1. Introduction to Simulation K.Sailaja Kumar 1 SYSTEM SIMULATION AND MODELLING Course Code: MCA 52 Faculty : Sailaja Kumar k

2. Introduction to Simulation K.Sailaja Kumar 2 What is this course about?(1/3) r Simulation is a technique to analyze and predict the behavior of existing or proposed systems by experimenting with representative models of the systems. r This course is primarily about imitating the operation of real-world systems using computer programs. Our focus will be on “discrete-event” simulations.

3. Introduction to Simulation K.Sailaja Kumar 3 What is this course about?(2/3) r We will learn how to: m Abstract real-world systems into models m Implement models using software m Experiment design r Systems modeling requires understanding of m Basic probability, statistics, elementary calculus

4. Introduction to Simulation K.Sailaja Kumar 4 What is this course about?(3/3) r In practice simulation models are mostly built on computer systems. Several languages and software packages facilitate the model building and experimentation process. r The Unified Modeling Language (UML) will be used for modeling and documentation when appropriate.

5. Introduction to Simulation K.Sailaja Kumar 5 Course Outline r Introduction to Simulation r Simulation Examples r General Principles & Queuing Models r Generating Random-Numbers r Generating Random-Variates r Input Modelling r Verification and Validation of Simulation Models r Output Analysis for a Single Model

6. Introduction to Simulation K.Sailaja Kumar 6 Books r Lecture material primarily drawn from: m Discrete-Event System Simulation (Third Edition), Banks, Carson, Nelson, and Nicol, Prentice-Hall, 2007. r References m Simulation Modeling and Analysis. (Fourth Edition), Averill M Law,W David Kelton, McGraw Hill m Discrete – Event Simulation: A First Course Lawrence M. Leemis, Stephen K. Park: Pearson / Prentice-Hall, 2006.

7. Introduction to Simulation K.Sailaja Kumar 7 Introduction to Modeling and Simulation r What is simulation? r Systems and system Environment r Components of a system r Discrete and continuous Systems r Model of a system r Types of models r Discrete-Event System Simulation r When Simulation is the appropriate Tool r When Simulation is not appropriate r Advantages and Disadvantages of Simulation r Application areas of simulation r Steps in a simulation study

8. Introduction to Simulation K.Sailaja Kumar 8 What is a simulation?(1/2) r Simulation – is imitation of the operation of a real world process or system over a time period, usually using a computer r The behavior of a system over a time period is studied by developing a simulation model r Simulation modeling can be used m As an analysis tool for predicting the effect of changes to existing systems m As a design tool to predict the performance of new systems under varying sets of circumstances.

9. Introduction to Simulation K.Sailaja Kumar 9 What is a simulation?(2/2) r A simple Simulation model can be developed by mathematical methods. r Many real world systems models are developed using numerical computer-based simulation methods. r The simulation-generated data is used to estimate the measures of performance of the system.

10. Introduction to Simulation K.Sailaja Kumar 10 When simulation is the appropriate tool (1/3) r To study and experiment with the internal interactions of a complex system or subsystem r To study the effect of informational, organizational and environmental changes on a system r Knowledge gained in designing a simulation model may help to suggest improvements r Changing inputs and observing resulting outputs reveals which variables are most important and how they interact r As a pedagogical device to reinforce analytic solution methodologies r To experiment with new designs or policies prior to implementation r To verify analytic results

11. Introduction to Simulation K.Sailaja Kumar 11 When simulation is the appropriate tool (2/3) r Simulation can be used for the following purposes: r Simulation enables the study of experiments with internal interactions r Informational, organizational, and environmental changes can be simulated to see the model’s behavior r Knowledge from simulations can be used to improve the system r Observing results from simulation can give insight to which variables are the most important ones r Simulation can be used as pedagogical device to reinforce the learning material r Simulations can be used to verify analytical results, e.g. queueing systems r Animation of a simulation can show the system in action, so that the plan can be visualized

12. Introduction to Simulation K.Sailaja Kumar 12 When to use simulations?(3/3) r Simulations can be used: m To study complex system, i.e., systems where analytic solutions are infeasible. m To compare design alternatives for a system that doesn’t exist. m To study the effect of alterations to an existing system. Why not change the system?? m To reinforce/verify analytic solutions.

13. Introduction to Simulation K.Sailaja Kumar 13 When simulation is not appropriate (1/2) Simulation should not be used, in the case r when problem is solvable by common sense r when the problem can be solved mathematically r when direct experiments are easier r when the simulation costs exceed the savings r when the simulation requires time, which is not available r when no (input) data is available, but simulations need data r when the simulation cannot be verified or validated r when the system behavior is too complex or unknown Example: human behavior is extremely complex to model

14. Introduction to Simulation K.Sailaja Kumar 14 When simulation is not appropriate (2/2) Simulations should not be used: m If model assumptions are simple such that mathematical methods can be used to obtain exact answers (analytical solutions)

15. Introduction to Simulation K.Sailaja Kumar 15 Advantages of simulation(1/3) r Policies, procedures, decision rules, information flows can be explored without disrupting the real system r New hardware designs, physical layouts, transportation systems can tested without committing resources r Hypotheses about how or why a phenomena occur can be tested for feasibility r Time can be compressed or expanded - Slow-down or Speed-up r Insight can be obtained about the interaction of variables

16. Introduction to Simulation K.Sailaja Kumar 16 Advantages of simulation(2/3) r Insight can be obtained about the importance of variables to the performance of the system r Bottleneck analysis can be performed to detect excessive delays r Simulation can help to understand how the system operates rather than how people think the system operates r “What if” questions can be answered

17. Introduction to Simulation K.Sailaja Kumar 17 Advantages of simulation(3/3) r Once a model is built, it can be used repeatedly r Simulation data can be less costly to obtain than data from the real system r Simulation methods can be easier to apply than analytical methods r Simulation models be more general and require fewer assumptions than analytical models r Sometimes simulation is the only way to derive a solution to a problem

18. Introduction to Simulation K.Sailaja Kumar 18 Disadvantages of simulation r Model building requires training, it is like an art. - Compare model building with programming. r Simulation results can be difficult to interpret - Most outputs are essentially random variables - Thus, not simple to decide whether output is randomness or system behavior r Simulation can be time consuming and expensive - Skimping in time and resources could lead to useless/wrong results

19. Introduction to Simulation K.Sailaja Kumar 19 Disadvantages of simulation r The disadvantages are offset as follows r Simulation packages contain models that only need input data r Simulation packages contain output-analysis capabilities r Sophistication in computer technology improves simulation times r For most of the real-world problems there are no closed form solutions

20. Introduction to Simulation K.Sailaja Kumar 20 Disadvantages of simulation r 1. Developing non-trivial simulation models may be costly r 2. Simulation is sometimes used when analytic techniques will suffice r 3. It is possible to become over-confident with the simulated results

21. Introduction to Simulation K.Sailaja Kumar 21 Advantages, disadvantages, and pitfalls in a simulation study r Advantages m Simulation allows great flexibility in modeling complex systems, so simulation models can be highly valid m Easy to compare alternatives m Control experimental conditions m Can study system with a very long time frame r Disadvantages m Stochastic simulations produce only estimates – with noise m Simulation models can be expensive to develop m Simulations usually produce large volumes of output – need to summarize, statistically analyze appropriately r Pitfalls m Failure to identify objectives clearly up front m In appropriate level of detail (both ways) m Inadequate design and analysis of simulation experiments m Inadequate education, training

22. Introduction to Simulation K.Sailaja Kumar 22 Systems and System Environment r System: A collection of objects that are joined together in some regular interaction or interdependence toward some purpose m E.g. A production system manufacturing automobiles. m Here the machines, component parts, and workers operate jointly to produce a high quality vehicle. r System Environment: changes occurring outside the system but affecting the system.

23. Introduction to Simulation K.Sailaja Kumar 23 Components of a System r Entity: Is an object of interest in the system. m E.g. Banking System Customers. r Attribute: It is a property of an entity m E.g. Checking balance in their account. r Activity: Represents a time period of specified length. m E.g. Making deposits.

24. Introduction to Simulation K.Sailaja Kumar 24 Components of a System r State : Is the collection of variables necessary to describe the system at any time, relative to the objectives of the study. m E.g. State variables for a Bank are Number of busy tellers, The number of customers waiting in line to be served and Arrival Time of next customer

25. Introduction to Simulation K.Sailaja Kumar 25 Components of a System r Event: It is an instantaneous occurrence that changes the state of the system. E.g. the arrival of a new customer. r Endogenous: used to describe activities and events occurring within a system. E.g. the completion of service of a customer r Exogenous: used to describe activities and events occurring in the environment that affect the system. E.g. the arrival of a customer

26. Introduction to Simulation K.Sailaja Kumar 26 How to study a system?

27. Introduction to Simulation K.Sailaja Kumar 27 Model of a System(1/2) r Model : It is defined as a representation of a system for the purpose of studying the system. m It is a simplification of the system. r Model represents only those aspects of the system that affect the problem under investigation r Model should be sufficiently detailed to permit valid conclusions to be drawn about the real system.

28. Introduction to Simulation K.Sailaja Kumar 28 Model of a System(2/2) r Model contains only those components that are relevant to the study r Different models of the same system are required for the purpose of investigation changes. r It is used to m study a system to understand the relationships between its components m Predict how the system will operate under a new policy.

29. Introduction to Simulation K.Sailaja Kumar 29 System vs. Its Model Simplification Abstraction Assumptions Real System Model

30. Introduction to Simulation K.Sailaja Kumar 30 Model Classification r Continuous-time vs. discrete-time models r Continuous-event vs. discrete-event models r Deterministic vs. probabilistic models r Static vs. dynamic models r Linear vs. non-linear models r Open vs. closed models

31. Introduction to Simulation K.Sailaja Kumar 31 r Physical model m Prototype of a system for the purpose of study. r Mathematical Model: m Uses symbolic notation and mathematical equations to represent a system. m E.g. A Simulation Model r Simulation Models: m Static vs. Dynamic m Deterministic vs. Stochastic m Discrete vs. Continuous

32. Introduction to Simulation K.Sailaja Kumar 32 Model Classification r Physical (prototypes) r Analytical (mathematical) r Computer (Monte Carlo Simulation) r Descriptive (performance analysis) r Prescriptive (optimization)

33. Introduction to Simulation K.Sailaja Kumar 33 Types of Simulation Models r Static/Dynamic m A static simulation model, sometimes called a Monte Carlo simulation, represent a system at a particular point in time. m A dynamic simulation model represents a system as it changes over time.

34. Introduction to Simulation K.Sailaja Kumar 34 Types of Simulation Models r Deterministic/Stochastic m Simulation models that contain no random variables are classified as deterministic m Deterministic models have a known set of inputs that will result in a unique set of outputs. m A stochastic simulation model has one or more random variables as inputs.

35. Introduction to Simulation K.Sailaja Kumar 35 Types of Simulation Models r Deterministic models produce deterministic results r Stochastic or probabilistic models are subject to random effects m Typically, they have one or more random inputs (e.g., arrival of customers, service time etc.). m Outputs from stochastic models are “estimates” of the true characteristics of the system m Need to repeat experiments number of times m Need to have confidence in the results

36. Introduction to Simulation K.Sailaja Kumar 36 Types of Simulation Models r Discrete/Continuous m State variables change instantaneously at separated points in time E.g Bank model: State changes occur only when a customer arrives or departs m State variables change continuously as a function of time E.g. Airplane flight: State variables like position, velocity change continuously

37. Introduction to Simulation K.Sailaja Kumar 37 Types of Simulation Models r Continuos systems in which the changes are predominantly smooth in time Natural events (rain, change of climate etc.) Mechanical systems Electrical systems r Discrete systems in which the state variable(s) change only at a discrete set of points in time Banks Manufacturing Computer systems

38. Introduction to Simulation K.Sailaja Kumar 38 Types of Simulation Models r Discrete systems: Is one in which the state variables change only at a discrete set of points in time. E.g. Banking system The number of customers changes only at discrete points in time

39. Introduction to Simulation K.Sailaja Kumar 39 Types of Simulation Models r Continuous systems: Is one in which the state variables change continuously over a time. E.g. The head of water behind a dam The head of water behind the dam, changes for this continuous system

40. Introduction to Simulation K.Sailaja Kumar 40 Continuous and Discrete-event models Distance traveled by plane Time (a) Continuous-event (b) Discrete-event Number of cust. in queue Time

41. Introduction to Simulation K.Sailaja Kumar 41 Types of Simulation Models r Continuous simulation – Typically, solve sets of differential equations numerically over time – May involve stochastic elements – Some specialized software available; some discrete-event simulation software will do continuous simulation as well r Combined discrete-continuous simulation – Continuous variables described by differential equations – Discrete events can occur that affect the continuously- changing variables – Some discrete-event simulation software will do combined discrete-continuous simulation as well

42. Introduction to Simulation K.Sailaja Kumar 42 Types of Simulation Models r Discrete-event simulation: a simulation using a discrete-event (also called discrete-state) model of the system m E.g., Widely used for studying computer systems r Continuous-event simulation: uses a continuous-state models m E.g., Widely used in chemical/pharmaceutical studies r Our focus will be on discrete-event systems.

43. Introduction to Simulation K.Sailaja Kumar 43 Discrete-event system simulation r Discrete and continuous models are defined similarly. r However, a discrete simulation model is not always used to model a discrete system, nor is a continuous model always used to model a continuous system. r Discrete-Event System Simulation r Discrete-event system simulation is widely used and is the focus of this course. r Discrete-event system simulation is the modeling of the systems in which the state variables change only at a discrete set of points in time.

44. Introduction to Simulation K.Sailaja Kumar 44 Discrete-event system simulation Modeling of a system as it evolves over time by a representation where the state variables change instantaneously at separated points in time More precisely, state can change at only a countable number of points in time These points in time are when events occur Event: Instantaneous occurrence that may change the state of the system Sometimes get creative about what an “event” is … e.g., end of simulation, Make a decision about a system’s operation Can in principle be done by hand, but usually done on computer

45. Introduction to Simulation K.Sailaja Kumar 45 More on models r Static and dynamic models m Static models – system state independent of time m Dynamic models - system state change with time r Linear and non-linear models m Linear models – output is a linear function of input parameters r Open and closed models (a) Open Model (b) Closed Model

46. Introduction to Simulation K.Sailaja Kumar 46 Areas of Application Application areas of simulation r Manufacturing applications r Semiconductor manufacturing r Construction engineering and project management r Military applications r Logistics, supply chain and distribution applications r Transportation models and traffic r Business process simulation r Health care r Call-center r Computers and Networks r Games r Human Systems

47. Introduction to Simulation K.Sailaja Kumar 47 Steps in a simulation study r 1. Problem formulation r 2. Setting objectives of study r 3. Model building r 4. Data collection r 5. Implementation r 6. Verification – is the implementation bug-free? r 7. Validation – is the model accurate? r 8. Experimental design r 9. Production runs & analysis r 10. Evaluate if results are satisfactory r 11. Report results

48. Introduction to Simulation K.Sailaja Kumar 48 Steps in a Simulation Study r 1. Problem formulation m Clearly understand problem m Reformulation of the problem r 2. Setting of objectives and overall project plan m Which questions should be answered? m Is simulation appropriate? m Costs? r 3. Model conceptualization m No general guide m Modeling tools in research, e.g. UML r 4. Data collection m How to get data? m Are random distributions appropriate? r 5. Model translation m Program

49. Introduction to Simulation K.Sailaja Kumar 49 Steps in a Simulation Study r 6. Verified? m Does the program that, what the model describes? r 7. Validated? m Do the results match the reality? m In cases with no real-world system, hard to validate r 8. Experimental design m Which alternatives should be run? m Which paramters should be varied? r 9. Production runs and analysis r 10. More runs? r 11. Documentation and reporting m Program documentation – how does the program work m Progress documentation – chronology of the work r 12. Implementation

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