Publications in Scientific Journals:
C. Kardos, C. La Flamme, V. Gallina, W. Sihn:
"Dynamic scheduling in a job-shop production system with reinforcement learning";
Procedia CIRP,
97
(2020),
104
- 109.
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, us- ing the data provided by intelligent elements of cyber-physical production systems opens up new pos- sibilities 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
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1016/f.procir.2020.05.210
Created from the Publication Database of the Vienna University of Technology.