Open Access Research Article

Designing a Blood Supply Chain Network: A Bi‐Objective Robust Optimization Approach

Authors

  • Mohammadhasan Zarei * Industrial Engineering, Sharif University of Technology, Tehran, Iran.
  • Mohsen Varmazyar Industrial Engineering, Sharif University of Technology, Tehran, Iran.

https://doi.org/10.48313/scodm.v3i1.52

Abstract

Managing blood supply chains and network design is challenging due to product perishability, uncertain supply and demand, and blood type compatibility. We present a two-stage robust optimization model for multi-product flows of red cells, platelets, and plasma, integrating strategic facility decisions with adaptive operational allocation under white, yellow, and red scenarios. An augmented ε-constraint method uncovers cost–time trade–offs, while robust optimization guards against extreme disruptions. In this study, we use real data from Tehran’s blood transfusion organization and solve the model with CPLEX. We demonstrate that allowing substitutions compatible with the ABO blood group system and the Rhesus factor (ABO–Rh) relieves inventory pressure and reduces expected costs. The resulting Pareto frontier guides decision-makers in balancing delivery time against operating expenses.

Keywords

Blood supply chain Robust optimization Network design ABO–Rh compatibility Multi-objective optimization Augmented ε-constraint
References (13)
  1. [1] Sun, H., Li, J., Wang, T., & Xue, Y. (2022). A novel scenario-based robust bi-objective optimization model for humanitarian logistics network under risk of disruptions. Transportation research part e: Logistics and transportation review, 157, 102578. https://doi.org/10.1016/j.tre.2021.102578

  2. [2] Katsaliaki, K. (2008). Cost-effective practices in the blood service sector. Health policy, 86(2), 276–287. https://doi.org/10.1016/j.healthpol.2007.11.004

  3. [3] Najafi, M., Ahmadi, A., & Zolfagharinia, H. (2017). Blood inventory management in hospitals: Considering supply and demand uncertainty and blood transshipment possibility. Operations research for health care, 15, 43–56. https://doi.org/10.1016/j.orhc.2017.08.006

  4. [4] Xu, Y., & Szmerekovsky, J. (2022). A multi-product multi-period stochastic model for a blood supply chain considering blood substitution and demand uncertainty. Health care management science, 25(3), 441–459. https://doi.org/10.1007/s10729-022-09593-5

  5. [5] Zhang, C., Ayer, T., White, C. C., Bodeker, J. N., & Roback, J. D. (2023). Inventory sharing for perishable products: Application to platelet inventory management in hospital blood banks. Operations research, 71(5), 1756–1776. https://doi.org/10.1287/opre.2022.2410

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How to Cite

Zarei, M. ., & Varmazyar, M. . (2026). Designing a Blood Supply Chain Network: A Bi‐Objective Robust Optimization Approach. Supply Chain and Operations Decision Making, 3(1), 63-75. https://doi.org/10.48313/scodm.v3i1.52

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