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.

翻译：本研究提出了一种优化医疗环境中ABLVR血管机器人采购、使用与维护的综合方法。医疗机器人技术，尤其在血管治疗领域，由于机器人及操作员维护的复杂性，需要精确的资源分配与优化。传统启发式方法虽直观，但常难以实现全局优化。为应对这些挑战，本研究提出了一种融合数学建模、混合遗传算法与ARIMA时间序列预测的新策略。考虑到动态的医疗环境，我们的方法包含针对机器人血管与操作员的鲁棒资源分配模型，并整合了操作员自适应学习过程的特殊需求及机器人组件的维护需求。该混合遗传算法融合了模拟退火与贪婪思想，能高效求解优化问题。此外，ARIMA时间序列预测用于预估血管机器人需求，进一步提升了策略的适应性。实验结果表明，本方法在优化效果、透明度及收敛速度方面均优于其他先进方法。