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General introduction to DEA

DEA (Data Envelopment Analysis) is the optimization method of mathematical programming to generalize the Farrell(1957) single-input/ single-output technical efficiency measure to the multiple-input/ multiple-output case by constructing a relative efficiency score as the ratio of a single virtual output to a single virtual input. Thus DEA become a new tool in operational research for measuring technical efficiency. It originally was developed by Charnes, Cooper, Rhodes(1978) with CRS and was extended by Banker, Charnes, Cooper(1984) to include variable returns to scale. So the basic DEA models are known as CCR and BCC. Since 1978 over 1000 articles, books and dissertation have been published and DEA has rapidly extended to returns to scale, dummy or categorical variables, discretionary and non-discretionary variables, incorporating value judgments, longitudinal analysis, weight restrictions, stochastic DEA, non-parametric Malmquist indices, technical change in DEA and many other topics. Up to now the DEA measure has been used to evaluate and compare educational departments (schools, colleges and universities), health care (hospitals, clinics) prisons, agricultural production, banking, armed forces, sports, market research, transportation (highway maintenance), courts, benchmarking, index number construction and many other applications.At the moment researchers follow wide ranges of DEA and related topics.
Here are some topics in DEA:
Returns to scale
Dummy or categorical variables
Discretionary and non-discretionary variables
Incorporating judgment
Longitudinal analysis
Weight restriction
Stochastic DEA
Fuzzy and imprecise DEA
Non-parametric Malmquist indices
Technical change in DEA
Dynamics of Data Envelopment Analysis
Sensitivity
and many more ….

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