用分层并行解码提升仓库拣货路由效率，兼顾速度与协同。
Learning to Solve the Min-Max Mixed-Shelves Picker-Routing Problem via Hierarchical and Parallel Decoding
- 分层并行解码生成多智能体联合动作分布，加快推理速度。
- 在大规模及分布外场景下，解质量与推理速度均达当前最优。
- 适合需要快速高效拣货调度的大型仓储系统部署。
混合货架拣货路由问题（MSPRP）是仓库物流中的核心挑战，要求拣货员在混合货架环境中高效获取商品。传统启发式与优化方法难以扩展，而现有机器学习方法多依赖串行决策，导致解算延迟高且智能体协调不足。本文提出一种基于多智能体强化学习的分层并行解码方法，解决MSPRP的最小化最大路径长度变体。该方法通过联合动作分布实现快速解码与有效协同，同时引入串行动作选择机制，避免多维动作空间中的冲突。实验表明，该方法在解质量与推理速度上均达到当前最优，尤其在大规模及分布外实例中表现突出。代码已公开于http://github.com/LTluttmann/marl4msprp。
原文摘要 · Abstract (English)
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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