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

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

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 …

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 …

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 …

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

Problem Definition: Are traditional deep reinforcement learning (DRL) algorithms, developed
for a broad range of purposes including game-play and robotics, the most suitable machine …

Determining optimum inventory replenishment decisions are critical for retail businesses
with uncertain demand. The problem becomes particularly challenging when multiple …

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