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Soft Computing

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06.08.2019 | Methodologies and Application

A novel distance-based multiple attribute decision-making with hesitant fuzzy sets

verfasst von: Bahram Farhadinia, Zeshui Xu

Erschienen in: Soft Computing | Ausgabe 7/2020

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Abstract

Up to now, various types of distance measures have been developed and investigated in-depth for hesitant fuzzy sets (HFSs). The analytical study of the existing distance measures for HFSs shows that they have still some limitations. In an attempt to overcome the limitations, this study develops a class of Hausdorff-based distances to measure the distance among HFSs which are not restricted to the same length of their hesitant fuzzy elements (HFEs) and of course the arranging order of values in the HFEs. Furthermore, these HFS distance measures do satisfy all well-known and essential axioms, specially, the triangle inequality property. Eventually, we present some examples to illustrate the efficiency of the new developed HFS distance measures together with a comparative analysis with other existing ones.

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Titel: A novel distance-based multiple attribute decision-making with hesitant fuzzy sets
verfasst von: Bahram Farhadinia
Zeshui Xu
Publikationsdatum: 06.08.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 7/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-019-04250-6

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