Similarity-Based Approach for Group Decision Making with Multi-Granularity Linguistic Information-Reference-Cited by-同舟云学术

Similarity-Based Approach for Group Decision Making with Multi-Granularity Linguistic Information

Published:2016-11-30 Issue:06 Volume:24 Page:873-900
ISSN:0218-4885
Container-title:International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
language:en
Short-container-title:Int. J. Unc. Fuzz. Knowl. Based Syst.

Author:

Lin Jian¹,Chen Riqing²,Zhang Qiang³

Affiliation:

1. College of Computer and Information, Fujian Agriculture and Forestry University, Fuzhou, 350002, P. R. China

2. Smart Agriculture and Big Data Research Centre, Fujian Agriculture and Forestry University, Fuzhou, 350002, P. R. China

3. School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, P. R. China

Abstract

The aim of this article is to investigate the approach for multi-attribute group decision-making, in which the attribute values take the form of multi-granularity multiplicative linguistic information. Firstly, to process multiple sources of decision information assessed in different multiplicative linguistic label sets, a method for transforming multi-granularity multiplicative linguistic information into multiplicative trapezoidal fuzzy numbers is proposed. Then, a formula for ranking multiplicative trapezoidal fuzzy numbers is given based on geometric mean. Furthermore, the concept of similarity degree between two multiplicative trapezoidal fuzzy numbers is defined. The attribute weights are obtained by solving some optimization models. An effective approach for group decision making with multi-granularity multiplicative linguistic information is developed based on the ordered weighted geometric mean operator and proposed formulae. Finally, a practical example is provided to illustrate the practicality and validity of the proposed method.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Information Systems,Control and Systems Engineering,Software

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0218488516500409

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