In the previous chapters, we formulated and analyzed various models on real-life problems. All the models were used with mathematical techniques to have analytical solutions. In certain cases, it might not be possible to formulate the entire problem or solve it through mathematical models. In such cases, simulation proves to be the most suitable method, which offers a near-optimal solution. Simulation is a reflection of a real system, representing the characteristics and behavior within a given set of conditions.
In simulation, the problem must be defined first. Secondly, the variables of the model are introduced with logical relationship among them. Then a suitable model is constructed. After developing a desired model, each alternative is evaluated by generating a series of values of the random variable, and the behavior of the system is observed. Lastly, the results are examined and the best alternative is selected the whole process has been summarized and shown with the help of a flow chart in the Figure.
Simulation technique is considered as a valuable tool because of its wide area of application. It can be used to solve and analyze large and complex real world problems. Simulation provides solutions to various problems in functional areas like production, marketing, finance, human resource, etc., and is useful in policy decisions through corporate planning models. Simulation experiments generate large amounts of data and information using a small sample data, which considerably reduces the amount of cost and time involved in the exercise.
For example, if a study has to be carried out to determine the arrival rate of customers at a ticket booking counter, the data can be generated within a short span of time can be used with the help of a computer.
ADVANTAGES AND DISADVANTAGES OF SIMULATION
Simulation is best suited to analyze complex and large practical problems when it is not possible to solve them through a mathematical method.
Simulation is flexible, hence changes in the system variables can be made to select the best solution among the various alternatives.
In simulation, the experiments are carried out with the model without disturbing the system.
Policy decisions can be made much faster by knowing the options well in advance and by reducing the risk of experimenting in the real system.
Simulation does not generate optimal solutions.
It may take a long time to develop a good simulation model.
In certain cases simulation models can be very expensive.
The decision-maker must provide all information (depending on the model) about the constraints and conditions for examination, as simulation does not give the answers by itself.