Lim, J.-B.; Jeong, J. Factory Simulation of Optimization Techniques Based on Deep Reinforcement Learning for Storage Devices. Appl. Sci.2023, 13, 9690.
Lim, J.-B.; Jeong, J. Factory Simulation of Optimization Techniques Based on Deep Reinforcement Learning for Storage Devices. Appl. Sci. 2023, 13, 9690.
Lim, J.-B.; Jeong, J. Factory Simulation of Optimization Techniques Based on Deep Reinforcement Learning for Storage Devices. Appl. Sci.2023, 13, 9690.
Lim, J.-B.; Jeong, J. Factory Simulation of Optimization Techniques Based on Deep Reinforcement Learning for Storage Devices. Appl. Sci. 2023, 13, 9690.
Abstract
In this study, reinforcement learning (RL) was used in factory simulation to optimize storage devices for use in Industry 4.0 and digital twins. First, we defined an RL environment, modeled it, and validated its ability to simulate a real physical system. Subsequently, we introduced a method to calculate reward signals and apply them to the environment to ensure the alignment of the behavior of the RL agent with the task objective. The stocker simulation model was used to validate the effectiveness of RL. The model is a storage device that simulates logistics in a manufacturing production area. The results revealed that RL is a useful tool for automating and optimizing complex logistics systems and increase the applicability of RL in logistics. We proposed a novel method for creating an agent through learning using the proximal policy optimization algorithm, and the agent was optimized by configuring various learning options. The application of reinforcement learning resulted in an effectiveness of 30% to 100%, and methods can be expanded to other fields.
Keywords
conceptualization; methodology; job allocation; reinforcement learning; stocker; digital twin; simulation; Industry 4.0
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
