  Radiation therapy (RT) is a medical treatment to kill cancer cells or shrink
tumors. To manually schedule patients for RT is a time-consuming and
challenging task. By the use of optimization, patient schedules for RT can be
created automatically. This paper presents a study of different optimization
methods for modeling and solving the RT patient scheduling problem, which can
be used as decision support when implementing an automatic scheduling algorithm
in practice. We introduce an Integer Programming (IP) model, a column
generation IP model (CG-IP), and a Constraint Programming model. Patients are
scheduled on multiple machine types considering their priority for treatment,
session duration and allowed machines. Expected future patient arrivals are
included in the models as placeholder patients. Since different cancer centers
can have different scheduling objectives, the models are compared using
multiple objective functions, including minimizing waiting times, and
maximizing the fulfillment of patients' preferences for treatment times. The
test data is generated from historical data from Iridium Netwerk, Belgium's
largest cancer center with 10 linear accelerators. The results demonstrate that
the CG-IP model can solve all the different problem instances to a mean
optimality gap of less than 1% within one hour. The proposed methodology
provides a tool for automated scheduling of RT treatments and can be generally
applied to RT centers.

Optimization and Control