Improving the discrimination power and weights dispersion in the data envelopment analysis

被引：68

作者：

Bal, Hasan ^{[1
]}

Orkcu, H. Hasan ^{[1
]}

Celebioglu, Salih ^{[1
]}

机构：

[1] Gazi Univ, Arts & Sci Fac, Dept Stat, TR-06500 Ankara, Turkey

来源：

COMPUTERS & OPERATIONS RESEARCH | 2010年 / 37卷 / 01期

关键词：

Data envelopment analysis; Coal programming; Discrimination power; Unit invariance; RELATIVE EFFICIENCY; DEA MODELS; RESTRICTIONS; RANKING; UNITS;

D O I：

10.1016/j.cor.2009.03.028

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Data envelopment analysis (DEA) has been a very popular method for measuring and benchmarking relative efficiency of peer decision making units (DMUs) with multiple input and outputs. Beside of its popularity, DEA has some drawbacks such as unrealistic input-output weights and lack of discrimination among efficient DMUs. In this study, two new models based on a multi-criteria data envelopment analysis (MCDEA) are developed to moderate the homogeneity of weights distribution by using goal programming (GP). These goal programming data envelopment analysis models, GPDEA-CCR and GPDEA-BCC, also improve the discrimination power of DEA. (C) 2009 Elsevier Ltd. All rights reserved.

引用

页码：99 / 107

页数：9

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