IDEAS home Printed from https://ideas.repec.org/a/spr/jglopt/v53y2012i4p699-736.html
   My bibliography  Save this article

An experimental methodology for response surface optimization methods

Author

Listed:
  • Daniel Lizotte
  • Russell Greiner
  • Dale Schuurmans

Abstract

No abstract is available for this item.

Suggested Citation

  • Daniel Lizotte & Russell Greiner & Dale Schuurmans, 2012. "An experimental methodology for response surface optimization methods," Journal of Global Optimization, Springer, vol. 53(4), pages 699-736, August.
    • Handle: RePEc:spr:jglopt:v:53:y:2012:i:4:p:699-736
    DOI: 10.1007/s10898-011-9732-z
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10898-011-9732-z
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10898-011-9732-z?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. D. Huang & T. Allen & W. Notz & N. Zeng, 2006. "Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models," Journal of Global Optimization, Springer, vol. 34(3), pages 441-466, March.
    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. Ehsan Mehdad & Jack P. C. Kleijnen, 2018. "Efficient global optimisation for black-box simulation via sequential intrinsic Kriging," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(11), pages 1725-1737, November.
    2. Zheng, Liang & Xue, Xinfeng & Xu, Chengcheng & Ran, Bin, 2019. "A stochastic simulation-based optimization method for equitable and efficient network-wide signal timing under uncertainties," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 287-308.
    3. Songhao Wang & Szu Hui Ng & William Benjamin Haskell, 2022. "A Multilevel Simulation Optimization Approach for Quantile Functions," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 569-585, January.
    4. Diariétou Sambakhé & Lauriane Rouan & Jean-Noël Bacro & Eric Gozé, 2019. "Conditional optimization of a noisy function using a kriging metamodel," Journal of Global Optimization, Springer, vol. 73(3), pages 615-636, March.
    5. Yuan, Jun & Shi, Xunpeng & He, Junliang, 2024. "LNG market liberalization and LNG transportation: Evaluation based on fleet size and composition model," Applied Energy, Elsevier, vol. 358(C).
    6. Qi Fan & Jiaqiao Hu, 2018. "Surrogate-Based Promising Area Search for Lipschitz Continuous Simulation Optimization," INFORMS Journal on Computing, INFORMS, vol. 30(4), pages 677-693, November.
    7. Donghun Lee, 2022. "Knowledge Gradient: Capturing Value of Information in Iterative Decisions under Uncertainty," Mathematics, MDPI, vol. 10(23), pages 1-20, November.
    8. Dellino, G. & Lino, P. & Meloni, C. & Rizzo, A., 2009. "Kriging metamodel management in the design optimization of a CNG injection system," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2345-2360.
    9. Mehdad, E. & Kleijnen, Jack P.C., 2014. "Global Optimization for Black-box Simulation via Sequential Intrinsic Kriging," Other publications TiSEM 8fa8d96f-a086-4c4b-88ab-9, Tilburg University, School of Economics and Management.
    10. Zhe Zhou & Fusheng Bai, 2018. "An adaptive framework for costly black-box global optimization based on radial basis function interpolation," Journal of Global Optimization, Springer, vol. 70(4), pages 757-781, April.
    11. Dawei Zhan & Jiachang Qian & Yuansheng Cheng, 2017. "Balancing global and local search in parallel efficient global optimization algorithms," Journal of Global Optimization, Springer, vol. 67(4), pages 873-892, April.
    12. Kleijnen, Jack P.C., 2009. "Kriging metamodeling in simulation: A review," European Journal of Operational Research, Elsevier, vol. 192(3), pages 707-716, February.
    13. Peter Frazier & Warren Powell & Savas Dayanik, 2009. "The Knowledge-Gradient Policy for Correlated Normal Beliefs," INFORMS Journal on Computing, INFORMS, vol. 21(4), pages 599-613, November.
    14. Taimoor Akhtar & Christine Shoemaker, 2016. "Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection," Journal of Global Optimization, Springer, vol. 64(1), pages 17-32, January.
    15. Emre Barut & Warren Powell, 2014. "Optimal learning for sequential sampling with non-parametric beliefs," Journal of Global Optimization, Springer, vol. 58(3), pages 517-543, March.
    16. Zilong Wang & Marianthi Ierapetritou, 2018. "Surrogate-based feasibility analysis for black-box stochastic simulations with heteroscedastic noise," Journal of Global Optimization, Springer, vol. 71(4), pages 957-985, August.
    17. Satyajith Amaran & Nikolaos V. Sahinidis & Bikram Sharda & Scott J. Bury, 2016. "Simulation optimization: a review of algorithms and applications," Annals of Operations Research, Springer, vol. 240(1), pages 351-380, May.
    18. Fu, Quanlu & Wu, Jiyan & Wu, Xuemian & Sun, Jian & Tian, Ye, 2024. "Managing network congestion with link-based incentives: A surrogate-based optimization approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 182(C).
    19. Raymond K. W. Wong & Curtis B. Storlie & Thomas C. M. Lee, 2017. "A frequentist approach to computer model calibration," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 635-648, March.
    20. Gyoung-Ja Lee & Won-Ju Hwang & Jin-Ju Park & Min-Ku Lee, 2019. "Study of Sensitive Parameters on the Sensor Performance of a Compression-Type Piezoelectric Accelerometer Based on the Meta-Model," Energies, MDPI, vol. 12(7), pages 1-11, April.

    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:spr:jglopt:v:53:y:2012:i:4:p:699-736. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.