Multi-attribute group decision making methods with proportional 2-tuple linguistic assessments and weights

DOI

10.1080/18756891.2014.960232How to use a DOI?

Keywords

multi-attribute group decision making, proportional 2-tuple linguistic model, linguistic weights, TOPSIS, ELECTRE, PROMETHEE

Abstract

The proportional 2-tuple linguistic model provides a tool to deal with linguistic term sets that are not uniformly and symmetrically distributed. This study further develops multi-attribute group decision making methods with linguistic assessments and linguistic weights, based on the proportional 2-tuple linguistic model. Firstly, this study defines some new operations in proportional 2-tuple linguistic model, including weighted average aggregation operator with linguistic weights, ordered weighted average operator with linguistic weights and the distance between proportional linguistic 2-tuples. Then, four multi-attribute group decision making methods are presented. They are the method based on the proportional 2-tuple linguistic aggregation operator, technique for order preference by similarity to ideal solution (TOPSIS) with proportional 2-tuple linguistic information, elimination et choice translating reality (ELECTRE) with proportional 2-tuple linguistic information, preference ranking organization methods for enrichment evaluations (PROMETHEE) with proportional 2-tuple linguistic information. Finally, an example is given to illustrate the effectiveness of the proposed methods.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Cong-Cong Li
AU  - Yucheng Dong
PY  - 2014
DA  - 2014/08/01
TI  - Multi-attribute group decision making methods with proportional 2-tuple linguistic assessments and weights
JO  - International Journal of Computational Intelligence Systems
SP  - 758
EP  - 770
VL  - 7
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2014.960232
DO  - 10.1080/18756891.2014.960232
ID  - Li2014
ER  -