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Performance based clustering for benchmarking of US airports

机译:基于性能的聚类,对美国机场进行基准测试

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Managing service operations is gaining significant attention in both academic and practitioner circles. In this broad area, performance evaluation and process improvement of airlines and air carriers has been the focus of several studies. Although efficient airport operations are critical for improved performance of airlines and air carriers, few studies have focused on airport performance measurement. This study evaluates the operational efficiencies of 44 major US airports across 5 years using multi-criteria non-parametric models. These efficiency scores are treated by a clustering method in identifying benchmarks for improving poorly performing airports. Efficiency measures are based on four resource input measures including airport operational costs, number of airport employees, gates and runways, and five output measures including operational revenue, passenger flow, commercial and general aviation movement, and total cargo transportation. The methodology presented here can be generalized to other industries and institutions.
机译:管理服务运营在学术界和从业者界都得到了极大的关注。在这个广阔的领域,航空公司和航空承运人的绩效评估和流程改进一直是多项研究的重点。尽管有效的机场运营对于改善航空公司和航空承运人的绩效至关重要,但很少有研究集中在机场绩效的评估上。这项研究使用多准则非参数模型评估了美国44个主要机场在5年内的运营效率。这些效率分数通过聚类方法进行处理,以确定用于改善业绩不佳的机场的基准。效率衡量标准基于四项资源投入衡量指标,包括机场运营成本,机场员工人数,登机口和跑道,以及五项产出衡量指标,包括运营收入,旅客流量,商业和通用航空运输以及总货运量。这里介绍的方法可以推广到其他行业和机构。

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