Management Issues Within Simulation Model Development and Testing

  • David J. Murray-Smith
Part of the Simulation Foundations, Methods and Applications book series (SFMA)


This chapter presents a case for the application of appropriate management strategies in the development, application and maintenance of simulation models. It is argued that the need for good management procedures is especially important in terms of the preparation and support of comprehensive documentation. Without well-designed and effective systems for model management and model documentation much effort can be wasted and there are increased risks that models will be used in inappropriate ways, leading to increased project costs and delays. User-friendly and reliable techniques for version handling are important, as are properly supported and maintained libraries of commonly used models and sub-models, with full documentation immediately available to all users. The chapter concludes with section in which the benefits versus costs of investing in sound model management procedures are considered.


Reverse Engineering System Identification Method Good Documentation Model Development Process Model Documentation 
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.


  1. 1.
    The Mitre Corporation (2014) Verification and validation of simulation models. In: Mitre systems engineering guide, pp 461–469. Accessed 24 Feb 2015
  2. 2.
    Balci O, Glasgow PA, Muessiny P et al (1996) Department of defense verification, validation and accreditation (V. V. & A.) recommended practices guide, defense modeling and simulation office, (1996). Defense Modeling and Simulation Office, Alexandria, VAGoogle Scholar
  3. 3.
    Birta LG, Abou-Rounia AO, Őren TI (1992) Reverse engineering in the simulation lifecycle. SAMS 9:69–84Google Scholar
  4. 4.
    Brade D (2003) A generalized process for the verification and validation of models and simulation results, dissertation submitted for award of the degree Dr rer. nat., Fakultät für Informatik, Universität der Bundeswehr München, GermanyGoogle Scholar
  5. 5.
    Kit E (1995) Software testing in the real world. Addison Wesley, HarlowGoogle Scholar
  6. 6.
    Brade D, Waldner C (2003) Automatic detection of behavioural specification violations. In: Proceedings of the 03 European interoperability workshop, Stockholm, Sweden. Simulation Interoperability Standardization OrganizationGoogle Scholar
  7. 7.
    US Department of Defense (2009) DoD modeling and simulation (M&S) verification, validation, and accreditation (VV&A). U. S. Department of Defense Instruction Number 5000.61. December 9, 2009. Accessed 1 Mar 2015
  8. 8.
    Cook DA, Skinner JM (2005). How to perform credible verification, validation and accreditation for modelling and simulation. J Def Softw Eng 18(5):20–24Google Scholar
  9. 9.
    Famme JB, Gallagher C, Raitch T (2009) Performance based design for fleet affordability. Nav Eng J 12(4):117–132. doi: 10.1111/j.1559-3581.2009.00233.x CrossRefGoogle Scholar
  10. 10.
    Kammel G, Voigt HM, Neβ K (2005) Development of a tool to improve the forecast accuracy of dynamic simulation models for the paper process. In: Kappen J, Manninen J, Ritala R (eds) Proceedings of model validation workshop, Oct. 6, 2005, Espoo, Finland. VTT Technical Research Centre, FinlandGoogle Scholar
  11. 11.
    Eddy DM, Hollingworth W, Caro JJ et al (2012) Model transparency and validation. A report of the ISPOR_Smdm Modeling Good Research Practices Task Force-7. Med Decis Making 35(5):733–743CrossRefGoogle Scholar
  12. 12.
    Smith MI, Murray-Smith DJ, Hickman D (2007) Verification and validation issues in a generic model of electro-optic sensor systems. Def Model J Simul 4(1):17–17Google Scholar
  13. 13.
    Foss BA, Lohmann B, Marquhardt W (1998) A field study of the industrial modelling process. Model Identif Control 19(3):153–174CrossRefzbMATHGoogle Scholar
  14. 14.
    Padfield GD (1991) The role of industry. In: Rotorcraft system identification, AGARD advisory report 280 (AGARD-AR-280), Section 3.3, AGARD, Neuilly-sur-Seine, FranceGoogle Scholar
  15. 15.
    Pace DK (2004) Modeling and simulation verification and validation challenges. John Hopkins APL Tech Dig 25(2):163–172MathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • David J. Murray-Smith
    • 1
  1. 1.School of EngineeringUniversity of GlasgowGlasgowUK

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