Radiotherapy Patient Scheduling During Pandemics

Abstract

With the Covid-19 outbreak happening worldwide, clinically vulnerable people should be of concern, as they are more likely to be exposed to the virus. Cancer patients with weak immune systems are a group of aforementioned people that often have to undergo radiotherapy treatment sessions every day for several weeks. Therefore, special measures are to take place for more protection. During the treatment process, they will be assigned to Linear Accelerator (LINAC) machines that are located in separate rooms of the radiotherapy center. During each visit, they are in close contact with other patients that are assigned to the same LINAC, but for different time slots. Our research focuses on scheduling radiotherapy patients, using two mixed-integer linear programming models, to minimize the total number of potential interactions between patients. A secondary objective is then proposed to choose among the set of optimal solutions, and the models' complexity growth is discussed. Then, we introduce a heuristic algorithm to increase the efficiency of the proposed model for large instances and use a visual step-by-step example to further elaborate the algorithm details. Finally, small numerical examples are used to demonstrate the effectiveness of the models, followed by larger instances from our partner clinic, the Grand River Regional Cancer Center (GRRCC). The results show that implementing the proposed model and the heuristic will decrease the number of interactions up to 75%, compared to the centre's original schedule.