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 Management Science 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 4000 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.

If you are new to DEA, you could familiarise yourself with the basic concepts of DEA by reading:

Thanassoulis, E (2001), Introduction to the Theory and Application of Data Envelopment Analysis: A Foundation Text with Integrated Software
Please note that the software integrated in the above book is Warwick DEA Software

Information about DEA and its application can also be found at:

"Ali Emrouznejad's Data Envelopment Analysis Homepage ( www.DEAzone.com )"