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ISE 195 Introduction to Industrial Engineering Lecture 2

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Title: ISE 195 Introduction to Industrial Engineering Lecture 2


1
ISE 195 Introduction to Industrial
Engineering Lecture 2
2
Modeling and Simulation (Topic of ISE 471 System
Performance Modeling)
3
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
  • Todays goal Cover general ideas, terminology,
    examples of applications, good/bad things, kinds
    of simulation, software options, how/when
    simulation is used

4
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.

5
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

6
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
  • Newtons 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

7
Example 1
  • An example of a simulation from the mechanical
    engineers perspective
  • Vehicle Suspension Simulation (Inventor)
  • http//www.youtube.com/watch?vL0R5elR6nck

8
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 its often impossible to do so in reality
    with the actual system
  • System doesnt 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

9
Using Models
  • Study the model instead of the real system
    usually much easier, faster, cheaper, safer
  • Can try wide-ranging ideas with the model
  • Make your mistakes on the computer where they
    dont count, rather than for real where they do
    count
  • Often, just building the model is instructive
    regardless of results
  • Model 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

10
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 dont apply what to do?

11
Simulation is just a sampling experiment that
is performed using a model.
12
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

13
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 thats for sure nothing is for
    sure
  • Danger of ignoring system variability
  • Model validity - is the system correctly captured

Youre walking along in the dark and see someone
on hands and knees searching the ground under a
street light. You Whats wrong? Can I help
you? Other person I dropped my car keys and
cant 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.
14
Advantages of Simulation (contd.)
  • 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

15
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?

16
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 Winstons Simulation of the 2010 NCAA Mens
    Basketball Tournament
  • http//waynewinston.com/wordpress/?p509

17
Example Monte Carlo Model in a Spreadsheet
18
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.

19
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

20
Example 2 Traffic Simulators
  • Vehicle Intersection Model with Pedestrians
    (VisSim)
  • http//www.youtube.com/watch?vYq9IAzNTAz0feature
    related

21
Example 3 Agent Based Models
  • Subway Station Simulation AnyLogic Subway
    Entrance Hall Model
  • http//www.xjtek.com/anylogic/demo_models/?applica
    tion_areaPedestrianDynamics

22
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

23
  • Questions?
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