For each scenario that is to be simulated, decisions need to be made concerning the 
length of the simulation run, the number of runs (also called replications), and the 
manner of initialization that is required.The simulation analyst 
will need to understand the difference between Sue and Bob's performance and 
how that can be included in the simulation as the service time of the attendant, and 
then make changes in the simulation to include that as an input parameter and not 
hardwired into the equations for the attendant.However, if 
the simulation has tens or hundreds of input parameters, conducting a run for each 
combination of the parameters can be unmanageable.If there are only a few input parameters, this approach will work.The simulation analysts may also want 
to use a technique called Monte Carlo analysis (described in a later chapter) that 
relies on repeated random sampling to compute the results of the simulation.One approach to conducting experiments is 
to vary the input data for each simulation run and observe the behavior of the sys tem.To illustrate the importance of thinking through experiments during the concep tual model development, what would happen if you were presenting the results of 
these runs to the client (i.e., gas station management), and they ask you whether 
the results change if Sue is working as an attendant versus Bob?The changes needed may not be that 
difficult, but it requires time and resources to change the conceptual model, col lect data, make the changes to the simulation, do the verification and validation, 
and run the experiments again.