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dc.contributor.authorFigueira, José
dc.contributor.authorGreco, Salvatore
dc.contributor.authorSlowinski, Roman
dc.date.accessioned2011-10-28T14:16:09Z
dc.date.available2011-10-28T14:16:09Z
dc.date.issued2009
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/7356
dc.language.isoenen
dc.subjectPreference modelen
dc.subjectMultiple criteria decision analysisen
dc.subjectValue functionen
dc.subjectOrdinal regressionen
dc.subjectIntensity of preferenceen
dc.subjectGRIP methoden
dc.subject.ddc003en
dc.titleBuilding a set of additive value functions representing a reference preorder and intensities of preference: GRIP methoden
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherInstitute of Computing Science, Poznań University of Technology;Pologne
dc.contributor.editoruniversityotherInstitute for Systems Research, Polish Academy of Sciences;Pologne
dc.contributor.editoruniversityotherCEG-IST, Center for Management Studies, Instituto Superior Técnico, Technical University of Lisbon;Portugal
dc.contributor.editoruniversityotherFaculty of Economics, University of Catania;Italie
dc.description.abstractenWe present a method called Generalized Regression with Intensities of Preference (GRIP) for ranking a finite set of actions evaluated on multiple criteria. GRIP builds a set of additive value functions compatible with preference information composed of a partial preorder and required intensities of preference on a subset of actions, called reference actions. It constructs not only the preference relation in the considered set of actions, but it also gives information about intensities of preference for pairs of actions from this set for a given decision maker (DM). Distinguishing necessary and possible consequences of preference information on the considered set of actions, GRIP answers questions of robustness analysis. The proposed methodology can be seen as an extension of the UTA method based on ordinal regression. GRIP can also be compared to the AHP method, which requires pairwise comparison of all actions and criteria, and yields a priority ranking of actions. As for the preference information being used, GRIP can be compared, moreover, to the MACBETH method which also takes into account a preference order of actions and intensity of preference for pairs of actions. The preference information used in GRIP does not need, however, to be complete: the DM is asked to provide comparisons of only those pairs of reference actions on particular criteria for which his/her judgment is sufficiently certain. This is an important advantage comparing to methods which, instead, require comparison of all possible pairs of actions on all the considered criteria. Moreover, GRIP works with a set of general additive value functions compatible with the preference information, while other methods use a single and less general value function, such as the weighted-sum.en
dc.relation.isversionofjnlnameEuropean Journal of Operational Research
dc.relation.isversionofjnlvol195en
dc.relation.isversionofjnlissue2en
dc.relation.isversionofjnldate2009
dc.relation.isversionofjnlpages460-486en
dc.relation.isversionofdoihttp://dx.doi.org/10.1016/j.ejor.2008.02.006
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherElsevieren
dc.subject.ddclabelRecherche opérationnelleen


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