Research On Multiple Attribute Group Decision Theory And Method Based On Probabilistic Linguistic Information

As an extension of Hesitant fuzzy linguistic term set,Probabilistic linguistic term set(PLTS)can measure experts' linguistic evaluation information more effectively.Based on the theory of PLTSs,this paper extends the traditional Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)method,grey relational analysis(GRA)Method,Combined Compromise Solution(CoCoSo)method,Dombi operator and Heronian mean(HM)operator to probabilistic linguistic environment,and constructs a multi-attribute group decision-making(MAGDM)model with probabilistic linguistic evaluation information.At the same time,considering the problem of attribute weight,the attribute weight is obtained by constructing an optimization model when the weight information is partially known.When the weight information is completely unknown,the entropy weight method and CRITIC method are used to obtain the attribute weight respectively.The new probabilistic linguistic TOPSIS(PL-TOPSIS)model,probabilistic linguistic GRA(PL-GRA)model,probabilistic linguistic CoCoSo(PL-CoCoSo)model and probabilistic linguitic Dombi Heronian mean(PLDHM)operator are applied to green supplier selection cases to verify the effectiveness of the proposed model.The model proposed in this paper is compared with the existing models to verify the scientificity of the model proposed in this paper.