  This study presents a comprehensive approach for optimizing the acquisition,
utilization, and maintenance of ABLVR vascular robots in healthcare settings.
Medical robotics, particularly in vascular treatments, necessitates precise
resource allocation and optimization due to the complex nature of robot and
operator maintenance. Traditional heuristic methods, though intuitive, often
fail to achieve global optimization. To address these challenges, this research
introduces a novel strategy, combining mathematical modeling, a hybrid genetic
algorithm, and ARIMA time series forecasting. Considering the dynamic
healthcare environment, our approach includes a robust resource allocation
model for robotic vessels and operators. We incorporate the unique requirements
of the adaptive learning process for operators and the maintenance needs of
robotic components. The hybrid genetic algorithm, integrating simulated
annealing and greedy approaches, efficiently solves the optimization problem.
Additionally, ARIMA time series forecasting predicts the demand for vascular
robots, further enhancing the adaptability of our strategy. Experimental
results demonstrate the superiority of our approach in terms of optimization,
transparency, and convergence speed from other state-of-the-art methods.
