Simulating Stable Stochastic Systems, I: General Multiserver Queues
Journal of the ACM (JACM)
Simulating Stable Stochastic Systems, II: Markov Chains
Journal of the ACM (JACM)
Steady-state simulation of queueing processes: survey of problems and solutions
ACM Computing Surveys (CSUR)
Advanced output analysis for simulation
WSC '92 Proceedings of the 24th conference on Winter simulation
On the use of MANOVA in the analysis of multiple-response simulation experiments
WSC '85 Proceedings of the 17th conference on Winter simulation
Statistical analysis of output processes
WSC '93 Proceedings of the 25th conference on Winter simulation
Advanced simulation output analysis for a single system
WSC '93 Proceedings of the 25th conference on Winter simulation
Output analysis for simulation
WSC '91 Proceedings of the 23rd conference on Winter simulation
Output analysis for simulation (tutorial session)
WSC' 90 Proceedings of the 22nd conference on Winter simulation
Regression-adjusted estimates for regenerative simulations, with graphics
Communications of the ACM - Special issue on simulation modeling and statistical computing
A tutorial on statistical analysis of simulation output data
WSC '80 Proceedings of the 12th conference on Winter simulation
| Hi-index | 48.22 |
This paper extends the use of the regenerative property of queueing systems in the analysis of simulation output. In particular, it describes a sequential estimation method which when used with the regenerative property allows results to be obtained with specified statistical accuracy. This method includes a test to check the normality assumption on which the sequential procedure relies. The paper illustrates the method using the empty and idle state as the regenerative state. A second example then describes how using the most frequently entered state as the regenerative state reduces the chance of making a costly error in a preliminary simulation run. The paper also described how a variance reduction method due to Page [9] can be used to obtain a specified accuracy with considerably fewer job completions than are required when no variance reduction technique is applied.