Improved genetic algorithm based on greedy and simulated annealing ideas for vascular robot ordering strategy.
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 rob...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0306990 |
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| _version_ | 1850278337291747328 |
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| author | Zixi Wang Yubo Huang Yukai Zhang Yifei Sheng Xin Lai Peng Lu |
| author_facet | Zixi Wang Yubo Huang Yukai Zhang Yifei Sheng Xin Lai Peng Lu |
| author_sort | Zixi Wang |
| collection | DOAJ |
| description | 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. The source code is available at https://github.com/ybfo/improved-GA. |
| format | Article |
| id | doaj-art-9274b72ab2bd43bbb163b7355e9d0e23 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-9274b72ab2bd43bbb163b7355e9d0e232025-08-20T01:49:32ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e030699010.1371/journal.pone.0306990Improved genetic algorithm based on greedy and simulated annealing ideas for vascular robot ordering strategy.Zixi WangYubo HuangYukai ZhangYifei ShengXin LaiPeng LuThis 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. The source code is available at https://github.com/ybfo/improved-GA.https://doi.org/10.1371/journal.pone.0306990 |
| spellingShingle | Zixi Wang Yubo Huang Yukai Zhang Yifei Sheng Xin Lai Peng Lu Improved genetic algorithm based on greedy and simulated annealing ideas for vascular robot ordering strategy. PLoS ONE |
| title | Improved genetic algorithm based on greedy and simulated annealing ideas for vascular robot ordering strategy. |
| title_full | Improved genetic algorithm based on greedy and simulated annealing ideas for vascular robot ordering strategy. |
| title_fullStr | Improved genetic algorithm based on greedy and simulated annealing ideas for vascular robot ordering strategy. |
| title_full_unstemmed | Improved genetic algorithm based on greedy and simulated annealing ideas for vascular robot ordering strategy. |
| title_short | Improved genetic algorithm based on greedy and simulated annealing ideas for vascular robot ordering strategy. |
| title_sort | improved genetic algorithm based on greedy and simulated annealing ideas for vascular robot ordering strategy |
| url | https://doi.org/10.1371/journal.pone.0306990 |
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