Group decision making-based TODIM under Linguistic Aggregation Majority Additive operator

This paper proposes an extension of TODIM (interactive multi-criteria decision making) under group decision making (GDM) using the Linguistic Aggregation Majority Additive (LAMA) operator. TODIM is an effective method in modelling experts' psychological behaviour in the decision-making process. However, under the GDM, the method is based solely on the average of all experts' judgments without any consideration to soft aggregation processes that include majority and/or minority concepts. In this work, the LAMA operator is used to be integrated with TODIM-GDM to aggregate the experts' opinions with respect to the majority concept under the linguistic domain. This is to provide a greater flexibility in reaching a consensus instead of only considering equally average using the classical averaging operators. Two linguistic representations, namely, symbolic approach and 2-tuple linguistic approach for LAMA operator are proposed to be utilised in the method. A numerical example in investment selection problem is provided to illustrate the applicability of the proposed method. Finally, the comparison of these two linguistic approaches is presented. The results show that LAMA under the 2-tuple linguistic approach is preferable to the symbolic approach in case of there is a tie between alternatives in the final ranking. (paper)