ISyE 6203
Transportation and Supply Chain Systems
Spring 2013
Case Study: Inventory Routing
Due Date: April 4, 2013
Read the materials below in preparation for discussion in class. The questions are intended to guide
your reading and thoughts, but are not
ISyE-6203 Transportation and Supply
Chain Systems
Assignment 2
Submitted by
Harish Babu A.P
902903594
Method 1: Simple Exponential Smoothing SA-1
The following table gives the standard deviation of the final inventory quantities with
a changing value of a
ISyE 6203 Transportation and Supply Chain Systems
Spring 2013
Assignment 5
Question 1
1. Determine the number of vehicles needed.
The minimum number of vehicles needed = = = 2
2. Write down a complete integer linear programming formulation for the vehicle
ISyE 6203 Transportation and Supply
Chain Systems
Homework 3
Submitted by
Harish Babu A.P
902903594
Problem Description
The problem presented is a stochastic vehicle routing problem with pickup and
delivery. There are some elements which are determined by
ISyE 6203 Transportation and Supply Chain Systems
Spring 2013
Case Study: Inventory Routing
Due Date: April 4, 2013
Questions
(a) Describe the companys products.
The products are industrial gases such as oxygen, nitrogen, carbon monoxide,
hydrogen, argon
Harish Babu A.P
902903594
ISyE 6203 Transportation and Supply Chain Systems
Case Study: Inventory Management
1) How does a base-stock policy work? How does a time-varying base-stock
policy work? How does an echelon base-stock policy work?
Base- Stock Poli
Improving the Distribution of Industrial Gases with an On-Line Computerized Routing and
Scheduling Optimizer
Author(s): Walter J. Bell, Louis M. Dalberto, Marshall L. Fisher, Arnold J. Greenfield, R.
Jaikumar, Pradeep Kedia, Robert G. Mack and Paul J. Pru
ISyE 6203 Transportation and Supply Chain Systems
Spring 2013
Assignment 5
Question 1
1. Determine the number of vehicles needed.
The minimum number of vehicles needed = = = 2
2. Write down a complete integer linear programming formulation for the vehicle
Submitted by
Harish Babu A.P
902903594
ISyE 6203 Transportation and Supply Chain Systems
Homework 4
Question 1
1. Complete Linear Program Formulation
The following formulation was used :
s.t.
Where
N = cfw_ A,B,C,D,E,F represent the nodes
E= cfw_ (A,B) (A
Submitted by
Harish Babu A.P (902903594)
Case Study: The Bullwhip Effect
1) List some problems associated with global supply chains
Long delivery lead times, high buffer stock, complex logistics and high cost of
coordination, as companies try to coordinat
Research Paper No. 1549
Information Sharing in a Supply Chain
Hau L. Lee
Seungjin Whang
RESEARCH PAPER SERIES
GRADUATE SCHOOL OF BUSINESS
STANFORD UNIVERSITY
Information Sharing in a Supply Chain
L.
and Seungjin
Department of Industrial Engineering and En
R Notes for
Generalized Linear Models
Fall 2013
Revised by Nicoleta Serban from Kathryn Roeder Larry Wassermans original
Regression Course notes
1
0.1 Example. This is a famous data set collected by Sir Richard Doll in the 1950s. I am following example 9.
LOGISTIC REGRESSION, POISSON
REGRESSION AND GENERALIZED
LINEAR MODELS
We have introduced that a continuous response, Y, could depend on continuous or
discrete variables X1, X2, Xp-1. However, dichotomous (binary) outcome is most
common situation in biolog
Binary Logistic Regression
Binary Logistic Regression
The test you choose depends on level of measurement:
Independent Variable
Dependent Variable
Test
Dichotomous
Interval-Ratio
Dichotomous
Independent Samples t-test
Nominal
Dichotomous
Nominal
Dichotomo
Lecture Notes for
Multiple Linear Regression
Fall 2013
Revised by Nicoleta Serban from Kathryn Roeder Larry Wassermans original
Regression Course notes
1
1
1.1
Bias-Variance Decomposition
The Bias-Variance Trade-off
If X and Y are random variables, recall
Lecture Notes for
Generalized Linear Models
Fall 2013
Revised by Nicoleta Serban from Kathryn Roeder Larry Wassermans original
Regression Course notes
1
1
Models
We can write the logistic regression model as
Yi Bernoulli(i )
g (i ) = XiT
where g (z ) = l
Regression Analysis
Homework 4: Logistic Regression
This homework is due Thursday, November 7th, in class BEFORE class starts. Late papers
will not be accepted.
Please remember to staple if you turn in more than one page.
You must SHOW ALL WORK. If you
Lecture Notes for
Logistic Regression
Fall 2013
Revised by Nicoleta Serban from Kathryn Roeder Larry Wassermans original
Regression Course notes
1
1
Model Estimation
1.1 Example. Our motivating example concerns the probability of extinction as a function
K WH R EGI O ND I VI S I OR EP O R T AB L E_D O M P C O N O F F 1 S L EEP 1 EC D EV L EC C H R G R GP L GE O T H ER MEM P H O M T EM P GO NES F R I G F R I G2 F R I G3 D I S HES C WAS H WWACU S ES O L AR S T L C F L P C F L EEL S EN G S EL P S EF O S EK E
2. (a) Give an expression for E[d(t1, t2)] and Var[d(t1, t2)].
(b) Assume that D(0), D(1), D(2), . . . , are independent. Give an expression for
Var[d(t1, t2)].
(c) The assumption that D(0), D(1), D(2), . . . , are independent is a restrictive
assumption.
Submitted by
Harish Babu A.P (902903594)
ISyE 6203 Transportation and Supply Chain Systems
Case Study: Barilla
Issued: January 29, 2013
Due: February 5, 2013
Questions
1) Describe Barillas supply chain?
Barilla had
A) Manufacturer- seven divisions (3 past