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

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

Blog Article

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 Counter/Bench Brushes rules (MDSRs) are proposed Custom Product 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.