A fuzzy linguistic representation model for decision making under uncertainty

Taking into account imprecise and incomplete information is very important for properly making a decision under uncertainty. However, due to inherent mechanism, many traditional approaches cannot deal with such problems. Motivated by this point, we propose a proportional fuzzy linguistic distribution model for decision making under uncertainty. It is shown that this new model not only allows experts to linguistically assess attributes by using the combinations of any number of adjacent evaluation grades, but is also applicable to the context that experts cannot supply complete linguistic assessments. In addition, we also introduce expected utility in proportional fuzzy linguistic distribution for the purpose of precisely ranking alternatives, and accordingly, conveniently making a final decision. Finally, a case study taken from the literature is used to illustrative the proposed model.