# Steps In The Simulation Process

## Steps (Algorithm)

1. Formulate the model. This step is almost the same as that for other operations research models. For a proper formulation, comprehensive study should be made regarding components of the problem, objective, composition of the organisation, etc.

Never add too much detail in a simulation model, it will consume a lot of computer time. Always keep your specific goal in mind.

2. Design the experiment. After building a simulation model, simulation experiments must be designed. In this step, you must decide the starting conditions of the model, parameter settings, time period required for each run, total number of runs, etc.

Work out the details of the experimental procedures before running the model, it will considerably reduce the chances of making blunders. Think carefully about the model to save your time and money

3. Develop the Computer Program: Using a high-speed electronic computer, simulation experiments can be performed. If the simulated model has a very simple structure, you can use a standard programming language, such as FORTRAN, PL/1, or ALGOL, to develop the computerized version. On the other hand, for a complex structure, you can use simulation languages like SIMSCRIPT or GPSS.

Never assume blindly that the entire simulated system is accurate & reliable, merely because each of the component parts seems accurate when considered in isolation.

## Monte-Carlo Simulation

The Monte-Carlo simulation method uses random numbers for generating some data by which a problem can be solved. These random numbers are helpful in creating a new set of hypothetical data for a problem whose behaviour is known from past experience. The random numbers are generated either on a computer or are picked up from a table. Most computers employ what is known as pseudorandomness. This means that the numbers are generated by a series of specific operations. Each number is generated by performing these operations on the previous number. After picking a random number, its value is compared with the cumulative probability distribution and the value of process parameters is obtained.

The cumulative probability distribution is most important instrument in the use of Monte-Carlo methods.