摘要
机场的地面保障是提高机场运行效率的关键问题之一。为全面掌握机场地面保障运行的关键影响因素,科学、客观地评价其运行状况,为现有机场及待建机场的改进提供参考依据,选择国内某机场,从人、机、环、管四个维度展开分析,构建了地面保障运行综合评价体系。传统的层次分析法虽能保证所评判内容的一致性,但缺乏对专家自身权重的度量,忽略了判断结果与群体共识之间的关系。采用了层次分析法与K-Means聚类相结合来确定指标的综合权重的方法,研究结果表明,地面保障设备性能、心理素质以及机场布局结构所占权重较高。研究认为,欲提高机场地面保障运行效率,应采取适时更新设备以保证作业效率,增加作业人员培训以提升抗压能力,合理改造现有机场布局以优化保障集结点与机位之间组合等措施。
Airport ground support is one of the key issues to improve airport operation efficiency,in order to fully grasp the key influencing factors of airport ground support operation,scientific and objective evaluation of its operation status,to provide a reference for the improvement of existing airports and airports to be built.This paper selects an airport in China,analyzes from four dimensions of people,equipment,environmentand management,and constructs a comprehensive evaluation system for ground support operation.Although the traditional analytic hierarchy process(AHP)can ensure the consistency of the evaluation content,it lacks the measurement of the weight of experts themselves and ignores the relationship between the judgment results and the group consensus.Therefore,this article uses the analytic hierarchy process combined with K-Means clustering to determine the comprehensive weight of the indicators.The research results show that the performance of ground support equipment,psychological quality and airport layout structure account for higher weights.In order to improve the operation efficiency of airport ground support,it is necessary to update the equipment timely to ensure the operation efficiency,and increase the training of operators to improve the anti-pressure ability,reasonable reconstruction of the existing airport layout to optimize the combination between the assembly point and the airport stand.
作者
何昕
孙强
杨得用
HE Xin;SUN Qiang;YANG De-yong(Civil Aviation Flight University of China,Guanghan 618000,China)
出处
《航空计算技术》
2021年第6期23-26,31,共5页
Aeronautical Computing Technique
基金
中国民用航空飞行学院面上项目资助(J2019-066)。
关键词
机场地面保障
层次分析
K-MEANS聚类
综合评价
airport ground support
analytic hierarchy process
K-means clustering
comprehensive evaluation