Multi-attribute decision making in contractor selection under hybrid uncertainty

Arbaiy Nureize, Junzo Watada

    Research output: Contribution to journalArticle

    8 Citations (Scopus)

    Abstract

    The successful of a construction industry project depends on contractor evaluation and selection. Further, human judgment and unknown evaluation risk make evaluation and selection increasingly complex. Such situations show that a contractor selection is influenced by multiple attributes that often have the hybrid uncertainty of fuzziness and probability. The objective of this study is therefore to propose a fuzzy random variable based multi-attribute decision scheme that enables us to solve such problems within the bounds of hybrid uncertainty by using a fuzzy random regression model. The proposed model is explained in examples and its usefulness is clarified. This decision model is facilitated in its use by evaluating alternatives and enables us to indicate the optimum choice in the presence of hybrid uncertainty.

    Original languageEnglish
    Pages (from-to)465-472
    Number of pages8
    JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
    Volume15
    Issue number4
    Publication statusPublished - 2011 Jun

    Fingerprint

    Contractors
    Decision making
    Construction industry
    Random variables
    Uncertainty

    Keywords

    • Contractor selection
    • Fuzzy random regression
    • Fuzzy random variables
    • Multi-attribute evaluation

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Vision and Pattern Recognition
    • Human-Computer Interaction

    Cite this

    Multi-attribute decision making in contractor selection under hybrid uncertainty. / Nureize, Arbaiy; Watada, Junzo.

    In: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 15, No. 4, 06.2011, p. 465-472.

    Research output: Contribution to journalArticle

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