Uncertainty and preference modelling for multiple criteria vehicle evaluation

Xinlian Xie, Jian Bo Yang, Dong Ling Xu, Anil Kumar Maddulapalli, Qiuping Yang

    Research output: Contribution to journalArticlepeer-review

    Abstract

    A general framework for vehicle assessment is proposed based on both mass survey information and the evidential reasoning (ER) approach. Several methods for uncertainty and preference modeling are developed within the framework, including the measurement of uncertainty caused by missing information, the estimation of missing information in original surveys, the use of nonlinear functions for data mapping, and the use of nonlinear functions as utility function to combine distributed assessments into a single index. The results of the investigation show that various measures can be used to represent the different preferences of decision makers towards the same feedback from respondents. Based on the ER approach, credible and informative analysis can be conducted through the complete understanding of the assessment problem in question and the full exploration of available information.
    Original languageEnglish
    Pages (from-to)688-708
    Number of pages20
    JournalInternational Journal of Computational Intelligence Systems
    Volume3
    Issue number6
    DOIs
    Publication statusPublished - Dec 2010

    Keywords

    • Evidence theory
    • Multiple criteria analysis
    • Uncertainty modeling
    • Vehicle evaluation

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