Steady-state simulation of queueing processes: survey of problems and solutions
ACM Computing Surveys (CSUR)
Evaluation of tests for initial-condition bias
WSC '92 Proceedings of the 24th conference on Winter simulation
Probability and statistics with reliability, queuing and computer science applications
Probability and statistics with reliability, queuing and computer science applications
Simulation output analysis: truncation point estimation using multiple replications in parallel
Proceedings of the 35th conference on Winter simulation: driving innovation
Probability and Random Processes For EE's (3rd Edition)
Probability and Random Processes For EE's (3rd Edition)
On an initial transient deletion rule with rigorous theoretical support
Proceedings of the 38th conference on Winter simulation
Using parallel replications for sequential estimation of multiple steady state quantiles
Proceedings of the 2nd international conference on Performance evaluation methodologies and tools
Using parallel replications for sequential estimation of multiple steady state quantiles
Proceedings of the 2nd international conference on Performance evaluation methodologies and tools
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The issue of the initial transient phase in steady state simulation has been widely discussed in simulation literature. Many methods have been proposed for deciding the duration of this phase of simulation, to determine a valid truncation point of the transient portion of output data. However, practically all these methods can only be used in simulations aimed at estimation of mean values. In this paper, we show that analyses of performance measures which do not represent mean values require different solutions, as the rate of convergence to steady state is different for mean values than, for example, for quantiles. We describe and present additional results for a new method of determining the duration of initial transient phase which can be applied in analysis of steady state quantiles and probability distributions. The method appears robust and applicable in analysis of arbitrary performance measures.