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dc.contributor.authorChiclana, Franciscoen
dc.contributor.authorTan, Xiaoen
dc.contributor.authorGong, Zaiwuen
dc.contributor.authorZhang, Ningen
dc.date.accessioned2017-09-04T09:26:35Z
dc.date.available2017-09-04T09:26:35Z
dc.date.issued2017-09-01
dc.identifier.citationTan, X., Gong, Z., Chiclana, F. and Zhang, N. (2017) Consensus modeling with probability and cost constraints under uncertainty opinions. Applied Soft Computing, in pressen
dc.identifier.urihttp://hdl.handle.net/2086/14457
dc.description.abstractGoal programming is often applied into uncertain group decision making to achieve the optimal solution. Exiting models focus on either the minimum cost (guaranteeing negotiation budget) or the maximum utility (improving satisfaction level). This paper constructs a stochastic optimization cost consensus group decision making model adopting the minimum budget and the maximum utility as objective function simultaneously to study the negotiation consensus with decision makers' opinions expressed in the forms of multiple uncertain preferences such as utility function and normal distribution. Thus, the proposed model is a generalization of the existing cost consensus model and utility consensus model, respectively. Furthermore in this model, utility priority coefficients cause acceptable budget range and chance constraint shows the probability of reaching consensus. Differing from previous optimization models, the proposed model designs a Monte Carlo simulation combined with Genetic Algorithm to reach an optimal solution, which makes it more applicable to real-world decision making.en
dc.language.isoenen
dc.publisherElsevieren
dc.subjectGroup decision makingen
dc.subjectCost consensusen
dc.subjectUncertain chance constraint;en
dc.subjectNormal distributionen
dc.subjectUtility functionen
dc.subjectGoal programming priorityen
dc.titleConsensus modeling with probability and cost constraints under uncertainty opinionsen
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1016/j.asoc.2017.08.049
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderNational Natural Science Foundation of China (71571104, 71171115, 70901043), Qing Lan Project, the Six Talent Peaks Project in Jiangsu Province (2014-JY-014), the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions and the Natural Science Foundation of Jiangsu, China (grant No. BK20141481); Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX17_0905).en
dc.projectidNational Natural Science Foundation of China (71571104, 71171115, 70901043), Qing Lan Project, the Six Talent Peaks Project in Jiangsu Province (2014-JY-014), the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions and the Natural Science Foundation of Jiangsu, China (grant No. BK20141481); Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX17_0905).en
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2017-08-26en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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