Multicriteria correlation preference information (MCCPI) with nonadditivity index for decision aiding

Huang, Li, Wu, Jian-Zhang and Beliakov, Gleb 2020, Multicriteria correlation preference information (MCCPI) with nonadditivity index for decision aiding, Journal of Intelligent and Fuzzy Systems, vol. 39, no. 3, pp. 3441-3452, doi: 10.3233/JIFS-191789.


Title Multicriteria correlation preference information (MCCPI) with nonadditivity index for decision aiding
Author(s) Huang, Li
Wu, Jian-Zhang
Beliakov, GlebORCID iD for Beliakov, Gleb orcid.org/0000-0002-9841-5292
Journal name Journal of Intelligent and Fuzzy Systems
Volume number 39
Issue number 3
Start page 3441
End page 3452
Total pages 12
Publisher IOS Press
Place of publication Amsterdam, The Netherlands
Publication date 2020
ISSN 1064-1246
1875-8967
Summary MCCPI (Multiple Criteria Correlation Preference Information) is a kind of 2 dimensional decision preference information obtained by pairwise comparison on the importance and interaction of decision criteria. In this paper, we introduce the nonadditivity index to replace the Shapley simultaneous interaction index and construct an undated MCCPI based decision scheme. We firstly propose a diagram to help decision maker obtain the nonadditivity index type MCCPI, then establish transform equations to normalize them into desired capacity and finally adopt a random generation MCCPI based comprehensive decision aid algorithm to explore the dominance relationships and creditable ranking orders of all decision alternatives. An illustrative example is also given to demonstrate the feasibility and effectiveness of the proposed decision scheme. It’s shown that based on some good properties of nonadditivity index in practice, the updated MCCPI model can deal with the internal interaction among decision criteria with relatively less model construction and calculation effort.
Language eng
DOI 10.3233/JIFS-191789
Indigenous content off
Field of Research 0801 Artificial Intelligence and Image Processing
1702 Cognitive Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30144690

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