Monte Carlo Simulation

Happy MLK jr day,

For those of you working today I thought I would give a little background on Monte Carlo Simulation (MCS). I have to admit that when asked I have tied MCS back to the gambling industry more than once. It sounds like a good story, however, it turns out the name comes from the resemblance to the act of playing and recording your results in a real gambling casino.  MCS techniques are often used in physical and mathematical problems and are most useful when it is difficult or impossible to obtain a closed-form expression, or in-feasible to apply a deterministic algorithm. Monte Carlo methods are mainly used in three distinct problem classes: optimization, numerical integration and generation of draws from a probability distribution.

The modern version of the Monte Carlo method was invented in the late 1940s by Stanislaw Ulam, while he was working on nuclear weapons projects at the Los Alamos National Labs. It was named by Nicholas Metropolis, after the Monte Carlo Casino, where Ulam’s uncle often gambled.  Immediately after Ulam’s breakthrough, John Von Neumann understood its importance and programmed the ENIAC computer to carry out Monte Carlo calculations.

So although gambling is a reference it’s not the reason for or used in the gaming industry… we at Isograph use MCS techniques to solve problems that cannot be solved using quantitative methods. Optimizing Preventive Maintenance Intervals, deciding the perfect number  of spares to have and where to keep them as well as optimizing the configuration of your system are just a few of the problems we can solve with the Availability Workbench using Monte Carlo Simulation. However, we cannot tell you where to place your bets or what number to choose on the Roulette Wheel.

The following is a short demonstration as to how we use MCS together with FMEA’s for RCM analysis or Block Diagrams to model complex systems logic.

Availability Workbench using Monte Carlo Simulation