Advertisement

The Modelling and Simulation Process

  • Louis G. Birta
  • Gilbert Arbez
Chapter
  • 3.2k Downloads
Part of the Simulation Foundations, Methods and Applications book series (SFMA)

Abstract

The discussion in this chapter continues the exploration of fundamental notions initiated in Chap. 1. However, a variety of important details relating to the modelling and simulation process are introduced. These set the stage for the discussions in the chapters that follow. Included here are the key notions of the observation interval, entities, data requirements, constants, parameters and (time) variables. The latter, in turn, includes input, state and output variables. The various phases of the modelling and simulation process are introduced. The essential need for clearly defined project goals for any simulation project is stressed throughout because these goals provide the basis for establishing a variety of key facets of model development, e.g. model granularity, input data requirements and output requirements. The successful completion of any modelling and simulation project can encounter many challenges, and care must be taken to avoid pitfalls. The notions of validation, verification and quality assurance are pertinent in this respect and these notions are explored. The chapter ends with the acknowledgement that modelling and simulation projects typically fall into one of two broad categories; these correspond to the study of discrete event dynamic systems (DEDS) and continuous time dynamic systems (CTDS). The two remaining parts of the book are separately focused on these two domains.

Keywords

Simulation Program Problem Description Observation Interval Project Goal Simulation Project 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Balci O (1994) Validation, verification, and testing techniques throughout the life cycle of a simulation study. Ann Oper Res 53:121–173MathSciNetCrossRefGoogle Scholar
  2. 2.
    Balci O (2001) A methodology for certification of modeling and simulation applications. ACM Trans Model Comput Simul 11:352–377CrossRefGoogle Scholar
  3. 3.
    Barlow J (2009) Simplification: ethical implications for modelling and simulation. In: Proceedings of the 18th World IMACS/MODSIM Congress, Cairns, Australia, ISBN: 978-0-9758400-7-8. http://www.mssanz.org.au/modsim09/F12/kragt.pdf, pp 432–438
  4. 4.
    Birta LG, Ozmizrak NF (1996) A knowledge-based approach for the validation of simulation models: the foundation. ACM Trans Model Comput Simul 6:67–98CrossRefGoogle Scholar
  5. 5.
    Boehm BW (1979) Software engineering: R&D trends and defence needs. In: Wegner P (ed) Research directions in software technology. MIT Press, Cambridge, MAGoogle Scholar
  6. 6.
    Cellier FE (1986) Combined discrete/continuous system simulation –application, techniques and tools. In: Proceedings of the 1986 winter simulation conference, Washington, IEEE Computer Society PressGoogle Scholar
  7. 7.
    Verification, Validation, & Accreditation (VV&A): Recommended Practices Guide (RPG) (2011) Available at http://www.msco.mil/VVA_RPG.html
  8. 8.
    General Accounting Office (1976) Report to the Congress: ways to improve management of federally funded computerized models. Report LCD-75-111. U.S. General Accounting Office, Washington, DCGoogle Scholar
  9. 9.
    General Accounting Office (1979) Guidelines for model evaluation. Report PAD-79-17. U.S. General Accounting Office, Washington, DCGoogle Scholar
  10. 10.
    General Accounting Office (1987) DOD simulations: improved assessment procedures would increase the credibility of results. Report GAO/PEMD-88-3. U.S. General Accounting Office, Washington, DCGoogle Scholar
  11. 11.
    Innis G, Rexstad E (1983) Simulation model simplification techniques. Simulation 41(7):7–15CrossRefGoogle Scholar
  12. 12.
    Ören TI (1981) Concepts and criteria to access acceptability of simulation studies. Commun ACM 24:180–189CrossRefGoogle Scholar
  13. 13.
    Ören TI (1971) GEST: general system theory implementor, A combined digital simulation language. Ph.D. dissertation, University of Arizona, Tucson, AZGoogle Scholar
  14. 14.
    Neelamkavil F (1987) Computer simulation and modeling. Wiley, ChichesterGoogle Scholar
  15. 15.
    Pace DK (2003) Verification, validation and accreditation of simulation models. In: Obaidat MS, Papadimitriou GI (eds) Applied system simulation: methodologies and applications. Kluwer Academic Publishers, BostonGoogle Scholar
  16. 16.
    Praehofer H (1991) System theoretic formalisms for combined discrete continuous system simulation. Int J Gen Syst 19:219–240CrossRefzbMATHGoogle Scholar
  17. 17.
    Ramadge PJG, Wonham WM (1992) The control of discrete event systems. In: Ho Y-C (ed) Discrete event dynamic systems. IEEE Press, Piscataway, pp 48–64Google Scholar
  18. 18.
    Robinson S (2006) Issues in conceptual modelling for simulation: setting a research agenda. In: Proceedings of the 2006 Operations Research Society simulation workshop, March, Lexington, EnglandGoogle Scholar
  19. 19.
    Shannon RE (1975) Systems simulation: the art and science. Prentice Hall, Englewood CliffsGoogle Scholar
  20. 20.
    Shannon RE (1998) Introduction to the art and science of simulation. In: Medeiros DJ, Watson EF, Carson JS, Manivannan MS (eds) Proceedings of the 1998 winter simulation conference, Washington, IEEE Computer Society Press, pp 7–14Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Louis G. Birta
    • 1
  • Gilbert Arbez
    • 1
  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada

Personalised recommendations