Inventory Control

Inventory control, aiming to optimize stock levels to balance supply and demand, is a crucial area of operations research with significant practical implications for businesses. Current research emphasizes the use of machine learning, particularly reinforcement learning algorithms like proximal policy optimization and deep reinforcement learning with architectures tailored to inventory network structures, to address challenges such as stochastic demand, lead times, and capacity constraints. These advanced methods are being applied to various inventory systems, including multi-product, multi-echelon, and those with complex arrival dynamics, often showing improvements over traditional approaches. The resulting improvements in efficiency and cost reduction have significant impact on supply chain management and logistics.