Indeed, each DM may have their own perception (no free of certain imprecision, uncertainty, or arbitrariness) about objective values, available and required resources. Such imperfect knowledge may impact the individual best solutions, on the collective preferences, and the consensus degree. Under imperfect information, the consensus search process is even more difficult and relevant since the diverse perceptions from the DMs and different levels of conservatism should be aggregated and, if possible, agreed. 5. In complex problems, some DMs with very different value systems and/or roles with respect to the other group members may consider different sets of objective functions and constraints. Such a case is not addressed by most of the methods to solve GDM-MOPs. This paper presents a proposal that strongly reduces the above criticisms. Since group (dis)satisfaction depends on the number of its (dis)satisfied members, the paper focuses on a consensus measure based on counting the number of DMs who are satisfied (respectively dissatisfied) with their respective objective values on a common point in the decision variable space. For each point in the feasible decision set, satisfaction or dissatisfaction from each group member is determined by a multi-criteria ordinal classification approach, based on comparing solutions with a limiting boundary between classes \"unsatisfactory\" and \"satisfactory\". By counting the group members who are (dis)satisfied with solutions in the decision variable space, group satisfaction and dissatisfaction can be simultaneously optimized. This paper has perhaps the merit to be the first in addressing consensus evaluation as multi-criteria ordinal classification problems solved by the entire set of group members. Consensus is identified by a high level of group satisfaction and a low level of dissatisfaction. The group moderator is in charge of making the final decision, finding the best compromise between collective satisfaction and dissatisfaction. Interval numbers are used to model imperfect information from each group member. This model is less sophisticated than more general fuzzy approaches, but, to a great extent, without their mathematical complexity. Choosing interval numbers as the model of imprecisions allows using recently proposed interval-based multi-criteria ordinal classification methods. Two different decision models are proposed in this paper; the first one uses the interval outranking approach and INTERCLASS-nB method by [PERSON] et al. in [20; 21]; the second one is based on building and exploiting an interval weighted-sum value function. On one hand, the outranking model is appropriate for handling non-compensatory preferences, allowing veto effects and incomparability situations. On the other hand, the weighted-sum function is, to a certain extent, a simpler and rougher model, which may be recommended to represent compensatory preferences. With a little loss of generality, we address multi-objective maximization problems under resource constraints (as typically in project portfolio optimization), but the method can be trivially extended to other kinds of problems. The structure of the paper follows: In Section 2, we outline the background on which our proposal is based. Section 2.1 refers to several relevant precedent papers. The concept of good consensus, some fundamental aspects about interval numbers, and an interval-based multi-criteria classification method are briefly described in Section 2.2, Section 2.3, Section 2.4. The problem is detailed in Section 3. Sections 4 and 5, combined with the concept of maximum consensus, form the core of the proposal. These sections present the mathematical model of what a satisfied/dissatisfied DM is from two points of view: the outranking model of preferences (Section 4) and the weighted-sum model (Section 5). The method is summarized in Section 6; the whole proposal is illustrated by a real-size many-objective project portfolio optimization problem in Section 7. Lastly, several concluding remarks are discussed in Section 8.