Advertisement

A Comparative Study on the Economic Development Level of the Countries by Fuzzy DEA Methodologies

  • Mujde Erol Genevois
  • Michele CedolinEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 896)

Abstract

In this work, we aim to compare the economic development level of the countries, regarding to their macroeconomic indicators, also considering their financial service accessibility, including as factor the number of bank branches and the automated teller machines. In this context, we took into account the data of the sixteen European countries, obtained by the consensus of financial experts and we applied different Data Envelopment Analysis (DEA) models for finding the most efficient countries in the selected context. We observed that although the models differ, we obtained similar results for each model.

Keywords

Fuzzy decision making Data envelopment analysis Performance evaluation 

Notes

Acknowledgments

This work has been financially supported by Galatasaray University Research Fund 18.402.011.

References

  1. 1.
    Hatefi, S.M., Torabi, S.A.: A common weight MCDA–DEA approach to construct composite indicators. Ecol. Econ. 70(1), 114–120 (2010)CrossRefGoogle Scholar
  2. 2.
    Doyle, J., Green, R.: Efficiency and cross-efficiency in DEA: derivations, meanings and uses. J. Oper. Res. Soc. 45, 567–578 (1994)CrossRefGoogle Scholar
  3. 3.
    Farrell, M.J.: The measurement of productive efficiency. J. R. Stat. Soc. 253–290 (1957)CrossRefGoogle Scholar
  4. 4.
    Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision making units. Eur. J. Oper. Res. 429–444 (1978)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Li, X.-B., Reeves, G.R.: A multiple criteria approach to data envelopment analysis. Eur. J. Oper. Res. 115, 507–517 (1999)CrossRefGoogle Scholar
  6. 6.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)CrossRefGoogle Scholar
  7. 7.
    Sengupta, J.K.: A fuzzy systems approach in data envelopment analysis. Comput. Math. Appl. 24, 259–266 (1992)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Triantis, K., Girod, O.: A mathematical programming approach for measuring technical efficiency in a fuzzy environment. J. Prod. Anal. 10, 85–102 (1998)CrossRefGoogle Scholar
  9. 9.
    Hatami-Marbini, A., Emrouznejad, A., Tavana, M.: A taxonomy and review of the fuzzy data envelopment analysis literature: two decades in the making. Eur. J. Oper. Res. 214, 457–472 (2011)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Karsak, E.E.: Using data envelopment analysis for evaluating flexible manufacturing systems in the presence of imprecise data. Int. J. Adv. Manuf. Technol. 35, 867–874 (2008)CrossRefGoogle Scholar
  11. 11.
    Saati, M.S., Memariani, A., Jahanshahloo, G.R.: Efficiency analysis and raking of DMUs with fuzzy data. Fuzzy Optim. Decis. Making 1(3), 255–267 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Industrial Engineering DepartmentGalatasaray UniversityIstanbulTurkey

Personalised recommendations