Assessing maintenance planning and scheduling using Deep Reinforcement Learning

Maintenance scheduling is critical in many industries, and recent advances in Deep Reinforcement Learning (DRL) have shown that it can optimise scheduling decisions in complex and dynamic contexts. Traditional methods of maintenance scheduling frequently confront obstacles, making DRL an appealing alternative. This study presents a novel approach for autonomously determining optimal maintenance scheduling decisions in production systems that blends a simulation-based model with a DRL agent. The learning agent makes intelligent judgements based on the chance of failure and machine availability through trial and error. The setup of the DRL setting, particularly the reward function, has a considerable impact on the approach's performance. The proposed hybrid simulation-based and DRL methodology outperforms existing heuristic methods in rigorous evaluation, demonstrating its promise for efficient and effective maintenance planning and scheduling. This work sets the way for better system reliability and productivity in companies that rely on complex systems.