Fuzzy Multi-attribute Group Decision Making Based On Structured Element

Group decision-making is an effective means as the forefront of science for decisionmaking theory. Multi-attribute group decision making has been widely used in political,economic, military and other fields as an important component of decision science. In order toachieve the effect of group decision making, every decision makers judge and make decisionindependently and then integrated the results of judgments and decisions as the main features inthe case of multiple properties under multiple programs. As the complexity of objective things,uncertainty and ambiguity of human thinking, the process of decision making need to deal with alot of fuzzy data. Therefore, multi-attribute decision making research has important theoreticaland practical significance in fuzzy environment.Fuzzy multi-attribute group decision making problem can be usually transformed into fuzzynumbers ranking problem. The power method for ranking fuzzy numbers and ranking fuzzynumbers based on centroid and fragmentation are proposed in order to elicit the research onfuzzy multi-attribute group decision making problem.In this paper, the structured element theory is introduced into fuzzy multi-attribute decisionmaking in order to simplify the complex operations of traditional fuzzy decision making. Paperproposes two methods of fuzzy multi-attribute decision making based on the structured elementtheory. One method considers the impact of the emphasis of decision-makers and consensusdegree of evaluation by decision-makers which can affect the result of group evaluation. Theother method extends classic ELECTRE method for fuzzy multi-attribute decision making whichovercomes the disadvantage that it is difficult to ranking fuzzy numbers and has to be turned intodetermine system. Two methods of fuzzy multi-attribute decision making based on the structuredelement theory are put forward which put complex operations of fuzzy numbers into that ofmonotone functions with same monotonic formal in order to simplify the complexdecision-making process. Finally, paper describes the effective and reasonable of two methodswith example.