Optimization of Production Time using the Reinforcement Learning Method as a Particular Case Of Improving the Efficiency of Automated Production Lines

This article examines the application of reinforcement learning methods to optimize the production time of an automated production line modeled as a graph. In this graph representation, nodes correspond to pieces of equipment capable of performing one or multiple manufacturing operations. Such an approach not only creates a precise model of the agent's operating environment but also enables the implementation of computer simulations. These simulations serve as a critical foundation for assessing the potential effectiveness of reinforcement learning methods in optimizing real-world production lines. By adopting this approach, the study explores opportunities to improve efficiency, optimize resource utilization, and enhance the reliability of production systems. A key focus of the article is the detailed investigation of the stages involved in the computer simulation of production time optimization. The simulation process consisted of several integral stages: preparation of input data, design and implementation of the simulation environment, construction of a Deep Q-Network (DQN) agent, execution of the learning algorithm, and evaluation of optimization efficiency. These stages are thoroughly analyzed, demonstrating the systematic approach required to integrate reinforcement learning into manufacturing processes. The research also emphasizes the advantages of modeling the production line as a graph, highlighting how it enables the simulation of dynamic and complex production environments. This graph-based framework provides the agent with a structured understanding of equipment connectivity and operational constraints, allowing it to develop effective decision-making policies. Through iterative interactions with the environment, the DQN agent identifies optimal production sequences, minimizes downtime, and enhances throughput. Furthermore, the article explores the practical implications of integrating reinforcement learning into industrial applications. Computer simulations not only validate the feasibility of these methods but also provide insights into their scalability and adaptability to diverse manufacturing scenarios. The findings underscore the potential of reinforcement learning to transform automated production lines into more intelligent, adaptive, and resilient systems. By addressing both theoretical and practical aspects, the study lays the groundwork for future research in applying artificial intelligence to industrial automation. This comprehensive approach enables stakeholders to better understand the value of advanced learning algorithms in boosting operational efficiency and ensuring sustainable growth in automated production.