Introductions
• I am:
– Graham Horton
– FIN-ISG / 2-212
– Email: [email protected]
Introduction
• You
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"Simulation 1"
are students of:
Computervisualistik (3rd year)
Wirtschaftsingenieur/Logistik (2nd year)
Wirtschaftsinformatik (2nd year)
...?
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Goals of the Course
• Your Studienordnungen refer to the course "Simulation 1"
• This semester, there are two introductory simulation courses:
– Introduction to Simulation (Horton)
– Simulation and Animation (Lorenz)
• Give an introduction to some important areas of simulation:
– Continuous simulation (ODEs & PDEs)
– Discrete-event (stochastic) simulation
• Content developed together with industry
• Learn to use the simulation software SIMPLEX3
• Both of these can be used as "Simulation 1"
• Solve some typical engineering problems using simulation
• Both are in English
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• Form the basis for advanced courses and project work
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General Information
Simulation Courses 01-02
Introductory
Level
• No previous knowledge is necessary
• The lectures will be in English
– Tue. 1pm, 22a-203 (WIF)
Introduction
to Simulation
– Wed. 1pm/odd, 22a-119 (WLO)
– Thu. 1pm, 22a-208 (CV)
Simulation
& Animation
• A complete script will be provided (via the web)
• Website: wwwisg.cs.uni-magdeburg.de/~graham/its
Graham Horton
Simulation Opportunities
Advanced
Discrete Sim.
Produktionssimulation
Kontinuierliche
Simulation
Petrinetze
Umwelt- und Unternehmenssimulation
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SIMPLEX3 and Assignments
• Wide range of courses
– various levels
– theoretical and practical
• We will use the simulation system SIMPLEX3
• SIMPLEX3 is free for students
• Internships (Industriepraktika)
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Graduate
level
Diskrete
Simulation
Simulationssysteme
– Wed. 11am/even, 22a-129 (WLO)
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Advanced
level
Simulation
Project
• Exercise classes / practical lectures:
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Intermediate
level
• SIMPLEX3 is installed on the RTL computers
BMW
Siemens
DaimlerChrysler
Fraunhofer Institut
• Most assignments will be simulation programming
• Many assignments developed together with industry
• Internal and external Diplomarbeiten
• PhD opportunities
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SIMPLEX3
Evaluation
• I would like to carry out a student evaluation of ItS
• What criteria make a good course?
• Examples:
– The lecturer responds well to questions
– The lecturer makes the material interesting
– The slides and/or script are clear and helpful
– The level of difficulty is appropriate
– I have learned a lot from this course
– I would recommend this course to other students
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Brockhaus: "Simulation"
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Brockhaus: "Modell"
• Simulation [lat. "imitate"] the representation or replication
• Model: a representation of nature which emphasises those
as a model of certain aspects of a real or planned cybernetic
properties that are considered to be important and ignores
system, in particular of its behaviour over time.
the aspects which are considered to be irrelevant.
• Simulation [lat. "nachahmen"] die modellhafte Darstellung
oder Nachbildung bestimmter Aspekte eines vorhandenen
oder zu entwickelnden kybernetischen Systems, insbesondere
• Modell: ein Abbild der Natur unter Hervorhebung für
wesentlich erachteter Eigenschaften und Außerachtlassen als
nebensächlich angesehener Aspekte.
auch seines Zeitverhaltens.
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Three Methods of Science
e
ris
eo
h
T
Experiment
Real system
Prediction
Mo
de
lb
uil
din
g
Simulation model
Experiment
Conceptual model
Modify
Compare
Real data
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Some Applications
Simulation data
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One well-known Example
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• Continuous simulation:
– All branches of (Natural) Science
– All branches of Engineering
– Operations Research
• Discrete simulation:
– Manufacturing and Automation
– Logistics and Transportation
– Reliability and Safety Engineering
– Operations Research
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Atmospheric Simulation
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Simulation is Interdisciplinary
• One major classification of systems (and their models)
Application
field
Time
cs
ati
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Stochastic and Deterministic Systems
Discrete
Continuous
Simulation
Continuous
Partial
Differential
Equations
Ordinary
Differential
Equations
Discrete
"Space"
Co
Sc mpu
ien ter
ce
m
the
Ma
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Continuous and Discrete Systems
??
Discrete-Event
Systems
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Static and Dynamic Simulations
• The other major classification of systems:
• Another major classification of simulations:
• Deterministic:
• Static:
– Randomness does not affect the behaviour of the
system.
The output of the system is not a random variable.
• Stochastic:
– A simulation of a system at one specific time, or a
simulation in which time is not a relevant parameter.
– Examples: Monte Carlo & steady-state simulations
• Dynamic:
– Randomness affects the behaviour of the system.
The output of the system is a random variable.
• N.B. Real system and model need not be of the same type!
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– A simulation representing a system evolving over time.
– Examples: The majority of simulation problems!
• N.B. Here also, real system and model may differ!
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Examples
When to use Simulation?
Queue length at a cash machine:
Stochastic, DT, DS
The motion of the planets:
Deterministic, CT, DS
• When to use simulation?
– Study internals of a complex system
– Examine effect of environmental changes
Logic circuit in a computer:
Deterministic, DT, DS
Flow of air around a car:
Deterministic, CT, CS
Closing prices of the 30 DAX shares: Stochastic, CT, DS
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– Study importance of variables
– Verify analytic solutions (theories)
– Test new designs or policies
– Impossible to influence/build the system
N.B. Other answers are also possible!
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– Suggest improvements
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Advantages of Simulation
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Difficulties of Simulation
• Advantages of simulation:
• Difficulties of simulation:
– Doesn't interrupt running system
– Provides only individual solutions
– Doesn't consume resources
– Manpower: Time-consuming
– Test hypotheses
– Computing: Memory- & time-intensive
– Manipulate time
– Difficult, skilled
– Study interactions
– Hard to interpret results
– Ask "what if" questions
– Expensive!
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A Classic Example
A Classic Example
• During the First World War, the population of fish in
the Adriatic Sea went down.
• This was surprising, because, owing to the war, there was
less fishing going on (so you would think the amount of
fish would go up.)
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A Classic Example
Enter
bank model:
Customers arrive at random intervals at a bank.
There is only one cashier.
Customers must wait in a queue.
Service times at the cashier are also random.
• Measured inter-arrival times (seconds):
– 25, 111, 56, 232, 97, 452, 153, 45, ...
• Measured service times (seconds):
– 45, 32, 11, 61, 93, 56, 30, ...
• Volterra (an Italian mathematician) built a model to
find out what was happening...
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• The
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A Classic Example
• Consider an area in which hares and foxes live
Service
• Denote the population of hares by h and of foxes by f
– Foxes must eat hares in order to survive
– Hares have an unlimited supply of food
• The resulting equations are:
Queue
• Compute:
– The average length of the queue
– The probability that the cashier is busy
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• The Lotka-Volterra equations
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dh
= a⋅h −c⋅h⋅ f
dt
df
= −b ⋅ f + c ⋅ h ⋅ f
dt
Graham Horton
A Classic Example
A Classic Example
• Simulation results for parameters
• Example: Petri-dish populations of
– h (0) = 400
– Paramecium Aurelia (predator)
– f (0) = 37
– Saccharomices Exiguns (prey)
– a = 0.175
– b = 0.125
– c = 0.001
– 0 < t < 150
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The Last Slide
• There will be no exercise classes this week
• Find the ItS web site
• Your homework assignment (if appropriate):
– Download SIMPLEX3 and the C compiler from the web
– Download the license file from the web site
– Install SIMPLEX3 on your own computer
– Check that you can start the program
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