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A fully fuzzified data envelopment analysis model

Author

Listed:
  • Adel Hatami-Marbini
  • Madjid Tavana
  • Alireza Ebrahimi

Abstract

In the conventional data envelopment analysis (DEA), all the data assumes the form of crisp numerical values. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. Some researchers have proposed various fuzzy methods for dealing with the imprecise and ambiguous data in DEA by constructing linear programming (LP) models with 'partial' fuzzy parameters. The main purpose of this study is to evaluate the performance of a set of decision making units (DMUs) in a fully fuzzified environment. We propose a novel fully fuzzified DEA (FFDEA) model by utilising a fully fuzzified LP (FFLP) model, where all decision parameters and variables are fuzzy numbers. The contribution of this paper is threefold: first, we consider ambiguous, uncertain and imprecise input and output data in DEA; second, we address the gap in the fuzzy DEA literature for solutions to fully fuzzified problems; and third, we present a numerical example to demonstrate the applicability and efficacy of the proposed model.

Suggested Citation

  • Adel Hatami-Marbini & Madjid Tavana & Alireza Ebrahimi, 2011. "A fully fuzzified data envelopment analysis model," International Journal of Information and Decision Sciences, Inderscience Enterprises Ltd, vol. 3(3), pages 252-264.
    • Handle: RePEc:ids:ijidsc:v:3:y:2011:i:3:p:252-264
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    Citations

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    Cited by:

    1. Awadh Pratap Singh & Musrrat Ali, 2023. "Development of Bi-Objective Fuzzy Data Envelopment Analysis Model to Measure the Efficiencies of Decision-Making Units," Mathematics, MDPI, vol. 11(6), pages 1-15, March.
    2. Mahsa Ghandi & Abbas Roozbahani, 2020. "Risk Management of Drinking Water Supply in Critical Conditions Using Fuzzy PROMETHEE V Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 595-615, January.
    3. Ali Ebrahimnejad & Madjid Tavana & Seyed Hadi Nasseri & Omid Gholami, 2019. "A New Method for Solving Dual DEA Problems with Fuzzy Stochastic Data," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 147-170, January.

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