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Modeling and Simulation

Modeling and Simulation. CS 4211. Course outlines. Introduction. (Definition, Brief History, Applications and advantage and disadvantages of simulation) Simulation Software Discrete event simulation Introducing the discrete event simulation Single server queuing systems simulation

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Modeling and Simulation

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  1. Modeling and Simulation CS 4211

  2. Course outlines • Introduction. (Definition, Brief History, Applications and advantage and disadvantages of simulation) • Simulation Software • Discrete event simulation • Introducing the discrete event simulation • Single server queuing systems simulation • Multiple server queuing systems simulation • Simulation of Inventory Systems • Simulation of Inventory Systems newspaper problem • Monte-Carlo simulation • Random number • Random number generating • Random number testing • Simulation Input and Output Data Analysis.

  3. Course evaluation

  4. Introduction • Definition • Brief History • Applications • “Real World” Applications • The advantage of simulation • The disadvantages of simulation

  5. 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)

  6. 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.

  7. EXAMPLES OF SYSTEMS AND COMPONENTS Exercise: Give examples of systems and components

  8. Types of systems • Discrete • 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

  9. 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”

  10. 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

  11. 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)

  12. 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

  13. 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

  14. 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…)

  15. 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.

  16. 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!

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

  18. The Process of Simulation

  19. Advantages of Simulation • Can be used to study existing systems without disrupting the ongoing operations. • Proposed systems can be “tested” before committing resources • Can handle large and complex systems • Can answer “what-if” questions • Does not interfere with the real system • Allows study of interaction among variables • “Time compression” is possible • Handles complications that other methods can’t

  20. Disadvantages of Simulation • Can be expensive and time consuming • Managers must choose solutions they want to try (“what-if” scenarios) • Each model is unique • Model building is an art as well as a science. The quality of the analysis depends on the quality of the model and the skill of the modeler (Remember: GIGO) • Simulation results are sometimes hard to interpret. • Should not be used when an analytical method would provide for quicker results.

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

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