IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v79y2002i2p93-100.html
   My bibliography  Save this article

Process optimization via neural network metamodeling

Author

Listed:
  • Chambers, M.
  • Mount-Campbell, C. A.

Abstract

No abstract is available for this item.

Suggested Citation

  • Chambers, M. & Mount-Campbell, C. A., 2002. "Process optimization via neural network metamodeling," International Journal of Production Economics, Elsevier, vol. 79(2), pages 93-100, September.
    • Handle: RePEc:eee:proeco:v:79:y:2002:i:2:p:93-100
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925-5273(00)00188-2
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Badiru, Adedeji B. & Sieger, David B., 1998. "Neural network as a simulation metamodel in economic analysis of risky projects," European Journal of Operational Research, Elsevier, vol. 105(1), pages 130-142, February.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. K A H Kobbacy & S Vadera & M H Rasmy, 2007. "AI and OR in management of operations: history and trends," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(1), pages 10-28, January.
    2. van der Gaast, Jelmer Pier & Weidinger, Felix, 2022. "A deep learning approach for the selection of an order picking system," European Journal of Operational Research, Elsevier, vol. 302(2), pages 530-543.
    3. Artur M. Schweidtmann & Alexander Mitsos, 2019. "Deterministic Global Optimization with Artificial Neural Networks Embedded," Journal of Optimization Theory and Applications, Springer, vol. 180(3), pages 925-948, March.
    4. Becker, Till & Illigen, Christoph & McKelvey, Bill & Hülsmann, Michael & Windt, Katja, 2016. "Using an agent-based neural-network computational model to improve product routing in a logistics facility," International Journal of Production Economics, Elsevier, vol. 174(C), pages 156-167.
    5. Azadeh, A. & Faiz, Z.S. & Asadzadeh, S.M. & Tavakkoli-Moghaddam, R., 2011. "An integrated artificial neural network-computer simulation for optimization of complex tandem queue systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(4), pages 666-678.
    6. B Dengiz & C Alabas-Uslu & O Dengiz, 2009. "Optimization of manufacturing systems using a neural network metamodel with a new training approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(9), pages 1191-1197, September.
    7. Reis dos Santos, Pedro M. & Isabel Reis dos Santos, M., 2009. "Using subsystem linear regression metamodels in stochastic simulation," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1031-1040, August.
    8. Chen-Fu Chien & Chung-Jen Kuo & Chih-Min Yu, 2020. "Tool allocation to smooth work-in-process for cycle time reduction and an empirical study," Annals of Operations Research, Springer, vol. 290(1), pages 1009-1033, July.

    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. Yang Changwei & Li Zonghao & Guo Xueyan & Yu Wenying & Jin Jing & Zhu Liang, 2019. "Application of BP Neural Network Model in Risk Evaluation of Railway Construction," Complexity, Hindawi, vol. 2019, pages 1-12, June.
    2. Angelini, Eliana & di Tollo, Giacomo & Roli, Andrea, 2008. "A neural network approach for credit risk evaluation," The Quarterly Review of Economics and Finance, Elsevier, vol. 48(4), pages 733-755, November.
    3. Reis dos Santos, Pedro M. & Isabel Reis dos Santos, M., 2009. "Using subsystem linear regression metamodels in stochastic simulation," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1031-1040, August.

    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:eee:proeco:v:79:y:2002:i:2:p:93-100. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    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.