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Modeling and Simulation
A model can be said to be a simplified representation of a system from the real world, which is designed at some point in time and space and is used for promoting understanding of the real system. System always exists and operates in real space and time. By the use of simulation the real world model compresses the aspects relating to time and space. This helps in viewing the interactions in a clearer and precise manner as the compression brings together the interaction which would otherwise be not apparent, because of their wide separations in time and space. The discipline of modeling and stimulation helps in studying the systems and their parts. It explores the unexplored boundaries of systems and their interactions and no other discipline provides as in-depth of study and increased level of understanding for all aspects of the system as simulation and modeling do.
All the parts of the system constantly interact with each other to give the system its continued existence. The model represents the system in a simple way, which promotes the better understanding of the real life system. The better understanding a model provides the more effective it is. The model should have the exact amount of detail required. More details can give the model unnecessary complication while lesser details can miss vital information. Simulation provides implications of the defined interactions of the model in its computerized version. These models are run over time and are again and again revised unless the necessary understanding develops.
Use of Simulation and Modeling
If one flies the simulator instead of a real airplane, the cost involved in the training program gets substantially cheaper. Thus models find wide usage in commerce and military industries. Real systems require high cost and also less amount of experimenting can be done on them. It is also sometime very dangerous to use real system and could well cost the life of the trainee. These dangers are avoided in the model simulation.
Simulation performs the perfect modeling technique for the dynamic processes. The dynamic systems and processes perform a certain degree of randomness, which add to their complexity. Simplification of these dynamic processes is required by the theoretical assumptions, which is not necessary in their simulations. Simulation can be done by plotting the points on the graph with necessary coordinates of each activity. The increase and decrease in units will give the desired results which can be further analyzed and its implications explored. A simulation is called a discrete event when the number of events is finite. Simulations can be performed on computers and manually as well.
In a discrete event system, the occurrence of events is plotted at various time intervals which evolve over time. Discrete event systems have wide real world applications. These include flexible manufacturing systems, traffic systems, coherent life systems, production lines and flow networks etc. The system changes from one state to another by the occurrence of events in the models of these systems.
There are a large number of control parameters in a stochastic system, which impacts its performance .By the use of senility analysis same changes are applied to the corresponding nominal values of input parameters. This provides the knowledge about behavior of the system.and the relative importance of input parameters can be established. The partial derivative is obtained which is the sensitivity of the performance measure with respect to the input parameter. Evaluation of sensitivities of performance with respect to the various parameters of interest is termed as senility analysis. Sensivity analysis helps in providing guidance to design and operational decisions and also helps to identify the most significant system parameters. This is done in various steps of problem formulation, data collection and analysis, simulation model development, and model validation, verification and calibration for getting the desired system scenario.
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