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Simulation Concepts
Modeling methodologies
• MonteCarlo
• Agent-based
• State/Action
For more information, see “Other modeling approaches” on page 49.
As you might expect, you can use different methods to model different aspects of real-world systems. For example, at a chemical plant you could model the chemical reactions as a contin- uous process, the control logic of the chemical process using discrete event modeling, and the tanks, valves, and flow of the production process with discrete rate.
It is good to note, however, that there is no such thing as “the” model of a system: a system can be modeled in any number of different ways, depending on what it is you want to accomplish. In general, how you model the system depends on the purpose of the model: what type, level, and fidelity of information you want to gather and the amount of detail, or level of abstraction or granularity, of the model. Once that has been determined, you can intelligently choose which type of model to build.
☞ The types of models that can be built depend on the ExtendSim product that was purchased.
Comparison of main modeling methodologies
The three main modeling methodologies are continuous, discrete event, and discrete rate. Con- tinuous modeling (sometimes known as process modeling) is used to describe a flow of values. Discrete event models track unique entities. Discrete rate models share some aspects of both continuous and discrete event modeling.
In all three types of simulations, what is of concern is the granularity of what is being modeled and what causes the state of the model to change.
• In continuous models, the time step is fixed at
the beginning of the simulation, time advances
in equal increments, and values change based
directly on changes in time. In this type of
model, values reflect the state of the modeled
system at any particular time, and simulated
time advances evenly from one time step to the next. For example, an airplane flying on autopilot represents a continuous system since its state (such as position or velocity) changes continuously with respect to time. Continuous simulations are analogous to a constant stream of fluid passing through a pipe. The volume may increase or decrease at each time step, but the flow is continuous.
• Indiscreteeventmodels,thesystemchanges
state as events occur and only when those
events occur; the mere passing of time has no
direct effect on the model. Unlike a continuous
model, simulated time advances from one
event to the next and it is unlikely that the time
between events will be equal. A factory that assembles parts is a good example of a discrete event system. The individual entities (parts) are assembled based on events (receipt or antic- ipation of orders). Using the pipe analogy for discrete event simulations, the pipe could be
01234
Time line for continuous simulation
0 2.3 2.7 4
Time line for discrete event simulation
Tutorial

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