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/).
Cite this article
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 -