Industrial Optimization in the Age of AI: Hybrid Models, Smart Decisions, and Future Directions

This study proposes a next-generation AI-driven framework for modeling and optimizing intelligent industrial systems, with a focus on enhancing operational performance within modern production environments. The main objective is to increase throughput, reduce energy consumption, and minimize defect rates in complex manufacturing lines by leveraging artificial intelligence, particularly reinforcement learning techniques. A discrete event simulation model is developed to represent a multi-station production line, integrating real-time sensor data such as machine temperature, vibration, energy usage, and cycle time. An AI agent, based on a Deep Q-Network (DQN), is deployed to interact with the virtual production environment, continuously learning and updating optimal machine parameters to achieve the best performance outcomes. Hypothetical results indicate notable improvements, including an ۱۸% increase in throughput, a ۱۴% decrease in energy consumption, and a ۱۰% reduction in defect rates. These outcomes demonstrate the potential of AI to transform traditional manufacturing systems into adaptive, self-optimizing structures capable of responding efficiently to dynamic industrial conditions. Furthermore, this research outlines future directions, including extending the proposed framework to diverse production sectors and integrating AI with complementary technologies such as the Internet of Things (IoT), edge computing, and blockchain. The overall contribution of this study lies in its practical modeling and optimization strategy, which supports the broader transition toward intelligent, sustainable, and highly responsive manufacturing systems.

Keywords:

Production Optimization , Reinforcement Learning , Discrete Simulation , Smart Manufacturing