Problem definition: Is deep reinforcement learning (DRL) effective at solving inventory
problems? Academic/practical relevance: Given that DRL has successfully been applied in …

Deep reinforcement learning (DRL) has shown great potential for sequential decision-
making, including early developments in inventory control. Yet, the abundance of choices …

Abstract The One-Warehouse Multi-Retailer (OWMR) system is the prototypical distribution
and inventory system. Many OWMR variants exist, eg demand in excess of supply may be …

Deep reinforcement learning (DRL) has proven to be an effective, general-purpose
technology to develop 'good'replenishment policies in inventory management. We show …

This article delves into the challenging supply chain management domain, explicitly
addressing the intricate issue of perishable inventory allocation within a two-echelon supply …

Problem definition: The beer game is widely used in supply chain management classes to
demonstrate the bullwhip effect and the importance of supply chain coordination. The game …

This study addresses the optimal inventory management problem for new smartphone
products as an effective example of a supply chain with a short product life cycle. The …

Deep reinforcement learning has been coined as a promising research avenue to solve
sequential decision-making problems, especially if few is known about the optimal policy …

This work provides a Deep Reinforcement Learning approach to solving a periodic review
inventory control system with stochastic vendor lead times, lost sales, correlated demand …

An inventory management problem is addressed for a make-to-order supply chain that has
inventory holding and/or manufacturing locations at each node. The lead times between …