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dc.contributor.authorChiclana, Franciscoen
dc.contributor.authorHerrera-Viedma, Enriqueen
dc.contributor.authorCabrerizo, F. J.en
dc.contributor.authorDong, Yuchengen
dc.contributor.authorLiu, Wenquen
dc.date.accessioned2016-06-02T15:23:20Z
dc.date.available2016-06-02T15:23:20Z
dc.date.issued2016-11-29
dc.identifier.citationDong, Y., Liu, W., Chiclana, F., Herrera-Viedma, E. and Javier Cabrerizo, F. (2016) Group decision-making based on heterogeneous preference relations with self-confidence. Fuzzy Optimization and Decision Making, 16 (4), pp. 429-447en
dc.identifier.urihttp://hdl.handle.net/2086/12113
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractPreference relations are very useful to express decision makers’ preferences over alternatives in the process of group decision-making. However, the multiple self-confidence levels are not considered in existing preference relations. In this study, we define the preference relation with self-confidence by taking multiple self-confidence levels into consideration, and we call it the preference relation with self-confidence. Furthermore, we present a two-stage linear programming model for estimating the collective preference vector for the group decision-making based on heterogeneous preference relations with self-confidence. Finally, numerical examples are used to illustrate the two-stage linear programming model, and a comparative analysis is carried out to show how self-confidence levels influence on the group decision-making results.en
dc.language.isoenen
dc.publisherSpringeren
dc.subjectPreference relationsen
dc.subjectself-confidence levelsen
dc.subjectcollective preference vectoren
dc.subjectlinear programming modelen
dc.titleGroup decision-making based on heterogeneous preference relations with self-confidenceen
dc.typeArticleen
dc.identifier.doihttps://dx.doi.org/10.1007/s10700-016-9254-8
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderThis work was supported in part by NSF of China under Grants Nos. 71171160 and 71571124, FEDER funds under Grant TIN2013-40658-P, and the Andalusian Excellence Project Grant TIC-5991.en
dc.projectidNSF of China Grants Nos. 71171160 and 71571124en
dc.projectidFEDER funds Grant TIN2013-40658-Pen
dc.projectidAndalusian Excellence Project Grant TIC-5991.en
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2016-05-15en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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