# Modeling and Simulation

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Modeling and Simulation
CS 4901

Course outlines Introduction Basic simulation models
Discrete-Event Simulation Random number Other simulation models

Introduction Definition Brief History Applications

Definition: “Simulation is the process of designing
a model of a real system and conducting experiments with this model for the purpose of either understanding the behavior of the system and/or evaluating various strategies for the operation of the system.” - Introduction to Simulation Using SIMAN (2nd Edition)

System definition: System is a collection of entities (people, parts, messages, machines, servers, …) that act and interact together toward some end (Schmidt and Taylor, 1970) In practice, depends on objectives of study Might limit the boundaries (physical and logical) of the system Judgment call: level of detail (e.g., what is an entity?) Usually assume a time element – dynamic system State of a system: Collection of variables and their values necessary to describe the system at that time Might depend on desired objectives, output performance measures Bank model: Could include number of busy tellers, time of arrival of each customer, etc.

EXAMPLES OF SYSTEMS AND COMPONENTS
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Types of systems Discrete Continuous
State variables change instantaneously at separated points in time Bank model: State changes occur only when a customer arrives or departs Continuous State variables change continuously as a function of time Head of water behind the dam : State variables Head amount change continuously Many systems are partly discrete, partly continuous

Ways to study a system Simulation is “method of last resort?” Maybe …
But with simulation there’s no need (or less need) to “look where the light is”

Modeling construct a conceptual framework that describes a system
Modeling is a way of looking at the world a system can have multiple contradictory models We need to use models because resources are limited Models simplified thing Using the appropriate model allows us to make decisions, even when the situation is complex or resources are limited We are always using models

Simulating “Simulation is the process of designing a model of a real system and conducting experiments with this model for the purpose of either understanding the behavior of the system and/or evaluating various strategies for the operation of the system.” To simulate any system we need at first to build a model for that system then simulate this model Types of simulation models: Physical simulation models Mathematical simulation models Static vs. dynamic Deterministic vs. stochastic Continuous vs. discrete (Most operational models are dynamic, stochastic, and discrete – will be called discrete-event simulation models)

simulation software (package)
Open source: Galatea, SimPy, Tortuga … etc. Commercial: Arena, Enterprise Dynamic, SIMUL8 …etc

Simulation allows us to:
Why we need simulation ? Because the real system is: too cumbersome (ثقيل) too costly too dangerous too slow Simulation allows us to: Model complex systems in a detailed way Describe the behavior of systems Construct theories or hypotheses that account for the observed behavior Use the model to predict future behavior, that is, the effects that will be produced by changes in the system Analyze proposed systems

Help-desk operator example
Help- desk operator has two states Busy helping someone waiting for a call to come in. Events: Customer starts describing problem to help desk Customer completes telephone conversation Simulation starts at t= t0 = 0 First customer arrives at t1, simulation time is now t= t0 + t1 Help desk requires ts to resolve issue First customer leaves at t= t0 + t1 + ts Second customer arrives at t2 If t2< t0 + t1 + ts then customer must wait If t2> t0 + t1 + ts then customer starts services

Brief History Not a very old technique...
World War II “Monte Carlo” simulation: originated with the work on the atomic bomb. Used to simulate bombing raids. Given the security code name “Monte-Carlo”. Still widely used today for certain problems which are not analytically solvable (for example: complex multiple integrals…)

Brief History (cont.) Late ‘50s, early ‘60s Computers improve
First languages introduced: SIMSCRIPT, GPSS (IBM) Simulation viewed at the tool of “last resort” Late ‘60s, early ‘70s Primary computers were mainframes: accessibility and interaction was limited GASP IV introduced by Pritsker. Triggered a wave of diverse applications. Significant in the evolution of simulation.

Brief History (cont.) Late ‘70s, early ‘80s
SLAM introduced in 1979 by Pritsker and Pegden. Models more credible because of sophisticated tools. SIMAN introduced in 1982 by Pegden. First language to run on both a mainframe as well as a microcomputer. Late ‘80s through present Powerful PCs Languages are very sophisticated (market almost saturated) Major advancement: graphics. Models can now be animated!

What can be simulated? Almost anything can and
almost everything has...

The Process of Simulation