Evaluating the Performance of Serial Production Lines Using Tree-Based Machine Learning Methods
- Publication Type
- Original research
- Authors
This study investigates the efficiency of serial production lines using advanced tree-based machine learning algorithms, including Random Forest (RF), XGBoost, LightGBM, and CatBoost. By analyzing various performance indicators such as throughput, machine reliability, and processing time, we construct predictive models to evaluate production line behavior. The comparative results demonstrate that LightGBM outperforms the other models in prediction accuracy and computational speed. These findings suggest that machine learning techniques, particularly ensemble tree models, can significantly enhance decision-making and operational performance in manufacturing systems.
Journal
- Title
- Reports in Mechanical Engineering
- Publisher
- PKP
- Publisher Country
- United States of America
- Indexing
- Scopus
- Impact Factor
- None
- Publication Type
- Both (Printed and Online)
- Volume
- 6
- Year
- 2025
- Pages
- --