Generalized mixture operators using weighting functions: A comparative study with WA and OWA

In the context of multiple attribute decision making, we present an aggregation scheme based on generalized mixture operators using weighting functions and we compare it with two standard aggregation methods: weighted averaging (WA) and ordered weighted averaging (OWA). Specifically, we consider linear and quadratic weight generating functions that penalize bad attribute performances and reward good attribute performances. An illustrative example, borrowed from the literature, is used to perform the operators’ comparison. We believe that this comparative study will highlight the potential and flexibility of generalized mixture operators using weighting functions that depend on attribute performances.