Evaluating the Performance of Serial Production Lines Using Tree-Based Machine Learning Methods

Authors

  • Feras Mabrouke Department of Industrial and Mechanical Engineering, Faculty of Engineering, An-Najah National University, Nablus, Palestine
  • Basel Al-Sahili Department of Industrial and Mechanical Engineering, Faculty of Engineering, An-Najah National University, Nablus, Palestine
  • Ramiz Assaf Department of Industrial and Mechanical Engineering, Faculty of Engineering, An-Najah National University, Nablus, Palestine
  • Abdalmuttaleb Al-Sartawi Accounting, Finance and Banking Department, Ahlia University, Bahrain
  • Salem Aljazzar Industrial Engineering Department, Jeddah College of Engineering, University of Business and Technology, Jeddah 21448, Kingdom of Saudi Arabia
  • Siraj Zahran Industrial Engineering Department, Jeddah College of Engineering, University of Business and Technology, Jeddah 21448, Kingdom of Saudi Arabia
  • Mohammad Kanan Industrial Engineering Department, Jeddah College of Engineering, University of Business and Technology, Jeddah 21448, Kingdom of Saudi Arabia

DOI:

https://doi.org/10.31181/rme444

Keywords:

Serial Production Line, Tree-based Models, Machine Learning, Multioutput Regression, Simulation, Synchronous, Buffered Systems, Operational Efficiency, Performance Evaluation

Abstract

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.

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Published

2025-06-24

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Vol. 6 No. 1 (2025): Reports in Mechanical Engineering

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Articles

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Evaluating the Performance of Serial Production Lines Using Tree-Based Machine Learning Methods. (2025). Reports in Mechanical Engineering, 6(1), 27-45. https://doi.org/10.31181/rme444