ISyE320: Simulation and Probabilistic
Modeling: Introduction to Simulation
Prof. Laura Albert McLay
Department of Industrial and Systems Engineering
University of Wisconsin-Madison
Chapter 2.1 2.4, 3, 6.1
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Prof. Albert McLay (UW-Madison)
ISyE
Chapter 5
Joint Probability Distributions and Random Samples
5.1 - Jointly Distributed Random Variables
5.2 - Expected Values, Covariance, and
Correlation
5.3 - Statistics and Their Distributions
5.4 - The Distribution of the Sample Mean
5.5 - The Di
Overflow from last time
Weibull
The Weibull distribution is commonly used in Reliability
X Weibull(, )
1 (x/)
x e
,x 0
F (x) = 1 e(x/)
f (x) =
is called the scale parameter:
X Weibull(1, ) X Weibull(, )
is called the shape parameter
Lets do inverse tra
Industrial and Systems Engineering
Junior Design Laboratory
Define the Problem: Mission
and Project Planning
Industrial and Systems Engineering
Junior Design Laboratory
Professor Radwin
Mission and Project Planning Tools
Project Charter
Gantt Cha
Exponential distribution: memorylessness
Proof:
P (X > s + t|X > t) =
P (X > s + t|X > t) =
P (X > s + t|X > t) =
P (X > s + t, X > t)
P (X > t)
P (X > s + t)
1 F (s + t)
=
P (X > t)
1 F (t)
e(t+s)
= es = 1 F (s) = P (X > s)
et
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Prof. Albert
STAT 312
Chapter 7 - Statistical Intervals Based on a Single Sample
7.1 - Basic Properties of Confidence Intervals
7.2 - Large-Sample Confidence Intervals for a Population Mean and Proportion
7.3 - Intervals Based on a Normal Population Distribution
Industrial and Systems Engineering
Junior Design Laboratory
Team Building
Industrial and Systems Engineering
Junior Design Laboratory
Professor Radwin
Ability to work in teams is among the
most valued skills for engineers
Professor Radwin
1
STAT 312
Chapter 7 - Statistical Intervals Based on a Single Sample
7.1 - Basic Properties of Confidence Intervals
7.2 - Large-Sample Confidence Intervals for a Population Mean and Proportion
7.3 - Intervals Based on a Normal Population Distribution
Design Project Mission Statement and Management Plan Assignment
Team: Cheeseheads
Team Members: Emily Egge, Bridget Roehrs, Vy Nguyen,
Saulo Filipe Mendes Couto, and Huimin Ou
Project Charter
Purpose
As a team of successful industrial engineers, we are wo
Nuclear Accident in Fukushima and Its Impacts to Society
Group Members: Mason Zhao, Collin Peters, Dokyung Lim, Brandon
Li, Isak Fruchtman, Minsun Lim.
Summarization of Fukushima Nuclear Accident
* The earthquake and tsunami struck the eastern part of Jap
Rubric for CEE 629 Group Project Presentation
weig
ht
Task Description: Group Project Presentation
Objectives: To able to identify the problems, define the scope of work, conduct the preliminary study, perform
engineering design, and write a technical rep
Industrial and Systems Engineering Junior Design Laboratory
Introduction to ROBOTC for TETRIX and LEGO MINDSTORMS
Introduction:
In this exercise you will build your first robot and learn how to download your first
Robot C program to the robot. Your team w
ISyE 321
1
Lab 6 - Intro to Input Analysis
Suzan Iloglu Afacan
3/1/17
Announcements!
2
Project !
Phase II due 3/29!
Collect Data!
Analyze!
Next week Exam Review (Attendance is Optional)!
The week before Spring Break Project Work Time
(Attendance is opti
Random Number Generation: Chap 7
Characteristics of Good Generators
Maximum Density
Want the values Ri , i = 1, 2, . . . to leave no large gaps on [0,1]
Problem: Each Ri is discrete (not continuous)
Solution: Use a very large integer m
Maximum Period
To a
STAT 312
Chapter 7 - Statistical Intervals Based on a Single Sample
Chapter 8 - Tests of Hypotheses Based on a Single Sample
Chapter 9 - Inferences Based on Two Samples
Chapter 10 - Analysis of Variance
Chapter 11 - Multifactor Analysis of Variance
Chapte
1
Learning Objectives
Explain work center scheduling.
Analyze scheduling problems using
priority rules and more specialized
techniques.
Apply scheduling techniques to the
manufacturing shop floor.
Analyze employee schedules in the
service sector.
22-2
Man
STAT 312
Chapter 9 - Inferences Based on Two Samples
Introduction
9.1 - Z-Tests and Confidence Intervals for a
Difference Between Two Population Means
9.2 - The Two-Sample T-Test and Confidence Interval
9.3 - Analysis of Paired Data
9.4 - Inferences
1
Learning Objectives
Explain the Theory of Constraints (TOC).
Analyze bottleneck resources and apply TOC
principles to controlling a process.
Compare TOC to conventional approaches.
Evaluate bottleneck scheduling problems by applying
TOC principles.
23-2
ISyE320: Simulation and Probabilistic
Modeling: Lecture 1
Prof. Laura McLay
Department of Industrial and Systems Engineering
University of Wisconsin-Madison
Chapter 1
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Prof. McLay (UW-Madison)
ISyE320
Lecture Notes
1 / 32
Simulation overview
See discussions, stats, and author profiles for this publication at: https:/www.researchgate.net/publication/227294404
Impact of Dam Construction on Water Quality
and Water Self-Purification Capacity of the
Lancang River, China
Article in Water Resources
Dynamic Simulation
Simulation Basics
Two worldviews to simulation modeling
Event-Scheduling
Concentrates on the events and how they affect the system
state
Simulation evolves by executing events in increasing order of
their occurrence
Procedural languages
Dynamic Simulation
Simulation Basics
System dynamics: number of people in system
2servers:
1 server from before:
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Prof. Albert McLay (UW-Madison)
ISyE320
Lecture Notes
36 / 59
Dynamic Simulation
Simulation Basics
Warning!
We looked at a First
ISyE320: Simulation and Probabilistic
Modeling: Probability Review
Prof. Laura Albert McLay
Department of Industrial and Systems Engineering
University of Wisconsin-Madison
Chapter 5.1, 5.3, 5.4, 5.5
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Prof. Albert McLay (UW-Madison)
ISyE320
L
STAT 312
Chapter 7 - Statistical Intervals Based on a Single Sample
7.1 - Basic Properties of Confidence Intervals
7.2 - Large-Sample Confidence Intervals for a Population Mean and Proportion
7.3 - Intervals Based on a Normal Population Distribution
The Poisson process
Consider an arrival process that starts at time t = 0.
Let N (t) denote the (random) cumulative number of arrivals by
time t (in interval [0, t]).
N (t) can be 0,1,2,.
The counting process cfw_N (t), t 0 is a Poisson process with avera
Special Methods
Reminder: The Inverse Transform Theorem is
magical
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Prof. Albert McLay (UW-Madison)
ISyE320
Lecture Notes
60 / 87
Special Methods
Geometric Distribution
Suppose we repeat independent random trials until we succeed
Probability