The transient behavior of the M/Ek/2 queue and steady-state simulation

The probabilistic structure for the transient M/Ek/2 queue is derived in discrete time, where Ek denotes a k-Erlang distribution. This queue has a two-dimensional state-space. Expressions for the expected delay in queue are formulated in terms of transition probabilities. Results are numerically evaluated for a few cases. The convergence behavior is similar to that seen in previous work on queues with one-dimensional state spaces. The implications for initialization of steady-state simulations are discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/27548/1/0000592.pd

2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease

The recommendations listed in this document are, whenever possible, evidence based. An extensive evidence review was conducted as the document was compiled through December 2008. Repeated literature searches were performed by the guideline development staff and writing committee members as new issues were considered. New clinical trials published in peer-reviewed journals and articles through December 2011 were also reviewed and incorporated when relevant. Furthermore, because of the extended development time period for this guideline, peer review comments indicated that the sections focused on imaging technologies required additional updating, which occurred during 2011. Therefore, the evidence review for the imaging sections includes published literature through December 2011

Stochastic initialization in steady state simulations.

The goal of steady-state simulation is often to obtain point and interval estimators for a steady-state parameter. The technique of making independent and identically distributed replications is a simple way to obtain these estimates; unfortunately, the initial conditions used to start a simulation frequently bias the estimators thus obtained. A common method to deal with this initialization bias is to delete an initial portion of the output, but this can necessitate the deletion of large amounts of data. In this dissertation, we investigate a method of initialization for steady-state simulations which reduces the initialization bias and , consequently, the amount of deletion required. We call this method stochastic initialization. We use a first-order autoregressive process with high autocorrelation to study the effect of stochastic initialization on various point and interval estimator criteria: bias, variance, mean squared error, coverage and expected half-length. We show that the method can be highly effective in reducing bias in the point estimator and increasing coverage to desired levels. We also demonstrate the effectiveness of stochastic initialization in the simulation of a few queueing systems; this study required the derivation of the transient behavior of the queueing systems, results of value in their own right. We also show that the effectiveness of our stochastic initialization procedure is not very sensitive to the specification of methodological parameters, allowing room for error in implementation by a simulation practitioner.Ph.D.Operations researchIndustrial engineeringUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/162102/1/8907110.pd

The transient response of the M/E[subscript K]/2 queue and steady-state simulation

http://deepblue.lib.umich.edu/bitstream/2027.42/6730/5/bam7939.0001.001.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/6730/4/bam7939.0001.001.tx