Airports Performance and Efficiency Evaluation Based on Multidimentional Tools
Airport benchmarking depends on airport operational performance and efficiency indicators, which are important issues for business, operational management, regulatory agencies, airlines and passengers. There are several sets of single and complex indicators to evaluate airports performance and efficiency as well as several techniques to benchmark such infrastructures.
The general aim of this work is the development of airport performance and efficiency predictive models using robust but flexible methodologies and incorporating simultaneously traditional indicators (number of movements and passengers, tons of cargo, number of runways and slots, area of terminals both of passenger and cargo) as well as new (emergent) constraints as ramp incidents and volcano ashes.
Specifically this work: firstly shows the performance and efficiency evolution of a set of airports under several constraints based on two multidimensional tools, Multicriteria Decision Analysis (MCDA, by the use of Macbeth – Measuring Attractiveness by a Categorical Based Evaluation Technique) and Data Envelopment Analysis (DEA); and secondly compares the obtained results either with Macbeth or with DEA.
Whilst DEA is a linear programming based technique for measuring the relative performance of organizational units in the presence of multiple inputs and outputs, MCDA/Macbeth uses performance and efficiency indicators to support benchmark results, being useful to evaluate not only the real importance of the selected indicators but also its correct weight.
This work is divided as follows: first, a state of the art review concerning airport operational performance and efficiency indicators, and DEA and MCDA tools and techniques; second, the impacts on airports operational performance and efficiency of emergent operational factors (ramp incidents) and sudden meteorological/natural phenomenon (volcano ashes); third, a study on the feasibility of the incorporation of such inputs in airport performance and efficiency predictive models; fourth, the presentation of some case studies concerning a set of selected airports; fifth, some insights and challenges about future research still under development.
We believe that new models are needed to benchmark airports simultaneously based on scientific techniques robust but flexible to accommodate new (emerging) constraints and useful for those responsible for airport management in different processes of decision making.