DEA based on strongly efficient and inefficient frontiers and its application on port efficiency measurement

Data envelopment analysis (DEA) is a non-parametric analytical methodology widely used in efficiency measurement of decision making units (DMUs). Conventionally, after identifying the efficient frontier, each DMU is compared to this frontier and classified as efficient or inefficient. This paper first introduces the strongly efficient frontier (SEF) and strongly inefficient frontier (SIF), and then proposes several models to calculate various distances between DMUs and both frontiers. Specifically, the distances considered in this paper include: (1) both the distance to SEF and the distance to SIF, where the former reveals a unit’s potential opportunity to become a best performer while the latter reveals its potential risk to become a worst performer, and (2) both the closest distance and the farthest distance to frontiers, which may provide different valuable benchmarking information for units. Subsequently, based on these distances, eight efficiency indices are suggested to rank DMUs. Due to different distances adopted in these indices, the efficiency of units can be evaluated from diverse perspectives with different indices employed. In addition, all units can be fully ranked by these indices. The efficiency of 24 major Asian container ports is analyzed with our study, where the potential opportunities and potential crises of these ports are revealed and some new insights about their efficiency are provided.

[1]  Zilla Sinuany-Stern,et al.  Review of ranking methods in the data envelopment analysis context , 2002, Eur. J. Oper. Res..

[2]  Juan Aparicio,et al.  Closest targets and minimum distance to the Pareto-efficient frontier in DEA , 2007 .

[3]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[4]  P. Ji,et al.  An Application of DEA Windows Analysis to Container Port Production Efficiency , 2004 .

[5]  Jesús T. Pastor,et al.  Units invariant and translation invariant DEA models , 1995, Oper. Res. Lett..

[6]  P. Andersen,et al.  A procedure for ranking efficient units in data envelopment analysis , 1993 .

[7]  Chulwoo Baek,et al.  The relevance of DEA benchmarking information and the Least-Distance Measure , 2009, Math. Comput. Model..

[8]  Y Roll,et al.  Port performance comparison applying data envelopment analysis (DEA) , 1993 .

[9]  Jose L. Tongzon,et al.  Efficiency measurement of selected Australian and other international ports using data envelopment analysis , 2001 .

[10]  T. Sexton,et al.  Data Envelopment Analysis: Critique and Extensions , 1986 .

[11]  Boaz Golany,et al.  Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions , 1985 .

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

[13]  M. Asmild,et al.  Using DEA and Worst Practice DEA in Credit Risk Evaluation , 2004 .

[14]  Song Jin Yu,et al.  Performance based stratification and clustering for benchmarking of container terminals , 2009, Expert Syst. Appl..

[15]  C. Barros.  A Benchmark Analysis of Italian Seaports Using Data Envelopment Analysis , 2006 .

[16]  C. Pestana Barros,et al.  THE MEASUREMENT OF EFFICIENCY OF PORTUGUESE SEA PORT AUTHORITIES WITH DEA , 2003 .

[17]  William W. Cooper,et al.  Handbook on data envelopment analysis , 2011 .

[18]  Chun-Chu Liu,et al.  Evaluating the operational efficiency of major ports in the Asia-Pacific region using data envelopment analysis , 2008 .

[19]  Ross Robinson,et al.  Asian hub/feeder nets: the dynamics of restructuring , 1998 .

[20]  Emmanuel Thanassoulis,et al.  Finding Closest Targets in Non-Oriented DEA Models: The Case of Convex and Non-Convex Technologies , 2003 .

[21]  Gholam Reza Jahanshahloo,et al.  A ranking method based on a full-inefficient frontier , 2006 .

[22]  Richard H. Silkman,et al.  Measuring efficiency : an assessment of data envelopment analysis , 1986 .

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

[24]  Patrick T. Harker,et al.  Projections Onto Efficient Frontiers: Theoretical and Computational Extensions to DEA , 1999 .

[25]  T. Anderson,et al.  The Fixed Weighting Nature of A Cross-Evaluation Model , 2002 .

[26]  Joe Zhu.  Robustness of the efficient DMUs in data envelopment analysis , 1996 .

[27]  W. Cooper,et al.  RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models, and Relations to Other Models and Measures in DEA , 1999 .

[28]  Rodney H. Green,et al.  Efficiency and Cross-efficiency in DEA: Derivations, Meanings and Uses , 1994 .

[29]  William W. Cooper,et al.  Introduction to Data Envelopment Analysis and Its Uses: With Dea-Solver Software and References , 2005 .

[30]  Martin Dresner,et al.  NORTH AMERICAN CONTAINERPORT PRODUCTIVITY: 1984-1997 , 2004 .