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<!DOCTYPE ArticleSet PUBLIC "-//NLM//DTD PubMed 2.7//EN" "https://dtd.nlm.nih.gov/ncbi/pubmed/in/PubMed.dtd">
<ArticleSet>
<Article>
<Journal>
<PublisherName>OICC Press</PublisherName>
<JournalTitle>International Journal of Mathematical Modelling &amp; Computations</JournalTitle>
<Issn>2228-6233</Issn>
<Volume>2</Volume>
<Issue>1</Issue>
<PubDate PubStatus="epublish">
<Year>2012</Year>
<Month>12</Month>
<Day>21</Day>
</PubDate>
</Journal>
<ArticleTitle>THE IDENTIFICATION OF EFFICIENCY BY USING FUZZY NUMBERS</ArticleTitle>
<VernacularTitle></VernacularTitle>
<FirstPage>53</FirstPage>
<LastPage>59</LastPage>
<ELocationID EIdType="doi"></ELocationID>
<Language>EN</Language>
<AuthorList>
<Author>
<FirstName>M.</FirstName>
<LastName>Sanei</LastName>
<Affiliation>Islamic Azad University, Central Tehran Branch, Iran.	
Iran, Islamic Republic of

Department of Mathematics</Affiliation>
<Identifier Source="ORCID"></Identifier>
</Author>
<Author>
<FirstName>R.</FirstName>
<LastName>Dehghan</LastName>
<Affiliation>Islamic Azad University, Masjed-Solyman Branch,Iran	
Iran, Islamic Republic of

Department of Mathematics</Affiliation>
<Identifier Source="ORCID"></Identifier>
</Author>
<Author>
<FirstName>A.</FirstName>
<LastName>Mahmoodi Rad</LastName>
<Affiliation>Islamic Azad University, Masjed-Solyman Branch,Iran	
Iran, Islamic Republic of

Department of Mathematics</Affiliation>
<Identifier Source="ORCID"></Identifier>
</Author>
</AuthorList>
<PublicationType>Journal Article</PublicationType>
<History>
<PubDate PubStatus="received">
<Year>2012</Year>
<Month>12</Month>
<Day>21</Day>
</PubDate>
</History>
<Abstract>In original Data Envelopment Analysis (DEA) models for measuring the relative efficiencies of a set of Decision Making Units (DMUs) using various inputs to produce various outputs are limited to crisp data. To deal with imprecise data, the notion of fuzziness has been introduced. this paper develops a procedure to measure the efficiencies of DMUs with fuzzy observations. The basic idea is to transform a fuzzy DEA model to family of conventional crisp DEA models by applying optimistic, intermediate and pessimistic concepts. A numerical example is given to show the efficiency.
</Abstract>
</Article>
</ArticleSet>