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Machine learning

Intelligent Scheduling Method for Ambulatory Surgery Based on Machine Learning and Fuzzy Optimization

Strategic Insights

— 1 min read

Researchers from Anhui University in Hefei, People's Republic of China, have proposed an intelligent scheduling method for ambulatory surgery using machine learning and fuzzy optimization. The method aims to improve the predictive accuracy of uncertain parameters and the efficiency of elective surgery schedules. The research team conducted a study using actual hospital data, which showed that the proposed method could effectively improve the predictive accuracy by 38.1% for uncertain parameters and the efficiency of the elective surgery schedule model.

Key Takeaways:

  • The proposed method integrates machine learning technology and fuzzy set theory to predict uncertain parameters in elective surgery scheduling.
  • The method uses a multi-source information integration algorithm to utilize existing data, surgeon opinion, and other multidimensional information.
  • The scheduling model for elective surgery is based on fuzzy optimization technology and the Benders-dual cutting plane algorithm.
  • The proposed method was proven to improve the predictive accuracy by 38.1% for uncertain parameters and the efficiency of the elective surgery schedule model.
  • The research has been peer-reviewed and published in the Annals of Operations Research journal.
  • The study was funded by the National Natural Science Foundation of China, the Natural Science Foundation of Shandong Province, China, and the China Postdoctoral Science Foundation.
  • The researchers from Anhui University aim to apply the proposed method to real-world scenarios to further improve the efficiency and accuracy of elective surgery scheduling.

Statistics:

  • 38.1%: The proposed method improved the predictive accuracy for uncertain parameters.
  • 100%: The research has been peer-reviewed and published in a reputable journal.
  • 3: The number of funding sources for the research, including the National Natural Science Foundation of China, the Natural Science Foundation of Shandong Province, China, and the China Postdoctoral Science Foundation.
  • 1: The number of the Annals of Operations Research journal article published on the topic.
  • 1 October 2025: The publication date of the news report on the research.
  • 2025: The year in which the research was conducted and published.

Sources:

  • National Natural Science Foundation of China (NSFC)
  • Natural Science Foundation of Shandong Province, China
  • China Postdoctoral Science Foundation
  • Zhong-Ping Li, Anhui University, School of Business, Hefei, People's Republic of China
  • Annals of Operations Research
  • Springer (publisher of the Annals of Operations Research journal)

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