Optimal Resource Utilization for Autonomous Laboratory Orchestrators

2026-07-01

AI Summary

This work addresses the challenge of efficient experimental scheduling in autonomous laboratories under heterogeneous hardware constraints across multiple instruments. The authors propose a two-stage optimization approach: first, an optimal schedule minimizing total makespan is generated using constraint programming; second, a task-state dependency mechanism is introduced to ensure execution robustness. This method achieves, for the first time, resource-aware scheduling on an autonomous platform for metal–organic framework synthesis that jointly accounts for hardware capacity limits, throughput constraints, and execution reliability. Experimental results demonstrate that the proposed algorithm significantly improves resource utilization in a real-world system while maintaining both scheduling efficiency and robustness.

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.

Problem

• autonomous laboratories
• resource utilization
• hardware constraints
• experiment scheduling
• instrument throughput

Innovation

• constraint programming
• resource scheduling
• autonomous laboratory
• status dependencies
• optimal experiment planning