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
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
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
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
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
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
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
Because of its competitive position, minimizing the total supply chain cost becomes an important
objective. Finally, during the decline phase, the number of sales points and sales channels through
which the product is distributed decreases. Because of the
Chapter 1. Introduction
Learning Objectives
After you have studied this chapter, you should
Know the relationships between warehousing, logistics, and supply chains
Know the major activities and processes in warehousing and the major characteristics of
wa
Notation for Parameters
Q = number of units loads per batch
W = unit load width along the aisle
L = load length perpendicular to the aisle
A = travel aisle width
SI = safety stock in loads at time of arrival
d = constant demand rate
z = stack height
22-Oc
From the first graph it is clear that the two alternative channels in this example, i.e. truck and rail, yield
nearly the same aggregate unit cost when leaving the destination facility. The minimum aggregate cost
is shown as a dashed line in the second gr
10.1. Introduction
Inventory Definition
The materials held in storage to satisfy a future demand are called inventory. The location where the
inventory is held is most of the time stationary and is called a warehouse. However, small caches of
inventory ar
Warehousing Systems:
Non Direct Access Storage
15-Oct-13
1.
Introduction
4.
3.
4.
5.
Warehouse Design Overview
Data Analysis
Unit Load Storage Policies
Non Direct Access Storage
Systems
Non Direct Access Storage Systems
Marc Goetschalckx
Non Direct Access
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
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
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
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
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
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
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