ISE 195 Introduction to Industrial Engineering Lecture 2

1. ISE 195Introduction to Industrial EngineeringLecture 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 • Today’s 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 • 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

7. Example 1 • An example of a “simulation” from the mechanical engineer’s perspective • Vehicle Suspension Simulation (Inventor) • http://www.youtube.com/watch?v=L0R5elR6nck

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 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

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 don’t 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 don’t 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 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.”

14. 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

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 Winston’s Simulation of the 2010 NCAA Men’s Basketball Tournament • http://waynewinston.com/wordpress/?p=509

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?v=Yq9IAzNTAz0&feature=related

21. Example 3: Agent Based Models • Subway Station Simulation: AnyLogic Subway Entrance Hall Model • http://www.xjtek.com/anylogic/demo_models/?application_area=Pedestrian++Dynamics

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?