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.

仓库操作管理中的拣货路径规划是一个关键问题。由于问题的复杂性和快速解决的需求，实践中经常使用次优算法。然而，强化学习提供了传统启发式方法的一个有吸引力的替代方案，潜在地在速度和准确性方面优于现有方法。我们介绍了一种基于注意力机制的神经网络模型来建模拣货路线，该模型使用强化学习进行训练。我们的方法在一系列问题参数上与现有启发式方法进行了评估，以展示其效力。我们提出的方法的一个关键优势是可以降低路径的感知复杂性。