SIMULATION INPUT MODELING

SIMULATION INPUT MODELING

1. Successful input modeling requires a close match between the input model and the true underlying probabilistic mechanism associated with the system.

2. COLLECTING DATA

   • The first is the classical approach, where a designed experiment is conducted to collect the data.
   • The second is the exploratory approach, where questions are addressed by means of existing data that the modeler had no hand in collecting.
   • there are several things that can be “wrong” about the data set
     • Wrong amount of aggregation.
     • Wrong distribution in time.
     • Wrong distribution in space.
     • Censored data.
     • Insufficient distribution resolution.

3. Input models

4. assessing independence.

   • autocorrelation
     • analysis of this data set includes plotting a histogram and calculating the values of some sample statistics
       • to be determined is whether a parametric or nonparametric model should be chosen for the process

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