Estimation of processing time using machine learning and real factory data for optimization of parallel machine scheduling problem

• Published in: Operations Research Perspectives Vol. 8; pp. 1 - 9
• Main Authors: Yamashiro, Hirochika, Nonaka, Hirofumi
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier 2021; Elsevier Ltd
• Subjects: Artificial neural networks; Gaussian process regression; Gradient boosted decision trees; Identical parallel machine scheduling; Machine learning; Operations research; Gaussian process regression; Gradient boosted decision trees; Identical parallel machine scheduling; Operations research; Machine learning; Artificial neural networks
• ISSN: 2214-7160; 2214-7160
• DOI: 10.1016/j.orp.2021.100196

Traditionally, mathematical optimization methods have been applied in manufacturing industries where production scheduling is one of the most important problems and is being actively researched. Extant studies assume that processing times are known or follow a simple distribution. However, the actual processing time in a factory is often unknown and likely follows a complex distribution. Therefore, in this study, we consider estimating the processing time using a machine-learning model. Although there are studies that use machine learning for scheduling optimization itself, it should be noted that the purpose of this study is to estimate an unknown processing time. Using machine-learning models, one can estimate processing times that follow an unknown and complex distribution while further improving the schedule using the computed importance variable. Based on the above, we propose a system for estimating the processing time using machine-learning models when the processing time follows a complex distribution in actual factory data. The advantages of the proposed system are its versatility and applicability to a real-world factory where the processing times are often unknown. The proposed method was evaluated using process information with the processing time for each manufacturing sample provided by research partner companies. The Light gradient-boosted machine (LightGBM) algorithm and Ridge performed the best with MAPE and RMSE. The optimization of parallel machine scheduling using estimated processing time by our method resulted in an average reduction of approximately 30% for the makespan. On the other hands, the results of probabilistic sampling methods which are Kernel Density Estimation, Gamma distribution, and Normal Distribution have shown poorer performance than ML approaches. In addition, machine-learning models can be used to deduce variables that affect the estimation of processing times, and in this study, we demonstrated an example of feature importance computed from experimental data. In addition, machine-learning models can be used to deduce variables that affect the estimation of processing times, and in this study, we demonstrated an example of feature importance computed from experimental data.