IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v84y1995i1p192-212.html
   My bibliography  Save this article

Dynamic grouping of parts in flexible manufacturing systems -- a self-organizing neural networks approach

Author

Listed:
  • Kulkarni, Uday R.
  • Kiang, Melody Y.

Abstract

No abstract is available for this item.

Suggested Citation

  • Kulkarni, Uday R. & Kiang, Melody Y., 1995. "Dynamic grouping of parts in flexible manufacturing systems -- a self-organizing neural networks approach," European Journal of Operational Research, Elsevier, vol. 84(1), pages 192-212, July.
  • Handle: RePEc:eee:ejores:v:84:y:1995:i:1:p:192-212
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/0377-2217(94)00326-8
    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. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    2. Ravi Kumar, K. & Kusiak, Andrew & Vannelli, Anthony, 1986. "Grouping of parts and components in flexible manufacturing systems," European Journal of Operational Research, Elsevier, vol. 24(3), pages 387-397, March.
    3. Kaparthi, Shashidhar & Suresh, Nallan C. & Cerveny, Robert P., 1993. "An improved neural network leader algorithm for part-machine grouping in group technology," European Journal of Operational Research, Elsevier, vol. 69(3), pages 342-356, September.
    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. Kiang, Melody Y., 2001. "Extending the Kohonen self-organizing map networks for clustering analysis," Computational Statistics & Data Analysis, Elsevier, vol. 38(2), pages 161-180, December.
    2. Yin, Yong & Yasuda, Kazuhiko, 2006. "Similarity coefficient methods applied to the cell formation problem: A taxonomy and review," International Journal of Production Economics, Elsevier, vol. 101(2), pages 329-352, June.
    3. Mohamed, Zubair M. & Kumar, Ashok & Motwani, Jaideep, 1999. "An improved part grouping model for minimizing makespan in FMS," European Journal of Operational Research, Elsevier, vol. 116(1), pages 171-182, July.
    4. Shouhong Wang, 2001. "Cluster analysis using a validated self‐organizing method: cases of problem identification," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(2), pages 127-138, June.

    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. Boutsinas, Basilis, 2013. "Machine-part cell formation using biclustering," European Journal of Operational Research, Elsevier, vol. 230(3), pages 563-572.
    2. Palocsay, Susan W. & Stevens, Scott P. & Brookshire, Robert G. & Sacco, William J. & Copes, Wayne S. & Buckman, Robert F. & Smith, J. Stanley, 1996. "Using neural networks for trauma outcome evaluation," European Journal of Operational Research, Elsevier, vol. 93(2), pages 369-386, September.
    3. Yu-Shan Chen & Ke-Chiun Chang, 2009. "Using neural network to analyze the influence of the patent performance upon the market value of the US pharmaceutical companies," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(3), pages 637-655, September.
    4. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    5. Maria H. Kim & Graham Partington, 2015. "Dynamic forecasts of financial distress of Australian firms," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 135-160, February.
    6. J.E. Boritz & D.B. Kennedy & Augusto de Miranda e Albuquerque, 1995. "Predicting Corporate Failure Using a Neural Network Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 4(2), pages 95-111, June.
    7. repec:hum:wpaper:sfb649dp2013-037 is not listed on IDEAS
    8. Mark T. Leung & An-Sing Chen, 2005. "Performance evaluation of neural network architectures: the case of predicting foreign exchange correlations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(6), pages 403-420.
    9. Sueyoshi, Toshiyuki, 2006. "DEA-Discriminant Analysis: Methodological comparison among eight discriminant analysis approaches," European Journal of Operational Research, Elsevier, vol. 169(1), pages 247-272, February.
    10. Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2009. "Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(6), pages 512-534.
    11. Jones, Stewart & Johnstone, David & Wilson, Roy, 2015. "An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 72-85.
    12. Mohamed, Zubair M. & Kumar, Ashok & Motwani, Jaideep, 1999. "An improved part grouping model for minimizing makespan in FMS," European Journal of Operational Research, Elsevier, vol. 116(1), pages 171-182, July.
    13. Callen, Jeffrey L. & Kwan, Clarence C. Y. & Yip, Patrick C. Y. & Yuan, Yufei, 1996. "Neural network forecasting of quarterly accounting earnings," International Journal of Forecasting, Elsevier, vol. 12(4), pages 475-482, December.
    14. Kattan, Michael W. & Cooper, Randolph B., 2000. "A simulation of factors affecting machine learning techniques: an examination of partitioning and class proportions," Omega, Elsevier, vol. 28(5), pages 501-512, October.
    15. En-Der Su & Shih-Ming Huang, 2010. "Comparing Firm Failure Predictions Between Logit, KMV, and ZPP Models: Evidence from Taiwan’s Electronics Industry," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 17(3), pages 209-239, September.
    16. Chrysovalantis Gaganis & Fotios Pasiouras & Charalambos Spathis & Constantin Zopounidis, 2007. "A comparison of nearest neighbours, discriminant and logit models for auditing decisions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(1‐2), pages 23-40, January.
    17. Greta Falavigna, 2008. "Nouveaux instruments d’évaluation pour le risque financier d’entreprise," CERIS Working Paper 200801, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.
    18. Dumitrescu, Elena & Hué, Sullivan & Hurlin, Christophe & Tokpavi, Sessi, 2022. "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1178-1192.
    19. Seyma Caliskan Cavdar & Alev Dilek Aydin, 2015. "An Empirical Analysis for the Prediction of a Financial Crisis in Turkey through the Use of Forecast Error Measures," JRFM, MDPI, vol. 8(3), pages 1-18, August.
    20. repec:hum:wpaper:sfb649dp2008-035 is not listed on IDEAS
    21. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
    22. Tonatiuh Peña & Serafín Martínez & Bolanle Abudu, 2011. "Bankruptcy Prediction: A Comparison of Some Statistical and Machine Learning Techniques," Dynamic Modeling and Econometrics in Economics and Finance, in: Herbert Dawid & Willi Semmler (ed.), Computational Methods in Economic Dynamics, pages 109-131, Springer.

    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:ejores:v:84:y:1995:i:1:p:192-212. 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/eor .

    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.