Chapter 12
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Determining the output assuming the most likely values for the random variables of a model.
Base-case scenario
Determining the output assuming the best values that can be expected for the random variables of a model.
Best-case scenario
A probability distribution where the possible values for a random variable can take any value between two specified values. The specified values can include negative and positive infinity.
Continuous probability distribution
Input to a simulation model that is selected by the decision maker.
Controllable input
A probability distribution where the possible values for a random variable can take on only specified discrete values.
Discrete probability distribution
A simulation model that describes how a system evolves over time by managing a discrete sequence of events (i.e., customer arrival or departure, over time).
Discrete-event simulation model
A simulation model used in situations where the state of the system affects how the system changes or evolves over time.
Dynamic simulation model
Numerical values that appear in the mathematical relationships of a model.
Parameters
A description of the range and relative likelihood of possible values of an uncertain variable.
Probability distribution
Input to a simulation model whose value is uncertain and described by a probability distribution.
Random variable or uncertain variable
The process of evaluating a decision in the face of uncertainty by quantifying the likelihood and magnitude of an undesirable outcome.
Risk analysis
A method that uses repeated random sampling of values to represent uncertainty in a model representing a real system and computes the values of model outputs.
Simulation
A simulation model in which each trial used in situations where the state of the system at one point in time does not affect the state of the system at future points in time. Each trial of the simulation is independent.
Static simulation model
The process of determining that a simulation model provides an accurate representation of a real system.
Validation
The process of determining that a computer program implements a simulation model as it is intended.
Verification
A trial-and-error approach to learning about the range of possible out puts for a model. Trial values are chosen for the model inputs (these are the what-ifs) and the value of the output(s) is computed.
What-if analysis
Determining the output assuming the worst values that can be expected for the random variables of a model.
Worst-case scenario