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

Authors

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

  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

  6. [6] Ben-Tal, A., & Nemirovski, A. (2000). Robust solutions of linear programming problems contaminated with uncertain data. Mathematical programming, 88(3), 411–424. https://doi.org/10.1007/PL00011380

  7. [7] Jabbarzadeh, A., Fahimnia, B., & Seuring, S. (2014). Dynamic supply chain network design for the supply of blood in disasters: A robust model with real world application. Transportation research part e: Logistics and transportation review, 70, 225–244. https://doi.org/10.1016/j.tre.2014.06.003

  8. [8] Hamdan, B., & Diabat, A. (2020). Robust design of blood supply chains under risk of disruptions using Lagrangian relaxation. Transportation research part e: Logistics and transportation review, 134, 101764. https://doi.org/10.1016/j.tre.2019.08.005

  9. [9] Khalilpourazari, S., & Hashemi Doulabi, H. (2023). A flexible robust model for blood supply chain network design problem. Annals of operations research, 328(1), 701–726. https://doi.org/10.1007/s10479-022-04673-9

  10. [10] Tirkolaee, E. B., Golpîra, H., Javanmardan, A., & Maihami, R. (2023). A socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: An interactive possibilistic programming approach for a real case study. Socio-economic planning sciences, 85, 101439. https://doi.org/10.1016/j.seps.2022.101439

  11. [11] Entezari, S., Abdolazimi, O., Fakhrzad, M. B., Shishebori, D., & Ma, J. (2024). A bi-objective stochastic blood type supply chain configuration and optimization considering time-dependent routing in post-disaster relief logistics. Computers & industrial engineering, 188, 109899. https://doi.org/10.1016/j.cie.2024.109899

  12. [12] Abdolazimi, O., Pishvaee, M. S., Shafiee, M., Shishebori, D., Ma, J., & Entezari, S. (2025). Blood supply chain configuration and optimization under the COVID-19 using benders decomposition based heuristic algorithm. International journal of production research, 63(2), 571–593. https://doi.org/10.1080/00207543.2023.2263088

  13. [13] Aghezzaf, E. H., Sitompul, C., & Najid, N. M. (2010). Models for robust tactical planning in multi-stage production systems with uncertain demands. Computers & operations research, 37(5), 880–889. https://doi.org/10.1016/j.cor.2009.03.012

Published

2026-02-25

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

Similar Articles

11-20 of 35

You may also start an advanced similarity search for this article.