A sequential stopping rule for a steady-state simulation based on time-series forecasting

Gerald T. Mackulak, Sungmin Park, John Fowler, Sonia E. Leach, J. Bert Keats

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


A sequential stopping procedure should collect enough steady-state data to overwhelm the influence of initial transient bias without requiring initial data truncation. The initial transient negatively affects the efficiency of the sequential procedure, but from a practical point of view, eliminating the difficulty of determining the data truncation point can lead to a more easily implemented algorithm for determining the appropriate length of a simulation run. A sequential stopping rule is presented that uses a time-series forecasting procedure to determine appropriate trade-offs between the efficiency and simplicity of the estimate of cycle time for a relevant constant mean process. Results show that the proposed sequential stopping rule terminates a simulation output process at a point when a stable estimate is obtained. Furthermore, the rule performs as well as the crossings-of-means data truncation technique yet is easier to implement.

Original languageEnglish (US)
Pages (from-to)643-654
Number of pages12
Issue number11
StatePublished - Nov 2002


  • Covariance stationary process
  • Cumulative sample mean
  • Forecasting
  • Sequential stopping rule
  • Steady-state simulation
  • The problem of the initial transient

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Graphics and Computer-Aided Design


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