Page 76 - DCAP601_SIMULATION_AND_MODELING

Simulation and Modelling

Notes

Notes Complex discrete, dynamic, stochastic systems frequently disobey an analytic
solution and are as a result studied in the course of simulation.

5.2 Random-number Generators

The simulation needs to generate random variables of various kinds, depending on the system
model. This is accomplished by one or more pseudorandom number generators. The use of
pseudorandom numbers as opposed to true random numbers is a benefit should a simulation
need a rerun with exactly the same behaviour.

One of the problems with the random number distributions used in discrete-event simulation is
that the steady-state distributions of event times may not be known in advance. As a result, the
initial set of events placed into the pending event set will not have arrival times representative
of the steady-state distribution. This problem is typically solved by bootstrapping the simulation
model. Only a limited effort is made to assign realistic times to the initial set of pending events.
These events, however, schedule additional events, and with time, the distribution of event
times approaches its steady state. This is called bootstrapping the simulation model. In gathering
statistics from the running model, it is important to either disregard events that occur before the
steady state is reached or to run the simulation for long enough that the bootstrapping behavior
is overwhelmed by steady-state behavior. (This use of the term bootstrapping can be contrasted
with its use in both statistics and computing.)

Tasks Analyze the problems that take place in random number distributions.

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