Dual-response surface optimization: A weighted MSE approach

Rui Ding; Dennis K.J. Lin; Duan Wei

doi:10.1081/QEN-120027940

Dual-response surface optimization: A weighted MSE approach

Rui Ding, Dennis K.J. Lin, Duan Wei

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83 Scopus citations

Abstract

Dual-response surface methodology is a powerful tool for simultaneously optimizing the mean and the variance of responses in quality engineering. In this article, we suggest a weighted mean squared error (MSE) approach to improve the optimization procedure. In addition, we propose a data-driven approach to determine the weights when the prior information is vague. This is based on the idea of an "efficient curve." Examples are given to illustrate the superiority of the proposed method, as compared with other existing procedures.

Original language	English (US)
Pages (from-to)	377-385
Number of pages	9
Journal	Quality Engineering
Volume	16
Issue number	3
DOIs	https://doi.org/10.1081/QEN-120027940
State	Published - Mar 2004

All Science Journal Classification (ASJC) codes

Safety, Risk, Reliability and Quality
Industrial and Manufacturing Engineering

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10.1081/QEN-120027940

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author = "Rui Ding and Lin, {Dennis K.J.} and Duan Wei",

note = "Funding Information: We thank Dr. Necip Doganaksoy for his constructive comments and helpful suggestions, which led to a significant improvement of this article. Dennis Lin is partially supported by the National Security Agency, via grant MDA904-02-1-0054.",

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AB - Dual-response surface methodology is a powerful tool for simultaneously optimizing the mean and the variance of responses in quality engineering. In this article, we suggest a weighted mean squared error (MSE) approach to improve the optimization procedure. In addition, we propose a data-driven approach to determine the weights when the prior information is vague. This is based on the idea of an "efficient curve." Examples are given to illustrate the superiority of the proposed method, as compared with other existing procedures.

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Dual-response surface optimization: A weighted MSE approach

Abstract

All Science Journal Classification (ASJC) codes

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