Robust finite-horizon scheduling/rescheduling of operating rooms with elective and emergency surgeries under resource constraints

Proper planning and scheduling of activities involved in health can improve productivity in this area. In this regard, hospitals are one of the most critical components, and in hospitals, the operating room is one of the most important ones. Since the operating room is a very costly facility, the scheduling of the patients and involved resources is an important issue. In this study, the problem of scheduling and rescheduling of the operating room in a finite horizon is investigated to minimize the total waiting time and tardiness of patients. The constraints in the problem under consideration are the number of surgeons and the number of beds available. Furthermore, emergency patients as well elective patients are considered simultaneously in our proposed model. In addition to operating room scheduling in this study, rescheduling is also done to canceled patients. To further fit the model presented with reality, uncertainties in parameters such as operating time and the number of beds in the post-anesthesia care unit are also considered. In this study, robust optimization is used to deal with uncertainties in the model. After applying the Bertsimas and Sim approach, due to the complexity of the problem under investigation, the genetic algorithm is used to solve the proposed model. To validate the mentioned algorithm, the particle swarm optimization algorithm is selected according to the literature. The results of the comparison show the superiority of the proposed algorithm compared to the particle swarm optimization algorithm in terms of the objective function and running time.