GSLM 53300
System Simulation
Yat-wah Wan
Room: B317; Email: ywan; Ext: 3166
 1 
Agenda
 house
keeping issues
 applications
 useful
of simulation
information from Arena
 systems,
models, and simulation
 2 
House-Keeping Issues
 prerequisite/background:
undergraduate
probability and statistics
 aims
and objectives: covering
 simulation
 Arena
of discrete-event systems
as the main tool, plus others (spreadsheet)
a
little bit on modeling and statistical issues of
simulation
 3 
Contents

first 13 lectures: first 8 Ch of the textbook
Ch 1
 Ch 2
 Ch 3
 Ch 4
 Ch 5
 Ch 6


Ch 7

Ch 8
What is simulation?
Fundamental simulation concepts
A guided tour through Arena
Modeling basic operations and inputs
Modeling detailed operations
Statistical analysis of output from terminating
simulations
Intermediate modeling and steady-state
statistical analysis
Entity transfter
 4 
Contents
 last
4 lectures
 special
features & unconventional models of Arena
 generation of random variates
 examples of simulation applications
 5 
Textbook and References

textbook


Kelton, W. David, Randall P. Sadowski, and David T.
Sturrock (2010) Simulation with Arena
references
Hoover, Stewart V. and Ronald F. Perry (1989)
Simulation: A Problem-Solving Approach
 Law, Averill M. and W. David Kelton (2000) Simulation
Modeling and Analysis
 Ripley, Brian D. (2006) Stochastic Simulation
 Ross, Sheldon M. (2006) Simulation

 6 
Assessments
 Assignments
30%
40%
30%
 Project
 Final
Examination
 7 
Applications of Simulation
 8 
Examples of Simulation

searching for “simulation” on web




Games
Solar System Simulator
Simulating Fire Patterns in Heterogeneous
Landscapes
Arena Software


Example 1, Example 2
….. etc.
 9 
Reasons to Use Simulation

mimic reality when the real system is







not available
costly to build
dangerous to operate
difficult to visualize
slow in evolution
difficult to predict
how about
analytical
methods?
both deterministic and stochastic systems
 10 
From System to Simulation
 11 
Applications of Simulation
Interfaces
simulation papers in 2007 & 2008



15 (out of 172) titles in two years
simulation
15
network
14
IP
17
logistics
6
LP
12
DP
2
Inventory
16
forecasting
16
Statistic
3
NLP
3
SCM
22
marketing
10
 12 
Useful Information from Arena
 13 
Download Arena 12.0

McGraw-Hill Web site


student center: download software,
installation: STUDENT
 14 
Useful Information from Arena

Start/All Programs/Rockwell
Software/Arena/Online Books

C:\Program Files\Rockwell
Software\Arena




Book Examples
Examples
Online Books
Smarts
 15 
Systems, Models, and Simulation
 16 
System, Model, & Solution
Role of models:
describe, explain, predict,
control, optimize
system
model
solution
Simulation: a special
way to find the
solution of a model
 17 
Our Simulation

required knowledge
modeling (state, dynamics, etc.)
 analysis (input, output, verification
and validation, variance reduction,
optimization)
 a computer language and a
simulation package

computer simulation, after all
most for stochastic systems, though …
 18 
Pillars of Simulation
modeling
computer:
simulation
analysis:
languages &
software
 Which
probability &
statistics
is the most important?
 19 
System, Model, & Solution




how to represent a real system?
 stochastic inputs
what information to carry
model correctly (setting the model right)?
correct model (setting the right model)?


system
model
 20 
art
how to analyze?
how to optimize?
solution
Issues to Simulate a System
first: the amount of information to carry to
represent the system (i.e., the state of the system)
 second: the evolution of the state of the system
(i.e., the dynamics of the system)
 third: the medium to realize the (evolution of the)
system
 fourth: the method to represent the system
dynamics in the selected medium
 fifth: the analysis, control, and optimization of the
simulation model

 21 
Examples
 simulation
projects of increasing complexity
 well-defined
dynamics
 chessboard
 differential
equations
 well-defined
problems
 simulation of real-life systems
 22 
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