Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply Chains
This work addresses inventory management for supply chain practitioners, but it is incremental as it applies existing methods to a specific domain.
This study tackled the supply chain inventory management problem by comparing deep reinforcement learning algorithms in a two-echelon environment with stochastic and seasonal demand, finding that they outperform traditional strategies like the static (s, Q)-policy.
In this study, we analyze and compare the performance of state-of-the-art deep reinforcement learning algorithms for solving the supply chain inventory management problem. This complex sequential decision-making problem consists of determining the optimal quantity of products to be produced and shipped across different warehouses over a given time horizon. In particular, we present a mathematical formulation of a two-echelon supply chain environment with stochastic and seasonal demand, which allows managing an arbitrary number of warehouses and product types. Through a rich set of numerical experiments, we compare the performance of different deep reinforcement learning algorithms under various supply chain structures, topologies, demands, capacities, and costs. The results of the experimental plan indicate that deep reinforcement learning algorithms outperform traditional inventory management strategies, such as the static (s, Q)-policy. Furthermore, this study provides detailed insight into the design and development of an open-source software library that provides a customizable environment for solving the supply chain inventory management problem using a wide range of data-driven approaches.