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Balancing the Subjective and Objective Weights for Correlated Multiresponse Optimization
Author(s) -
Zhang Liuyang,
Ma Yizhong,
Ouyang Linhan,
Liu Jian
Publication year - 2016
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1794
Subject(s) - function (biology) , mean squared error , mathematical optimization , computer science , regression , regression analysis , mathematics , statistics , biology , evolutionary biology
Desirability function approach is very popular for multiresponse optimization problems. However, the approach ignores the correlations among multiple responses and does not consider how to reasonably determine the relative weights of multiple responses. In this paper, an integrative desirability function approach is proposed to simultaneously consider the correlations among the responses and the weight determination method. For the proposed approach, the root mean square error performance is regarded as a new response, and then the seemingly unrelated regression estimation is utilized to fit the models. Through balancing the subjective and objective information, the proposed approach can be used to make more reasonable decisions for correlated multiresponse optimization. Two examples are employed to validate the effectiveness of the proposed approach. Copyright © 2015 John Wiley & Sons, Ltd.