Skip to main content

A Software Tool for the Evaluation of Transient Removal Methods in Discrete Event Stochastic Simulations

  • Chapter
  • First Online:
  • 1384 Accesses

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

Simulations are often started from an empty initial system state, which leads to a transient bias from the desired stationary results. This paper compares several state-of-the-art transient removal algorithms and proposes a software framework for a systematic comparison of such algorithms. This helps simulation engineers in selecting a suitable bias-removal algorithm, with special attention to the determination of the quality of the simulation after the removal. It also allows comparison of new bias-removal algorithms against a set of tests, whose implementation would otherwise be time-consuming. A set of quantitative evaluation criteria is also proposed and used for the evaluation of the implemented methods.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. S. Asmussen, P. Glynn, Stochastic Simulation: Algorithms and Analysis. Stochastic Modelling and Applied Probability, vol. 57 (Springer, Berlin, 2007)

    Google Scholar 

  2. G.S.M. Bertoli, G. Casale, An overview of the JMT queueing network simulator. Tr 2007.2, Politecnico di Milano, DEI (2007)

    Google Scholar 

  3. C.G. Cassandras, S. Lafortune, Introduction to Discrete Event Systems (Kluwer, Boston, 1999)

    Book  Google Scholar 

  4. R.W. Conway, Some tactical problems in digital simulation. Manag. Sci. 10(1), 47–61 (1963)

    Article  MathSciNet  Google Scholar 

  5. G.S. Fishman, Bias considerations in simulation experiments. Oper. Res. 20(4), 785–790 (1972)

    Article  Google Scholar 

  6. G.S. Fishman, Monte Carlo: Concepts, Algorithms and Applications. (Springer, New York, 1996)

    Google Scholar 

  7. A. Freeth, A sequential steady-state detection method for quantitative discrete-event simulation. PhD thesis, University of Canterbury (2012)

    Google Scholar 

  8. A.V. Gafarian, C.J. Ancker, T. Morisaku, Evaluation of commonly used rules for detecting “steady state” in computer simulation. Nav. Res. Logist. Q. 25(3), 511–529 (1978)

    Article  Google Scholar 

  9. W.K. Grassmann, Factors affecting warm-up periods in discrete event simulation. Simulation 90(1), 11–23 (2014)

    Article  Google Scholar 

  10. K. Hoad, S. Robinson, R. Davies, Automating warm-up length estimation. J. Oper. Res. Soc. 61, 1389–1403 (2009)

    Article  Google Scholar 

  11. K. Hoad, S. Robinson, R. Davies, AutoSimOA: a framework for automated analysis of simulation output. J. Simul. 5(1), 9–24 (2011)

    Article  Google Scholar 

  12. A.M. Law, D.M. Kelton, Simulation Modeling and Analysis, 3rd edn. (McGraw-Hill Higher Education, New York, 1999)

    MATH  Google Scholar 

  13. Y.H. Lee, K.H. Kyung, C.S. Jung, On-line determination of steady state in simulation outputs. Comput. Ind. Eng. 33(3), 805–808 (1997)

    Article  Google Scholar 

  14. P.S. Mahajan, R.G. Ingalls, Evaluation of methods used to detect warm-up period in steady state simulation, in Proceedings of the 36th Conference on Winter Simulation, WSC ’04, Winter Simulation Conference (2004), pp. 663–671

    Google Scholar 

  15. D. McNickle, G.C. Ewing, K. Pawlikowski, Some effects of transient deletion on sequential steady-state simulation. Simul. Modell. Pract. Theory 18(2), 177–189 (2010)

    Article  Google Scholar 

  16. D. Mcnickle, K. Pawlikowski, G. Ewing, AKAROA2: a controller of discrete-event simulation which exploits the distributed computing resources of networks, in Proceedings of European Conference on Modelling and Simulation (ECMS 2010) (2010)

    Google Scholar 

  17. S. Nagaraj, A. Zimmermann. fDRIT - an evaluation tool for transient removal methods in discrete event stochastic simulations, in Proceedings of 10th International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS 2016), Taormina, October 2016

    Google Scholar 

  18. R. Pasupathy, B. Schmeiser, The initial transient in steady-state point estimation: contexts, a bibliography, the MSE criterion, and the MSER statistic, in Simulation Conference (WSC), Proceedings of the 2010 Winter, December 2010, pp. 184–197

    Google Scholar 

  19. K. Pawlikowski, Steady-state simulation of queueing processes: survey of problems and solutions. ACM Comput. Surv. 22(2), 123–170 (1990)

    Article  Google Scholar 

  20. L. Schruben, Confidence interval estimation using standardized time series. Oper. Res. 31(6), 1090–1108 (1983)

    Article  Google Scholar 

  21. L. Schruben, H. Singh, L. Tierney, Optimal tests for initialization bias in simulation output. Oper. Res. 31(6), 1167–1178 (1983)

    Article  Google Scholar 

  22. K.P. White, A simple rule for mitigating initialization bias in simulation output: comparative results, in IEEE International Conference on Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century, October 1995, vol. 1, pp. 206–211

    Google Scholar 

  23. K.P. White, M.A. Minnox, Minimizing initialization bias in simulation output using a simple heuristic, in Proceedings of IEEE International Conference on Systems, Man and Cybernetics, October 1994, vol. 1, pp. 215–220

    Google Scholar 

  24. S. Yousefi, MSER-5Y: an improved version of MSER-5 with automatic confidence interval estimation. Master’s thesis, North Carolina State University (2011)

    Google Scholar 

  25. A. Zimmermann, Stochastic Discrete Event Systems (Springer, Berlin, 2007)

    Google Scholar 

  26. A. Zimmermann, Modelling and performance evaluation with TimeNET 4.4, in Quantitative Evaluation of Systems - 14th International Conference, QEST 2017, pp. 300–303, Berlin, September 2017

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Armin Zimmermann .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Nagaraj, S., Zimmermann, A. (2019). A Software Tool for the Evaluation of Transient Removal Methods in Discrete Event Stochastic Simulations. In: Puliafito, A., Trivedi, K. (eds) Systems Modeling: Methodologies and Tools. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-92378-9_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-92378-9_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92377-2

  • Online ISBN: 978-3-319-92378-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics