Annals of Operations Research

, Volume 116, Issue 1–4, pp 225–242 | Cite as

Review of Methods for Increasing Discrimination in Data Envelopment Analysis

  • Lidia Angulo-Meza
  • Marcos Pereira Estellita Lins
Article

Abstract

We present a review of methods for increasing discrimination between efficient DMUs in Data Envelopment Analysis. These methods were classified into two groups: those that incorporate a priori information and those that do not use or minimize the use of such a priori information. We also compare these methodologies regarding their specific characteristics.

Data Envelopment Analysis weight restrictions Value Efficiency Analysis cross-evaluation super efficiency multiple objectives model 

References

  1. [1]
    R. Allen, A. Athanassopoulos, R.G. Dyson and E. Thanassoulis, Weights restrictions and value judgements in data envelopment analysis: evolution, development and future directions, Annals of Operations Research 73 (1997) 13-34.Google Scholar
  2. [2]
    P. Andersen and N.C. Petersen, A procedure for ranking efficient units in data envelopment analysis, Management Science 39 (1993) 1261-1264.Google Scholar
  3. [3]
    T.R. Anderson, A. Uslu and K.B. Holingsworth, Revisiting extensions in efficiency measurement of alternate machine component grouping solutions via data envelopment analysis, Working paper (1998).Google Scholar
  4. [4]
    J.E. Beasley, Comparing university departments, Omega International Journal of Management Science 18 (1990) 171-183.Google Scholar
  5. [5]
    A. Charnes, W.W. Cooper and E. Rhodes, Measuring the efficiency of decision-making units, European Journal of Operational Research 2 (1978) 429-444.Google Scholar
  6. [6]
    A. Charnes, W.W. Cooper, Z.M. Huang and D.B. Sun, Polyhedral cone-ratio DEA models with an illustrative application to large commercial banks, Journal of Econometrics 46 (1990) 73-91.Google Scholar
  7. [7]
    A. Charnes, W.W. Cooper, Q.L.Wei and Z.M. Huang, Cone ratio data envelopment analysis and multiple objective linear programming, International Journal of Management Science 20 (1989) 1099-1118.Google Scholar
  8. [8]
    W.W. Cooper, L.M. Seiford and K. Tone, Data Envelopment Analysis, A Comprehensive Text with Models, Applications, References and DEA-Solver Software (Kluwer, Boston, 2000).Google Scholar
  9. [9]
    J.R. Doyle and R.H. Green, Efficiency and cross-efficiency in DEA: derivations, meanings and uses, Journal of the Operational Research Society 45 (1994) 567-578.Google Scholar
  10. [10]
    J.H. Dulá and B.L. Hickman, Effects of excluding the column being scored from the DEA envelopment LP technology matrix, Journal of the Operational Research Society 48 (1997) 1001-1012.Google Scholar
  11. [11]
    R.G. Dyson and E. Thanassoulis, Reducing weight flexibility in data envelopment analysis, Journal of the Operational Research Society 39 (1988) 563-576.Google Scholar
  12. [12]
    R. Green, J.R. Doyle and W.D. Cook, Preference voting and project ranking using DEA and crossevaluation, European Journal of the Operational Research 90 (1996) 461-472.Google Scholar
  13. [13]
    M. Halme and P. Korhonen, Restricting weights in value efficiency analysis, European Journal of Operational Research 126 (2000) 175-188.Google Scholar
  14. [14]
    M. Halme, T. Joro, P. Korhonen, S. Salo and J. Wallenius, Value efficiency analysis for incorporating preference information in DEA, Management Science 45 (2000) 103-115.Google Scholar
  15. [15]
    T. Joro, P. Korhonen and J. Wallenius, Structural comparison of data envelopment analysis and multiple objective linear programming, Management Science 44 (1998) 962-970.Google Scholar
  16. [16]
    P. Korhonen and J. Wallenius, A Pareto race, Naval Research Logistics 35 (1988) 615-623.Google Scholar
  17. [17]
    P. Korhonen, A. Siljamäki and M. Soismaa, Practical aspects of value efficiency analysis, IIASA Interim report IR-98-042, IIASA, Finland (1998).Google Scholar
  18. [18]
    P. Korhonen, A. Siljamäki and M. Soismaa, On the use of value efficiency analysis and some further developments, Journal of Productivity Analysis 17 (2002) 49-64.Google Scholar
  19. [19]
    P. Korhonen, R. Tainio and J. Wallenius, Value efficiency analysis of academic research, European Journal of Operational Research 130 (2001) 121-132.Google Scholar
  20. [20]
    J.S.M. Kornbluth, Analysing policy effectiveness using cone restricted DEA, Journal of the Operational Research Society 42 (1991) 1097-1104.Google Scholar
  21. [21]
    X.B. Li and G.R. Reeves, A multiple criteria approach to data envelopment analysis, European Journal of Operational Research 115 (1999) 507-517.Google Scholar
  22. [22]
    R. Pedraja-Chaparro, J. Salinas-Jimenes, J. and P. Smith, On the role of weight restrictions in DEA, Journal of Productivity Analysis 8 (1997) 215-230.Google Scholar
  23. [23]
    Y. Roll, W. Cook and B. Golany, Controlling factor weights in DEA, IIE Transactions 23 (1991) 2-9.Google Scholar
  24. [24]
    L.M. Seiford and J. Zhu, Infeasibility of super efficiency data envelopment analysis models, Information Systems and Operational Research 27 (1999) 174-187.Google Scholar
  25. [25]
    T.R. Sexton, Measuring Efficiency: An Assessment of Data Envelopment Analysis, ed. R.H. Silkman (Jossey-Bass, San Francisco, 1986).Google Scholar
  26. [26]
    R.E. Steuer, An interactive multiple objective linear programming procedure, TIMS Studies in the Management Sciences 6 (1977) 225-239.Google Scholar
  27. [27]
    T.J. Stewart, Relationships between DEA e multicriteria decision analysis, Journal of the Operational Research Society 47 (1996) 654-665.Google Scholar
  28. [28]
    S. Talluri and J. Sarkis, Extensions in efficiency measurement of alternate machine component grouping solutions via data envelopment analysis, IEEE Transactions on Engineering Management 44 (1997) 27-31.Google Scholar
  29. [29]
    R.G. Thompson, F.D. Jr. Singleton, R.M. Thrall and B.A. Smith, Comparative site evaluations for locating a high-energy physics lab in Texas, Interfaces 16 (1986) 35-49.Google Scholar
  30. [30]
    R.M. Thrall, Duality, classification and slacks in DEA, The Annals of Operations Research 66 (1996) 109-138.Google Scholar
  31. [31]
    Y.-H. Wong and J.E. Beasley, Restricting weight flexibility in DEA, Journal of the Operational Research Society 41 (1990) 829-835.Google Scholar
  32. [32]
    J. Zhu, Robustness of the efficient DMUs in data envelopment analysis, European Journal of Operational Research 90 (1996) 451-460.Google Scholar
  33. [33]
    J. Zhu, Data envelopment analysis with preference structure, Journal of the Operational Research Society 47 (1996) 136-150.Google Scholar
  34. [34]
    J. Zhu, Super-efficiency and DEA sensitivity analysis, European Journal of Operational Research 129 (2001) 443-455.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Lidia Angulo-Meza
    • 1
  • Marcos Pereira Estellita Lins
    • 1
  1. 1.Programa de Engenharia de Produção, COPPE/UFRJRio de Janeiro, RJBrazil

Personalised recommendations