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 …

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

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

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

We propose a framework that uses deep neural networks (DNN) to optimize inventory
decisions in complex multi-echelon supply chains. We first introduce pairwise modeling of …

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 …

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

With the global trade competition becoming further intensified, Supply Chain Management
(SCM) technology has become critical to maintain competitive advantages for enterprises …

Inventory cost is a significant factor in Supply Chain Management (SCM), and an effective
replenishment strategy can reduce warehouse operation costs. However, traditional …

Reinforcement Learning (RL) is an effective method to solve stochastic sequential decision-
making problems. This is a problem description common to supply chain operations …