An improved optimal fuzzy information fusion method and its application in group decision

A group of decision-makers may differ in their choice of alternatives while making a decision. So, in any decision-making problem concerning decisions made by a group, the question arises how best we can aggregate individual choices into a general consensus choice. In most consensus-based group decision systems, the degree of similarity between each decision maker plays an important role and may greatly influence the final decision. Many methods such as the similarity aggregation method (SAM) and the optimal aggregation method (OAM) are presented based on different similarity measure. However, all the methods still have some drawbacks due to two main reasons. One is that the fuzzy opinions are modeled as normal fuzzy numbers, which cannot reflect the confidence level of the decision makers. The other is that the similarity measure used in previous work cannot correctly determine the degree of similarity in some situations. In order to solve these problems, an improved optimal aggregation method (IOAM) is proposed in this paper. In our method, the opinions of decision makers are modeled as generalized fuzzy numbers so that the aggregation algorithm is more intelligent and flexible than existing methods. In addition, a new reasonable similarity measure is used so that the aggregation result is more accurate. Finally, a numerical example is used to show the procedure of our method in a fuzzy group decision-making environment.