Deep Reinforcement Learning for Picker Routing Problem in Warehousing
This addresses the need for efficient routing solutions in warehouse operations management, though it appears incremental as it builds on existing reinforcement learning and attention mechanisms.
The paper tackled the order picker routing problem in warehousing by introducing an attention-based neural network trained with reinforcement learning, which demonstrated efficacy in speed and accuracy compared to existing heuristics across various problem parameters.
Order Picker Routing is a critical issue in Warehouse Operations Management. Due to the complexity of the problem and the need for quick solutions, suboptimal algorithms are frequently employed in practice. However, Reinforcement Learning offers an appealing alternative to traditional heuristics, potentially outperforming existing methods in terms of speed and accuracy. We introduce an attention based neural network for modeling picker tours, which is trained using Reinforcement Learning. Our method is evaluated against existing heuristics across a range of problem parameters to demonstrate its efficacy. A key advantage of our proposed method is its ability to offer an option to reduce the perceived complexity of routes.