A sequential three-way decision-based group consensus method under probabilistic linguistic term sets

For complex decision-making problems, the joint evaluation of multiple experts is indis-pensable for efficiently addressing them. Therefore, multi-attribute group decision -making (MAGDM) problems have become popular in recent decades, however the develop-ment of MAGDM problems in the environment of probabilistic linguistic term sets (PLTSs) is not mature. To this end, the paper aims to study group consensus under PLTSs. Specifically, since some existing distance measures do not meet the basic principle of distance mea-sures, and some ones can not measure the distance between PLTSs of different lengths, a new distance measure is defined to eliminate these two drawbacks. In the consensus reaching process, most of common orientation rules choose to change all evaluation infor-mation of experts, which is actually hard in reality. For the sake of guaranteeing the orig-inal information as much as possible with consensus reaching, the attribute discriminant value is proposed via using the idea of sequential three-way decision eth STWD THORN . Then, an STWD-based group consensus method is constructed under PLTSs, namely PLTS-STWD. Meanwhile, dynamic modification parameters are developed associated with attribute dis-criminant values. Afterwards, the regret-rejoice function is employed to rank all alterna-tives. Finally, the constructed method is applied to a realistic case and compared with other consensus methods to verify its effectiveness and feasibility.(c) 2022 Elsevier Inc. All rights reserved.