Simulation

1. Intro

A simulation is an attempt to mimic the real world using analytical methodology. They are ideal to forecast systems where the true relationship is too difficult to estimate or the system is easily modeled. Simulations are not necessarily alternatives to heuristic rules and statistical techniques but an alternative method for forecasting using those techniques.  To build a simulation model you may have to rely on either statistics and/or heuristics to build the core logic. Alternatively, you could use theoretical models as the core of your simulation. Simulations are powerful predictive tools as well as useful for running what-if scenarios. Example simulation models:

a) Supply and Demand

b) Queuing models, prisons, phone,…

c) Factory floor

2. Monte Carlo Simulations

Monte Carlo simulations are stochastic models. They simulate the real world by assuming it is a random or stochastic process.

a. Random Number Generators

Most of the time we do not have truly random numbers but pseudo-random numbers typically generated from the date and time the number was created. These random numbers are drawn from a uniform distribution.

One way to generate a random number drawn from a particular distribution is to calculate the probability density function (PDF) for the random variable then using a random number from a uniform distribution as the probability of that random number. In cases where we cannot easily use the PDF often times a simple algorithm will work.

b. Markov Chains

A Markov chain is a sequence of random numbers that are independent of one another. A classic example of a Markov chain is a random walk. A random walk, or drunkard s walk, is when, if walking each successive step is in a random direction. Studying Markov chains is not an excuse to hang out in bars more often; a real drunk has an intended direction but impaired capacity for executing their intension.

c. Example Model (Queuing)

1) Intro

Queuing models have one or more servers that process either people or items. If a server cannot instantaneously process people and if more then one person arrives a line forms behind the server.

2) Open Jackson Network Queuing

1. Arrival follows a Poisson process

2. Service time is independent and exponentially distributed

3. Probability of complete one node and going onto another is independent

4. Is open so can re-enter the system

5. Assume an infinite number of servers.

Note: Use an Erlang instead of Poisson distribution when you have a finite number of servers.