F Stranieri, F Stella, C Kouki - International Journal of Production …, 2024 - Taylor & Francis
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 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 work investigates the application of Multi-Agent Deep Reinforcement Learning (MADRL) on decentralized inventory management problems with multiple echelons …
Problem definition: Is deep reinforcement learning (DRL) effective at solving inventory problems? Academic/practical relevance: Given that DRL has successfully been applied in …
Is Deep Reinforcement Learning (DRL) effective at solving inventory problems? Given that DRL has successfully been applied in computer games and robotics, supply chain …
F Stranieri, F Stella - arXiv preprint arXiv:2204.09603, 2022 - ewrl.wordpress.com
This paper leverages recent developments in reinforcement learning and deep learning to solve the supply chain inventory management (SCIM) problem, a complex sequential …
Deep reinforcement learning (DRL) has shown great potential for sequential decision- making, including early developments in inventory control. Yet, the abundance of choices …
NN Sultana, H Meisheri, V Baniwal, S Nath… - arXiv preprint arXiv …, 2020 - arxiv.org
This paper describes the application of reinforcement learning (RL) to multi-product inventory management in supply chains. The problem description and solution are both …
Deep reinforcement learning (DRL) has proven to be an effective, general-purpose technology to develop 'good'replenishment policies in inventory management. We show …