Study On Stochastic Multi-criterion Decision-making Methods With Incomplete Information In Fuzzy Environments

The stochastic multi-criterion decision-making which is in a fuzzy environment is called fuzzy-stochastic multi-criterion decision-making. It is an important research branch of the uncertain multi-criterion decision-making, and it's also a kind of common problems in the unpredictable and complex society. In brief, what it solves is the problem of selecting, ranking or classifying the alternatives when their evaluations are fuzzy-stochastic variables on multiple criteria. In the actual decision-making processes, due to the complexity of the decision-making environment and the uncertainty of the decision-makers'subjective perception, it is general that the evaluations of the alternatives are fuzzy-stochastic variables or even missing on some criteria and the information of the decision-making factors, such as the weights, the state probabilities, the risk attitude and the preference given by the decision-makers, may be uncertain or incomplete. At present, there is little concern on these issues. Therefore, the academic value of systemic research on them is great. That these methods are applied to the public service management activities and assist the relevant managers in the decision making could optimize the decision-making process, reduce the decision-making risk and improve the decision-making result. So they have important practical significance. To this end, based on the comprehensive and in-depth analysis of the relevant literatures,the thesis systematacially studies on the stochastic multi-criterion decision-making methods with incomplete information in the fuzzy environments. The key ideas and main innovations are shown as follows.(1)After the preliminary discussion on the basic theories of the uncertain multi-criterion decision-making is carried out, some phased outcomes are obtained.According to the extent of the complexity, the uncertain information is split into the simple uncertain information and the multiple uncertain information.The trapezoidal fuzzy-stochastic variables, interval-valued fuzzy-stochastic variables, intuitionistic fuzzy-stochastic variables and linguistic-stochastic variables are defined. The state probabilities with incomplete information are introducted. The four usual methods of dealing with the stochastic variables are systematacially summarized such as the expected utility value, stochastic simulation technology, stochastic dominance rule and information aggregation operator. After the stochastic multi-criterion decision-making is renamed, the classification system of the stochastic multi-criterion decision-making is initially established. And the different types of stochastic multi-criterion decision-making are defined. The above results build the basic theories and methods of the study.(2)With respect to the fuzzy-stochastic multi-criterion decision-making problems with consistent state space, incomplete certain state probabilities and incomplete certain criterion weights, six decision-making methods are proposed. In line with the features of the different types of fuzzy-stochastic variables, these methods extend the appliance region of the ordered weighted averaging operator, cut set of fuzzy set, analysis of systematic deviation, cumulative prospect theory, score function, operation rules of interval numbers and similarity degree. These methods are separately the trapezoidal fuzzy-stochastic multi-criterion decision-making method base on TC-OWA operator, trapezoidal fuzzy-stochastic multi-criterion decision-making method base onα-cut set, interval-valued fuzzy-stochastic multi-criterion decision-making method base on cumulative prospect theory, interval-valued fuzzy-stochastic multi-criterion decision-making method base on score function, intuitionistic fuzzy-stochastic multi-criterion decision-making method base on interval operation and intuitionistic fuzzy-stochastic multi-criterion decision-making method base on similarity degree. The above methods point the way of sloving the similar problems, and can be extended in the inconsistent state space.(3) With respect to the linguistic-stochastic multi-criterion decision-making problems with inconsistent state space and incomplete information, two decision-making methods are proposed. In terms of the features of the different types of linguistic-stochastic variables, these methods extend the appliance region of the superiority-inferiority ranking and cloud model. These methods are separately the discrete linguistic-stochastic multi-criterion decision-making method base on superiority-inferiority ranking and linguistic-stochastic multi-criterion decision-making method base on cloud model. The above methods provide the mentality of sloving the similar problems, and can be extended in the problems with missing information.(4)Some examples are analyzed in the domain of the appraisement and selection to the alternatives for the urban public traffic line network optimization. The results show the effectiveness and scientificalness of the above methods. It provides a useful reference for the relevant applications in the other areas.