Bayesian-optimized ensemble deep learning models for demand forecasting in the volatile situations: A case study of grocery demand during Covid-19 outbreaks

• AL Theeb, Nader ^[1] ; Smadi, Hazem ^[1] ; Al-qaydeh, Naser ^[1]
  1. [1] Jordan University of Science and Technology

     Jordan University of Science and Technology

     Jordania

     
• Localización: Journal of Industrial Engineering and Management, ISSN-e 2013-0953, Vol. 18, Nº. 1, 2025 (Ejemplar dedicado a: In process), págs. 193-213
• Idioma: inglés
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• Resumen

  • Purpose: Lockdown and movement restrictions that imposed by governments have significantly changed customers behavior, making the planning and decision-making processes more challenging. Providing accurate estimation of the demand, enable managers to take more successful decisions and allow optimizing inventory and resources; this is the main purpose of this study.Design/methodology/approach: An ensemble model that based on combining Bayesian-optimized Long Short-Term Memory (BO-LSTM) and Gated Recurrent Unit (BO-GRU). Experiments were carried out on actual dataset obtained from company specialized in food industries during the volatile situation of Covid-19.Findings: The proposed model significantly outperformed all hand-tuned ones and reduced the mean Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) by 2.80% and 4.74% compared to BO-LSTM and 3.14% and 3.60% compared to BO-GRU respectively. Furthermore, using BO algorithm for hyperparameters tuning improved the forecasting accuracy.Originality/value: The suggested model was statistically compared to its members in addition to other machine learning models using the t-test. Findings demonstrated the superiority of the proposed method over all benchmark models.