α-complete information in factor space

In daily life, we normally describe our concepts and problems in linguistic terms. Due to the vagueness of our natural languages, the classical approach is unable to fully capture the properties (factors) of such concepts and problems and, hence, cannot provide decision-makers´ complete information for making an appropriate decision. Therefore, in this paper, we first classify general fuzzy data of a problem which are presented by human linguistic terms into different categories and based on their properties, each of them is described by an appropriate fuzzy set. Then, by investigating the properties of a problem as factors of a system, a fuzzy multiobjective linear programming (FMOLP) model is proposed from the viewpoint of evidence theory and information theory to measure the uncertainty of a fuzzy problem. A learning procedure is also designed to inquire the complete information according to the required level of sufficiency α. Finally, an example of mobile phone service (MPS) is presented to show that the proposed model can aid decision-makers to identify representative (significant) factors and obtain complete information of the MPS within a few steps