Abstract
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 language | English (US) |
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Pages (from-to) | 643-654 |
Number of pages | 12 |
Journal | SIMULATION |
Volume | 78 |
Issue number | 11 |
DOIs | |
State | Published - Nov 2002 |
Keywords
- 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