A Method for Multi-Criteria Group Decision Making with 2-Tuple Linguistic Information Based on Cloud Model

This paper presents a new approach to solve the multi-criteria group decision making (MCGDM) problem where criteria values take the form of 2-tuple linguistic information. Firstly, a 2-tuple hybrid ordered weighted geometric (THOWG) operator is proposed, which synthetically considers the importance of both individual and the ordered position so as to overcome the defects of existing operators. Secondly, combining the advantages of the cloud model and 2-tuple linguistic variable, a new generating cloud method is proposed to transform 2-tuple linguistic variables into clouds. Thirdly, we further define some new cloud algorithms, such as cloud possibility degree and cloud support degree which can be respectively used to compare clouds and determine the criteria weights. Furthermore, a new approach for 2-tuple linguistic group decision making is presented on the basis of the THOWG operator, the improved generating cloud method as well as the new cloud algorithms. Finally, an example of assessing the social effects of biomass power plants (BPPS) is illustrated to verify the application and feasible of the developed approach, and a comparative analysis is also conducted to validate the effectiveness of the proposed method