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On variable reductions in data envelopment analysis with an illustrative application to a gas company

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  • Toloo, Mehdi
  • Babaee, Seddigheh

Abstract

Data envelopment analysis (DEA) is a non-parametric data oriented method for evaluating relative efficiency of the number of decision making units (DMUs) based on pre-selected inputs and outputs. In some real DEA applications, the large number of inputs and outputs, in comparison with the number of DMUs, is a pitfall that could have major influence on the efficiency scores. Recently, an approach was introduced which aggregates collected inputs and outputs in order to reduce the number of inputs and outputs iteratively. The purpose of this paper is to show that there are three drawbacks in this approach: instability due to existence of an infinitesimal epsilon, iteratively which can be improved to just one iteration, and providing non-radial inputs and outputs and then capturing them. In order to illustrate the applicability of the improved approach, a real data set involving 14 large branches of National Iranian Gas Company (NIGC) is utilized.

Suggested Citation

  • Toloo, Mehdi & Babaee, Seddigheh, 2015. "On variable reductions in data envelopment analysis with an illustrative application to a gas company," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 527-533.
    • Handle: RePEc:eee:apmaco:v:270:y:2015:i:c:p:527-533
    DOI: 10.1016/j.amc.2015.06.122
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    References listed on IDEAS

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    1. Adler, Nicole & Yazhemsky, Ekaterina, 2010. "Improving discrimination in data envelopment analysis: PCA-DEA or variable reduction," European Journal of Operational Research, Elsevier, vol. 202(1), pages 273-284, April.
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    5. Mehdi Toloo & Mona Barat & Atefeh Masoumzadeh, 2015. "Selective measures in data envelopment analysis," Annals of Operations Research, Springer, vol. 226(1), pages 623-642, March.
    6. Mehdi Toloo & Atefeh Masoumzadeh & Mona Barat, 2015. "Finding an Initial Basic Feasible Solution for DEA Models with an Application on Bank Industry," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 323-336, February.
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    8. Mehdi Toloo & Mona Barat & Atefeh Masoumzadeh, 2015. "Erratum to: Selective measures in data envelopment analysis," Annals of Operations Research, Springer, vol. 235(1), pages 821-821, December.
    9. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, December.
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    Cited by:

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    3. Toloo, Mehdi & Hančlová, Jana, 2020. "Multi-valued measures in DEA in the presence of undesirable outputs," Omega, Elsevier, vol. 94(C).
    4. Capece, Guendalina & Costa, Roberta & Di Pillo, Francesca, 2021. "Benchmarking the efficiency of natural gas distribution utilities in Italy considering size, ownership, and maturity," Utilities Policy, Elsevier, vol. 72(C).
    5. Kasiri, Mohammadreza & Mirnezami, Seyed Reza, 2023. "How can the compensation structure of Iran's natural gas distribution services be modified based on incentive-based regulations?," Energy, Elsevier, vol. 285(C).
    6. Villanueva-Cantillo, Jeyms & Munoz-Marquez, Manuel, 2021. "Methodology for calculating critical values of relevance measures in variable selection methods in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 290(2), pages 657-670.
    7. Toloo, Mehdi & Keshavarz, Esmaeil & Hatami-Marbini, Adel, 2021. "Selecting data envelopment analysis models: A data-driven application to EU countries," Omega, Elsevier, vol. 101(C).

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