Lexicographical dynamic goal programming approach to a robust design optimization within the pharmaceutical environment

Nha, Vo Thanh; Shin, Sangmun; Jeong, Seong Hoon

doi:10.1016/j.ejor.2013.02.017

Cited by 26 publications

(19 citation statements)

References 30 publications

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“…Notably, there are different optimization approaches available on dual response methodology where some of them are referenced in (Ardakani & Noorossana, 2008;Beyer & Sendhoff, 2007;Nha et al, 2013;Yanikoglu et al, 2016), so here just for instance some common methods of them are mentioned in Table 3.…”

Section: Dual Response Surface Methodsmentioning

confidence: 99%

“…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%

See 1 more Smart Citation

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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Section: Dual Response Surface Methodsmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…Robust design is the most powerful method available for reducing product cost, improving quality, and simultaneously reducing development time. In process robustness studies, it is desirable to minimize the influence of noise input control factors and output responses, robust design methods can disclose robust solutions that are less sensitive to causes of variations [5]. It is commonly accepted that the Taguchi's principles are useful and very appropriate for industrial product design [6].…”

Section: Quality Loss Function (Qlf)mentioning

confidence: 99%

Robust Product Design: A Modern View of Quality Engineering in Manufacturing Systems

Parnianifard¹,

Azfanizama²,

Ariffin³

et al. 2018

Preprint

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ABSTRACT:One of the main technological and economic challenges for an engineer is designing high-quality products in manufacturing processes. Most of these processes involve a large number of variables included the setting of controllable (design) and uncontrollable (noise) variables. Robust Design (RD) method uses a collection of mathematical and statistical tools to study a large number of variables in the process with a minimum value of computational cost. Robust design method tries to make high-quality products according to customers' viewpoints with an acceptable profit margin. This paper aims to provide a brief up-to-date review of the latest development of RD method particularly applied in manufacturing systems. The basic concepts of the quality loss function, orthogonal array, and crossed array design are explained. According to robust design approach, two classifications are presented, first for different types of factors, and second for different types of data. This classification plays an important role in determining the number of necessity replications for experiments and choose the best method for analyzing data. In addition, the combination of RD method with some other optimization methods applied in designing and optimizing of processes are discussed.

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“…To the pharmaceutical formulation development associated with in-vitro in-vivo correlation, a weight assignment problem is an unresolved significant issue. The preference information between characteristics from the decision maker was not concerned in Shin et al (2011), Choi et al (2012 and Truong et al (2011) while the priority between the gelation kinetics and drug release was mentioned in Nha et al (2013). Therefore, it is necessary to provide an alternative approach to solve the problem with the information from the decision maker by considering the tradeoff between gelation kinetics and drug release.…”

Section: Introductionmentioning

confidence: 99%

“…A set of accommodations between these conflicted responses is often created in multi-objective optimization problems by the consideration of the trade-off between objectives based on the preference information from a human decision maker's opinions concerning to the multiple criteria. Shin et al (2011), Choi et al (2012 and Nha et al (2013) developed a two directions approach to model the multiple time-oriented responses as a function of control factors and time for pharmaceutical problems. Truong et al (2011) integrated the inverse problem to robust design modeling for the generic drug development.…”

Section: Introductionmentioning

confidence: 99%

Augmented Weighted Tchebycheff Modeling and Robust Design Optimization on a Drug Development Process

Ho¹,

Shin²

2013

Journal of Korean Institute of Industrial Engineers

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The quality of the products/processes has been improved remarkably since robust design (RD) methodology is applied into the practice manufacturing processes. A model building method based on the dual responses methods for multiple and time oriented responses on a drug development process is employed in this paper instead of the previous methods that handle the static nature of data and single response. Subsequently, the optimal solutions of a multiple and time series RD problem are obtained by using the proposed augmented weighted Tchebycheff method that has a significant flexibility on assigning weights. Finally, a pharmaceutical case study associated with a generic drug development process is conducted in order to illustrate the efficient optimal solutions from the proposed model.

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Lexicographical dynamic goal programming approach to a robust design optimization within the pharmaceutical environment

Cited by 26 publications

References 30 publications

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

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

Robust Product Design: A Modern View of Quality Engineering in Manufacturing Systems

Augmented Weighted Tchebycheff Modeling and Robust Design Optimization on a Drug Development Process

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