Summary

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Simulation has become an increasingly important management science technique in recent years . Various surveys have shown simulation to be one of the techniques most widely applied to real-world problems. Evidence of this popularity is the number of specialized simulation languages that have been developed by the computer industry and academia to deal with complex problem areas.

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The popularity of simulation is due in large part to the flexibility it allows in analyzing systems, compared to more confining analytical techniques. In other words, the problem does not have to fit the model (or technique)the simulation model can be constructed to fit the problem. A primary benefit of simulation analysis is that it enables us to experiment with the model. For example, in our queuing example we could expand the model to represent more service facilities, more queues, and different arrival and service times; and we could observe their effects on the results. In many analytical cases, such experimentation is limited by the availability of an applicable formula. That is, by changing various parts of the problem, we may create a problem for which we have no specific analytical formula. Simulation, however, is not subject to such limitations. Simulation is limited only by one's ability to develop a computer program.

Simulation is one of the most important and widely used management science techniques .

Simulation provides a laboratory for experimentation on a real system .

Simulation is a management science technique that does not usually result in an optimal solution. Generally, a simulation model reflects the operation of a system , and the results of the model are in the form of operating statistics, such as averages. However, optimal solutions can sometimes be obtained for simulation models by employing search techniques .

Simulation does not usually provide a recommended decision as does an optimization model; it provides operating characteristics .

However, in spite of its versatility, simulation has limitations and must be used with caution. One limitation is that simulation models are typically unstructured and must be developed for a system or problem that is also unstructured. Unlike some of the structured techniques presented in this text, they cannot simply be applied to a specific type of problem. As a result, developing simulation models often requires imagination and intuitiveness that are not required by some of the more straightforward solution techniques we have presented. In addition, the validation of simulation models is an area of serious concern. It is often impossible realistically to validate simulation results, to know if they accurately reflect the system under analysis. This problem has become an area of such concern that "output analysis" of simulation results is developing into a new field of study. Another limiting factor in simulation is the cost in money and time of model building. Because simulation models are developed for unstructured systems, they often take large amounts of staff time, computer time, and money to develop and run. For many business companies, these costs can be prohibitive.

Simulation has certain limitations .