Articlehybrid2022

Multi-Echelon Inventory Optimization Using Deep Reinforcement Learning

Kevin Geevers · Lotte van Hezewijk · Martijn Mes

50 citations0/0 files storedCentral European Journal of Operations Research

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Supply Chain and Inventory Management Reinforcement Learning in Robotics Advanced Manufacturing and Logistics Optimization

Abstract

Abstract 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 backorder costs. First, we conduct an extensive literature review to map the current applications of reinforcement learning in multi-echelon inventory systems. Next, we apply our deep reinforcement learning method to three cases with different network structures (linear, divergent, and general structures). The linear and divergent cases are derived from literature, whereas the general case is based on a real-life manufacturer. We apply the proximal policy optimization (PPO) algorithm, with a continuous action space, and show that it consistently outperforms the benchmark solution. It achieves an average improvement of 16.4% for the linear case, 11.3% for the divergent case, and 6.6% for the general case. We explain the limitations of our approach and propose avenues for future research.

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References

2006A Robust Optimization Approach to Inventory Theory 1999Managing supply chain inventories: A multiple retailer, one warehouse, multiple supplier model 2021A Deep Q-Network for the Beer Game: Deep Reinforcement Learning for Inventory Optimization 1978Introduction to materials management 2002Inventory management in supply chains: a reinforcement learning approach 2022Can Deep Reinforcement Learning Improve Inventory Management? Performance on Lost Sales, Dual-Sourcing, and Multi-Echelon Problems 2001Optimal Policies for Multiechelon Inventory Problems with Markov-Modulated Demand 2003Integrated inventory model for deteriorating items under a multi-echelon supply chain environment

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2024Performance of deep reinforcement learning algorithms in two-echelon inventory control systems 2025Deep Controlled Learning for Inventory Control 2024Neuroevolution reinforcement learning for multi-echelon inventory optimization with delivery options and uncertain discount

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