mlsanityData to 14 Oct 2026

Deep Reinforcement Learning for Picker Routing Problem in Warehousing

arXiv 2402.03525 ↗First posted 5 Feb 2024cs.LG cs.AIcited by 1

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. First posted on arXiv 5 Feb 2024.

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Citing papers per year: 2024: 1.

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  1. 2024Deep Reinforcement Learning for Dynamic Order Picking in Warehouse Operationscited by 1

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