Theory Thursday- Simulation of a Poisson Process

You are using discrete-event simulation to analyze a process or system. Imagine that your arrivals occur according to a Poisson Process. Without using specialized simulation software, this post shows how to code up a Poisson arrival process. Y_n will represent the n’th arrival time.

Stationary arrivals:
If your arrival rate does not vary over time, then your task is easy. You have two main options:
1. Generate exponential random variables X_1, X_2, … , representing interarrival times. Set Y_1 = X_1, Y_2 = Y_1+X_2, Y_3=Y_2+X_3,… Stop generating random variables when Y_m>T for some m, where T is your time interval length that you want to simulate.
2. Generate a Poisson random variable N(T), representing the number of arrivals over a time interval of length T. Then generate N(T) uniform [0,T] random variables, representing arrival times. Sort the arrival times in ascending order to obtain Y_1, Y_2, …

Non-stationary arrivals:
If your arrival rate varies over time, you’ll need an extra step or two of logic to generate your arrival times. Here are two options for simulation:
1. Find the highest arrival rate in your time interval, \lambda_{max}. Generate a stationary Poisson process with rate \lambda_{max} as in option 1 in the stationary arrival section above. For each arrival simulated, generate a uniform[0,1] random variable. If this uniform random variable is less than \lambda(t)/\lambda_{max}, where \lambda(t) is the arrival rate at the generated arrival time, accept the arrival as “real” and keep it. If not, discard the arrival. The “real” arrivals make up an accurate non-stationary Poisson arrival process.
2. Divide the time period [0,T] into small time increments. For each time increment, generate a uniform[0,1] random variable. For each time increment, if the uniform variable is less than or equal to \lambda(t) dt, where dt is the time increment size, then an arrival occurs during that increment. Assign the arrival time to be a random time in the increment. This method is only approximately correct, but it is good enough in most cases and may be faster to simulate in certain cases.

There are other options, but these will get you started in simulating your Poisson processes.

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