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dc.contributor.authorSaati, S.en
dc.contributor.authorHatami-Marbini, A.en
dc.contributor.authorTavana, M.en
dc.contributor.authorAgrell, P. J.en
dc.date.accessioned2017-02-28T16:02:17Z
dc.date.available2017-02-28T16:02:17Z
dc.date.issued2013
dc.identifier.citationSaati, S. et al. (2013) A fuzzy data envelopment analysis for clustering operating units with imprecise data. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems , 21 (1), 29en
dc.identifier.urihttp://hdl.handle.net/2086/13349
dc.description.abstractData envelopment analysis (DEA) is a non-parametric method for measuring the efficiency of peer operating units that employ multiple inputs to produce multiple outputs. Several DEA methods have been proposed for clustering operating units. However, to the best of our knowledge, the existing methods in the literature do not simultaneously consider the priority between the clusters (classes) and the priority between the operating units in each cluster. Moreover, while crisp input and output data are indispensable in traditional DEA, real-world production processes may involve imprecise or ambiguous input and output data. Fuzzy set theory has been widely used to formalize and represent the impreciseness and ambiguity inherent in human decision-making. In this paper, we propose a new fuzzy DEA method for clustering operating units in a fuzzy environment by considering the priority between the clusters and the priority between the operating units in each cluster simultaneously. A numerical example and a case study for the Jet Ski purchasing decision by the Florida Border Patrol are presented to illustrate the efficacy and the applicability of the proposed method.en
dc.language.isoenen
dc.publisherWorld Scientificen
dc.subjectData envelopment analysisen
dc.subjectClusteringen
dc.subjectPriorityen
dc.subjectRankingen
dc.subjectFuzzy input and output dataen
dc.subjectFlorida border patrolen
dc.titleA fuzzy data envelopment analysis for clustering operating units with imprecise dataen
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1142/S0218488513500037
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceN/Aen
dc.researchinstituteCentre for Enterprise and Innovation (CEI)en


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