Study On The Aggregation Methods For Fuzzy Multiple Attribute Group Decision Making

In this paper, the aggregation method of multiple attributes group decision making is studied. First, the experts' opinions are aggregated, and then the fuzzy multiple attribute decision making is solved.Let opinion of experts among group decision making be represented as L — R fuzzy number. The fuzzy uncertainty of fuzzy number reflects directly the human judgment to object, so the fuzzy number is obtained from the process of human psychology. Based on the psychology, when people judges object, the cases of the worst, the most possible and the best are taken into account, so the fuzzy number's form is middle form in generally. And the case of the worst reflects that the attitude of people is pessimistic, that is pessimistic opinion; the case of the best reflects that the attitude of people is optimistic, that is optimistic opinion; the most possible reflects the best possible attitude of people. Then for the benefit attribute, the pessimistic opinion of expert's is reflected by the left-hand membership function while the optimistic opinion is reflected by the right-hand one, and the most possible opinion is reflected by the support set which is a closed interval. In this paper, the consensus opinion of group decision making based on the opinion that the pessimistic opinion should be compared with pessimistic one and so should the optimistic one. The difference of two experts' opinions is reflected by two distances which are called the left-hand side distance and the right-hand side one. The distances are employed to construct a new similarity function to measure the similarity degrees between the experts by using an exponential operation. At last, the consensus opinion of group decision making is obtained by the relative agreement degrees and the important degrees of experts.At the same time, some numeric examples are shown to illustrate this method. Calculating by the software matlab, this method is feasible. And this method can solve the case that the two experts have no common intersection. This method can calculate L — R fuzzy numbers.