Fuzzy stochastic data envelopment analysis with application to base realignment and closure (BRAC)

Madjid Tavana, Rashed Khanjani Shiraz, Adel Hatami-Marbini, Per J. Agrell, Khalil Paryab

Research output: Contribution to journalArticlepeer-review

47 Citations (Scopus)


Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of decision-making units (DMUs) on the basis of multiple inputs and outputs. Conventional DEA models assume that inputs and outputs are measured by exact values on a ratio scale. However, the observed values of the input and output data in real-world problems are often vague or random. Indeed, decision makers (DMs) may encounter a hybrid uncertain environment where fuzziness and randomness coexist in a problem. Several researchers have proposed various fuzzy methods for dealing with the ambiguous and random data in DEA. In this paper, we propose three fuzzy DEA models with respect to probability-possibility, probability-necessity and probability-credibility constraints. In addition to addressing the possibility, necessity and credibility constraints in the DEA model we also consider the probability constraints. A case study for the base realignment and closure (BRAC) decision process at the U.S. Department of Defense (DoD) is presented to illustrate the features and the applicability of the proposed models.

Original languageEnglish
Pages (from-to)12247-12259
Number of pages13
JournalExpert Systems with Applications
Issue number15
Early online date26 Apr 2012
Publication statusPublished - 1 Nov 2012
Externally publishedYes


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