Determining the optimal amount of blood sent to hospitals in the blood transfusion network (Case study: Mashhad blood transfusion center)

Document Type : Original Article

Authors

1 Ferdowsi of Mashhad University

2 management, ferdowsi university of mashhad

3 Faculty members of Ferdowsi University of Mashhad

4 Head of Information Technology Department of Mashhad Blood Transfusion Organization

Abstract

Inventory managers at blood transfusion centers are always looking to create sufficient reserves to increase access to various blood products and reduce losses due to the expiration date of blood. Because timely, appropriate and sufficient response of its suppliers to consumers is considered very important and necessary due to the corruption of blood, the uncertainty of blood demand and the direct relationship of its presence or absence with human life. This requires full knowledge of the demand of the covered hospitals as base users and decision-making on how to meet their needs in the best possible way in order to minimize the shortages and losses in the network. Due to the importance of this issue, in this article, we have tried to design and implement an optimal model of how to respond to the demand of hospitals in the Mashhad blood transfusion network with the aim of reducing the cost of waste and blood shortages. In other words, the proposed model determines when the blood transfusion centers receive orders from their consumers, considering how to respond optimally to the orders, given the available inventory, which results in the least losses and blood shortages. Have. In order to validate the proposed model, statistical parameters such as mean relative absolute error, mean absolute error, constant R^2and mean square error were used.

Keywords

References

  1. H. Lowalekar, Integrated Model for Blood Bank Operations, Fourth National Conference on Management Science and Practice, 2013
  2. J.T. Blake, M. Hardy, A generic modelling framework to evaluate network blood management policies: The Canadian Blood Services experience. Operations Research for Health Care 3 (2014),pp. 116–128

  3. M. Zarezade, Z, Najiazimi, A, Moravati Sharif Abadi, M.A. Pirayesh. Designing a model for allocating zero blood products under conditions

    Uncertainty (Case Study: Yazd Blood Transfusion Organization). Modern Researches in Decision Making, Vol 4, no 2, pp 71-96, 2019.

  4. M. Yegul, Simulation analysis of the blood supply chain and a case study, A thesis submitted to graduate school of natural and applied sciences of middle east technical university. 2007. 
  5. Pierskalla, William P. (2004). OPERATIONS RESEARCH AND HEALTH CARE. Chapter5. SUPPLY CHAIN MANAGEMENT OF BLOOD BANKS, pp104-145
  6. M. Dillon, F. Oliveira, B. Abbasi, A two-stage stochastic programming model for inventory management in the blood supply chain, International Journal of Production Economics, 187 (2017), pp. 27–41
  7. S. Gunpinar, G. Centeno. Stochastic integer programming models for reducing wastages and shortages of blood products at hospitals. Computers & Operations Research, 54(2015). pp. 127-141
  8. Z, safdari, M. R. Ramezanian, K. Yakide. Comparison of artificial neural network performance and logistic regression Tubin q in index detection analysis. Modern Researches in Decision Making, Vol 3, no 4, pp 1-23, 2018.
  9. K. Katsaliaki. Cost-effective practices in the blood service sector, Health Policy 86 (2008).pp.  276–287
  10. M. Aghyani, A. Jabarzadeh, S.J. Sajadi, Providing a Sustainable Optimization Model for Designing a Supply Chain Network in Crises with Considering Reliability, Engineering Issue and Quality Management,5(2)(2013). 
  11. S.M. Zahraeea, M. Rohani, A. Firouzi, A. Shahpanaha. Efficiency improvement of blood supply chain system using Taguchi method and dynamic simulation, 2nd International Materials, Industrial, and Manufacturing Engineering Conference, MIMEC2015, 4-6 February 2015, Bali Indonesia, Procedia Manufacturing 2 ( 2015 ) 1 – 5.
  12. R. Khaldi, E.A. Abedllatif, Ch. Raddouane, F. Rduan. Artificial neural network based approach for blood demand forecasting: Fez transfusion blood center case study. Proceedings of the 2nd international conference in Big Data, Cloud and Applications Article, No. 59, 2017.

13. Sh, Abrishami, A, Kalate Ahani, F, Dehghanian. Optimizing a two-tier blood supply chain to reduce hospital shortages, 2nd International Conference on Industrial and Systems Engineering.(2016)

  1. F, Firoozi Jahantegh B, Fonudi, S, Providing a model for forecasting demand in the blood platelet supply chain with the approach of artificial neural network and Arima models. Blood Research Quarterly, Volume 14, Number 4, pp. 335-345.(2017).

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