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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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    1. Mohamadreza Fazli-Khalaf & Soheyl Khalilpourazari & Mohammad Mohammadi, 2019. "Mixed robust possibilistic flexible chance constraint optimization model for emergency blood supply chain network design," Annals of Operations Research, Springer, vol. 283(1), pages 1079-1109, December.
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    5. Soheyl Khalilpourazari & Alireza Arshadi Khamseh, 2019. "Bi-objective emergency blood supply chain network design in earthquake considering earthquake magnitude: a comprehensive study with real world application," Annals of Operations Research, Springer, vol. 283(1), pages 355-393, December.
    6. Behzad Zahiri & Mir Saman Pishvaee, 2017. "Blood supply chain network design considering blood group compatibility under uncertainty," International Journal of Production Research, Taylor & Francis Journals, vol. 55(7), pages 2013-2033, April.
    7. Soheyl Khalilpourazari & Shima Soltanzadeh & Gerhard-Wilhelm Weber & Sankar Kumar Roy, 2020. "Designing an efficient blood supply chain network in crisis: neural learning, optimization and case study," Annals of Operations Research, Springer, vol. 289(1), pages 123-152, June.
    8. Dehghani, Maryam & Abbasi, Babak & Oliveira, Fabricio, 2021. "Proactive transshipment in the blood supply chain: A stochastic programming approach," Omega, Elsevier, vol. 98(C).
    9. Ghelichi, Zabih & Saidi-Mehrabad, Mohammad & Pishvaee, Mir Saman, 2018. "A stochastic programming approach toward optimal design and planning of an integrated green biodiesel supply chain network under uncertainty: A case study," Energy, Elsevier, vol. 156(C), pages 661-687.
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    Cited by:

    1. 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.
    2. Mehdi Safaei & Khalid Yahya & Saleh Al Dawsari, 2024. "A Comprehensive Evaluation Model for Sustainable Supply Chain Capabilities in the Energy Sector," Sustainability, MDPI, vol. 16(21), pages 1-25, October.
    3. 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).

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