Journal of Industrial Management Perspective

Journal of Industrial Management Perspective

Presenting a Robust Optimization Model to Design a Comprehensive Blood Supply Chain under Supply and Demand Uncertainties

Document Type : Original Article

Authors
Taher Kouchaki Tajani 1
Ali Mohtashami 2
Maghsoud Amiri 3
Reza Ehtesham Rasi 4
1 Ph.D. Student in Industrial Management, Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
2 Associate Professor of Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
3 Professor, Department of Industrial Management, Allameh Tabataba'i University, Tehran, Iran.
4 Assistant Professor of Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
10.52547/jimp.11.1.81
Abstract
Neglecting the supply chain management of perishable goods could create a lot of costs for organizations and companies. Blood is a perishable product in the healthcare supply chain, the  shortage of which could prove quite problematic and disastrous. Any improvements in the blood supply chain management operations may increase service efficiency and decrease the cost of the healthcare system, saving the lives of lots of people. In this paper, a mixed-integer nonlinear programming model is proposed for comprehensive blood supply chain management, which includes gathering, processing and distributing blood and blood products by taking into account the demand lifetime and age. This model aims at decreasing supply chain costs and blood product deficiency. Robust optimization is utilized to take into account the inherent uncertainty and volatility of the demand and supply. The proposed model is first tested on a small-scale numerical example in GAMS software. Then a large-scale problem is solved using Whale and Imperialist Competition algorithms and the results are compared. In addition, a case study is presented to show the applicability of the proposed model.
Keywords
Blood Supply Chain
Mathematical Modeling
Robust Modeling
Whale Metaheuristic Algorithm
Imperialist Competitive Algorithm
  1. Arvan, M., R. (2015). Tavakkoli-Moghaddam, and M. Abdollahi, Designing a bi-objective and multi-product supply chain network for the supply of blood. Uncertain Supply Chain Management, 3(1), 57-68.
  2. Atashpaz-Gargari, E., & Lucas., C. (2007). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. in 2007 IEEE congress on evolutionary computation. Ieee.
  3. Bain, B.J., (2014). Blood cells: a practical guide. John Wiley & Sons.
  4. Bertsimas, D., & Sim, M. (2002). Robust discrete optimization and network flows 1 introduction. Operations Research, 71(January), 1-26.
  5. Bozorgi Amiri, A., S. Mansoori, and M.S. Pishvaee, Multi-objective Relief Chain Network Design for Earthquake Response under Uncertainties. Journal of Industrial Management Perspective, 2017. 7(Issue 1, Spring 2017): p. 9-36. (In Persian).
  6. Daneshvar, A., Homayounfar, M., & Farahmandnejad, A. (2020). Developing an Intelligent Multi Criteria Clustering Method Based on PROMETHEE. Journal of Industrial Management Perspective, 9(Issue 4), 41-61. (In Persian)
  7. Derikvand, H., et al., (2020). A robust stochastic bi objective model for blood inventory-distribution management in a blood supply chain. European Journal of Industrial Engineering, 14(3), 369-403. (In Persian)
  8. Dillon, M., Oliveira, F., & Abbasi, B. (2017). A two-stage stochastic programming model for inventory management in the blood supply chain. International Journal of Production Economics, 187, 27-41.
  9. Doodman, M., & Bozorgi Amiri, A. (2020). Integrate Blood Supply Chain Network Design with Considering Lateral Transshipment under Uncertainty. Journal of Industrial Management Perspective, 9(Issue 4), 9-40. (In Persian)
  10. Ema, (2007). Guideline on influenza vaccines prepared from viruses with the potential to cause pandemic and intended for use outside of the core dossier context (EMEA/CHMP/VWP/263499/2006). Guideline, 24.
  11. Eskandari-Khanghahi, M., et al. (2018). Designing and optimizing a sustainable supply chain network for a blood platelet bank under uncertainty. Engineering Applications of Artificial Intelligence, 71, 236-250.
  12. Farrokh, M., A. Azar, and G. Jandaghi, Developing a Robust Fuzzy Programming Approach for Closed Loop Supply Chain Design. Journal of Industrial Management Perspective, 2016. 6(Issue 2, Summer 2016): p. 9-43. (In Persian).
  13. Gholami, H.R., Mehdizadeh, E., & Naderi, B. (2018). Algorithm for Assembly Flowshops. Journal of Industrial Management Perspective, 8(Issue 1), 93-111. (In Persian)
  14. Gunpinar, S. & Centeno, G. (2015). Stochastic integer programming models for reducing wastages and shortages of blood products at hospitals. Computers & Operations Research, 54, 129-141.
  15. Hamdan, B., & Diabat, A. (2019). A two-stage multi-echelon stochastic blood supply chain problem. Computers & Operations Research, 101, 130-143.
  16. Heidari-Fathian, H. & Pasandideh, S.H.R. (2018). Green-blood supply chain network design: Robust optimization, bounded objective function & Lagrangian relaxation. Computers & Industrial Engineering, 122, 95-105.
  17. Horng, M.-F., T.-K. Dao, & Shieh, C.-S. (2017). A multi-objective optimal vehicle fuel consumption based on whale optimization algorithm, in Advances in Intelligent Information Hiding and Multimedia Signal Processing. Springer, 371-380.
  18. Hosseini-Motlagh, S.-M., M.R.G. Samani, & Cheraghi, S. (2020). Robust and stable flexible blood supply chain network design under motivational initiatives. Socio-Economic Planning Sciences, 70, 100725.
  19. Hosseini-Motlagh, S.-M., M.R.G. Samani, & Homaei, S. (2020). Blood supply chain management: robust optimization, disruption risk, and blood group compatibility (a real-life case). Journal of Ambient Intelligence and Humanized Computing, 11(3), 1085-1104.
  20. Jokar, M., M. Mozafari, and A. Akbari, A Weighted Robust Two-Stage Stochastic Optimization Model for Supplier Selection and Order Allocation under Uncertainty. Journal of Industrial Management Perspective, 2020. 10(Issue 2, Summer 2020): p. 111-135. (In Persian).
  21. Kamyabniya, A., et al., (2018). Robust Platelet Logistics Planning in Disaster Relief Operations Under Uncertainty: a Coordinated Approach. Information Systems Frontiers, 20(4), 759-782.
  22. Larimi, N.G. & Yaghoubi, S. (2019). A robust mathematical model for platelet supply chain considering social announcements and blood extraction technologies. Computers & Industrial Engineering, 137, 106014.
  23. Medicines, E.D.f.t.Q.o., (2013). Guide to the preparation, use and quality assurance of blood components, in Recommendation No. R (95) 15.
  24. Mirjalili, S. & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67.
  25. Nagurney, A., A.H. Masoumi, and M. Yu, Supply chain network operations management of a blood banking system with cost and risk minimization. Computational Management Science, 2012. 9(2): p. 205-231.
  26. Nahmias, S. (1982). Perishable inventory theory: A review. Operations research, 30(4), 680-708.
  27. Osorio, A.F., et al., (2016). Simulation-optimization model for production planning in the blood supply chain. Health care management science, 1-17.
  28. Pierskalla, W.P. (2005). Supply chain management of blood banks, in Operations research and health care., Springer. p. 103-145.
  29. Prastacos, G.P. (1984). Blood inventory management: an overview of theory and practice. Management Science, 30(7), 777-800
  30. Ramezanian, R., & Behboodi, Z. (2017). Blood supply chain network design under uncertainties in supply and demand considering social aspects. Transportation Research Part E: Logistics and Transportation Review, 104, 69-82.
  31. Zahiri, B. & Pishvaee, M.S. (2016). Blood supply chain network design considering blood group compatibility under uncertainty. International Journal of Production Research, 55(7), 2013-2033.
  32. Zahiri, B., et al. (2018). A multi-stage stochastic programming approach for blood supply chain planning. Computers & Industrial Engineering, 122, 1-14.