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Open AccessArticle

The Multi-Attribute Group Decision-Making Method Based on Interval Grey Trapezoid Fuzzy Linguistic Variables

by Kedong Yin 1,2, Pengyu Wang 1 and Xuemei Li 1,2,*
1
School of Economics, Ocean University of China, Qingdao 266100, China
2
Ocean Development Research Institute, Major Research Base of Humanities and Social Sciences of Ministry of Education, Ocean University of China, Qingdao 266100, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2017, 14(12), 1561; https://doi.org/10.3390/ijerph14121561
Received: 3 November 2017 / Revised: 6 December 2017 / Accepted: 6 December 2017 / Published: 13 December 2017
(This article belongs to the Special Issue Decision Models in Green Growth and Sustainable Development)
With respect to multi-attribute group decision-making (MAGDM) problems, where attribute values take the form of interval grey trapezoid fuzzy linguistic variables (IGTFLVs) and the weights (including expert and attribute weight) are unknown, improved grey relational MAGDM methods are proposed. First, the concept of IGTFLV, the operational rules, the distance between IGTFLVs, and the projection formula between the two IGTFLV vectors are defined. Second, the expert weights are determined by using the maximum proximity method based on the projection values between the IGTFLV vectors. The attribute weights are determined by the maximum deviation method and the priorities of alternatives are determined by improved grey relational analysis. Finally, an example is given to prove the effectiveness of the proposed method and the flexibility of IGTFLV. View Full-Text
Keywords: interval grey trapezoid fuzzy linguistic variables; grey relation analysis; multi-attribute group decision making interval grey trapezoid fuzzy linguistic variables; grey relation analysis; multi-attribute group decision making
MDPI and ACS Style

Yin, K.; Wang, P.; Li, X. The Multi-Attribute Group Decision-Making Method Based on Interval Grey Trapezoid Fuzzy Linguistic Variables. Int. J. Environ. Res. Public Health 2017, 14, 1561.

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