P‐235: Late‐News Poster: Optimization of Display Production Scheduling with Reinforcement Learning

Abstract

The display module process is characterized by having to deal with a wide range of customers and product groups with a short turnaround time (TAT). In this study, a new concept of simulation tool reflecting the characteristic of the module process was developed. This tool reflects the TAT of the equipment using the cumulative distribution function of the process time, and the frequency and time of down of the equipment are modeled using statistical techniques. This tool has confirmed a high accuracy of 92% in comparison with actual production. The tool provides rule‐based operation guidance to manufacturing engineers. However, rule‐based operation is not easy to adequately respond to all manufacturing situations in the actual field. Thus, reinforcement learning (RL) was applied to the module scheduler system for optimal operation. We applied RL algorithm such as deep Q‐Networks (DQN), double DQN, dueling network. Through a test comparing reinforcement learning‐based operations and rule‐based operations under the same conditions, it was found that reinforcement learning‐based operations are better in terms of productivity and efficiency than rule‐based operations. This result shows that the paradigm of module scheduling has shifted from traditional rule‐based operations to reinforcement learning‐based AI operations.