Variance Reduction in the Simulation of Stochastic Activity Networks.

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 variances converge faster than 1/K, where K denotes the number of replications collected in the experiment. The paper demonstrates how accuracy diminishes for a given K with increasing dimensionality of the network and shows how a procedure that uses a cutset of the network together with convolution can reduce dimensionality and increase accuracy. Two examples illustrate the benefits of using quasirandom points together with a cutset and then convolution. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1983
Accession Number
ADA124251

Entities

People

  • George S. Fishman

Organizations

  • University of North Carolina at Chapel Hill

Tags

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Business Administration
  • Convolution
  • Distribution Functions
  • Estimators
  • Mathematics
  • Monte Carlo Method
  • North Carolina
  • Numerical Integration
  • Operations Research
  • Plastic Explosives
  • Random Variables
  • Security
  • Simulations
  • Statistical Algorithms
  • Statistical Sampling

Fields of Study

  • Computer science

Readers

  • Mathematics or Statistics
  • Operations Research
  • Statistical inference.