veripax.blogg.se - Random number generator algorithm example

The linear congruential generator (LCG) has the form: Perhaps the most common type of pseudo-random number generation algorithm, with respect to use in simulation languages, is the linear congruential generator (Lehmer, 1951). Schmeiser (1980) provides a comprehensive survey. Other approaches for generating pseudo-random numbers are given in Banks, Carson, Nelson, and Nicol (2009) as well as Law (2007). This means for example that if the probability distribution modeling the operation time at station A were changed, the times between arrivals would remain the same.Īs in the previous section, one approach to pseudo-random number generation will be presented. For example in the two stations in a series model, the time between arrivals and the operation time at station A would be assigned different streams. Having multiple streams of random numbers allows sampling from each particular probability distribution used in a model to be associated with a particular stream. Imagine the difficulty of removing a bug from a model if the results were randomly different each time the model was executed!Ī sequence of pseudo-random numbers is called a stream.

This is important in simulation both for debugging and experimentation using common random numbers.

All possible numbers in the sequence are generated before any number repeats.īecause the pseudo-random number generation algorithms are deterministic, a sequence of numbers can be regenerated whenever necessary.

There are many, many (at least 1,000,000) numbers in sequence.

The numbers appear to be uniformly distributed in the range (0,1).

The numbers do not exhibit any statistical correlation with each other.

However, the properties of the sequence of pseudo-random numbers make them look random. Fortunately, there are several well known algorithms for generating such samples, called pseudo - random numbers. To get the number in range 0 to max, we are using modulus operator to get the remainder.įor the seed value we are providing the time(0) function result into the srand() function.All of the random sampling strategies discussed in the previous section require a random sample uniformly distributed in the range (0,1). To get the number we need the rand() method. This is an integer value to be used as seed by the pseudo-random number generator algorithm. The declaration of srand() is like below: void srand(unsigned int seed) The function void srand(unsigned int seed) seeds the random number generator used by the function rand. To perform this operation we are using the srand() function. Here we are generating a random number in range 0 to some value.

Let us see how to generate random numbers using C++.