Metamodel-Based Robust Simulation-Optimization: An Overview

Dellino, Gabriella; Kleijnen, J.P.C.; Meloni, Carlo

doi:10.1007/978-1-4899-7547-8_2

Cited by 17 publications

(19 citation statements)

References 61 publications

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“…Therefore, meta-models could be used to simulate and approximate the relationship between output and inputs parameters in the process. The metamodel and its counterpart as robust design approach have been studied, to guarantee that the problem keeps its tractability under uncertainties with at least computational costs (Dellino et al, 2015). Naturally, it is up to the process engineer to decide which method is the best for a particular problem.…”

Section: Basic Informationmentioning

confidence: 99%

“…In this context, we concentrate more in Taguchi philosophy for the uncertain and noisy condition of the problem in the real world. Recent comprehensive overview of historical and technical aspects of robust optimization methods can be found in (Bertsimas et al, 2011;Beyer & Sendhoff, 2007;Dellino et al, 2015;Gabrel et al, 2014;Geletu & Li, 2014;Wang & Shan, 2011).…”

Section: Uncertaintymentioning

confidence: 99%

“…The application of robust design optimization has been contributed by great researchers to quality improvement of various processes or product design in practice, and several appropriate studies have been reviewed the application of Taguchi methodology in real case studies, (e.g. Beyer & Sendhoff, 2007;Dellino et al, 2015;Gabrel et al, 2014;Geletu & Li, 2014;Park & Lee, 2006;Wang & Shan, 2011). In current reviewing of studies, the application of robust design methodology on optimizing the process in two types of production and project management were considered, whose results are described in section 4.…”

Section: Positive and Negative Points Of View On Taguchi Approachmentioning

confidence: 99%

“…So, it could not propose the best design by considering all responses at the same time. Thirdly Taguchi method just investigates the best levels of design variables in the discrete region and could not treat in whole design ranges (Dellino et al, 2015;Park & Lee, 2006).…”

Section: Positive and Negative Points Of View On Taguchi Approachmentioning

confidence: 99%

“…There are different number of methods which have been introduced as meta-models to approximate the relationship between response and design variables of process, and they can be found in several comprehensive technical surveys in literature. In addition, Investigating in literature shows that two versatile methods, RSM and Kriging, have been applied more in different optimization problems in the real world (See Dellino et al, 2015;Jin et al, 2003;Simpson et al, 2001;Wang & Shan, 2007).…”

Section: Robust Design Optimization Hybrid Metamodelingmentioning

confidence: 99%

See 4 more Smart Citations

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: Basic Informationmentioning

confidence: 99%

Section: Uncertaintymentioning

confidence: 99%

Section: Positive and Negative Points Of View On Taguchi Approachmentioning

confidence: 99%

Section: Positive and Negative Points Of View On Taguchi Approachmentioning

confidence: 99%

Section: Robust Design Optimization Hybrid Metamodelingmentioning

confidence: 99%

See 3 more Smart Citations

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

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Observational process data analytics using causal inference

Yang

Bequette

2022

AIChE Journal

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Voluminous process data are available with the paradigm shift toward smart manufacturing. However, most historical data are observational, containing noncausal correlations due to confounders and mediators. Estimating causal effects from observational data remains a bottleneck in leveraging them for active applications such as optimization and control. This work aims to introduce a causal modeling framework for analyzing observational process data and extracting quantitative causal information. We demonstrate a real-world application in steel manufacturing where causal inference is used to analyze observational production data and improve the steelmaking process.Additionally, we propose a novel formulation for identifying critical process parameters from observational data, where causal inference is combined with variance-based methods to estimate corresponding risks of interventions to the manufacturing system.The proposed methods are compared with statistical ones to illustrate that causally interpreting statistical correlation leads to problematic results, while the provided workflow generates satisfactory strategies for process improvement.

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