A Stacking Ensemble Learning Model for Waste Prediction in Offset Printing

The production of quality printing products requires a highly complex and uncertain process, which leads to the unavoidable generation of printing defects. This common phenomenon has severe impacts on many levels for Offset Printing manufacturers, ranging from a direct economic loss to the environmental impact of wasted resources. Therefore, the accurate estimation of the amount of paper waste expected during each press run, will minimize the paper consumption while promoting environmentally sustainable principles. This work proposed a Machine Leaning (ML) framework for proactively predicting paper waste for each printing order. Based on a historical dataset extracted by an Offset Printing manufacturer, a two-level stacking ensemble learning model combining Support Vector Machine (SVM), Kernel Ridge Regression (KRR) and Extreme Gradient Boosting (XGBoost) as base learners, and Elastic Net as a meta-learner, was trained and evaluated using cross-validation. The evaluation outcomes demonstrated the ability of the proposed framework to accurately estimate the amount of waste expected to be generated for each printing run, by significantly outperforming the rest of the benchmarking models.