Development of a Materials Requirements Planning (MRP) Software.
V.O. Oladokun, Ph.D.* and O.A. Olaitan, M.Sc.
Department of Industrial and Production Engineering, University of Ibadan, Ibadan, Nigeria.
E-mail: [email protected]
ABSTRACT
Lack of
In-depth
Implementing online or hybrid courses in a
traditional university
Author
Jacqueline L. Cahill
Doctoral Student (e-Learning
and Educational Technology),
Ph.D. Northcentral
University
[email protected]
com
Tags
eLearning, blended learning,
Lagrangian relaxation algorithms for hybrid flow-shop
scheduling problems with limited buffers
Takashi Irohara
Faculty of Science and Technology
Sophia University
7-1 Kioi-cho, Chiyoda-ku,
Tokyo, 102-8554, Japan
E-mail: [email protected]
Abstract: In t
ERP & WEB BASED SUPPLY
Chain Management
Chapter 3
Overview of ERP Systems
Overview of ERP Systems
Basic ERP System
Basic ERP system has a central database which is
used for financial,manufacturing & inventory
applications.There are service applications
co
ERP & WEB BASED SUPPLY
Chain Management
Chapter 3
Overview of ERP Systems
Overview of ERP Systems
Basic ERP System
Basic ERP system has a central database which is
used for financial,manufacturing & inventory
applications.There are service applications
co
Introduction
Process mapping in
successful ERP
implementations
Michael D. Okrent and
Robert J. Vokurka
The authors
Michael D. Okrent is based at Southern Connecticut State
University, New Haven, Connecticut, USA.
Robert J. Vokurka is based at Texas A&M Un
Syracuse University
SUrface
Mechanical and Aerospace Engineering
L.C. Smith College of Engineering and Computer
Science
1-1-2007
Enterprise Resource Planning (ERP): a review of
the literature
Young Moon
Syracuse University, [email protected]
Follow this and
Introductions
LEAN and ERP
A Marriage Made In Hellven
Curtis Roberts, CPIM
APICS Instructor
Varied Experience in Operations
Business Systems Consultant
Generally tolerated most of the time
Curtis Roberts, CPIM
What I hear
ERP is old thinking. Were going l
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The Use of the Gradient in Image Processing
Robert Reams
CTO/Founder. Streaming Applainces
Figure 1: An electron microscope image. The left image shows the original image and
the right image shows an edge map of the cell.
Introduction
The goal with this w
Minimizing Multimodal Functions of Continuous Variables with the Simulated Annealing Algorithm
A. CORANA, M. MARCHESI, C. MARTINI, and S. RIDELLA lstituto per i Circuiti Elettronici-C.N.R.
A new global optimization algorithm for functions of continuous va
Deection and Stiness
2
1
DEFLECTION AND STIFFNESS
A mechanical element, which does not bent, deect, or twist too much when
an external force, moment or torque is applied on it, is called rigid. But, if
the movement due to an external disturbance is large,
Spring Design Problem
A helical compression spring, made of music wire, has preload length of 3.5 inches. The
spring is to operate an indefinite number of times through a deflection of 0.75 inches
with the free length of the spring is 4.125 inches. We wis
Chapter 8: Robust Design
CHAPTER 8
ROBUST DESIGN
1 Introduction
In the real world, almost all designs are subject to variation. Such variation can arise from
multiple sources, including manufacturing processes, material properties, changing operating
cond
Limestone Slurry Pipeline Design Problem
Introduction
The objective of this project is to optimize the design of a pipeline for transporting
crushed limestone from a limestone quarry to a supply station located a distance away
from the quarry, as shown in
Design of a Hollow Fiber Artificial Kidney
Hemodialysis is a medical procedure used for patients with acute kidney failure. The
goal of hemodialysis is to reduce the amount of undesirable solutes (such as urea) in the
bloodstream of the patient to an acce
Design Optimization
Genetic Algorithm HW
1.
Minimize the function f = 0.2 + x12 + x22 - 0.1cos(6x1) - 0.1cos(6x2), subject to
variable bounds -1 x 1, using the genetic algorithm. Besides the starting population,
create two new generations (children and gr
Design Optimization
Simulated Annealing HW
Execute 3 cycles of the simulated Annealing algorithm to minimize the following
function:
f = 0.2 + x12 + x22 - 0.1cos(6x1) - 0.1cos(6x2), subject to variable bounds -1 x 1.
The argument for cos is evaluated in r
Chapter 1: Optimization-Based Design
CHAPTER 1
INTRODUCTION TO OPTIMIZATION-BASED DESIGN
1. What is Optimization?
Engineering is a profession whereby principles of nature are applied to build useful objects.
A mechanical engineer designs a new engine, or
Chapter 6: Constrained Optimization 1
CHAPTER 6
CONSTRAINED OPTIMIZATION 1: K-T CONDITIONS
1 Introduction
We now begin our discussion of gradient-based constrained optimization. Recall that in
Chapter 3 we looked at gradient-based unconstrained optimizati
ME 575: Spring Design
The specifications and modeling equations for compression spring design are given below.
We wish to determine the spring design that maximizes the force of a spring at its preload
height, ho, of 1.0 inches. The spring is to operate a
Chapter 3: Unconstrained Optimization
CHAPTER 3
UNCONSTRAINED OPTIMIZATION
1.
Preliminaries
1.1. Introduction
In this chapter we will examine some theory for the optimization of unconstrained functions.
We will assume all functions are continuous and diff
Chapter 5: Genetic and Evolutionary Optimization
CHAPTER 5
GENETIC AND EVOLUTIONARY OPTIMIZATION
1. Introduction
Gradient-based algorithms have some weaknesses relative to engineering optimization.
Specifically, it is difficult to use gradient-based algor
Chapter 2: Modeling Concepts
CHAPTER 2
MODELING CONCEPTS
1 Introduction
As was discussed in the previous chapter, in order to apply optimization methods we must
have a model to optimize. As we also mentioned, obtaining a good model of the design
problem i
Chapter 4: Introduction to Discrete Variable Optimization
CHAPTER 4
INTRODUCTION TO DISCRETE VARIABLE OPTIMIZATION
1. Introduction
1.1. Examples of Discrete Variables
One often encounters problems in which design variables must be selected from among a se
Branch and Bound Exercise
Consider the optimization problem with the following objective function.
2
min x14 2 x2 x12 x2 x12 2 x1 5
s.t. 2 x1 2
2 x2 4
A contour plot of the objective function is shown below:
Use the associated contour plot to evaluate ob
Nonlinear Programming
In previous chapters, we have studied linear programming problems. For an LP, our goal was
to maximize or minimize a linear function subject to linear constraints. But in many interesting
maximization and minimization problems, the o
3.6. DIRECTIONAL DERIVATIVES AND THE GRADIENT VECTOR 157
3.6
3.6.1
Directional Derivatives and the Gradient Vector
Functions of two Variables
Directional Derivatives
Let us rst quickly review, one more time, the notion of rate of change. Given
y = f (x),