Evaluating the Impact of the Level of Robustness in Operating Room Scheduling Problems

Area of Science:

• Operations Research
• Healthcare Management
• Applied Mathematics

Background:

• Uncertainty in surgery durations poses significant challenges to operating room (OR) scheduling, impacting patient care and hospital efficiency.
• Traditional deterministic scheduling approaches often fail to adequately address this inherent variability.

Purpose of the Study:

• To develop and evaluate a cardinality-constrained robust optimization model for OR scheduling that accounts for uncertain surgery durations.
• To optimize patient-centered outcomes, including waiting times and urgency prioritization, while ensuring operational resilience.

Main Methods:

• A robust optimization model was formulated for OR scheduling, incorporating patient waiting times, urgency levels, and delay penalties.
• The model's performance was analyzed by assessing its sensitivity to various robustness levels and its effectiveness in different uncertainty scenarios.

Main Results:

• The robust optimization model demonstrated significant improvements in patient outcomes, such as reduced waiting times and fewer missed surgeries.
• Prioritization of urgent cases was enhanced, and the model showed effectiveness in handling diverse uncertainty scenarios.
• Analysis provided insights into the impact of robustness parameters on OR scheduling performance.

Conclusions:

• The proposed robust optimization approach offers a reliable and adaptive solution for OR scheduling under uncertainty.
• Healthcare providers can leverage this model to enhance operational efficiency, improve patient satisfaction, and mitigate scheduling disruptions.