Measuring the Efficiency and Congestion of Iranian Airports in 2008 with Data Envelopment Analysis

Data envelopment analysis (DEA) is a non-parametric method in operations research for measuring the efficiency of a set of decision making units (DMUs) such as firms with multiple inputs and outputs. Efficiency is the ability to produce the outputs with a minimum input required. Congestion is evidenced when the attainment of maximal output requires a reduction in one or more of the input amounts used. This study measures the efficiency and congestion of 45 Iranian airports in 2008 with considering three inputs such as airport area, runway area and terminal area and there outputs such as the number of operational flights, the number of passengers and cargo handle. The results illustrated that most airports were not profitable and require increasing their outputs significantly. In other words, they require increasing the passenger movement, aircraft movements and cargo handle for being more efficient. Moreover, the study demonstrates that most airports have congestion in their airport area and suggests that those airports should decrease their airport area to increase their efficiencies.

[1]  F. Tapiador,et al.  The geographical efficiency of Spain's regional airports: A quantitative analysis , 2008 .

[2]  R. Färe,et al.  Measuring congestion in production , 1983 .

[3]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[4]  William W. Cooper,et al.  Inefficiency and Congestion in Chinese Production Before and after the 1978 Economic Reforms , 1998 .

[5]  Concepción Román,et al.  A Benchmarking Analysis of Spanish Commercial Airports. A Comparison Between SMOP and DEA Ranking Methods , 2006 .

[6]  Pedro Simões,et al.  Measuring the Influence of Congestion on Efficiency in Worldwide Airports , 2010 .

[7]  David Gillen,et al.  DEVELOPING MEASURES OF AIRPORT PRODUCTIVITY AND PERFORMANCE: AN APPLICATION OF DATA ENVELOPMENT ANALYSIS , 1997 .

[8]  M. Farrell The Measurement of Productive Efficiency , 1957 .

[9]  Ming-Miin Yu,et al.  Measuring physical efficiency of domestic airports in Taiwan with undesirable outputs and environmental factors , 2004 .

[10]  Biresh K. Sahoo,et al.  Evaluating cost efficiency and returns to scale in the Life Insurance Corporation of India using data envelopment analysis , 2005 .

[11]  Elton Fernandes,et al.  EFFICIENT USE OF AIRPORT CAPACITY , 2002 .

[12]  William W. Cooper,et al.  A ONE-MODEL APPROACH TO CONGESTION IN DATA ENVELOPMENT ANALYSIS , 2002 .

[13]  W. Cooper,et al.  Using DEA to improve the management of congestion in Chinese industries (1981-1997) , 2001 .

[14]  R. Pacheco,et al.  Managerial efficiency of Brazilian airports , 2003 .

[15]  Sebastián Lozano,et al.  Slacks-based measure of efficiency of airports with airplanes delays as undesirable outputs , 2011, Comput. Oper. Res..

[16]  Joseph Sarkis An analysis of the operational efficiency of major airports in the United States , 2000 .

[17]  Kazuyuki Sekitani,et al.  DEA congestion and returns to scale under an occurrence of multiple optimal projections , 2009, Eur. J. Oper. Res..

[18]  Hong Yan,et al.  Congestion and returns to scale in data envelopment analysis , 2004, Eur. J. Oper. Res..

[19]  R. Färe,et al.  Slacks and congestion: a comment , 2000 .

[20]  Nicole Adler,et al.  Measuring airport quality from the airlines' viewpoint: an application of data envelopment analysis , 2001 .

[21]  R. Färe,et al.  The measurement of efficiency of production , 1985 .

[22]  C. Barros,et al.  Performance evaluation of Italian airports: A data envelopment analysis , 2007 .

[23]  Kaoru Tone,et al.  Degree of scale economies and congestion: A unified DEA approach , 2002, Eur. J. Oper. Res..

[24]  William W. Cooper,et al.  Slacks and congestion: response to a comment by R. Färe and S. Grosskopf☆ , 2001 .

[25]  A. U.S.,et al.  Measuring the efficiency of decision making units , 2003 .

[26]  Gholam Reza Jahanshahloo,et al.  Suitable combination of inputs for improving outputs in DEA with determining input congestion: Considering textile industry of China , 2004, Appl. Math. Comput..