用物流算法优化实验室液体操作，提速近四成。
Optimization of Robotic Liquid Handling as a Capacitated Vehicle Routing Problem
- 将液体操作建模为有容量限制的路径规划问题，借用物流优化算法。
- 随机任务下执行时间最多减少37%，真实实验中节省61分钟。
- 无需改硬件，适合药物筛选、材料研发等高通量实验场景。
我们提出一种优化策略，以减少自动化化学实验室中液体操作的执行时间。通过将任务建模为有容量限制的车辆路径问题（CVRP），利用传统物流与运输规划中的启发式求解器来优化任务执行时长。以8通道可独立控制尖端的移液器为例，该方法在不同板型（如微孔板、管架）下均表现出稳健的优化性能，相比基线排序方法，随机生成的任务执行时间最多减少37%。进一步应用于真实世界高通量材料发现项目，仅3分钟优化时间即带来相比最优排序策略61分钟的执行时间节省。结果表明，该方法可在不进行任何硬件改动的前提下显著提升自动化实验室的吞吐量与效率。此策略为药物组合筛选、反应条件优化、材料开发及配方工程等领域的组合实验加速提供了实用且可扩展的解决方案。
原文摘要 · Abstract (English)
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.
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