1. Computer Simulation A Laboratory to Evaluate “What-if” Questions

2. Nature of Computer Simulation A system is a combination of elements that interact to accomplish an objective. A computer simulation model generates estimates of system performance. An animated simulation allows one to view the system activity in accelerated time. Discrete event simulation is driven by events that occur at certain points in time. Systems are both dynamic and stochastic.

3. Process of System Simulation Define problem and objectives Collect empirical data to determine probability distributions Begin model development with a process flowchart Verify that the model works as intended Validate that the model predicts system performance based on historical data Run scenarios (What ifs) one at a time under controlled conditions Document and prepare graphic presentations Implementation of results

4. Monte Carlo Simulation Use random numbers (RN) to generate probabilistic events. Note that RN is uniformly distributed on the interval 0 to 1. Random variable are either discrete or continuous

5. Generating Discrete Random Variable Days x Prob. p(x) Cum. Prob. F(x) RN Assignment 10.1 0.0≤RN<0.1 20.50.60.1≤RN<0.6 30.41.00.6≤RN<1.0 Example of Lead Time Delay for Order

6. Generating Continuous Random Variable Uniform Distribution Negative Exponential Distribution Normal Distribution