Towards optimal assembly line order sequencing with reinforcement learning: A case study

S. Shafiq, C. Mayr-Dorn, A. Mashkoor, A. Egyed. Towards optimal assembly line order sequencing with reinforcement learning: A case study. pages 982-989, DOI https://doi.org/10.1109/ETFA46521.2020.9211982, 9, 2020.

Autoren
  • Saad Shafiq
  • Christoph Mayr-Dorn
  • Atif Mashkoor
  • Alexander Egyed
BuchProceedings of the 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)
TypIn Konferenzband
VerlagIEEE
DOIhttps://doi.org/10.1109/ETFA46521.2020.9211982
ISBN978-1-7281-8956-7
Monat9
Jahr2020
Seiten982-989
Abstract

The new era of Industry 4.0 is leading towards selflearning and adaptable production systems requiring efficient and intelligent decision making. Achieving high production rate in a short span of time, continuous improvement, and better utilization of resources is crucial for such systems. This paper discusses an approach to achieve production optimization by finding optimal sequences of orders, which yield high throughput using reinforcement learning. The feasibility of our approach is evaluated by simulating a plant modelled on a higher level of abstraction taken from a real assembly line. The applicability of the proposed approach is demonstrated in the form of code utilizing the simulation model. The obtained results show promising accuracy of sequences against corresponding throughput during the simulation process.