Robust parameter design using the weighted metric method - The case of 'the smaller the better'

Mostafa Kamali Ardakani; Rassoul Noorossana; Seyed Taghi Akhavan Niaki; Homayoun Lahijanian

International Journal of Applied Mathematics and Computer Science (2009)

  • Volume: 19, Issue: 1, page 59-68
  • ISSN: 1641-876X

Abstract

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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.

How to cite

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Mostafa Kamali Ardakani, et al. "Robust parameter design using the weighted metric method - The case of 'the smaller the better'." International Journal of Applied Mathematics and Computer Science 19.1 (2009): 59-68. <http://eudml.org/doc/207921>.

@article{MostafaKamaliArdakani2009,
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.},
author = {Mostafa Kamali Ardakani, Rassoul Noorossana, Seyed Taghi Akhavan Niaki, Homayoun Lahijanian},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {multiobjective decision making; regression estimation; response surface methodology; robust parameter design; weighted metric method},
language = {eng},
number = {1},
pages = {59-68},
title = {Robust parameter design using the weighted metric method - The case of 'the smaller the better'},
url = {http://eudml.org/doc/207921},
volume = {19},
year = {2009},
}

TY - JOUR
AU - Mostafa Kamali Ardakani
AU - Rassoul Noorossana
AU - Seyed Taghi Akhavan Niaki
AU - Homayoun Lahijanian
TI - Robust parameter design using the weighted metric method - The case of 'the smaller the better'
JO - International Journal of Applied Mathematics and Computer Science
PY - 2009
VL - 19
IS - 1
SP - 59
EP - 68
AB - 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.
LA - eng
KW - multiobjective decision making; regression estimation; response surface methodology; robust parameter design; weighted metric method
UR - http://eudml.org/doc/207921
ER -

References

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