IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v30y1988i4p347-370.html
   My bibliography  Save this article

On the efficient generation of discrete event sample paths under different system parameter values

Author

Listed:
  • Ho, Yu-Chi
  • Li, Shu
  • Vakili, Pirooz

Abstract

The techniques of perturbation analysis (PA) are generalized from the viewpoint of efficiently generating sample paths of discrete event dynamic systems (DEDS) operating under different parameter values. The central idea, denoted as ‘cut-and-paste’, is simply to utilize one segment of a sample path generated under one parameter as legitimate sample paths of as many different parameter values of the DEDS as possible. Viewed in this light, many new ways to perform discrete event simulation for parametric study appear possible. Both analytical and experimental results are offered to substantiate our case.

Suggested Citation

  • Ho, Yu-Chi & Li, Shu & Vakili, Pirooz, 1988. "On the efficient generation of discrete event sample paths under different system parameter values," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 30(4), pages 347-370.
    • Handle: RePEc:eee:matcom:v:30:y:1988:i:4:p:347-370
    DOI: 10.1016/S0378-4754(98)90005-2
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475498900052
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/S0378-4754(98)90005-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rubinstein, Reuven Y., 1986. "The score function approach for sensitivity analysis of computer simulation models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 28(5), pages 351-379.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ho, Yu-Chi & Cassandras, C.G. & Makhlouf, M., 1993. "Parallel simulation of real-time systems via the Standard Clock approach," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 35(1), pages 33-41.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rubinstein, Reuven Y., 1991. "Modified importance sampling for performance evaluation and sensitivity analysis of computer simulation models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 33(1), pages 1-22.
    2. Laub, Patrick J. & Salomone, Robert & Botev, Zdravko I., 2019. "Monte Carlo estimation of the density of the sum of dependent random variables," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 161(C), pages 23-31.
    3. Marrel, Amandine & Chabridon, Vincent, 2021. "Statistical developments for target and conditional sensitivity analysis: Application on safety studies for nuclear reactor," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    4. Soumyadip Ghosh & Henry Lam, 2019. "Robust Analysis in Stochastic Simulation: Computation and Performance Guarantees," Operations Research, INFORMS, vol. 67(1), pages 232-249, January.
    5. Rubinstein, Reuven Y. & Shapiro, Alexander, 1990. "Optimization of static simulation models by the score function method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 32(4), pages 373-392.
    6. Li, Jinghui & Mosleh, Ali & Kang, Rui, 2011. "Likelihood ratio gradient estimation for dynamic reliability applications," Reliability Engineering and System Safety, Elsevier, vol. 96(12), pages 1667-1679.
    7. Jingxu Xu & Zeyu Zheng, 2023. "Gradient-Based Simulation Optimization Algorithms via Multi-Resolution System Approximations," INFORMS Journal on Computing, INFORMS, vol. 35(3), pages 633-651, May.
    8. Marie Chiron & Jérôme Morio & Sylvain Dubreuil, 2023. "Local Sensitivity of Failure Probability through Polynomial Regression and Importance Sampling," Mathematics, MDPI, vol. 11(20), pages 1-19, October.
    9. Marvin K. Nakayama & Perwez Shahabuddin, 1998. "Likelihood Ratio Derivative Estimation for Finite-Time Performance Measures in Generalized Semi-Markov Processes," Management Science, INFORMS, vol. 44(10), pages 1426-1441, October.
    10. Chabridon, Vincent & Balesdent, Mathieu & Bourinet, Jean-Marc & Morio, Jérôme & Gayton, Nicolas, 2018. "Reliability-based sensitivity estimators of rare event probability in the presence of distribution parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 164-178.
    11. Schweinberger, Michael & Snijders, Tom A.B., 2007. "Markov models for digraph panel data: Monte Carlo-based derivative estimation," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4465-4483, May.
    12. Yijie Peng & Li Xiao & Bernd Heidergott & L. Jeff Hong & Henry Lam, 2022. "A New Likelihood Ratio Method for Training Artificial Neural Networks," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 638-655, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:matcom:v:30:y:1988:i:4:p:347-370. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.