Akademik Veri Yönetim Sistemi
The 22nd Conference of the International Federation of Operational Research Societies, Seoul, Güney Kore, 23 - 27 Ağustos 2021, ss.29-30
In this study, we consider estimating the cost matrix parameters of the multivariate quality loss function which is commonly used in product and process design parameter optimization. Multivariate quality loss functions consider multiple responses simultaneously for determining the optimal levels of design parameters that yield a high-quality performance. They are also used in quality improvement decision making and statistical tolerancing. To that end, we propose an interactive and evolutionary method for estimating the cost matrix parameters of the multivariate quality loss functions. We present the applicability of the method on a real-life example based on the honing operation of the automotive industry. Additionally, we conduct a computational experiment to show that the problem converges to the underlying loss function after a few iterations even when the information provided by the decision-maker contains certain degrees of errors. The convergence is also shown for di erent variance-covariance structures.