Multi-criteria optimization with multiple responses for technological innovation

Abstract

This study integrated a multi-criteria response factor design and a multi-criteria optimization method to develop a new model and found the technical innovation multiple responses with multi-criteria optimization design of products and manufacturing processes. This research combined the multiple responses with the multi-criteria optimization design of products and manufacturing processes, and utilized the principle component analysis to compute the principle score of five indicators of innovation ability as dependent variables. Utilizing the factor analysis, the variables were retrenched and the factor scores were computed as independent variables. Furthermore, this research established the response surface models by using principle scores as dependent variables and factor scores as independent variables. Finally, this research analyzed the key influence factors on innovation ability by desirability function and sensitivity analysis. This research proposed the most complete innovation measurement indicators and contributed to the present innovation theory and academic. The results of this research indicated the optimal combination of innovation sources and pointed out that firm supporting, external information sources, evaluation on marketing effect, feasibility study and professional innovation information were the main factors that had a positive impact on innovation performance, while innovation uncertainty was the only factor that had a negative influence on innovation performance in a company. The contribution of this study is seen, more obviously, in the electronic industry. This research enhanced the innovation ability of the industry and analyzed the optimized combination of innovation ability. Key words: Multi-criteria optimization, response surface model, technological innovation.