Inventory Policy

Inventory policy research focuses on optimizing the management of stock levels to balance costs and customer demand, aiming to maximize profitability and minimize waste. Current research emphasizes the application of reinforcement learning (RL), particularly deep RL models and contextual bandit algorithms, often coupled with generative models for demand forecasting and incorporating capacity constraints. These advanced techniques are being rigorously evaluated through theoretical frameworks like VC theory and tested against traditional methods using both simulated and real-world data, leading to improved inventory control strategies across various industries.