Question

Under what conditions is it necessary to use Monte Carlo analysis in the study of a queuing system?

Answer 1

Monte Carlo simulations are used to model the probability of different outcomes in a process that cannot easily be predicted due to the intervention of random variables. It is a technique used to understand the impact of risk and uncertainty in prediction and forecasting models.

Example

One way to employ a Monte Carlo simulation is to model possible movements of asset prices using Excel or a similar program. There are two components to an asset's price movements: drift, which is a constant directional movement, and a random input, which represents market volatility. By analyzing historical price data, you can determine the drift, standard deviation, variance, and average price movement for a security. These are the building blocks of a Monte Carlo simulation.

Monte Carlo simulations prove to be very beneficial in areas such as in meteorology, astronomy and particle physics.

It can be used under following conditions:

• When the population proportion is very large and it is difficult to have a proper comparison.
• When any individual has left the population and a queue is joined by him.
• When the capacity of the service is increased then to decrease the time and waiting line, Monte Carlo analysis must be used.
• When faced with significant uncertainty in the process of making a forecast or estimation, rather than just replacing the uncertain variable with a single average number, the Monte Carlo Simulation might prove to be a better solution.