Robust optimization of multistage process: response surface and multi-response optimization approaches

被引:4
作者
Moslemi, Amir [1 ]
Seyyed-Esfahani, Mirmehdi [2 ]
机构
[1] Islamic Azad Univ, Fac Engn, Dept Ind Engn, West Tehran Branch, Tehran, Iran
[2] Amirkabir Univ Technol, Dept Ind Engn & Management Syst, Tehran, Iran
关键词
epsilon-constraints; global criterion (GC) method; multi-response optimization; multi-response surface; multistage process; multivariate robust regression; REGRESSION; MULTICOLLINEARITY; IMPROVEMENT; ESTIMATOR; SYSTEMS; SQUARES;
D O I
10.1515/ijnsns-2017-0003
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
A multistage system refers to a system contains multiple components or stages which are necessary to finish the final product or service. To analyze these problems, the first step is model building and the other is optimization. Response surfaces are used to model multistage problem as an efficient procedure. One regular approach to estimate a response surface using experimental results is the ordinary least squares (OLS) method. OLS method is very sensitive to outliers, so some multivariate robust estimation methods have been discussed in the literature in order to estimate the response surfaces accurately such as multivariate M-estimators. In optimization phase, multi-response optimization methods such as global criterion (GC) method and E-constraints approaches are different methods to optimize the multi-objective-multistage problems. An example of the multistage problem had been estimated considering multivariate robust approaches, besides applying multi-response optimization approaches. The results show the efficiency of the proposed approaches.
引用
收藏
页码:163 / 175
页数:13
相关论文
共 31 条
  • [11] Quality chain design and optimization by multiple response surface methodology
    Hejazi, Taha Hossein
    Seyyed-Esfahani, Mirmehdi
    Mahootchi, Masoud
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2013, 68 (1-4) : 881 - 893
  • [12] Robust regression and outlier detection in the evaluation of robustness tests with different experimental designs
    Hund, E
    Massart, DL
    Smeyers-Verbeke, J
    [J]. ANALYTICA CHIMICA ACTA, 2002, 463 (01) : 53 - 73
  • [13] A general framework for multiresponse optimization problems based on goal programming
    Kazemzadeh, Reza B.
    Bashiri, Mahdi
    Atkinson, Anthony C.
    Noorossana, Rassoul
    [J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2008, 189 (02) : 421 - 429
  • [14] L-ESTIMATION FOR LINEAR-MODELS
    KOENKER, R
    PORTNOY, S
    [J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1987, 82 (399) : 851 - 857
  • [15] Last Mark, 2001, P 2 INT C INT TECHN, P292
  • [16] Multiresponse optimization of a multistage manufacturing process using a patient rule induction method
    Lee, Dong-Hee
    Yang, Jin-Kyung
    Kim, Kwang-Jae
    [J]. QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2020, 36 (06) : 1982 - 2002
  • [17] Optimizing mean and variance of multiresponse in a multistage manufacturing process using operational data
    Lee, Dong-Hee
    Yang, Jin-Kyung
    Kim, So-Hee
    Kim, Kwang-Jae
    [J]. QUALITY ENGINEERING, 2020, 32 (04) : 627 - 642
  • [18] ROBUST REGRESSION THROUGH ROBUST COVARIANCES
    MARONNA, R
    MORGENTHALER, S
    [J]. COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 1986, 15 (04) : 1347 - 1365
  • [19] Montgomery D., 2010, Design and Analysis of Experiments
  • [20] Robust surface estimation in multi-response multistage statistical optimization problems
    Moslemi, Amir
    Seyyed-Esfahani, Mirmehdi
    Niaki, Seyed Taghi Akhavan
    [J]. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2018, 47 (03) : 762 - 782