Application Of Fuzziness And Polarity In Multi-attribute Decision Making

Due to the complexity of the objective things, a variety of multi-attribute decisions in the uncertain environment has aroused people’s attention. Fuzziness and bipolarity are the general characters of all things in the world. Moreover multi-attribute decisions often are a dynamic process, so fuzziness, bipolarity, dynamic are the important natures when researching multi-attribute decisions. Multiple attribute decision is a preferred alternative process, using the method of decision-making information and programs through a certain way in the given program. In uncertain multi-attribute decisions, fuzziness is often related to membership and the non-membership degree, membership and the membership degree is reflected in the form of point and interval.bipolarity is a either this or that nature; dynamic reflects that the decision-making information usually changes during the process.combined with the above three properties, Interval intuitionistic fuzzy sets and dynamic interval bipolar-value fuzzy soft sets have been discussed. New methods to deal with multiple attribute decision making have been obtained. The main contents are as follows:Firstly, two kinds of methods for interval intuitionistic fuzzy sets are introduced. Both of these methods have common taking hesitation degrees into consideration The first method defines a new scoring function, hesitation degrees has been distributed in a reasonable way. The second method defines a proportionality coefficient which can give a way that hesitation degrees distributed to membership and the non-membership degreeSecondly, two intuitionistic fuzzy sets can been regarded as two vectors, so that not only the size of the problem but also the direction should been considered. Combing with vector features,the relative formulas has been defined. For the last,the same example dealt with different approaches illustrated the correctness and feasibility of knowledge.Finally, combining characteristics of dynamic multi-attribute decision,dynamic interval bipolar-value fuzzy soft sets with relative definitions and theorems has been defined. The way how to get the time weight and attribute weight has been given. A numerical examples show the feasibility and effectiveness of this method.