Ali Emrouznejad's DEA Homepage, The most comprehensive source of Data Envelopment Analysis!!

About DEA

This is a 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.
The main research areas of DEA are:

Returns to scale
Dummy or categorical variables
Discretionary and non-discretionary variables
Incorporating judgment
Longitudinal analysis
Weight restriction
Stochastic DEA
Non-parametric Malmquist indices
Technical change in DEA
Dynamics of Data Envelopment Analysis
Sensitivity
and many other topics

For a further details of DEA see the Tutorial

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Copyright @1995-2001
Ali Emrouznejad

If using this homepage please refrence to:
Emrouznejad, A (1995-2001), " Ali Emrouznejad's DEA HomePage", Warwick Business School, Coventry CV4 7AL, UK

	This is a 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. The main research areas of DEA are: Returns to scale Dummy or categorical variables Discretionary and non-discretionary variables Incorporating judgment Longitudinal analysis Weight restriction Stochastic DEA Non-parametric Malmquist indices Technical change in DEA Dynamics of Data Envelopment Analysis Sensitivity and many other topics
	For a further details of DEA see the Tutorial