reader is referred to [5]. Some other modern challenges faced by researchers in GDM are: (i) large-scale problems (LSGDM) involving more than twenty decision-makers (e.g., [6; 7]); and (ii) manipulation and group dictatorship (e.g., [8]). These challenges have gained increasing importance as a consequence of the modern digital economy and the relevance of social networks. The main processes for solving GDM are the consensus reaching process (CRP) and the selection process (SP) [9; 10; 11]. CRPs have the objective of obtaining the maximum degree of satisfaction from the DMs with the collective decision [9]. The search for consensus is an active process, with repetitive interactions (rounds, consensus steps) among group members, which contains a measure of consensus and a feedback method [10]; the last one suggests whether the group members should change their preferences, beliefs, and judgments to allow a better agreement [11; 12]. Usually, the CRP is guided by a moderator [13]. The final solution is found through an SP, considering the preferences, beliefs, and judgments of the group members. Frequently, the SP consists of two phases: (A) aggregation of preferences, beliefs, and judgments from the members; and (B) use of these preferences, beliefs, and judgments, that were aggregated collectively, to find a solution which should correspond, as much as possible, to the aggregated group opinions [9; 14]. In this paper, our interest is limited to problems in which the decision alternatives are described by multiple criteria, the so-called multi-person-multi-criteria decision making [5]. Here, the aggregation of the group member's preferences on conflicting criteria plays a crucial role in identifying an acceptable collective agreement. Usually, the selection process concerns a relatively small set of options; many methods have been proposed to address such cases (e.g., [3; 4; 15; 16; 17]). Alternatively, the group multi-objective decision problem on very large decision sets (characterized by constraints) has received comparatively very little attention. According to [18], we call this problem GDM-MOP, denoting the combination of group decision making and multi-objective optimization. To the best of our knowledge, the existing GDM-MOP approaches are not free of some of the following criticisms:  
1. Most interactive methods implicitly assume that group preferences are transitive and comparable relations, although the lack of transitivity is a well-established characteristic of voting systems (e.g., [19]). Even in the case of a single DM, transitivity, and comparability of their preference relation are subject to question, mainly in the presence of veto conditions, and/or when the number of objectives overcomes the cognitive limitations of the human mind. 2. Many methods are susceptible to manipulation. According to classical voting theory, under very general conditions, every voting procedure can be manipulated by some voters by declaring insincere preferences (e.g., [19]). 3. Popular interactive approaches help to obtain acceptable agreements because each DM learns the preferences from the other DMs and correspondingly fits their own. However, the final accepted solution may significantly differ from those that each DM would have considered as satisfactory if the decision had depended solely of them. Thus, the consensus does not result from the search in the set of possible solutions but from mutual concessions. Group satisfaction is partial because it is only achieved by recognizing that a more satisfactory result is not possible. 4. The handle of imprecision, uncertainty, and ill-definition in GDM-MOP is a real concern. GDM-MOP approaches typically assume that the whole group agrees on the resource availability, the resource consumption, and objective values for each point in the decision variable space. However, there could be several sources of imperfect information which affect that assumption.