1. MODES-650 Advanced System Simulation 5.11.2010 Presented by Olgun Karademirci http://www.karademirci.net REPRESENTING AND GENERATING UNCERTAINTY EFFECTIVELY W. David Kelton

2. 4 W. DAVID KELTON Professor Director, Master of Science in Quantitative Analysis (MSQA) Program Department of Quantitative Analysis and Operations Management University of Cincinnati Cincinnati, Ohio His Interests  Computer simulation methods and applications  Applied stochastic processes  Operations research  Statistical methods

3. OUTLINE Scope and purposeDifferent kinds of simulation models and inputsCommon assumption about random inputsGenerating and representing random input to a simulationAssigning random numbers to improve precisionConclusions

4. Scope and purpose  Suggestions for new ways of generating random inputs in simulation-modeling software is the scope of this study.  Purpose of the author in writing this proceeding is to discuss approaches for effective generation of uncertain inputs in computer-simulation models.

5. Different kinds of simulation models and inputs  Deterministic Simulations – Stochastic Simulations  Static Simulation Model – Dynamic Simulation Model  Structural Components – Quantitative Components  Deterministic Inputs – Random Inputs

6. Common assumption about random inputs  Mutually independent random inputs  Random inputs itself as a stream of independent and identically distrubution

7. Common assumption about random inputs  Example-1; A patient arriving to an urgent-care facility  Example-2; A telecommunications system  Example-2; A call center

8. Generating and representing random input to a simulation  Using actual data (observed)  Fitting data  Empirical distribution

9. Assigning random numbers to improve precision  Random number generation  Pseudo  True

10. Conclusions  Importance of input random process  Model’s Validity – Model Precision