Discrete Event Simulation

Jim Herbst

Modeling & Simulation

November 3, 2012

Strayer University

Dr. Abdel Ismail

CIS 530: Simulation & Modeling

Fall 2012

Abstract

Discrete event simulation uses stochastic models through a computer to imitate a real-life situation. It is like other mathematical models, but it explicitly incorporates uncertainty in one or more input quantities. Stochastic models allow random quantities to take on various values, and keep track of any resulting output quantities of interest. In this way, discrete event simulation allows the user to see how the outputs vary as a function of the varying inputs.

The modeling matches the activities to probability distributions while acknowledging that activities/scope can be complicated, but their outcomes are finite. Matching activities/scope to probability distributions accurately reflects the complexity of those activities. Queuing theories hold that many behaviors can follow probability distributions.

There is a probability distribution for just about any behavior. The Exponential Distribution models inter-arrival times such as:

– Customers arriving every x minutes to a store

– Calls arriving every y minutes to a call center

– Workers taking breaks every z minutes etc…

The Gamma Distribution often models the amount of time it takes to complete a task such as repairing a machine or packaging inventory.

The modeling allows inferences to be drawn about system processes virtually without building a proposed system or disturbing a current system that is in practice. This allows managers and analysts to evaluate proposed systems virtually to solve problems. Once built, the model lends itself to numerous experiments and alternatives, which are generally less costly than other methods while providing insight into how randomness affects a system or process.

History of Simulation

Through my search the origin of modeling and simulation goes back to the Society...