Assessment aggregation in the evidential reasoning approach to MADM under uncertainty: orthogonal versus weighted sum

Authors:
Van-Nam Huynh;Yoshiteru Nakamori;Tu-Bao Ho
Affiliations:
School of Knowledge Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan;School of Knowledge Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan;School of Knowledge Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan
Venue:
ASIAN'04 Proceedings of the 9th Asian Computing Science conference on Advances in Computer Science: dedicated to Jean-Louis Lassez on the Occasion of His 5th Cycle Birthday
Year:
2004

Citing 5
Cited 0

The transferable belief model

Artificial Intelligence
Fuzzy sets and decision analysis

Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Fuzzy Multiple Attribute Decision Making: Methods and Applications

Fuzzy Multiple Attribute Decision Making: Methods and Applications
On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Nonlinear information aggregation via evidential reasoning in multiattribute decision analysis under uncertainty

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we revisit the evidential reasoning (ER) approach to multiple-attribute decision making (MADM) with uncertainty. The attribute aggregation problem in MADM under uncertainty is generally formulated as a problem of evidence combination. Then several new aggregation schemes are proposed and simultaneously their theoretical features are explored. A numerical example traditionally examined in published sources on the ER approach is used to illustrate the proposed techniques.