• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.1
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• pp.1-9
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• 2010

Quality design methodologies have received constituent attention from a number of researchers and practitioners for more than twenty years. Specially, the quality design for drug products must be carefully considered because of the hazards involved in the pharmaceutical industry. Conventional pharmaceutical formulation design problems with mixture experiments have been typically studied under the assumption of an unconstrained experimental region with a single quality characteristic. However, real-world pharmaceutical industrial situations have many physical limitations. We are often faced with multiple quality characteristics with constrained experimental regions. ln order to address these issues, the main objective of this paper is to propose a robust desirability function (RDF) model using a desirability function (DF) and mean square error (MSE) to simultaneously consider a number of multiple quality characteristics. This paper then present L-pseudocomponents and U-pseudocomponents to handle physical constraints. Finally, a numerical example shows that the proposed RDF can efficiently be applied to a pharmaceutical process design.

• Journal of Korean Society for Quality Management
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• v.27 no.2
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• pp.126-142
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• 1999

Robust design is an approach to reducing performance variation of quality characteristic values in quality engineering. Taguchi has an idea that mean and variation are handled simultaneously to reduce the expected loss in products and processes. Taguchi parameter design has a great deal of advantages but it also has some disadvantages. The various research efforts aimed at developing alternative methods. In the Taguchi parameter design, the product-array approach using orthogonal arrays is mainly used. However, it often requires an excessive number of experiments. An alternative approach, which is called the combined-array approach, was suggested by Welch et. al. ( 1990) and studied by others. In these studies, only single quality characteristic was considered. In this paper we propose how to simultaneously optimize multiple quality characteristics using desirability function when we used the combined-array approach to assign control and noise factors. An example is illustrated to show the difference between the Taguchi's product-array approach and the combined-array approach.

• Asian-Australasian Journal of Animal Sciences
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• v.20 no.4
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• pp.605-614
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• 2007

Feeding pigs used to be a means of managing domestic resources that may otherwise have been wasted into valuable animal protein. Feeding pigs thus was a form of husbandry. Following recent rapid industrial development, pig rearing has changed from extensive to intensive, but this transformation has been associated with major concerns. The concentration of large amounts of pig manure in small arrears is environmentally hazardous. Moreover, high densities of animals in intensive production systems also impose a health threat for both animals and humans. Furthermore, the use of growth promoters and preventive medicines for higher production efficiencies, such as in-feed antibiotics, also induces microbial resistance thus affects human therapeutics. In addition, consumers are questioning the ethics of treating animals in intensive production systems. Animal welfare, environmental and bio-safe issues are re-shaping the nature of pig production systems. Feeding pigs thus involves not only the consideration of economic traits, but also welfare traits and environmental traits. Thus, a focus on technological feasibility, environmental sustainability and social desirability is essential for successful feeding operations. Feeding pigs now involves multiple projects with different sustainability goals, but goal conflicts exist since no pattern or scenario can fulfill all sustainability goals and the disagreements are complicated by reduced or even no use of in-feed antibiotics. Thus it is difficult to feed pigs in a manner that meets all goals of high quality, safe product, eco- and bio-sustainability, animal welfare and profit. A sustainable pig production system thus requires a prioritization of goals based on understanding among consumers, society and producers and needs to view from both a local and global perspective.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.39-44
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• 2004

A desirability function approach to a multiresponse problem is proposed considering process parameter fluctuation as well as distance-to-target of response and response variance. The variation of process parameters amplifies the variance of responses. It is called POE (propagation of error), which is defined as the standard deviation of the transmitted variability in the response as a function of process parameters. In order to obtain more robust process parameters, this variability should be considered in the optimization problem. The proposed method is illustrated using a rubber product case.

• Journal of Integrative Natural Science
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• v.6 no.1
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• pp.39-45
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• 2013

Robust design is an approach to reducing performance variation of quality characteristic values in quality engineering. Taguchi has an idea that mean and variation are handled simultaneously to reduce the expected loss in products and processes. In the Taguchi parameter design, the product-array approach using orthogonal arrays is mainly used. However, 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 quality characteristic (or response) was considered. In this paper we propose how to simultaneously optimize for multiple quality characteristics (or multiresponse) using desirability function when we used the combined-array approach to assign control and noise factors.

• The Research Journal of the Costume Culture
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• v.22 no.1
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• pp.158-166
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• 2014

Apparel design is an economic activity to create values for users over the value chain of a product. In this paper, the contribution of apparel design is defined as the enhancement of users' perceived values by improving users' experience of products. In this context, the value of a product corresponds to compensation for experience or a promise for experience of a product. Experience can be sensory or psychological benefits to users. To evaluate the value of apparel design, the researcher identified and analyzed the apparel design parameters affecting users' experience and benefits of products such as macro-, micro-environmental factors, value chain factors, apparel designer factors, and user factors. For an analytical modeling of the values of apparel design, this paper introduces the concept of a utility function from economics. In economics, utility is a measure of desirability or satisfaction that can be correlative to need or desire. The measure of value can be found in the price which a user is willing to pay for the fulfillment or satisfaction of need or desire via the experience of a product.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.685-696
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• 2001

Robust design is an approach to reducing performance variation of response values in products and processes. In the Taguchl parameter design, the product-array approach using orthogonal arrays is mainly used. However, it often requires an excessive number of experiments. An alternative approach, which is called the combined-array approach, was suggested by Welch et. al. (1990) and studied by others. In these studies, only single response variable was considered. We propose how to simultaneously optimize multiple responses when there are correlations among responses, and when we use the combined-array approach to assign control and noise factors. An example is illustrated to show the difference between the Taguchi's product-array approach and the combined-array approach.

• Journal of Korean Society for Quality Management
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• v.27 no.2
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• pp.218-236
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• 1999

The purpose of parameter design is to determine optimal settings of design parameters of a product or a process such that the performance characteristics of a product exhibit small variabilities around their target values. Taguchi made significant contributions in this area. However, his analysis of the problem focused on only one performance characteristic or response, although in product and process design, multiple characteristics are more common. The critical problem in dealing with multiple characteristics is how to compromise the conflict among the selected levels of the design parameters for each individual characteristic. In this paper, Methodology using SN ratio optimized by univariate technique is proposed and a parameter design procedure to achieve the optimal balance among several different response variables is developed. Existing case studies are solved by the proposed method and the results are compared with ones by the sum of SN ratios, the expected weighted loss, the desirability function, and EXTOPSIS model.

• 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.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.3
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• pp.198-207
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• 2012

In product or process parameter design, the case of multiple performance characteristics appears more commonly than that of a single characteristic. Numerous methods have been developed to deal with such multi-characteristic parameter design (MCPD) problems. Among these, this paper considers three representative methods, which are respectively based on the desirability function (DF), grey relational analysis (GRA), and principal component analysis (PCA). These three methods are then used to solve the MCPD problems in ten case studies reported in the literature. The performance of each method is evaluated for various combinations of its algorithmic parameters and alternatives. Relative performances of the three methods are then compared in terms of the significance of a design parameter and the overall performance value corresponding to the compromise optimal design condition identified by each method. Although no method is significantly inferior to others for the data sets considered, the GRA-based and PCA-based methods perform slightly better than the DF-based method. Besides, for the PCA-based method, the compromise optimal design condition depends much on which alternative is adopted while, for the GRA-based method, it is almost independent of the algorithmic parameter, and therefore, the difficulty involved in selecting an appropriate algorithmic parameter value can be alleviated.