In external-beam radiotherapy (RT), delays in the start of treatment can negatively affect the patient’s outcome and quality of life. Radiotherapy pre-treatment operations (CT, MRI, tumor delineation, treatment planning, etc.) are tightly and time-wise connected, personnel are highly specialized, and the required equipment may be dedicated to patients with specific tumor sites. Managing patient flows in such an environment is challenging and is further complicated by the high levels of uncertainty in the process, such as a highly fluctuating patient inflow. As the number of patients diagnosed with cancer increases and treatment pathways become more and more personalized, avoiding delays in the start of treatment and under/over utilization of resources becomes increasingly difficult for RT centers. In this thesis, we studied, designed, and developed several Operations Research (OR) models for the logistical optimization of RT processes to support decision-makers use their resources more efficiently. We propose innovative approaches for solving logistical problems encountered in the whole RT chain of operations, from pre-treatment to treatment, while optimizing for the most important KPIs related to timeliness and patient-centeredness. The developed research work is practice-oriented, with models being built, validated, and tested using real-world information and data provided by six collaborating RT centers.
|Qualification||Doctor of Philosophy|
|Award date||26 Jun 2020|
|Place of Publication||Enschede|
|Publication status||Published - 26 Jun 2020|