Study On The Intertelated Problems For Group Decision Making Based On Unbalanced Linguistic Information

In decision analysis, because the human's mind is fuzzy and uncertain and the decision making problem is complex, the decision analysis method with linguistic phrases is more convenient and has caused wide attention. So far, about how to solve the consistency for the group decision making problem (GDM) based on the linguistic term, the domestic and foreign scholars have maken a series of theories and applications, which is based on the fuzzy extension principle method and the symbolic translation method.However, in the process of dealing with GDM, there exist some problems whose linguistic terms are unsymmetrical and uniform. This paper calles this type of linguistic term unbalanced linguistic term set. Based on the GDM with unbalanced linguistic term, this paper reseaches the consensus problem and the adverse judgement problem.About the consistency problem, this paper gives a new consensus algorithm.The consistency model proposed by Herrera is divided into two processes:the concensus process and the selection process. The model exists some phenomena of complex computing and the opinion's adjustment with random. To the problem, we introduce the concepts of similarity function and the Hadamard product of preference matrices, propose a much simpler consensus algorithm. Finally, we carry out comparative analysis with Herrera's example. The new algorithm has two advantages:the calculation process is brief; the expert's opinion is adjusted in accordance with the concise principle.About the adverse judgement problem for GDM based on the unbalanced linguistic information, the paper defines the concepts of similarity degree, satisfactory degree, close degree, combines the compatibility and consensus, gives a compatibility analysis. Then, the paper merges the mathematical statistics and compatibility analysis and gives a better algorithm by dimensional analysis for data index. It makes up the sided phenomenon for the first two methods, improves the adverse judgement process of group decision making.