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SAGAS: Simulated annealing and greedy algorithm scheduler for laboratory automation
Yuya Arai1, Ko Takahashi2, Takaaki Horinouchi3
1College of Biological Sciences, School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan; Bioinformatics Laboratory, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan.
This study introduces SAGAS, a fast scheduler for life science automation. It efficiently finds optimal experimental schedules, reducing computation time for laboratory automation and configuration design.
Area of Science:
- Life Science Automation
- Computational Biology
- Operations Research
Background:
- Coordinating instruments and personnel is crucial for minimizing execution time in automated life science experiments.
- The scheduling for laboratory automation in biology (S-LAB) problem, considering time constraints by mutual boundaries (TCMB), faces challenges with existing methods for large-scale, real-time applications.
- Efficient scheduling is vital for optimizing throughput and resource utilization in high-throughput biological research.
Purpose of the Study:
- To develop a fast and effective scheduling method for the S-LAB problem.
- To address the limitations of existing methods in handling large-scale scheduling problems within practical timeframes.
- To enable systematic exploration of laboratory automation configurations for minimized execution times.
Main Methods:
- Proposed SAGAS (Simulated Annealing and Greedy Algorithm Scheduler), a hybrid approach combining simulated annealing and the greedy algorithm.
- Implemented and tested SAGAS on real-world experimental protocols.
- Evaluated SAGAS for its ability to find feasible or optimal solutions within practicable computation times.
Main Results:
- SAGAS demonstrated the capability to find feasible or optimal scheduling solutions for S-LAB problems in practical computation times.
- The method significantly reduced computation time compared to existing approaches, making it suitable for real-time applications.
- Simulations using SAGAS facilitated systematic searching for laboratory automation configurations that minimize overall execution time.
Conclusions:
- SAGAS provides an efficient scheduling solution for life science automation laboratories.
- The reduced computational demands of SAGAS open new avenues for designing and optimizing laboratory automation systems.
- This work contributes to advancing the efficiency and design possibilities of automated biological research environments.
