IDEA Approach for Solving Multi-Responses Fuzziness Problem in Robust Design

Most manufacturing applications on data envelopment analysis (DEA) models assume that all data have the form of specific numerical values. In some circumstances, however, the data may be imprecise. This research, therefore, proposes an approach for solving the multi-responses fuzziness problem in the Taguchi method using the Imprecise Data Envelopment Analysis (IDEA) approach. The response fuzziness is caused by complex and vague behavior of the process itself. Thus, each response is defined by an interval bounded by lower and upper values. The multiple response values correspond to each combination factor settings are transformed into a single measure with lower and upper values, called the relative interval efficiency. The combination of factor settings at each experiment is treated as a decision making unit (DMU). Two IDEA models are employed to calculate DMU's upper and lower efficiency values. Finally, a fuzzy multi objective data envelopment analysis (DEA) model is used to determine the best combination of factor settings. Two case studies are adopted for illustration; in both of which the proposed approach achieved competitive improvements against the principle component analysis and desirability function.