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dc.contributor.authorWang, Haichao
dc.contributor.authorLahdelma, Risto
dc.contributor.authorSalminen, Pekka
dc.date.accessioned2018-10-30T06:48:45Z
dc.date.available2018-10-30T06:48:45Z
dc.date.issued2018
dc.identifier.citationWang, H., Lahdelma, R., & Salminen, P. (2018). Complementary Judgment Matrix Method with Imprecise Information for Multicriteria Decision-Making. <i>Mathematical Problems in Engineering</i>, <i>2018</i>, Article 3695627. <a href="https://doi.org/10.1155/2018/3695627" target="_blank">https://doi.org/10.1155/2018/3695627</a>
dc.identifier.otherCONVID_28665716
dc.identifier.otherTUTKAID_79170
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/60005
dc.description.abstractThe complementary judgment matrix (CJM) method is an MCDA (multicriteria decision aiding) method based on pairwise comparisons. As in AHP, the decision-maker (DM) can specify his/her preferences using pairwise comparisons, both between different criteria and between different alternatives with respect to each criterion. The DM specifies his/her preferences by allocating two nonnegative comparison values so that their sum is 1. We measure and pinpoint possible inconsistency by inconsistency errors. We also compare the consistency of CJM and AHP trough simulation. Because preference judgments are always more or less imprecise or uncertain, we introduce a way to represent the uncertainty through stochastic distributions, and a computational method to treat the uncertainty. As in Stochastic Multicriteria Acceptability Analysis (SMAA), we consider different uncertainty levels: precise comparisons, imprecise comparisons with a stochastic distribution, and missing comparisons between criteria. We compute rank acceptability indices for the alternatives, describing the probability of an alternative to obtain a given rank considering the level of uncertainty and study the influence of the uncertainty on the SMAA-CJM results.fi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherHindawi Publishing Corporation
dc.relation.ispartofseriesMathematical Problems in Engineering
dc.rightsCC BY 4.0
dc.subject.othercomplementary judgment matrix (CJM) method
dc.subject.othermulticriteria decision-making
dc.subject.othercomparisons
dc.subject.otherstochastic multicriteria acceptability analysis (SMAA)
dc.subject.otheruncertainty levels
dc.titleComplementary Judgment Matrix Method with Imprecise Information for Multicriteria Decision-Making
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201810174442
dc.contributor.laitosKauppakorkeakoulufi
dc.contributor.laitosSchool of Business and Economicsen
dc.contributor.oppiaineBasic or discovery scholarshipfi
dc.contributor.oppiaineLaskentatoimifi
dc.contributor.oppiaineBasic or discovery scholarshipen
dc.contributor.oppiaineAccountingen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2018-10-17T12:15:14Z
dc.description.reviewstatuspeerReviewed
dc.relation.issn1024-123X
dc.relation.numberinseries0
dc.relation.volume2018
dc.type.versionpublishedVersion
dc.rights.copyright© 2018 Haichao Wang et al.
dc.rights.accesslevelopenAccessfi
dc.subject.ysopäätösteoria
dc.subject.ysostokastinen monikriteerinen arvostusanalyysi
dc.subject.ysoepävarmuus
dc.subject.ysomatriisit
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p13478
jyx.subject.urihttp://www.yso.fi/onto/yso/p23107
jyx.subject.urihttp://www.yso.fi/onto/yso/p1722
jyx.subject.urihttp://www.yso.fi/onto/yso/p18099
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1155/2018/3695627


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