An Multiple Attribute Decision Making Of Intuitionistic Trapezoid Fuzzy Numbers

In the real decision-making, due to the increasing complexity and uncertainty of the social economic environment, there is affirmation, negation and hesitation in the cognitive results. As in all election events, in addition to support and against, often occur about the abstention. Because of intuitionistic fuzzy sets based on fuzzy set by adding a new parameter--non membership, the fuzzy nature can be more delicate description and characterization of the objective world. The intuitionistic fuzzy sets are widely used in the fuzzy multiple attribute decision-making and group decision-making in the field. In this paper, whether the attribute weights are known. We discuss the method of intuitionistic trapezoidal fuzzy multiple attribute decision making.This paper introduces the basic algorithms and related integrated operator of intuitionistic trapezoidal fuzzy numbers, discusses the intuitionistic trapezoidal fuzzy number reciprocal operation, and gives a new integrated operator of the harmonic averaging operator. Finally, the mean score function and accuracy function distance functions are given about the intuitionistic trapezoidal fuzzy number.For multiple attribute decision making problems, in which the information on attribute weights are known, and the attribute value are intuitionistic trapezoidal fuzzy numbers. Respectively we put forward decision-making model and algorithm of harmonic averaging operator based on the score function. Finally, we give the corresponding examples.With incomplete known of information about attribute weights in multiple attribute decision making problems for attribute values is intuitionistic trapezoidal fuzzy number. The alternative is ranked base on the score function and distance function and we give the corresponding examples.For unknown attribute weights, attribute values are intuitionistic trapezoidal fuzzy number. And the scheme is biased or unbiased multi-attribute decision making problems. We are presented to determine the weight deviation decision model and algorithm based on maximum and minimum deviation. The alternative is ranked by harmonic averaging operators. Finally, the corresponding example is given.