A new method for design parameter optimization of products or processes with an ordinal categorical response

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2022-8

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Erdoğan, Pınar

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It is an important design problem to obtain the best parameter values that are insensitive to the variability of input, process or environmental factors of a product or process. Therefore, many industrial organizations use robust parameter design (RPD). While there are many methods in the literature for design parameter optimization with continuous quality characteristics, fewer studies are found for ordered categorical quality characteristics. This study proposes a new method for finding robust levels of design parameters of products and processes with ordinal categorical responses. Many approaches use expected value and variance as measures of location and dispersion respectively when the response is categorical. However, these measures used for numerical data are not meaningful to summarize categorical data. The developed method uses the median value and coefficient of ordinal variation (COV) for ordinal categorical data as location and dispersion measures, respectively. In addition, the Extreme Gradient Boosting (XGBoost) algorithm is presented as an alternative to Random Forests and Logistic Regression, which have been used in RPD studies in the literature. The classification performances of these algorithms are compared with Multi-Objective Decision Making methods. Based on this comparison, Random Forests algorithm is used to predict the category probabilities. These probabilities are used to calculate median and COV. Ordinal Logistic Regression and least squares regression are used to model these measures as functions of design parameters. The best parameter values are determined by solving a non-linear optimization problem. The proposed method is applied to different problems, and the results are discussed.

Subject Keywords

Robust Parameter Design, Coefficient of Ordinal Variation (COV), Median, XGBoost, Random Forest

URI

https://hdl.handle.net/11511/99548

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

P. Erdoğan, “A new method for design parameter optimization of products or processes with an ordinal categorical response,” M.S. - Master of Science, Middle East Technical University, 2022.