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

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.

[1]  M Hamada,et al.  Analysis of censored data from higly fractionated experiments , 1991 .

[2]  Lee-Ing Tong,et al.  A non‐parametric method for experimental analysis with censored data , 1997 .

[3]  G. J. Hahn,et al.  A Simple Method for Regression Analysis With Censored Data , 1979 .

[4]  Muh-Cherng Wu,et al.  An enhanced Taguchi method for optimizing SMT processes , 1992 .

[5]  R. Lippmann,et al.  An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.

[6]  Hal S. Stern,et al.  Neural networks in applied statistics , 1996 .

[7]  Jiju Antony,et al.  Simultaneous Optimisation of Multiple Quality Characteristics in Manufacturing Processes Using Taguchi's Quality Loss Function , 2001 .

[8]  J. L Lin,et al.  The use of the orthogonal array with grey relational analysis to optimize the electrical discharge machining process with multiple performance characteristics , 2002 .

[9]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[10]  Gerald J. Hahn,et al.  Linear Estimation of a Regression Relationship from Censored Data Part I—Simple Methods and Their Application , 1972 .

[11]  A. Charnes,et al.  Data Envelopment Analysis Theory, Methodology and Applications , 1995 .

[12]  Madhan Shridhar Phadke,et al.  Quality Engineering Using Robust Design , 1989 .

[13]  Chao-Ton Su,et al.  Neural network procedures for experimental analysis with censored data , 1998 .

[14]  C. Su,et al.  Multi-response robust design by principal component analysis , 1997 .

[15]  N. Logothetis,et al.  Characterizing and optimizing multi‐response processes by the taguchi method , 1988 .

[16]  Rodney H. Green,et al.  Efficiency and Cross-efficiency in DEA: Derivations, Meanings and Uses , 1994 .

[17]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[18]  Joseph J. Pignatiello,et al.  STRATEGIES FOR ROBUST MULTIRESPONSE QUALITY ENGINEERING , 1993 .

[19]  Gerald J. Hahn,et al.  A Comparison of Methods for Analyzing Censored Life Data to Estimate Relationships Between Stress and Product Life , 1974 .

[20]  Kaoru Tone,et al.  Data Envelopment Analysis , 1996 .

[21]  Y. S. Tarng,et al.  Optimization of the electrical discharge machining process based on the Taguchi method with fuzzy logics , 2000 .