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Sensitivity and stability analysis in DEA with bounded uncertainty

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Abstract

Measurement errors, incomplete information and noisy input and output data create difficulties in assessing the efficiency of data envelopment analysis (DEA). Previous studies have addressed uncertainty using interval analysis to extend the classical DEA problem to the case of bounded uncertainties. This paper proposes an approach to analyze the sensitivity and stability radius. By assuming that the data vary within a bounded interval, all of the decision making units (DMUs) can be classified as \(\hbox {E}^{++}, \hbox {E}^{+},\) and \(\hbox {E}^{-}\). To determine how sensitive these classifications are to possible data perturbations, the paper develops programs to calculate the stability radius within which the percentage data variation does not change the class of efficiency unit. In addition, the data changes are applied to not only the DMU that is being evaluation but also all of the DMUs and the various input and output subsets.

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References

  1. Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2(6), 429–444 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  2. Cook, W.D., Seiford, L.M.: Data envelopment analysis (DEA)-thirty years on. Eur. J. Oper. Res. 192(1), 1–17 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  3. Liu, J.S., Lu, L.Y.Y., Lu, W.M., Lin, B.J.Y.: A survey of DEA applications. Omega 41(5), 893–902 (2013)

    Article  Google Scholar 

  4. Wilson, P.W.: Detecting influential observations in data envelopment analysis. J. Prod. Anal. 6(1), 27–46 (1995)

    Article  Google Scholar 

  5. Banker, R.D., Chang, H., Cooper, W.W.: Simulation studies of efficiency, returns to scale and misspecification with nonlinear functions in DEA. Ann. Oper. Res. 66(4), 233–253 (1996)

    Article  MATH  Google Scholar 

  6. Neralic, L.: Sensitivity in data envelopment analysis for arbitrary perturbations of data. Glas. Mat. 32(2), 315–335 (1997)

    MathSciNet  MATH  Google Scholar 

  7. Neralic, L.: Preservation of efficiency and inefficiency classification in data envelopment analysis. Math. Commun. 9(1), 51–62 (2004)

    MathSciNet  MATH  Google Scholar 

  8. Charnes, A., Neralic, L.: Sensitivity analysis of the additive model in data envelopment analysis. Eur. J. Oper. Res. 48(3), 332–341 (1990)

    Article  MATH  Google Scholar 

  9. Charnes, A., Neralic, L.: Sensitivity analysis of the proportionate change of inputs (or outputs) in data envelopment analysis. Glas. Mat. 27(47), 393–405 (1992)

    MathSciNet  MATH  Google Scholar 

  10. Zhu, J.: Super-efficiency and DEA sensitivity analysis. Eur. J. Oper. Res. 129(2), 443–455 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  11. Abri, A.G., Shoja, N., Jelodar, M.F.: Sensitivity and stability radius in data envelopment analysis. Int. J. Ind. Math. 3(1), 227–234 (2009)

    Google Scholar 

  12. Jahanshahloo, G.R., Hosseinzadeh, F., Shoja, N., Sanei, M., Tohidi, G.: Sensitivity and stability analysis in DEA. Appl. Math. Comput. 169, 897–904 (2005)

    MathSciNet  MATH  Google Scholar 

  13. Jahanshahloo, G.R., Lotfi, F.H., Shoja, N., Abri, A.G., Jelodar, M.F., Firouzabadi, K.J.: Sensitivity analysis of inefficient units in data envelopment analysis. Math. Comput. Model. 53(5), 587–596 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  14. Bruni, M.E., Conforti, D., Beraldi, P., Tundis, E.: Probabilistically constrained models for efficiency and dominance in DEA. Int. J. Prod. Econ. 117(1), 219–228 (2009)

    Article  Google Scholar 

  15. Branda, M.: Diversification-consistent data envelopment analysis with general deviation measures. Eur. J. Oper. Res. 226(3), 626–635 (2013)

  16. Wei, G., Chen, J., Wang, J.: Stochastic efficiency analysis with a reliability consideration. Omega 48, 1–19 (2014)

    Article  Google Scholar 

  17. Cooper, W.W., Park, K.S., Yu, G.: IDEA and AR-IDEA: models for dealing with imprecise data in DEA. Manag. Sci. 45(4), 597–607 (1999)

    Article  MATH  Google Scholar 

  18. Despotis, D.K., Smirlis, Y.G.: Data envelopment analysis with imprecise data. Eur. J. Oper. Res. 140(1), 24–36 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  19. Entani, T., Maeda, Y., Tanaka, H.: Dual models of interval DEA and its extension to interval data. Eur. J. Oper. Res. 136(1), 32–45 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  20. Esmaeili, M.: An Enhanced Russell Measure in DEA with interval data. Appl. Math. Comput. 219(4), 1589–1593 (2012)

    Google Scholar 

  21. Jahanshahloo, G.R., Matin, R.K., Vencheh, A.H.: On FDH efficiency analysis with interval data. Appl. Math. Comput. 159, 47–55 (2004)

    MathSciNet  MATH  Google Scholar 

  22. Inuiguchi, M., Mizoshita, F.: Qualitative and quantitative data envelopment analysis with interval data. Ann. Oper. Res. 195(1), 189–220 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  23. Wang, Y.M., Greatbanks, R., Yang, J.B.: Interval efficiency assessment using data envelopment analysis. Fuzzy Set. Syst. 153(3), 347–370 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  24. Jahanshahloo, G.R., Lotfi, F.H., Malkhalifeh, M.R., Namin, M.A.: A generalized model for data envelopment analysis with interval data. Math. Comput. Model. 33(7), 3237–3244 (2009)

    MathSciNet  MATH  Google Scholar 

  25. Yang, F., Ang, S., Xia, Q., Yang, C.C.: Ranking DMUs by using interval DEA cross efficiency matrix with acceptability analysis. Eur. J. Oper. Res. 223(2), 483–488 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  26. Jahanshahloo, G.R., Lofti, F., Moradi, M.: Sensitivity and stability analysis in DEA with interval data. Appl. Math. Comput. 156(2), 463–477 (2004)

    MathSciNet  MATH  Google Scholar 

  27. Charnes, A., Haag, S., Jaska, P., Semple, J.: Sensitivity of efficiency classifications in the additive model of data envelopment analysis. Int. J. Syst. Sci. 23(5), 789–798 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  28. Charnes, A., Rousseau, J., Semple, J.: Sensitivity and stability of efficiency classifications in data envelopment analysis. J. Prod. Anal. 7(1), 5–18 (1996)

    Article  Google Scholar 

  29. Andersen, P., Petersen, N.C.: A procedure for ranking efficient units in DEA. Manag. Sci. 39(10), 1261–1264 (1993)

    Article  MATH  Google Scholar 

  30. Seiford, L.M., Zhu, J.: Sensitivity analysis of DEA models for simultaneous changes in all the data. J. Oper. Res. Soc. 49(10), 1060–1071 (1998b)

    Article  MATH  Google Scholar 

  31. Seiford, L.M., Zhu, J.: Infeasibility of super-efficiency data envelopment analysis. INFOR 37(2), 174–187 (1999a)

    Google Scholar 

  32. Charnes, A., Cooper, W.W., Thrall, R.M.: A structure for classifying and characterizing efficiencies and inefficiencies in DEA. J. Prod. Anal. 2(3), 197–237 (1991)

    Article  Google Scholar 

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Acknowledgments

We appreciate the support from Projects No. 71272160 and No. 71172011 of the National Natural Science Foundation of China, and the major Project No. KJW-A-1410 of strategic research of the Science and Technology Commission of Ministry of Education. This paper was completed as expected.

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Correspondence to Xiaoning Xu.

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He, F., Xu, X., Chen, R. et al. Sensitivity and stability analysis in DEA with bounded uncertainty. Optim Lett 10, 737–752 (2016). https://doi.org/10.1007/s11590-015-0895-2

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