Volume 7, Issue 4, August 2014, Pages 758 - 770
Multi-attribute group decision making methods with proportional 2-tuple linguistic assessments and weights
- Cong-Cong Li, Yucheng Dong
- Corresponding Author
- Cong-Cong Li
Available Online 9 January 2017.
- https://doi.org/10.1080/18756891.2014.960232How to use a DOI?
- multi-attribute group decision making, proportional 2-tuple linguistic model, linguistic weights, TOPSIS, ELECTRE, PROMETHEE
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Cong-Cong Li AU - Yucheng Dong PY - 2017 DA - 2017/01 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 - https://doi.org/10.1080/18756891.2014.960232 ID - Li2017 ER -