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record of what happens during the simulation and recording and summarizing the
simulation output. A variety of special-purpose simulation packages are available,
Ò Ò Ò Ò
including GPSS , SIMSCRIPT , SLAM and Arena . These packages have built-
in simulation clocks, simplified methods for generating probabilistic inputs and
procedures for collecting and summarizing the simulation output. Special-purpose
simulation packages enable quantitative analysts to simplify the process of develop-
ing and implementing the simulation model. Indeed, Arena 6.0 was used to develop
the simulation model described in the Management Science in Action, Preboard
Screening at Vancouver International Airport.
Simulation models can also be developed using general-purpose computer pro-
gramming languages such as BASIC, FORTRAN, PASCAL, C and C++. The
disadvantage of using these languages is that special simulation procedures are not
built in. One command in a special-purpose simulation package often performs the
The computational and computations and record-keeping tasks that would require several BASIC, FOR-
record-keeping aspects TRAN, PASCAL, C or C++ statements to duplicate. The advantage of using a
of simulation models are general-purpose programming language is that they offer greater flexibility in terms
assisted by special
simulation software of being able to model more complex systems.
packages. The packages To decide which software to use, an analyst will have to consider the relative merits
ease the tasks of of a spreadsheet, a special-purpose simulation package and a general-purpose com-
developing a computer puter programming language. The goal is to select the method that is easy to use
simulation model.
while still providing an adequate representation of the system being studied.

Verification and Validation
An important aspect of any simulation study involves confirming that the simulation
model accurately describes the real system. Inaccurate simulation models cannot be
expected to provide worthwhile information. So, before using simulation results to
draw conclusions about a real system, we have to take steps to verify and validate the
simulation model.
Verification is the process of determining that the computer procedure that
performs the simulation calculations is logically correct. Verification is largely a
debugging task to make sure that no errors are in the computer procedure that
implements the simulation. In some cases, an analyst may compare computer results
for a limited number of events with independent hand calculations. In other cases,
tests may be performed to verify that the probabilistic inputs are being generated
correctly and that the output from the simulation model seems reasonable. The
verification step is not complete until the user develops a high degree of confidence
that the computer procedure is error free.
Validation is the process of ensuring that the simulation model provides an
accurate representation of a real system. Validation requires an agreement among
analysts and managers that the logic and the assumptions used in the design of the
simulation model accurately reflect how the real system operates. The first phase of
the validation process is done prior to, or in conjunction with, the development of
the computer procedure for the simulation process. Validation continues after the
computer program has been developed with the analyst reviewing the simulation
output to see whether the simulation results closely approximate the performance of
the real system. If possible, the output of the simulation model is compared to the
output of an existing real system to make sure that the simulation output closely
approximates the performance of the real system. If this form of validation is not
possible, an analyst can experiment with the simulation model and have one or more
individuals experienced with the operation of the real system review the simulation
output to determine whether it is a reasonable approximation of what would be
obtained with the real system under similar conditions.

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