The Large Scale System of Multiple Criteria Decision Making; Pre-***processing

Large-scale engineering optimization, including large amounts of variables and objectives requires significant computing power and efficient algorithms. Multiple criteria decision making (MCDM) is a modeling tool for dealing with such complex engineering problems. One approach of MCDM for dealing with complex design evaluation models is to develop meta-models to replace the large scale design space. However there is a lack of research in the large-scale design optimization area applying MCDM to address further approximation using meta-models. Increasing the number of input variables is one of the sources of complexity. In this situation mining the MCDM data as a preprocessing stage could make a huge difference in terms of reducing the number of input variables and minimizing the design space. This paper aims to introduce the classification task of data mining as an effective option for identifying the most effective variables of the MCDM systems. In order to evaluate the effectiveness of the proposed method an example has been given.