Data Envelopment Analysis

History, Models and Interpretations
  • William W. Cooper
  • Lawrence M. Seiford
  • Joe Zhu
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 71)

Abstract

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

Key words

Data envelopment analysis (DEA) Efficiency Performance 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Afriat, S., 1972, Efficiency estimation of production functions, International Economic Review 13, 568–598.MATHMathSciNetGoogle Scholar
  2. 2.
    Ahn, T., A. Charnes and W.W. Cooper, 1988, “Efficiency Characterizations in Different DEA Models,” Socio-Economic Planning Sciences 22, 253–257.Google Scholar
  3. 3.
    Arnold, V., I. Bardhan, W.W. Cooper and A. Gallegos, 1998, “Primal and Dual Optimality in Computer Codes Using Two-Stage Solution Procedures in DEA” in J. Aronson and S. Zionts, eds., Operations Research Methods, Models and Applications (Westpost, Conn: Quorum Books).Google Scholar
  4. 4.
    Banker, R., A. Charnes and W.W. Cooper, 1984, Some models for estimating technical and scale inefficiencies in data envelopment analysis, Management Science 30, 1078–1092.Google Scholar
  5. 5.
    Banker, R.D. and R.C. Morey, 1986a, Efficiency analysis for exogenously fixed inputs and outputs, Operations Research 34,No. 4, 513–521.Google Scholar
  6. 6.
    Banker, R.D. and R.C. Morey, 1986b, The use of categorical variables in data envelopment analysis, Management Science 32,No. 12, 1613–1627.Google Scholar
  7. 7.
    Bardhan, I., W.F. Bowlin, W.W. Cooper and T. Sueyoshi, 1996, “Models and Measures for Efficiency Dominance in DEA, Part I: Additive Models and MED Measures,” Journal of the Operational Research Society of Japan 39, 322–332.MathSciNetGoogle Scholar
  8. 8.
    Brockett, P.L., A. Charnes, W.W. Cooper, Z.M. Huang, and D.B. Sun, 1997, Data transformations in DEA cone ratio envelopment approaches for monitoring bank performances, European Journal of Operational Research 98,No. 2, 250–268.CrossRefGoogle Scholar
  9. 9.
    Brockett, P.L., W.W. Cooper, H.C. Shin and Y. Wang, 1998, Inefficiency and congestion in Chinese production before and after the 1978 economic reforms. Socio-Econ Plann Sci 32, 1–20.Google Scholar
  10. 10.
    Charnes, A. and W.W. Cooper, 1962, Programming with linear fractional functionals, Naval Research Logistics Quarterly 9, 181–185.MathSciNetGoogle Scholar
  11. 11.
    Charnes, A., W.W. Cooper, and E. Rhodes, 1978, Measuring the efficiency of decision making units, European Journal of Operational Research 2, 429–444.MathSciNetGoogle Scholar
  12. 12.
    Charnes A, C.T. Clark, W.W. Cooper, B. Golany, 1985, A developmental study of data envelopment analysis in measuring the efficiency of maintenance units in the US air forces. In R.G. Thompson and R.M. Thrall, Eds., Annals Of Operation Research 2:95–112.Google Scholar
  13. 13.
    Charnes, A., W.W. Cooper, DB. Sun, and Z.M. Huang, 1990, Polyhedral cone-ratio DEA models with an illustrative application to large commercial banks, Journal of econometrics 46, 73–91.CrossRefGoogle Scholar
  14. 14.
    Charnes, A., W.W. Cooper, Q.L. Wei, and Z.M. Huang, 1989, Cone ratio data envelopment analysis and multi-objective programming, International Journal of Systems Science 20, 1099–1118.MathSciNetGoogle Scholar
  15. 15.
    Charnes, A. and W.W. Cooper, 1961, Management Models and Industrial Applications of Linear Programming, 2 vols., with A. Charnes (New York: John Wiley and Sons, Inc.).Google Scholar
  16. 16.
    Charnes, A., W.W. Cooper, B. Golany, L. Seiford and J. Stutz, (1985) “Foundations of Data Envelopment Analysis for Pareto-Koopmans Efficient Empirical Production Functions,” Journal of Econometrics (1985), 30, pp. 91–107.MathSciNetCrossRefGoogle Scholar
  17. 17.
    Cook, W.D., M. Kress, and L.M. Seiford, 1992, Prioritization models for frontier decision making units in DEA, European Journal of Operational Research 59,No. 2, 319–323.CrossRefGoogle Scholar
  18. 18.
    Cooper, W.W., R.G. Thompson, and R.M. Thrall, 1996, Extensions and new developments in data envelopment analysis, Annals of Operations Research 66, 3–45.MathSciNetGoogle Scholar
  19. 19.
    Cooper, W.W., Seiford, L.M. and Tone, K., 2000, Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software, Kluwer Academic Publishers, Boston.Google Scholar
  20. 20.
    Cooper, W.W., Seiford, L.M. and Zhu, Joe, A unified additive model approach for evaluating inefficiency and congestion with associated measures in DEA. Socio-Economic Planning Sciences, Vol. 34,No. 1 (2000), 1–25.CrossRefGoogle Scholar
  21. 21.
    Cooper, W.W., H. Deng, Z. M. Huang and S. X. Li, 2002, A one-model approach to congestion in data envelopment analysis, Socio-Economic Planning Sciences 36, 231–238.CrossRefGoogle Scholar
  22. 22.
    Cooper, W.W., K.S. Park and J.T. Pastor, 2000, “Marginal Rates and Elasticities of Substitution in DEA.” Journal of Productivity Analysis 13, 2000, pp. 105–123.Google Scholar
  23. 23.
    Cooper, W.W., K.S. Park and J.T. Pastor, 1999, “RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models and Relations to Other Models and Measures in DEA”, Journal of Productivity Analysis 11, 5–42.CrossRefGoogle Scholar
  24. 24.
    Debreu, G., 1951, The coefficient of resource utilization, Econometrica 19, 273–292.MATHGoogle Scholar
  25. 25.
    Dyson, R.G. and E. Thanassoulis, 1988, Reducing weight flexibility in data envelopment analysis, Journal of the Operational Research Society 39,No. 6, 563–576.Google Scholar
  26. 26.
    Färe, R., S. Grosskopf and C.A.K. Lovell, 1985, The Measurement of Efficiency of Production. Boston: Kluwer Nijhoff Publishing Co.Google Scholar
  27. 27.
    Färe, R., S. Grosskopf and CAK Lovell, 1994, Production Frontiers (Cambridge: Cambridge University Press).Google Scholar
  28. 28.
    Farrell, M.J., 1957, The measurement of productive efficiency, Journal of Royal Statistical Society A 120, 253–281.Google Scholar
  29. 29.
    Koopmans, T.C., 1951, Analysis of Production as an efficient combination of Activities, in T.C. Koopmans, ed. Wiley, New York.Google Scholar
  30. 30.
    Roll, Y., W.D. Cook, and B. Golany, 1991, Controlling factor weights in data envelopment analysis, IIE Transactions, 23, 2–9.Google Scholar
  31. 31.
    Shephard, R.W., 1970, Theory of Cost and Production Functions, Princeton University Press, Princeton, NJ.Google Scholar
  32. 32.
    Takamura, T. and K. Tone, 2003, “A Comparative Site Evaluation Study for Relocating Japanese Government Agencies Out of Tokyo,” Socio-Economic Planning Sciences 37, 85–102.CrossRefGoogle Scholar
  33. 33.
    Tavares, G., 2003, “A Bibliography of Data Envelopment Analysis (1978–2001),” Socio-Economic Planning Sciences (to appear).Google Scholar
  34. 34.
    Thompson, R.G., F.D. Jr. Singleton, R.M. Thrall, and B.A. Smith, 1986, Comparative site evaluation for locating a high-energy physics lab in Texas, Interfaces 16, 35–49.CrossRefGoogle Scholar
  35. 35.
    Thompson, R.G., L. Langemeier, C. Lee, E. Lee, and R. Thrall, 1990, The role of multiplier bounds in efficiency analysis with application to Kansas farming, Journal of Econometrics 46, 93–108.CrossRefGoogle Scholar
  36. 36.
    Tone, K. and B.K. Sahoo, 2003, “A Reexamination of Cost Efficiency and Cost Elasticity in DEA,” Management Science (forthcoming).Google Scholar
  37. 37.
    Wong, Y.-H.B. and J.E. Beasley, 1990, Restricting weight flexibility in data envelopment analysis, Journal of the Operational Research Society 41, 829–835.Google Scholar
  38. 38.
    Zhu, J., 1996a, DEA/AR analysis of the 1988–1989 performance of the Nanjing Textiles Corporation, Annals of Operations Research 66, 311–335.MATHCrossRefGoogle Scholar
  39. 39.
    Zhu, J., 1996b, Data envelopment analysis with preference structure, Journal of the Operational Research Society 47,No. 1, 136–150.MATHGoogle Scholar
  40. 40.
    Zhu, J., 2000, Multi-factor performance measure model with an application to Fortune 500 companies. European Journal of Operational Research 123,No. 1, 105–124.MATHCrossRefGoogle Scholar
  41. 41.
    Zhu, J. 2002, Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets and DEA Excel Solver, Kluwer Academic Publishers, Boston.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • William W. Cooper
    • 1
  • Lawrence M. Seiford
    • 2
  • Joe Zhu
    • 3
  1. 1.Red McCombs School of BusinessUniversity of Texas at AustinAustinUSA
  2. 2.Department of Industrial and Operations EngineeringAnn ArborUSA
  3. 3.Department of ManagementWorcester Polytechnic InstituteWorcesterUSA

Personalised recommendations