Talks and Poster Presentations (without Proceedings-Entry):
C. Kardos, C. La Flamme, V. Gallina, W. Sihn:
"Dynamic scheduling in a job-shop production system with reinforcement learning";
Talk: 8th CIRP CATS 2020,
Athen;
2020-09-29
- 2020-10-01.
English abstract:
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.
Keywords:
dynamic scheduling; reinforcement learning; simulation; smart factory
Created from the Publication Database of the Vienna University of Technology.