<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>In process robustness studies, it is desirable to minimize the influence of noise factors on the system and simultaneously determine the levels of controllable factors optimizing the overall response or outcome. In the cases when a random effects model is applicable and a fixed effects model is assumed instead, an increase in the variance of the coefficient vector should be expected. In this paper, the impacts of this assumption on the results of the experiment in the context of robust parameter design are investigated. Furthermore, two criteria are considered to determine the optimum settings for the control factors. In order to better understand the proposed method and to evaluate its performances, a numerical example for the case of ‘the smaller the better’ is included.</p></abstract>

In process robustness studies, it is desirable to minimize the influence of noise factors on the system and simultaneously determine the levels of controllable factors optimizing the overall response or outcome. In the cases when a random effects model is applicable and a fixed effects model is assumed instead, an increase in the variance of the coefficient vector should be expected. In this paper, the impacts of this assumption on the results of the experiment in the context of robust parameter design are investigated. Furthermore, two criteria are considered to determine the optimum settings for the control factors. In order to better understand the proposed method and to evaluate its performances, a numerical example for the case of ‘the smaller the better’ is included.

Robust Parameter Design Using the Weighted Metric Method—The Case of ‘the Smaller the Better’

Department of Civil Engineering, The Catholic University of America, Washington DC 20064, USA

Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran

International Journal of Applied Mathematics and Computer Science

In process robustness studies, it is desirable to minimize the influence of noise factors on the system and simultaneously determine the levels of controllable...

Robust Parameter Design Using the Weighted Metric Method—The Case of ‘the Smaller the Better’

Published Online: Apr 02, 2009

Page range: 59 - 68

DOI: https://doi.org/10.2478/v10006-009-0005-7

Keywords
multiobjective decision making, regression estimation, response surface methodology, robust parameter design, weighted metric method

This content is open access.

Robust Parameter Design Using the Weighted Metric Method—The Case of ‘the Smaller the Better’

Published Online: Apr 02, 2009

Page range: 59 - 68

DOI: https://doi.org/10.2478/v10006-009-0005-7

Keywordsmultiobjective decision making, regression estimation, response surface methodology, robust parameter design, weighted metric method

This content is open access.

Keywords
multiobjective decision making, regression estimation, response surface methodology, robust parameter design, weighted metric method