IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v39y2012i3p599-613.html
   My bibliography  Save this article

An augmented approach to the desirability function

Author

Listed:
  • Hsiu-Wen Chen
  • Weng Kee Wong
  • Hongquan Xu

Abstract

The desirability function is widely used in the engineering field to tackle the problem of optimizing multiple responses simultaneously. This approach does not account for the variability in the predicted responses and minimizing this variability to have narrower prediction intervals is desirable. We propose to add this capability in the desirability function and also incorporate the relative importance of optimizing the multiple responses and minimizing the variances of the predicted responses at the same time. We show that the benefits of our augmented approach using two real data sets by comparing our solutions with those obtained from the desirability approach. In particular, it is shown that our approach offers greater flexibility and the solutions can reduce the variances of all the predicted responses resulting in narrower prediction intervals.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:599-613
    DOI: 10.1080/02664763.2011.605437
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2011.605437
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2011.605437?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kim, Kwang-Jae & Lin, Dennis K.J., 2006. "Optimization of multiple responses considering both location and dispersion effects," European Journal of Operational Research, Elsevier, vol. 169(1), pages 133-145, February.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    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. 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.
    5. Nitidetch Koohathongsumrit & Pongchanun Luangpaiboon, 2022. "An integrated FAHP–ZODP approach for strategic marketing information system project selection," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(6), pages 1792-1809, September.
    6. 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.
    7. 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.
    8. Linhan Ouyang & Yizhong Ma & Jianxiong Chen & Zhigang Zeng & Yiliu Tu, 2016. "Robust optimisation of Nd: YLF laser beam micro-drilling process using Bayesian probabilistic approach," International Journal of Production Research, Taylor & Francis Journals, vol. 54(21), pages 6644-6659, November.
    9. 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.
    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. Wang, Jiacheng & Tan, Xianfeng & Zhao, Zhihong & Chen, Jinfan & He, Jie & Shi, Qipeng, 2024. "Coupled thermo-hydro-mechanical modeling on geothermal doublet subject to seasonal exploitation and storage," Energy, Elsevier, vol. 293(C).
    12. 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.
    13. Jessenberger, J. & Weihs, Claus, 2004. "Desirability to characterize process capability," Technical Reports 2004,73, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    14. 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.
    15. 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.
    16. Ouyang, Linhan & Ma, Yizhong & Wang, Jianjun & Tu, Yiliu, 2017. "A new loss function for multi-response optimization with model parameter uncertainty and implementation errors," European Journal of Operational Research, Elsevier, vol. 258(2), pages 552-563.
    17. 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.
    18. R. L. J. Coetzer & R. F. Rossouw & D. K. J. Lin, 2008. "Dual response surface optimization with hard‐to‐control variables for sustainable gasifier performance," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(5), pages 567-587, December.
    19. 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.
    20. Liu, Guihong & Zhao, Zhihong & Xu, Haoran & Zhang, Jinping & Kong, Xiangjun & Yuan, Lijuan, 2022. "A robust assessment method of recoverable geothermal energy considering optimal development parameters," Renewable Energy, Elsevier, vol. 201(P1), pages 426-440.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:599-613. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.