ABSTRACT
Simulation models imitate the behavior of real-world systems, most frequently via computers. These models study interactions among elements of the system, as well as the system’s operating characteristics. The simulation can be static or dynamic. In a simulation model, one can assume that changes in an observed system occur either continuously, or only at discrete points in time. The Monte Carlo simulation method is based on the idea of using sampling in order to estimate performances of an observed system. In this method, random numbers are used to obtain samples from probability distributions. Sampling from the relevant probability distribution is based on the utilization of the random numbers. The random numbers are uniformly distributed in the interval. In other words, every one of the values in the interval has the same chance to happen. The Poisson random variable represents counts of the number of times that the observed event happens within the given time interval.
