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Publication details, including instructions
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Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
INFORMS is located in Maryland, USA
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INFORMS is located in Maryland, USA
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IE 403-Lab
(Article 4)
Improving Patient Access to Chemotherapy
Treatment at Duke Cancer Institute
Article
Was written by Jonathan C. Woodall, Tracy
Gosselin, Amy Boswell and Sebestian
Urrutia, Michael Murr and Brian T. Denton.
Interfaces Vol. 43, No. 5,
IE 403-Lab
(Article 6)
Casualty Collection Points Optimization: A
Study for the District of Columbia
Article
Was written by Aruna Apte, Curtis Heidtke
and Javier Salmern
Published online in Articles in Advance 05
Sep 2014
Key Words
Casualty collection poi
IE 403-Lab
(Article 5)
Annual Distribution Budget in the Beverage
Industry: A Case Study
Article
Was written by Luis GuimarQaes, Pedro
Amorim, Fabrcio Sperandio, Fabo
Moreira,Bernardo Almada-Lobo.
Interfaces 2012 ,pp.1-22
Key Words
Tactical Distribution p
Nonlinear Models
IE 403
1
2
LPs
Constant returns to scale
Use of a resource by an activity is
proportional to the level of the activity.
Proportionality
The total use of a resource is the sum
of the uses by the individual activities.
Additivity
No x12
Simple Modeling Techniques I
IE 403
1
2
A Financial Management
Problem
A bank offers five types of loans with
different interest rates.
You have some money to invest.
How should you divide up your
investments to maximize the yield on
investment in loan
Structured LP Models
IE 403
1
2
Large Problems
Large problems many times have a
structure to them
The main problem consists of several
subproblems
2
3
Block Angular Structures
A0
A1
A2
An
B1
b0
Common constraint
b1
B2
Bn
bn
3
4
Staircase Structures
Objec
Simple Modeling Techniques II
IE 403
1
2
Precedence Constraints
These type of constraints are typically
needed for modeling scheduling problems.
e.g., project management problem
Example: Task B cannot be completed
before task A is finished.
Let xA and
Simple Modeling Techniques II More
IE 403
1
2
Piecewise Linear Functions
Some problems involve nonlinear
continuous functions that can be
approximated by piecewise linear functions.
Piecewise linear functions can be linearized by
using binary variables.
Ch01 Introduction-3e-SI 12-15, 16/06, 06/04/07
Answer: Flexible automation is an extension of programmable automation. A flexible automated system is
capable of producing a variety of parts (or products) with virtually no time lost for changeovers from on
Outlook
Sunny
Sunny
Overcast
Rainy
Rainy
Rainy
Overcast
Sunny
Sunny
Rainy
Sunny
Overcast
Overcast
Rainy
Temperature
Humidity
Hot
High
Hot
High
Hot
High
Mild
High
Cool
Normal
Cool
Normal
Cool
Normal
Mild
High
Cool
Normal
Mild
Normal
Mild
Normal
Mild
High
H
Data Exploration and Visualization with R
Yanchang Zhao
http:/www.RDataMining.com
30 September 2014
1 / 39
Outline
Introduction
Have a Look at Data
Explore Individual Variables
Explore Multiple Variables
More Explorations
Save Charts to Files
Further Read
Decision Trees
M.Erdal BALABAN
IE485 IntoductIon to Data MInIng
A simple example
You are coach of Fenerbahe Football Team and you want to guess/predict/estimate
the outcome of next week's soccer game between the Fenerbahe and the
Galatasaray.
What we know
IE 402 Operations Research
II
Major Distributions
1
Bernoulli Distribution
A random variable X with the Bernoulli
distribution with parameter p has two
possible values, 0 and 1 where
p(0)=1-p and p(1)=p
E[X]=p and Var[X]=p(1-p)
2
Binomial Distribution
Let
IE 402 Operations Research
III
Probability Review
Random Variables
A random variable is a variable that takes
on its values by chance.
Use capital letters, say X, to denote a
random variable and lowercase letters,
say x, to denote its value.
A random vari
Data Mining
The process of employing one or more
computer learning techniques to
automatically analyze and extract
knowledge from data.
Knowledge Discovery in
Databases (KDD)
The application of the scientific
method to data mining. Data mining is
one step
IE 402 - Homework 1
Q.1. Random variables X and Y are independent and have the probability mass func1
1
tions: PX (0) = 1 , PX (3) = 2 ; PY (1) = 6 , PY (2) = 1 , PY (3) = 1 . Determine the
2
3
2
probability mass function of the sum Z = X + Y . In other w
IE 402 Operations Research III - Fall 2014
Instructor
Mehmet Onal
Industrial Engineering Dept.
Room No: AMF 327
Phone: +90 (216) 528 7138
e-mail: [email protected]
Class Schedule and Venue
Wednesday 7; Friday 1,2
@DK 107
Textbooks
Wayne L. Winston, Opera
Sales T ransactions: July 14
Cust ID Region
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Seller
Julia
Chris
Julia
Luise
Julia
Mike
Mike
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Mike
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Chris
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C
COURSE PROFILE
Course Number : IE 485
Course Title: Introduction to Data Mining
Required / Elective : Elective
Pre-requisite : IE Senior Standing or consent
of the Instructor
Catalog Description :
Textbook / Required Material :
Basic concepts in data mini
This article was downloaded by: [88.255.242.18] On: 25 August 2014, At: 07:59
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INFORMS is located in Maryland, USA
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Loan Purpose
Small Appliance
Furniture
New Car
Furniture
Education
Furniture
New Car
Business
Small Appliance
Small Appliance
Business
New Car
Business
New Car
New Car
Used Car
Furniture
New Car
Repairs
Education
Furniture
Furniture
Furniture
Furniture
Ne
How to Write a Java Program Basics
First you need to install NetBeans IDE (Integrated Development Environment) (or a similar
environment) to be able to easily write, compile, debug and run your programs.
Click and start NetBeans IDE.
Choose from the menu
1.10 Sums and Other Functions of Random Variables
We have seen some examples of sums of random variables.
Example 1. We roll a die twice. Let X1 = result of first roll and X2 = result of second roll. Let T
be the sum of the two rolls. Then T is the sum of