Skip to main content

Most Favorable Russell Measures of Efficiency: Properties and Measurement

  • Conference paper
  • First Online:
Mathematical Optimization Theory and Operations Research (MOTOR 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12095))

  • 648 Accesses

Abstract

Conventional radial efficiency measurement models in data envelopment analysis are unable to produce appropriate efficiency scores for production units lying outside the cone generated by the convex hull of the extreme efficient production units. In addition, in the case of production technologies with variable returns to scale, the efficiency scores measured from the input and output sides are usually different. To solve these problems, the Russell measure of efficiency, which takes both the inputs and outputs into account, has been proposed. However, the conventional Russell efficiency is measured under the least favorable conditions, rather than the general custom of measuring under the most favorable ones. This paper develops a model to measure Russell efficiency under the most favorable conditions in two forms, the average and the product. They can be transformed into a second-order cone program and a mixed integer linear program, respectively, so that the solution can be obtained efficiently. A case of Taiwanese commercial banks demonstrates that they are more reliable and representative than the radial measures. Since the most favorable measures are higher than the least favorable measures, and the targets for making improvements are the easiest to reach, they are more acceptable to the production units to be evaluated.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ando, K., Kai, A., Maeda, Y., Sekitani, K.: Least distance based inefficiency measures on the Pareto-efficient frontier in DEA. J. Oper. Res. Soc. Jpn. 55, 73–91 (2012)

    Article  MathSciNet  Google Scholar 

  2. Aparicio, J.: A survey on measuring efficiency through the determination of the least distance in data envelopment analysis. J. Cent. Cathedra 9, 143–167 (2016)

    Article  Google Scholar 

  3. Aparicio, J., Cordero, J.M., Pastor, J.T.: The determination of the least distance to the strongly efficient frontier in Data Envelopment Analysis oriented models: modelling and computational aspects. Omega 71, 1–10 (2017)

    Article  Google Scholar 

  4. Aparicio, J., Pastor, J.T.: A well-defined efficiency measure for dealing with closest target in DEA. Appl. Math. Comput. 219(17), 9142–9154 (2013)

    MathSciNet  MATH  Google Scholar 

  5. Aparicio, J., Pastor, J.T.: Closest targets and strong monotonicity on the strongly efficient frontier in DEA. Omega 44, 51–57 (2014)

    Article  Google Scholar 

  6. Aparicio, J., Ruiz, J.L., Sirvent, I.: Closest targets and minimum distance to the Pareto-efficient frontier in DEA. J. Prod. Anal. 28(3), 209–218 (2007). https://doi.org/10.1007/s11123-007-0039-5

    Article  Google Scholar 

  7. Banker, R.D., Charnes, A., Cooper, W.W.: Some models for estimating technical and scale efficiencies in data envelopment analysis. Manage. Sci. 30, 1078–1092 (1984)

    Article  Google Scholar 

  8. Briec, W.: Minimum distance to the complement of a convex set: duality result. J. Optim. Theory Appl. 93(2), 301–319 (1997). https://doi.org/10.1023/A:1022697822407

    Article  MathSciNet  MATH  Google Scholar 

  9. Briec, W.: Hölder distance function and measurement of technical efficiency. J. Prod. Anal. 11(2), 111–131 (1999). https://doi.org/10.1023/A:1007764912174

    Article  Google Scholar 

  10. Charnes, A., Cooper, W.W.: Programming with linear fractional functionals. Nav. Res. Logist. Q. 9, 181–186 (1962)

    Article  MathSciNet  Google Scholar 

  11. Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2, 429–444 (1978)

    Article  MathSciNet  Google Scholar 

  12. Färe, R., Grosskopf, S., Lovell, C.A.K.: The Measurement of Efficiency of Production. Kluwer-Nijhoff, Dordrecht (1985)

    Book  Google Scholar 

  13. Färe, R., Lovell, C.A.K.: Measuring the technical efficiency of production. J. Econ. Theory 19(1), 150–162 (1978)

    Article  MathSciNet  Google Scholar 

  14. Førsund, F.R., Lovell, C.A.K., Schmidt, P.: A survey of frontier production functions and of their relationship to efficiency measurement. J. Econom. 13, 5–25 (1980)

    Article  Google Scholar 

  15. Fukuyama, H., Maeda, Y., Sekitani, K., Shi, J.: Input-output substitutability and strongly monotonic p-norm least distance DEA measures. Eur. J. Oper. Res. 237, 997–1007 (2014)

    Article  MathSciNet  Google Scholar 

  16. Gonzaléz, E., Álvarez, A.: From efficiency measurement to efficiency improvement: the choice of a relevant benchmark. Eur. J. Oper. Res. 133, 512–520 (2001)

    Article  Google Scholar 

  17. Halická, M., Trnovská, M.: The Russell measure model: Computational aspects, duality, and profit efficiency. Eur. J. Oper. Res. 268, 386–397 (2018)

    Article  MathSciNet  Google Scholar 

  18. Kao, C., Liu, S.T.: Predicting bank performance with financial forecasts: a case of Taiwan commercial banks. J. Bank. Finance 28, 2353–2368 (2004)

    Article  Google Scholar 

  19. Olesen, O.B., Petersen, N.C.: Indicators of ill-conditioned data sets and model misspecification in data envelopment analysis: an extended facet approach. Manage. Sci. 42(2), 205–219 (1996)

    Article  Google Scholar 

  20. Pastor, J.T., Ruiz, J.L., Sirvent, I.: An enhanced DEA Russell graph efficiency measure. Eur. J. Oper. Res. 115, 596–607 (1999)

    Article  Google Scholar 

  21. Petersen, N.C.: Directional distance functions in DEA with optimal endogenous directions. Oper. Res. 66(4), 1068–1085 (2018)

    Article  MathSciNet  Google Scholar 

  22. Sueyoshi, T., Sekitani, K.: Computational strategy for Russell measure in DEA: second-order cone programming. Eur. J. Oper. Res. 180, 459–471 (2007)

    Article  MathSciNet  Google Scholar 

  23. Tone, K.: A slacks-based measure of efficiency in data envelopment analysis. Eur. J. Oper. Res. 130, 498–509 (2001)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgment

This research was partially supported by the Ministry of Science and Technology of the Republic of China (Taiwan), under grant MOST108-2410-H-006-102-MY3.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chiang Kao .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kao, C. (2020). Most Favorable Russell Measures of Efficiency: Properties and Measurement. In: Kononov, A., Khachay, M., Kalyagin, V., Pardalos, P. (eds) Mathematical Optimization Theory and Operations Research. MOTOR 2020. Lecture Notes in Computer Science(), vol 12095. Springer, Cham. https://doi.org/10.1007/978-3-030-49988-4_29

Download citation

Publish with us

Policies and ethics