A data envelopment analysis method for optimizing multi-response problem with censored data in the Taguchi method

Abstract

Abstract Taguchi method is an efficient method used in off-line quality control in that the experimental design is combined with the quality loss. This method including three stages of systems design, parameter design, and tolerance design has been deeply discussed in Phadke [Quality engineering using robust design (1989)]. It is observable that most industrial applications solved by Taguchi method belong to single-response problems. However, in the real world more than one quality characteristic should be considered for most industrial products, i.e. most problems customers concern about are multi-response problems. As a result, Taguchi method is not appropriate to optimize a multi-response problem. At present, it is still necessary to rely on the engineering judgment to optimize the multi-response problem; therefore uncertainty will be increased during the decision-making process. On the other hand, due to some uncontrollable causes occurring, only a portion of experiment can be completed so that the censored data will be produced. Traditional approaches for analysis of censored data are computationally complicated. In order to overcome above two shortages, this article proposes an effective procedure on the basis of the neural network (NN) and the data envelopment analysis (DEA) to optimize the multi-response problems. A case study of improving the quality of hard disk driver in Su and Tong [ Total Quality Management 8 (1997) 409] is resolved by the proposed procedure. The result indicates that it yields a satisfactory solution.