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Estimating Network Characteristics in Stochastic Activity Networks

Author

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  • George S. Fishman

    (Curriculum in Operations Research and Systems Analysis, University of North Carolina, Chapel Hill, North Carolina 27514)

Abstract

This paper describes a Monte Carlo method based on the theory of quasirandom points for estimating the distribution functions and means of network completion time and shortest path time in a stochastic activity network. In particular, the method leads to estimators whose absolute errors converge as (log K) N /K, where K denotes the number of replications collected in the experiment and N is the number of dimensions for sampling. This rate compares favorably with the standard error of estimate O(1/K 1/2 ) which obtains for experiments that use random sampling. Moreover, since quasirandom points are nonrandom the upper bound (log K) N /K is deterministic in contrast to the random sampling rate O(1/K 1/2 ) which is probabilistic. The paper demonstrates how the use of a cutset of the network reduces N in the bound when estimating the distribution functions. Two examples illustrate the benefits and costs of using quasirandom points.

Suggested Citation

  • George S. Fishman, 1985. "Estimating Network Characteristics in Stochastic Activity Networks," Management Science, INFORMS, vol. 31(5), pages 579-593, May.
    • Handle: RePEc:inm:ormnsc:v:31:y:1985:i:5:p:579-593
    DOI: 10.1287/mnsc.31.5.579
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    Cited by:

    1. N-H Shih, 2005. "Estimating completion-time distribution in stochastic activity networks," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(6), pages 744-749, June.

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