Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2013
Best Practices in
Knowledge Management
Andy Moore . . . . . . . . . . . . . . . . . . . . . . . . . 2 KM: The World Changer We Love To Hate
I once asked a conference audience: By a show of hands, how
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2014
COURSE: OPTIMIZATION TECHNIQUE AAOC C222
ASSIGNMENT  6
Date of Assignment: 10/11/09
Date of Submission: 27/11/09 (Common Hour)
Maximum marks: 5
Q1. Solve the following by Branch and Bound Algorithm.
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2014
COURSE: OPTIMIZATION TECHNIQUE AAOC C222
ASSIGNMENT2
Date of Assignment: 11/09/09 Date of Submission 18/8/09 (Common Hour)
Maximum marks: 5
1(a) Why artificial variables are not called slack?
1(b) Wh
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2014
BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE, PILANI
SemesterI, 200708 AAOC C222 (Optimization)
Assignment 4
*
Note: Submit handwritten solutions of the assignment to your instructor in tutorial
class
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2014
COURSE: OPTIMIZATION TECHNIQUE AAOC C222
ASSIGNMENT1
Date of Assignment: 21/08/09 Date of Submission 28/8/09 (Common Hour)
Maximum marks: 5
Q1. A call center has the following minimal daily requireme
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2014
BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE, PILANI
I Semester 200708 AAOC C222 (Optimization)
Assignment 3
*
Note: Submit handwritten solutions of the assignment to your instructor on regular class
(i
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2014
COURSE: OPTIMIZATION TECHNIQUE AAOC C222
ASSIGNMENT  5
Date of Assignment: 06/11/09
Date of Submission: 13/11/09 (Common Hour)
Maximum marks: 5
Q1. Solve the given assignment model (refer to Table 1)
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2014
INTEGER LINEAR PROGRAMMING
There are many LP problems in which the decision
variables will take only integer values. If all the
decision variables will only take integer values it is
called a pure int
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2014
CLASSICAL OPTIMIZATION THEORY
Quadratic forms
Let
x1
x
2
X .
.
xn
be a nvector.
Let A = ( aij) be a nn symmetric matrix.
We define the kth order principal minor as
the kk determinant
a11
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2014
QUADRATIC
PROGRAMMING
Quadratic Programming
A quadratic programming problem is a nonlinear
programming problem of the form
Maximize
Subject to
T
z c X X DX
A X b , X 0
Here
x1
b1
x
b
2 2
X .
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2014
Dual simplex method for
solving the primal
In this lecture we describe the
important Dual Simplex method
and illustrate the method by doing
one or two problems.
Dual Simplex Method
Suppose a basic sol
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2014
GAME THEORY
Life is full of conflict and competition.
Numerical examples involving adversaries in
conflict include parlor games, military battles,
political campaigns, advertising and
marketing campai
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2014
Iterative
computations of the
Transportation
algorithm
Iterative computations of the Transportation algorithm
After determining the starting BFS by any one of the
three methods discussed earlier, we u
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2014
Sensitivity Analysis
The optimal solution of a LPP is based on the
conditions that prevailed at the time the LP model
was formulated and solved. In the real world, the
decision environment rarely rema
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2014
CPM and
PERT
CPM and PERT
CPM (Critical Path Method) and PERT
(Program Evaluation and Review Technique)
are network based methods designed to assist
in the planning, scheduling, and control of
project
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2014
The Assignment Model
" The best person for job" is an apt description of
the assignment model.
The general assignment model with n workers
and n jobs is presented below:
Jobs
1 2 .
n
1 c11 c12
c1n
Wo
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2014
Previous Page
The direct compressive stress (ac) in the column due to the weight of the water
tank is given by
Mg
Mg
bd
XxX2
and the buckling stress for a fixedfree column (ab) is given by [1.71]
_ /
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2014
4
LINEAR PROGRAMMING II:
ADDITIONAL TOPICS AND
EXTENSIONS
4.1 INTRODUCTION
If a LP problem involving several variables and constraints is to be solved by
using the simplex method described in Chapter
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2013
See discussions, stats, and author profiles for this publication at: https:/www.researchgate.net/publication/263855292
CFD MODELING FOR ANALYSIS OF CALCINERS
IN CEMENT INDUSTRIES
Conference Paper Nove
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2013
Aptitude Test
Please read the following instructions carefully.
1. Total duration to complete the test is 45 minutes.
2. General structure of the test:
3.
4.
5.
6.
Section
No. of Questions
Duration Ma
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2013
r
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Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2013
PONDICHERRY UNIVERSITY
(A Central University)
DIRECTORATE OF DISTANCE EDUCATION
Human Resource Information Systems
Paper Code : MBHR 4004
MBA  HRM
IV  Semester
Author
Dr. S. Riasudeen,
Asst. Profess
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2013
Application of HRIS in
International Human
Resource Management
HUMAN RESOURCE INFORMATION SYSTEM
GROUP NO. 10
Introduction
Globalization
creates a need for
internationally engaged firms to operate
mo
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2013
Waitrose
Waitrose
The retail management graduate scheme at Waitrose attracts thousands of applications each year: in
2010/11, there were over 3,500 applicants for just 30 places. Every application is
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2013
Performance Based Incentive Systems of Modern Bank of India
The case addresses the issues related to performance Management quite
critically. Its evident from the case study that the HR department of
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2013
Performance Based Incentive Systems of Modern Bank of India
The case addresses the issues related to performance Management quite
critically. Its evident from the case study that the HR department of
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2014
Previous Page
Since the integrand
n
F=H S
Pixcfw_
(12.73)
/= i
depends on x, u, and t, there are n + m dependent variables (x and u) and
hence the EulerLagrange equations become
?7,(T)
dxt
=0
'
'!
Birla Institute of Technology & Science, Pilani  Hyderabad
Engineering optimization
ME 313

Spring 2014
ii
STOCHASTIC PROGRAMMING
11.1 INTRODUCTION
Stochastic or probabilistic programming deals with situations where some or
all of the parameters of the optimization problem are described by stochastic
(o