Constraint programming can optimize experiment scheduling in autonomous labs by finding time-minimal plans that respect hardware constraints, with status dependencies ensuring robust real-world execution.
This paper tackles how to efficiently schedule experiments in autonomous labs when multiple instruments have different speeds and capacities. The authors use constraint programming to find optimal schedules that minimize total time while respecting hardware limits, then add a dependency system to ensure reliable execution of those schedules.