Study: Algorithm offers smarter OR scheduling under pressure

Editor's Note

A robust scheduling model can significantly improve OR efficiency and stability when surgery and recovery times are unpredictable, according to research published in the journal Mathematics. Using a Genetic Algorithm for Robust Scheduling (GARS), the authors demonstrate a practical and computationally efficient method for minimizing makespan across a three-stage surgical workflow—pre-op holding, surgery, and PACU—under strict no-wait constraints (patients could not be delayed between stages). The primary objective was to minimize makespan, defined as the longest time required to complete all surgeries.

GARS evaluates scheduling solutions by averaging performance across multiple sampled scenarios, making it less sensitive to variations in case durations. In simulated tests with up to 30 surgeries and moderate uncertainty levels (δ = 0.5), GARS achieved:

• Average makespan: 633.85 minutes
• Standard deviation: 40.81 minutes
• Worst-case performance ratio (WPR): 1.00

These results were superior to those from the deterministic genetic algorithm (GAD), which had an average makespan of 673.75 and a WPR of 1.11 under the same conditions.The study also compared GARS with three other algorithms: GARS_SA (which adds simulated annealing), GRIS (a greedy randomized insertion and swap heuristic), and BRS (a baseline random search). GARS and GARS_SA demonstrated the lowest average makespans, standard deviations, and WPRs. While GARS_SA showed marginally better performance, GARS achieved comparable results with a shorter runtime (236 s vs. 333 s), making it the more computationally efficient option.

Though based on synthetic data rather than clinical environments, the findings suggest that robust algorithms like GARS can support more resilient scheduling under variable surgical durations. “The proposed GARS algorithm provides a practical tool for hospital administrators to generate schedules that remain effective even under uncertain surgery and recovery durations,” authors write. “By focusing on robustness rather than only deterministic performance, this algorithm can improve OR utilization, reduce bottlenecks in PACUs, and mitigate the impact of variability on daily operations.”