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Simultaneous optimization of mechanical properties of steel by maximizing exponential desirability functions

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  • Kwang‐Jae Kim
  • Dennis K. J. Lin

Abstract

A modelling approach to optimize a multiresponse system is presented. The approach aims to identify the setting of the input variables to maximize the degree of overall satisfaction with respect to all the responses. An exponential desirability functional form is suggested to simplify the desirability function assessment process. The approach proposed does not require any assumptions regarding the form or degree of the estimated response models and is robust to the potential dependences between response variables. It also takes into consideration the difference in the predictive ability as well as relative priority among the response variables. Properties of the approach are revealed via two real examples—one classical example taken from the literature and another that the authors have encountered in the steel industry.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssc:v:49:y:2000:i:3:p:311-325
    DOI: 10.1111/1467-9876.00194
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    Cited by:

    1. Bera, Sasadhar & Mukherjee, Indrajit, 2012. "An ellipsoidal distance-based search strategy of ants for nonlinear single and multiple response optimization problems," European Journal of Operational Research, Elsevier, vol. 223(2), pages 321-332.
    2. Abbas Al-Refaie & Wafa’a Al-Alaween & Ali Diabat & Ming-Hsien Li, 2017. "Solving dynamic systems with multi-responses by integrating desirability function and data envelopment analysis," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 387-403, February.
    3. Bera, Sasadhar & Mukherjee, Indrajit, 2016. "A multistage and multiple response optimization approach for serial manufacturing system," European Journal of Operational Research, Elsevier, vol. 248(2), pages 444-452.
    4. Zhen He & Jing Wang & Jinho Oh & Sung H. Park, 2010. "Robust optimization for multiple responses using response surface methodology," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 26(2), pages 157-171, March.
    5. Jeong, In-Jun & Kim, Kwang-Jae, 2009. "An interactive desirability function method to multiresponse optimization," European Journal of Operational Research, Elsevier, vol. 195(2), pages 412-426, June.
    6. Hsu, Chih-Ming, 2004. "An integrated approach to enhance the optical performance of couplers based on neural networks, desirability functions and tabu search," International Journal of Production Economics, Elsevier, vol. 92(3), pages 241-254, December.
    7. Murat Köksalan & Robert D. Plante, 2003. "Interactive Multicriteria Optimization for Multiple-Response Product and Process Design," Manufacturing & Service Operations Management, INFORMS, vol. 5(4), pages 334-347, May.
    8. Chao-Ton Su & Mu-Chen Chen & Hsiao-Ling Chan, 2005. "Applying neural network and scatter search to optimize parameter design with dynamic characteristics," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(10), pages 1132-1140, October.
    9. Jessenberger, J. & Weihs, Claus, 2004. "Desirability to characterize process capability," Technical Reports 2004,73, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    10. Riva, Lorenzo & Nielsen, Henrik Kofoed & Skreiberg, Øyvind & Wang, Liang & Bartocci, Pietro & Barbanera, Marco & Bidini, Gianni & Fantozzi, Francesco, 2019. "Analysis of optimal temperature, pressure and binder quantity for the production of biocarbon pellet to be used as a substitute for coke," Applied Energy, Elsevier, vol. 256(C).
    11. Chiang, Tai-Lin & Su, Chao-Ton, 2003. "Optimization of TQFP molding process using neuro-fuzzy-GA approach," European Journal of Operational Research, Elsevier, vol. 147(1), pages 156-164, May.
    12. 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.
    13. Hsiu-Wen Chen & Weng Kee Wong & Hongquan Xu, 2012. "An augmented approach to the desirability function," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(3), pages 599-613, July.
    14. Paul L. Goethals & Natalie M. Scala, 2018. "Eliminating the Weakest Link Approach to Army Unit Readiness," Decision Analysis, INFORMS, vol. 15(2), pages 110-130, June.
    15. Mouhamadou Mansour Mbow & Christelle Grandvallet & Frederic Vignat & Philippe Rene Marin & Nicolas Perry & Franck Pourroy, 2022. "Mathematization of experts knowledge: example of part orientation in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1209-1227, June.
    16. Shi, Liangxing & Lin, Dennis K.J. & Peterson, John J., 2016. "A confidence region for the ridge path in multiple response surface optimization," European Journal of Operational Research, Elsevier, vol. 252(3), pages 829-836.

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