Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 39, Issue 15, Pages 12247-12259Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2012.04.049
Keywords
Data envelopment analysis; Fuzzy random variable; Base realignment and closure; Probability-possibility; Probability-necessity; Probability-credibility
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Funding
- U.S. Naval Research Laboratory [N00014-08-1-0160]
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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. (C) 2012 Elsevier Ltd. All rights reserved.
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