Optimal Resource Utilization for Autonomous Laboratory Orchestrators
Summary
This paper presents a two-step method for optimizing resource utilization in autonomous laboratories using constraint programming for scheduling and status dependencies for robust execution, demonstrated on a platform for metal-organic framework synthesis.
View Cached Full Text
Cached at: 07/02/26, 05:41 AM
# Optimal Resource Utilization for Autonomous Laboratory Orchestrators Source: [https://arxiv.org/abs/2607.01188](https://arxiv.org/abs/2607.01188) [View PDF](https://arxiv.org/pdf/2607.01188) > Abstract: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\. ## Submission history From: Austin McDannald Ph\.D\. \[[view email](https://arxiv.org/show-email/3f6bb6eb/2607.01188)\] **\[v1\]**Wed, 1 Jul 2026 17:20:45 UTC \(520 KB\)
Similar Articles
From Prompts to Protocols: An AI Agent for Laboratory Automation
This paper presents an AI agent that integrates large language models with laboratory orchestration software, allowing scientists to create, monitor, and manage automated lab protocols using natural language. Evaluated on three simulated labs, the agent achieves a 97% first-attempt protocol generation success rate and requires far fewer interface actions.
OR-Space: A Full-Lifecycle Workspace Benchmark for Industrial Optimization Agents
OR-Space is a benchmark for evaluating large language model agents in industrial operations research workflows, focusing on multi-stage task lifecycles and persistent workspaces beyond simple text generation.
Agent-Orchestration in Autonomous Chip Design
This paper explores using large language models and AI agents for autonomous chip design, modeling it as an AI-organization and discussing action spaces for black-box optimization in chip design scenarios.
Using OR-Tools CP-SAT for Scheduling Problems
The article discusses using Google's OR-Tools CP-SAT solver to optimize maintenance scheduling for cloud infrastructure at Akamai, addressing complex constraints like capacity and concurrency.
Sim2Schedule: A Simulator-Guided LLM Framework for Autonomous Open-Pit Mine Scheduling
This paper introduces Sim2Schedule, a simulator-guided LLM framework for autonomous open-pit mine scheduling that achieves 94-99% of the optimal NPV from MILP while scaling linearly in computation time, operating zero-shot without fine-tuning.