New closeness coefficients for fuzzy similarity based fuzzy TOPSIS: an approach combining fuzzy entropy and multidistance
type: Item
instance of: scholarly article
title: New closeness coefficients for fuzzy similarity based fuzzy TOPSIS: an approach combining fuzzy entropy and multidistance
zbMATH Open document ID: 1390.90344
DOI: 10.1155/2015/251646
author: Mikael Collan
author: Mario Fedrizzi
author: Pasi Luukka
published in: Advances in Fuzzy Systems
publication date: 2018-05-18T00:00:00Z
review text: Summary: This paper introduces new closeness coefficients for fuzzy similarity based TOPSIS. The new closeness coefficients are based on multidistance or fuzzy entropy, are able to take into consideration the level of similarity between analysed criteria, and can be used to account for the consistency or homogeneity of, for example, performance measuring criteria. The commonly known OWA operator is used in the aggregation process over the fuzzy similarity values. A range of orness values is considered in creating a fuzzy overall ranking for each object, after which the fuzzy rankings are ordered to find a final linear ranking. The presented method is numerically applied to a research and development project selection problem and the effect of using two new closeness coefficients based on multidistance and fuzzy entropy is numerically illustrated.
Mathematics Subject Classification ID: 90B50
Mathematics Subject Classification ID: 68T37
Mathematics Subject Classification ID: 94A17
zbMATH DE Number: 6870099
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/251646
OpenAlex ID: W1554419471
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Wikidata QID: Q59101905
description: scientific article; zbMATH DE number 6870099