With the rapid progress in Multi-Agent Path Finding (MAPF), researchers have studied how MAPF algorithms can be deployed to coordinate hundreds of robots in large automated warehouses. While most works try to improve the throughput of such warehouses by developing better MAPF algorithms, we focus on improving the throughput by optimizing the warehouse layout. We show that, even with state-of-the-art MAPF algorithms, commonly used human-designed layouts can lead to congestion for warehouses with large numbers of robots and thus have limited scalability. We extend existing automatic scenario generation methods to optimize warehouse layouts. Results show that our optimized warehouse layouts (1) reduce traffic congestion and thus improve throughput, (2) improve the scalability of the automated warehouses by doubling the number of robots in some cases, and (3) are capable of generating layouts with user-specified diversity measures. We include the source code at: \url{https://github.com/lunjohnzhang/warehouse_env_gen_public}

翻译：随着多智能体路径规划（MAPF）技术的快速发展，研究人员已开始探索如何部署MAPF算法以协调大型自动化仓储中数百台机器人。现有研究大多致力于通过改进MAPF算法来提升此类仓库的吞吐量，而本文则聚焦通过优化仓库布局来提高吞吐量。研究表明，即使采用最先进的MAPF算法，传统人工设计的布局在机器人数量庞大的仓储场景中仍会导致拥堵问题，进而限制系统的可扩展性。我们扩展了现有自动化场景生成方法以优化仓库布局。实验结果表明，优化后的仓库布局能够：（1）缓解交通拥堵，从而提升吞吐量；（2）在某些情况下使机器人数量翻倍，从而显著提升自动化仓储的可扩展性；（3）支持生成具有用户指定多样性指标的布局方案。相关源代码已开源至：\url{https://github.com/lunjohnzhang/warehouse_env_gen_public}