Taguchi's Parameter Design: A Panel Discussion

Abraham, Bovas; MacKay, Jock; Box, George E. P.; Kacker, Raghu N.; Lorenzen, Thomas J.; Lucas, James M.; Myers, Raymond H.; Vining, G. Geoffrey; Nelder, J. A.; Phadke, M. S.; Sacks, Jerome; Welch, William J.; Shoemaker, Anne C.; Tsui, Kwok Leung; Taguchi, Shin; Wu, Changbao; Nair, Vijayan N.

doi:10.2307/1269231

Cited by 294 publications

(115 citation statements)

References 70 publications

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“…We adopt Taguchi's view but not his statistical methods, which have been criticized by many statisticians; see Nair (1992) and the Online Supplement for further details. Instead of these methods, we use Kriging and include designs such as Latin hypercube sampling (LHS).…”

Section: Taguchi's Worldviewmentioning

confidence: 99%

Robust Optimization in Simulation: Taguchi and Krige Combined

Dellino

Kleijnen

Meloni

2012

INFORMS Journal on Computing

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O ptimization of simulated systems is the goal of many methods, but most methods assume known environments. We, however, develop a "robust" methodology that accounts for uncertain environments. Our methodology uses Taguchi's view of the uncertain world but replaces his statistical techniques by design and analysis of simulation experiments based on Kriging (Gaussian process model); moreover, we use bootstrapping to quantify the variability in the estimated Kriging metamodels. In addition, we combine Kriging with nonlinear programming, and we estimate the Pareto frontier. We illustrate the resulting methodology through economic order quantity (EOQ) inventory models. Our results suggest that robust optimization requires order quantities that differ from the classic EOQ. We also compare our results with results we previously obtained using response surface methodology instead of Kriging.

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Section: Taguchi's Worldviewmentioning

confidence: 99%

Robust Optimization in Simulation: Taguchi and Krige Combined

Dellino

Kleijnen

Meloni

2012

INFORMS Journal on Computing

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“…For specific details of these issues, see G. E. P. Box (1988), G. E. P. Box et al (1988), Hunter (1985Hunter ( , 1989, Pignatiello and Ramberg (1992), and Myers et al (2009). Many of these concerns are also summarized in the extensive panel discussion in the May 1992 issue of Technometrics (see Nair 1992).…”

Section: Background and History Of Designed Experimentsmentioning

confidence: 99%

Designed Experiments for the Defense Community

Johnson

Hutto

Simpson

et al. 2012

Quality Engineering

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The areas of application for design of experiments principles have evolved, mimicking the growth of U.S. industries over the last century, from agriculture to manufacturing to chemical and process industries to the services and government sectors. In addition, statistically based quality programs adopted by businesses morphed from total quality management to Six Sigma and, most recently, statistical engineering (see Hoerl and Snee 2010). The good news about these transformations is that each evolution contains more technical substance, embedding the methodologies as core competencies, and is less of a ''program.'' Design of experiments is fundamental to statistical engineering and is receiving increased attention within large government agencies such as the National Aeronautics and Space Administration (NASA) and the Department of Defense. Because test policy is intended to shape test programs, numerous test agencies have experimented with policy wording since about 2001. The Director of Operational Test & Evaluation has recently (2010) published guidelines to mold test programs into a sequence of well-designed and statistically defensible experiments. Specifically, the guidelines require, for the first time, that test programs report statistical power as one proof of sound test design. This article presents the underlying tenents of design of experiments, as applied in the Department of Defense, focusing on factorial, fractional factorial, and response surface design and analyses. The concepts of statistical modeling and sequential experimentation are also emphasized. Military applications are presented for testing and evaluation of weapon system acquisition, including force-on-force tactics, weapons employment and maritime search, identification, and intercept.KEYWORDS factorial design, optimal design, power, response surface methodology, space filling design, test and evaluation WHY DOES THE DEFENSE COMMUNITY NEED DESIGN OF EXPERIMENTS?Any organization serious about testing should embrace methods and a general strategy that will cover the range of product employment, extract the most information in limited trials, and identify parameters affecting

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“…A justificativa para fixar o modelo para a dispersão como Gama é que o desvio tem uma distribuição próxima da distribuição Gama até mesmo quando Y não tem uma distribuição normal (Nair, 1992). Note que a distribuição para dispersão é Gama exata se Y tem uma distribuição normal.…”

Section: A Quase Verossimilhança E Quase Verossimilhança Estendidaunclassified

Modelagem conjunta da média e dispersão de Nelder e Lee como alternativa aos métodos de Taguchi

Pinto¹

2006

Pesqui. Oper.

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ResumoNos últimos anos, uma coleção de técnicas para a melhoria da qualidade foi desenvolvida, no Japão, por Genichi Taguchi no planejamento, na evolução e na fabricação de produtos industrializados. Esta revolução no campo da qualidade despertou o interesse, no mundo inteiro, de vários engenheiros e estatísticos, que propuseram vários métodos alternativos, mais claros e eficientes do que aqueles propostos por Taguchi. Nelder e Lee observaram que a metodologia de Taguchi conduz a um modelo conjunto para a média e dispersão, usando modelos lineares generalizados. Eles mostraram como esta classe é geral e suficiente para a análise desses modelos. O objetivo deste artigo é fazer uma síntese da modelagem conjunta da média e dispersão, proposta por Nelder e Lee, explicitando, de uma forma concisa, os principais pontos da teoria.Palavras-chave: modelagem conjunta da média e dispersão; quase verossimilhança estendida; métodos de Taguchi. AbstractIn the last years, in Japan, Genichi Taguchi developed some techniques to improve the quality of the products during its design, evolution and manufacture. His ideas have raised the attention of engineers and statisticians around the world who have proposed some alternative methods that are simpler and more efficient than Taguchi's methods. Nelder and Lee observed that Taguchi's methodology lead up to a joint model for the mean and dispersion employing generalized linear models. They showed that this class is general and sufficient to analyze Taguchi's Models. The purpose of this work is to describe joint modeling of mean and dispersion that was proposed by Nelder and Lee and the key points of the theory in a concise manner.

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Taguchi's Parameter Design: A Panel Discussion

Cited by 294 publications

References 70 publications

Robust Optimization in Simulation: Taguchi and Krige Combined

Robust Optimization in Simulation: Taguchi and Krige Combined

Designed Experiments for the Defense Community

Modelagem conjunta da média e dispersão de Nelder e Lee como alternativa aos métodos de Taguchi

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