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CLC number: O159; O22

On-line Access: 2016-03-07

Received: 2015-06-24

Revision Accepted: 2015-09-26

Crosschecked: 2015-12-30

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Xiao-xiong Zhang


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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.3 P.237-249


A consensus model for group decision making under interval type-2 fuzzy environment

Author(s):  Xiao-xiong Zhang, Bing-feng Ge, Yue-jin Tan

Affiliation(s):  College of Information System and Management, National University of Defense Technology, Changsha 410073, China

Corresponding email(s):   zxxandxx@163.com

Key Words:  Group decision making (GDM), Interval type-2 fuzzy sets (IT2 FSs), Feedback mechanism

Xiao-xiong Zhang, Bing-feng Ge, Yue-jin Tan. A consensus model for group decision making under interval type-2 fuzzy environment[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(3): 237-249.

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T1 - A consensus model for group decision making under interval type-2 fuzzy environment
A1 - Xiao-xiong Zhang
A1 - Bing-feng Ge
A1 - Yue-jin Tan
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1500198

We propose a new consensus model for group decision making (GDM) problems, using an interval type-2 fuzzy environment. In our model, experts are asked to express their preferences using linguistic terms characterized by interval type-2 fuzzy sets (IT2 FSs), because these can provide decision makers with greater freedom to express the vagueness in real-life situations. Consensus and proximity measures based on the arithmetic operations of IT2 FSs are used simultaneously to guide the decision-making process. The majority of previous studies have taken into account only the importance of the experts in the aggregation process, which may give unreasonable results. Thus, we propose a new feedback mechanism that generates different advice strategies for experts according to their levels of importance. In general, experts with a lower level of importance require a larger number of suggestions to change their initial preferences. Finally, we investigate a numerical example and execute comparable models and ours, to demonstrate the performance of our proposed model. The results indicate that the proposed model provides greater insight into the GDM process.

The paper provides a consensus model to deal with a linguistic decision making framework based on type 2 fuzzy sets, and clearly the paper provides novel content on this hot topic related with consensus. The topic is very interesting and the model proposed in this contribution is good.




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