Intelligent Decision Optimization for Industrial Supply Chain Based on Reinforcement Learning and Deep Q Network

The management of industrial supply chains involves a series of complex decision-making processes, where the uncertainty of demand, supply, and inventory poses significant challenges. Understanding the essence of these challenges, this paper seeks to introduce an avant-garde approach to address them. At the heart of our proposal is the application of an intelligent decision optimization framework constructed on the foundational principles of reinforcement learning (RL). This approach, distinct from traditional methods, leverages the power of machine learning to guide decision-making, offering notable advantages in adaptability and precision.