A Reinforcement Learning Approach to Dynamic Scheduling in a Product-Mix Flexibility Environment

Machine bottlenecks, resulting from shifting and unbalanced machine loads caused by resource capacity limitations, impair product-mix flexibility production systems. Thus, the knowledge base (KB) of a dynamic scheduling control system should be dynamic and include a knowledge revision mechanism for...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.106542-106553
Main Authors: Shiue, Yeou-Ren, Lee, Ken-Chuan, Su, Chao-Ton
Format: Article
Language:English
Subjects:
Dynamic scheduling
Flexibility
Flexible manufacturing systems
Frequency modulation
Job shop scheduling
Job shops
Knowledge bases (artificial intelligence)
Machine learning
Manufacturing execution system
Production systems
Q-learning
reinforcement learning
Scheduling
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Summary:Machine bottlenecks, resulting from shifting and unbalanced machine loads caused by resource capacity limitations, impair product-mix flexibility production systems. Thus, the knowledge base (KB) of a dynamic scheduling control system should be dynamic and include a knowledge revision mechanism for monitoring crucial changes that occur in the production system. In this paper, reinforcement learning (RL)-based dynamic scheduling and a selection mechanism for multiple dynamic scheduling rules (MDSRs) are proposed to support the operating characteristics of a flexible manufacturing system (FMS) and semiconductor wafer fabrication (FAB). The proposed RL-based dynamic scheduling MDSR selection mechanism consisted of initial MDSR KB generation and revision phases. According to various performance criteria, the presented approach yields a system performance that is superior to those of the fixed-decision scheduling approach, the machine learning classification approach, and the classical MDSR selection mechanism.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3000781