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 development of complex equipment faces prominent challenges, including unequal status among participating agents, multi-objective full confrontation, strong super-conflict among multi-indicators, dynamic risk evolution, delayed on-site perception, and the absence of collaborative negotiation. Traditional risk management and control methods, based on the ideal assumptions of equal subjects and independent indicators, struggle to characterize and resolve super-conflict games dominated by super decision-makers. Furthermore, they lack dynamic early warning and closed-loop execution mechanisms linked to real-time perception, commonly suffering from drawbacks such as low early warning accuracy, high decision-making conflict, delayed response, and inefficient collaboration. To address these issues, this paper integrates multi-agent conflict negotiation with intelligent perception and learning technologies to propose an intelligent risk early warning and closed-loop control method for complex equipment development. The three-dimensional risk evolution dynamics model and Virtual Risk Center (VRC) are employed to decouple super-conflict indicators, while the industrial inspection unmanned aerial vehicle (UAV) perception relative motion model enables the unified mapping of physical risks and decision-making games. A super-conflict gray target negotiation (SCGTN) model is constructed to achieve stable consensus decisions among multiple parties under conflicting indicators. Based on Markov Decision Processes and the PPO algorithm, the optimal virtual risk trajectory is generated, which is then combined with the Archimedean spiral convergence trajectory to synthesize executable control trajectories. This forms an integrated system of UAV real-time perception → super-conflict resolution → intelligent decision-making → closed-loop regulation. Validated through a case study of large-scale complex aviation equipment development, the proposed method achieves field-validated risk early warning accuracy of 94.7% evaluated against real-world on-site ground truth labels. The numerical simulation results, whose parameters are fully calibrated against real-world engineering datasets, indicate that under simulated test conditions, our method yields simulation-predicted performance: it reduces decision-making conflict intensity by 49.3%, controls risk deviation error within 1.38%, and shortens closed-loop response time to 158 ms. Note that conflict reduction level, risk deviation error, and closed-loop response time are pure simulation outputs and have not been directly measured from physical on-site closed-loop experiments. Under the same simulation setup, the end-to-end response speed is 7.6 times faster than the peer dynamic closed-loop Digital Twin-Proximal Policy Optimization (DT-PPO) benchmark algorithm with identical online sensing and reinforcement learning architecture and roughly 4700 times faster than simulated counterparts of traditional static offline evaluation modes that rely on periodic manual statistics and offline meetings. It is adaptable to complex equipment development scenarios characterized by strong super-conflict, high dynamics, and unequal subjects, providing a theoretical framework and technical support for intelligent risk prevention and control throughout the full lifecycle of complex equipment.

Quality risks in digitalized equipment manufacturing processes are increasingly characterized by multi-source coupling, chain-like transmission, and latent evolution, making it difficult for single-method approaches alone to fully reveal their transmission mechanisms and key driving factors. This study proposes a mixed-methods framework integrating grounded theory, covariance-based structural equation modeling (CB-SEM), and XGBoost-SHAP. First, grounded theory was applied to in-depth interview data to develop a five-dimensional quality risk framework covering process design and transformation, data flow and collaboration, production execution and process control, quality inspection and data traceability, and personnel capability matching. Second, based on 420 valid questionnaire responses, CB-SEM was used to validate the chain transmission path of risks along “process design–data collaboration–production execution–inspection and traceability” and to reveal the pervasive effect of personnel capability matching risk, with indirect effects accounting for 43.5%. Finally, the XGBoost-SHAP framework was introduced to capture nonlinear and item-level effects, identifying risk items with high predictive contributions, such as the absence of pre-job certification, underlying control failures, and distorted simulation boundary conditions. By integrating qualitative construction, linear validation, and nonlinear attribution, this study provides cross-method evidence for identifying the driving factors and transmission mechanisms of quality risks in equipment digital manufacturing processes. The findings deepen the understanding of how quality risks are formed and transmitted across equipment digital manufacturing processes and provide decision support for agile and targeted digital quality control in equipment manufacturing enterprises.

Quality risks in digitalized equipment manufacturing processes are increasingly characterized by multi-source coupling, chain-like transmission, and latent evolution, making it difficult for single-method approaches alone to fully reveal their transmission mechanisms and key driving factors. This study proposes a mixed-methods framework integrating grounded theory, covariance-based structural equation modeling (CB-SEM), and XGBoost-SHAP. First, grounded theory was applied to in-depth interview data to develop a five-dimensional quality risk framework covering process design and transformation, data flow and collaboration, production execution and process control, quality inspection and data traceability, and personnel capability matching. Second, based on 420 valid questionnaire responses, CB-SEM was used to validate the chain transmission path of risks along “process design–data collaboration–production execution–inspection and traceability” and to reveal the pervasive effect of personnel capability matching risk, with indirect effects accounting for 43.5%. Finally, the XGBoost-SHAP framework was introduced to capture nonlinear and item-level effects, identifying key risk drivers such as the absence of pre-job certification, underlying control failures, and distorted simulation boundary conditions. By integrating qualitative construction, linear validation, and nonlinear attribution, this study provides cross-method evidence for identifying the driving factors and transmission mechanisms of quality risks in equipment digital manufacturing processes. The findings deepen the understanding of how quality risks are formed and transmitted across equipment digital manufacturing processes and provide decision support for agile and targeted digital quality control in equipment manufacturing enterprises.

To address the fuzziness of multi-criteria evaluation information and the bounded rationality of decision-makers in small UAV procurement, this paper proposes a procurement decision-making method that incorporates the dual psychological aversion of regret and disappointment. Firstly, an evaluation index system for small UAV procurement suitable for border patrol missions is constructed, including endurance, communication reliability, wind resistance level, protection level, infrared detection range, and procurement cost. Secondly, intuitionistic fuzzy sets are employed to represent expert evaluation information, and an intuitionistic fuzzy weighted averaging operator is used to aggregate the evaluation information from multiple experts. On this basis, the intuitionistic fuzzy CRITIC method is applied to objectively determine the weights of the criteria. Subsequently, regret theory and disappointment theory are integrated to construct a comprehensive perceived utility function, which is then used to rank and select the candidate UAV models. Finally, a case study of a border patrol mission scenario is conducted to verify the feasibility and effectiveness of the proposed method. Sensitivity analysis demonstrates that within a reasonable range of psychological parameters, the core selection conclusions of the model remain highly robust.