A new loss function for multi-response optimization with model parameter uncertainty and implementation errors

DOI10.1016/j.ejor.2016.09.045zbMath1394.90357OpenAlexW2534417664MaRDI QIDQ1751673

Yizhong Ma, Linhan Ouyang, Yiliu Tu, Jian-Jun Wang

Publication date: 25 May 2018

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2016.09.045

zbMATH Keywords

robustnessquality managementimplementation errorsmodel parameter uncertaintymulti-response optimization

Mathematics Subject Classification ID

Applications of statistics in engineering and industry; control charts (62P30) Management decision making, including multiple objectives (90B50) Response surface designs (62K20)

Related Items (2)

Sequential design strategies for mean response surface metamodeling via stochastic kriging with adaptive exploration and exploitation ⋮ Integrated parameter and tolerance design based on a multivariate Gaussian process model

Cites Work

• Unnamed Item
• Unnamed Item
• A new Bayesian approach to multi-response surface optimization integrating loss function with posterior probability
• A posterior preference articulation approach to multiresponse surface optimization
• Robust optimization using computer experiments
• Bayesian credible intervals for response surface optima
• A robust desirability function method for multi-response surface optimization considering model uncertainty
• An interactive desirability function method to multiresponse optimization
• Factor screening for simulation with multiple responses: sequential bifurcation
• Optimization of multiple responses considering both location and dispersion effects
• Process optimization. A statistical approach.
• Robust Optimization in Simulation: Taguchi and Krige Combined
• A Bayesian Framework for Quantifying Uncertainty in Stochastic Simulation
• Stochastic Kriging for Simulation Metamodeling
• Interval approach to robust design
• A Bayesian Approach for Multiple Response Surface Optimization in the Presence of Noise Variables
• Robust optimization for multiple responses using response surface methodology
• An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias
• Design and analysis of simulation experiments

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