SIMULATION

To simulate is to initiate. In general terms, simulation involves developing a model of some real phenomenon and then performing experiments on the model evolved. It is a descriptive, and not an optimizing, technique. In simulation, a given system is copied and the variables and constants associated with it are manipulated in an artificial environment to examine the behaviour of the system. Using simulation, an analyst can introduce the constants and variables related to the problem, set up the possible courses of action and establish criteria, which act as measures of effectiveness. The benefit of simulation from the view point of the analyst stems from the fact that the results of taking a particular course of action can be estimated prior to its implementation in the real world. Instead of using hunches and intuition to determine what may happen, the analyst using simulation can test and evaluate various alternatives and select the one that gives the best results. Broadly, there are four phases of the simulation process. They are:
a. Definition of the problem and statement of objectives.
b. Construction of an appropriate model.
c. Experimentation with the model constructed, and
d. Evaluation of the results of simulation.
Monte Carlo Simulation
It is also known as probabilistic simulation method. It can be described as a numerical technique that involves modeling with the objectives of predicting the system's behaviour. The chance element is a very significant feature of Monte Carlo simulation and this approach can be used when the given process has a random, or chance component.
Advantages and Disadvantages of Simulation
Advantage The chief merit of the simulation technique is its capacity to lend itself to problems that are cumbersome, or impossible to handle mathematically using analytical methods. Not only this, the technique allows the analyst to experiment with the system behaviour without subjecting it to the risks that would be inherent in experimenting with the real system. It also compresses time to enable the manager to visualize the long-term effects in a quick manner. Besides, simulation is often used to test proposed analytic solutions as well.
Disadvantage: It does not represent a methodology for derivation of optimal solutions to the given problems. This approach is designed merely to provide characterisation of the behaviour of the system in question for a given set of inputs. Further, the simulation approach is not precise in the sense that it yields only estimates which are subject to sampling error. Of course, the sampling error can be reduced by increasing the sample size.
Another drawback is that it may not prove economical, as it requires lot of efforts to develop a suitable model.
It is a tool of solution evaluation and does not generate problem solution. Thus the analyst has to develop the proposed solution; then simulation can be used to test the relative desirability of those solutions.

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