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dc.contributor.authorLiu, Yatingen
dc.contributor.authorDong, Yuchengen
dc.contributor.authorChiclana, Franciscoen
dc.contributor.authorCabrerizo, Francisco Javieren
dc.contributor.authorHerrera-Viedma, Enriqueen
dc.date.accessioned2017-03-16T10:53:54Z
dc.date.available2017-03-16T10:53:54Z
dc.date.issued2017-08-24
dc.identifier.citationLiu, Y. et al. (2017) Strategic Weight Manipulation in Multiple Attribute Decision Making in an Incomplete Information Context. Proceedings of FUZZ-IEEE 2017,en
dc.identifier.urihttp://hdl.handle.net/2086/13636
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.abstractIn some real-world multiple attribute decision making (MADM) problems, a decision maker can strategically set attribute weights to obtain her/his desired ranking of alternatives, which is called the strategic weight manipulation of the MADM. Sometimes, the attribute weights are given with imprecise or partial information, which is called incomplete information of attribute weights. In this study, we propose the strategic weight manipulation under incomplete information on attributes weights. Then, a series of mixed 0-1 linear programming models (MLPMs) are proposed to derive a strategic weight vector for a desired ranking of an alternative. Finally, a numerical example is used to demonstrate the validity of our models.en
dc.language.isoenen
dc.publisherIEEE Xploreen
dc.subjectmultiple attribute decision makingen
dc.subjectstrategic weight manipulationen
dc.subjectrankingen
dc.subjectincomplete informationen
dc.titleStrategic Weight Manipulation in Multiple Attribute Decision Making in an Incomplete Information Contexten
dc.typeConferenceen
dc.identifier.doihttps://dx.doi.org/10.1109/FUZZ-IEEE.2017.8015419
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderThis work was supported in part by the NSF of China under Grant 71571124.en
dc.projectidThis work was supported in part by the NSF of China under Grant 71571124.en
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2017-03-14en


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