SAGAS: Simulated annealing and greedy algorithm scheduler for laboratory automation

During laboratory automation of life science experiments, coordinating specialized instruments and human experimenters for various experimental procedures is important to minimize the execution time. In particular, the scheduling of life science experiments requires the consideration of time constraints by mutual boundaries (TCMB) and can be formulated as the scheduling for laboratory automation in biology (S-LAB) problem. However, existing scheduling methods for the S-LAB problems have difficulties in obtaining a feasible solution for large-size scheduling problems at a time sufficient for real-time use. In this study, we proposed a fast schedule-finding method for S-LAB problems, SAGAS (Simulated annealing and greedy algorithm scheduler). SAGAS combines simulated annealing and the greedy algorithm to find a scheduling solution with the shortest possible execution time. We have performed scheduling on real experimental protocols and shown that SAGAS can search for feasible or optimal solutions in practicable computation time for various S-LAB problems. Furthermore, the reduced computation time by SAGAS enables us to systematically search for laboratory automation with minimum execution time by simulating scheduling for various laboratory configurations. This study provides a convenient scheduling method for life science automation laboratories and presents a new possibility for designing laboratory configurations.

Automated summary

During laboratory automation of life science experiments, it is important to coordinate specialized instruments and human experimenters to minimize execution time. Existing scheduling methods for large-scale scheduling problems have difficulties in obtaining feasible solutions in real-time. In this study, we proposed a fast schedule-finding method called SAGAS, which combines simulated annealing and the greedy algorithm to find the shortest execution time for life science experiments.