Kelton_Simulation_with_Arena

Kelton_Simulation_with_Arena - Statistical Analysis In...

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Unformatted text preview: Statistical Analysis In Chapter 4, we showed you the kind of modeling you can do with modules that were primarily from the Basic Process panel. These are relatively high-level and easy-to-use modules that will usually take you a long way toward building a model at a level of detail you need. Sometimes it’s all you’ll need. But sometimes it isn’t. As you gain experience in modeling, and as your models be- come bigger, more complex, and more detailed, you might find that you’d like to be able to control or model things at a lower level, in more detail, or just differently from what the modules of the Basic Process panel have to offer. Arena doesn’t strand you at this level, forcing you to accept a limited number of “canned” modeling constructs. Nor does it force you to learn a programming language or some pseudo-programming syntax to capture complicated system aspects. Rather, it offers a rich and deep hierarchy of several different modeling levels that you can fathom to get the flexibility you might need to model some peculiar logic just right. It’s probably a good idea to start with the high-level modules, take them as far as they’ll go (maybe that’s all the way), and when you need greater flexibility than they provide, go to a lower and more detailed level. This structure allows you to exploit the easy high-level modeling to the extent possible, yet allows you to drill down lower when you need to. And because all of this modeling power is pro- vided by standard Arena modules, you’ll already be familiar with how to use them; to put them to work, you simply need to become familiar with what they do. This chapter explores some (certainly not all) of the detailed, lower-level modeling constructs available in the Advanced Process and Blocks panels; the latter panel provides the lowest-level model logic where modules correspond to the blocks in the SIMAN simulation language that underlies Arena. The example we’ll use for this is a fairly com- plex telephone call center, including technical support, sales, and order-status checking. We’ll also touch on the important topics of nonstationary (time-dependent) arrival pro- cesses, model debugging, and a greater level of customization in animation. Using the models we develop as laboratory animals, we’ll also get into the topic of statistical analy- sis of simulation output data. Section 5.1 describes the system and Section 5.2 talks about how to model it using some new Arena modeling concepts. Section 5.3 describes our basic modeling strategy. The model logic is developed in Section 5.4. The unhappy (but inevitable) issue of debug- ging is taken up in Section 5.5. Corresponding to the more detailed modeling in this chap- ter, Section 5.6 indicates some ways you can fine-tune your animations to create some nonstandard effects. In Section 5.7, we’ll talk about streamlining a model for faster ex- ecution and developing overall economic measures of performance; the resulting model will be used in Section 5.8 to discuss the design and analysis of simulation experiments. will be used in Section 5....
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This note was uploaded on 12/19/2009 for the course INDUSTRIAL ie500 taught by Professor Mathematicalprogrammibg during the Spring '09 term at Bilkent University.

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Kelton_Simulation_with_Arena - Statistical Analysis In...

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