چکیده مقاله
Accurate demand forecasting plays a critical role in blood supply chain management due to the perishable nature of blood products and uncertainty in demand patterns This study develops a Long Short Term Memory LSTM neural network model to forecast blood demand using real world data collected from healthcare centers in Fars province After performing data preprocessing, normalization, and time series structuring, the LSTM model was trained and evaluated using RMSE, and MAPE performance indicators Results demonstrate that the proposed deep learning approach effectively captures nonlinear temporal patterns and seasonal fluctuations Furthermore, a scenario based framework is constructed to incorporate uncertainty into forecasting outputs The findings confirm that deep learning based forecasting significantly enhances decision making reliability in blood supply planning
کلیدواژهها
نویسندگان
اکرم احمدی
استاد راهنما ومشاور انجمن علمی دانشجویی کسب وکار دانشگاه علمی کاربردی غرب استان تهران خانه کارگر واحد اسلامشهر
Mohammadamin Khosravi
Department of Industrial Engineering , Yazd University, Yazd, Iran
هادی احمدی
دانشجوی مقطع کارشناسی وعضو انجمن علمی دانشجویی دانشگاه علمی کاربردی خانه کارگر واحد اسلامشهر
Hassan Khademizare
Department of Industrial Engineering, Yazd University, Yazd, Iran
علی رمضانی
دانشجوی مقطع کارشناسی وعضو انجمن علمی دانشجویی دانشگاه علمی کاربردی خانه کارگر واحد اسلامشهر
Hassan Hosseininasab
Department of Industrial Engineering, Yazd University, Yazd, Iran
Davood Shishebori
Department of Industrial Engineering, Yazd University, Yazd, Iran
شیوه ارجاع
Khosravi, Mohammadamin and Khademizare, Hassan and Hosseininasab, Hassan and Shishebori, Davood,1405,Deep Learning-Based Demand Forecasting for Blood Supply Systems: A Case Study Using LSTM Networks ,16th International Conference on Interdisciplinary Studies in Management & Engineering,Tehran
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