Stochastic operating room scheduling: a new model for solving problem and an approach for determining the factors that affect operation time variations

The most common situation encountered in the operating room scheduling is the uncertainty of operation times. This situation may cause the operations to be delayed or canceled. In this study, stochastic operating room scheduling is discussed under the uncertainty of operation times. In real life, variability may vary depending on many factors from operation to operation/patient to patient. These factors are the surgeon's experience, the difficulty of the operation, the patient's weight, age, disease history, etc. In this study, separate coefficients of variability were determined for each operation, taking into account the variability factors. Operations are scheduled, taking into account the operation-specific coefficients of variation. To evaluate the variability factors, analytical network process method was used considering the interaction between them. The level of uncertainty/coefficient of variation of each operation was determined with the PROMETHEE method. Finally, the logical modeling power of constraint programming is used to solve the operating room scheduling problem. In the proposed constraint programming model, the flexibility of the goal programming method was utilized. For the modeling of uncertainties, a chance-constrained approach was used. The case study demonstrates that the proposed approach is a novel and outstanding technique, and the proposed CP model is efficient in solving the problem. As a result of the study, the uncertainty in the operation time of each patient was calculated as the variability according to the factor weights, and the tables were reconstructed according to this situation. The performance and effectiveness of the new schedules obtained under variability are shown.