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Performance evaluation and ranking of electricity companies using fuzzy network data envelopment analysis: a case study of Iranian regional electricity organisations

Author

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  • Negar Roghaee
  • Emran Mohammadi
  • Nilofar Varzgani

Abstract

Data envelopment analysis (DEA) is one of the most appropriate methods for a data-oriented approach to evaluate the performance of a set of entities called decision making units (DMUs). This study aims at evaluating the efficiency of Iranian regional power companies using data envelopment analysis (DEA), under the uncertainty conditions in the fuzzy necessity scheme. In this study, we determine the total efficiency as well as the efficiency of each stage including the production, transmission and distribution using fuzzy network DEA (FNDEA). Since the Iranian power industries have a network structure, the network DEA process has been used for evaluating the efficiency of the processes of companies. Sixteen Iranian regional power companies were selected and analysed based on the performance of the production, transferring, and distribution.

Suggested Citation

  • Negar Roghaee & Emran Mohammadi & Nilofar Varzgani, 2020. "Performance evaluation and ranking of electricity companies using fuzzy network data envelopment analysis: a case study of Iranian regional electricity organisations," International Journal of Management and Decision Making, Inderscience Enterprises Ltd, vol. 19(4), pages 450-472.
    • Handle: RePEc:ids:ijmdma:v:19:y:2020:i:4:p:450-472
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    Cited by:

    1. Pejman Peykani & Farhad Hosseinzadeh Lotfi & Seyed Jafar Sadjadi & Ali Ebrahimnejad & Emran Mohammadi, 2022. "Fuzzy chance-constrained data envelopment analysis: a structured literature review, current trends, and future directions," Fuzzy Optimization and Decision Making, Springer, vol. 21(2), pages 197-261, June.
    2. Svetlana V. Ratner & Artem M. Shaposhnikov & Andrey V. Lychev, 2023. "Network DEA and Its Applications (2017–2022): A Systematic Literature Review," Mathematics, MDPI, vol. 11(9), pages 1-24, May.

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