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Annals of Operations Research
Article . 2024 . Peer-reviewed
License: CC BY
Data sources: Crossref
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OWA-based multi-criteria decision making based on fuzzy methods

Authors: Livia De Giovanni; Pierpaolo D'Urso; Jalal Chachi; Ahmad Kazemifard;

OWA-based multi-criteria decision making based on fuzzy methods

Abstract

AbstractOne of the most important challenges in Multi-Attribute Decision Making (MADM) problems is how can the optimal weights of the criteria can be determined properly by the decision maker. In the relevant research literature, various methods based on the requirements and assumptions of the problem were introduced to determine the weights of the criteria. In this regard, in particular, the Yager’s OWA operator is one of the most significant and widely used approaches to evaluate the weights of criteria. But there is a drawback, that is, the results of Yager’s OWA operator depend only on the level of decision-maker’s risk and the number of the criteria. Therefore, in this paper, using a multi-objective decision making approach, we try to express this MADM challenge in the form of a generalization of the Yager’s OWA operators and Ahn’s method. One of the advantages of this generalization is that the proposed method uses all the information in the decision matrix compared to the methods proposed by Yager’s OWA operators and the Ahn’s method. The proposed approach is also able to enter various types of preferences considered by the decision maker for the criteria calculations as crisp or fuzzy quantities. Numerical examples and real dataset analysis based on a survey of students’ opinions on teaching activities are provided.

Country
Italy
Keywords

OWA operators Fuzzy preferences Constrained OWA optimization L-p metric and Goal Programming MADM TOPSIS

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    11
    popularity
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    Top 10%
    influence
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citations
The total number of citations received by all articles of the researcher of interest.
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Top 10%
Average
Top 10%
Green
hybrid