Reinforcement Learning in a Digital Twin for Galvano Hoist Scheduling

Reinforcement Learning (RL) has evolved as a dominant AI method to move towards optimal control of complex systems. In the domain of manufacturing, production control has emerged as one of the major application areas, where material flow is governed by an RL agent that is fed with the real-time information flow of the system through a digital twin. A digital twin framework facilitates efficient and effective RL training. The coordination task performed in discrete, flexible manufacturing offers multiple decisions that cannot be found in all cases. Hoist scheduling for galvanic equipment introduces additional constraints as parts cannot be left inside a galvanic bath arbitrarily long. Even short deviations critically affect product quality, which is even more complicated in high mix high volume environments. The proposed RL agent learns superior control compared to the state-of-the-art and simple heuristic rules can be derived for everyday application in the absence of digital twins.