IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v27y1981i3p322-335.html
   My bibliography  Save this article

A Perspective on the Use of Control Variables to Increase the Efficiency of Monte Carlo Simulations

Author

Listed:
  • S. S. Lavenberg

    (IBM Thomas J. Watson Research Center)

  • P. D. Welch

    (IBM Thomas J. Watson Research Center)

Abstract

This is a survey paper on the application of control variables to increase the efficiency of discrete event simulations. The emphasis is on the practical problems and potential of applying the method in the simulation of complex systems. The basic theory of control variables is reviewed and the equivalence of control variables and multiple estimators is discussed. Techniques for generating control variables are described. Inefficiencies resulting from the statistical estimation of control variable coefficients and the problem of confidence interval generation are treated. This is done both within the context of the method of independent replications and the regenerative method. The application literature is reviewed and the conditions under which control variables could be profitably applied in practical simulations are described. Finally, there is a set of recommended directions for future research.

Suggested Citation

  • S. S. Lavenberg & P. D. Welch, 1981. "A Perspective on the Use of Control Variables to Increase the Efficiency of Monte Carlo Simulations," Management Science, INFORMS, vol. 27(3), pages 322-335, March.
    • Handle: RePEc:inm:ormnsc:v:27:y:1981:i:3:p:322-335
    DOI: 10.1287/mnsc.27.3.322
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.27.3.322
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.27.3.322?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
    ---><---

    Citations

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


    Cited by:

    1. Pellizzari, P., 2005. "Static hedging of multivariate derivatives by simulation," European Journal of Operational Research, Elsevier, vol. 166(2), pages 507-519, October.
    2. Assereto, Martina & Byrne, Julie, 2021. "No real option for solar in Ireland: A real option valuation of utility scale solar investment in Ireland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    3. Zhang, M.M. & Zhou, P. & Zhou, D.Q., 2016. "A real options model for renewable energy investment with application to solar photovoltaic power generation in China," Energy Economics, Elsevier, vol. 59(C), pages 213-226.
    4. Erik Hintz & Marius Hofert & Christiane Lemieux & Yoshihiro Taniguchi, 2022. "Single-Index Importance Sampling with Stratification," Methodology and Computing in Applied Probability, Springer, vol. 24(4), pages 3049-3073, December.
    5. Tsai, Shing Chih & Chu, I-Hao, 2012. "Controlled multistage selection procedures for comparison with a standard," European Journal of Operational Research, Elsevier, vol. 223(3), pages 709-721.
    6. Mingming Zhang & Dequn Zhou & Hao Ding & Jingliang Jin, 2016. "Biomass Power Generation Investment in China: A Real Options Evaluation," Sustainability, MDPI, vol. 8(6), pages 1-22, June.
    7. Zhang, M.M. & Zhou, D.Q. & Zhou, P. & Chen, H.T., 2017. "Optimal design of subsidy to stimulate renewable energy investments: The case of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 873-883.
    8. Kämpke, Thomas, 1989. "Multiple use of random numbers in discrete-event simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 31(3), pages 171-176.
    9. Kenneth W. Bauer & James R. Wilson, 1992. "Control‐variate selection criteria," Naval Research Logistics (NRL), John Wiley & Sons, vol. 39(3), pages 307-321, April.
    10. Shing Chih Tsai & Chen Hao Kuo, 2012. "Screening and selection procedures with control variates and correlation induction techniques," Naval Research Logistics (NRL), John Wiley & Sons, vol. 59(5), pages 340-361, August.
    11. Zhang, M.M. & Wang, Qunwei & Zhou, Dequn & Ding, H., 2019. "Evaluating uncertain investment decisions in low-carbon transition toward renewable energy," Applied Energy, Elsevier, vol. 240(C), pages 1049-1060.
    12. Michael P. Bailey & Marcelo C. Bartroli & Keebom Kang & Alexander J. Callahan, 1992. "Establishing Reliability Goals for Naval Major‐Caliber Ammunition," Naval Research Logistics (NRL), John Wiley & Sons, vol. 39(7), pages 877-892, December.
    13. Barry L. Nelson, 2004. "50th Anniversary Article: Stochastic Simulation Research in Management Science," Management Science, INFORMS, vol. 50(7), pages 855-868, July.
    14. Russell Davidson & James G. Mackinnon, 1990. "Regression-Based Methods for Using Control and Antithetic Variates in Monte Carlo Experiments," Working Paper 781, Economics Department, Queen's University.
    15. Amano, Tomoyuki & Taniguchi, Masanobu, 2011. "Control variate method for stationary processes," Journal of Econometrics, Elsevier, vol. 165(1), pages 20-29.
    16. Tsai, Shing Chih, 2011. "Selecting the best simulated system with weighted control-variate estimators," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(4), pages 705-717.
    17. Grant, Floyd H. & Solberg, James J., 1983. "Variance reduction techniques in stochastic shortest route analysis: application procedures and results," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 25(4), pages 366-375.
    18. Ben-Alexander Cassell & Michael P. Wellman, 2012. "Asset pricing under ambiguous information: an empirical game-theoretic analysis," Computational and Mathematical Organization Theory, Springer, vol. 18(4), pages 445-462, December.
    19. Boyle, Phelim & Broadie, Mark & Glasserman, Paul, 1997. "Monte Carlo methods for security pricing," Journal of Economic Dynamics and Control, Elsevier, vol. 21(8-9), pages 1267-1321, June.

    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:inm:ormnsc:v:27:y:1981:i:3:p:322-335. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.