Learning to Solve the Min-Max Mixed-Shelves Picker-Routing Problem via Hierarchical and Parallel Decoding

• URL: http://arxiv.org/abs/2502.10233v1
• Date: Fri, 14 Feb 2025 15:42:30 GMT
• Title: Learning to Solve the Min-Max Mixed-Shelves Picker-Routing Problem via Hierarchical and Parallel Decoding
• Authors: Laurin Luttmann, Lin Xie,
• Abstract summary: The Mixed-Shelves Picker Routing Problem (MSPRP) is a fundamental challenge in logistics, where pickers must navigate a mixed-shelves environment to retrieve SKUs efficiently.<n>We propose a novel hierarchical and parallel decoding approach for solving the min-max variant of the MSPRP via multi-agent reinforcement learning.<n> Experiments show state-of-the-art performance in both solution quality and inference speed, particularly for large-scale and out-of-distribution instances.
• Score: 0.3867363075280544
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Mixed-Shelves Picker Routing Problem (MSPRP) is a fundamental challenge in warehouse logistics, where pickers must navigate a mixed-shelves environment to retrieve SKUs efficiently. Traditional heuristics and optimization-based approaches struggle with scalability, while recent machine learning methods often rely on sequential decision-making, leading to high solution latency and suboptimal agent coordination. In this work, we propose a novel hierarchical and parallel decoding approach for solving the min-max variant of the MSPRP via multi-agent reinforcement learning. While our approach generates a joint distribution over agent actions, allowing for fast decoding and effective picker coordination, our method introduces a sequential action selection to avoid conflicts in the multi-dimensional action space. Experiments show state-of-the-art performance in both solution quality and inference speed, particularly for large-scale and out-of-distribution instances. Our code is publicly available at http://github.com/LTluttmann/marl4msprp.

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