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# Simulation Powerpoint- Lecture Notes

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### Simulation Powerpoint- Lecture Notes

1. 1. Introduction To Modeling and Simulation Lecture 1 Introduction 1
2. 2. Goals Of This CourseIntroduce ModelingIntroduce SimulationDevelop an Appreciation for the Need forSimulationDevelop Facility in Simulation ModelBuilding“Learn by Doing”--Lots of Case Studies Introduction 2
3. 3. What Is A Model ?A Representation of an object, asystem, or an idea in some formother than that of the entity itself. (Shannon) Introduction 3
4. 4. Types of Models:Physical (Scale models, prototype plants,…)Mathematical (Analytical queueing models, linear programs, simulation) Introduction 4
5. 5. What is Simulation?A Simulation of a system is the operation of amodel, which is a representation of that system.The model is amenable to manipulation whichwould be impossible, too expensive, or tooimpractical to perform on the system which itportrays.The operation of the model can be studied, and,from this, properties concerning the behavior ofthe actual system can be inferred. Introduction 5
6. 6. Applications:Designing and analyzing manufacturingsystemsEvaluating H/W and S/W requirements for acomputer systemEvaluating a new military weapons system ortacticsDetermining ordering policies for aninventory systemDesigning communications systems andmessage protocols for them Introduction 6
7. 7. Applications:(continued)Designing and operating transportationfacilities such as freeways, airports,subways, or portsEvaluating designs for service organizationssuch as hospitals, post offices, or fast-foodrestaurantsAnalyzing financial or economic systems Introduction 7
8. 8. Steps In Simulation and Model Building1. Define an achievable goal2. Put together a complete mix of skills on the team3. Involve the end-user4. Choose the appropriate simulation tools5. Model the appropriate level(s) of detail6. Start early to collect the necessary input data Introduction 8
9. 9. Why Teach with Simulations?1.Deep Learning• Instructional simulations have the potential to engage students in "deep learning" that empowers understanding as opposed to "surface learning" that requires only memorization.• A good summary of how deep learning contrasts with surface learning is given at the Engineering Subject Centre
10. 10. Deep learning means student can learn scientific methods including• the importance of model building.• Experiments and simulations are the way scientists do their work.• Using instructional simulations gives students concrete formats of what it means to think like a scientist and do scientific work.• the relationships among variables in a model or models. Simulation allows students to change parameter values and see what happens.• Students develop a feel for what variables are important and the significance of magnitude changes in parameters.
11. 11. • data issues, probability and sampling theory. Simulations help students understand probability and sampling theory.• Instructional simulations have proven their worth many times over in the statistics based fields.• The ability to match simulation results with an analytically derived conclusion is especially valuable in beginning classes, where students often struggle with sampling theory.• Given the utility of data simulation, it is not surprising that SERC has an existing module on teaching with data simulation.• how to use a model to predict outcomes.• Simulations help students understand that scientific knowledge rests on the foundation of testable hypotheses.
12. 12. Learn to reflect on and extend knowledge by:• actively engaging in student-student or instructor- student conversations needed to conduct a simulation. Instructional simulations by their very nature cannot be passive learning.• Students are active participants in selecting parameter values, anticipating outcomes, and formulating new questions to ask.• transferring knowledge to new problems and situations. A well done simulation is constructed to include an extension to a new problem or new set of parameters that requires students to extend what they have learned in an earlier context.
13. 13. • understanding and refining their own thought processes. A well done simulation includes a strong reflection summary that requires students to think about how and why they behaved as they did during the simulation.• seeing social processes and social interactions in action. This is one of the most significant outcomes of simulation in social science disciplines such as sociology and political science.
14. 14. How to Teach with Simulations? Instructor Preparation is Crucial• The good news is that instructional simulations can be very effective in stimulating student understanding.• The bad news is that many simulations require intensive pre-simulation lesson preparation.• Lesson preparation varies with the type and complexity of the simulation
15. 15. Active Student Participation is Crucial• Students learn through instructional simulations when they are actively engaged.• Students should predict and explain the outcome they expect the simulation to generate.• Every effort should be made to make it difficult for students to become passive during the simulation. Students must submit timely input and not rely on classmates to play for them.• Instructors should anticipate ways the simulation can go wrong and include this in their pre-simulation discussion with the class.
16. 16. Post-Simulation Discussion is Crucial• Post-simulation discussion with students leads to deeper learning. The instructor should:• 1. Provide sufficient time for students to reflect on and discuss what they learned from the simulation• 2. Integrate the course goals into the post-simulation discussion• 3. Ask students explicitly asked how the simulation helped them understand the course goals or how it may have made the goals more confusing. Extremely significant or important=crucial
17. 17. Steps In Simulation and Model Building(cont’d)7. Provide adequate and on-going documentation8. Develop a plan for adequate model verification (Did we get the “right answers ?”)9. Develop a plan for model validation (Did we ask the “right questions ?”)10. Develop a plan for statistical output analysis Introduction 17
18. 18. Define An Achievable Goal“To model the…” is NOT a goal!“To model the…in order to select/determine feasibility/…is a goal.Goal selection is not cast in concreteGoals change with increasing insight Introduction 18
19. 19. Put together a complete mix of skills on the teamWe Need:-Knowledge of the system under investigation-System analyst skills (model formulation)-Model building skills (model Programming)-Data collection skills-Statistical skills (input data representation) Introduction 19
20. 20. Put together a completemix of skills on the team(continued)We Need:-More statistical skills (output data analysis)-Even more statistical skills (design of experiments)-Management skills (to get everyone pulling in the same direction) Introduction 20
21. 21. INVOLVE THE END USER-Modeling is a selling job!-Does anyone believe the results?-Will anyone put the results into action?-The End-user (your customer) can (and must) do all of the above BUT, first he must be convinced!-He must believe it is HIS Model! Introduction 21
22. 22. Choose The Appropriate Simulation Tools Assuming Simulation is the appropriate means, three alternatives exist:1. Build Model in a General Purpose Language2. Build Model in a General Simulation Language3. Use a Special Purpose Simulation Package Introduction 22
23. 23. MODELLING W/ GENERAL PURPOSE LANGUAGESAdvantages:– Little or no additional software cost– Universally available (portable)– No additional training (Everybody knows…(language X) ! )Disadvantages:– Every model starts from scratch– Very little reusable code– Long development cycle for each model– Difficult verification phase Introduction 23
24. 24. GEN. PURPOSE LANGUAGES USED FOR SIMULATIONFORTRAN – Probably more models than any other language.PASCAL – Not as universal as FORTRANMODULA – Many improvements over PASCALADA – Department of Defense attempt at standardizationC, C++ – Object-oriented programming language Introduction 24
25. 25. MODELING W/ GENERALSIMULATION LANGUAGESAdvantages:– Standardized features often needed in modeling– Shorter development cycle for each model– Much assistance in model verification– Very readable codeDisadvantages:– Higher software cost (up-front)– Additional training required– Limited portability Introduction 25
26. 26. GENERAL PURPOSESIMULATION LANGUAGESGPSS– Block-structured Language– Interpretive Execution– FORTRAN-based (Help blocks)– World-view: Transactions/FacilitiesSIMSCRIPT II.5– English-like Problem Description Language– Compiled Programs– Complete language (no other underlying language)– World-view: Processes/ Resources/ Continuous Introduction 26
27. 27. GEN. PURPOSE SIMULATION LANGUAGES (continued) MODSIM III – Modern Object-Oriented Language – Modularity Compiled Programs – Based on Modula2 (but compiles into C) – World-view: Processes SIMULA – ALGOL-based Problem Description Language – Compiled Programs – World-view: Processes Introduction 27
28. 28. GEN. PURPOSE SIMULATION LANGUAGES (continued) SLAM – Block-structured Language – Interpretive Execution – FORTRAN-based (and extended) – World-view: Network / event / continuous CSIM – process-oriented language – C-based (C++ based) – World-view: Processes Introduction 28
29. 29. MODELING W/ SPECIAL-PURPOSE SIMUL. PACKAGES Advantages – Very quick development of complex models – Short learning cycle – No programming--minimal errors in usage Disadvantages – High cost of software – Limited scope of applicability – Limited flexibility (may not fit your specific application) Introduction 29
30. 30. SPECIAL PURPOSEPACKAGES USED FOR SIMUL.NETWORK II.5– Simulator for computer systemsOPNET– Simulator for communication networks, including wireless networksCOMNET III– Simulator for communications networksSIMFACTORY– Simulator for manufacturing operations Introduction 30
31. 31. THE REAL COST OF SIMULATIONMany people think of the cost of a simulation only in terms of the software package price.There are actually at least three components to the cost of simulation:1. Purchase price of the software2. Programmer / Analyst time3. “Timeliness of Results” Introduction 31
32. 32. TERMINOLOGYSystem– A group of objects that are joined together in some regular interaction or interdependence toward the accomplishment of some purpose.– Entity– An object of interest in the system.– E.g., customers at a bank Introduction 32
33. 33. TERMINOLOGY (continued)Attribute– a property of an entity– E.g., checking account balanceActivity– Represents a time period of specified length.– Collection of operations that transform the state of an entity– E.g., making bank deposits Introduction 33
34. 34. TERMINOLOGY (continued)Event:– change in the system state.– E.g., arrival; beginning of a new execution; departureState Variables– Define the state of the system– Can restart simulation from state variables– E.g., length of the job queue. Introduction 34
35. 35. TERMINOLOGY (continued)Process– Sequence of events ordered on timeNote:– the three concepts(event, process,and activity) give rise to three alternative ways of building discrete simulation models Introduction 35
36. 36. A GRAPHIC COMPARISON OFDISCRETE SIMUL. METHODOLOGIES A1 A2 P1 E1 E2 /E3 E4 A1 A2 P2 E1’ E2’ E3’ E4’ Simulation Time Introduction 36
37. 37. EXAMPLES OF SYSTEMS AND COMPONENTS System Entities Attributes Activities Events State Variables Banking Customers Checking Making Arrival; # of busy account deposits Departure tellers; # of balance customers waitingNote: State Variables may change continuously (continuous sys.)over time or they may change only at a discrete set of points(discrete sys.) in time. Introduction 37
38. 38. SIMULATION “WORLD- VIEWS”Pure Continuous SimulationPure Discrete Simulation– Event-oriented– Activity-oriented– Process-orientedCombined Discrete / Continuous Simulation Introduction 38
39. 39. Examples Of Both Type ModelsContinuous Time and Discrete TimeModels:CPU scheduling model vs. number ofstudents attending the class. Introduction 39
40. 40. Examples (continued)Continuous State and Discrete StateModels:Example: Time spent by students in aweekly class vs. Number of jobs in Q. Introduction 40
41. 41. Other Type Models Deterministic and Probabilistic Models: Output Output Input Input Static and Dynamic Models: CPU scheduling model vs. E = mc2 Introduction 41
42. 42. Stochastic vs. Deterministic System Model 1Deterministic Deterministic 3 2 Stochastic Stochastic 4 Introduction 42
43. 43. MODEL THE APPROPRIATE LEVEL(S) OF DETAIL Define the boundaries of the system to be modeled. Some characteristics of “the environment” (outside the boundaries) may need to be included in the model. Not all subsystems will require the same level of detail. Control the tendency to model in great detail those elements of the system which are well understood, while skimming over other, less well - understood sections. Introduction 43
44. 44. START EARLY TO COLLECTTHE NECESSARY INPUT DATAData comes in two quantities: TOO MUCH!! TOO LITTLE!!With too much data, we need techniques for reducing it to a form usable in our model.With too little data, we need information which can be represented by statistical distributions. Introduction 44
45. 45. PROVIDE ADEQUATE ANDON-GOING DOCUMENTATIONIn general, programmers hate to document. (They love to program!)Documentation is always their lowest priority item. (Usually scheduled for just after the budget runs out!)They believe that “only wimps read manuals.”What can we do? – Use self-documenting languages – Insist on built-in user instructions(help screens) – Set (or insist on) standards for coding style Introduction 45
46. 46. DEVELOP PLAN FOR ADEQUATE MODEL VERIFICATIONDid we get the “right answers?” (No such thing!!)Simulation provides something that no other technique does:Step by step tracing of the model execution.This provides a very natural way of checking the internal consistency of the model. Introduction 46
47. 47. DEVELOP A PLAN FOR MODEL VALIDATIONVALIDATION: “Doing the right thing” Or “Asking the right questions”How do we know our model represents thesystem under investigation? – Compare to existing system? – Deterministic Case? Introduction 47
48. 48. DEVELOP A PLAN FORSTATISTICAL OUTPUT ANALYSISHow much is enough? Long runs versus ReplicationsTechniques for Analysis Introduction 48