A note on the Bayesian analysis of experiments with correlated multiple responses using a matrix‐logarithmic covariance model

Hamada, Michael S.; Jaramillo, Brandon M.; Chiao, Chih‐Hua

doi:10.1002/qre.2540

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…For a replicated experimental design data, the RS models for predicting the standard deviation (or variance) and correlation (or covariance) between the response pairs can also be estimated (Chiao and Hamada, 2001; Kim and Lin, 2006). However, if the number of replicates is few, point estimates of the variance-covariance may not guarantee positive semi-definite condition and non-singularity of the matrix (Hamada et al , 2019). Hamada et al (2019) claimed that this condition could only be satisfied if the number of replicates in each run is greater than or equal to K+1.…”

Section: Proposed Robust Guided Multi-objective Optimisation-based So...mentioning

confidence: 99%

“…However, if the number of replicates is few, point estimates of the variance-covariance may not guarantee positive semi-definite condition and non-singularity of the matrix (Hamada et al , 2019). Hamada et al (2019) claimed that this condition could only be satisfied if the number of replicates in each run is greater than or equal to K+1.…”

Section: Proposed Robust Guided Multi-objective Optimisation-based So...mentioning

confidence: 99%

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A robust multiobjective solution approach for mean-variance optimisation of correlated multiple quality characteristics

Sharma

Mukherjee

Bera

et al. 2021

IJQRM

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PurposeThe primary objective of this study is to propose a robust multiobjective solution search approach for a mean-variance multiple correlated quality characteristics optimisation problem, so-called “multiple response optimisation (MRO) problem”. The solution approach needs to consider response surface (RS) model parameter uncertainties, response uncertainties, process setting sensitivity and response correlation strength to derive the robust solutions iteratively.Design/methodology/approachThis study adopts a new multiobjective solution search approach to determine robust solutions for a typical mean-variance MRO formulation. A fine-tuned, non-dominated sorting genetic algorithm-II (NSGA-II) is used to derive efficient multiobjective solutions for varied mean-variance MRO problems. The iterative search considers RS model uncertainties, process setting uncertainties and response correlation structure to derive efficient fronts. The final solutions are ranked based on two different multi-criteria decision-making (MCDM) techniques.FindingsFive different mean-variance MRO cases are selected from the literature to verify the efficacy of the proposed solution approach. Results derived from the proposed solution approach are compared and contrasted with the best solution(s) derived from other approaches suggested in the literature. Comparative results indicate significant superiorities of the top-ranked predicted robust solutions in nondominated frequency, closeness-to-target and response variabilities.Research limitations/implicationsThe solution approach depends on RS modelling and considers continuous search space.Practical implicationsIn this study, promising robust solutions are expected to be more suitable for implementation than point estimate-based MOO solutions for a real-life MRO problem.Originality/valueNo evidence of earlier research demonstrates the superiority of a MOO-based iterative solution search approach for mean-variance MRO problems by simultaneously considering model uncertainties, response correlation and process setting sensitivity.

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Section: Proposed Robust Guided Multi-objective Optimisation-based So...mentioning

confidence: 99%

Section: Proposed Robust Guided Multi-objective Optimisation-based So...mentioning

confidence: 99%

A robust multiobjective solution approach for mean-variance optimisation of correlated multiple quality characteristics

Sharma

Mukherjee

Bera

et al. 2021

IJQRM

View full text Add to dashboard Cite

show abstract

An incomplete taxonomy of Bayesian models with examples from industrial statistics applications

Hamada

2020

Quality Engineering

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A note on the Bayesian analysis of experiments with correlated multiple responses using a matrix‐logarithmic covariance model

Cited by 2 publications

References 9 publications

A robust multiobjective solution approach for mean-variance optimisation of correlated multiple quality characteristics

A robust multiobjective solution approach for mean-variance optimisation of correlated multiple quality characteristics

An incomplete taxonomy of Bayesian models with examples from industrial statistics applications

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