仓库布局优化不用再跑几千次模拟了，SRA 用十九分钟就能搞定，还能让机器人数量翻一倍，值得看看。
研究者提出 Stress-Relief Annealing（SRA）算法，这是一种多项式时间的免仿真仓库布局优化方法。SRA 将任务需求转化为逐顶点的应力场，其峰值可证明地限制吞吐量。实验显示，SRA 能将人工设计的仓库可承载的机器人数量大约翻倍。相比进化基线，SRA 在单 CPU 核心上仅需 19 分钟，而基线需要 25,000 次模拟和 64 核机器上的 25 小时。该方法在不同多智能体路径规划算法和非均匀任务需求下均能泛化。
Stress-Relief Annealing: Polynomial-Time Simulation-Free Layout Optimization for Automated Warehouses
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.