---
title: "自主实验室中资源最优利用的两步方法"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.01188v1"
permalink: "https://feed.astralor.com/items/itm_d3a3adaf83a6f2ef79746a6d"
published: "2026-07-01T17:20:45.000Z"
score: 53
channel: "ai"
tags: ["约束编程","自主实验室","调度优化","金属有机框架"]
---

# 自主实验室中资源最优利用的两步方法

## 速览

自主实验室中AI代理建议实验批次，但资源规划面临硬件约束挑战。本文针对金属有机框架合成平台，提出两步法：先用约束编程找到最小化总时间的最优调度，再通过任务状态依赖系统确保调度稳健执行。该方法有效提升了多设备异构条件下的资源利用率。

## 关键点

- 两步法结合约束编程与状态依赖系统，解决自主实验室资源调度问题
- 针对金属有机框架合成平台，处理多设备容量与吞吐量差异
- 约束编程生成最小总时间调度，满足硬件限制
- 状态依赖系统保证最优调度的稳健执行

## 全文

In autonomous laboratories, AI agents suggest the next batch of experiments to do. However, planning and executing those tasks taking full advantage of the available resources is a completely different question. This can be challenging when dealing with real-world hardware constraints, especially so when there are multiple instruments with different capacities and throughputs. Here we demonstrate a 2-step method to address resource utilization for our autonomous platform for metal-organic framework synthesis. First, we use constraint programming to find optimal schedules. This finds schedules that minimizes the total time while still satisfying the limitations and capacities of the hardware. Secondly, we use a system of status dependencies for each task, which allows for the robust execution of the optimal schedules.

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