Machine learning for online scheduling in manufacturing: A systematic literature review

Global trends such as mass customization and dynamical disturbances in manufacturing systems demand robustness for steady and efficient production control. These dynamical disturbances significantly affect scheduling as a central task of production control. The task allocation procedure is designed reactively in online scheduling, making it suitable for application in dynamic manufacturing environments. Recently, the usability and accuracy of machine learning (ML) methods improved significantly, leveraging online scheduling performance to deal with the above-described upcoming challenges in manufacturing. Since no up-to-date reviews exist that focus on machine learning in online scheduling, this paper presents a systematic and specific literature review. The review was conducted according to the PRISMA meta-analysis framework to ensure repeatability. The findings reveal the diverse applicability and performance of supervised, unsupervised, and reinforcement learning techniques in various manufacturing scenarios, aiding researchers and practitioners in the selection and deployment of ML methods. Moreover, the review identifies future research trends in ML methods for online scheduling applications, aligning with the overarching goal of accelerating manufacturing processes.