Study On Methods For Hybrid Multiple Attribute Group Decision Making

There are a lot of multi-attribute group decision making in many domains such as society、economy and military. These problems often contain quantitative attributes and qualitative attributes, these values of attribute are precise number, interval number, fuzzy number and linguistic values, etc., and in order to avoid wrong decisions due to the mistakes of individual decision-makers lead to adverse consequences to improve decision-making level and efficiency of decision-making processes, there require multiple decision-makers involved in the decision-making process, which form hybrid multi-attribute group decision-making (HMAGDM)problems. Main researches of the multiple attribute group decision-making focused on how to aggregate individual judgment to form group judgment, to select the relative satisfactory alternatives, to rank alternatives, to classify or sort alternatives according to the attribute value of alternatives. Because of quantitative attributes and qualitative attributes are handled in HMAGDM, these values of attribute are various types of data, which improve the complexity of problems. The study for HMAGDM problem has important theoretical significance and practical application background. In this thesis, some problems of HMAGDM in which its attribute values are precise numbers, interval numbers, fuzzy numbers and linguistic values are investigated, some specific solutions are proposed. The main contributions of this thesis are summarized as follows:(l)The problem of consensus among group opinions in hybrid multi-attribute group decision making is studied. Several consensus analysis methods for two cases which decision makers’assessment information is complete and decision makers’assessment information is incomplete are separately proposed. Aimed at the case of complete assessment information, this paper proposes a consensus analysis method which based on the degree of difference-the degree of consensus in the attribute level, so, in the process of calculating, data type does not need any conversion, which, in turn, secures no information losses. When the group is not in consensus, experts targeted revising the assessment information of relevant attribute making the group agree each other as soon as possible. At the same time, excessive modification of assessment information can be avoided. Then, aimed at the case of incomplete assessment information, two new consensus analysis methods are proposed according to two ways of processing incomplete information. In fist method, a linear programming model is build up according to a certain constraint conditions for filling missing value, through the calculation of the model to get missing value, so, incomplete assessment matrix is converted complete assessment matrix, then consensus analysis is done in this complete assessment matrix. In the second method, missing value not to be filled, consensus analysis is directly done in the incomplete assessment matrix. Finally the three methods of consensus analysis are compared. The compared result shows that in the case of incomplete assessment information, consensus analysis is directly done on the incomplete assessment matrix without change to initial assessment information and that is more consistent with the actual situation. In addition, aimed at the case of incomplete assessment information, the completely degree of assessment matrix, alternative’s complete degree and attribute’s completely degree were discussed.(2)Ranking problem in hybrid multi-attribute group decision making is researched. Four ranking methods based on dominance degree and dominance relation are proposed according to four cases of the completeness of the assessment information and the compensatory among attributes namely complete assessment information and complete mutual compensation among attributes, complete assessment information and incomplete mutual compensation among attributes, incomplete assessment information and complete mutual compensation among attributes and incomplete assessment information and incomplete mutual compensation among attributes,and these methods were compared with some existing methods.These methods rank alternatives by comparing the dominance degree of alternative.These methods avoid information loss and information distortion be caused by uniform the preference information in the most of the existing ranking methods of hybrid decision making problems. Furthermore, these methods omitted the process to find positive ideal solution (PIS) and negative ideal solution (NIS) with the expanded TOPSIS method to solve hybrid group decision making problems,and that directly calculated the dominance degree between alternatives,the results are also more accurately. In order to calculate the dominance degree between alternatives, methods of calculation for dominance degree between data on the each data type are respectively proposed.(3) This paper proposed several different sorting methods to solve sorting problem in hybrid multi-attribute group decision making. When the limit profiles of between adjacent classes are known, a sorting method based on the dominance degree and dominance relation is proposed. when the limit profiles between adjacent classes are unknown but reference alternatives which represent each class are known, the clustering method based on the distance is expanded to the sorting of group decision making problems, a sorting method based on distance is established. When experts given their rating opinion about each alternative, a sorting method based on the probability is proposed. These methods provide new ways to solve sorting problems of hybrid multi-attribute group decision making.(4)Some examples such as supplier selection, suppliers sorting, etc are given to illustrate the feasibility and validity of these proposed methods, and, application of the proposed ranking method in enterprise cooperation innovation partner selection is studied.The above mentioned contributions has further enriched the study of hybrid multi-attribute group decision-making, and provide new methods for consensus analysis in HMAGDM,ranking under certain conditions in HMAGDM and sorting in HMAGDM.