed extensive worst-case analyses to demonstrate the robustness of the results. In the end, we provide critical managerial insights to enhance the effectiveness of the supply chain.

Suggested Citation

  • Soheyl Khalilpourazari & Hossein Hashemi Doulabi, 2023. "A flexible robust model for blood supply chain network design problem," Annals of Operations Research, Springer, vol. 328(1), pages 701-726, September.
    • Handle: RePEc:spr:annopr:v:328:y:2023:i:1:d:10.1007_s10479-022-04673-9
    DOI: 10.1007/s10479-022-04673-9
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    References listed on IDEAS

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    Cited by:

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    2. Yin, Yunqiang & Xu, Xinrui & Wang, Dujuan & Yu, Yugang & Cheng, T.C.E., 2024. "Two-stage recoverable robust optimization for an integrated location–allocation and evacuation planning problem," Transportation Research Part B: Methodological, Elsevier, vol. 182(C).
    3. Rizgar R. Zebari & Gheyath M. Zebari & Adel Al-zebari & Marwan Aziz Mohammed, 2025. "LSTM-XGBoost: An Ensemble Model for Blood Demand Distribution Forecasting – A Case Study in Zakho City, Kurdistan Region, Iraq," SN Operations Research Forum, Springer, vol. 6(1), pages 1-22, March.

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