An Artificial Intelligence Approach for Online Optimization of Flexible Manufacturing Systems
This paper addresses the problem of efficiently operating a flexible manufacturing machine in an electricity micro-grid featuring a high volatility of electricity prices. The problem of finding the optimal control policy is formulated as a sequential decision making problem under uncertainty where, at every time step the uncertainty comes from the lack of knowledge about fu-ture electricity consumption and future weather dependent energy prices. We propose to address this problem using deep reinforcement learning. To this purpose, we designed a deep learning architecture to forecast the load profile of future manufacturing schedule from past production time series. Combined with the forecast of future energy prices, the reinforcement-learning algorithm is trained to perform an online optimization of the production ma-chine in order to reduce the long-term energy costs. The concept is empirical-ly validated on a flexible production machine, where the machine speed can be optimized during the production.
Jörg Franke, Michael Scholz and Annika Höft
J. Bakakeu et al., "An Artificial Intelligence Approach for Online Optimization of Flexible Manufacturing Systems", Applied Mechanics and Materials, Vol. 882, pp. 96-108, 2018