A Sequential Stopping Rule For A Steady-State Simulation Based On Time-Series Forecasting  

Oleh:

Mackulak, Gerald T. ; Park, Sungmin ; Fowler, John W.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Simulation vol. 78 no. 11 (Nov. 2002), page 643-654.
Topik: Sequential stopping rule; steady-state simulation; covariance stationary process; the problem of the initial transient; cumulative sample mean; forecasting
Fulltext: 643.pdf (184.46KB)

Isi artikel

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 timeseries 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.

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