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

Area of Science:

• Medical Robotics
• Operations Research
• Healthcare Management

Background:

• Vascular robotics require complex resource allocation and maintenance.
• Traditional methods struggle with global optimization for medical robots.
• Efficient management of robotic assets is crucial in healthcare.

Purpose of the Study:

• To develop an optimized strategy for acquiring, utilizing, and maintaining ABLVR vascular robots.
• To address the limitations of heuristic methods in medical robotics optimization.
• To enhance resource allocation for robotic systems and operators in vascular treatments.

Main Methods:

• Mathematical modeling combined with a hybrid genetic algorithm (simulated annealing and greedy approaches).
• ARIMA time series forecasting for predicting vascular robot demand.
• Incorporation of operator adaptive learning and robotic component maintenance needs.

Main Results:

• The proposed approach demonstrates superior optimization compared to state-of-the-art methods.
• Enhanced transparency and faster convergence speed in optimization tasks.
• Effective prediction of vascular robot demand and improved resource allocation.

Conclusions:

• The novel strategy effectively optimizes vascular robot operations in healthcare.
• The hybrid approach offers a robust solution for complex resource management in medical robotics.
• This work provides a foundation for improving efficiency and effectiveness in robotic vascular surgery.