Optimization of simulated production process performance using machine learning

This paper investigates integration of the supervised machine learning algorithms (model trees, neural networks) into a production plan realized in a physics-based realistic simulator. Proposed novelty is in that the learning capability is integrated into the control process which allows for online learning and on the fly control code modification. Running the process in a simulated environment enables hazardless experimenting with the system´s setup and integral acquisition of data. Yielded optimization times obtained through learning outperform times of a production process solely based on averaging.