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Performance of deep reinforcement learning algorithms in two-echelon inventory control systems

Francesco Stranieri · Fabio A. Stella · Chaaben Kouki

32 citations0/0 files storedInternational Journal of Production Research

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

Abstract

This study conducts a comprehensive analysis of deep reinforcement learning (DRL) algorithms applied to supply chain inventory management (SCIM), which consists of a sequential decision-making problem focussed on determining the optimal quantity of products to produce and ship across multiple capacitated local warehouses over a specific time horizon. In detail, we formulate the problem as a Markov decision process for a divergent two-echelon inventory control system characterised by stochastic and seasonal demand, also presenting a balanced allocation rule designed to prevent backorders in the first echelon. Through numerical experiments, we evaluate the performance of state-of-the-art DRL algorithms and static inventory policies in terms of both cost minimisation and training time while varying the number of local warehouses and product types and the length of replenishment lead times. Our results reveal that the Proximal Policy Optimization algorithm consistently outperforms other algorithms across all experiments, proving to be a robust method for tackling the SCIM problem. Furthermore, the (s, Q)-policy stands as a solid alternative, offering a compromise in performance and computational efficiency. Lastly, this study presents an open-source software library that provides a customisable simulation environment for addressing the SCIM problem, utilising a wide range of DRL algorithms and static inventory policies.

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References

2022The role of artificial intelligence in supply chain management: mapping the territory 2022A review on reinforcement learning algorithms and applications in supply chain management 2021A Deep Q-Network for the Beer Game: Deep Reinforcement Learning for Inventory Optimization 2021Deep reinforcement learning for inventory control: A roadmap 2022Can Deep Reinforcement Learning Improve Inventory Management? Performance on Lost Sales, Dual-Sourcing, and Multi-Echelon Problems 2020Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail 2023From natural language to simulations: applying AI to automate simulation modelling of logistics systems 2008A reinforcement learning model for supply chain ordering management: An application to the beer game

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2025Deep Controlled Learning for Inventory Control 2025A systematic review of machine learning approaches in inventory control optimization 2025Hierarchical production control and distribution planning under retail uncertainty with reinforcement learning 2025A machine learning approach to inventory stockout prediction

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