1. Chapter 10 Introduction to Simulation Modeling Monte Carlo Simulation

2. 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.

3. Introduction continued

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

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

6. Symmetric vs. skewed

8. 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

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

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

11. 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

12. Built-in Excel tools and @Risk Monte Carlo simulation models –Using built-in Excel functions –Using @RISK Excel add-in by Palisade Corporation The Decision Analysis Tool Kit