A guide to simulation (2nd ed.)
A guide to simulation (2nd ed.)
Improved batching for confidence interval construction in steady-state simulation
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Discrete-event simulation
Ranking and selection for steady-state simulation
Proceedings of the 32nd conference on Winter simulation
Simulation with Arena
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Using Ordinal Optimization Approach to Improve Efficiency of Selection Procedures
Discrete Event Dynamic Systems
Output analysis: ASAP2: an improved batch means procedure for simulation output analysis
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
ASAP3: a batch means procedure for steady-state simulation analysis
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Experimental performance evaluation of histogram approximation for simulation output analysis
WSC '04 Proceedings of the 36th conference on Winter simulation
A multi-objective selection procedure of determining a Pareto set
Computers and Operations Research
Proceedings of the 40th Conference on Winter Simulation
ADAPT Selection procedures to process correlated and non-normal data with batch means
Winter Simulation Conference
Finite-Sample Performance of Absolute Precision Stopping Rules
INFORMS Journal on Computing
A simulation-optimization heuristic for configuring a selective pallet rack system
Proceedings of the Winter Simulation Conference
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Batch means are sample means of subsets of consecutive subsamples from a simulation output sequence. Independent and normally distributed batch means are not only the requirement for constructing a confidence interval for the mean of the steady-state distribution of a stochastic process, but are also the prerequisite for other simulation procedures such as ranking and selection (R&S). We propose a procedure to generate approXimately independent and normally distributed batch means, as determined by the von Neumman test of independence and the chi-square test of normality, and then to construct a confidence interval for the mean of a steady-state eXpected simulation response. It is our intention for the batch means to play the role of the independent and identically normally distributed observations that confidence intervals and the original versions of R&S procedures require. We perform an empirical study for several stochastic processes to evaluate the performance of the procedure and to investigate the problem of determining valid batch sizes.