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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/4659

Title: Multiple Attribute Decision Making Under Uncertainty: The Evidential Reasoning Approach Revisited
Authors: Huynh, Van-Nam
Nakamori, Yoshiteru
Ho, Tu-Bao
Murai, Tetsuya
Keywords: assessment
evidence combination
evidential reasoning (ER)
multiple-attribute decision making (MADM)
Issue Date: 2006-07
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Volume: 36
Number: 4
Start page: 804
End page: 822
DOI: 10.1109/TSMCA.2005.855778
Abstract: 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.
Rights: Copyright (c)2006 IEEE. Reprinted from IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 36(4), 2006, 804-822. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of JAIST's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
URI: http://hdl.handle.net/10119/4659
Material Type: publisher
Appears in Collections:a10-1. 雑誌掲載論文 (Journal Articles)

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