Reinforcement Learning for Dynamic Quality Optimization in Inventory Flow

Abstract

Efficient inventory flow management requires balancing cost, service level, and product quality in environments characterized by uncertainty and dynamic demand. Traditional inventory optimization models often rely on static assumptions and predefined decision rules, limiting their ability to adapt to real-time changes in quality degradation, demand variability, and operational constraints. This paper proposes a reinforcement learning–based framework for dynamic quality optimization in inventory flow systems. By modeling inventory decisions as a sequential decision-making problem, the proposed approach enables an intelligent agent to learn optimal ordering, holding, and allocation policies that minimize quality loss while maintaining operational efficiency. The framework incorporates quality-aware reward functions and state representations that capture inventory levels, product age, demand uncertainty, and quality decay dynamics. Experimental results demonstrate that reinforcement learning policies outperform conventional inventory control models in reducing quality-related losses, improving service levels, and adapting to fluctuating operating conditions. The findings highlight the potential of reinforcement learning as a powerful tool for achieving dynamic, data-driven inventory quality optimization in modern supply chain systems.