Interval Multi-attribute Decision-making And Optimization Method

Multi-attribute decision-making problems which have been applied to a wide range of areas are endemic in our daily lives. Interval multi-attribute decision-making takes up a significant part in multi-attribute decision-making. Fruitful achievements have been made in this theoretical study, but the theoretical research is imperfect because the research started late, the further investigation on it is still needed. Therefore, this paper will do the major work based on the previous scholars' study on the interval multi-attribute decision-making as follows.(1)Focusing on the known attributes weight range multi-attribute decision making problems, this paper proposes two new methods on the basis of traditional interval Multi-attribute methods based on possibility and projection. Compared with the traditional method, the raised method based on possility in this paper calculate the possibility degree to the the positive ideal point and negative ideal point for each planning to rank all alternatives. With the risk attitude of decision-maker, it will get a general priority of the problem. The method based on the projection avoids direct comparison of the interval numbers, and calculates the projection to the the positive ideal point and negative ideal point for each planning to rank all alternatives. It builds a new evaluation indicator. In the end, it will get a priority of the problem.(2)Focusing on attribute weight incomplete information interval multi-attribute decision-making problems, this paper proposes two new decision-making methods: attributes weight incomplete information interval multi-attribute TOPSIS method and interval multi-attribute decision-making method based on satisfaction. Two methods both fully take into account the weight of the information presented by decision-maker to control the selection of positive and negative points. Compared with the traditional method, it avoids the randomness of negative and positive ideal point, and makes close to the decision-maker's subjective requirements. The first method is based on the traditional method of TOPSIS, which determine weight through single-objective linear programming. Then it gets the priority of the problem; The second method gets the rant through building a optimal model based on the satisfaction. New methods with full consideration on the given weight information by the decision-makers combine the subjective with objective factors and help to make a more rational decision.(3) This thesis proposes the multi-attribute decision-making optimization method based on the compatibility and different degrees. At present, scholars have put forward a number of multi-attribute decision-making methods. It hasn't a evaluation standards so decision-makers don't know which method is better. When we use different multi-attribute decision-making methods to solve a problem, the results may be different, sometimes will vary greatly. So when we use a single method, it will reduce the reliability and scientific of decision-making; when we apply a variety of methods simultaneously, the results of the decision-making will not be in consistency. But the multi-attribute decision-making optimization method with models based on the compatibility and different degrees, which is a synthesis and optimization method of a variety of traditional multi-attribute evaluation methods, and these reserve the consistent part of the traditional methods and get a new evaluation results according to maximization of compatibility and minization of difference. It can evaluate the pros and cons of sorting methods.