A novel MAGDM approach with proportional hesitant fuzzy sets
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 256-271 |
| Journal / Publication | International Journal of Computational Intelligence Systems |
| Volume | 11 |
| Issue number | 1 |
| Publication status | Published - 2018 |
Link(s)
| DOI | DOI |
|---|---|
| Attachment(s) | Documents
Publisher's Copyright Statement
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| Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85045618401&origin=recordpage |
| Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(c45f1a53-09d9-40a8-a7dc-b5b1e0efde1e).html |
Abstract
In this paper, we propose an extension of hesitant fuzzy sets, i.e., proportional hesitant fuzzy sets (PHFSs), with the purpose of accommodating proportional hesitant fuzzy environments. The components of PHFSs, which are referred to as proportional hesitant fuzzy elements (PHFEs), contain two aspects of information provided by a decision-making team: the possible membership degrees in the hesitant fuzzy elements and their associated proportions. Based on the PHFSs, we provide a novel approach to addressing fuzzy multi-attribute group decision making (MAGDM) problems. Different from the traditional approach, this paper first converts fuzzy MAGDM (expressed by classical fuzzy numbers) into proportional hesitant fuzzy multi-attribute decision making (represented by PHFEs), and then solves the latter through the proposal of a proportional hesitant fuzzy TOPSIS approach. In this process, preferences of the decision-making team are calculated as the proportions of the associated membership degrees. Finally, a numerical example and a comparison are provided to illustrate the reliability and effectiveness of the proposed approach.
Research Area(s)
- Fuzzy sets, Hesitant fuzzy sets, Multi-attribute group decision making, Proportional hesitant fuzzy sets
Citation Format(s)
A novel MAGDM approach with proportional hesitant fuzzy sets. / Xiong, Sheng-Hua; Chen, Zhen-Song; Chin, Kwai-Sang.
In: International Journal of Computational Intelligence Systems, Vol. 11, No. 1, 2018, p. 256-271.
In: International Journal of Computational Intelligence Systems, Vol. 11, No. 1, 2018, p. 256-271.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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