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This short chapter will cover just the basics of queueing theory not queueing simulation , since familiarity with this material and the terminology is important for developing many simulation models. The relatively simple mathematical formulas from elementary queueing theory can also prove valuable for verification of simulation models of queueing systems helping to determine that the simulations are correct. Queueing-theory models can do that by providing a benchmark against which simulation results can be compared, if the simulation model is simplified probably unrealistically to match the more stringent assumptions of queueing theory e.

Queueing theory is an enormous subject, and has been studied mathematically since at least , at first by A. There are many entire books written on it e. Ross or on other application-oriented topics like manufacturing e. The foundational ideas of a probabilistic experiment , the sample space for the experiment, and events. Expected values of RVs and their distributions a.

In days gone by books using probability notions contained lots of pages of distribution facts like definitions of PMFs, PDFs, and giving CDFs, expected values, variances, and so on, for many distributions. But this book does not have such a compendium since that material is readily available elsewhere, including the Simio documentation itself described in Section 6. References to further material on distributions are in Section 6. In Section 2. Section 2. Finally, in Section 2.

The urgent-care clinic described earlier, and in Figure 2. If there are multiple individual parallel servers at a queueing node e. The numbers by the arcs in Figure 2. When a server finishes service on an entity and there are other entities in queue for that queueing node, we need to decide which specific entity in queue will be chosen to move into service next — this is called the queue discipline.

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