Answers:
The Monte-Carlo method is a simulation technique in which statistical distribution functions are created by using a series of random numbers. This approach has the ability to develop many months or years of data in a matter of few minutes on a digital computer.
The method is generally used to solve the problems that cannot be adequately represented by mathematical models or where solution of the model is not possible by analytical method.
Step 1: Define the problem:
a) Identify the objectives of the problem.
b) Identify the main factors that have the greatest effect on the objectives of the problem.
Step 2: Construct an appropriate model:
a) Specify the variables and parameters of the model.
b) Formulate the appropriate decision rules, i.e., state the conditions under which the experiment is to be performed.
c) Identity the type of distribution that will be used. Models use either theoretical distributions or empirical distributions to state the patterns of occurrence associated with the variables.
d) Specify the manner in which time will change.
e) Define the relationship between the variables and parameters.
Step 3: Prepare the model for experimentation:
a) Define the starting conditions for the simulation.
b) Specify the number of runs of simulation to be made.
Step 4: Using steps 1 to 3, experiment with the model:
a) Define a coding system that will correlate the factors defined in step 1 with the random numbers to be generated for the simulation.
b) Select a random number generator and create the random numbers to be used in the simulation.
c) Associate the generated random numbers with the factors identified in step1 and coded in step 4(a).
Step 5: Summarise and examine the results obtained in step 4.
Step 6: Evaluate the results of the simulation.
Step 7: Formulate proposals for advice to management on the course of action to be adopted and modify the model, if necessary.