Authors
Sharareh Taghipour, Hamed A Namoura, Mani Sharifi, Mageed Ghaleb
Publication date
2024/5/2
Journal
INFOR: Information Systems and Operational Research
Volume
62
Issue
2
Pages
186-210
Publisher
Taylor & Francis
Description
In the real-time scheduling (RTS) research field, it has been shown that employing multiple dispatching rules (MDRs) for the components in a flexible manufacturing system will improve production performance much more than a single dispatching rule (SDR). To fulfill the goal of Industry 4.0 in production control and improve production performance, this study deploys a deep reinforcement learning-based multi-agent (DRLBMA) approach for real-time scheduling. The proposed approach uses the MDRs strategy by integrating two main methodologies: an off-line learning module and a Deep Q-learning-based multi-agent module. The proposed method employs a two-level self-organizing map (SOM) to determine the system’s states. The proposed methodology determines the best MDRs decision. The approach is applied to a case study of a smart manufacturing system. The results of the proposed method are …
Total citations
Scholar articles
S Taghipour, HA Namoura, M Sharifi, M Ghaleb - INFOR: Information Systems and Operational Research, 2024