  In robust optimization, the uncertainty set is used to model all possible
outcomes of uncertain parameters. In the classic setting, one assumes that this
set is provided by the decision maker based on the data available to her. Only
recently it has been recognized that the process of building useful uncertainty
sets is in itself a challenging task that requires mathematical support.
  In this paper, we propose an approach to go beyond the classic setting, by
assuming multiple uncertainty sets to be prepared, each with a weight showing
the degree of belief that the set is a "true" model of uncertainty. We consider
theoretical aspects of this approach and show that it is as easy to model as
the classic setting. In an extensive computational study using a shortest path
problem based on real-world data, we auto-tune uncertainty sets to the
available data, and show that with regard to out-sample performance, the
combination of multiple sets can give better results than each set on its own.
