A TWO-STAGE ROBUST METHOD FOR FLEXIBLE INTEGRATED MASTER SURGICAL SCHEDULING AND CASE-MIX PLANNING

This paper investigates the integrated master surgical scheduling and case-mix planning problem with the objective of a weighted sum of minimizing the costs of overtime and idle time, increasing surgeons' preferences, and reducing uncovered demands. Considering the uncertainty in surgery demands, a robust optimization model is proposed. A two-stage method is designed for creating and updating this scheduling with respect to downstream resources. The first stage allocates the time blocks to each surgeon to determine the mix of surgery types assigned to each block. In the second stage, having the weekly waiting list of patients, the schedule is updated on the weekly horizon to cope with demand fluctuations and to maximize the use of operating rooms capacity. The proposed method is validated using actual data from a teaching hospital in Iran. The results of the proposed models significantly outperformed the actual plan of a hospital, which indicated the efficiency of the designed models. Comparing the deterministic and robust models shows that robust models lead to better results in over 70% of the test instances