(A) methodology and group support system for multi-attribute decision making with incomplete information

Abstract

This thesis deals with the multiple attribute decision making problem. Since the increasing complexity of decision making problem requires multiple participants and narrowly specified attributes, method for multi-attribute decision making with multiple decision makers and hierarchically structured attributes is needed for decision making. Articulating preferences in incomplete manner must be more convenient for decision makers than providing cardinal or numerical preferences. Accordingly, it is necessary to develop the multi-attribute group decision making method which considers the attribute hierarchy and incomplete information. This thesis proposes several mathematical models which are used for the multi-attribute decision method and procedure with incomplete information. Also, a group support system named HIPS is developed to support the method and procedure. A mathematical model is formulated which can derive the value interval on any attribute in attribute hierarchy. Also, to handle the tree structure, we break down the attribute tree into sub-trees. Since the model has recursive structure, the optimization results from sub-trees can be utilized in computing the value interval on the topmost attribute. To illustrate how to obtain the pairwise value on the topmost attribute, an algorithm based on the recursive model is proposed. The pairwise values derived from the algorithm can be used in final choice making of multi-attribute decision with single or multiple decision maker(s). With the incomplete information and multiple decision makers, however, a selection is not generally made in a single step and some additional information are required to get a final decision. From this point of view, an interactive procedure is proposed to support multi-attribute group decision making with incomplete information. Basic solution technique is summarized in order to aggregate each group member``s preference information. The probabilistic measure which is an indicativ...