Deep Reinforcement Learning for Picker Routing Problem in Warehousing

TL;DR

This paper presents a reinforcement learning approach with an attention-based neural network to optimize picker routing in warehouses, aiming to outperform traditional heuristics in speed and accuracy.

Contribution

It introduces a novel attention-based neural network trained with reinforcement learning for picker routing, addressing complexity and efficiency issues in warehouse operations.

Findings

Outperforms existing heuristics in speed and accuracy

Reduces perceived complexity of routing problems

Effective across various problem parameters

Abstract

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.