  Robust Ordinal Regression (ROR) is a way of dealing with Multiple Criteria
Decision Aiding (MCDA), by considering all sets of parameters of an assumed
preference model, that are compatible with preference information given by the
Decision Maker (DM). As a result of ROR, one gets necessary and possible
preference relations in the set of alternatives, which hold for all compatible
sets of parameters or for at least one compatible set of parameters,
respectively. In this paper, we extend the MCDA methods based on ROR, by
considering one important aspect of decision problems: imprecise evaluations.
To deal with imprecise evaluations of some alternatives on particular criteria,
we extend the set of considered variables to define necessary and possible
preference relations taking into account this imprecision. In consequence, the
concepts of necessary and possible preference represent not only all compatible
sets of parameters, but also all possible values of the imprecise evaluations
of alternatives.
