1. Introduction To Modeling and Simulation Rabie A. Ramadan Rabie@rabieramadan.org http://www.rabieramadan.org/classes/2012/m odeling / Lecture 1

2. 2 Welcome Back

3. Class Organization 3 Attendance is very important Assignments Projects Quizzes

4. Textbooks 4

5. Exams 5 Do not worry about the exam as long as : You are attending Done with your project Done with your presentation Assignments are delivered

6. Why should I attend ? 6 We will have group activities in class. Some materials will be taught from outside our textbook(s). Some materials will be skipped or left for you to read

7. Projects 7 There will be a term project Only 4 persons per project You can select your own project after my approval Project

8. Things need to be with you in class 8 For the group activities

9. Goals Of This Course 9 l Introduce Modeling l Introduce Simulation l Develop an Appreciation for the Need for Simulation l Develop Facility in Simulation Model Building

10. What is M&S ? 10 Discipline of understanding and evaluating the interaction of parts of a real or theoretical system by; Designing its representation (model) and Executing (running) the model including the time and space dimension (simulation).

11. What is a System 11 An entity (unit, process, event...), which exists and operates in time and space through the interaction of its parts.

12. What Is A Model ? 12 A Representation of an object, a system, or an idea in some form other than that of the entity itself. (Shannon)

13. Types of Models: 13 Physical (Scale models, prototype plants,…) Mathematical (Analytical queuing models, linear programs, simulation)

14. What is Simulation? 14 l A Simulation of a system is the operation of a model, which is a representation of that system. l The model is amenable to manipulation which would be impossible, too expensive, or too impractical to perform on the system which it represents. l The operation of the model can be studied, and, from this, properties concerning the behavior of the actual system can be inferred.

15. What is a Simulation ? The manipulation of a model in such a way that it operates in time or space to summarize it.

16. Applications : 16 l Designing and analyzing manufacturing systems l Evaluating H/W and S/W requirements for a computer system l Evaluating a new military weapons system or tactics l Determining ordering policies for an inventory system l Designing communications systems and message protocols for them

17. Applications:(continued) 17 l Designing and operating transportation facilities such as freeways, airports, subways, or ports l Evaluating designs for service organizations such as hospitals, post offices, or fast-food restaurants l Analyzing financial or economic systems

18. Steps In Simulation and Model Building 18 1. Define an achievable goal 2. Put together a complete mix of skills on the team 3. Involve the end-user 4. Choose the appropriate simulation tools 5. Model the appropriate level(s) of detail 6. Start early to collect the necessary input data

19. Steps In Simulation and Model Building(cont’d) 19 7. Provide adequate and on-going documentation 8. 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

20. Define An Achievable Goal 20 “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 concrete Goals change with increasing insight

21. Put together a complete mix of skills on the team 21 We 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)

22. Put together a complete mix of skills on the team(continued) 22 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)

23. Choose The Appropriate Simulation Tools 23 Assuming Simulation is the appropriate means, three alternatives exist: 1.Build Model in a General Purpose Language 2.Build Model in a General Simulation Language 3.Use a Special Purpose Simulation Package

24. MODELLING With GENERAL PURPOSE LANGUAGES 24 l Advantages: Little or no additional software cost Universally available (portable) No additional training (Everybody knows…(language X) ! ) l Disadvantages: Every model starts from scratch Very little reusable code Long development cycle for each model Difficult verification phase

25. GEN. PURPOSE LANGUAGES USED FOR SIMULATION 25 FORTRAN Probably more models than any other language. PASCAL Not as universal as FORTRAN MODULA Many improvements over PASCAL ADA Department of Defense attempt at standardization C, C++ Object-oriented programming language What Language are you professional at?

26. MODELING W/ GENERAL SIMULATION LANGUAGES 26 l Advantages: Standardized features often needed in modeling Shorter development cycle for each model Much assistance in model verification Very readable code l Disadvantages: Higher software cost (up-front) Additional training required Limited portability

27. GENERAL PURPOSE SIMULATION LANGUAGES 27 l GPSS Block-structured Language Interpretive Execution FORTRAN-based (Help blocks) World-view: Transactions/Facilities l SIMSCRIPT II.5 English-like Problem Description Language Compiled Programs Complete language (no other underlying language) World-view: Processes/ Resources/ Continuous

28. GEN. PURPOSE SIMULATION LANGUAGES (continued) 28 l MODSIM III Modern Object-Oriented Language Modularity Compiled Programs Based on Modula2 (but compiles into C) World-view: Processes l SIMULA Problem Description Language Compiled Programs World-view: Processes

29. GEN. PURPOSE SIMULATION LANGUAGES (continued) 29 l SLAM Block-structured Language Interpretive Execution FORTRAN-based (and extended) World-view: Network / event / continuous l CSIM process-oriented language C-based (C++ based) World-view: Processes

30. MODELING W/ SPECIAL-PURPOSE SIMUL. PACKAGES 30 l Advantages Very quick development of complex models Short learning cycle No programming--minimal errors in usage l Disadvantages High cost of software Limited scope of applicability Limited flexibility (may not fit your specific application)

31. SPECIAL PURPOSE PACKAGES USED FOR SIMUL. 31 l NETWORK II.5 Simulator for computer systems l OPNET Simulator for communication networks, including wireless networks l COMNET III Simulator for communications networks l SIMFACTORY Simulator for manufacturing operations

32. THE REAL COST OF SIMULATION 32 Many 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 software 2. Programmer / Analyst time 3. “Timeliness of Results” Being at the right time.

33. TERMINOLOGY 33 l System 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

34. TERMINOLOGY (continued) 34 l Attribute a property of an entity E.g., checking account balance l Activity Represents a time period of specified length. Collection of operations that transform the state of an entity E.g., making bank deposits

35. TERMINOLOGY (continued) 35 l Event: change in the system state. E.g., arrival; beginning of a new execution; departure l State Variables Define the state of the system Can restart simulation from state variables E.g., length of the job queue.

36. TERMINOLOGY (continued) 36 l Process Sequence of events ordered on time W Note: the three concepts(event, process,band activity) give rise to three alternative ways of building discrete simulation models

37. SIMULATION “WORLD-VIEWS” 37 l Pure Continuous Simulation l Pure Discrete Simulation Event-oriented Activity-oriented Process-oriented l Combined Discrete / Continuous Simulation

38. Examples Of Both Type Models 38 l Continuous Time and Discrete Time Models: CPU scheduling model vs. number of students attending the class. a) N(t) value is defined all the time b) N(t) value is defined at discrete point in time, not all the time.

39. Examples (continued) 39 l Continuous State and Discrete State Models: Example: Time spent by students in a weekly class vs. Number of jobs in Q. a) state takes continuous values (fraction) b) state takes discrete values (only integers, ON/OFF, Busy/Idle).

40. Other Type Models 40 Static and Dynamic Models: CPU scheduling model vs. E = mc 2 Input Output Input Output lDeterministic and Probabilistic/Stochastic Models:

41. START EARLY TO COLLECT THE NECESSARY INPUT DATA 41 Data 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. Use BestFit software by Law

42. DEVELOP PLAN FOR ADEQUATE MODEL VERIFICATION 42 Did 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.

43. DEVELOP A PLAN FOR MODEL VALIDATION 43 VALIDATION:“Doing the right thing” Or“Asking the right questions” How do we know our model represents the system under investigation? Compare to existing system? Deterministic Case?

44. DEVELOP A PLAN FOR STATISTICAL OUTPUT ANALYSIS 44 l How much is enough? Long runs versus Replications l Transient and steady state