CLC number: TP13; C934
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2017-11-06
Cited: 0
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Jie Ding, Ze-shui Xu, Hu-chang Liao. Consensus-reaching methods for hesitant fuzzy multiple criteria group decision making with hesitant fuzzy decision making matrices[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(11): 1679-1692.
@article{title="Consensus-reaching methods for hesitant fuzzy multiple criteria group decision making with hesitant fuzzy decision making matrices",
author="Jie Ding, Ze-shui Xu, Hu-chang Liao",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="11",
pages="1679-1692",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601546"
}
%0 Journal Article
%T Consensus-reaching methods for hesitant fuzzy multiple criteria group decision making with hesitant fuzzy decision making matrices
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%A Hu-chang Liao
%J Frontiers of Information Technology & Electronic Engineering
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%P 1679-1692
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%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601546
TY - JOUR
T1 - Consensus-reaching methods for hesitant fuzzy multiple criteria group decision making with hesitant fuzzy decision making matrices
A1 - Jie Ding
A1 - Ze-shui Xu
A1 - Hu-chang Liao
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 11
SP - 1679
EP - 1692
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601546
Abstract: Group decision making plays an important role in various fields of management decision and economics. In this paper, we develop two methods for hesitant fuzzy multiple criteria group decision making with group consensus in which all the experts use hesitant fuzzy decision matrices (HFDMs) to express their preferences. The aim of this paper is to present two novel consensus models applied in different group decision making situations, which are composed of consensus checking processes, consensus-reaching processes, and selection processes. All the experts make their own judgments on each alternative over multiple criteria by hesitant fuzzy sets, and then the aggregation of each hesitant fuzzy set under each criterion is calculated by the aggregation operators. Furthermore, we can calculate the distance between any two aggregations of hesitant fuzzy sets, based on which the deviation between any two experts is yielded. After introducing the consensus measure, we develop two kinds of consensus-reaching procedures and then propose two step-by-step algorithms for hesitant fuzzy multiple criteria group decision making. A numerical example concerning the selection of selling ways about ‘Trade-Ins’ for Apple Inc. is provided to illustrate and verify the developed approaches. In this example, the methods which aim to reach a high consensus of all the experts before the selection process can avoid some experts’ preference values being too high or too low. After modifying the previous preference information by using our consensus measures, the result of the selection process is much more reasonable.
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Open peer comments: Debate/Discuss/Question/Opinion
<1>
Kajal Chatterjee@National Institute of Technology<chatterjeekajal7@gmail.com>
2017-04-15 18:48:42
I am a PhD researcher working in MCDM under various uncertain domains. I want to collaborate with Chinese researchers working in the area of MCDM, who have publications both in English and Chinese. I want to explore the papers written only in Chinese.