• Journal of Korean Society for Quality Management
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• v.25 no.1
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• pp.56-68
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• 1997

The object of multiresponse optimization is to determine conditions on hte independent variables that lead to optimal or nearly optimal values of the response variables. Derringer and Suich (1980) extended Harrington's (1965) procedure by introducing more general transformations of the response into desirability functions. The core of the desirability a, pp.oach condenses a multivariate optimization into a univariate one. But because of the subjective nature of this a, pp.oach, inexperience on the part of the user in assessing a product's desirability value may lead to inaccurate results. To compensate for this defect, a weighted desirability function is introduced which takes into consideration the vriances of the responses.

• Knowledge Management Research
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• v.20 no.3
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• pp.39-47
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• 2019

Response surface methodology (RSM) is a group of statistical modeling and optimization methods to improve the quality of design systematically in the quality engineering field. Its final goal is to identify the optimal setting of input variables optimizing a response. RSM is a kind of knowledge management tool since it studies a manufacturing or service process and extracts an important knowledge about it. In a real problem of RSM, it is a quite frequent situation that considers multiple responses simultaneously. To date, many approaches are proposed for solving (i.e., optimizing) a multi-response problem: process capability function approach, desirability function approach, loss function approach, and so on. The process capability function approach first estimates the mean and standard deviation models of each response. Then, it derives an individual process capability function for each response. The overall process capability function is obtained by aggregating the individual process capability function. The optimal setting is given by maximizing the overall process capability function. The existing process capability function methods usually use the arithmetic mean or geometric mean as an aggregation operator. However, these operators do not guarantee the Pareto optimality of their solution. Moreover, they may bring out an unacceptable result in terms of individual process capability function values. In this paper, we propose a maximin-based process capability function method which uses a maximin criterion as an aggregation operator. The proposed method is illustrated through a well-known multiresponse problem.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.3
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• pp.317-326
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• 2016

In this study, running stability and ride quality analyses, applying the 'ISO 3888 (double lane change)' and 'ISO 2631-1' (mechanical vibration and shock) tests, were performed for the suspension optimization of the Korean personal rapid transit (PRT) vehicle. The suspension optimization results for running stability and ride quality were derived by applying the multiresponse surface method. From the comparisons of the optimization results for different ratios of the objective functions of running stability and ride quality, we derived the best objective function ratio of 3.9-to-6.1 to improve both the running stability and the ride quality. With the optimized results, the suspension stiffness became 30.68 N/mm, between the value of the $S_2$ and $S_3$ models, and the damping coefficient equaled that of the $D_1$ model. When compared with the suspension of the current PRT vehicle, the roll angle, yaw rate, sideslip angle, and ride comfort were improved by 0.37, 0.37, 2.8, and 5, respectively.