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A general framework for multiresponse optimization problems based on goal programming

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  • Kazemzadeh, Reza B.
  • Bashiri, Mahdi
  • Atkinson, Anthony C.
  • Noorossana, Rassoul

Abstract

Setting of process variables to meet a required specification of quality characteristic (or response variable) in a process, is one of the common problems in the process quality control. But generally there are more than one quality characteristics in the process and the experimenter attempts to optimize all of them simultaneously. Since response variables are different in some properties such as scale, measurement unit, type of optimality and their preferences, there are different approaches in model building and optimization of MRS problems. This study propose a general framework in MRS problems according to some existing works and some types of related decision makers and attempts to aggregate all of characteristics in one approach. The proposed framework contains four non-desirability parts of bias, response variation, errors in predictions and separation from responses' specific region. We demonstrate the proposed framework with two examples of the literature and the results has been discussed with comparing of mentioned existing works.

Suggested Citation

  • Kazemzadeh, Reza B. & Bashiri, Mahdi & Atkinson, Anthony C. & Noorossana, Rassoul, 2008. "A general framework for multiresponse optimization problems based on goal programming," European Journal of Operational Research, Elsevier, vol. 189(2), pages 421-429, September.
    • Handle: RePEc:eee:ejores:v:189:y:2008:i:2:p:421-429
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    References listed on IDEAS

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    1. Kwang‐Jae Kim & Dennis K. J. Lin, 2000. "Simultaneous optimization of mechanical properties of steel by maximizing exponential desirability functions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(3), pages 311-325.
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    1. Nha, Vo Thanh & Shin, Sangmun & Jeong, Seong Hoon, 2013. "Lexicographical dynamic goal programming approach to a robust design optimization within the pharmaceutical environment," European Journal of Operational Research, Elsevier, vol. 229(2), pages 505-517.
    2. Wang, Jianjun & Ma, Yizhong & Ouyang, Linhan & Tu, Yiliu, 2016. "A new Bayesian approach to multi-response surface optimization integrating loss function with posterior probability," European Journal of Operational Research, Elsevier, vol. 249(1), pages 231-237.
    3. Mahdi Bashiri & Seyed Javad Hosseininezhad, 2012. "Fuzzy Development of Multiple Response Optimization," Group Decision and Negotiation, Springer, vol. 21(3), pages 417-438, May.
    4. Hejazi, Taha-Hossein & Badri, Hossein & Yang, Kai, 2019. "A Reliability-based Approach for Performance Optimization of Service Industries: An Application to Healthcare Systems," European Journal of Operational Research, Elsevier, vol. 273(3), pages 1016-1025.
    5. Jones, Dylan & Jimenez, Mariano, 2013. "Incorporating additional meta-objectives into the extended lexicographic goal programming framework," European Journal of Operational Research, Elsevier, vol. 227(2), pages 343-349.
    6. He, Zhen & Zhu, Peng-Fei & Park, Sung-Hyun, 2012. "A robust desirability function method for multi-response surface optimization considering model uncertainty," European Journal of Operational Research, Elsevier, vol. 221(1), pages 241-247.

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