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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016
Publisher	IEEE Computer Society
Pages	1938-1941
Number of pages	4
ISBN (Electronic)	9781509036653
DOIs	https://doi.org/10.1109/IEEM.2016.7798216
Publication status	Published - 27 Dec 2016
Event	2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016 - Bali, Indonesia Duration: 4 Dec 2016 → 7 Dec 2016

Publication series

Name	IEEE International Conference on Industrial Engineering and Engineering Management
Volume	2016-December
ISSN (Print)	2157-3611
ISSN (Electronic)	2157-362X

Conference

Conference	2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016
Country/Territory	Indonesia
City	Bali
Period	4/12/16 → 7/12/16

Keywords

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

Access to Document

10.1109/IEEM.2016.7798216

Cite this

Li, S., & Wang, W. (2016). A general framework for multiple responses optimization based on Bayesian posterior predictive method. In 2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016 (pp. 1938-1941). Article 7798216 (IEEE International Conference on Industrial Engineering and Engineering Management; Vol. 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. in 2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016., 7798216, IEEE International Conference on Industrial Engineering and Engineering Management, vol. 2016-December, IEEE Computer Society, pp. 1938-1941, 2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016, Bali, Indonesia, 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. p. 1938-1941 7798216 (IEEE International Conference on Industrial Engineering and Engineering Management; Vol. 2016-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A general framework for multiple responses optimization based on Bayesian posterior predictive method

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

UR - http://www.scopus.com/inward/record.url?scp=85009836994&partnerID=8YFLogxK

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. In 2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016. IEEE Computer Society. 2016. p. 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

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this