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Radiation therapy (RT) is a medical treatment to kill cancer cells or shrinktumors. To manually schedule patients for RT is a time-consuming andchallenging task. By the use of optimization, patient schedules for RT can becreated automatically. This paper presents a study of different optimizationmethods for modeling and solving the RT patient scheduling problem, which canbe used as decision support when implementing an automatic scheduling algorithmin practice. We introduce an Integer Programming (IP) model, a columngeneration IP model (CG-IP), and a Constraint Programming model. Patients arescheduled on multiple machine types considering their priority for treatment,session duration and allowed machines. Expected future patient arrivals areincluded in the models as placeholder patients. Since different cancer centerscan have different scheduling objectives, the models are compared usingmultiple objective functions, including minimizing waiting times, andmaximizing the fulfillment of patients' preferences for treatment times. Thetest data is generated from historical data from Iridium Netwerk, Belgium'slargest cancer center with 10 linear accelerators. The results demonstrate thatthe CG-IP model can solve all the different problem instances to a meanoptimality gap of less than 1% within one hour. The proposed methodologyprovides a tool for automated scheduling of RT treatments and can be generallyapplied to RT centers.

Comparing Optimization Methods for Radiation Therapy Patient Scheduling using Different Objectives