Aggregation with generalized mixture operators using weighting functions

This paper regards weighted aggregation operators in multiple attribute decision making and its main goal is to investigate ways in which weights can depend on the satisfaction degrees of the various attributes (criteria). We propose and discuss two types of weighting functions that penalize poorly satisfied attributes and reward well satisfied attributes. We discuss in detail the characteristics and properties of both functions. Moreover, we present an illustrative example to clarify the behaviour of such weighting functions, comparing the results with those of standard weighted averaging.