Multi-attribute Group Decision Model Based On Probabilistic Linguistic Group Consensus Degree And Application

With the development of social economy and the progress of science and technology,the decision-making problems in people’s lives and work have become more and more complex and difficult.Many decision-making problems involve multiple factors,need to consider multiple goals,multiple constraints,and have strong uncertainty.In this case,it is often difficult for a single decision-maker to make the best decision,and multi-faceted experts are required to participate in the decision-making.In the decision-making process in which many people participate,these participants have different knowledge and experience.There are cognitive deviations,information asymmetry and other problems between individuals,which are likely to lead to inaccurate and unstable decision-making results.Collaboration between social groups requires more effective decision-making methods and tools to reduce conflicts and misunderstandings in the process of collaboration and improve the efficiency and quality of decision-making.Group decision-making consensus research is developed to meet this demand.Research on how to improve group consensus,coordinate different opinions,and finally reach group consensus has become an important research field.This paper studies a group consensus model based on the probability language terminology set,which is used to solve the consensus problem in group decision-making.The main research content is as follows:(1)This paper uses the probability language glossary to describe the uncertainty and ambiguity of group members on decision-making problems,and establishes three levels of comprehensive consensus indicators to measure the degree of consensus of decision makers.These indicators take into account both individual preferences and collective preferences,and measure the degree of consistency between them.(2)Based on individual similarity and group similarity,this paper establishes a dynamic consensus threshold based on probability language,provides personalized feedback,and has a consensus improvement model with minimum adjustment.Different group decisionmaking problems involve different factors,stakeholders and constraints,so it is necessary to select different group consensus thresholds for decision-making to meet the specific needs of different decision-making problems,so as to improve decision-making efficiency and improve decision-making effect.This article promotes consensus among group members by dynamically adjusting the consensus threshold.When the group consensus is lower than the threshold,group members need to adjust and negotiate to reach a higher consensus.In order to better allow group members to accept the modification opinions to reach group consensus,this model uses the concept of minimum adjustment,that is,when group members adjust and negotiate,they need to achieve the minimum decision value adjustment based on consensus growth.Set the minimum adjustment feedback mechanism based on personalized feedback parameters.While providing experts with decision modification opinions,you can provide personalized modification opinions for decision makers according to personalized parameters to prevent the problem of improving the consensus threshold,so that the decision maker’s evaluation decision is adjusted too large,forcing the decision maker to modify the opinion,etc.Fruit.Through comparative analysis and verification,the model can effectively improve group consensus,improve group decision-making efficiency,and have good application prospects.(3)This study provides a new idea and method for improving group consensus.Based on the above consensus research model,this paper builds a PL-CRITIC-TODIM model based on the probability language glossary set.The model uses similarity to solve expert weights,uses the improved CRITIC method to solve attribute weights,and conducts the final comprehensive evaluation through the PL-TODIM method,to get the final decision result.After comparative analysis,the effectiveness of this method is verified,and the above model is applied to solve the problem of emergency logistics supplier selection.