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Some Problems of Digital Systems Simulation

Author

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  • R. W. Conway

    (Department of Industrial and Engineering Administration, Cornell University)

  • B. M. Johnson

    (Department of Industrial and Engineering Administration, Cornell University)

  • W. L. Maxwell

    (Department of Industrial and Engineering Administration, Cornell University)

Abstract

Our objective is to discuss the technique of digital system simulation. This procedure has already achieved a considerable stature in industrial and research organizations and promises to attain even greater importance in the future. Yet, with few exceptions (5) 1 , the published literature in the area consists of introductory expositions (10) or of descriptions of the solution of a particular problem (7) where the technique itself is correctly relegated to secondary position. The only publication devoted entirely to the topic (12) is a compendium of papers of the latter type with little attempt to summarize or generalize. Simulations are, of course, as varied as the systems which they represent but they do have certain common characteristics and problems. An identification of these problems would at least allow the investigator to anticipate them and plan accordingly. This paper consists of three sections. The first is an attempt to describe characteristics common to many system simulations; the second is a discussion of some problems involved in the construction of a digital simulator; and the third concerns problems that arise in the use of such a simulator.

Suggested Citation

  • R. W. Conway & B. M. Johnson & W. L. Maxwell, 1959. "Some Problems of Digital Systems Simulation," Management Science, INFORMS, vol. 6(1), pages 92-110, October.
    • Handle: RePEc:inm:ormnsc:v:6:y:1959:i:1:p:92-110
    DOI: 10.1287/mnsc.6.1.92
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    Citations

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    Cited by:

    1. Song, Wheyming Tina, 1996. "On the estimation of optimal batch sizes in the analysis of simulation output," European Journal of Operational Research, Elsevier, vol. 88(2), pages 304-319, January.
    2. Enrico Cozzoni & Carmine Passavanti & Cristina Ponsiglione & Simonetta Primario & Pierluigi Rippa, 2021. "Interorganizational Collaboration in Innovation Networks: An Agent Based Model for Responsible Research and Innovation in Additive Manufacturing," Sustainability, MDPI, vol. 13(13), pages 1-17, July.
    3. Barry L. Nelson, 2004. "50th Anniversary Article: Stochastic Simulation Research in Management Science," Management Science, INFORMS, vol. 50(7), pages 855-868, July.
    4. Rand, William & Rust, Roland T., 2011. "Agent-based modeling in marketing: Guidelines for rigor," International Journal of Research in Marketing, Elsevier, vol. 28(3), pages 181-193.
    5. Vagnani, Gianluca, 2009. "The Black-Scholes model as a determinant of the implied volatility smile: A simulation study," Journal of Economic Behavior & Organization, Elsevier, vol. 72(1), pages 103-118, October.
    6. Gianluca Vagnani, 2009. "The Black-Scholes model as a determinant of the implied volatility smile: A simulation study," Post-Print hal-00736952, HAL.
    7. Peter Moran & Michele Simoni & Gianluca Vagnani, 2011. "Becoming the best: by beating or ignoring the best? Toward an expanded view of the role of managerial selection in complex and turbulent environments," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 15(3), pages 447-481, August.
    8. Xuefei Lu & Alessandro Rudi & Emanuele Borgonovo & Lorenzo Rosasco, 2020. "Faster Kriging: Facing High-Dimensional Simulators," Operations Research, INFORMS, vol. 68(1), pages 233-249, January.
    9. Wright, A. & Dent, J. Barry, 1969. "The Application Of Simulation Techniques To The Study Of Grazing Systems," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 13(2), pages 1-10, December.

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