Multiple-attribute decision making under uncertainty: the evidential reasoning approach revisited

In multiple-attribute decision making (MADM) problems, one often needs to deal with decision information with uncertainty. During the last decade, Yang and Singh (1994) have proposed and developed an evidential reasoning (ER) approach to deal with such MADM problems. Essentially, this approach is ba...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 36; no. 4; pp. 804 - 822
Main Authors Huynh, V.-N., Nakamori, Y., Tu-Bao Ho, Murai, T.
Format Journal Article
LanguageEnglish
Published IEEE 01.07.2006
Subjects
Agglomeration
Algorithms
Artificial intelligence
Assessment
Axioms
Cybernetics
Decision making
Design engineering
Engineering management
Erbium
evidence combination
Evidential reasoning
evidential reasoning (ER)
Human
Information science
Marine safety
Marine vehicles
Motorcycles
multiple-attribute decision making (MADM)
Uncertainty
Online AccessGet full text

Cover

More Information
Summary:In multiple-attribute decision making (MADM) problems, one often needs to deal with decision information with uncertainty. During the last decade, Yang and Singh (1994) have proposed and developed an evidential reasoning (ER) approach to deal with such MADM problems. Essentially, this approach is based on an evaluation analysis model and Dempster's rule of combination in the Dempster-Shafer (D-S) theory of evidence. This paper reanalyzes the ER approach explicitly in terms of D-S theory and then proposes a general scheme of attribute aggregation in MADM under uncertainty. In the spirit of such a reanalysis, previous ER algorithms are reviewed and two other aggregation schemes are discussed. Theoretically, it is shown that new aggregation schemes also satisfy the synthesis axioms, which have been recently proposed by Yang and Xu (2002) for which any rational aggregation process should grant. A numerical example traditionally examined in published sources on the ER approach is used to illustrate the discussed techniques
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1083-4427
1558-2426
DOI:10.1109/TSMCA.2005.855778