The evidential reasoning approach for multi-attribute decision analysis under interval uncertainty

Dong Ling Xu, Jian Bo Yang, Ying Ming Wang

    Research output: Contribution to journalArticlepeer-review

    Abstract

    The evidential reasoning (ER) approach is a method for multiple attribute decision analysis (MADA) under uncertainties. It improves the insightfulness and rationality of a decision making process by using a belief decision matrix (BDM) for problem modelling and the Dempster-Shafer (D-S) theory of evidence for attribute aggregation. The D-S theory provides scope and flexibility to deal with interval uncertainties or local ignorance in decision analysis, which is not explored in the original ER approach and will be investigated in this paper. Firstly, interval uncertainty will be defined and modelled in the ER framework. Then, an extended ER algorithm, IER, is derived, which enables the ER approach to deal with interval uncertainty in assessing alternatives on an attribute. It is proved that the original ER algorithm is a special case of the IER algorithm. The latter is demonstrated using numerical examples. © 2005 Elsevier B.V. All rights reserved.
    Original languageEnglish
    Pages (from-to)1914-1943
    Number of pages29
    JournalEuropean Journal of Operational Research
    Volume174
    Issue number3
    DOIs
    Publication statusPublished - 1 Nov 2006

    Keywords

    • Decision support systems
    • Evidential reasoning
    • Intelligent decision system
    • Interval uncertainty modelling
    • Multiple attribute decision analysis

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