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Neural Networks for the MS/OR Analyst: An Application Bibliography

Author

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  • Ramesh Sharda

    (College of Business Administration, Oklahoma State University, Stillwater, Oklahoma 74078)

Abstract

From a management scientist's perspective, the neural networks offer three opportunities: statistical methods, optimization methods, and a problem domain in which to apply OR algorithms. This annotated bibliography of application literature should offer a nontechnical fast track to someone getting started in this exciting area. The research published thus far on the applications of neural networks for statistical and optimization problems shows promise for the interface of neural networks and management science.

Suggested Citation

  • Ramesh Sharda, 1994. "Neural Networks for the MS/OR Analyst: An Application Bibliography," Interfaces, INFORMS, vol. 24(2), pages 116-130, April.
    • Handle: RePEc:inm:orinte:v:24:y:1994:i:2:p:116-130
    DOI: 10.1287/inte.24.2.116
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    Citations

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    Cited by:

    1. Sander van der Hoog, 2017. "Deep Learning in (and of) Agent-Based Models: A Prospectus," Papers 1706.06302, arXiv.org.
    2. Angelos Mimis & Antonis Rovolis & Marianthi Stamou, 2013. "Property valuation with artificial neural network: the case of Athens," Journal of Property Research, Taylor & Francis Journals, vol. 30(2), pages 128-143, June.
    3. Eleimon Gonis & Salima Paul & Jon Tucker, 2012. "Rating or no rating? That is the question: an empirical examination of UK companies," The European Journal of Finance, Taylor & Francis Journals, vol. 18(8), pages 709-735, September.
    4. Sander Hoog, 2019. "Surrogate Modelling in (and of) Agent-Based Models: A Prospectus," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1245-1263, March.
    5. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    6. Khurshid Kiani, 2005. "Detecting Business Cycle Asymmetries Using Artificial Neural Networks and Time Series Models," Computational Economics, Springer;Society for Computational Economics, vol. 26(1), pages 65-89, August.
    7. James R. Coakley & Carol E. Brown, 2000. "Artificial neural networks in accounting and finance: modeling issues," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(2), pages 119-144, June.
    8. Khurshid Kiani, 2011. "Fluctuations in Economic and Activity and Stabilization Policies in the CIS," Computational Economics, Springer;Society for Computational Economics, vol. 37(2), pages 193-220, February.
    9. Li, Hongtao & Bai, Juncheng & Li, Yongwu, 2019. "A novel secondary decomposition learning paradigm with kernel extreme learning machine for multi-step forecasting of container throughput," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    10. Davalos, Sergio & Gritta, Richard D. & Adrangi, Bahram, 2007. "Deriving Rules for Forecasting Air Carrier Financial Stress and Insolvency: A Genetic Algorithm Approach," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 46(2).
    11. Yijie Peng & Li Xiao & Bernd Heidergott & L. Jeff Hong & Henry Lam, 2022. "A New Likelihood Ratio Method for Training Artificial Neural Networks," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 638-655, January.
    12. Mostafa, Mohamed M. & El-Masry, Ahmed A., 2013. "Citizens as consumers: Profiling e-government services’ users in Egypt via data mining techniques," International Journal of Information Management, Elsevier, vol. 33(4), pages 627-641.
    13. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    14. Schewe, Gerhard & Leker, Jens, 1996. "Empirische Insolvenzforschung: Ein Vergleich ausgewählter Instrumente," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 401, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    15. Jeeeun Kim & Sungjoo Lee, 2017. "Forecasting and identifying multi-technology convergence based on patent data: the case of IT and BT industries in 2020," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 47-65, April.
    16. Juan Camilo Santana, 2008. "La curva de rendimientos: una revisión metodológica y nuevas aproximaciones de estimación," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, July.

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    Keywords

    computers: artificial intelligence;

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