A Minimum Adjustment Binary Categorical Large Group Consensus Decision Making Approach from a Probabilistic Linguistic Distribution Perspective
Aiming at the problem of large group decision-making considering the ac-tual classification needs,this paper investigates the binary classification large group consensus decision-making model from a perspective of probability distribution of preference information and minimum adjustment.Firstly,by combining the probabil-ity distribution vector of the data with the dominance degree,an alternative ranking method is proposed based on the PROMETHEE Ⅱ method.Secondly,according to the classification needs,a binary classification selection process is established.Then,a binary classification consensus optimization model based on minimum adjustment is constructed,which can minimize the amount of consensus adjustment of decision makers while obtaining the classification consensus.Finally,a numeral example is used to illustrate the validity and feasibility of the proposed method.
Probability distributiondominance degreebinary classificationmini-mum adjustmentgroup consensus
NETL
NSTL
万方数据
