A multi-stage multi-criteria data analytics approach to rank commercial service airports

The continuous performance evaluation of commercial service airports is an important part of airport operations. Despite its significance, the existing airport ranking approaches by various aviation organizations and agencies does not provide a comprehensive methodology to rank the airports based on multiple criteria. This paper proposes a comprehensive multi-stage multi-criteria data analytics approach that combines data envelopment analysis, best-worst method, and multi-attributive border approximation area comparison method to provide a robust ranking framework. The ranking procedure is based on seven criteria, i.e. passenger throughput, number of direct routes, number of check-in counters, terminal area, number of gates, number of flights, and number of runways. The proposed method ranks the shortlisted airports from the best to the worst and evaluates the strengths and weaknesses of each of the candidate airports. A real-world example involving the ranking of the efficient major commercial service airports in China is demonstrated to validate the applicability of the proposed approach. The insights generated have implications for the benchmarking of airports.

Automated summary

This paper proposes a comprehensive multi-stage multi-criteria data analytics approach that combines data envelopment analysis, best-worst method, and multi-attributive border approximation area comparison method to provide a robust ranking framework for commercial service airports. The proposed method ranks the shortlisted airports based on seven criteria and evaluates their strengths and weaknesses. A real-world example in China is used to validate the applicability of the proposed approach and generate insights for airport benchmarking.