IE 415/515 – Simulation
Today’s Agenda
Information on syllabus
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Office hours
Prerequisites
Text
Grading
Exams & Homework
Class format
Introduction to IE simulation
Continue lecture material
Office Hours
Mondays/Wednesdays 3-4:30PM
By appointment
Office 424 Rogers
E-mail: No HW/technical questions!
TA: Faisal Alfayez, Zahra Mohktari
 Office hours TBD
Prerequisites
Stat 314 or equivalent will be needed.
Computer programming experience – Helpful
but not critical. If specific material is needed,
it will be covered for course purposes.
Knowledge of Windows and Excel is
assumed. Experience programming and
debugging is helpful.
Some ENGR 390 (engineering economics)
background.
Course Information
Course homepage :
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http://classes.engr.oregonstate.edu/mime/winter2015/ie415-001
 Syllabus
 Lecture slides for note taking
 Handouts
 This introductory presentation
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Information sheet
 Homework and lab assignments
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Check the page for course information and
announcements.
References
Kelton, W.D., Sadowski, R.P. and N.B. Swets, (2010).
Simulation With Arena 5th Edition, McGraw-Hill Inc.
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Valley Library -11 copies on one-day reserve.
Law, A.M. and W.D. Kelton, (2000). Simulation
Modeling and Analysis 3rd Edition, McGraw-Hill Inc.
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Two earlier editions on 3-hour reserve
Banks, J., Carson, J.S., Nelson, B.L., Nicol, D.M.,
(2010). Discrete Event System Simulation 5th Edition,
Prentice Hall. On Reserve.
Arena online books.
Crystal Ball online documentation.
Grading – Allocation
Mid-term Exam (2/15/2015)
25%
Final Exam (Monday 3/16/2015 12:001:50PM)
35%
Lab Exams (Week 4 lab, Week 9 lab)
20%
Homework/Lab Assignments
(12-14 Labs/HW)
Class Participation (includes information
sheet if applicable)
Class participation based on:
1. Participation in class – answering questions
2. In-class exercises
3. Random attendance taken
4. Submitting information sheet
15%
5%
Grading Scale
92 or above
A
89-92
A-
86-89
B+
82-86
B
79-82
B-
76-79
C+
72-76
C
69-72
C-
59-69
D
Exams, Homework, Labs
Homework
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5-7 homework assignments will be given.
Some solutions for programming problems will be
provided after assignment is turned in.
Group study is encouraged but each person
should understand all problems.
Due at the beginning of class – Late HW penalized
 After the final call for homework – 2 out of 10.
 After 12 noon the day immediately following the final call
– assignment not accepted.
Exams, Homework, Labs
Labs
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Labs start in week 1.
Seven total lab assignments – No labs in week 10.
Due by the end of lab.
Counts the same as a HW assignment.
Switching lab sections
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Only with prior approval of the TAs or instructor.
The number approved requests for switching
sections will be limited.
Exams, Homework, Labs
Lab exams
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Two lab exams: week 4 and week 9.
Tests simulation modeling with specific software.
Graded by the TAs.
Different sections will be given different versions
of the exam.
 The paper copies of the exam are to be returned to the
TAs.
 No photographs of the exam are allowed.
Exams, Homework, Labs
In-class Exams
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Open book and open note exams – No laptop computers,
tablets, smartphones, etc. and no electronic communication
permitted.
Based on homework, lecture material (in-class exercises and
examples), labs.
Exams will only be distributed in class and in office hours for
viewing and will then be returned to the instructor. No
photos of the exam are allowed.
Grading questions/modifications must be brought to the
instructor within one week after the exam is returned in
class.
Recipe for Failure
Low effort on HW
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Utilize solutions from prior terms
Split problems with classmates
Turn in late HW
Low effort on labs
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Rely on your partner to complete lab
Focus on procedures instead of what the procedure
accomplishes
Don’t attend class
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Physically
Mentally
Things To Do
Do the opposite of Recipe for Failure
Get your points
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Get your HW and lab points
At some point do the HW and labs with good effort
Seek help early in the term if needed
Do problems under a time constraint
IE 415 vs. IE 515
IE 415/515 differences
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IE 515 – additional homework (may
require study outside of class material)
One or more questions on each exam will
differ
Grading will be harder for 515 students
Lecture Format
The first part of class will be devoted to questions.
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Unreasonably long questions will be handled one on one.
Lecture
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Ask questions
End of Class – Will try to leave time for questions.
Lecture Format
Material will be delivered on slides using a tablet PC.
 Material will be added to the slides during class.
 Examples will be completed electronically on the slides.
 There will be periodic in-class problem solving
sessions.
 Solutions completed electronically on slides.
Minor changes to the slides may be made just before
class.
All added (hand written) material is your responsibility
– They will not be available on the website.
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Class Rules
Turn off/quiet cell & smart phones and other
communication devices.
No web surfing.
No newspapers.
No completing homework or other
assignments.
No sleeping.
Use common sense and be considerate of
others.
18
Questions ?
IE 415/515 - Introduction
Simulation
Example 1
As an IE working at a manufacturing plant, you are
asked to help evaluate a potential investment in a new
machine (at a highly utilized process step). The
company has a number of different types of jobs that
undergo processing at this step. Currently there are five
machines, and each machine can only process a subset
of the jobs. Each existing machine also experiences
random failures. The new machine can process any of
the currently produced jobs. Jobs arrive in batches
(each batch with different job types of known
composition). Assuming the percentages of different job
types remains the same, you are asked to evaluate the
increased throughput realizable by purchasing this
machine.
A,B
Jobs to be processed
C,D
Completed jobs at rate?
.
.
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J,K
New Machine – All job types
Example 2
Applying engineering economic analysis
to evaluate the NPV of two alternatives
for fork lift purchases.
In addition to NPV, evaluate each
alternative with respect to cost
risk/uncertainty.
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There is uncertainty in many of the
parameters used in these calculations.
Example 2
Known parameters
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Initial costs
Approximate fuel costs (e.g., gas vs.
electric) in the near future.
Unknown parameters
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Breakdown/maintenance costs
Salvage/resale value
Future fuel costs
Example
How do you proceed?
Approaches
Experience/Intuition
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Often effective but limited in very complex
situations
Analytical models – Mathematical equations
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Usually preferred if available
Usually very fast – many types of “what if”
Provide insight into key parameters
Limited availability/accessibility
Computer simulations
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Applicable to the most complex situations given
enough time
Simulation Questions
How do you simulate these systems?
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What software choices?
How are system dynamics represented/simulated?
How do you represent randomness in the system?
What is the form of the answer?
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How do you interpret simulation results?
What data needs to be collected?
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…
How is the data processed?
Simulation
Dictionary definition – “to look or act
like”
Almost everything done in engineering
is simulation
Engineers build models to predict and
understand the performance of all types
of things, systems, and processes
Examples
Equations predicting what happens in
physical systems – thermodynamics, statics,
…
Physical prototypes of products for
development, test and validation
Final product testing
Process validation – Soft tooling
Flight simulators
Arcade games
Types of Systems IEs Simulate
They are big and costly
Involve people
Random events/values occur over time
The systems are too big to build physical
prototypes
A calculation may involve the combination of
multiple random components
The systems may not exist
Systems IEs Simulate - Examples
Production line performance
Call centers performance
Plant floor layout – material movement
Scheduling of resources
Network performance
Inventory control/ordering points
Distribution and routing
Engineering economic calculations incorporating
randomness
…
Characteristics of Systems IEs Simulate
System operation is often dictated by manmade rules, or the focus is on establishing
efficient rules
Examples
 Staffing for a desired level of customer
performance.
 Sizing/allocation of storage areas.
 The number of machines to use at a
workstation.
 The scheduling of work.
 Etc.
IE Computer Simulations
In practice, simulation refers to the process
of designing and creating computerized
models of a system and doing numerical
computer-based experiments.
Real power - application to complex systems.
Industry acceptance.
Objectives of IE Analysis
Estimate performance
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Throughput of a production line.
Average wait time for customers.
Minimum investment to achieve a target.
Distribution of NPV values.
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Evaluate designs
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Plant layouts
Scheduling rules
Production system configurations
…
IE Computer Simulations - Types
Deterministic/Stochastic
Discrete/Continuous state
Static/Dynamic
IE 415/515 will focus on Stochastic,
Discrete, Static & Dynamic simulations.
Example
Expected value (average) of the max
value from two rolls of a die
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Approaches
 Experience/intuition
 Analytical
 Simulation
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Physical
Computer simulation
Example
Experience/Intuition
Example – Analytical Model
Expected value (average) of the max value
from two rolls of a die
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Analytical (can also enumerate for this example).
E[max( X 1 , X 2 )]  E[ E[max( X 1 , X 2 | X 1 )]]
1 6
 *  E[max( X 1 , X 2 | X 1  i )]
6 i 1
i
6  (i  1)
i
E[max( X 1 , X 2 | X 1  i )]  (i * ) 
* (1  )
6
2
6
42  i  i 2

12
1 6 42  i  i 2
 E[max( X 1 , X 2 )]  
 4.47
6 i 1
12
Simulation
Expected value (average) of the max
value from two rolls of a die
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Physical simulation
Example
Expected value (average) of the max
value from two rolls of a die
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Computer simulation
Example
Computer simulation answer is not a
single value
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More work – more precision
95 % Confidence Intervals for the Avg.
150 Trials
Half Width
Low Limit
Upper Limit
0.23292914
4.12707086
4.592929141
1500 Trials
Half Width
Low Limit
Upper Limit
0.07068044
4.34931956
4.49068044
Static Stochastic Simulation
Spreadsheet packages
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@Risk
Crystal Ball
Dynamic Stochastic Simulation
The passing of time is a fundamental
part of the simulation.
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For IEs this time is normally the time a
system (e.g., a plant) is operating.
Dynamic stochastic simulations are
often animated
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Validation
Communication
Example
M/M/1 Queuing System
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Avg. # in queue, Avg. time in system
Example
Experience/Intuition
Example – Analytical Model
M/M/1 Queuing System
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Many results have been obtained.
Example – Physical Simulation
M/M/1 Queuing System
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Most likely not possible – Instead, the real
system can be observed.
Example – Computer Simulation
M/M/1 Queuing System
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Arena software utilized.
Dynamic Stochastic Simulations
Physical simulations too costly or not
possible.
Analytical models do not exist - System
is too complex.
Demo
Dynamic Stochastic Simulations
Normally executed with “simulation software”
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General-purpose languages (C++)
 Tedious, low-level, error-prone
 Almost complete flexibility
 Can be used to program static stochastic simulations too
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Support packages
 Subroutines for list processing, bookkeeping, time
advance
 Widely distributed, widely modified
Dynamic Stochastic Simulations
Simulation languages
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GPSS, SIMSCRIPT, SLAM, SIMAN
Learning curve for features, effective use, syntax
High-level simulators
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Very easy, graphical interface
Domain-restricted (manufacturing,
communications)
Limited flexibility — model validity?
Dynamic Stochastic Simulations
Demo
Warnings
Simulation is very time consuming
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Model development
Data collection
This often makes simulation infeasible
Simulations are complicated – Easy to make
errors (logical), validation is often difficult
Garbage in – Garbage out
Simulation output has randomness
Goals of this Course
Students successfully completing this course
should (independent of the simulation
system):
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Understand the basic mechanics of how almost all
discrete event simulation systems operate
Be able to carry out a “complete” simulation
project
Understand pros and cons of using simulation to
study dynamic systems
Simulation Coverage Outline
Start with static stochastic simulations
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Probability and statistics required
Cover fundamentals in lecture
We will use Excel/Crystal Ball Software in the lab
Move to discrete dynamic stochastic
simulations
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Cover fundamentals in lecture
We will use Arena software in the lab