Simulation Experiments

Model Characterization

A simulation model is typically both stochastic and dynamic, as described in the paragraphs that follow.

A system model is either deterministic or stochastic. A deterministic system model has no stochastic (random) components. For example, provided the conveyor belt and machine never fail, a model of a constant velocity conveyor belt feeding parts to a machine with a constant service time is deterministic. At some level of detail, however, all systems have some stochastic components: Machines fail, people respond in a random fashion, and so on. One attractive feature of simulation modeling is that stochastic components can be accommodated, usually without a dramatic increase in the complexity of the system model at the computational level.

A system model is static or dynamic. A static system model is one in which time is not a significant variable. Many static models can be analyzed by Monte Carlo simulation, which performs a random experiment repeatedly to estimate, for example, a probability associated with a random event. The passage of time always plays a significant role in dynamic models.

A dynamic system model is continuous or discrete. Most of the traditional dynamic systems studied in classical mechanics have state variables that evolve continuously. A particle moving in a gravitational field, an oscillating pendulum, and a block sliding down an inclined plane are examples. In each of these cases, the motion is characterized by one or more differential equations that model the continuous time evolution of the system. In contrast, models involving queuing, machine repair, inventory systems, and so forth, are discrete because the state of the system is a piecewise-constant function of time. For example, the number of jobs in a queuing system is a natural state variable that changes value only at those discrete times when a job arrives (to be served) or departs (after being served).

A discrete-event simulation model is defined by three attributes: (a) The model is stochastic—at least some of the system state variables are random; (b) the model is dynamic—the time evolution of the system state variables is significant; and (c) the model has discrete event times—significant changes in the system state variables are associated with events that occur at discrete time instances only.

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