1 / 10

MODES-650 Advanced System Simulation

MODES-650 Advanced System Simulation. REPRESENTING AND GENERATING UNCERTAINTY EFFECTIVELY. W. David Kelton. Presented by Olgun Karademirci http://www.karademirci.net. 5.11. 20 10. 4. W. DAVID KELTON. Professor Director , Master of Science in Quantitative Analysis (MSQA) Program

beth
Télécharger la présentation

MODES-650 Advanced System Simulation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. 4 W. DAVID KELTON ProfessorDirector, 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

  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

More Related