Radiation Therapy Patient Scheduling: An Operations Research Approach
Time: Fri 2023-05-26 10.00
Location: F3, Lindstedtsvägen 26 & 28, Stockholm
Subject area: Applied and Computational Mathematics, Optimization and Systems Theory
Doctoral student: Sara Frimodig , Optimeringslära och systemteori
Opponent: Professor Willem-Jan van Hoeve, Carnegie Mellon University, USA
Supervisor: Per Enqvist, Optimeringslära och systemteori; Jan Kronqvist, Optimeringslära och systemteori; Mats Carlsson, RISE Research Institutes of Sweden
The manual scheduling of patients for radiation therapy is difficult and labor-intensive. With the increase in cancer patient numbers, efficient resource planning is an important tool to achieve short waiting times and equal right to care. This thesis studies an operations research approach to the radiation therapy scheduling problem. The four appended papers each provide incremental steps towards a clinical implementation of an automated scheduling algorithm.
In Paper A, three models for the radiation therapy scheduling problem in a simplified clinical setup are proposed. It is shown that the two constraint programming models find feasible solutions more quickly, while the integer programming model proves optimality faster. However, none of the models can solve large problem instances in sufficient time. In Paper B a collaboration with a large cancer center with ten linear accelerators is initiated. The previous models are refined and adapted to a more realistic clinical setup. Moreover, a column generation approach is introduced. The models are compared using different objective function combinations designed to mimic the scheduling objectives at different cancer centers. The column generation approach outperforms the other methods on all problem instances, regardless of what objective is optimized. In Paper C the column generation approach is further developed to include additional medical and technical constraints. Different methods to ensure that there are available resources for high priority patients at arrival are compared. Finally, in Paper D the potential for clinical implementation of the column generation approach is evaluated. The schedules generated by the column generation model are clinically validated. Compared to manually constructed, historical schedules for a time period of one year, the automatically generated schedules are shown to decrease the average patient waiting time by 80%, improve the consistency in treatment times between appointments by 80%, and increase the number of treatments scheduled the machine best suited for the treatment by more than 90%, without loss of performance in other quality metrics.
Since the constraints between radiotherapy centers are similar and multiple objective functions are presented, the column generation approach can be generally used for automated patient scheduling in radiation therapy. This would allow radiotherapy centers to save time during the scheduling process and improve the quality of the schedules.