Solving dynamic systems with multi-responses by integrating desirability function and data envelopment analysis

Today’s complexity of product design requires improving multiple quality characteristics. This research proposes an approach that integrates the desirability function and data envelopment analysis to enhance process performance with dynamic multi-responses. Firstly, the desirability function is employed. Then, data envelopment analysis is used to obtain the best settings of controllable factor levels that make the effect of noise factors as small as possible. Three case studies are utilized to demonstrate the effectiveness of the proposed approach, in all of which the proposed approach is found an effective procedure in reducing the effect of noise factors, does not need huge data for the analysis or any subjective information and human judgment, and can be used regardless of linearity or nonlinearity relationship between the signal factor and responses. Such advantages should provide great assistance to product engineers in improving performance of dynamic systems with multiple responses.