Deep Reinforcement Learning for Picker Routing Problem in Warehousing
George Dunn Hadi Charkhgard Ali Eshragh Sasan Mahmoudinazlou Elizabeth Stojanovski
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.