A fully fuzzified data envelopment analysis model

Adel Hatami-Marbini, Madjid Tavana, Alireza Ebrahimi

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)252-264
Number of pages13
JournalInternational Journal of Information and Decision Sciences
Issue number3
Publication statusPublished - 28 Jul 2011
Externally publishedYes


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