Newsvendor Problem

The Newsvendor problem addresses the challenge of optimizing inventory levels under uncertain demand, balancing the costs of overstocking and understocking. Current research focuses on data-driven approaches, employing algorithms like Sample Average Approximation and gradient descent methods (often within a reinforcement learning framework), to learn optimal ordering policies from historical data, sometimes incorporating contextual information and privacy considerations. These advancements improve decision-making in various applications, from supply chain management and retail to energy trading, by providing more accurate and robust inventory strategies.