Volume 24, Issue 2 (IJIEPR 2013)                   IJIEPR 2013, 24(2): 113-121 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
HosseinHejazi T, Ramezani M, Seyyed-Esfahani M, Kimiagari A M. Multiple Response Optimization with Probabilistic Covariates Using Simultaneous Equation Systems (Quality Engineering Conference Paper). IJIEPR 2013; 24 (2) :113-121
URL: http://ijiepr.iust.ac.ir/article-1-502-en.html
Multiple Response Optimization with Probabilistic Covariates Using Simultaneous Equation Systems (Quality Engineering Conference Paper)
Taha HosseinHejazi1 , Majid Ramezani2 , Mirmehdi Seyyed-Esfahani3 , Ali Mohammad Kimiagari 4
1- PhD Candidate, Department of Industrial Engineering and management Systems, Amirkabir University of Technology, Tehran, Iran
2- PhD Candidate, Department of Industrial Engineering and management Systems, Amirkabir University of Technology, Tehran, Iran,
3- Associate Professor, Department of Industrial Engineering and management Systems, Amirkabir University of Technology, Tehran, Iran,
4- Associate Professor, Department of Industrial Engineering and management Systems, Amirkabir University of Technology, Tehran, Iran, , kimiagar@aut.ac.ir
Abstract:   (7039 Views)
control of production processes in an industrial environment needs the correct setting of input factors, so that output products with desirable characteristics will be resulted at minimum cost. Moreover, such systems havetomeetset of qualitycharacteristicstosatisfycustomer requirements.Identifyingthemosteffectivefactorsindesignoftheprocesswhichsupportcontinuousandcontinualimprovement isrecentlydiscussedfromdifferentviewpoints.Inthisstudy, we examined the quality engineering problems in which several characteristics and factors are to be analyzed through a simultaneous equations system. Besides, the several probabilistic covariates can be included to the proposed model. The main purpose of this model is to identify interrelations among exogenous and endogenous variables, which give important insight for systematic improvements of quality. At the end, the proposed approach is described analytically by a numerical example.
Keywords: Design of experiments, Multiple Response Optimization (MRO), robust design, probabilistic covariate, System regression.
Full-Text [PDF 501 kb]   (3033 Downloads)    
Type of Study: Research | Subject: Optimization Techniques
Received: 2013/01/28 | Accepted: 2013/06/17 | Published: 2013/07/15
Add your comments about this article : Your username or Email:
CAPTCHA
Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.