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The Prediction of Demand for Blood Bank Products for Each Blood Group Using Neural Networks (Case Study:Blood Transfusion Network of Zanjan Province) | ||
| مدیریت زنجیره تأمین | ||
| Volume 23, Issue 70, July 2021, Pages 5-13 PDF (979.87 K) | ||
| Document Type: Scientific Paper | ||
| Authors | ||
| Ali Asghar Moslemi Birami; Mahdi Yousefi Nejad Attari* | ||
| Assistant Professor, Department of industrial engineering, Bonab branch, Islamic Azad University, Bonab, Iran | ||
| Receive Date: 10 July 2020, Revise Date: 14 October 2020, Accept Date: 12 June 2021 | ||
| Abstract | ||
| The supply and demand management and planning of the blood supply chain have complexities considering the uncertainty in demand and supply. The supply uncertainty often results from the random demand as well as unpredictable and irregular behavior of the people involved in blood donation as the only source of blood supply. In addition, the perishability and short lifespan of blood and blood products are the problems that should be considered. Adequate and healthy blood supply plays an essential role in health systems. Therefore, the prediction of demand to prevent endangering the patients’ health due to the inventory shortages on one hand, and increasing inventory which increases wastages and government expenses on the other hand, have attracted the attention of researchers. Accordingly, the aim of the present study is to predict the demand for blood bank products using neural networks. Neural networks have the ability to properly predict the future based on certain trends of the past by adjusting some parameters. To collect the necessary data for this research, the statistics, databases and computer networks of the blood transfusion network of Zanjan province have been selected. For data analysis, the existing facilities and functions of the MATLAB software have been used. Based on previous demands of blood products, the research findings demonstrate that the best neural network model to predict demand has two delays and five neurons in the hidden layer. The results also show that the error values in all three blood products are close to each other but have different values. | ||
| Keywords | ||
| Blood products; Neural network; Demand in the supply chain | ||
| References | ||
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