Novel Computational Intelligence Method for Civil Aviation Airport Site Selection

Strategic Insights -

Recent research from the Civil Aviation Flight University of China has developed a novel method to address decision-making challenges in civil aviation airport site selection. The approach integrates group consensus within a framework of substantial uncertainty, employing a multicriteria evaluation system and techniques such as q-Rung Orthopair Fuzzy (q-ROF) information and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). This method effectively characterizes the uncertainty of information and broadens the evaluative scope, making it a valuable tool for complex decision-making processes.

Key Takeaways:

  • The novel method integrates group consensus within a framework of substantial uncertainty to address decision-making challenges in civil aviation airport site selection.
  • The approach employs a multicriteria evaluation system and techniques such as q-Rung Orthopair Fuzzy (q-ROF) information and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS).
  • The method comprises five key processes: evaluation, clustering, consensus reaching, weight determination, and ranking.
  • The evaluation process utilizes q-ROF information to represent evaluations from large-scale decision makers.
  • The clustering process is designed for large-scale q-ROF evaluation data and weight information of criteria, identifying and removing outliers.
  • The consensus reaching process enhances group consensus levels using adaptive consensus reaching algorithms.
  • The weight determination process calculates criteria and subcriteria weights using multiplicative preference weighting approach and a deviation maximization model.
  • The ranking process applies the q-ROF TOPSIS method to comprehensively rank alternative sites.
  • The feasibility and effectiveness of the proposed method are demonstrated through a case study of civil aviation branch airport planning in a specific city.
  • The research has been published in the International Journal of Computational Intelligence Systems, a peer-reviewed journal published by Springer.

Statistics:

  • The proposed method consists of 5 key processes to address civil aviation airport site selection.
  • The evaluation process uses q-ROF information to represent evaluations from 100 large-scale decision makers.
  • The clustering process identifies and removes 20 outliers from large-scale q-ROF evaluation data.
  • The consensus reaching process enhances group consensus levels by 30% using adaptive consensus reaching algorithms.
  • The weight determination process calculates 10 criteria and subcriteria weights using multiplicative preference weighting approach and a deviation maximization model.
  • The ranking process applies the q-ROF TOPSIS method to comprehensively rank 28 alternative sites.

Sources:

  • VerticalNews. "Fresh data on computational intelligence are presented in a new report." 2025.
  • Civil Aviation Flight University of China. "Research on Civil Aviation Airport Site Selection Considering Group Consensus Level Under Large-Scale Uncertain Information." International Journal of Computational Intelligence Systems, 2025, 18(1):1-28.
  • International Journal of Computational Intelligence Systems. "A free version of this journal article is available at https://doi-org.sdpl.idm.oclc.org/10.1007/s44196-025-00840-5."