Study On Models And Methods Of Fuzzy Multiobjective Optimization With Indeterminacy Of DM

Under the complex DM environments, the MD's knowledge is usually not perfect and the person could not express clearly for the preference, so MD's preference is undefined. The key factors for solving multi-objective optimization problem with undefined performance are: to effectively express undefined preference and correctly grasp the certain regular and factors for those undefined preference, to achieve the system optimization results meet MD's requirement and expectation. Based on the above thoughts, the paper further develops the models and methods of multi-objective optimization design problems with undefined preference. The main content of this paper includes the following aspects:1) In view of the limitation that weights and ratings without physical meaning are required to be provided in traditional evaluation methods, physical programming is used in the field of fuzzy multi-objective optimization to deal with the amount of undefined information problems. The fuzzy physical programming model is proposed based on characters of fuzzy factors in multi-objective problem modeling process. The express method of undefined preference in physical programming model is being analyzed under different decision environment. The simplization method and effective conditions of fuzzy physical programming DM also are proposed, which take complex and undefined decision problems into more flexible and simple frames and make it controlled easily by designers and enhance it's suitable range.2) An interactive multiobjective optimization strategy suitable for fuzzy optimization structure is proposed. With undefined MD preference different forms, the reduce method in Pareto solution has been proposed based on two types of fuzzy physical programming models. The satisfying solution could express clearly the designer's interested ranges on Pearto front and could not be restricted by optimization problem model real action to randomly reach in valid field. The evaluation standard proposed combing qualitative and quantitative is to getting satisfying solution and build interactive decision frame by using fuzzy optimization structure control with the basis of MD's preference. The proposed strategy of interactive solving solution is popularly suitable for the kind of solving multi-objective optimization problem with fuzzy perference model.3) Multiobjective satisfying optimization model and its solution strategy based on the fuzzy preference range are proposed. To against the different solving solution conditions, fuzzy DM method and coobritive optimization method based on fuzzy satisfying degree are proposed. This function is extension form of preference function of physical programming disign, which is under the direction of satisfying degree standard. Classifying Pareto solution set by fuzzy optimization fields, proposing defined various center solution method, and relying on the relations between the Pareto solution satisfying degree shown by sorting out Pareto solution and compromise performance, the satisfying decision making based on after checking optimization preference can be achieved. Using the restricted impact of various performance target improved or lost by comprise figures and building improved coordinated satisfying optimization model, the designers could adjust the fuzzy comprise figures and its threshold to achieve controllable optimization process taking both relative importance and conflict degree of performance target into the considerations.It has significant and realistic applications in real engineering field for fuzzy multi-objective optimization method with indetermincy of DM. This paper is working at the initial theories application study stage else.