ArXiv

Stress-Relief Annealing: Polynomial-Time Simulation-Free Layout Optimization for Automated Warehouses

2026-08-02 · 05:59 UTC ·Xiangjie Luo, Yulun Zhang, Miyuki Koshimura... ·1 min read
Authors
Xiangjie Luo, Yulun Zhang, Miyuki Koshimura...
Categories
cs.MA, cs.AI, cs.RO
arXiv
https://arxiv.org/abs/2608.01024v1
PDF
https://arxiv.org/pdf/2608.01024v1

Brief

Stress-Relief Annealing (SRA) is a polynomial-time, simulation-free algorithm that maps task demand into a per-vertex stress field whose peak provably caps throughput in automated warehouse layouts. Experiments show SRA roughly doubles robot capacity of a human-designed warehouse and matches or outperforms evolutionary baselines while running in 19 minutes on one CPU (vs 25,000 simulations and ~25 hours on 64 cores); gains hold across different MAPF algorithms, non-uniform task distributions, and enlarged warehouse dimensions.

Why it matters

SRA (Stress-Relief Annealing) is a polynomial-time, simulation-free layout optimizer that converts task demand into a per-vertex "stress field"; the field's peak provably upper-bounds throughput.

Key details

  • Empirically, SRA roughly doubles the number of robots a human-designed warehouse can sustain and matches or exceeds evolutionary baselines while taking 19 minutes on one CPU (vs 25,000 simulations and ~25 hours on a 64-core machine); improvements generalize across MAPF algorithms, non-uniform demands, and doubled warehouse dimensions.
Source evidence

Abstract

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.