Open AccessAn intelligent demand forecasting model using a hybrid of metaheuristic optimization and deep learning algorithm for predicting concrete block production
Author(s) -
Huthaifa Al-Khazraji,
Ahmed R. Nasser,
Sohaib Khlil
Publication year - 2022
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.341
H-Index - 7
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v11.i2.pp649-657
Subject(s) - computer science , block (permutation group theory) , mean absolute percentage error , demand forecasting , mean squared error , metaheuristic , production (economics) , algorithm , artificial intelligence , deep learning , data mining , machine learning , artificial neural network , operations research , statistics , engineering , mathematics , geometry , economics , macroeconomics
Demand forecasting aims to optimize the production planning of industrial companies by ensuring that the production planning meets the future demand. Demand forecasting utilizes historical data as an input to predict future trends of the demand. In this paper, a new approach for developing an intelligent demand forecasting model using a hybrid of metaheuristic optimization and deep learning algorithm is presented. Firey algorithmbased gated recurrent units (FA-GRU) is used to tackle the production forecasting problem. The proposed model has been evaluated and compared with the standard gated recurrent unit (GRU) and standard long short-term memory model (LSTM) using historical data of 36 months of concrete block manufacturing at dler company in Iraq. The prediction accuracy of the three models is evaluated using the root mean square error (RMSE), the mean absolute percentage error (MAPE) and the statistical coefficient of determination (R2 ) indicators. The outcomes of the study show that the proposed FA-GRU gives better forecasting results compared to the standard GRU and standard LSTM.