OPTIMIZING MULTI-RESPONSE PROBLEMS IN THE TAGUCHI METHOD BY FUZZY MULTIPLE ATTRIBUTE DECISION MAKING

Lee‐Ing Tong · Chao-Ton Su

185 citations0/0 files storedQuality and Reliability Engineering International

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Optimal Experimental Design Methods Multi-Criteria Decision Making Quality Function Deployment in Product Design

Abstract

One of the conventional approaches used in off-line quality control is the Taguchi method. However, most previous Taguchi method applications have only dealt with a single-response problem and the multi-response problem has received only limited attention. The theoretical analysis in this study reveals that Taguchi's quadratic loss function and the indifference curve in the TOPSIS (Technique for order preference by similarity to ideal solution) method have similar features. The Taguchi method deals with a one-dimensional problem and TOPSIS handles multi-dimensional problems. As a result, the relative closeness computed in TOPSIS can be used as a performance measurement index for optimizing multi-response problems in the Taguchi method. Next, an effective procedure is proposed by applying fuzzy set theory to multiple attribute decision making (MADM). The procedure can reduce the uncertainty for determining a weight of each response and it is a universal approach which can simultaneously deal with continuous and discrete data. Finally, the effectiveness of the proposed procedure is verified with an example of analysing a plasma enhanced chemical vapour deposition (PECVD) process experiment. © 1997 by John Wiley & Sons, Ltd.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 185 citations exist upstream.

References

1981Multiple Attribute Decision Making 1980Simultaneous Optimization of Several Response Variables 1990Quality Engineering Using Robust Design 1992Fuzzy Multiple Attribute Decision Making 1985Off-Line Quality Control, Parameter Design, and the Taguchi Method 1990Combining Taguchi and Response Surface Philosophies: A Dual Response Approach 1988Signal-to-Noise Ratios, Performance Criteria, and Transformations 1981Simultaneous Optimization of Multiple Responses Represented by Polynomial Regression Functions

Cited by

2005Multi-response optimization using weighted principal component 2008The use of the Taguchi method with grey relational analysis to optimize the thin-film sputtering process with multiple quality characteristic in color filter manufacturing 2004Solving a multiresponse simulation-optimization problem with discrete variables using a multiple-attribute decision-making method 2001Analyzing Experiments with Correlated Multiple Responses 2004Optimization of multiple responses using principal component analysis and technique for order preference by similarity to ideal solution

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.