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We present two recent methods, called UTA0 and GRIP, from the viewpoint of robust ranking of multi-criteriaalternatives. In these methods, the preference information provided by a single or multiple Decision Makers (DMs) is com-posed of holistic judgements of some selected alternatives, called reference alternatives. The judgements express pairwisecomparisons of some reference alternatives, and comparisons of selected pairs of reference alternatives fromthe viewpoint of intensity of preference (itt GRIP). C?rdinal regression is used to find additive value functions compatiblewith this preference information. The whole set of compatible value functions is then used in Linear Programming (LP) tocalculate a necessary and possible weak preference relations in the set of all alternatives, and in the set of all pairs of alterna-lives. While the necessary relation is true for all compatible value functions, the possible relation is true for at least onecompatible value function. The necessary relation is a partial prcorder and the possible relation is a complete and negativelytransitive relation. The necessary relations show consequences of the given preference information which are robust because“always true”. We illustrate this methodology with an example.