SYSTEM SIMULATION AND MODELLING
Course Code: MCA 52
Faculty : Sailaja Kumar k
Introduction to Simulation
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K.Sailaja Kumar
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.
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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
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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
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Lecture material primarily drawn from:
 Discrete-Event System Simulation (Third
Edition), Banks, Carson, Nelson, and Nicol,
Prentice-Hall, 2007.
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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
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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
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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 unknown
Example: human behavior is extremely complex to model
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When simulation is not appropriate
(2/2)
Simulations should not be used:
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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
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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.
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 System Environment: changes occurring
outside the system but affecting the system.
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Components of a System
 Entity: Is an object of interest in the system.
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E.g. Banking System
• Customers.
 Attribute: It is a property of an entity
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E.g. Checking balance in their account.
 Activity: Represents a time period of specified
length.
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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.
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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
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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.
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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
Real System
Introduction to Simulation
• Abstraction
• Assumptions
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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
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Prototype of a system for the purpose of study.
 Mathematical Model:
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Uses symbolic notation and mathematical equations to
represent a system.
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E.g. A Simulation Model
 Simulation Models:
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Static vs. Dynamic
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Deterministic vs. Stochastic
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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/Dynamic
A
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
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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
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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 time
E.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
time
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Types of Simulation Models
 Continuous systems:
change continuously over a time.
Is one in which the state variables
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
Distance
traveled
by plane
Number
of cust.
in queue
Time
Time
(b) Discrete-event
(a) Continuous-event
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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 continuouslychanging 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
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E.g., Widely used for studying computer systems
 Continuous-event simulation: uses a
continuous-state models
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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 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
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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
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Linear models – output is a linear function of input
parameters
 Open and closed models
(b) Closed Model
(a) Open 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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