system simulation and modelling course code: mca 52 faculty : sailaja kumar k
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SYSTEM SIMULATION AND MODELLING Course Code: MCA 52 Faculty : Sailaja Kumar k. What is this course about?(1/3). Simulation is a technique to analyze and predict the behavior of existing or proposed systems by experimenting with representative models of the systems. - PowerPoint PPT PresentationTRANSCRIPT
Introduction to Simulation K.Sailaja Kumar1
SYSTEM SIMULATION AND MODELLING
Course Code: MCA 52
Faculty : Sailaja Kumar k
Introduction to Simulation K.Sailaja Kumar2
What is this course about?(1/3)
Simulation is a technique to analyze and predict the behavior of existing or proposed systems by experimenting with representative models of the systems.
This course is primarily about imitating the operation of real-world systems using computer programs. Our focus will be on “discrete-event” simulations.
Introduction to Simulation K.Sailaja Kumar3
What is this course about?(2/3)
We will learn how to: Abstract real-world systems into models Implement models using software Experiment design
Systems modeling requires understanding of Basic probability, statistics, elementary
calculus
Introduction to Simulation K.Sailaja Kumar4
What is this course about?(3/3)
In practice simulation models are mostly built on computer systems. Several languages and software packages facilitate the model building and experimentation process.
The Unified Modeling Language (UML) will be used for modeling and documentation when appropriate.
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Course Outline
Introduction to Simulation Simulation Examples General Principles & Queuing Models Generating Random-Numbers Generating Random-Variates Input Modelling Verification and Validation of Simulation
Models Output Analysis for a Single Model
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Books Lecture material primarily drawn from:
Discrete-Event System Simulation (Third Edition), Banks, Carson, Nelson, and Nicol, Prentice-Hall, 2007.
References Simulation Modeling and Analysis. (Fourth
Edition), Averill M Law,W David Kelton, McGraw Hill
Discrete – Event Simulation: A First Course Lawrence M. Leemis, Stephen K. Park:
Pearson / Prentice-Hall, 2006.
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Introduction to Modeling and Simulation What is simulation? Systems and system Environment Components of a system Discrete and continuous Systems Model of a system Types of models Discrete-Event System Simulation When Simulation is the appropriate Tool When Simulation is not appropriate Advantages and Disadvantages of Simulation Application areas of simulation Steps in a simulation study
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What is a simulation?(1/2) Simulation – is imitation of the operation of a
real world process or system over a time period, usually using a computer
The behavior of a system over a time period is studied by developing a simulation model
Simulation modeling can be used As an analysis tool for predicting the effect of changes
to existing systems
As a design tool to predict the performance of new systems under varying sets of circumstances.
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What is a simulation?(2/2)
A simple Simulation model can be developed by mathematical methods.
Many real world systems models are developed using numerical computer-based simulation methods.
The simulation-generated data is used to estimate the measures of performance of the system.
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When simulation is the appropriate tool (1/3)
To study and experiment with the internal interactions of a complex system or subsystem To study the effect of informational, organizational and environmental changes on a system Knowledge gained in designing a simulation model may help to suggest improvements Changing inputs and observing resulting outputs reveals which variables are most important and how they interact As a pedagogical device to reinforce analytic solution methodologies To experiment with new designs or policies prior to implementation To verify analytic results
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When simulation is the appropriate tool (2/3) Simulation can be used for the following purposes: Simulation enables the study of experiments with internal interactions Informational, organizational, and environmental changes can
be simulated to see the model’s behavior Knowledge from simulations can be used to improve the
system Observing results from simulation can give insight to which variables are the most important ones Simulation can be used as pedagogical device to reinforce the learning material Simulations can be used to verify analytical results, e.g. queueing systems Animation of a simulation can show the system in action, so that the plan can be visualized
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When to use simulations?(3/3) Simulations can be used:
To study complex system, i.e., systems where analytic solutions are infeasible.
To compare design alternatives for a system that doesn’t exist.
To study the effect of alterations to an existing system. Why not change the system??
To reinforce/verify analytic solutions.
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When simulation is not appropriate (1/2)Simulation should not be used, in the case when problem is solvable by common sense when the problem can be solved mathematically when direct experiments are easier when the simulation costs exceed the savings when the simulation requires time, which is not
available when no (input) data is available, but simulations
need data when the simulation cannot be verified or validated when the system behavior is too complex or
unknownExample: human behavior is extremely complex to
model
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When simulation is not appropriate(2/2)Simulations should not be used:
If model assumptions are simple such that mathematical methods can be used to obtain exact answers (analytical solutions)
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Advantages of simulation(1/3)
Policies, procedures, decision rules, information flows can be explored without disrupting the real system
New hardware designs, physical layouts, transportation systems can tested without committing resources
Hypotheses about how or why a phenomena occur can be tested for feasibility
Time can be compressed or expanded - Slow-down or Speed-up Insight can be obtained about the interaction of
variables
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Advantages of simulation(2/3)
Insight can be obtained about the importance of variables to the performance of the system
Bottleneck analysis can be performed to detect excessive delays
Simulation can help to understand how the system operates rather than how people think the system operates
“What if” questions can be answered
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Advantages of simulation(3/3)
Once a model is built, it can be used repeatedly Simulation data can be less costly to obtain than data from the real system Simulation methods can be easier to apply than analytical methods Simulation models be more general and require fewer assumptions than analytical models Sometimes simulation is the only way to derive a solution to a problem
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Disadvantages of simulation
• Model building requires training, it is like an art. - Compare model building with programming. • 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 • Simulation can be time consuming and
expensive - Skimping in time and resources could lead to useless/wrong results
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Disadvantages of simulation
The disadvantages are offset as follows • Simulation packages contain models that only
need input data • Simulation packages contain output-analysis
capabilities • Sophistication in computer technology
improves simulation times • For most of the real-world problems there are
no closed form solutions
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Disadvantages of simulation
1. Developing non-trivial simulation models may be costly
2. Simulation is sometimes used when analytic
techniques will suffice 3. It is possible to become over-confident
with the simulated results
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Advantages, disadvantages, and pitfalls in a simulation study
Advantages Simulation allows great flexibility in modeling complex
systems, so simulation models can be highly valid Easy to compare alternatives Control experimental conditions Can study system with a very long time frame
Disadvantages Stochastic simulations produce only estimates – with
noise Simulation models can be expensive to develop Simulations usually produce large volumes of output –
need to summarize, statistically analyze appropriately Pitfalls
Failure to identify objectives clearly up front In appropriate level of detail (both ways) Inadequate design and analysis of simulation
experiments Inadequate education, training
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Systems and System Environment System: A collection of objects that are
joined together in some regular interaction or interdependence toward some purpose E.g. A production system manufacturing
automobiles. Here the machines, component parts, and
workers operate jointly to produce a high quality vehicle.
System Environment: changes occurring outside the system but affecting the system.
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Components of a SystemEntity: Is an object of interest in the system.
E.g. Banking System• Customers.
Attribute: It is a property of an entity
E.g. Checking balance in their account.
Activity: Represents a time period of specified
length.
E.g. Making deposits.
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Components of a System
State : Is the collection of variables necessary to describe the system at any time, relative to the objectives of the study.
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
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Components of a System Event: It is an instantaneous occurrence that changes
the state of the system. E.g. the arrival of a new customer.
Endogenous: used to describe activities and events occurring within a system.E.g. the completion of service of a customer
Exogenous: used to describe activities and events occurring in the environment that affect the system.E.g. the arrival of a customer
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How to study a system?
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Model of a System(1/2)Model : It is defined as a representation of a
system for the purpose of studying the system.
It is a simplification of the system.
Model represents only those aspects of the system that affect the problem under investigation
Model should be sufficiently detailed to permit valid conclusions to be drawn about the real system.
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Model of a System(2/2)
Model contains only those components that are relevant to the study
Different models of the same system are required for the purpose of investigation changes.
It is used to study a system to understand the relationships
between its components Predict how the system will operate under a
new policy.
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System vs. Its Model
• Simplification• Abstraction• Assumptions
Real System Model
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Model Classification
Continuous-time vs. discrete-time models
Continuous-event vs. discrete-event
models
Deterministic vs. probabilistic models
Static vs. dynamic models
Linear vs. non-linear models
Open vs. closed models
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Physical model Prototype of a system for the purpose of study.
Mathematical Model: Uses symbolic notation and mathematical equations to
represent a system.
E.g. A Simulation Model
Simulation Models: Static vs. Dynamic
Deterministic vs. Stochastic
Discrete vs. Continuous
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Model Classification
Physical (prototypes)
Analytical (mathematical) Computer (Monte Carlo
Simulation) Descriptive (performance analysis) Prescriptive (optimization)
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Types of Simulation Models
Static/DynamicA static simulation model, sometimes called a Monte Carlo simulation,
represent a system at a particular point in time.
A dynamic simulation model represents a
system as it changes over time.
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Types of Simulation Models
Deterministic/Stochastic Simulation models that contain no random variables are classified as deterministic
Deterministic models have a known set of inputs that will result in a unique set of outputs.
A stochastic simulation model has one or more random variables as inputs.
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Types of Simulation Models
Deterministic models produce deterministic results
Stochastic or probabilistic models are subject to random effects Typically, they have one or more random inputs
(e.g., arrival of customers, service time etc.). Outputs from stochastic models are “estimates”
of the true characteristics of the system Need to repeat experiments number of times Need to have confidence in the results
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Types of Simulation Models
Discrete/Continuous State variables change instantaneously at
separated points in time E.g Bank model: State changes occur only
when a customer arrives or departs
State variables change continuously as a function of timeE.g. Airplane flight: State variables like position, velocity change continuously
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Types of Simulation Models
Continuos systems in which the changes are predominantly smooth in time
• Natural events (rain, change of climate etc.)• Mechanical systems• Electrical systems
Discrete systems in which the state variable(s) change only at a discrete set of points in time
• Banks• Manufacturing• Computer systems
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Types of Simulation Models
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
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Types of Simulation Models
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
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Continuous and Discrete-event models
Distancetraveled by plane
Time
(a) Continuous-event (b) Discrete-event
Number of cust. in queue
Time
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Types of Simulation Models 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
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
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Types of Simulation Models Discrete-event simulation: a simulation
using a discrete-event (also called discrete-state) model of the system E.g., Widely used for studying computer
systems Continuous-event simulation: uses a
continuous-state models E.g., Widely used in chemical/pharmaceutical
studies Our focus will be on discrete-event
systems.
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Discrete-event system simulation Discrete and continuous models are defined similarly. 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.
Discrete-Event System Simulation
Discrete-event system simulation is widely used and is the focus of this course.
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.
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Discrete-event system simulationModeling 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 systemSometimes 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
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More on models Static and dynamic models
Static models – system state independent of time Dynamic models - system state change with time
Linear and non-linear models Linear models – output is a linear function of
input parameters Open and closed models
(a) Open Model (b) Closed Model
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Areas of Application
Application areas of simulation • Manufacturing applications • Semiconductor manufacturing • Construction engineering and project management • Military applications • Logistics, supply chain and distribution
applications • Transportation models and traffic • Business process simulation • Health care • Call-center • Computers and Networks • Games • Human Systems
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Steps in a simulation study
1. Problem formulation 2. Setting objectives of study 3. Model building 4. Data collection 5. Implementation 6. Verification – is the implementation bug-free? 7. Validation – is the model accurate? 8. Experimental design 9. Production runs & analysis 10. Evaluate if results are satisfactory 11. Report results
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Steps in a Simulation Study
1. Problem formulation Clearly understand problem Reformulation of the problem
2. Setting of objectives and overall project plan Which questions should be answered? Is simulation appropriate? Costs?
3. Model conceptualization No general guide Modeling tools in research, e.g. UML
4. Data collection How to get data? Are random distributions appropriate?
5. Model translation Program
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Steps in a Simulation Study
6. Verified? Does the program that, what the model describes?
7. Validated? Do the results match the reality? In cases with no real-world system, hard to validate
8. Experimental design Which alternatives should be run? Which paramters should be varied?
9. Production runs and analysis 10. More runs? 11. Documentation and reporting
Program documentation – how does the program work Progress documentation – chronology of the work
12. Implementation
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