Maintaining a balance between the supply and demand of products by optimizing replenishment decisions is one of the most important challenges in the supply chain industry. This paper presents a novel reinforcement learning framework called MARLIM, to address the inventory management problem for a single-echelon multi-products supply chain with stochastic demands and lead-times. Within this context, controllers are developed through single or multiple agents in a cooperative setting. Numerical experiments on real data demonstrate the benefits of reinforcement learning methods over traditional baselines.
@article{arxiv.2308.01649,
title = {MARLIM: Multi-Agent Reinforcement Learning for Inventory Management},
author = {Rémi Leluc and Elie Kadoche and Antoine Bertoncello and Sébastien Gourvénec},
journal= {arXiv preprint arXiv:2308.01649},
year = {2023}
}
Comments
Accepted at NeurIPS 2022 Workshop: Reinforcement Learning for Real Life (https://nips.cc/virtual/2022/workshop/50014)