Setup Time Prediction Using Machine Learning Algorithms: A Real-World Case Study

In this paper, we explore the use of machine learning regression algorithms for setup time prediction and we apply them to a real-world scheduling application arising in the color printing industry. As the complexities associated with setup time predictions have received limited attention from the literature, we aim at exploiting a data-driven approach based on machine learning algorithms to enhance the quality of setup time evaluations and narrow the gap between scheduling theory and practice. Using a real-world industrial dataset, we train three different machine learning models: linear regression, random forests, and gradient boosting machines. The experimental results demonstrate that the gradient boosting machine approach obtains the best performance overall, immediately followed by random forests. The accuracy of the obtained predictions shows the effectiveness of the proposed approach in setup time evaluation. The obtained results are particularly significant and valuable due to the versatile nature of the proposed machine learning approaches. These methods can be applied to various scheduling scenarios, making them suitable for integration into scheduling algorithms to potentially improve their accuracy.