Improved genetic algorithm based on greedy and simulated annealing ideas for vascular robot ordering strategy.

ABSTRACT: 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

SUBMITTER: Wang Z 

PROVIDER: S-EPMC11841910 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature