Optimizing Autonomous Factory Operations Using Reinforcement Learning and Deep Neural Networks

Abstract

This paper presents a novel approach to enhancing the efficiency of autonomous factory operations through the integration of reinforcement learning (RL) and deep neural networks (DNNs). The study addresses the increasing demand for advanced automation solutions in manufacturing environments, where traditional methods often fall short in dynamically complex and uncertain settings. We propose a hybrid model that leverages RL to enable adaptive decision-making in real-time, while DNNs provide robust feature extraction and predictive analytics. Our approach focuses on optimizing several operational aspects, including resource allocation, process scheduling, and fault detection. The method was evaluated in a simulated smart factory environment, replicating a diverse range of production scenarios. Results demonstrate significant improvements in operational efficiency, with a reduction in energy consumption by 15% and an increase in production throughput by 20%, compared to standard automation techniques. Additionally, the system showcases improved adaptability to unforeseen disturbances, maintaining optimal performance under varying conditions. These findings highlight the potential of RL and DNNs to revolutionize industrial operations, paving the way for the development of fully autonomous factories that can autonomously learn and adapt to their environment without human intervention. The paper concludes with a discussion on potential challenges, future research directions, and implications for industry adoption.