Robust Parameter Design Using the Weighted Metric Method—The Case of ‘the Smaller the Better’

Ardakani, Mostafa K.; Noorossana, Rassoul; Niaki, Seyed Taghi Akhavan; Lahijanian, H.

doi:10.2478/v10006-009-0005-7

Cited by 11 publications

(6 citation statements)

References 38 publications

(41 reference statements)

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“…Although different terminologies have been used for the weighted p ‐norm formulation and often just a special case of it is utilized, the formulation has been widely applied in RSM literature such as Box and Jones, Lin and Tu, Ding et al ., Köksoy, and Lu et al . Also, Ardakani and Noorossana and Ardakani et al . used this formulation for Nominal The Best and Smaller The Better scenarios, respectively.…”

Section: Optimization Formulations For Multi‐response Surface Problemsmentioning

confidence: 99%

“…21 This formulation is based on the p-norm function in order to provide the best compromise solutions and combines multiple objectives into a single objective function. Although different terminologies have been used for the weighted p-norm formulation and often just a special case of it is utilized, the formulation has been widely applied in RSM literature such as Box and Jones, 22 Lin and Tu, 23 Ding et al, 24 Köksoy, 25 and Lu et al 26 Also, Ardakani and Noorossana 27 and Ardakani et al 28 used this formulation for Nominal The Best and Smaller The Better scenarios, respectively. In addition, Noorossana and Ardakani 29 employed it for multiple RPD and performed sensitivity analysis.…”

Section: Weighted P-norm Formulation (L P )mentioning

confidence: 99%

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The Impacts of Errors in Factor Levels on Robust Parameter Design Optimization

Ardakani

2015

Qual. Reliab. Engng. Int.

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In robust parameter design (RPD), the ultimate goal is to identify the settings of control factors, which lead to an optimal mean with minimum process variation. In order to achieve this goal, usually two objective functions corresponding to the mean and variance of the desired quality characteristic are considered. Next, settings for the control variables (factors) are determined such that the values achieved for the two objective functions are as close to their ideal values as possible. This article highlights the impact of the miss‐specification of noise variables as fixed factors in RPDs. The miss‐specification or error in factor levels causes inappropriate estimates of the response model, which consequently affects the optimal settings of the control variables. The results are illustrated through an experimental example. Moreover, three different formulations are applied to determine the optimal settings for the case of Larger The Better (LTB). The performance of the formulations is also evaluated. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: Optimization Formulations For Multi‐response Surface Problemsmentioning

confidence: 99%

Section: Weighted P-norm Formulation (L P )mentioning

confidence: 99%

The Impacts of Errors in Factor Levels on Robust Parameter Design Optimization

Ardakani

2015

Qual. Reliab. Engng. Int.

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“…The weighted p ‐norm method applies the p ‐norm to the functional components to obtain optimal solutions. Specific versions of this method have appeared in the RSM literature . This approach generates a variety of multiobjective methods such as displaced ideal method, GP, global criterion, neutral compromise solution, and weighting method.…”

Section: Review Of Multiobjective Approachesmentioning

confidence: 99%

“…Specific versions of this method have appeared in the RSM literature. [17][18][19][20][21] This approach generates a variety of multiobjective methods such as displaced ideal method, GP, global criterion, neutral compromise solution, and weighting method. The p-norm metric measures distance from the ideal solution using the formulation…”

Section: Weighted P-normmentioning

confidence: 99%

An Overview of Optimization Formulations for Multiresponse Surface Problems

Ardakani

Wulff

2012

Quality & Reliability Eng

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Many industrial applications involve more than one quality characteristic. For example, in robust parameter design, the quality characteristics include the process mean and process variance. Such applications lead to multiresponse surface problems in which it is necessary to determine optimal operating conditions according to some specified optimization criterion involving the quality characteristics. The purpose of this article is to address this problem from a multiobjective decision‐making framework. The foremost approaches in multiresponse optimization are categorized and integrated. Guidelines are presented to help select appropriate formulations. Moreover, the applicability and computational aspects of the methods in various decision‐making contexts are discussed. Numerical examples are also provided. Copyright © 2012 John Wiley & Sons, Ltd.

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“…In probabilistic or stochastic robust optimization methods, the designer performs the problem by employing the probability distribution of variables, particularly the mean and variation of uncertain or noise variables. It is clear that accuracy of obtained optimization results strongly depends on the accuracy of assumed probability distribution, in (Ardakani et al, 2009;Khan et al, 2015;Nha et al, 2013;Park & Leeds, 2015;Simpson et al, 2001) some applications of these types of robust optimization methods have been illustrated. Sometimes, the probability distribution of variables might be unknown or often difficult to obtain.…”

Section: Uncertaintymentioning

confidence: 99%

An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty

Parnianifard¹,

Azfanizam²,

Ariffin³

et al. 2018

10.5267/j.ijiec

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Nowadays, process optimization has been an interest in engineering design for improving the performance and reducing cost. In practice, in addition to uncertainty or noise parameters, a comprehensive optimization model must be able to attend other circumstances which might be imposed in problems under real operational conditions such as dynamic objectives, multiresponses, various probabilistic distribution, discrete and continuous data, physical constraints to design parameters, performance cost, computational complexity and multi-process environment. The main goal of this paper is to give a general overview on topics with brief systematic review and concise discussions about the recent development on comprehensive robust design optimization methods under hybrid aforesaid circumstances. Both optimization methods of mathematical programming based on Taguchi approach and robust optimization based on scenario sets are briefly described. Metamodels hybrid robust design is discussed as an appropriate methodology to decrease computational complexity in problems under uncertainty. In this context, the authors' policy is to choose important topics for giving a systematic picture to those who wish to be more familiar with recent studies about robust design optimization hybrid metamodels, also by attending real circumstances in practice. In particular, production and project management are considered as two important methodologies that could be improved by applications of advanced robust design combining with metamodel methods.

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Robust Parameter Design Using the Weighted Metric Method—The Case of ‘the Smaller the Better’

Cited by 11 publications

References 38 publications

The Impacts of Errors in Factor Levels on Robust Parameter Design Optimization

The Impacts of Errors in Factor Levels on Robust Parameter Design Optimization

An Overview of Optimization Formulations for Multiresponse Surface Problems

An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty

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