simulation
simulation is next best thing to observing a real system in operation. it allows us to collect pertinent information about the behavior of the system by executing a computerized model. The collected data are then used to design the system. Simulation is not an optimization teachnique. Rather, it is a technique for estimating the measures of performance of the modeled system.
Simulation has been used in all aspects of science and technology as the following partial list demonstrates:
1. Basic science
(a) Estimation of the area under a curve or, more generally, evaluation of multiple integrals
(b) Estimation of the constant π (=3.14159)
(c) Matrix inversion
(d) Study of particle diffusion
2. Practical situations
(a) Industrial problems, including the design of queueing systems. Communication networks, inventory control, and chemical processes
(b) Business and economic problems, including consumer behavior, price determination, economic forecasting, and total firm operation
(c) Behavioral and social problems, including population dynamics, environmental health effects, epidemiological studies, and group behavior
(d) Biomedical systems, including fluid balance, electrolyte distribution in the human body, blood cell proliferation, and brain activities
(e) War strategies and tactics
Estimation of simulation output in based on random sampling, much the same way we do when observing a real situation. This means that the output of simulation is subject to random variations, and thus, as in any statistical experiment, must be examined using formal statistical inference tests. This important point is stressed throughout the chapter.