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dc.contributor.authorDong, Yuchengen
dc.contributor.authorLiu, Yatingen
dc.contributor.authorLiang, Haimingen
dc.contributor.authorChiclana, Franciscoen
dc.contributor.authorHerrera-Viedma, Enriqueen
dc.date.accessioned2017-03-09T10:07:06Z
dc.date.available2017-03-09T10:07:06Z
dc.date.issued2017-03-18
dc.identifier.citationDong, Y. et al. (2017) Strategic weight manipulation in multiple attribute decision making. Omega: The International Journal of Management Science, 75, pp. 154-164en
dc.identifier.urihttp://hdl.handle.net/2086/13492
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. In this paper, we define the concept of the ranking range of an alternative in the MADM, and propose a series of mixed 0-1 linear programming models (MLPMs) to show the process of designing a strategic attribute weight vector. Then, we reveal the conditions to manipulate a strategic attribute weight based on the ranking range and the proposed MLPMs. Finally, a numerical example with real background is used to demonstrate the validity of our models, and simulation experiments are presented to show the better performance of the ordered weighted averaging operator than the weighted averaging operator in defending against the strategic weight manipulation of the MADM problems.en
dc.language.isoenen
dc.publisherElsevieren
dc.subjectmultiple attribute decision makingen
dc.subjectstrategic weight manipulationen
dc.subjectthe ordered weighted averaging operatoren
dc.subjectrankingen
dc.titleStrategic weight manipulation in multiple attribute decision makingen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1016/j.omega.2017.02.008
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderThis work was supported by the grants (Nos.71171160 and 71571124) from NS F of China, the grants (No. skqy201606) from Sichuan University, the grants (Nos.TI N2013-40658-P and TIN2016-75850-R) from the FEDER funds, and the grant (No.TIC –5991) from the Andalusi-an Excellence Project.en
dc.projectidThis work was supported by the grants (Nos.71171160 and 71571124) from NS F of China, the grants (No. skqy201606) from Sichuan University, the grants (Nos.TI N2013-40658-P and TIN2016-75850-R) from the FEDER funds, and the grant (No.TIC –5991) from the Andalusi-an Excellence Project.en
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2017-02-20en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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