IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v132y2004i1p47-6810.1023-banor.0000045276.18995.c8.html
   My bibliography  Save this article

Better Optimization of Nonlinear Uncertain Systems (BONUS): A New Algorithm for Stochastic Programming Using Reweighting through Kernel Density Estimation

Author

Listed:
  • Kemal Sahin
  • Urmila Diwekar

Abstract

A new nonlinear programming algorithm is proposed for stochastic programming problems. This method relies on sampling to estimate the probabilistic objective function and constraints. The computational burden of excessive model calculations for determining the search direction is bypassed through a reweighting method using Kernel Density Estimation. The improvements accomplished by this algorithm called Better Optimization of Nonlinear Uncertain Systems (BONUS) are presented through two real world case studies involving parameter design for off-line quality control of a chemical reactor, and optimal capacity expansion for electric utilities in uncertain markets. Copyright Kluwer Academic Publishers 2004

Suggested Citation

  • Kemal Sahin & Urmila Diwekar, 2004. "Better Optimization of Nonlinear Uncertain Systems (BONUS): A New Algorithm for Stochastic Programming Using Reweighting through Kernel Density Estimation," Annals of Operations Research, Springer, vol. 132(1), pages 47-68, November.
    • Handle: RePEc:spr:annopr:v:132:y:2004:i:1:p:47-68:10.1023/b:anor.0000045276.18995.c8
    DOI: 10.1023/B:ANOR.0000045276.18995.c8
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/B:ANOR.0000045276.18995.c8
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1023/B:ANOR.0000045276.18995.c8?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Salazar, Juan M. & Diwekar, Urmila & Constantinescu, Emil & Zavala, Victor M., 2013. "Stochastic optimization approach to water management in cooling-constrained power plants," Applied Energy, Elsevier, vol. 112(C), pages 12-22.
    2. Rajib Mukherjee & Urmila M. Diwekar, 2021. "Optimizing TEG Dehydration Process under Metamodel Uncertainty," Energies, MDPI, vol. 14(19), pages 1-20, September.
    3. Sen, Pallabi & Sen, Kinnar & Diwekar, Urmila M., 2016. "A multi-objective optimization approach to optimal sensor location problem in IGCC power plants," Applied Energy, Elsevier, vol. 181(C), pages 527-539.
    4. Pablo T. Rodriguez-Gonzalez & Vicente Rico-Ramirez & Ramiro Rico-Martinez & Urmila M. Diwekar, 2019. "A New Approach to Solving Stochastic Optimal Control Problems," Mathematics, MDPI, vol. 7(12), pages 1-13, December.
    5. Adarsh Vaderobli & Dev Parikh & Urmila Diwekar, 2020. "Optimization under Uncertainty to Reduce the Cost of Energy for Parabolic Trough Solar Power Plants for Different Weather Conditions," Energies, MDPI, vol. 13(12), pages 1-17, June.
    6. Lee, Adrian J. & Diwekar, Urmila M., 2012. "Optimal sensor placement in integrated gasification combined cycle power systems," Applied Energy, Elsevier, vol. 99(C), pages 255-264.

    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:spr:annopr:v:132:y:2004:i:1:p:47-68:10.1023/b:anor.0000045276.18995.c8. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.