Facility Layout Problem (FLP) aims to optimize arrangement of facilities to enhance productivity and minimize costs. Traditional methods face challenges in dealing with the complexity and non-linearity of modern manufacturing environments. This study introduced an approach combining Reinforcement Learning (RL) and simulation to optimize manufacturing line layouts. Deep Q-Network (DQN) learns to reduce unused space, improve path efficiency, and maximize space utilization by optimizing facility placement and material flow. Simulations were used to validate layouts and evaluate performance based on production output, path length, and bending frequency. This RL-based method offers a more adaptable and efficient solution for FLP than traditional techniques, addressing both physical and operational optimization.

The importance of safety and emergency preparedness of nuclear power plants (NPPs) has been increasingly emphasized since the Fukushima accident. Recently, the Nuclear Robot and Diagnosis Team at Korea Atomic Energy Research Institute (KAERI) initiated research on an unmanned emergency response robotics system. The objective of the research was to provide a practical means that countermeasure the initial accident stages of NPPs. Considering that the robotic systems that tried to mitigate the damage caused by the Fukushima accident did not work adequately, the robotic system to be developed should be tested in the testbed simulating the accident site of NPPs. In this paper, the recent domestic works on a robotic system for the safety of NPP were introduced.