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29.01.2015 | Methodologies and Application

Fuzzy measure identification for criteria coalitions using linguistic information

verfasst von: R. Bernal, M. Karanik, J. I. Peláez

Erschienen in: Soft Computing | Ausgabe 4/2016

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Abstract

Modeling interactions between criteria in multiple criteria decision analysis (MCDA) is a complex task. Such complexity arises when there are visible redundancies and synergies among criteria, which traditional MCDA methods cannot deal with. The Choquet integral is a model that has been conceived to deal with these issues, but an appropriate fuzzy measure must be defined. This article shows how to compute a fuzzy measure for criteria coalitions using linguistic information efficiently. Due to the complexity to identify an adequate fuzzy measure when the criteria set cardinality increases, the proposed model reduces the effort to determine the measure of each criteria combination by focusing on relevant interactions. Then, this fuzzy measure is used on Choquet integral to establish the best alternative in a decision-making problem. Finally, a comparison between the arithmetic mean, the OWA operator and the proposed method is presented.

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To keep equidistance to 0.

\(V\)does not include the Null label because a criterion with weight equal to 0 should not be taken into account for the decision process.

\(W\) includes the Null label because there may exist criteria without interaction.

The coefficients of \(\emptyset \) and \(N\) are determined by definition: \(\mu (\emptyset )=0\); \(\mu ( N)=1\).

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Titel: Fuzzy measure identification for criteria coalitions using linguistic information
verfasst von: R. Bernal
M. Karanik
J. I. Peláez
Publikationsdatum: 29.01.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 4/2016
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-015-1589-5

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