Multiple attribute decision making based on induced OWA operator

Aggregation operators are crucial to multiple attribute decision makers when they make decisions. While minimum and maximum can only represent optimistic and pessimistic extremes, an Ordered Weighted Aggregation (OWA) operator is able to reflect varied human attitudes lying between the two extremes by using distinct weight vectors. However, the OWA operator has a disadvantage of overlooking the importance of given argument itself. By combining the given argument itself with the ordered position argument and considering their importance, the authors of this paper first present an induced ordered weighted geometric averaging (IOWGA) operator for aggregating data information, and then give an IOWGA operator-based method applying to multiple attribute decision making (MADM) problems. Both the theoretical analysis and the numerical results show that IOWGA can better reflect the real situations in practical applications, and finally an illustrative example is given.