(1373-B) Scheduling methods for streamlining and designing laboratory automation
Monday, February 5, 2024
2:00 PM – 3:00 PM EST
Location: Exhibit Halls AB
Abstract: In laboratories automated with a variety of instruments, scheduling algorithms play a crucial role in optimizing instrument allocation to minimize the time needed to complete experimental procedures. However, previous studies on laboratory automation scheduling have not adequately focused on the time constraints by mutual boundaries (TCMBs) among operations, a key factor in procedures involving live cells or unstable biomolecules. To bridge this gap, we define the "scheduling for laboratory automation in biology" (S-LAB) problem as one involving automated laboratories where operations with TCMBs are executed by multiple diverse instruments [1]. This S-LAB problem is formulated as a mixed-integer programming (MIP) problem, and we have developed a method utilizing the branch-and-bound algorithm to solve it [1]. Our simulations show that this method can effectively create schedules meeting time constraints while also minimizing total execution time. Moreover, for handling large-size scheduling problems in a timeframe suitable for real-time application, we introduced a fast schedule-finding method for S-LAB problems, named SAGAS (Simulated annealing and greedy algorithm scheduler) [2]. SAGAS's effectiveness is validated across different experimental protocols, and its reduced computation time allows systematic exploration of laboratory automation configurations to minimize execution time. This approach opens new possibilities for designing laboratory configurations. By integrating parallel scheduling and collaboration across various instrument types, our methods would facilitate large-scale automation in life science experiments.
References: [1] Itoh and Horinouchi et al., SLAS Technology (2021) [2] Arai et al., SLAS Technology (2023)