  The number of cancer cases per year is rapidly increasing worldwide. In
radiation therapy (RT), radiation from linear accelerators is used to kill
malignant tumor cells. Scheduling patients for RT is difficult both due to the
numerous medical and technical constraints, and because of the stochastic
inflow of patients with different urgency levels. In this paper, a Column
Generation (CG) approach is proposed for the RT patient scheduling problem. The
model includes all the constraints necessary for the generated schedules to
work in practice, including for example different machine compatibilities,
individualized patient protocols, and multiple hospital sites. The model is the
first to include planned interruptions in treatments due to maintenance on
machines, which is an important aspect when scheduling patients in practice, as
it can create bottlenecks in the patient flow. Different methods to ensure that
there are available resources for high priority patients at arrival are
compared, including static and dynamic time reservation. Data from Iridium
Netwerk, the largest cancer center in Belgium, is used to evaluate the CG
approach. The results show that the dynamic time reservation method outperforms
the other methods used to handle uncertainty in future urgent patients. A
sensitivity analysis also shows that the dynamic time reservation method is
robust to fluctuations in arrival rates. The CG approach produces schedules
that fulfill all the medical and technical constraints posed at Iridium Netwerk
with acceptable computation times.

Optimization and Control