Synthetic Instance Generation for the Radiotherapy Scheduling Problem

The Radiotherapy Scheduling Problem (RTSP) concerns finding an optimal appointment schedule for patients undergoing radiation treatments. Given its considerable impact on clinical outcomes, this problem has been studied in previous research, relying on datasets obtained from various hospital settings; however, such real-world datasets are rarely made public--mainly for ethical approval issues--while synthetic problem instances are rare and not standardised, which makes the comparison between scheduling algorithms harder. To address this gap, we propose a Benchmark Instance Management Tool that comprehends 1) a generator of new instances that samples from statistical distributions tuned on two real-world reference datasets from a big Belgian cancer centre and a small Italian radiotherapy unit, 2) a set of solvers, such as Mixed Integer Linear Programming, Simulated Annealing and two heuristics, and 3) a validator to check solution feasibility. Moreover, we propose a standardised input and output format. The generated instances are compared to real-world data by means of Instance Space Analysis on features extracted directly from instances. The results reveal two sharply separated clusters for the real-world reference datasets in the 2-dimensional feature space, while the newly generated instances not only populate those clusters but also fill the gap between them.