Advertisement

Superefficiency and Multiplier Adjustment in Data Envelopment Analysis

  • Jorge SantosEmail author
  • Luís Cavique Santos
  • Armando B. Mendes
Chapter
  • 886 Downloads

Abstract

Superefficiency is an important extension of DEA that overcomes some limitations of the traditional models, specifically allowing ranking of efficient units and a unique set of weights for those units. Weights restriction is a well-known technique in the DEA field. When those techniques are applied, weights cluster around its new limits, making its evaluation dependent of its levels. This chapter introduces a new approach to weights adjustment by goal programming techniques, avoiding the imposition of hard restrictions that can even lead to unfeasibility. This method results in models that are more flexible.

Keywords

Data envelopment analysis Superefficiency Weights restriction Evaluation Goal programming 

References

  1. Andersen P, Petersen NC (1993) A procedure for ranking efficient units in data envelopment analysis. Manag Sci 39(10):1261–1264CrossRefGoogle Scholar
  2. Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci 30(9):1078–1092CrossRefGoogle Scholar
  3. Boussofiane A, Dyson RG, Thanassoulis E (1991) Applied data envelopment analysis. Eur J Oper Res 52:1–15CrossRefGoogle Scholar
  4. Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444CrossRefGoogle Scholar
  5. Charnes A, Cooper W, Wei Q, Huang Z (1989) Cone ratio data envelopment analysis and multi-objective programming. Int J Syst Sci 20:1099–1118CrossRefGoogle Scholar
  6. Durchholz M, Barr R (1997) Parallel and hierarchical decomposition approaches for solving large-scale data envelopment analysis models. Ann Oper Res 73:339–372. doi: 10.1023/A:1018941531019 CrossRefGoogle Scholar
  7. Dyson RG, Thanassoulis E (1988) Reducing weight flexibility in data envelopment analysis. J Oper Res Soc 39(6):563–576Google Scholar
  8. Hillier FS, Lieberman GJ (1990) Introduction to operations research. Mc Graw Hill, New YorkGoogle Scholar
  9. Roll Y, Cook W (1991) Controlling factor weights in data envelopment analysis. IIE Trans 23(1):2–9CrossRefGoogle Scholar
  10. Santos J (1994) Ordenação de unidades eficientes por técnicas de data envelopment analysis. Unpublished MsC. thesis, Instituto Superior Técnico, Technical University of LisbonGoogle Scholar
  11. Santos J (2008) Issues of universal feasibility and multiplier adjustment in data envelopment analysis (DEA) with an application. Unpublished PhD thesis, Universidade de ÉvoraGoogle Scholar
  12. Scheel H (2000) EMS: efficiency measurement system user’s manual version 1.3. http://www.microtheory.uni-jena.de/download/ems.pdf
  13. Thompson RG, Singleton FD, Thrall RM, Smith BA (1986) Comparative site evaluations for locating a high-energy physics lab in Texas. Interfaces 16(6):35–49CrossRefGoogle Scholar
  14. Thompson RG, Langemeier LN, Lee CT, Thrall RM (1990) The role of multiplier bounds in efficiency analysis with application to Kansas farming. J Econ 46:93–108Google Scholar
  15. Wong Y-HB, Beasley JE (1990) Restricting weight flexibility in data envelopment analysis. J Oper Res Soc 41(9):829–835Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Jorge Santos
    • 1
    Email author
  • Luís Cavique Santos
    • 2
  • Armando B. Mendes
    • 3
  1. 1.CIMA, Departamento de MatemáticaUniversidade de ÉvoraÉvoraPortugal
  2. 2.LabMAg, Science and Technology DepartmentUniversidade AbertaLisboaPortugal
  3. 3.CEEAplA, Departamento de MatemáticaUniversidade dos AçoresPonta Delgada, AçoresPortugal

Personalised recommendations