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C. Kardos, C. La Flamme, V. Gallina, W. Sihn:
"Dynamic scheduling in a job-shop production system with reinforcement learning";
Vortrag: 8th CIRP CATS 2020, Athen; 29.09.2020 - 01.10.2020.

Fluctuating customer demands, expected short delivery times and the need for quick order confirmation creates a fast-paced scheduling environment for modern production systems. In this turbulent scene, using the data provided by intelligent elements of cyber-physical production systems opens up new possibilities for dynamic scheduling. The paper introduces a reinforcement learning approach, in particular Q-Learning, to reduce the average lead-time of production orders in a job-shop production system. The intelligent product agents are able to choose a machine for every production step based on real-time information. A performance comparison against standard dispatching rules is given, which shows that in the presented dynamic scheduling use-cases the application of RL reduces the average lead-time.

dynamic scheduling; reinforcement learning; simulation; smart factory