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dc.contributor.authorChiclana, Franciscoen
dc.contributor.authorZhang, Ningen
dc.contributor.authorGong, Zaiwuen
dc.date.accessioned2017-07-24T12:08:31Z
dc.date.available2017-07-24T12:08:31Z
dc.date.issued2017-07-25
dc.identifier.citationZhang, N., Gong, Z. and Chiclana, F. (2017) Minimum Cost Consensus Models based on Random Opinions. Expert Systems with Applications, 89, pp. 149-159en
dc.identifier.urihttp://hdl.handle.net/2086/14325
dc.description.abstractIn some complex group decision making cases, the opinions of decision makers (DMs) present random characteristic. However, it is difficult to determine the range of opinions by knowing only their probability distributions. In this paper, we construct cost consensus models with random opinions. The objective function is obtaining the minimum consensus budget under a certain confidence level. Nonetheless, the constraints restrict the upper limit of the consensus cost, the lower limit of DMs' compensations, and the opinions deviation between DMs and the moderator. As such, probabilistic planning based on a genetic algorithm is designed to resolve the minimum cost consensus models based on China's urban demolition negotiation, which can better simulate the consensus decision-making process and obtain a satisfactory solution for the random optimization consensus models. The proposed models generalize both Ben-Arieh's minimum cost consensus model and Gong's consensus model with uncertain opinions. Considering that the opinions of DMs and the moderator obey various distributions, the models simulate the opinion characteristics more effectively. In the case analysis, a sensitivity analysis method is adopted to obtain the minimum budget, and probabilistic planning based on genetic algorithm to obtain a satisfactory solution that is closer to reality.en
dc.language.isoenen
dc.publisherElsevieren
dc.subjectGroup decision makingen
dc.subjectConsensusen
dc.subjectProbability distributionen
dc.subjectProbabilistic planningen
dc.subjectGenetic algorithmen
dc.titleMinimum Cost Consensus Models based on Random Opinionsen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2017.07.035
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2017-07-23en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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