Dynamic Scheduling Method for Job-Shop Manufacturing Systems by Deep Reinforcement Learning with Proximal Policy Optimization
1
College of Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UK
2
School of Science, Technology and Health, York St John University, York YO31 7EX, UK
*
Authors to whom correspondence should be addressed.
Academic Editors: Mosè Gallo, Massimo Bertolini, Mattia Neroni and Patrick Dallasega
Sustainability 2022, 14(9), 5177; https://doi.org/10.3390/su14095177
Received: 30 March 2022
/
Revised: 21 April 2022
/
Accepted: 22 April 2022
/
Published: 25 April 2022
(This article belongs to the Special Issue Industry 4.0 Impacts on Lean Production Systems: Sustainable Practices)
With the rapid development of Industrial 4.0, the modern manufacturing system has been experiencing profoundly digital transformation. The development of new technologies helps to improve the efficiency of production and the quality of products. However, for the increasingly complex production systems, operational decision making encounters more challenges in terms of having sustainable manufacturing to satisfy customers and markets’ rapidly changing demands. Nowadays, rule-based heuristic approaches are widely used for scheduling management in production systems, which, however, significantly depends on the expert domain knowledge. In this way, the efficiency of decision making could not be guaranteed nor meet the dynamic scheduling requirement in the job-shop manufacturing environment. In this study, we propose using deep reinforcement learning (DRL) methods to tackle the dynamic scheduling problem in the job-shop manufacturing system with unexpected machine failure. The proximal policy optimization (PPO) algorithm was used in the DRL framework to accelerate the learning process and improve performance. The proposed method was testified within a real-world dynamic production environment, and it performs better compared with the state-of-the-art methods.
View Full-Text
Keywords:
Industry 4.0; manufacturing sustainability; dynamic scheduling; deep reinforcement learning; artificial neural networks
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
MDPI and ACS Style
Zhang, M.; Lu, Y.; Hu, Y.; Amaitik, N.; Xu, Y. Dynamic Scheduling Method for Job-Shop Manufacturing Systems by Deep Reinforcement Learning with Proximal Policy Optimization. Sustainability 2022, 14, 5177. https://doi.org/10.3390/su14095177
AMA Style
Zhang M, Lu Y, Hu Y, Amaitik N, Xu Y. Dynamic Scheduling Method for Job-Shop Manufacturing Systems by Deep Reinforcement Learning with Proximal Policy Optimization. Sustainability. 2022; 14(9):5177. https://doi.org/10.3390/su14095177
Chicago/Turabian StyleZhang, Ming, Yang Lu, Youxi Hu, Nasser Amaitik, and Yuchun Xu. 2022. "Dynamic Scheduling Method for Job-Shop Manufacturing Systems by Deep Reinforcement Learning with Proximal Policy Optimization" Sustainability 14, no. 9: 5177. https://doi.org/10.3390/su14095177
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
