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 multiresponse
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.
