This paper studies the applicability of a deep reinforcement learning approach to three
different multi-echelon inventory systems, with the objective of minimizing the holding and …

This study conducts a comprehensive analysis of deep reinforcement learning (DRL)
algorithms applied to supply chain inventory management (SCIM), which consists of a …

Reinforcement Learning has lead to considerable break-throughs in diverse areas such as
robotics, games and many others. But the application to RL in complex real-world decision …

This thesis is written at ORTEC in order to develop a deep reinforcement learning method
that can solve various inventory problems. With this method, we solved three inventory …

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 …

This paper leverages recent developments in reinforcement learning and deep learning to
solve the supply chain inventory management (SCIM) problem, a complex sequential …

This work investigates the application of Multi-Agent Deep Reinforcement Learning
(MADRL) on decentralized inventory management problems with multiple echelons …

Multi-echelon inventory systems are commonly used in practice to satisfy widely distributed
random demands of spare parts in an efficient and cost-effective manner. Optimization of a …

We introduce a novel heuristic designed to address the supply chain inventory management
problem in the context of a two-echelon divergent supply chain. The proposed heuristic …

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