Rockwell Automation Arena OptQuest Users Guide - Manual (Page 24)

A RENA O PT Q UEST U SER ’ S G UIDE
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Conceptually, an optimization model might resemble the figure below.
The solution to an optimization model provides a set of values for the controls that
optimizes (maximizes or minimizes) the associated objective. If the world were simple and
the future were predictable, all data in an optimization model would be constant (making
the model deterministic), and you could use techniques such as linear or nonlinear
programming to find optimal solutions.
However, a deterministic optimization model can’t capture all the relevant intricacies of a
practical decision environment. When model data is uncertain and can only be described
probabilistically, the objective is not represented by a single value but rather by a proba-
bility distribution that varies with any chosen set of values for the controls. You can find
an approximation of this probability distribution by simulating the model using Arena.
An optimization model with uncertainty has several additional elements:
Assumptions Capture the uncertainty of model data using probability distributions.
Assumptions are primarily modeled by choosing appropriate probability
distributions for each stochastic activity in the simulation model.
Responses An output from the simulation model, such as resource utilization, cycle
time, or queue length. A response has an underlying probability
distribution that can be empirically approximated with a simulation
model.
Response Statistics Summary values of a response, such as the mean, standard deviation, or
variance. You may control the optimization by maximizing, minimizing,
or restricting response statistics; for example, the average waiting time
or the maximum queue length.