چکیده مقاله
Demand forecasting is the basis of many planning activities in the supply chain Pharmaceuticalindustry, which deal with human health, require the implementation of an effective demand forecastingmodel Due to demand volatility, businesses find it challenging to forecast customer demand accuratelyusing traditional models In this study, a comparative analysis is performed based on machine learningtechniques such as Support vector regression SVR , Random forest RF , Light gradient boostingmachine LGBM , and Extreme gradient boosting XGB models for demand forecasting inpharmaceutical products The effectiveness of machine learning models is greatly affected by choosingthe appropriate hyperparameter configuration Therefore, Bayesian optimization BO algorithm withthe Gaussian process GP is combined with Time series cross validation to determine the optimalcombination of model hyperparameters The results show that the Extreme gradient boosting modeloutperforms the other forecasting models in terms of Root Mean Squared Error RMSE , MeanAbsolute Error MAE , and 𝑅2 score This method can effectively forecast future demand to improvepharmaceutical supply chain management
کلیدواژهها
نویسندگان
Reza Shirazi Zadeh
M Sc, Department of Industrial Engineering, Yazd University;
Hasan Hosseini Nasab
Professor, Department of Industrial Engineering, Yazd University;
Mohammad Bagher Fakhrzad
Professor, Department of Industrial Engineering, Yazd University;
شیوه ارجاع
Shirazi Zadeh, Reza and Hosseini Nasab, Hasan and Fakhrzad, Mohammad Bagher,1402,Demand Forecasting Model for Pharmaceutical Products Using MachineLearning Techniques with Bayesian Hyperparameter Optimization,The 9th International Conference on Industrial and Systems Engineering,Mashhad
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