Ensemble Learning-Based Approach for Forecasting Inventory Data in Prefabricated Component Warehousing

Accurately predicting the storage area of prefabricated components facilitates transshipment scheduling and prevents the waste of storage space. Due to the influence of numerous factors, precise prediction remains challenging. Currently, limited research has addressed the prediction of storage areas for prefabricated components, and effective solutions are lacking. To address this issue, a GRU model with an attention mechanism based on ensemble learning was proposed. The model employed the Bo-Bi-ATT-GRU approach to address the time series prediction of storage areas. A Bayesian optimization algorithm was utilized to enhance parameter tuning and training efficiency, while an ensemble learning framework improved model stability. In this study, a port container dataset was used for experimentation, with root mean square error (RMSE) and mean absolute percentage error (MAPE) as evaluation metrics. Compared with the GM model, the R2 of the proposed model improved by 3.38%. Experimental results demonstrated that the ensemble learning-based prediction model offered superior performance in forecasting the storage area of prefabricated components.