Calculating of the optimal number and location of blood supply centers in the case of East Azerbaijan

Document Type : Research Paper

Author

Department of Industrial Engineering, Bonab branch, Islamic Azad University, Bonab, Iran

Abstract

Background and Objectives: One of the key issues in determining location for blood supply center is the design of blood supply chain. To minimize the cost of blood supply, the donors should be reached easily with appropriate distribution of blood and blood products to the hospital. The aim of this study was calculating of the optimal number and location of different various types blood supply centers 
Methods: This was mathematical modeling study of potential donors in the East Azerbaijan Province cities. The cost of construction and operation for each facility was calculated based on the activities and after which a mathematical model has been used. Blood supply centers was included fixed centers and mobile teams. Data collection for this study was obtained in March 2014 to September 2015. The mathematical model developed by software 24.1 GAMS 

Results: The location of Blood Transfusion Centers in the city of Tabriz in East Azerbaijan province were showed that optimal location for constructing of preparation and processing centers of East Azerbaijan province are cities of Maragheh, Mianeh and Marand. 
Establishing fixed blood supply centers in the cities of Ahar, Tabriz, Shabestar, Azarshahr, Ajab Shir, Bonab, Malekan, Bostanabad and Sarab had the lowest opening and transportation cost. Therefore, optimal situation for mobile teams were Julfa,Varzaqan,Khodaafarin, Harris, Tabriz, Osku, Maragheh, Khoda Afarin, Hashtrud and Charuymaq.

Conclusion: The appropriate allocation of satellite, fixed centers and mobile teams for the cities of East Azerbaijan reduces the cost of supplying blood. Observing this can reduce transportation costs. Therefore, the blood transfusion organization should choose the places to set up the blood supply centers to reduce its costs.

References

Farrugia A. The regulatory pendulum in transfusion medicine (2002). Transfuse Med Rev; 16(4): 273-82.
Amin M, Fergusson D, Aziz A, Wilson K, Coyle D, Herbert P. The cost of allogeneic red blood cell-a systematic review (2003). Transfuse Med; 13(5): 275-85.
Gharehbaghian A, Abolghasemi H, TabriziNamini M. Status of blood Transfusion services in Iran (2009). Asian J TransfSci; 2(1): 13-17.
Cheraghali AM. Overview of blood transfusion system of Iran: 2002–2011 (2012). Iran J Public Health; 41(8):89–93.
Maghsudlu M, Nasizadeh S, Abolghasemi H, Ahmadyar SH. Blood donation and donor recruitment in Iran from 1998 through 2007: ten years’ experience (2009). Transfusion; 49(11):2346–51.a
Omidkhoda A, AminiKafi-Abad S, Pourfatollah AA, Maghsudlu M. Blood collection, components preparation and distribution in Iran, 2008–2012 (2016). Transfusion and Apheresis Science; 54: 117–121.
Zahraee SM, Rohani JM, Firouzi A, Shahpanah A. Efficiency Improvement of Blood Supply Chain System Using Taguchi Method and Dynamic Simulation (2015). ProcediaManuf; 2: 1–5.
Zahiri B, Torabi SA, Mousazadeh M, Mansouri SA. Blood collection management: Methodology and application (2015). Appl Math Mode; 39 (23–24): 7680–7696.
Fahimnia B, Jabbarzadeh A, Ghavamifar A, Bell M. Supply chain design for efficient and effective blood supply in disasters (2015);Int J Prod Econ; Nov.
Jabbarzadeh A, Fahimnia B, Seuring S. Dynamic supply chain network design for the supply of blood in disasters: A robust model with real world application (2014).Transp Res Part E LogistTransp Rev; 70: 225–244.
Hasani A, Khosrojerdi A. Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study (2016).Transp Res Part E LogistTransp Rev;87: 20–52
Gunpinar S, Centeno G. Stochastic integer programming models for reducing wastages and shortages of blood products at hospitals (2015). ComputOper Res; 54: 129–141.
Beliën J, Forcé H. Supply chain management of blood products: A literature review (2012). Eur J Oper Res; 217(1): 1–16.
Vanany I, Maryani A, Amaliah B, Rinaldy F, Muhammad F. Blood Traceability System for Indonesian Blood Supply Chain (2015). Procedia Manuf; 4: 535–542.
Davoudi-Kiakalayeh A, Paridar M, ToogehGh. Cost unit analysis of blood transfusion centers in Guilanprovince (2012). Sci J Iran Blood Transfus Organ; 9(3): 346-352. [Artic le in Farsi]
Gharehbaghian A, JalilzadehKhoei M, Honarkaran N, Davoodi F. Estimsation and comparison of the production cost of blood and blood products in 28 IBTO centers in 1381 (2005). Sci J Iran Blood Transfus Organ; 2(1): 61-69. [Article in Farsi]
Attari, Y. N. A., pasandide H., Agaie, A., Akhavan Niaki ST. Presenting a stochastic multi choice goal programming model for reducing wastages and shortages of blood products at hospitals. Journal of Industrial and Systems Engineering. 2017 Mar 22; 10(special issue on healthcare):81-96.
Osorio, A. F., Brailsford, S. C., & Smith, H. K. (2017).Whole blood or apheresis donations? A multi-objective  stochastic optimization approach. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2017.09.005.
Osorio, A. F., Brailsford, S. C., Smith, H. K., Forero-Matiz, S. P., & Camacho-Rodríguez, B. A. (2016). Simulation-optimization model for production planning in the blood supply chain. Health Care ManagementScience. https://doi.org/10.1007/s10729-016-9370-6.
Kazemi, S. M., Rabbani, M., Tavakkoli-Moghaddam, R., & Shahreza, F. A. (2017). Blood inventory-routing problem under uncertainty. Journal of Intelligent & Fuzzy Systems, 32(1), 467–481.
Kaveh, A., & Ghobadi,M. (2017). A multistage algorithm for blood banking supply chain allocation problem.International Journal of Civil Engineering, 15(1), 103–112. https://doi.org/10.1007/s40999-016-0032-3
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. https://doi.org/10.1016/j.cor.2014.08.017.
Grant, D. B. (2010). Integration of supply and marketing for a blood service. Management Research Review,33(2), 123–133. https://doi.org/10.1108/01409171011015810.
Duan, Q.,&Liao, T.W. (2013).Anewage-based replenishment policy for supply chain inventory optimization of highly perishable products. International Journal of Production Economics, 145(2), 658–671. https://doi.org/10.1016/j.ijpe.2013.05.020.
Blake, J., & Hardy, M. (2013). Using simulation to evaluate a blood supply network in the Canadian Maritime Provinces. Journal of Enterprise Information Management, 26(1/2), 119–134. https://doi.org/10.1108/17410391311289587.
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.
Samani, M. R. G., Hosseini-Motlagh, S. M., & Ghannadpour, S. F. (2019). A multilateral perspective towards blood network design in an uncertain environment: Methodology and implementation. Computers & Industrial Engineering, 130, 450-471.
Attari, M. Y. N., & Jami, E. N. (2018). Robust stochastic multi-choice goal programming for blood collection and distribution problem with real application. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-19.
Samani, M. R. G., & Hosseini-Motlagh, S. M. (2018). An enhanced procedure for managing blood supply chain under disruptions and uncertainties. Annals of Operations Research, 1-50.
Ensafian, Hamidreza, and Saeed Yaghoubi. "Robust optimization model for integrated procurement, production and distribution in platelet supply chain." Transportation Research Part E: Logistics and Transportation Review 103 (2017): 32-55.
Samani, Mohammad Reza Ghatreh, S. Ali Torabi, and Seyyed-Mahdi Hosseini-Motlagh. "Integrated blood supply chain planning for disaster relief." International journal of disaster risk reduction 27 (2018): 168-188.
Zahiri, B., & Pishvaee, M. S. (2017). Blood supply chain network design considering blood group compatibility under uncertainty. International Journal of Production Research, 55(7), 2013-2033.
Liu, X., & Song, X. (2019). Emergency operations scheduling for a blood supply network in disaster reliefs. IFAC-PapersOnLine.
Cheraghi, S., Hosseini-Motlagh, S. M., & Ghatreh Samani, M. (2017). Integrated planning for blood platelet production: a robust optimization approach. Journal of Industrial and Systems Engineering, 10(special issue on healthcare), 55-80.
Hosseini-Motlagh, S. M., Samani, M. R. G., & Homaei, S. (2019). Blood supply chain management: robust optimization, disruption risk, and blood group compatibility (a real-life case). Journal of Ambient Intelligence and Humanized Computing, 1-20.
Jafarkhan, F., & Yaghoubi, S. (2018). An efficient solution method for the flexible and robust inventory-routing of red blood cells. Computers & Industrial Engineering, 117, 191-206.
Hosseini-Motlagh, S. M., Cheraghi, S., & Ghatreh Samani, M. (2016). A robust optimization model for blood supply chain network design. International Journal of Industrial Engineering & Production Research, 27(4), 425-444.
International Journal of Hospital Research
Volume 7, Issue 4
Autumn 2018
Pages 102-115

Files

Share

How to cite

Statistics