A general framework for multiple responses optimization based on Bayesian posterior predictive method

Suyi Li; Wenjia Wang

doi:10.1109/IEEM.2016.7798216

A general framework for multiple responses optimization based on Bayesian posterior predictive method

Suyi Li, Wenjia Wang

China Association for Quality

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

Response surface methodology (RSM) has been widely used in practice, which can optimize single response versus several factors. Naturally people are not only interested in single response optimization, but also multiple responses optimization. In this paper we propose a general framework for multiple responses optimization using Bayesian posterior predictive method. This method can account for the effects of variances, the correlation among the responses, and the model parameter uncertainty. We develop our approach as a guideline for the practitioners, and give an example to illustrate it.

源语言	英语
主期刊名	2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016
出版商	IEEE Computer Society
页	1938-1941
页数	4
ISBN（电子版）	9781509036653
DOI	https://doi.org/10.1109/IEEM.2016.7798216
出版状态	已出版 - 27 12月 2016
活动	2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016 - Bali, 印度尼西亚期限: 4 12月 2016 → 7 12月 2016

出版系列

姓名	IEEE International Conference on Industrial Engineering and Engineering Management
卷	2016-December
ISSN（印刷版）	2157-3611
ISSN（电子版）	2157-362X

会议

会议	2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016
国家/地区	印度尼西亚
市	Bali
时期	4/12/16 → 7/12/16

访问文件

10.1109/IEEM.2016.7798216

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Li, S., & Wang, W. (2016). A general framework for multiple responses optimization based on Bayesian posterior predictive method. 在 2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016 (页码 1938-1941). 文章 7798216 (IEEE International Conference on Industrial Engineering and Engineering Management; 卷 2016-December). IEEE Computer Society. https://doi.org/10.1109/IEEM.2016.7798216

@inproceedings{a354fca3703a4403825e0c170eb4dc00,

title = "A general framework for multiple responses optimization based on Bayesian posterior predictive method",

abstract = "Response surface methodology (RSM) has been widely used in practice, which can optimize single response versus several factors. Naturally people are not only interested in single response optimization, but also multiple responses optimization. In this paper we propose a general framework for multiple responses optimization using Bayesian posterior predictive method. This method can account for the effects of variances, the correlation among the responses, and the model parameter uncertainty. We develop our approach as a guideline for the practitioners, and give an example to illustrate it.",

keywords = "Bayesian method, Design of experiment (DOE), Quality loss function, Response surface methodology (RSM), Simulation",

author = "Suyi Li and Wenjia Wang",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016 ; Conference date: 04-12-2016 Through 07-12-2016",

year = "2016",

month = dec,

day = "27",

doi = "10.1109/IEEM.2016.7798216",

language = "English",

series = "IEEE International Conference on Industrial Engineering and Engineering Management",

publisher = "IEEE Computer Society",

pages = "1938--1941",

booktitle = "2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016",

address = "United States",

}

Li, S & Wang, W 2016, A general framework for multiple responses optimization based on Bayesian posterior predictive method. 在 2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016., 7798216, IEEE International Conference on Industrial Engineering and Engineering Management, 卷 2016-December, IEEE Computer Society, 页码 1938-1941, 2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016, Bali, 印度尼西亚, 4/12/16. https://doi.org/10.1109/IEEM.2016.7798216

A general framework for multiple responses optimization based on Bayesian posterior predictive method. / Li, Suyi; Wang, Wenjia.
2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016. IEEE Computer Society, 2016. 页码 1938-1941 7798216 (IEEE International Conference on Industrial Engineering and Engineering Management; 卷 2016-December).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

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AU - Li, Suyi

AU - Wang, Wenjia

PY - 2016/12/27

Y1 - 2016/12/27

N2 - Response surface methodology (RSM) has been widely used in practice, which can optimize single response versus several factors. Naturally people are not only interested in single response optimization, but also multiple responses optimization. In this paper we propose a general framework for multiple responses optimization using Bayesian posterior predictive method. This method can account for the effects of variances, the correlation among the responses, and the model parameter uncertainty. We develop our approach as a guideline for the practitioners, and give an example to illustrate it.

AB - Response surface methodology (RSM) has been widely used in practice, which can optimize single response versus several factors. Naturally people are not only interested in single response optimization, but also multiple responses optimization. In this paper we propose a general framework for multiple responses optimization using Bayesian posterior predictive method. This method can account for the effects of variances, the correlation among the responses, and the model parameter uncertainty. We develop our approach as a guideline for the practitioners, and give an example to illustrate it.

KW - Bayesian method

KW - Design of experiment (DOE)

KW - Quality loss function

KW - Response surface methodology (RSM)

KW - Simulation

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U2 - 10.1109/IEEM.2016.7798216

DO - 10.1109/IEEM.2016.7798216

M3 - Conference contribution

AN - SCOPUS:85009836994

T3 - IEEE International Conference on Industrial Engineering and Engineering Management

SP - 1938

EP - 1941

BT - 2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016

PB - IEEE Computer Society

T2 - 2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016

Y2 - 4 December 2016 through 7 December 2016

ER -

Li S, Wang W. A general framework for multiple responses optimization based on Bayesian posterior predictive method. 在 2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016. IEEE Computer Society. 2016. 页码 1938-1941. 7798216. (IEEE International Conference on Industrial Engineering and Engineering Management). doi: 10.1109/IEEM.2016.7798216

A general framework for multiple responses optimization based on Bayesian posterior predictive method

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