Graph Neural Networks and Deep Reinforcement Learning for Warehouse Order Picking and Representation Learning

Order picking is one of the most resource-intensive warehouse operations; therefore, improving routing efficiency remains an important challenge. Deep reinforcement learning (DRL) has shown promise in complex optimization problems. However, its application to warehouse order picking is still limited, and graph-based representation learning using graph neural networks (GNNs) in this context remains largely unexplored. This paper proposes a GNN-based DRL method that models warehouse layouts as graphs to optimize order-picking paths while simultaneously learning graph-based structural embeddings of storage locations. The approach is evaluated against exact optimal solutions for smaller instances and against classical heuristic baselines, including the Lin–Kernighan algorithm, in simulated warehouse environments of different scales. The results show that the proposed GNN–DRL approach produces routing solutions with low optimality gaps across different order sizes and remains effective across different warehouse sizes when fine-tuned. In addition, a preliminary small-scale multi-picker experiment illustrates that the proposed framework could be extended toward more complex warehouse optimization settings. Moreover, the learned node embeddings capture meaningful structural properties of warehouse layouts and adapt to different operational contexts, highlighting the potential of integrating GNNs and DRL as a flexible foundation for advanced warehouse optimization.