An extended of multiple criteria data envelopment analysis models for ratio data

One of the problems of the data envelopment analysis traditional models in the multiple form that is the weights corresponding to certain inputs and outputs are considered zero in the calculation of efficiency and this means that not all input and output components are utilized for the evaluation of efficiency, as some are ignored. The above issue causes the efficiency score of the under evaluation unit not to be calculated correctly. One of the ways to deal with the pseudo-inefficiency is to use data envelopment analysis models with multi-criteria structure. In this regard, we first investigate the models of data envelopment analysis with multi-criteria structure and further, with regard to the ability of the ratio-based data envelopment analysis models, we develop data envelopment analysis models with a multi-criteria structure for ratio data and the feasibility and the bounded condition of the above models and their efficiency intervals are described. By presenting a numerical example, we compare the efficiency scores obtained from the models presented with the previous models and we show that the proposed models can be used to deal with the pseudo-inefficiency and efficiency underestimation. Finally, we present the results.

[1]  Cláudia S. Sarrico,et al.  Restricting virtual weights in data envelopment analysis , 2004, Eur. J. Oper. Res..

[2]  Chih-Hung Tsai,et al.  A study of developing an input-oriented ratio-based comparative efficiency model , 2011, Expert Syst. Appl..

[3]  R. Dyson,et al.  Reducing Weight Flexibility in Data Envelopment Analysis , 1988 .

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

[5]  Joe Zhu,et al.  DEA Cobb–Douglas frontier and cross-efficiency , 2014, J. Oper. Res. Soc..

[6]  Moussa Larbani,et al.  Multiobjective data envelopment analysis , 2009, J. Oper. Res. Soc..

[7]  Chih-Hung Tsai,et al.  Using the DEA-R model in the hospital industry to study the pseudo-inefficiency problem , 2011, Expert Syst. Appl..

[8]  Mohammad Askari Sajedi,et al.  Improving Cross-Efficiency Evaluation Using Fuzzy Concepts , 2012 .

[9]  Russell G. Thompson,et al.  The role of multiplier bounds in efficiency analysis with application to Kansas farming , 1990 .

[10]  Jiazhen Huo,et al.  Super-efficiency based on a modified directional distance function , 2013 .

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

[12]  Cláudia S. Sarrico,et al.  Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software , 2001, J. Oper. Res. Soc..

[13]  Gary R. Reeves,et al.  A multiple criteria approach to data envelopment analysis , 1999, Eur. J. Oper. Res..

[14]  Hasan Bal,et al.  A new method based on the dispersion of weights in data envelopment analysis , 2008, Comput. Ind. Eng..

[15]  K. Chin,et al.  The use of OWA operator weights for cross-efficiency aggregation , 2011 .

[16]  Hasan Bal,et al.  Improving the discrimination power and weights dispersion in the data envelopment analysis , 2010, Comput. Oper. Res..

[17]  Kwai-Sang Chin,et al.  A neutral DEA model for cross-efficiency evaluation and its extension , 2010, Expert Syst. Appl..

[18]  Joe Zhu,et al.  Super-efficiency infeasibility and zero data in DEA , 2012, Eur. J. Oper. Res..

[19]  Ana S. Camanho,et al.  The measurement of relative efficiency using data envelopment analysis with assurance regions that link inputs and outputs , 2010, Eur. J. Oper. Res..

[20]  Joe Zhu,et al.  Super-efficiency DEA in the presence of infeasibility , 2011, Eur. J. Oper. Res..

[21]  Yao Chen,et al.  Measuring super-efficiency in DEA in the presence of infeasibility , 2005, Eur. J. Oper. Res..

[22]  İhsan Alp,et al.  A New Proposed Model of Restricted Data Envelopment Analysis By Correlation Coefficients , 2013 .

[23]  Kaoru Tone,et al.  A slacks-based measure of efficiency in data envelopment analysis , 1997, Eur. J. Oper. Res..

[24]  Emmanuel Thanassoulis,et al.  Simulating Weights Restrictions in Data Envelopment Analysis by Means of Unobserved Dmus , 1998 .

[25]  A. Charnes,et al.  Polyhedral Cone-Ratio DEA Models with an illustrative application to large commercial banks , 1990 .

[26]  Chih-Hung Tsai,et al.  Exploration of efficiency underestimation of CCR model: Based on medical sectors with DEA-R model , 2011, Expert Syst. Appl..

[27]  Liang Liang,et al.  Super-efficiency DEA in the presence of infeasibility: One model approach , 2011, Eur. J. Oper. Res..

[28]  Majid Soleimani-Damaneh,et al.  A comment on "Measuring super-efficiency in DEA in the presence of infeasibility" , 2006, Eur. J. Oper. Res..

[29]  A. A. Foroughi A note on "A new method for ranking discovered rules from data mining by DEA", and a full ranking approach , 2011, Expert Syst. Appl..

[30]  Ali Emrouznejad,et al.  A bi-objective weighted model for improving the discrimination power in MCDEA , 2014, Eur. J. Oper. Res..

[31]  Barton A. Smith,et al.  Comparative Site Evaluations for Locating a High-Energy Physics Lab in Texas , 1986 .