Multiple Criteria Decision-Making Preprocessing Using Data Mining Tools

TL;DR

This paper proposes using data mining classification techniques as a preprocessing step in Multi-Criteria Decision-Making to identify key variables, thereby simplifying complex multiobjective optimization problems in engineering design.

Contribution

It introduces a novel approach of applying data mining classification for variable selection in MCDM, enhancing the efficiency of multiobjective optimization processes.

Findings

Data mining classification effectively identifies influential variables.

Preprocessing improves the efficiency of MOO in engineering design.

Application to 3D wing design demonstrates practical benefits.

Abstract

Real-life engineering optimization problems need Multiobjective Optimization (MOO) tools. These problems are highly nonlinear. As the process of Multiple Criteria Decision-Making (MCDM) is much expanded most MOO problems in different disciplines can be classified on the basis of it. Thus MCDM methods have gained wide popularity in different sciences and applications. Meanwhile the increasing number of involved components, variables, parameters, constraints and objectives in the process, has made the process very complicated. However the new generation of MOO tools has made the optimization process more automated, but still initializing the process and setting the initial value of simulation tools and also identifying the effective input variables and objectives in order to reach the smaller design space are still complicated. In this situation adding a preprocessing step into the MCDM procedure could make a huge difference in terms of organizing the input variables according to their effects on the optimization objectives of the system. The aim of this paper is to introduce the classification task of data mining as an effective option for identifying the most effective variables of the MCDM systems. To evaluate the effectiveness of the proposed method an example has been given for 3D wing design.