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

Authors Saad Shafiq
Christoph Mayr-Dorn
Atif Mashkoor
Alexander Egyed
Editors
Title Towards optimal assembly line order sequencing with reinforcement learning: A case study
Booktitle Proceedings of the 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)
Type in proceedings
Publisher IEEE
ISBN 978-1-7281-8956-7
Month September
Year 2020
Pages 982-989
SCCH ID# 20042
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.