Effective management of cancer treatment facility for radiation therapy depends mainly on optimizing the utilization of linear accelerators. In this project, we are scheduling patients on those machines while taking into account their priority for treatment, the maximum waiting time before the first treatment and the duration of treatment. We collaborate with the “Centre Intégré de Cancérologie de Laval” to determine the best scheduling policy. The goal is to find, at each patient arrival, the best sequence of appointments that respects some constraints. Furthermore, we integrate the uncertainty related to the arrival of patients at the center. We develop an hybrid method combining stochastic optimization and online optimization to better meet the needs of central planning. Therefore, we use the information of future arrivals of patients to capture the most accurate picture of the expected utilization of resources. Randomly generated data allow us to study the behavior of our online algorithm according to its parameters. Tests on real data show that our method outperforms strategies typically used in such treatment centers.