In order to improve the ability of airspace management, a multi-attribute decision-making tool based on q-rung orthopair probability hesitant fuzzy GRA-TOPSIS is proposed to solve the problem of airspace operation effectiveness evaluation; this is in view of the fact that there are few airspace operation effectiveness evaluation methods in general aviation airports. Firstly, taking general aviation airports as the research object, a complete airspace operation effectiveness evaluation system is newly established, its evaluation indicators are introduced, and its multi-attribute decision-making ideas are explained. Then, based on the q-rung orthopair probability hesitant fuzzy set, a new distance measure and information aggregation operator are defined, which can better deal with symmetry information. Secondly, we build a deviation maximization model to calculate the attribute weights of indicator elements in the decision-making process. Then, we combine the GRA method and TOPSIS method to rank the airspace operation effectiveness evaluation schemes. Finally, combined with calculation examples and comparative analysis, the reliability and rationality of the method proposed in this paper are verified, and the symmetry relationship between the evaluation results and the actual situation is better reflected. Experiments show that the method proposed in this paper can obtain more accurate airspace operation effectiveness evaluation results, and can provide reference for related research.

To enhance the operational management capabilities of general aviation airports, this paper proposes a method for evaluating the runway capacity of general aviation airports based on the runway expansion system. Firstly, it provides a brief introduction to the flight rules of general aviation airports and arrival and departure flight procedures with symmetrical characteristics, which serve as a theoretical basis for establishing the runway expansion system. Subsequently, a runway expansion system that covers symmetrical flight activities such as departure and arrival under a visual flight rule and an instrument flight rule is proposed, providing a conceptual model for evaluating the runway capacity of general aviation airports. On this foundation, the classical space–time analysis model is improved to establish a single runway arrival, departure, and mixed operation capacity evaluation model for general aviation airports. Finally, the reliability and rationality of this method are verified through case evaluations and three sets of numerical experiments with symmetrical relationships. The experiments demonstrate that this method can better reflect the actual conditions of the runways at general aviation airports while ensuring flight safety, and it can provide a reference for related research.

Currently, airport fleet maintenance is characterized by diverse aircraft models, densely concurrent tasks, and tightly coupled professional skills. Traditional specialized personnel allocation models often lead to severe human resource redundancy and high costs, whereas fully generalized models struggle to meet the strict safety and professional qualification requirements for complex maintenance. Scientifically allocating maintenance personnel to balance support costs and operational efficiency under the constraints of broad task scopes, varied personnel qualifications, and strict time limits has become a critical issue for ensuring stable fleet operations and overall airport efficiency. To address this issue, this study proposes an optimization method for aircraft fleet maintenance support personnel allocation based on an Improved Genetic Algorithm. First, the practical challenges are analyzed and a modeling framework is established by systematically examining the implementation processes, professional skill requirements, and time-consuming characteristics of common maintenance tasks across various aircraft models. Next, the minimum required number of maintenance personnel is calculated as a baseline constraint based on the man-hour method. Subsequently, incorporating practical engineering constraints such as cross-type aircraft support, an Improved Genetic Algorithm integrating adaptive crossover/mutation operators and an elite local hill-climbing strategy is designed to solve the allocation optimization problem. Finally, case studies under four allocation schemes and multiple ablation experiments are performed to comprehensively verify the reliability and rationality of the proposed method. The experimental results demonstrate that the optimal personnel allocation scheme obtained with this method saves approximately 10% to 11% in total human resource costs, when compared to the traditional independent support model. This study provides a scientific decision-making basis and technical support for the refined allocation of human resources in the context of fleet maintenance.

The journal retracts the article “Airspace Operation Effectiveness Evaluation Based on q-Rung Orthopair Probabilistic Hesitant Fuzzy GRA and TOPSIS”