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