Improved Genetic Algorithm Based on Greedy and Simulated Annealing Ideas for Vascular Robot Ordering Strategy

TL;DR

This paper introduces a hybrid genetic algorithm combined with simulated annealing, greedy strategies, and ARIMA forecasting to optimize vascular robot resource management in healthcare, improving efficiency and adaptability.

Contribution

It presents a novel hybrid optimization method integrating multiple techniques for vascular robot management, addressing complex healthcare resource allocation challenges.

Findings

Outperforms existing methods in optimization accuracy

Achieves faster convergence and better resource utilization

Enhances adaptability with demand forecasting

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 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.