Chapter 10 Introduction to Simulation Modeling Monte Carlo Simulation

Introduction Simulation model is a computer model that imitates a real-life situation Incorporates uncertainty in input variables You allow random values for the input variables that imitate reality Keep track of any resulting output variables of interest Analyze how the outputs vary as a function of the varying inputs.

Introduction continued

Probability distributions for input variables –Discrete versus continuous –Symmetric versus skewed –Bounded versus unbounded –Nonnegative versus unrestricted.

Discrete vs. continuous Discrete = Finite number of possible values Continuous = Possible values are on a continuum

Symmetric vs. skewed

Bounded vs. unbounded Bounded if there is a minimum possible value and a maximum possible value. Unbounded if either there is no minimum, or maximum, or neither minimum nor maximum

Nonnegative vs. unrestricted Nonnegative = only possible values are nonnegative Unrestricted = no such restriction

Common probability distributions Discrete: Poisson, Uniform, Binomial, etc. Continuous: Normal, Uniform, Exponential, Beta, Triangular, etc.

Simulation and the flaw of averages Deterministic models use average values for input variables Average value for an input variable rarely occurs in reality Simulation takes into account all possible values

Built-in Excel tools Monte Carlo simulation models –Using built-in Excel functions Excel add-in by Palisade Corporation The Decision Analysis Tool Kit