ISE 195Introduction to Industrial

EngineeringLecture 2

Modeling and Simulation

(Topic of ISE 471 System Performance Modeling)

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Simulation Is …

Simulation – very broad term – methods and applications to imitate or mimic real systems, usually via computer

Applies in many fields and industries

Very popular and powerful method, in fact many surveys list simulation as among the most used techniques

Today’s goal – Cover general ideas, terminology, examples of applications, good/bad things, kinds of simulation, software options, how/when simulation is used

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Simulation Is …

Simulation is the process of designing a model of a real or imagined system and conducting experiments with that model

The purpose of simulation experiments is to understand the behavior of the system or evaluate strategies for the operation of the system Simulation is a “descriptive” technique, it generally requires

something to evaluate

Definition of Simulation: The technique of imitating the behavior of some situation or system by means of an analogous model, situation, or apparatus, either to gain information more conveniently or to train personnel.

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Systems

System – facility or process, actual or planned Many Examples …

Manufacturing facility Bank or other personal-service operation Transportation/logistics/distribution operation Hospital facilities (emergency room, operating room, admissions) Computer network Freeway system Business process (insurance office) Criminal justice system Chemical plant Fast-food restaurant Supermarket Theme park Flight-line maintenance modeling Simulator training systems Emergency-response system

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What is a Model in Engineering?

A system used to study another system Physical: A prototype or mock-up of a system

Live-action exercises Flight Simulators

Mathematical Systems of Simultaneous Linear Equations “Closed Form” expressions (Force = mass x acceleration)

Logical A chemical reaction Description of input/output of a logic circuit

Computational: A combination of logical and mathematical with a computer engine

Numerical methods– Newton’s method for finding a minimum of a convex function– Iterative solutions to differential equations

Computer Simulation: Using a computer-based model to mimic a real system as it evolves through time

– Includes both mathematical aspects and logical aspects

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Example 1

An example of a “simulation” from the mechanical engineer’s perspective

Vehicle Suspension Simulation (Inventor) http://www.youtube.com/watch?v=L0R5elR6nck

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Why Not Work With the Actual System?

Study the system – measure, improve, design, control Maybe just play with the actual system

Advantage — unquestionably looking at the right thing

But it’s often impossible to do so in reality with the actual system System doesn’t exist Would be disruptive, expensive, or dangerous

Examples: Examine configurations without disrupting manufacturing

operations Examine customer flows without re-configuring the store Examine new tactics without endangering planes or people

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Using Models

Study the model instead of the real system … usually much easier, faster, cheaper, saferCan try wide-ranging ideas with the model Make your mistakes on the computer where they don’t

count, rather than for real where they do count

Often, just building the model is instructive – regardless of resultsModel validity (any kind of model … not just simulation) Care in building to mimic reality faithfully Level of detail incorporated must be determined Should get same conclusions from the model as from

system More on this during verification and validation material

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Studying Mathematical or Logical Models

If model is simple enough, use ISE mathematical analysis … get exact results, lots of insight into model Queueing theory Differential equations Linear programming

But complex systems can seldom be validly represented by a simple analytic model Danger of over-simplifying assumptions … model

validity? The simplified model can provide valid bounds

Often, a complex system requires a complex model, and analytical methods don’t apply … what to do?

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Simulation is just a sampling experiment that is performed using a model.

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When Should We Use Computer Simulation?

Can be used to study simple systems Usually not necessary if an analytical solution is

available You will often study simple systems via simulation in

classwork, its worth the effort to search for a

Real power of simulation is in studying complex models Simulation can support complex models

Good for comparing alternative designs More complex techniques allow “optimization” using a

simulation model

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Advantages of Simulation

Flexibility to model things as they are (even if messy and complicated) Avoid looking where the light is:

Allows uncertainty, nonstationarity in modeling The only thing that’s for sure: nothing is for sure Danger of ignoring system variability Model validity - is the system correctly captured

You’re walking along in the dark and see someone on hands and knees searching the ground under a street light.You: “What’s wrong? Can I help you?”Other person: “I dropped my car keys and can’t find them.”You: “Oh, so you dropped them around here, huh?”Other person: “No, I dropped them over there.” (Points into the darkness.)You: “Then why are you looking here?”Other person: “Because this is where the light is.”

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Advantages of Simulation (cont’d.)

Advances in computing/cost ratios Estimated that 75% of computing power is used for

various kinds of simulations Dedicated machines (e.g., real-time shop-floor control)

Advances in simulation software Modern Tools are far easier to use (GUIs)

There is a down-side to this

No longer as restrictive in modeling constructs (hierarchical languages exist, can program down to C) For ISE 471 we use ARENA

Statistical design & analysis capabilities However, practitioners do not solely rely on these packaged

results

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Dangers of Simulation Modeling

Tendency to be too convinced by results without validation of the model Animation is very compelling Numbers are very compelling

Results must be checked using statistical techniques Did you collect enough data? Are you sure of your conclusions? How sure are you about your conclusions?

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ISE Simulation Models

Monte Carlo Simulation Using “Sampling” to estimate measures from systems

NCAA Tournament Pool Example Can you estimate the probability of picking the national

champion in Basketball if you could assign probabilities to each game in your bracket? Could use probability theory, if you knew how to combine

probabilities Could use simulation to try it out many times on the computer,

and see what happens in many trial runs of the tournament Wayne Winston’s Simulation of the 2010 NCAA Men’s

Basketball Tournament– http://waynewinston.com/wordpress/?p=509

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Example – Monte Carlo Model in a Spreadsheet

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Discrete Event Simulation

“A model of a system as it evolves over time where the state of the system changes at discrete points in time”

Necessary when systems involve humans and logical connections between components

The “engine” of common ISE simulation software is built on the discrete event approach: ARENA (used in ISE 471), FlexSim, etc.

The “interface” for the common ISE simulation software is built on the “process flow” approach.

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Process Flow Description of Systems

Systems consist of: Entities (Customers, Parts) Resources (Machines, People) Routings (Logic, Networks) Input Data (Times, Probabilities) Performance Measures (Times, Utilizations)

ARENA Model of a Single Server System (Service Counter at a Bank)

ARENA Model of a Truck Assembly Line

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Example 2: Traffic Simulators

Vehicle Intersection Model with Pedestrians (VisSim) http://www.youtube.com/watch?

v=Yq9IAzNTAz0&feature=related

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Example 3: Agent Based Models

Subway Station Simulation: AnyLogic Subway Entrance Hall Model http://www.xjtek.com/anylogic/demo_models/?

application_area=Pedestrian++Dynamics

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Some Primary Uses of Simulation Models in Operations

Find the bottlenecks

How are resources utilized

Capacity planning

Impact of configuration changes

Understand the system dynamics

Questions?