A comparative analysis of the efficient operating room scheduling models using robust optimization and upper partial moment

Abstract

Operating room scheduling problems are difficult to solve due to the uncertainties inherent in operating time. This study develops a multi-stage linear mixed integer optimization model to optimize the operation time and resource assignment. The decision tree approach addresses the uncertainties in each level of the tree. The proposed model considers three optimistic, most probable, and pessimistic scenarios. Robust Optimization and Upper Partial Moments methods (UPM) are then applied to propose efficient solutions under uncertainty. A case study is presented to compare the solutions proposed by Robust Optimization and UPM methods. The results are compared according to the variance, solution time, and waiting time. We show that the UPM method is superior to the Robust Optimization method in terms of variance and waiting time, while it solves the problem slower.