• International Journal of Quality Innovation
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• v.5 no.1
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• pp.43-51
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• 2004

During process design or process optimization, it is quite common for experimenters to find optimum operating conditions for several responses simultaneously. The traditional multiresponse surfaces optimization methods do not consider the uncertain relationship among these responses sufficiently. For this reason, the authors propose an optimization method based on evidential reasoning theory by Dempster and Shafer. By maximizing the basic probability assignment function, which indicates the degree of belief that certain operating condition is the solution of this multiresponse surfaces optimization problem, the desirable operating condition can be found.

• Journal of Applied Reliability
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• v.1 no.2
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• pp.165-173
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• 2001

The desirability function approach by Derringer and Suich (1980) and the generalized distance approach by Khuri and Conlon (1981) are two major approaches to multiresponse optimization for improvement of quality of a product or process. So far, the desirability function method has been the only tool for multiresponse optimization in the situations where there are the goal regions for the respective responses. For such situations, we propose a multiresponse optimization method based on the generalized distance approach.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.730-739
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• 2005

A common problem encountered in product or process design is the selection of optimal parameter levels which involve simultaneous consideration of multiresponse variables. A multiresponse problem is solved through three major stages: data collection, model building, and optimization. To date, various methods have been proposed for the optimization stage, including the desirability function approach and loss function approach. In this paper, we first propose a framework classifying the existing studies and then propose some promising directions for future research.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.377-380
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• 2006

A common problem encountered in product or process design is the selection of optimal parameter levels which involve simultaneous consideration of multiresponse variables. To date, various methods have been proposed for multiresponse optimization. In this paper, we briefly review the existing methods and then discuss some recent advances in this field.

• Journal of Integrative Natural Science
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• v.10 no.1
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• pp.51-57
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• 2017

The Taguchi parameter design in industry is an approach to reducing performance variation of quality characteristic in products and processes. In the Taguchi parameter design, the product array approach using orthogonal arrays is mainly used. It often requires an excessive number of experiments. An alternative approach, which is called the combined array approach, was studied. In these studies, only single response was considered. In this paper we propose how to simultaneously optimize multiresponse for the robust design using the combined array approach.

• Journal of Korean Society for Quality Management
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• v.39 no.3
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• pp.377-390
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• 2011

A common problem encountered in product or process design is the selection of optimal parameter levels which involves simultaneous consideration of multiple response variables. This is called a multiresponse problem. A multiresponse problem is solved through three major stages: data collection, model building, and optimization. Up to date, various methods have been proposed for the optimization, including the desirability function approach and loss function approach. In this paper, the existing studies in multiresponse optimization are reviewed and a future research direction is then proposed.

• Journal of Korean Society for Quality Management
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• v.33 no.1
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• pp.73-82
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• 2005

This article proposes a practical approach, which is based on the concept of the expected squared relative error, that can consider both the prediction quality and the practitioner's subjectivity in simultaneously optimizing multiple responses. Through a case study, multiresponse optimization using the expected squared relative error approach is illustrated, and the SAS program to implement the proposed method is provided.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.451-459
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• 1998

The analysis of data from a multiresponse experiment requires careful consideration of the multivariate nature of the data. In a multiresponse sitation, the optimization problem is more complex than in the single response case. The biplot is a graphical tool which make the analyst to understand the correlation of the response variables, the relation of the response variables arid the explanatory variables and the relative importance of the explanatory variables. In case of good fitting of the first order model, we can draw the biplot with the first order experimental design. Otherwise, we can make the biplot with the second order experimental design by adding other experimental points.

• Journal of the Korean Statistical Society
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• v.19 no.2
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• pp.145-150
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• 1990

In this article we develop the marginal posterior density of the model parameters in the multiresponse regression models when missing values exist only in one response. The resulting density resolves a couple of problems in the estimation approach proposed by Box, Draper, and Hunter (1970) and provides a general interpretation for relationship between the estimates of the missing values and the parameters.

• Journal of Korean Society for Quality Management
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• v.27 no.1
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• pp.165-194
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• 1999

Taguchi's parameter design seeks proper choice of levels of controllable factors (Parameters in Taguchi's terminology) that makes the qualify characteristic of a product optimal while making its variability small. This aim can be achieved by response surface techniques that allow flexibility in modeling and analysis. In this article, a collection of response surface modeling and analysis techniques is proposed to deal with the multiresponse optimization problem in experimentation with Taguchi's signal and noise factors.