Research On Optimization Methodology For Multiple Quality Characteristics Based On TOPSIS Method

The optimization methodology research for multiple quality characteristics in products or process design is considered as hot topic research issue on Design for Six Sigma in quality engineering. In the consideration of the demands of ordinary technical persons with not much statistical knowledge and the requirements of easy to be implemented and popularized, based on the conventional TOPSIS methodology, this paper brings you an advanced research on optimization methodology for multiple characteristics in the design of products or process. The main content is described as follow:First of all, you will find, in this paper, through the analysis of the problems and difficulties on optimization issue research for multiple quality characteristics in the design of product or process, the author has indicated the conventional design's defaults and shown you the importance of concurrent quality engineering and Design for Six Sigma in the quality improvement of product or process.After that, the author focuses her research on the self-particularity of the nominal-the-best type optimization with multiple quality characteristics and then puts out the shortcomings of conventional TOPSIS method. The positive ideal solution suitable for nominal-the-best type quality characteristic is presented in the proposed method, and the negative ideal solution is determined reasonably by the requirement of tolerance limits in reality. Thus, the order of alternatives and the optimal factor level combination can be determined by the improved optimization process and assessing index.Thirdly, the optimization method to meet the demand of robustness is studied. After the limitation analysis of conventional TOPSIS method, an improved methodology taking robustness into consideration is proposed, and then the decision matrix based on mean value and standard deviation are established, and the preference of decision maker and requirement of reality are both considered in the proposed method. Besides, the balance between mean value and standard deviation of characteristics can be done by using combined relative closeness which enhances the robustness of optimization.Finally, the author addresses her research on the requirements of deviation and robustness. An improved method considering both deviation and robustness is proposed to make up the defects of conventional TOPSIS method, and the decision matrix based on root of mean square error is established, thus the tradeoff between closing to the target and decreasing variation can be done by the decision maker which increases the flexibility of decision process.On conclusion, this paper proposed some improved methods which are simple, practical, and effective, also these methods require technical persons to know not much knowledge of statistics and mathematics, and the model optimization can be done through any normal soft wares such as Minitab or Excel. Moreover, it makes the decision making process more flexible and reasonable after taking into consideration of the decision maker's preference and practical requirements of robustness and deviation, which makes the result of optimization correspond more with the reality and decision maker get more satisfactions about the optimization resolution.