Abstract
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.
Highlights
In the 21st century, the advanced high-tech industry will inevitably face global market competition
Ishii and Talavage [31] presented a heuristic algorithm that applies the multiple dynamic scheduling rules (MDSRs) strategy to bottlenecks and unbalanced machines by using predictions based on multi-pass simulations, which confirmed that the MDSR strategy can improve the performance of a flexible manufacturing system (FMS) by up to 15.9% compared with the best result obtained using the single dynamic scheduling rule (SDSR) strategy
Based on this design philosophy, we present a reinforcement learning (RL)-based MDSR selection mechanism by assigning various scheduling rules for all machines in a dynamically complex production system, such as a FMS or FAB, during a given scheduling period
Summary
In the 21st century, the advanced high-tech industry will inevitably face global market competition. Ishii and Talavage [31] presented a heuristic algorithm that applies the MDSR strategy to bottlenecks and unbalanced machines by using predictions based on multi-pass simulations, which confirmed that the MDSR strategy can improve the performance of a FMS by up to 15.9% compared with the best result obtained using the SDSR strategy Their method did not work well with dynamic scheduling using machine learning classification methods. We developed a dynamic scheduling system that uses a RL-based MDSR selection mechanism to support the complex product-mix flexibility environment. During FAB operation, the Q-learning-based agent is assumed to perceive information on the FAB environment and is autonomously in charge of decision-making for the order release control and dispatch rule selection of wafer lots using the intrabay and stocker.
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