Quantitative Methods with Modeling and Simulation - Simulation and Modelling
Summary
TLDRThis video introduces modeling and simulation, explaining how simulations are used to imitate real-world systems through computer models. It covers the benefits of simulation, such as optimizing performance and preventing issues, and details the process of developing a simulation model, from problem identification to model validation. The video also discusses different types of simulations, including live, virtual, and constructive, and highlights common pitfalls to avoid in simulation studies. Overall, the video emphasizes the value of simulation in analyzing and improving complex systems safely and efficiently.
Takeaways
- đ ïž Simulation is the process of creating an imitation of a real-world system using a computer model.
- âïž A flight simulator on a PC is an example of a simulation, showing how controls and views from a cockpit can be mimicked.
- đ Simulation is used to reduce failure risks, predict bottlenecks, optimize resources, and enhance system performance before altering or building new systems.
- đĄ Simulation can answer complex questions about the best design, resource requirements, and performance impacts in telecommunications networks.
- đ Modeling involves creating a representation of a system to predict outcomes based on different actions, and is simpler than the real system it represents.
- đĄ Models, like flight simulators, are used because experimenting with real systems can be costly, dangerous, or impossible.
- đ Simulation is beneficial when multiple factors influence system behavior and allows for simultaneous handling and analysis of these factors.
- đ» Simulations can be manual or, more commonly, computer-based, involving software to implement the model over time.
- 𧩠There are three types of simulations: live (real people and systems), virtual (real people, simulated systems), and constructive (simulated people and systems).
- đ§ Developing a simulation model involves identifying the problem, formulating it, collecting data, creating the model, validating it, and documenting for future use.
- đ Benefits of simulation modeling include better system understanding, hypothesis testing, time compression or expansion for observation, and safe experimentation with system models.
- đ§ Simulation modeling helps identify driving variables, bottlenecks, and allows for the use of multiple performance metrics and a system approach to problem-solving.
- â ïž Common pitfalls in simulation include unclear objectives, invalid or overly complex models, undocumented assumptions, wrong input distributions, bugs, and poor communication.
Q & A
What is simulation in the context of operations research?
-Simulation in operations research is the process of pretending to deal with a real system by working with an imitation, typically a computer model, of the simulated reality.
How does a flight simulator serve as an example of simulation?
-A flight simulator on a PC models some aspects of a flight, showing the controls and the pilot's view from the cockpit. It allows training and experimentation without the risks and costs associated with real flights.
What are the key purposes of using simulation before altering or building a system?
-Simulation is used to reduce the chances of failure to meet specifications, identify unforeseen bottlenecks, prevent under or over-utilization of resources, and optimize system performance.
What types of questions can simulation help answer regarding telecommunications networks?
-Simulation can help determine the best design for a new network, resource requirements, performance under increased traffic load, the impact of new routing algorithms, and the effects of link failures.
What is a model in the context of modeling and simulation?
-A model is a representation of the construction and working of a system, intended to predict what happens if certain actions are taken. It is simpler than the actual system but serves to forecast the effects of changes.
Why are models used instead of real systems for experiments?
-Models are used because experimenting with real systems can be costly, dangerous, and sometimes impossible. Adequate models can save money, reduce risk, and provide valuable insights without disrupting the real system.
What are the three types of commonly used simulations?
-The three types are live simulations (involving real people operating real systems), virtual simulations (involving real people operating simulated systems), and constructive simulations (involving simulated people operating simulated systems).
What are the main steps involved in developing a simulation model?
-The steps include identifying the problem, formulating the problem, collecting real system data, formulating and developing the model, validating the model, and documenting the model for future use.
What are some benefits of simulation modeling and analysis?
-Benefits include better understanding of the system, hypothesis testing, time compression or expansion to observe phenomena, studying the effects of changes without disrupting the real system, and identifying driving variables and interrelationships.
What are some common pitfalls to guard against in simulation modeling?
-Pitfalls include unclear objectives, using simulation when an analytic solution is appropriate, invalid or overly complex models, undocumented assumptions, incorrect input probability distributions, wrong performance measures, and poor communication among personnel.
Outlines
đ Introduction to Modeling and Simulation
This video provides an introduction to modeling and simulation. Simulation involves creating a computer model to imitate reality, such as a flight simulator. It is used to reduce failure risks, identify bottlenecks, optimize resources, and enhance system performance. Modeling creates a representation of a system to predict outcomes. Examples include using flight simulators for safe and cost-effective training. Simulations help analyze system interactions and improve performance by addressing factors influencing system behavior.
đ Types of Simulations
There are three main types of simulations: live, virtual, and constructive. Live simulations involve real people operating real systems. Virtual simulations have real people interacting with simulated systems, focusing on skills like motor control and decision-making. Constructive simulations involve simulated people operating simulated systems, allowing for analysis and prediction of outcomes without real people determining the results. Each type has distinct applications and benefits in analyzing and improving system performance.
đ Developing a Simulation Model
Developing a simulation model involves several steps. First, identify the problem by outlining issues with the existing system or requirements for a new system. Next, formulate the problem by setting system boundaries, objectives, and performance measures. Collect real system data and develop the model using simulation software. Validate the model by comparing its performance with real system data and ensuring accuracy through expert review. Document the model thoroughly for future use and reference.
đĄ Benefits of Simulation Modeling and Analysis
Simulation modeling and analysis offer numerous benefits. They provide a better understanding of systems by allowing detailed observation over time. Hypotheses about system feasibility can be tested without disrupting the real system. Simulation allows for the study of long-term and complex phenomena and the effects of various changes on system operations. It helps identify critical variables, analyze system configurations using multiple performance metrics, and develop robust systems, ultimately reducing development time and risks.
â ïž Pitfalls to Avoid in Simulation
Several pitfalls must be avoided in simulation modeling. Objectives must be clear, and simulation should not be used when an analytic solution is possible. The model should be appropriately complex and assumptions documented. Using incorrect input distributions or performance measures can lead to inaccurate results. Ensuring independence in simulation output data and avoiding biases are crucial. Multiple simulation runs are necessary for reliable results. Effective communication among team members is vital for a successful simulation study.
Mindmap
Keywords
đĄSimulation
đĄModeling
đĄOperational Research
đĄComputer Model
đĄPerformance Measures
đĄSystem Entities
đĄInput Variables
đĄFunctional Relationships
đĄLive Simulation
đĄVirtual Simulation
đĄConstructive Simulation
Highlights
Introduction to modeling and simulation.
Definition of simulation: pretending to deal with a real thing while working with an imitation.
Simulation is used to reduce the chances of failure and optimize system performance.
Examples of simulation questions: best design for a new network, resource requirements, impact of traffic load increase.
Definition of modeling: producing a representation of a system to predict outcomes of certain actions.
Purpose of models: safer and cheaper experimentation compared to real systems.
Simulation is useful when many factors influence system behavior.
Simulations can be performed manually or using computer programs and software.
Types of simulations: live, virtual, and constructive.
Live simulation: real people operating real systems.
Virtual simulation: real people operating simulated systems, involving motor control, decision, and communication skills.
Constructive simulation: simulated people operating simulated systems, with real people providing inputs but not determining outcomes.
Steps in developing a simulation model: identifying the problem, formulating the problem, collecting real system data, developing the model, validating the model, and documenting the model.
Benefits of simulation modeling: better understanding of systems, testing hypotheses, observing long-term phenomena, studying system changes without real-world disruption.
Pitfalls to guard against in simulation: unclear objectives, invalid models, wrong input distributions, and poor communication.
Transcripts
good day everyone
this video will give you an introduction
on modeling and simulation
so let's start
what is simulation simulation
in general is to pretend that
one deals with a real thing while really
working with an
imitation in
operations research the imitation
is a computer model of the simulated
reality
for example a flight simulator on a pc
is also a computer model of some aspects
of the flight
it shows on the screen the controls and
what the pilot is supposed to see
from the cockpit now based on this
example
we can say that simulation is used
before
an existing system is altered or a new
system
is built to reduce the chances of
failure to meet specifications
to imitate unforeseen bottlenecks
to prevent under or over utilization of
resources
and to optimize system performance
simulation can be used to answer
questions like what is the best design
for a new telecommunications network
what are the associated resources
requirements
how will a telecommunication networks
perform
when the traffic load increases by 50
how will a new routing algorithm affects
its performance
which network protocol optimizes network
performance and what will the
uh what will be the impact of a link
failure
next is modeling modeling is the process
of producing a model a model
is a representation of the construction
and working of
some system of interest a model
is descript is a description of some
system
intended to predict what happens if a
certain action
are taken a model is similar
to but simpler than the systems
or system it represents one purpose of a
model is to enable the analyst
to predict the effects of changes
to the system
now why do we use models
let us take again the flight simulator
on the pc as an example
flying in a simulator is safer and
cheaper than the real airplane
for precisely this reason models are
used it is very costly
dangerous and often impossible to make
experiments
with real systems provided that the
model
are adequate descriptions of reality
experimenting with them can save money
suffering
and even time
so when do we use a simulation or
simulation
the use of simulation becomes apparent
as soon as a number of factors
are considered to influence the behavior
of
a system many approaches especially
statistical ones are available
to analyze interactions in systems
simulation modeling constitutes a unique
approach
that it enables the simply the
simultaneous handling
end range of such factors and see
their influence on the behavior of a
system
the next is how is simulation
performed
simulations may be performed manually
most often however the system model
is written either as a computer program
or as some kind of input
into sim simulator software
as you can see on the example
illustration
so what are the different types of
simulator
a simulator is a device or a computer
program
or a system that performs
simulation a simulation
is a method for implementing a model
over time there are three
types of commonly used
simulations the live
virtual and constructive
now live is a simulation involving real
people
operating real system
so uh in the live
simulations it involves individual or
groups
may use actual equipment should provide
a similar area of operations
should be close to replicating the
actual activity
the second type is the virtual
simulation involving real people
operating
same simulated system virtual
simulations inject human
in the loop in a central role
by exercising it involves
motor control skills decision skills and
communication skills
the third is the constructive simulation
now constructive simulation involves
simulated people operating simulated
systems
real people can stimulate
or make inputs but are not involved in
determining the outcomes
constructive simulation offered the
ability to
analyze concepts predict possible
outcomes
stress large organizations
make measurements generate statistics
and perform analysis
we now go to developing a simulation
model
simulation model consists of the
following components
system entities input variables
performance measures and
functional relationships
simulation modeling comprises of the
following steps
the first step is identifying the
problem
when uh in step one
uh enumerate problems with an existing
system
produce requirements for a proposed
system step 2
[Music]
formulate the problem
this means that select the bounds of the
system
the problem or a partner of
to be studied define overall
objective of the study in a few specific
issues
to be addressed we also need to define
performance measures quantitative
criteria
on the basis of which different system
configurations will be compared
and rank we have to identify also
briefly at this stage the
configurations of interest and formulate
hypotheses
about system performance
we also need to decide the time frame of
the study
example will be the model used
for one time decision
or over a period of time on a regular
basis
and lastly identify the end user of the
simulation model
the third step is collect the process
real system data this means that
collect data on system specifications
input variables as well as performance
of
the existing system
step four formulate and develop a model
in step four developing semantics and
network diagrams of the system
translate these conceptual models to
simulation software acceptable form
verify that the simulation model
executes
as intended step 5
validate the model this means that we
need to
compare the model's performance under
known conditions
with the performance of the real system
perform statistical interference test
and get the model examined
by system experts assess the confidence
that the end user places on the model
and address problems if there are any
lastly document model
for future use document objectives
assumptions and input variables
in detail
the next topic is what are the benefits
of simulation modeling and analysis
according to practitioners
simulation modeling and analysis is one
of the most
frequently used operations research
techniques when used judiciously
simulation modeling and analysis makes
it possible
to
first obtain a better understanding of
the system
by developing a mathematical model of a
system
of interest and observing the system's
operation
in detail over long periods
of time next test hypothesis
about the simula about the system
for visibility
next we have compressed time to observe
certain phenomena
over long periods or expand time to
observe
a complex phenomenon in detail
study the effects of certain information
organizational environmental and policy
changes
on the operation of a system by altering
the system's model
this can be done without disrupting the
real system
and significantly reduces the risk of
experimenting with the real system
lastly
experiment with you or unknown
situations
about which only weak information is
available
another benefits of simulation modeling
analysis
is
identify the driving variables
ones that performance measures are most
sensitive
to and the interrelationships
among them next
another bottlenecks in the flow
of entities or information
next is use multiple performance metrics
for analyzing system configuration
another is employ a system approach to
problem solving
and lastly develop well-designed
and robust systems and reduce system
development time
we now go to our last topic which is the
pitfalls of guard
against pitfalls to guard against
in simulation
can be a time consuming and complex
exercise
for modeling through output analysis
that necessitates the involvement of
residents
experts and decision makers in the
entire process
following is a checklist of pitfalls to
guard
against
first and clear
objective second
using simulation when analytic solution
is appropriate
next is invalid model
another is simulation model is too
complex or too simple
next is a red news assumptions
another is undocumented assumptions
this is extremely important and it is
strongly suggested
that assumptions made at each stage of
dissemination modeling and analysis
exercise
by documented thoroughly
another is using the wrong input
probability distribution
next is replacing a distribution by its
means
next is using the wrong performance
measure
next is bugs in the simulation program
another is using standard statistical
formulas that assume
independence in simulation output
analysis
next is initial bias in output data
another is making one simulation run for
a configuration
next is sports schedule and budget
planning
and the last poor communication among
the personnel involved
in the simulation study
and that concludes this lesson thanks
for listening
5.0 / 5 (0 votes)