| Decision making under uncertainty, especially uncertain group decision making (GDM), has already become a hot point in the domain of decision science owing to the partial ignorance, randomicity, difficult-description of decision problems, the uncertainty and imperfection of decision information, and the unbounded rationality (BR) of decision makers.Focusing on uncertain GDM problems, many approaches have been emerged, including fuzzy multiattribute GDM approach, linguistic assessment based GDM approach, evidence theory based GDM approach, and etc.. Almost all kings of approaches concentrate on the expressions of uncertain information, the analysis of group consensus, the aggregation of group opinions, and the unified dimensional ranking order of alternatives. However, their corresponding GDM processes take no enough confidence into account, which is shown as lack of consideration for the confidence of group assessments, the non-distinct preference of group assessments, and etc.. For lack of full confidence, based on Dempster-Shafer theory (DST), this dissertation investigates the construction of the GDM process on the basis of confidence belief functions and its real application. The main detailed contents and innovative points include:(1) The analysis of consistency degree among belief functions. This dissertation improves the conflict analysis among belief functions proposed by Liu Weiru in order to overcome the intrinsic difference between two dimensions of Liu's conflict measure and the irrationality of Liu's conflict measure based suggestions on the applicability of Dempster's rule. A consistency measure between a pair of belief functions is proposed to divide beliefs'pairwise relationships (BPRs) into three categories composed of conflict, compatible, and consistency. Three BPRs are further used to validly recommend the applicability of Dempster's rule. The consistency measure provides necessary foundations for the constructions of confidence belief functions from experts and group consensus.(2) The construction of confidence belief functions from experts. Regarding the BR of Simon as theoretical guidance, this dissertation analyzes the drawbacks of qualitatively and quantitatively producing a belief function from an expert, and proposes the qualitative definition of a confidence belief function from the expert based on a set of belief functions sampled from the expert within a given time interval. In terms of the qualitative definition of confidence belief function, a set of belief functions are partitioned into a finite number of consistent subsets with BPRs on the basis of consistency measure, and the confidence belief function is quantitatively defined under a statistical framework. Furthermore, a general procedure for generating a confidence belief function is elaborated. The strategy upgrade of a communication company is examined to demonstrate the construction of a confidence belief function and its application in intention decision. In order to deal with the exponential increasing of computational complexity when constructing confidence belief functions, we discuss how to select an appropriate frame by experimenting on computer and hierarchically partition original problems.(3) The construction of confidence belief functions from objective observation data. The instantaneous exceptions of constructing belief functions from objective observation data in representative engineering applications are analyzed. On the basis of the maximal consistent subset generated from a set of belief functions constructed from objective observation data, a confidence belief function from objective observation data is qualitatively defined. Because of the drawbacks of consistently partitioning a set of belief functions based on objective observation data using BPRs, a structure-equivalent rule is constructed and its five characteristics are explained. According to the qualitative definition of confidence belief function, the quantitative definition of the confidence belief function is given and its validity is analyzed. Taking Rough Set as theoretical foundations, we discuss a general procedure for generating a confidence belief function from a set of belief functions.(4) The confidence belief functions based GDM process using extended TOPSISs (Technique for Order Preference by Similarity to Ideal Solutions). Three extended TOPSIS models which are composed of premodel, intermodel, and postmodel, are constructed and used to build a GDM process based on confidence belief functions. A representative example is examined to make solutions using three extended TOPSIS models and compare them. The GDM process is suitable to be used in such applications that is not allowed by objective conditions or is not specified by decision rules.(5) The confidence belief functions based GDM process oriented to group consensus (GC). A GDM process based on confidence belief functions and constrained by GC is proposed. The GC in three levels including the attribute level, the alternative level, and the global level, is constructed. Solving the uncertain GDM problem based on GC in a specific level is studied. Hereinto, the main research contents include the unification of opinions of different experts using unified utilities, the confirmation of the relative weight of each expert on each attribute for each alternative, the aggregation of group utilities of assessment grades for alternatives, and the detailed interpretation of the whole process. An engineering project management software selection problem in the research institute of passenger car in an automobile manufacturing company is made to describe a whole GDM process. The GDM process is suitable to be used in such applications that is allowed and required by objective conditions or is specified by decision rules.The above research enriches the DST based uncertain GDM approaches and developes a confidence belief functions based GDM process. This makes the approaches more flexible to be used in practical applications, and further more rational and effective to solve real problems.
|
PDF Full Text Request