Rockwell Automation Arena Contact Center Edition Users Guide - Manual (Page 21)

2 • I NTRODUCTION TO S IMULATION
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Optimization: determining exactly which combination of factor levels produces the
best overall system response.
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Functional relations: establishing the nature of the relationships among one or more
significant factors and the system’s response.
Although not exhaustive, this list identifies the most common simulation goals or
purposes. The explicit purpose of the model has significant implications for the entire
model-building and experimentation process. For example, if a model’s goal is to evaluate
a proposed (or existing) system in an absolute sense, then the model must be accurate; and
there must be a high degree of correspondence between the model and the real system. On
the other hand, if the goal for a model is the relative comparison of two or more systems
or operating procedures, the model can be valid in a relative sense even though the
absolute magnitude of responses varies widely from that which would be encountered in
the real system. The entire process of designing the model, validating it, designing
experiments, and drawing conclusions from the resulting experimentation must be closely
tied to the specific purpose of the model. No one should build a model without having an
explicit experimental goal in mind. Unfortunately, the analyst does not always understand
the real-world problem well enough at first to ask the right questions. Therefore, the
model should have an easily modified structure so that additional questions arising from
early experimentation can be answered later.
Style definition and model formulation
The essence of the modeling art is abstraction and simplification. We try to identify that
small subset of characteristics or features of the system that is sufficient to serve the
specific objectives of the study. So, after we have specified the goal or purpose for which
the model is to be constructed, we then begin to identify the pertinent components. This
process entails itemizing all system components that contribute to the effectiveness or
ineffectiveness of its operation. After we have specified a complete list, we determine
whether each component should be included in our model; this determination may be
difficult because, at this stage of model development, a component’s significance to the
overall goal is not always clear. One of the key questions to be answered is whether a
particular component should be considered part of the model or part of the outside
environment, which is represented as inputs to the model.
In general, we have little difficulty deciding on the output variables. If we have done a
good job specifying the goals or purposes of the study, the required output variables
become apparent. The real difficulty arises when we try to determine which input and
status variables produce the effects observed and which can be manipulated to produce the
effects desired.
We also face conflicting objectives. On the one hand, we try to make the model as simple
as possible for ease of understanding, ease of formulation, and computational efficiency.