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

Zixi Wang, Yubo Huang, Yukai Zhang, Yifei Sheng, Xin Lai, Peng Lu

TL;DR

The paper addresses the problem of efficiently acquiring, deploying, and maintaining ABLVR vascular robots and their operators in a dynamic hospital environment. It proposes a comprehensive framework that combines a robust resource allocation model, a greedy-initialized genetic algorithm with simulated annealing refinements, and ARIMA time-series forecasting to predict demand and guide procurement. Key contributions include a cost-focused procurement formulation with maintenance and learning costs, a hybrid optimization engine that accelerates convergence while avoiding local optima, and demand forecasting that enhances strategic adaptability. The findings demonstrate lower costs and faster convergence compared with state-of-the-art methods, highlighting the practical impact for scalable, cost-effective deployment of vascular robotics in clinical workflows.

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.

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

TL;DR

The paper addresses the problem of efficiently acquiring, deploying, and maintaining ABLVR vascular robots and their operators in a dynamic hospital environment. It proposes a comprehensive framework that combines a robust resource allocation model, a greedy-initialized genetic algorithm with simulated annealing refinements, and ARIMA time-series forecasting to predict demand and guide procurement. Key contributions include a cost-focused procurement formulation with maintenance and learning costs, a hybrid optimization engine that accelerates convergence while avoiding local optima, and demand forecasting that enhances strategic adaptability. The findings demonstrate lower costs and faster convergence compared with state-of-the-art methods, highlighting the practical impact for scalable, cost-effective deployment of vascular robotics in clinical workflows.

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.
Paper Structure (13 sections, 34 equations, 13 figures, 1 table, 1 algorithm)

This paper contains 13 sections, 34 equations, 13 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Methodology
  3. Assumptions
  4. Mathematical Modeling
  5. Optimal decision making based on the single-objective genetic algorithm
  6. ARIMA Sequences and Seasonal Sequence Forecasting
  7. Improved Genetic Algorithm Based on Greedy and Simulated Annealing Ideas
  8. Experiments and Discussions
  9. Experiments
  10. Discussions
  11. Traditional Methods
  12. Machine Learning Approaches
  13. Conclusions

Figures (13)

  • Figure 1: Model Analysis of Optimization Strategies for Vascular Robots.
  • Figure 2: Flowchart of hybrid genetic algorithm combining simulated annealing and greedy methods.
  • Figure 3: Result by using GA with a greedy algorithm, including specific week, number of vessel boats purchased, number of operators purchased, number of operators maintained, number of vessel boats maintained, number of operators involved in training (including "skilled" and "novice" workers) and total cost
  • Figure 4: Result by using GA without the greedy algorithm, including specific week, number of vessel boats purchased, number of operators purchased, number of operators maintained, number of vessel boats maintained, number of operators involved in training (including "skilled" and "novice" workers) and total cost
  • Figure 5: Schematic diagram of simulated annealing and genetic correlation curves
  • ...and 8 more figures