Computational and Statistical Issues in Discrete-Event Simulation

Abstract

Discrete-event simulation is one of the most important techniques available for studying complex stochastic systems. In this paper we review the principal methods available for analyzing both the transient and steady-state simulation problems in sequential and parallel computing environments. Next we discuss several of the variance reduction methods designed to make simulations run more efficiently. Finally, a short discussion is given of the methods available to study optimization using simulation.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1989
Accession Number
ADA210743

Entities

People

  • Donald Iglehart
  • Peter W. Glynn

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

  • Applied Mathematics
  • Classification
  • Computer Programs
  • Computers
  • Environment
  • Estimators
  • Markov Chains
  • Monte Carlo Method
  • Operations Research
  • Optimization
  • Parallel Computing
  • Parallel Processing
  • Random Variables
  • Sampling
  • Simulations
  • Steady State
  • Stochastic Processes

Readers

  • Computational Modeling and Simulation