Annals of Operations Research

, Volume 283, Issue 1–2, pp 679–703 | Cite as

Developing a robust stochastic model for designing a blood supply chain network in a crisis: a possible earthquake in Tehran

  • Faraz SalehiEmail author
  • Masoud Mahootchi
  • Seyed Mohammad Moattar Husseini
Applications of OR in Disaster Relief Operations


In a natural disaster such as an earthquake, very often due to the extensive number of severe injuries, demands for blood units sharply increase in emergency hospitals. Regarding such a problem, we propose a new robust two-stage multi-period stochastic model for the blood supply network design with the consideration of a possible natural disaster. The demand for blood units from different types and their derivatives including plasma and platelets are uncertain variables. As a novel contribution, the possibility of transfusion of one blood type as well as its derivatives to other types based on the medical requirements is considered in the optimization model. The pertinent network consists of three layers including the donated areas, the collection blood centers, and the transfusion blood center, which is usually a governmental organization. The model is also constructed for considering a likely earthquake in Tehran (the capital of Islamic Republic of Iran) using a professional report prepared in the year 1999 and also updated in a next research work. The scenarios for the demands of blood units and their derivatives are generated based on these reports. The mathematical model is implemented and assessed in a proper way using the simulation method.


Network design Blood supply chain Blood types Blood products Uncertainty Two-stage stochastic programming Robust model 


  1. Abolghasemi, H., Radfar, M. H., Tabatabaee, M., Hosseini-Divkolayee, N. S., & Burkle, F. M. (2008). Revisiting blood transfusion preparedness: Experience from the Bam earthquake response. Prehospital and Disaster Medicine, 23(05), 391–394.CrossRefGoogle Scholar
  2. 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.CrossRefGoogle Scholar
  3. Alem, D., Clark, A., & Moreno, A. (2016). Stochastic network models for logistics planning in disaster relief. European Journal of Operational Research, 255(1), 187–206.CrossRefGoogle Scholar
  4. Barbarosoglu, G., & Arda, Y. (2004). A two-stage stochastic programming framework for transportation planning in disaster response. Journal of the Operational Research Society, 55(1), 43–53.CrossRefGoogle Scholar
  5. Beliën, J., & Forcé, H. (2012). Supply chain management of blood products: A literature review. European Journal of Operational Research, 217(1), 1–16.CrossRefGoogle Scholar
  6. Birge, J. R., & Louveaux, F. (2011). Introduction to stochastic programming. London: Springer.CrossRefGoogle Scholar
  7. Bozorgi-Amiri, A., Jabalameli, M. S., & Mirzapour Al-e-Hashem, S. M. (2013). A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty. OR Spectrum, 35(4), 1–29.Google Scholar
  8. Brito Junior, I., Leiras, A., & Yoshizaki, H. (2013). Stochastic optimization applied to the pre-positioning of disaster relief supplies decisions in Brazil. In Proceedings of the 24th annual conference of the production and operations management society (POMS) (pp. 1–10).Google Scholar
  9. Cetin, E., & Sarul, L. S. (2009). A blood bank location model: A multiobjective approach. European Journal of Pure and Applied Mathematics, 2(1), 112–124.Google Scholar
  10. De Rosa, V., Gebhard, M., Hartmann, E., & Wollenweber, J. (2013). Robust sustainable bi-directional logistics network design under uncertainty. International Journal of Production Economics, 145(1), 184–198.CrossRefGoogle Scholar
  11. Delen, D., Erraguntla, M., Mayer, R. J., & Wu, C. N. (2011). Better management of blood supply-chain with GIS-based analytics. Annals of Operations Research, 185(1), 181–193.CrossRefGoogle Scholar
  12. Esmaeilikia, M., Fahimnia, B., Sarkis, J., Govindan, K., Kumar, A., & Mo, J. (2014). Tactical supply chain planning models with inherent flexibility: Definition and review. Annals of Operations Research, 244(2), 1–21.Google Scholar
  13. Fahimnia, B., Jabbarzadeh, A., Ghavamifar, A., & Bell, M. (2015). Supply chain design for efficient and effective blood supply in disasters. International Journal of Production Economics, 183, 700–709.Google Scholar
  14. Golmohammadi, M., & Mahootchi, M. (2016). Developing a new stochastic model considering bi-directional relations in a natural disaster: A real case study in Tehran (the capital of Islamic Republic of Iran) (pp. 346–352), submitted to Annals of Operation Research, under revision. Google Scholar
  15. Gunpinar, S. (2013). Supply chain optimization of blood products. Ph.D. Dissertation, University of South Florida, 2013.Google Scholar
  16. Hess, J. R., & Thomas, M. J. G. (2003). Blood use in war and disaster: Lessons from the past century. Transfusion, 43(11), 1622–1633.CrossRefGoogle Scholar
  17. Hoyos, M. C., Morales, R. S., & Akhavan-Tabatabaei, R. (2015). OR models with stochastic components in disaster operations management: A literature survey. Computers & Industrial Engineering, 82, 183–197.CrossRefGoogle Scholar
  18. 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.CrossRefGoogle Scholar
  19. Kiakalayeh, A. D., Paridar, M., & Toogeh, G. H. (2012). Cost unit analysis of blood transfusion centers in Guilan province. Science Journal of Iranian Blood Transfusion Organization, 9(3).Google Scholar
  20. Manopiniwes, W., & Irohara, T. (2017). Stochastic optimization model for integrated decisions on relief supply chains: Preparedness for disaster response. International Journal of Production Research, 55(4), 979–996.CrossRefGoogle Scholar
  21. Melo, M. T., Nickel, S., & Saldanha-da-Gama, F. (2009). Facility location and supply chain management: A review. European Journal of Operational Research, 196(2), 401–412.CrossRefGoogle Scholar
  22. Mete, H. O., & Zabinsky, Z. B. (2010). Stochastic optimization of medical supply location and distribution in disaster management. International Journal of Production Economics, 126(1), 76–84.CrossRefGoogle Scholar
  23. Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations Research, 43(2), 264–281.CrossRefGoogle Scholar
  24. Nagurney, A., & Masoumi, A. H. (2012). Supply chain network design of a sustainable blood banking system. In Sustainable supply chains (pp. 49–72). New York: Springer.Google Scholar
  25. Nagurney, A., Masoumi, A. H., & Yu, M. (2012). Supply chain network operations management of a blood banking system with cost and risk minimization. Computational Management Science, 9(2), 205–231.CrossRefGoogle Scholar
  26. Overstreet, R. E., Hall, D., Hanna, J. B., & Kelly Rainer, R, Jr. (2011). Research in humanitarian logistics. Journal of Humanitarian Logistics and Supply Chain Management, 1(2), 114–131.CrossRefGoogle Scholar
  27. Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2012). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy Sets and Systems, 206, 1–20.CrossRefGoogle Scholar
  28. Rabinowitz, M. (1973). Blood bank inventory policies: A computer simulation. Health Services Research, 8(4), 271.Google Scholar
  29. Ruan, J. H., Wang, X. P., Chan, F. T. S., & Shi, Y. (2016). Optimizing the intermodal transportation of emergency medical supplies using balanced fuzzy clustering. International Journal of Production Research, 54(14), 4368–4386.CrossRefGoogle Scholar
  30. Şahin, G., Süral, H., & Meral, S. (2007). Locational analysis for regionalization of Turkish Red Crescent blood services. Computers & Operations Research, 34(3), 692–704.CrossRefGoogle Scholar
  31. Salmerón, J., & Apte, A. (2010). Stochastic optimization for natural disaster asset prepositioning. Production and Operations Management, 19(5), 561–574.CrossRefGoogle Scholar
  32. Sha, Y., & Huang, J. (2012). The multi-period location-allocation problem of engineering emergency blood supply systems. Systems Engineering Procedia, 5, 21–28.CrossRefGoogle Scholar
  33. Tabatabaie, M., Ardalan, A., Abolghasemi, H., Naieni, K. H., Pourmalek, F., Ahmadi, B., et al. (2010). Estimating blood transfusion requirements in preparation for a major earthquake: The Tehran, Iran study. Prehospital and Disaster Medicine, 25(03), 246–252.CrossRefGoogle Scholar
  34. Toyasaki, F., & Wakolbinger, T. (2014). Impacts of earmarked private donations for disaster fundraising. Annals of Operations Research, 221(1), 427–447.CrossRefGoogle Scholar
  35. Trifunac, M. D., & Brady, A. G. (1975). A study on the duration of strong earthquake ground motion. Bulletin of the Seismological Society of America, 65(3), 581–626. doi:  10.1016/j.ijpe.2015.11.007.CrossRefGoogle Scholar
  36. Wang, X., Wu, Y., Liang, L., & Huang, Z. (2016). Service outsourcing and disaster response methods in a relief supply chain. Annals of Operations Research, 240(2), 471–487.CrossRefGoogle Scholar
  37. Williamson, L. M., & Devine, D. V. (2013). Challenges in the management of the blood supply. The Lancet, 381(9880), 1866–1875.CrossRefGoogle Scholar
  38. Yadavalli, V. S., Sundar, D. K., & Udayabaskaran, S. (2015). Two substitutable perishable product disaster inventory systems. Annals of Operations Research, 233(1), 517–534.CrossRefGoogle Scholar
  39. Yang, F., Yuan, Q., Du, S., & Liang, L. (2016). Reserving relief supplies for earthquake: A multi-attribute decision making of China Red Cross. Annals of Operations Research, 247(2), 759–785.CrossRefGoogle Scholar
  40. Yu, C. S., & Li, H. L. (2000). A robust optimization model for stochastic logistic problems. International Journal of Production Economics, 64(1), 385–397.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Faraz Salehi
    • 1
    Email author
  • Masoud Mahootchi
    • 1
  • Seyed Mohammad Moattar Husseini
    • 1
  1. 1.Department of Industrial Engineering and Management SystemsAmirkabir University of TechnologyTehranIran

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