Decision-making based on probabilistic linguistic term sets without loss of information

Abstract Probabilistic linguistic term set (PLTS) provides a much more effective model to compute with words and express the uncertainty in pervasive natural language by probability information. In this paper, avoid loss of information, we redefine classical probabilistic sets (PLTSs) multiple distributions from an ambiguity perspective present some basic operations using Archimedean t-(co)norms. Different PLTSs, reformulated PLTSs are not necessarily normalized beforehand for further investigations. Moreover, based facilitate incorporation different attitudes DMs their score values deviation, thus comparisons. Then Decision-Making Trial Evaluation Laboratory (DEMATEL) method is extended PLTS frame incorporating With these newly developed elements DEMATEL attributes decision-making proposed. The illustrative example comparison analysis show that over feasible valid, has advantage processing without any information (i.e., normalization) fully exploration DMs-preference knowledge.