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Generation of simulation input scenarios using bootstrap methods

Author

Listed:
  • O F Demirel

    (Rensselaer Polytechnic Institute)

  • T R Willemain

    (Rensselaer Polytechnic Institute)

Abstract

Simulation modellers frequently face a choice between fidelity and variety in their input scenarios. Using an historical trace provides only one realistic scenario. Using the input modelling facilities in commercial simulation software may provide any number of unrealistic scenarios. We ease this dilemma by developing a way to use the moving blocks bootstrap to convert a single trace into an unlimited number of realistic input scenarios. We do this by setting the bootstrap block size to make the bootstrap samples mimic independent realizations in terms of the distribution of distance between pairs of inputs. We measure distance using a new statistic computed from zero crossings. We estimate the best block size by scaling up an estimate computed by analysing subseries of the trace.

Suggested Citation

  • O F Demirel & T R Willemain, 2002. "Generation of simulation input scenarios using bootstrap methods," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(1), pages 69-78, January.
    • Handle: RePEc:pal:jorsoc:v:53:y:2002:i:1:d:10.1057_palgrave.jors.2601251
    DOI: 10.1057/palgrave.jors.2601251
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    Citations

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    Cited by:

    1. Kleijnen, Jack P.C. & Deflandre, David, 2006. "Validation of regression metamodels in simulation: Bootstrap approach," European Journal of Operational Research, Elsevier, vol. 170(1), pages 120-131, April.
    2. Deflandre, D. & Kleijnen, J.P.C., 2002. "Statistical Analysis of Random Simulations : Bootstrap Tutorial," Other publications TiSEM 351056e6-c63d-4fec-8c00-a, Tilburg University, School of Economics and Management.
    3. Shankhajyoti De & Arabin Kumar Dey & Deepak Kumar Gouda, 2022. "Construction of Confidence Interval for a Univariate Stock Price Signal Predicted Through Long Short Term Memory Network," Annals of Data Science, Springer, vol. 9(2), pages 271-284, April.
    4. Manikas, Andrew & Chang, Yih-Long & Ferguson, Mark, 2009. "BlueLinx can benefit from innovative inventory management methods for commodity forward buys," Omega, Elsevier, vol. 37(3), pages 545-554, June.
    5. Shankhyajyoti De & Arabin Kumar Dey & Deepak Gauda, 2020. "Construction of confidence interval for a univariate stock price signal predicted through Long Short Term Memory Network," Papers 2007.00254, arXiv.org.
    6. Mitra, Sovan & Lim, Sungmook & Karathanasopoulos, Andreas, 2019. "Regression based scenario generation: Applications for performance management," Operations Research Perspectives, Elsevier, vol. 6(C).

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    Keywords

    simulation; statistics;

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