发表机构
Kyushu University; Carnegie Mellon University（九州大学; 卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自动化仓库布局优化现有方法样本效率低的问题，本文提出多项式时间无模拟的应力释放退火算法，大幅提升吞吐量与可扩展性，且效率远高于进化基线方法，增益可推广至多种场景。
AI 中文摘要
我们研究自动化仓库的物理布局优化问题，该场景中需协调数百至数千个机器人完成包裹运输。已有研究表明，优化仓库布局（如存储货架的物理位置）可显著提升吞吐量。然而，当前最先进的布局优化方法基于进化优化技术，将整个仓库视为黑箱，依赖随机变异搜索高质量布局。尽管优化效果可观，但这些方法需要大量模拟来评估候选解，导致样本效率低下。本文提出应力释放退火（Stress-Relief Annealing，SRA），这是一种多项式时间的无模拟布局优化算法。SRA将任务需求转化为每个顶点的“应力场”，用于预测仓库中交通集中的位置；该场的峰值可证明能限制吞吐量。实验结果显示：（1）SRA可同时提升人工设计仓库的吞吐量和可扩展性，大致使其可承载的机器人数量翻倍；（2）其吞吐量与进化基线方法相当或更优，且仅在单个CPU核心上耗时19分钟，而进化基线方法需25000次模拟，在64核机器上耗时25小时；（3）该增益可推广至不同的多智能体路径查找算法、非均匀任务需求及尺寸翻倍的仓库场景。
英文摘要
We study the problem of optimizing physical layouts for automated warehouses, where hundreds to thousands of robots are coordinated to transport packages. Previous works have shown that optimizing the warehouse layout (e.g., the physical location of the storage shelves) significantly improves throughput. However, state-of-the-art layout optimization approaches are based on evolutionary optimization methods, which treat the entire warehouse as a black box and rely on random mutation to search for high-quality layouts. While the optimization outcomes are promising, these methods require a massive number of simulations to evaluate candidate solutions, making them sample-inefficient. In this paper, we present Stress-Relief Annealing (SRA), a polynomial-time simulation-free layout optimization algorithm. SRA turns the task demand into a per-vertex \emph{stress field} that predicts where traffic will concentrate in the warehouse; the field's peak provably caps the throughput. Our experimental results show that (1) SRA improves both the throughput and the scalability of a human-designed warehouse, roughly doubling the number of robots it can sustain, (2) it matches or exceeds the throughput of the evolutionary baselines while taking only $19$ minutes on one CPU core, against their $25{,}000$ simulations and $25$ hours on a $64$-core machine, and (3) the gain generalizes across different Multi-Agent Path Finding algorithms, non-uniform task demands, and a warehouse with doubled dimensions.
