… Thus, when the target is a new product, it is difficult to optimize inventory control from its … deepreinforcement learning with high sample efficiency and propose an inventorymanagement …
… Because it is conventional in computer science literature to ‘maximize rewards’ instead of ‘minimizing costs’ in inventorymanagement, we will define the reward as the negation of the …
I Kaynov, M van Knippenberg, V Menkovski… - International Journal of …, 2024 - Elsevier
… DeepReinforcement Learning (DRL) is a general purpose technique for sequential decision making that has yielded good results for various challenging inventory … warehouse inventory…
… To allow for a stringent performance analysis of the A3C algorithm in inventorymanagement, we develop two sets of experiments. The first experiment considers small-scale settings for …
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 inventorymanagement (SCIM) problem, a complex sequential decision…
… As we have recently witnessed some applications of DRL in inventorymanagement, we believe the technique also bears substantial further promise for our field. The most apparent …
… paper proposes use of DeepReinforcement Learning (RL) techniques for the inventory management of perishable goods. The paper models the retailers inventory limitation factors and …
A Oroojlooyjadid, MR Nazari… - … Management, 2022 - pubsonline.informs.org
… Methodology: We propose a deepreinforcement learning (RL) algorithm to play the beer game. Our algorithm makes no assumptions about costs or other settings. As with any …
C Cuartas, J Aguilar - Journal of intelligent manufacturing, 2023 - Springer
… algorithm based on reinforcement learning and the inventorymanagement methodology called … For this, the inventorymanagement problem is formulated as a Markov Decision Process …