Research On Ranking Method Of Different Linguistic Information Based On Possibility Degree

In this paper,the multi-attribute decision making problems with uncertain inter-val linguistic,hesitant fuzzy linguistic,probabilistic linguistic,interval-valued proba-bilistic linguistic and probabilistic uncertain linguistic:information are systematically studied based on the possibility degree.Firstly,the existing interval number possibility degree formula is extended,and a new possibility degree formula of interval uncerta.in linguistic information is given,and some properties of this formula is proved,and the rationality of the newly defined formula in solving multi-attribute decision-making problems is illustrated by an ex-ample.Secondly,a new formula of relative dominance degree of interval uncertain linguistic information is proposed by using the existing formula of relative dominance degree of interval numbers,and some properties of this formula is proved,and the rationality of the new formula in solving multi-attribute decision-making problems is illustrated by an examples.Thirdly,under the condition of analysis the defects of the existing interval number possibility degree forlula,the multi-attribute decision-making method of hesitant fuzzy linguistic term set based on the possibility degree formula,and the drawbacks of these decision-making methods are introduced,the concept of G function is given,and the priority method proposed based on the function and the average operator are combined,a decision-making method based on the priority method is proposed,and it shows that the decision-making method is more reasonable in solving multi-attribute decision-making problems.Fourthly,the problems of the existing possibility degree method is analysed,and it is proved that the new possibility degree method is more reasonable in solving the ranking problem of probabilistic linguistic term set,probabilistic linguistic weight-ed average(PLWA)operator and probabilistic linguistic ordered weighted(PLOWA)operator arc proposed;the multi-attribute decision-inaking method with evaluation value of probabilistic linguistic term set is presented,and it is illustrated that the new decision making method is more reasonable in solving multi-attribute decision-making problems.Fifthly,the problems of the existing possibility degree method of interval-valued prob-abilistic linguistic term set are given,the possibility degree metliod of now interval-valued probabilistic linguistic term set is introduced by using the priority degree for-mula and interval number and the possibility degree formula of probabilistic linguistic term set,and the new possibility degree method is illustrated in solving the ranking problem of interval-valued probabilistic linguistic term sets is more reasonable;the interval-valued probabilistic geometric Bonferroni mean(IVPLGBM)operator and weighted interval-valued probabilistic geometric Bonferroni mean(WIVPLGBM)op-erator are proposed,a multi-attribute decision-making method with evaluation value of interval-valued probabilistic linguistic term set is given,and the rationality of the new decision-making method in solving multi-attribute decision-making problems is illustrated by an example.Sixthly,the existing priority formula of interval number in the third part is extended,the priority formula of interval uncertain linguistic information is given,and some properties of this formula are proved,and the priority formula of probabilistic un-certain linguistic term set is proposed by combining the possibility degree formula of probabilistic linguistic term set,and some properties of this formula are proved,the existing basic operation rules of probabilistic uncertain linguistic term set is improved;the probabilistic uncertain linguistic ordered weighted average(PULOWA)operator are proposed,and the multi-attribute decision-making method with evaluation value of probabilistic uncertain linguistic term set is presented,and the rationality of the new decision-making method in solving multi-attribute decision-making problems is illustrated with examples.