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Conditional Monte Carlo: A Simulation Technique for Stochastic Network Analysis

Author

Listed:
  • John M. Burt, Jr.

    (University of California, Los Angeles)

  • Mark B. Garman

    (University of California, Berkeley)

Abstract

This paper is concerned with a simulation procedure for estimating the distribution functions of the time to complete stochastic networks. The procedure, called conditional Monte Carlo, is shown to be substantially more efficient (in terms of the computational effort required) than traditional simulation methods. The efficacy of conditional Monte Carlo and its use in conjunction with other Monte Carlo methods is illustrated for the Wheatstone bridge network. The applicability of the procedure to larger networks, as well as other stochastic systems, is discussed, and a general method is given for its implementation.

Suggested Citation

  • John M. Burt, Jr. & Mark B. Garman, 1971. "Conditional Monte Carlo: A Simulation Technique for Stochastic Network Analysis," Management Science, INFORMS, vol. 18(3), pages 207-217, November.
    • Handle: RePEc:inm:ormnsc:v:18:y:1971:i:3:p:207-217
    DOI: 10.1287/mnsc.18.3.207
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    Cited by:

    1. Barry L. Nelson, 2004. "50th Anniversary Article: Stochastic Simulation Research in Management Science," Management Science, INFORMS, vol. 50(7), pages 855-868, July.
    2. Fatemi Ghomi, S. M. T. & Hashemin, S. S., 1999. "A new analytical algorithm and generation of Gaussian quadrature formula for stochastic network," European Journal of Operational Research, Elsevier, vol. 114(3), pages 610-625, May.
    3. Barry L. Nelson & Alan T. K. Wan & Guohua Zou & Xinyu Zhang & Xi Jiang, 2021. "Reducing Simulation Input-Model Risk via Input Model Averaging," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 672-684, May.
    4. Daniel Reich & Leo Lopes, 2011. "Preprocessing Stochastic Shortest-Path Problems with Application to PERT Activity Networks," INFORMS Journal on Computing, INFORMS, vol. 23(3), pages 460-469, August.
    5. Azaron, Amir & Katagiri, Hideki & Sakawa, Masatoshi & Kato, Kosuke & Memariani, Azizollah, 2006. "A multi-objective resource allocation problem in PERT networks," European Journal of Operational Research, Elsevier, vol. 172(3), pages 838-854, August.
    6. Athanassios N. Avramidis & Kenneth W. Bauer & James R. Wilson, 1991. "Simulation of stochastic activity networks using path control variates," Naval Research Logistics (NRL), John Wiley & Sons, vol. 38(2), pages 183-201, April.
    7. Brucker, Peter & Drexl, Andreas & Mohring, Rolf & Neumann, Klaus & Pesch, Erwin, 1999. "Resource-constrained project scheduling: Notation, classification, models, and methods," European Journal of Operational Research, Elsevier, vol. 112(1), pages 3-41, January.
    8. Lee, Heejung & Suh, Hyo-Won, 2008. "Estimating the duration of stochastic workflow for product development process," International Journal of Production Economics, Elsevier, vol. 111(1), pages 105-117, January.
    9. Bregman, Robert L., 2009. "A heuristic procedure for solving the dynamic probabilistic project expediting problem," European Journal of Operational Research, Elsevier, vol. 192(1), pages 125-137, January.
    10. Sigal, C.E. & Pritsker, A.A.B. & Solberg, J.J., 1979. "The use of cutsets in Monte Carlo analysis of stochastic networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 21(4), pages 376-384.
    11. Siqian Shen & J. Cole Smith & Shabbir Ahmed, 2010. "Expectation and Chance-Constrained Models and Algorithms for Insuring Critical Paths," Management Science, INFORMS, vol. 56(10), pages 1794-1814, October.
    12. Tetsuo Iida, 2000. "Computing bounds on project duration distributions for stochastic PERT networks," Naval Research Logistics (NRL), John Wiley & Sons, vol. 47(7), pages 559-580, October.
    13. Azaron, Amir & Katagiri, Hideki & Kato, Kosuke & Sakawa, Masatoshi, 2006. "Longest path analysis in networks of queues: Dynamic scheduling problems," European Journal of Operational Research, Elsevier, vol. 174(1), pages 132-149, October.

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