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A note on data envelopment analysis with missing values: an interval DEA approach

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Abstract

Smirlis et al. (Appl Math Comput 177(1):1–10, 2006) have proposed a pair of interval data envelopment analysis (DEA) models for computation of the efficiency of decision-making units (DMUs) in the presence of missing data. In this paper, we show that the interval DEA models presented by Smirlis et al. have some drawbacks due to the use of variable production frontier for computation of the efficiency intervals of DMUs. To overcome these drawbacks, this paper presents new interval DEA models based on interval arithmetic. It is shown that the proposed interval DEA models do not need extra variable changes and use a fixed, unified production frontier for computation of the efficiency intervals of the DMUs with interval input and output data. A numerical example is presented to illustrate the potential applications of the new interval DEA models and their effectiveness for measuring the interval efficiencies of the DMUs.

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References

  1. Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444. doi:10.1016/0377-2217(78)90138-8

    Article  MathSciNet  MATH  Google Scholar 

  2. Amirteimoori A, Kordrostami S (2011) Production planning: a DEA-based approach. Int J Adv Manuf Technol 56(1):369–376. doi:10.1007/s00170-011-3178-9

    Article  Google Scholar 

  3. Mousavi S, Zandieh M, Amiri M (2011) An efficient bi-objective heuristic for scheduling of hybrid flow shops. Int J Adv Manuf Technol 54(1):287–307. doi:10.1007/s00170-010-2930-x

    Article  Google Scholar 

  4. Azadeh A, Asadzadeh S, Bukhari A, Izadbakhsh H (2011) An integrated fuzzy DEA algorithm for efficiency assessment and optimization of wireless communication sectors with ambiguous data. Int J Adv Manuf Technol 52(5):805–819. doi:10.1007/s00170-010-2741-0

    Article  Google Scholar 

  5. O'Neal PV, Ozcan YA, Ma Y (2002) Benchmarking mechanical ventilation services in teaching hospitals. J Med Sys 26(3):227–240. doi:10.1023/a:1015058217867

    Article  Google Scholar 

  6. Jafarian-Moghaddam A, Ghoseiri K (2012) Multi-objective data envelopment analysis model in fuzzy dynamic environment with missing values. Int J Adv Manuf Technol 61(5–8):771–785. doi:10.1007/s00170-011-3730-7

    Google Scholar 

  7. Cook WD, Harrison J, Rouse P, Zhu J (2012) Relative efficiency measurement: the problem of a missing output in a subset of decision making units. Eur J Oper Res 220(1):79–84. doi:10.1016/j.ejor.2012.01.022

    Article  MathSciNet  MATH  Google Scholar 

  8. Kao C, Liu ST (2000) Data envelopment analysis with missing data: an application to university libraries in Taiwan. J Oper Res Soc 51(8):897–905. doi:10.1057/palgrave.jors.2600056

    MATH  Google Scholar 

  9. Kuosmanen T (2008) Data envelopment analysis with missing data. J Oper Res Soc 60(12):1767–1774. doi:10.1057/jors.2008.132

    Article  Google Scholar 

  10. Smirlis YG, Maragos EK, Despotis DK (2006) Data envelopment analysis with missing values: an interval DEA approach. Appl Math Comput 177(1):1–10. doi:10.1016/j.amc.2005.10.028

    Article  MathSciNet  MATH  Google Scholar 

  11. Wang Y-M, Greatbanks R, Yang J-B (2005) Interval efficiency assessment using data envelopment analysis. Fuzzy Set Syst 153(3):347–370. doi:10.1016/j.fss.2004.12.011

    Article  MathSciNet  MATH  Google Scholar 

  12. Azizi H, Ajirlu HG (2011) Measurement of the worst practice of decision-making units in the presence of non-discretionary factors and imprecise data. Appl Math Model 35(9):4149–4156. doi:10.1016/j.apm.2011.02.038

    Article  MathSciNet  MATH  Google Scholar 

  13. Farzipoor Saen R (2010) Developing a new data envelopment analysis methodology for supplier selection in the presence of both undesirable outputs and imprecise data. Int J Adv Manuf Technol 51(9):1243–1250. doi:10.1007/s00170-010-2694-3

    Article  Google Scholar 

  14. Charnes A, Cooper WW (1962) Programming with linear fractional functionals. Nav Res Logist Q 9(3–4):181–186. doi:10.1002/nav.3800090303

    Article  MathSciNet  MATH  Google Scholar 

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  1. Islamic Azad University, Parsabad Moghan Branch, Parsabad Moghan, Iran

    Hossein Azizi

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  1. Hossein Azizi
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Correspondence to Hossein Azizi.

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Azizi, H. A note on data envelopment analysis with missing values: an interval DEA approach. Int J Adv Manuf Technol 66, 1817–1823 (2013). https://doi.org/10.1007/s00170-012-4461-0

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  • DOI: https://doi.org/10.1007/s00170-012-4461-0

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