A two-phase approach for the Radiotherapy Scheduling Problem.

Abstract

The Radiotherapy Scheduling Problem (RTSP) focuses on optimizing the planning of radiotherapy treatment sessions for cancer patients. In this paper, we propose a two-phase approach for the RTSP. In the first phase, radiotherapy sessions are assigned to specific linear accelerators (linacs) and days. The second phase then decides the sequence of patients on each day/linac and the specific appointment times. For the first phase, an Integer Linear Programming (IP) model is proposed and solved using CPLEX. For the second phase, a Mixed Integer Linear Programming (MIP) and a Constraint Programming (CP) model are proposed. The test data is generated based on real data from CHUM, a large cancer center in Montréal, Canada, with an average of 3,500 new patients and 40,000 radiotherapy treatments per year. The results show that in the second phase, CP is better at finding good solutions quickly while MIP is better at closing optimality gaps with more run time. Lastly, a simulation is conducted to evaluate the impact of different scheduling strategies on the outcome of the scheduling. Preliminary results show that batch scheduling reduces patients' waiting time and overdue time.