Multiple Attribute Decision Making Methods With Unknown Weights And Their Applications

Multiple attribute decision-making plays an important role in modern politics, economy, culture, life and so on. The essence of the multiple attribute decision making is for sorting and preferred a series of limited alternatives by use of existing decision-making information through some ways or methods. It is mainly composed of two parts:one is the information acquisition of decision-making. Decision-making information includes attribute weights and attribute values, generally. The other one is using some methods to aggregate the decision-making information, and using some ways to rank and optimize the alternatives. Therefore, research on multiple attribute decision making method is mainly embodied in the study of determination of attribute weights and integrated operator, which the attribute weights are determined is an important research of multiple attribute decision making. Focus on the multiple attribute decision-making problems of attribute weights are unknown, this paper discussed multiple attribute decision-making problems and their application based on attribute weights are acquired under the interval-valued intuitionistic trapezoidal fuzzy number, intuitionistic trapezoidal fuzzy number and linguistic information, respectively. The latter two problems are solved by entropy.Chapter1:Introduce the development process of multi-attribute decision making, multi-attribute decision-making method of interval-valued intuitionistic trapezoidal fuzzy number and multi-attribute decision making based on entropy and the domestic and foreign current research situation, and give the paper’s research framework.Chapter2:Introduce the concepts of intuitionistic fuzzy number, Interval-valued intuitionistic trapezoidal fuzzy number and linguistic information, respectively. Then discuss their operations and relevant properties.Chapter3:Research two kinds of multi-attribute decision making problems based on interval-valued intuitionistic trapezoidal fuzzy number, in which the weights of attribute weight is incomplete and complete unknown. Focus on the problem of multiple attribute decision making, in which the weights of attribute weight is incomplete, defining the deviation between attribute and ideal solution of each scheme, constructing programming model based on the maximizing deviation, and then the attribute weight is determined. For the problem of multi-attribute decision making, in which the weight of attribute is complete unknown, relative degree of approximation and approximation degree are defined based on the TOPSIS, the model of multi-attribute decision making is constructed based on the min of the approximation degree, thereby the weight of attribute is obtained. Then decision methods are proposed respectively after giving the weight of attribute, and the examples analysis show the feasibility of the two methods.Chapter4:Research the multi-attribute decision making problems, in which the weight of attribute is completely unknown, decision methods and applications based on entropy. Firstly, for the problem of multi-attribute decision making, in which the attribute values are intuitionistic trapezoidal fuzzy number and the weights are complete unknown, intuitionistic trapezoidal fuzzy number of mathematical expectation is defined. By use of the cross-entropy formula of intuitionistic fuzzy numbers to measure the discrimination information between each object’s attribute values and its positive ideal solution, thereby the calculation formula of the attribute weights is obtained. Secondly, focus on the multiple attribute group decision making problem under linguistic information, in which the attribute weights and the expert weights are completely unknown, and the attribute values take the form of linguistic variables. First, giving the partition of alternatives, and then entropy and relative entropy are proposed for acquiring attribute weights and the expert weights, respectively. Finally, giving the decision making methods and the examples analysis show the effectiveness of the two methods.Chapter5:The full articles are summarized and looking into the distance.