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Data Envelopment Analysis: History, Models, and Interpretations

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Book cover Handbook on Data Envelopment Analysis

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 164))

Abstract

In about 30 years, Data Envelopment Analysis (DEA) has grown into a powerful quantitative, analytical tool for measuring and evaluating the performance. DEA has been successfully applied to a host of many different types of entities engaged in a wide variety of activities in many contexts worldwide. This chapter discusses the basic DEA models and some of their extensions.

Part of the material in this chapter is adapted from the Journal of Econometrics, Vol. 46, Seiford, L.M. and Thrall, R.M., Recent developments in DEA: The mathematical programming approach to frontier analysis, 7–38, 1990, with permission from Elsevier Science.

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References

  • Afriat S. Efficiency estimation of production functions. Int Econ Rev. 1972;13:568–98.

    Article  Google Scholar 

  • Ahn T, Charnes A, Cooper WW. Efficiency characterizations in different DEA models. Soc Econ Plann Sci. 1988;22:253–7.

    Article  Google Scholar 

  • Arnold V, Bardhan I, Cooper WW, Gallegos A. Primal and dual optimality in computer codes using two-stage solution procedures in DEA. In: Aronson J, Zionts S, editors. Operations research methods, models and applications. Westpost, CT: Quorum Books; 1998.

    Google Scholar 

  • Banker R, Charnes A, Cooper WW. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci. 1984;30:1078–92.

    Article  Google Scholar 

  • Banker RD, Morey RC. Efficiency analysis for exogenously fixed inputs and outputs. Oper Res. 1986a;34(4):513–21.

    Article  Google Scholar 

  • Banker RD, Morey RC. The use of categorical variables in data envelopment analysis. Manag Sci. 1986b;32(12):1613–27.

    Article  Google Scholar 

  • Bardhan I, Bowlin WF, Cooper WW, Sueyoshi T. Models and measures for efficiency dominance in DEA, part I: additive models and MED measures. J Oper Res Soc Jpn. 1996;39:322–32.

    Google Scholar 

  • Bessent A, Bessent W, Charnes A, Cooper WW, and Thorogood (1983). Evaluation of Educational Proposals by means of DEA, Educational Administrative Quarterly 14, 82–107. Reprinted in Management Science in Higher Education: Methods and Studies (New York: Elsevier Publishing co., 1987).

    Google Scholar 

  • Brockett PL, Charnes A, Cooper WW, Huang ZM, Sun DB. Data transformations in DEA cone ratio envelopment approaches for monitoring bank performances. Eur J Oper Res. 1997;98(2):250–68.

    Article  Google Scholar 

  • Bulla S, Cooper WW, Parks KS, Wilson D. Evluating efficiencies in Turbo-Fan jet engines in multiple inputs–outputs context approaches. Propul Power. 2000;16:431–9.

    Article  Google Scholar 

  • Charnes A, Cooper WW. Management models and industrial applications of linear programming. New York, NY: Wiley; 1961.

    Google Scholar 

  • Charnes A, Cooper WW. Programming with linear fractional functionals. Nav Res Logist Q. 1962;9:181–5.

    Article  Google Scholar 

  • Charnes A, Cooper WW, Rhodes E. 1978, Measuring the efficiency of decision making units, European Journal of Operational Research 2, 429–444. Also, 1979, Short Communication, European Journal of Operational Research 3, 339–340.

    Google Scholar 

  • Charnes A, Clarke CT, Cooper WW, Golany B. A developmental study of data envelopment analysis in measuring the efficiency of maintenance units in the US air forces. Annals of operation research. 1985. Vol. 2 p. 95–112.

    Google Scholar 

  • Charnes A, Cooper WW, Sun DB, Huang ZM. Polyhedral cone-ratio DEA models with an illustrative application to large commercial banks. J Econ. 1990;46:73–91.

    Google Scholar 

  • Charnes A, Cooper WW, Wei QL, Huang ZM. Cone ratio data envelopment analysis and multi-objective programming. Int J Syst Sci. 1989;20:1099–118.

    Article  Google Scholar 

  • Charnes A, Cooper WW, Golany B, Seiford L, Stutz J. Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. J Econ. 1985b;30:91–l07.

    Google Scholar 

  • Chen Y, Zhu J. Measuring information technology’s indirect impact on firm performance. Inform Technol Manag J. 2004;5(1–2):9–22.

    Article  Google Scholar 

  • Cook WD, Seiford LM. Data envelopment analysis (DEA) – Thirty years on. Eur J Oper Res. 2009;192:1–17.

    Article  Google Scholar 

  • Cook WD, Kress M, Seiford LM. On the use of ordinal data in data envelopment analysis. J Oper Res Soc. 1993;44:133–40.

    Google Scholar 

  • Cook WD, Kress M, Seiford LM. Data envelopment analysis in the presence of both quantitative and qualitative factors. J Oper Res Soc. 1996;47:945–53.

    Google Scholar 

  • Cook WD, Zhu J. Context-dependent assurance regions in DEA. Oper Res. 2008;56(1):69–78.

    Article  Google Scholar 

  • Cook WD, Liang L, Zhu J. Measuring performance of two-stage network structures by DEA: a review and future perspective. Omega. 2010;38:423–30.

    Article  Google Scholar 

  • Cook WD, Hababou M, Tuenter H. Multi-component efficiency measurement and shared inputs in data envelopment analysis: an application to sales and service performance in bank branches. J Product Anal. 2000;14:209–24.

    Article  Google Scholar 

  • Cook WD, Green RH. Evaluating power plant efficiency: a hierarchical model. Comput Oper Res. 2005;32:813–23.

    Article  Google Scholar 

  • Cook WD, Chai D, Doyle J, Green RH. Hierarchies and groups in DEA. J Product Anal. 1998;10:177–98.

    Article  Google Scholar 

  • Cook WD, Zhu J. Rank order data in DEA: a general framework. Eur J Oper Res. 2006;174(2):1021–38.

    Article  Google Scholar 

  • Cooper WW, Thompson RG, Thrall RM. Extensions and new developments in data envelopment analysis. Ann Oper Res. 1996;66:3–45.

    Google Scholar 

  • Cooper WW, Seiford LM, Tone 2nd K, editors. Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software. Boston: Kluwer; 2007.

    Google Scholar 

  • Cooper WW, Seiford LM, Zhu J. A unified additive model approach for evaluating inefficiency and congestion with associated measures in DEA. Soc Econ Plann Sci. 2000a;34(1):1–25.

    Article  Google Scholar 

  • Cooper WW, Park KS, Pastor JT. Marginal rates and Elasticities of substitution in DEA. J Product Anal. 2000b;13(2000):105–23.

    Article  Google Scholar 

  • Cooper WW, Park KS, Pastor JT. RAM: a range adjusted measure of inefficiency for use with additive models and relations to other models and measures in DEA. J Product Anal. 1999a;11:5–42.

    Article  Google Scholar 

  • Cooper WW, Park KS, Yu G. IDEA and AR-IDEA: models for dealing with imprecise data in DEA. Manag Sci. 1999b;45:597–607.

    Article  Google Scholar 

  • Debreu G. The coefficient of resource utilization. Econometrica. 1951;19:273–92.

    Article  Google Scholar 

  • Dyson RG, Thanassoulis E. Reducing weight flexibility in data envelopment analysis. J Oper Res Soc. 1988;39(6):563–76.

    Google Scholar 

  • Emrouznejad A, Parker BR, Tavares G. Evaluation of research in efficiency and productivity: a survey and analysis of the first 30 years of scholarly literature in DEA. Soc Econ Plann Sci. 2008;42:151–7.

    Article  Google Scholar 

  • Färe R, Grosskopf S, Lovell CAK. The measurement of efficiency of production. Boston: Kluwer Nijhoff Publishing Co.; 1985.

    Google Scholar 

  • Färe R, Grosskopf S, Lovell CAK. Production frontiers. Cambridge: Cambridge University Press; 1994.

    Google Scholar 

  • Färe R, Grosskopf S. Intertemporal production frontiers: with dynamic DEA. Boston, MA: Kluwer Academic; 1996.

    Google Scholar 

  • Färe R, Grosskopf S. Modelling undesirable factors in efficiency evaluation: Comment. Eur J Oper Res. 2004;157:242–5.

    Article  Google Scholar 

  • Farrell MJ. The measurement of productive efficiency. J Roy Stat Soc A. 1957;120:253–81.

    Article  Google Scholar 

  • Hua Z, Bin Y. DEA with undesirable factors. (Chapter 6). In: Zhu J, Cook WD, editors. Modeling data irregularities and structural complexities in data envelopment analysis. Boston: Springer Science; 2007.

    Google Scholar 

  • Koopmans TC, editor. Analysis of production as an efficient combination of activities. New York: Wiley; 1951.

    Google Scholar 

  • Liang LF, Yang F, Cook WD, Zhu J. DEA models for supply chain efficiency evaluation. Ann Oper Res. 2006;145(1):35–49.

    Article  Google Scholar 

  • Liang L, Cook WD, Zhu J. DEA Models for two-stage processes: game approach and efficiency decomposition. Nav Res Logist. 2008;55:643–53.

    Article  Google Scholar 

  • Roll Y, Cook WD, Golany B. Controlling factor weights in data envelopment analysis. IIE Trans. 1991;23:2–9.

    Article  Google Scholar 

  • Scheel H. Undesirable outputs in efficiency valuations. Eur J Oper Res. 2001;132:400–10.

    Article  Google Scholar 

  • Seiford LM, Zhu J. Modeling undesirable factors in efficiency evaluation. Eur J Oper Res. 2002;142(1):16–20.

    Article  Google Scholar 

  • Shephard RW. Theory of cost and production functions. Princeton, NJ: Princeton University Press; 1970.

    Google Scholar 

  • Takamura T, Tone K. A comparative site evaluation study for relocating Japanese Government Agencies out of Tokyo. Soc Econ Plann Sci. 2003;37:85–102.

    Article  Google Scholar 

  • Thompson RG, Jr FD, Singleton RMThrall, Smith BA. Comparative site evaluation for locating a high-energy physics lab in Texas. Interfaces. 1986;16:35–49.

    Article  Google Scholar 

  • Thompson RG, Langemeier L, Lee C, Lee E, Thrall R. The role of multiplier bounds in efficiency analysis with application to Kansas farming. J Econ. 1990;46:93–108.

    Google Scholar 

  • Tone K Sahoo BK. A reexamination of cost efficiency and cost elasticity in DEA. 2003. Working paper. National Graduate Institute for Policy Studies, Tokyo, Japan.

    Google Scholar 

  • Wong Y-HB, Beasley JE. Restricting weight flexibility in data envelopment analysis. J Oper Res Soc. 1990;41:829–35.

    Google Scholar 

  • Zhu J. DEA/AR analysis of the 1988–1989 performance of the Nanjing Textiles Corporation. Ann Oper Res. 1996a;66:311–35.

    Article  Google Scholar 

  • Zhu J. Data envelopment analysis with preference structure. J Oper Res Soc. 1996b;47(1):136–50.

    Google Scholar 

  • Zhu J. Quantitative models for performance evaluation and benchmarking: data envelopment analysis with spreadsheets and DEA excel solver. Boston: Kluwer; 2003a.

    Google Scholar 

  • Zhu J. Imprecise data envelopment analysis (IDEA): a review and improvement with an application. Eur J Oper Res. 2003b;144(3):513–29.

    Article  Google Scholar 

  • Zhu J. Quantitative models for performance evaluation and benchmarking: data envelopment analysis with spreadsheets. 2nd ed. Boston: Springer Science; 2009.

    Book  Google Scholar 

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Author information

Authors and Affiliations

  1. Red McCombs School of Business, University of Texas at Austin, Austin, TX, 78712, USA

    William W. Cooper

  2. Department of Industrial and Operations Engineering, University of Michigan at Ann Arbor, Ann Arbor, MI, 48102, USA

    Lawrence M. Seiford

  3. School of Business, Worcester Polytechnic Institute, Worcester, MA, 01609, USA

    Joe Zhu

Authors
  1. William W. Cooper
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  2. Lawrence M. Seiford
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  3. Joe Zhu
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Corresponding author

Correspondence to Joe Zhu .

Editor information

Editors and Affiliations

  1. McCombs School of Business, Dept. Management Science &, University of Texas, Austin, University Station 1, Austin, 78712-0212, Texas, USA

    William W. Cooper

  2. Dept. Industrial and, Operations Engineering, University of Michigan, Beal Avenue 1205, Ann Arbor, 48109-2117, Michigan, USA

    Lawrence M. Seiford

  3. Dept. Management, Worcester Polytechnic Institute, Institute Road 100, Worcester, 01609, Massachusetts, USA

    Joe Zhu

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Cooper, W.W., Seiford, L.M., Zhu, J. (2011). Data Envelopment Analysis: History, Models, and Interpretations. In: Cooper, W., Seiford, L., Zhu, J. (eds) Handbook on Data Envelopment Analysis. International Series in Operations Research & Management Science, vol 164. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6151-8_1

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