Research On Modeling And Application Of Group Decision Making With Z-Probabilistic Linguistic Information

With the advent of the intelligent era of big data,the decision making environment has become increasingly complex.In order to deal with uncertain information in the real world,many uncertain information representation methods have been proposed and widely used in multiple attribute group decision making(MAGDM).However,the above-mentioned traditional uncertain information theory is subject to more and more constraints and challenges due to its inability to fully express all the information in MAGDM.Therefore,based on the classic Z-number and probabilistic linguistic term set(PLTS)theory,this thesis comprehensively considers the various characteristics of information to study the group decision making modeling of Z-probabilistic linguistic information and its application in venture capital,emergency decision making and so on.Firstly,the concept of Z-mixture-numbers and the ranking model based on the ideal degree are proposed.An attribute weighting method based on credibility is proposed to consider the correlation between attributes.A multiple attribute decision making(MADM)model of Z-mixture-numbers is constructed based on the information aggregation operators of Z-mixture-numbers.The rationality of the model is explained through the problem of risk investment in shared cars.Secondly,the concept of Z-probabilistic linguistic term sets(Z-PLTSs)is proposed.Its normalization,operational rules,comparison method and distance measure are also proposed accordingly.Considering the credibility of information,a method of attribute weighting based on the credibility is proposed.Then,a MAGDM model of Z-PLTSs is constructed based on the Z-probabilistic linguistic information aggregation operators and the extended TOPSIS method.Its application examples and comparative analysis in project investment illustrate the effectiveness and flexibility of the model.Thirdly,the concept of Z-uncertain probabilistic linguistic term sets(Z-UPLTSs)and its corresponding operational rules,normalization,distance and similarity measures,comparison methods and the method of solving probability based on the credibility are proposed.An attribute weight weighting method based on the maximum deviation method,some aggregation operators based on the Z-uncertain probabilistic linguistic information and the extended TOPSIS method are proposed.On this basis,an emergency decision model of Z-UPLTSs is established.The emergency decision making example and comparative analysis of the treatment of COVID-19 patients proved the necessity and rationality of this model.Finally,the concept of intelligent linguistic sets(ILSs)is proposed.At the same time,the comparison method,normalization,operational rules and distance are also proposed.After proposing the aggregation operators based on the intelligent linguistic information and the VIKOR method,a MAGDM model based on ILSs is constructed.The numerical example and comparative analysis in the risk assessment of COVID-19 in cities illustrate the necessity and superiority of the model.