Optimization of Robotic Liquid Handling as a Capacitated Vehicle Routing Problem

2025-06-03

AI Summary

Liquid handling operations in automated chemical laboratories suffer from prolonged execution times, limiting experimental throughput. Method: This work pioneers the formulation of pipetting tasks as a Capacitated Vehicle Routing Problem (CVRP), leveraging established logistics heuristics—Clarke-Wright savings and the Lin-Kernighan Heuristic (LKH)—for optimal task scheduling. The approach requires no hardware modification, enabling cross-platform compatibility (e.g., microtiter plates, vial racks) via independent multi-channel pipette control and a generic container adaptation mechanism. Contribution/Results: Experiments demonstrate up to 37% reduction in execution time under randomized task scenarios. In a real-world high-throughput materials discovery workflow, a mere 3-minute computation yields a 61-minute runtime reduction. This is the first application of the CVRP framework to robotic liquid handling optimization, significantly enhancing experimental throughput and resource utilization in automated laboratories.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Learning & Optimization for ROB
• Computer Vision: Learning & Optimization for CV

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

We present an optimization strategy to reduce the execution time of liquid handling operations in the context of an automated chemical laboratory. By formulating the task as a capacitated vehicle routing problem (CVRP), we leverage heuristic solvers traditionally used in logistics and transportation planning to optimize task execution times. As exemplified using an 8-channel pipette with individually controllable tips, our approach demonstrates robust optimization performance across different labware formats (e.g., well-plates, vial holders), achieving up to a 37% reduction in execution time for randomly generated tasks compared to the baseline sorting method. We further apply the method to a real-world high-throughput materials discovery campaign and observe that 3 minutes of optimization time led to a reduction of 61 minutes in execution time compared to the best-performing sorting-based strategy. Our results highlight the potential for substantial improvements in throughput and efficiency in automated laboratories without any hardware modifications. This optimization strategy offers a practical and scalable solution to accelerate combinatorial experimentation in areas such as drug combination screening, reaction condition optimization, materials development, and formulation engineering.

Problem

• Optimizing liquid handling time in automated labs
• Applying vehicle routing heuristics to lab workflows
• Reducing execution time without hardware changes

Innovation

• Formulates liquid handling as capacitated vehicle routing problem
• Uses heuristic solvers for logistics to optimize execution times
• Achieves up to 37% time reduction without hardware changes