An Interval Linguistic Distribution Model for Multiple Attribute Decision Making Problems with Incomplete Linguistic Information

In this paper, the authors propose an interval linguistic distribution model for multiple attribute decision making MADM problems with incomplete linguistic information, in which they use intervals as evaluators' confidence levels indicating their belief degrees that a linguistic term fits an evaluation. By introducing the extent of ignoring information into this model, it can deal with incomplete linguistic assessments and then allow evaluators to avoid the dilemma that they have to supply complete assessments when not available. In addition, the uncertain subjective judgments on attributes of alternatives are represented as distributions on the linguistic term set used as an instrument for assessment in this model. This feature can be regarded as a measure for evaluators to handle uncertain information. The aggregation operators and expected utilities are also introduced for the purpose of aggregation and ranking problems of interval linguistic distributions. Finally, an example is used to illuminate the proposed model.