Effect of Normalization Techniques on MEMS Digital Micromirror Selection using MADM Methodology

The main objective of this work is to evaluate the effect of different normalization techniques within multiple attribute decision making (MADM) methods. The application of the work is dedicated to MEMS digital micro-mirror selection for a given industrial application. To this end, the general scheme of the decision model is first presented, with close attention to the context of digital micro-mirror selection. Subsequently, TOPSIS is employed to rank the digital micro-mirror. Finally, by the introduction of different norms namely vector normalization, logarithmic normalization, linear max-min normalization, non-linear normalization and Lia & Hwang method are used in this work to the solution algorithm. A simple multiaxial strategy is also recommended from which better engineering decisions may be attained by obtaining different ranks for different micro-mirror alternatives.