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Modelling simulation (1)
- 1. INTRODUCTION TO MODELLING AND SIMULATION
- 2. Goals Of This Course History of Modeling and Simulation Introduce Modeling Introduce Simulation Develop an Appreciation for the Need for Simulation “Learn by Doing”--Lots of Case Studies
- 3. History of Modeling and Simulation LIVE REHERSAL GAMES VIRTUAL REALITTY INTEROPERABILITY SYSTEMS ANALYSIS DISCRETE ENTERTAINMENT
- 4. What Is A Model ? A Representation of an object, a system, or an idea in some form other than that of the entity itself. (Shannon)
- 5. Types of Models: Physical (Scale models, prototype plants,…) Mathematical (Analytical queueing models, linear programs, simulation)
- 6. MATHEMATICAL MODEL • An abstract and simplified representation of a system • Specifies – Important components – Assumptions/approximations about how the system works • Not an exact re-creation of the original system! • If model is simple enough, study it with Queuing Theory, Linear Programming, Differential Equations... • If model is complex, Simulation is the only way!!!
- 7. GETTING ANSWERS FROM MODELS MODEL Operating Policies •Single queue, parallel servers •FIFO Input Parameters •No of servers •Inter-arrival Time Distribution •Service Time Distributions Output Parameters •Waiting Times •System Size •Utilizations (X) (Y) Y = f (X) ACTUAL SYSTEM
- 8. STOCHASTIC MODELS • Randomness or uncertainty is inherent • Example: Bank with customers and tellers ACTUAL SYSTEM µ µ µ QUEUEING MODEL λ
- 9. APPLICATIONS Systems – facility or process, actual or planned Examples Manufacturing facility Bank operation Airport operations (passengers, security, planes, crews, baggage) 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 Emergency-response system
- 10. CLASSIFICATION OF SIMULATION MODELS Static (Monte Carlo) Dynamic Systems Represents the system at a particular point in time IID observations Represents the system behaviour over time Continuous Simulation: • (Stochastic) Differential Equations Discrete Event Simulation: • System quantities (state variables) change with events • Estimation of π • Risk Analysis in Business • Water Level in a Dam • Queueing Systems • Inventory Systems
- 11. HOW TO SIMULATE • By hand – Buffon Needle and Cross Experiments (see Kelton et al.) • Spreadsheets • Programming in General Purpose Languages – Java, UML • Simulation Languages – SIMAN • Simulation Packages – Arena, MAYA, STELLA Issue: Modeling Flexibility vs. Ease of Use
- 13. A Simulation of a system is the operation of a model, which is a representation of that system The imitation of the operation of a real-world process or system over time… – Most widely used tool for decision making – Usually on a computer with appropriate software – An analysis (descriptive) tool – can answer what if questions – A synthesis (prescriptive) tool – if complemented by other tools WHAT IS SIMULATION?
- 14. Applied to complex systems that are impossible to solve mathematically We shall focus on one form of simulation modelling – discrete-event simulation modelling The model is amenable to manipulation which would be impossible, too expensive, or too impractical to perform on the system which it portrays. The operation of the model can be studied, and, from this, properties concerning the behavior of the actual system can be inferred. WHAT IS SIMULATION?
- 15. Applications: Designing and analyzing manufacturing systems Evaluating and testing H/W and S/W requirements for a computer system Evaluating a new military weapons system or tactics Determining ordering policies for an inventory system Designing communications systems and message protocols for them
- 16. Applications:(continued) Designing and operating transportation facilities such as freeways, airports, subways, or ports Evaluating designs for service organizations such as hospitals, post offices, or fast-food restaurants Analyzing - financial or economic systems Predicting what is to come in the future based on what you have now
- 17. Steps In Simulation and Model Building 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
- 18. Steps In Simulation and Model Building(cont’d) 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
- 19. 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 concrete Goals change with increasing insight
- 20. Put together a complete mix of skills on the team 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)
- 21. Put together a complete mix 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)
- 22. 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!
- 23. Choose The Appropriate Simulation Tools 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. The Role of Computers in Simulation General-purpose languages Visual Basic, C++, Java Special-purpose simulation languages GPSS/H, SLAM II, SIMSCRIPT II.5 1. require less programming 2. more efficient and easier to check for errors 3. have random number generators built in Special Purpose Simulation Package Extend, AutoMod, ALPHA/Sim, SIMUL8,STELLA, Arena, AweSim!, SLX, etc.
- 25. MODELLING W/ GENERAL PURPOSE LANGUAGES Advantages: – Little or no additional software cost – Universally available (portable) – No additional training (Everybody knows…(language X) ! ) – Open source and free Disadvantages: – Every model starts from scratch – Very little reusable code – Long development cycle for each model – Difficult verification phase
- 26. GEN. PURPOSE LANGUAGES USED FOR SIMULATION 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++, Java – Object-oriented programming language
- 27. MODELING W/ GENERAL SIMULATION LANGUAGES Advantages: – Standardized features often needed in modeling – Shorter development cycle for each model – Much assistance in model verification – Very readable code Disadvantages: – Higher software cost (up-front) – Additional training required – Limited portability
- 28. GENERAL PURPOSE SIMULATION LANGUAGES GPSS – Block-structured Language – Interpretive Execution – FORTRAN-based (Help blocks) – World-view: Transactions/Facilities SIMSCRIPT II.5 – English-like Problem Description Language – Compiled Programs – Complete language (no other underlying language) – World-view: Processes/ Resources/ Continuous
- 29. 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
- 30. 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
- 31. 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)
- 32. SPECIAL PURPOSE PACKAGES USED FOR SIMULATION NETWORK II.5 – Simulator for computer systems OPNET – Simulator for communication networks, including wireless networks COMNET III – Simulator for communications networks SIMFACTORY – Simulator for manufacturing operations MAYA – Simulator for computer systems STELLA – Simulator for manufacturing operations, computer systems etc GIS
- 33. THE REAL COST OF SIMULATION 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”
- 34. ADVANTAGES OF SIMULATION • When mathematical analysis methods are not available, simulation may be the only investigation tool • When mathematical analysis methods are available, but are so complex that simulation may provide a simpler solution • Allows comparisons of alternative designs or alternative operating policies • Allows time compression or expansion
- 35. DISADVANTAGES OF SIMULATION • For a stochastic model, simulation estimates the output while an analytical solution, if available, produces the exact output • Often expensive and time consuming to develop • An invalid model may result with confidence in wrong results.
- 36. The Nature of Simulation • Impediments to acceptance, use of simulation –Models of large systems are usually very complex • But now have better modeling software … more general, flexible, but still (relatively) easy to use –Can consume a lot of computer time • But now have faster, bigger, cheaper hardware to allow for much better studies than just a few years ago … this trend will continue • However, simulation will also continue to push the envelope on computing power in that we ask more and more of our simulation models
- 37. The Nature of Simulation cont… –Impression that simulation is “just programming” • There’s a lot more to a simulation study than just “coding” a model in some software and running it to get “the answer” • Need careful design and analysis of simulation models – simulation methodology
- 38. Pitfalls with Computer Simulation • Failure to have a well-defined set of objectives at the beginning of the simulation. • Failure to have the entire project team involved at the beginning of the study. • Inappropriate level of model detail. • Failure to communicate with management throughout the course of the simulation study. • Misunderstanding of simulation by the management. • Treating simulation as if it were primarily an exercise in computer programming. • Failure to have people with a knowledge of simulation methodology and statistics on the modeling team.
- 39. Pitfalls with Computer Simulation • Failure to collect good system data, e.g. not enough data to create a good model. • Inappropriate simulation software. • Belief that easy-to-use simulation tools require a significantly lower level of technical competence. • Failure to account correctly for sources of randomness in the system under consideration. • Using arbitrary random distributions as input to the simulation. • Analyzing the output from the simulation using formulas that assumes independence.
- 40. Pitfalls with Computer Simulation • Making single replication of a particular system design and treating the output statistics as true answers. • Failure to have warm up period, if the steady state behavior of a system is of interest. • Comparing alternative system designs on the basis of one replica for each design. • Using wrong performance measures.