Robust Operating Room Scheduling Model with Violation Probability Consideration under Uncertain Surgery Duration.

Area of Science:

• Operations Research
• Healthcare Management
• Optimization

Background:

• Traditional operating room (OR) scheduling models often overlook the impact of variable surgery durations.
• This oversight can lead to suboptimal patient waiting times and increased operational costs.

Purpose of the Study:

• To develop a robust operating room (OR) scheduling model that incorporates uncertainty in surgery durations.
• To minimize the combined costs of OR operation, patient waiting penalties, and OR overtime.

Main Methods:

• Formulated a robust OR scheduling model using a discrete approach to handle uncertain surgery durations.
• Employed a box uncertainty set and a robustness coefficient to manage uncertainty.
• Transformed the intractable robust model into a solvable Mixed Integer Linear Programming (MILP) model using robust discrete optimization and strong dual theories.

Main Results:

• Demonstrated a trade-off between total management cost and constraint violation probability, controlled by the robustness coefficient.
• Identified that total management cost is sensitive to small changes in the robustness coefficient but plateaus at higher values.
• Observed increased sensitivity of total management cost to perturbation factors as constraint violation probability decreases.

Conclusions:

• The proposed robust OR scheduling model effectively balances costs and reliability in the face of uncertain surgery durations.
• The robustness coefficient provides a tunable parameter for managing the trade-off between cost efficiency and operational risk.
• The model offers a practical framework for improving OR scheduling efficiency and patient flow.