SIMULATION THEORY/
MONTE CARLO SIMULATION
WHAT IS MONTE CARLO SIMULATION?
A mathematical technique that is used
to estimate the possible outcomes of
an uncertain event.
PROCEDURES FOR MONTE CARLO
SIMULATION:
1. Establish a probability distribution for the variables
to be analyzed.
2. Find the cumulative probability distribution for
each variable.
3. Set Random Number intervals for variables and
generate random numbers.
4. Simulate the experiment by selecting random
numbers from numbers tables until the required
number of simulations is generated.
5. Examine the result and validate the model.
COMMON PROBABILITY DISTRIBUTIONS
1. Normal or Bell Curve
2. Loganormal
3. Uniform
4. Triangular
COMMON PROBABILITY DISTRIBUTIONS
6.PERT
7. Discrete
ADVANTAGE OVER DETERMINSIC
Probabilistic Result
Graphical Result
Sensitivity Analysis
Scenario Analysis
Correlation of Inputs
COMMON PROBABILITY DISTRIBUTIONS
1. Normal or Bell Curve
2. Loganormal
3. Uniform
4. Triangular