Introduction to Simulation
A simulation is the imitation of the operation of a real-world process or system over time. Whether done by hand or on a computer, simulation involves the generation of an artificial history of a system, and the observation of that artificial history to draw inferences concerning the operating characteristics of the real system. The behavior of a system as it evolves over time is studied by developing a simulation model. This model usually takes the form of a set of assumptions concerning the operation of the system. These assumptions are expressed in mathematical, logical, and symbolic relationships between the entities, or objects of interest, of the system. Once developed and validated. a model can be used to investigate a wide variety of "what if" questions about the real world system. Potential changes to the system can first be simulated in order to predict their impact on system performance. Simulation can also be used to study systems in the design stage, before such systems are built. Thus, simulation modeling can be used both as an analysis tool for predicting the effect of changes to existing systems, and as a design tool to predict the performance of new systems under varying sets of circumstances.
In some instances, a model can be developed which is simple enough to be "solved" by mathematical methods. Such solutions may be found by the use of differential calculus, probability theory, algebraic methods, or other mathematical techniques. The solution usually consists of one or more numerical parameters, which are called measures of performance of the system. However, many real-world systems are so complex that models of these systems are virtually impossible to solve mathematically. In these instances, numerical, computer-based simulation can be used to imitate the behavior of the system over time. From the simulation, data are collected as if a real system were being observed. This simulation-generated data is used to estimate the measures of performance of the system.
1.1 When Simulation Is the Appropriate Tool
The availability of special-purpose simulation languages, massive computing capabilities at a decreasing cost per operation, and advances in simulation methodologies have made simulation one of the most widely used and accepted tools in operations research and systems analysis. Circumstances under which simulation is the appropriate tool to use have been discussed by many authors, from Naylor et al.  to Banks et al. . Simulation can be used for the following purposes:
1.Simulation enables the study of, and experimentation with, the internal interactions of a complex system, or of a subsystem within a complex system. 2.Informational, organizational, and environmental changes can be simulated, and the effect of these alterations on the model's behavior can be observed. 3.The knowledge gained in designing a simulation model may be of great value toward suggesting improvement in the system under investigation. 4.By changing simulation inputs and observing the resulting outputs, valuable insight may be obtained into which variables are most important and how variables interact. 5.Simulation can be used as a pedagogical device to reinforce analytic solution methodologies.
6.Simulation can be used to experiment with new designs or policies prior to implementation, so as to prepare for what may happen.
7.Simulation can be used to verify analytic solutions.
8.By simulating different capabilities for a machine, requirements can be determined. 9.Simulation models designed for training allow learning without the cost and disruption of on-the-job learning. 10. Animation shows a system in simulated operation so that the plan can be visualized. 11. The modern system (factory, wafer fabrication plant, service organization, etc.) is so complex that the interactions can be treated only through simulation.