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Mathematical description of a discrete event simulation (DEVS) using factor analytic method

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Journal of the Operational Research Society

Abstract

Appropriately capturing the logic or results of a detailed simulation through some kind of aggregation to pass up a model hierarchy is a difficult task. This aggregation task involves two main questions: ‘what’ to aggregate and ‘how’ to perform this aggregation. This paper provides a well-defined approach for the ‘what’ portion of the aggregation process for hierarchical simulation models. We start by characterizing a simulation model in a mathematical format. We then use this mathematical description to identify what portion of a lower-level model can be aggregated, or to capture the logical flow of the full lower-level model. A useful mathematical representation of the simulation structure and logic is through the development of a network representation of the model. This, in turn, can be systematically decomposed into smaller sub-networks by performing model decomposition by means of factor analytic methods, which is the main topic of discussion.

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References

  • Axtell RL (1992). Theory of model aggregation for dynamical systems with application to problems of global change. PhD Dissertation, Carnegie-Mellon University.

  • Barton R (1992). Metamodels for simulation input–output relations. In: Swain JJ, Goldsman D, Crain RC and Wilson JR (eds). Proceedings 1992 Winter Simulation Conference, IEEE Press: Piscataway, NJ, pp 289–299.

    Google Scholar 

  • Bauer Jr. K, Kochar B and Talavage J (1985). Simulation model decomposition by factor analysis. In: Gantz D, Blais G and Solomon S (eds). Proceedings of 1985 Winter Simulation Conference, IEEE Press: Piscataway, NJ, pp. 185–188.

    Google Scholar 

  • Bauer Jr. K, Kochar B and Talavage J (1991). Discrete event simulation model decomposition by principal component analysis. ORSA Journal on Computing 3 (1): 23–32.

    Article  Google Scholar 

  • Committee on Technology for Future Naval Forces (1997). Technology for the United States Navy and Marine Corps, 2000–2035, Becoming a 21st-Century Force Vol. 9, Modeling and Simulation, National Academy Press: Washington DC.

  • Dillon W and Goldstein M (1991). Multivariate Analysis: Methods and Applications. Wiley: New York.

    Google Scholar 

  • Faas P (2003). Simulation of autonomic logistics system (ALS) sortie generation. Masters Thesis, AFIT/GOR/ENS/03M-07 Air Force Institute of Technology, Wright-Patterson AFB OH, March.

  • Faas P and Miller JO (2003). Impact of an autonomic logistics system (ALS) on the sortie generation process. In: Chick S, Sanchez PJ, Ferrin D and Morrice DJ (eds). Proceedings 2003 Winter Simulation Conference, IEEE Press: Piscataway, NJ, pp 1021–1025.

    Chapter  Google Scholar 

  • Fonseca DJ, Navaresse DO and Moynihan GP (2003). Simulation metamodeling through artificial neural networks. Engineering Applications of Artificial Intelligence 16 (3): 177–183.

    Article  Google Scholar 

  • Guo Y, Yin X and Gong W (1998). ART 2 neural network clustering for hierarchical simulation. Proceedings of SPIE—The International Society for Optical Engineering 3369: 35–48.

    Google Scholar 

  • Harman HH (1967). Modern Factor Analysis, 2nd edn. University of Chicago Press: Chicago, pp 293–313.

    Google Scholar 

  • Jackson JE (1991). A User's Guide to Principal Components. Wiley: New York.

    Book  Google Scholar 

  • Kaiser HF (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika 23 (3): 187–200.

    Article  Google Scholar 

  • Kaiser HF (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement 20 (1): 141–151.

    Article  Google Scholar 

  • Kilmer RA (1994). Artificial Neural Network Metamodels of Stochastic Computer Simulations. PhD Dissertation. Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, PA.

  • Kleijnen JPC (1987). Statistical Tools for Simulation Practitioners. Marcel Dekker: New York, NY.

    Google Scholar 

  • Matthes S (1988). Discrete event simulation model decomposition. Masters Thesis, AFIT/GOR/ENS/88 M Air Force Institute of Technology, Wright-Patterson AFB OH, March.

  • Nasereddin M and Mollaghasemi M (1999). The development of a methodology for the use of neural networks and simulation modeling in system design. In: Proceedings of 1999 Winter Simulation Conference. December, Phoenix, AZ, pp 537–542.

  • Neuhaus JO and Wrigley C (1954). The quartimax method: An analytical approach to orthogonal simple structure. British Journal of Statistical Psychology 7 (2): 81–91.

    Article  Google Scholar 

  • Rodriguez J (2008). Metamodeling techniques to aid in the aggregation process of Large hierarchical simulation models. PhD Dissertation, AFIT/DS/ENS/08-03 Air Force Institute of Technology, Wright Patterson AFB, OH, August.

  • Saunders DR (1961). The rationale for an ‘oblimax’ method of transformation in factor analysis. Psychometrika 26 (3): 317–324.

    Article  Google Scholar 

  • Sisti A (1998). Enabling Technologies for Simulation Science. IEEE Information Technology Conference, Syracuse, NY, 1–3 September, pp 33–36.

  • Thurstone LL (1947). Multiple-Factor Analysis: A Development and Expansion of the Vectors of Mind. The University of Chicago Press: Chicago, IL, p. 335.

    Google Scholar 

  • West D (2001). Introduction to Graph Theory, 2nd edn. Prentice-Hall: Upper Saddle River, NJ.

    Google Scholar 

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Disclaimer: The views expressed in this paper are those of the authors and do not necessarily reflect the official policy or position of the US Air Force, the Department of Defense, or the US Government.

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Rodriquez, J., Miller, J. & Bauer, K. Mathematical description of a discrete event simulation (DEVS) using factor analytic method. J Oper Res Soc 63, 1179–1188 (2012). https://doi.org/10.1057/jors.2011.107

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  • DOI: https://doi.org/10.1057/jors.2011.107

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