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Optimal Scheduling for Laboratory Automation of Life Science Experiments with Time Constraints
Takeshi D Itoh1,2, Takaaki Horinouchi3,4, Hiroki Uchida5
1Mathematical Informatics Laboratory, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, Japan.
SLAS Technology
|June 25, 2021
Summary
This study introduces a new scheduling method for automated biological laboratories, optimizing instrument allocation to minimize experiment time. The approach effectively handles time constraints crucial for sensitive biological samples.
Area of Science:
- Laboratory Automation
- Computational Biology
- Operations Research
Background:
- Automated laboratories require efficient scheduling algorithms to minimize experimental procedure times.
- Existing algorithms often overlook time constraints by mutual boundaries (TCMBs), critical for time-sensitive biological samples like live cells or unstable biomolecules.
Purpose of the Study:
- To define and address the
- scheduling for laboratory automation in biology
- (S-LAB) problem, incorporating TCMBs.
- To develop an optimal scheduling method for automated biological experiments that minimizes execution time while respecting TCMBs.
Main Methods:
- Formulated the S-LAB problem as a mixed-integer programming (MIP) problem.
- Developed a scheduling method utilizing the branch-and-bound algorithm to solve the MIP formulation.
- Conducted simulations to validate the scheduling method's performance.
Main Results:
- The proposed branch-and-bound method successfully finds optimal schedules for S-LAB problems.
- The method minimizes overall execution time for automated laboratory procedures.
- All implemented time constraints by mutual boundaries (TCMBs) were satisfied.
Conclusions:
- The developed scheduling method is effective for optimizing automated biological experiments with TCMBs.
- This approach can be used for simulation-based design of laboratory automation job definitions and configurations.
- Addresses a critical gap in laboratory automation scheduling for time-sensitive biological applications.
