  In the realm of medical science, the pursuit of enhancing treatment efficacy
and patient outcomes continues to drive innovation. This study delves into the
integration of biomimicry principles within the domain of Radiation Therapy
(RT) to optimize patient scheduling, ultimately aiming to augment treatment
results. RT stands as a vital medical technique for eradicating cancer cells
and diminishing tumor sizes. Yet, the manual scheduling of patients for RT
proves both laborious and intricate. In this research, the focus is on
automating patient scheduling for RT through the application of optimization
methodologies. Three bio-inspired algorithms are employed for optimization to
tackle the complex online stochastic scheduling problem. These algorithms
include the Genetic Algorithm (GA), Firefly Optimization (FFO), and Wolf
Optimization (WO). These algorithms are harnessed to address the intricate
challenges of online stochastic scheduling. Through rigorous evaluation,
involving the scrutiny of convergence time, runtime, and objective values, the
comparative performance of these algorithms is determined. The results of this
study unveil the effectiveness of the applied bio-inspired algorithms in
optimizing patient scheduling for RT. Among the algorithms examined, WO emerges
as the frontrunner, consistently delivering superior outcomes across various
evaluation criteria. The optimization approach showcased in this study holds
the potential to streamline processes, reduce manual intervention, and
ultimately improve treatment outcomes for patients undergoing RT.
