IDEAS home Printed from https://ideas.repec.org/p/tiu/tiutis/903e51c8-bed3-4e97-990f-c052a3686676.html
   My bibliography  Save this paper

Constrained Optimization in Simulation : Efficient Global Optimization and Karush-Kuhn-Tucker Conditions

Author

Listed:
  • Kleijnen, Jack

    (Tilburg University, School of Economics and Management)

  • van Nieuwenhuyse, I.
  • van Beers, W.C.M.

    (Tilburg University, School of Economics and Management)

Abstract

No abstract is available for this item.

Suggested Citation

  • Kleijnen, Jack & van Nieuwenhuyse, I. & van Beers, W.C.M., 2022. "Constrained Optimization in Simulation : Efficient Global Optimization and Karush-Kuhn-Tucker Conditions," Other publications TiSEM 903e51c8-bed3-4e97-990f-c, Tilburg University, School of Economics and Management.
    • Handle: RePEc:tiu:tiutis:903e51c8-bed3-4e97-990f-c052a3686676
    as

    Download full text from publisher

    File URL: https://pure.uvt.nl/ws/portalfiles/portal/63646687/2022_020.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Kleijnen, Jack P.C. & Mehdad, Ehsan, 2014. "Multivariate versus univariate Kriging metamodels for multi-response simulation models," European Journal of Operational Research, Elsevier, vol. 236(2), pages 573-582.
    2. Sadoughi, Mohammadkazem & Li, Meng & Hu, Chao, 2018. "Multivariate system reliability analysis considering highly nonlinear and dependent safety events," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 189-200.
    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. Liu, Yushan & Li, Luyi & Zhao, Sihan & Song, Shufang, 2021. "A global surrogate model technique based on principal component analysis and Kriging for uncertainty propagation of dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    2. Sheibani, Mohamadreza & Ou, Ge, 2021. "Adaptive local kernels formulation of mutual information with application to active post-seismic building damage inference," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Rabbi, Khan Md. & Sheikholeslami, M. & Karim, Anwarul & Shafee, Ahmad & Li, Zhixiong & Tlili, Iskander, 2020. "Prediction of MHD flow and entropy generation by Artificial Neural Network in square cavity with heater-sink for nanomaterial," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    4. Zhang, Jinhao & Xiao, Mi & Gao, Liang, 2019. "An active learning reliability method combining Kriging constructed with exploration and exploitation of failure region and subset simulation," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 90-102.
    5. Rahat, Alma A.M. & Wang, Chunlin & Everson, Richard M. & Fieldsend, Jonathan E., 2018. "Data-driven multi-objective optimisation of coal-fired boiler combustion systems," Applied Energy, Elsevier, vol. 229(C), pages 446-458.
    6. Jiang, Chen & Qiu, Haobo & Gao, Liang & Wang, Dapeng & Yang, Zan & Chen, Liming, 2020. "EEK-SYS: System reliability analysis through estimation error-guided adaptive Kriging approximation of multiple limit state surfaces," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    7. Yang, Seonghyeok & Lee, Mingyu & Lee, Ikjin, 2023. "A new sampling approach for system reliability-based design optimization under multiple simulation models," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    8. Yang, Seonghyeok & Jo, Hwisang & Lee, Kyungeun & Lee, Ikjin, 2022. "Expected system improvement (ESI): A new learning function for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    9. Xiao, Mi & Zhang, Jinhao & Gao, Liang, 2020. "A system active learning Kriging method for system reliability-based design optimization with a multiple response model," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    10. Menafoglio, Alessandra & Secchi, Piercesare, 2017. "Statistical analysis of complex and spatially dependent data: A review of Object Oriented Spatial Statistics," European Journal of Operational Research, Elsevier, vol. 258(2), pages 401-410.
    11. Kleijnen, Jack & van Nieuwenhuyse, I. & van Beers, W.C.M., 2022. "Constrained Optimization in Simulation : Efficient Global Optimization and Karush-Kuhn-Tucker Conditions (revision of 2021-031)," Other publications TiSEM 31a06a3b-dfc4-4431-a141-5, Tilburg University, School of Economics and Management.
    12. Binois, M. & Ginsbourger, D. & Roustant, O., 2015. "Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations," European Journal of Operational Research, Elsevier, vol. 243(2), pages 386-394.
    13. Huang, Shi-Ya & Zhang, Shao-He & Liu, Lei-Lei, 2022. "A new active learning Kriging metamodel for structural system reliability analysis with multiple failure modes," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    14. Moustapha, Maliki & Parisi, Pietro & Marelli, Stefano & Sudret, Bruno, 2024. "Reliability analysis of arbitrary systems based on active learning and global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    15. Wang, Jian & Sun, Zhili & Cao, Runan, 2021. "An efficient and robust Kriging-based method for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    16. Furtado Júnior, Juarez Corrêa & Palacio, José Carlos Escobar & Leme, Rafael Coradi & Lora, Electo Eduardo Silva & da Costa, José Eduardo Loureiro & Reyes, Arnaldo Martín Martínez & del Olmo, Oscar Alm, 2020. "Biorefineries productive alternatives optimization in the brazilian sugar and alcohol industry," Applied Energy, Elsevier, vol. 259(C).
    17. Theodore T. Allen & Olivia K. Hernand & Abdullah Alomair, 2020. "Optimal Off-line Experimentation for Games," Decision Analysis, INFORMS, vol. 17(4), pages 277-298, December.
    18. Sadoughi, Mohammadkazem & Li, Meng & Hu, Chao, 2018. "Multivariate system reliability analysis considering highly nonlinear and dependent safety events," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 189-200.
    19. Li, Meng & Sadoughi, Mohammadkazem & Hu, Zhen & Hu, Chao, 2020. "A hybrid Gaussian process model for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 197(C).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:tiu:tiutis:903e51c8-bed3-4e97-990f-c052a3686676. 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: Richard Broekman (email available below). General contact details of provider: https://www.tilburguniversity.edu/about/schools/economics-and-management/ .

    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.