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Spreadsheet Modeling
& Decision Analysis
A Practical Introduction to
Management Science
5th edition
Cliff T. Ragsdale
1
Chapter 8
Nonlinear Programming &
Evolutionary Optimization
2
Introduction to Nonlinear Programming
(NLP)
An NLP problem has a nonline
Chapter 13
Queuing Theory
13.0 Introduction
Sometimes it seems as if we spend most of our lives waiting in lines. We wait in lines at
grocery stores, banks, airports, hotels, restaurants, theaters, theme parks, post ofces,
and trafc lights. At home, we ar
Chapter 1
Introduction to Modeling
and Decision Analysis
1.0 Introduction
This book is titled Spreadsheet Modeling and Decision Analysis: A Practical Introduction to
Management Science, so lets begin by discussing exactly what this title means. By the
ver
Index
801
Index
A
ACA problem. See American Car Association (ACA) problem
Accuracy measures, 486487
descriptions, 486487
goodness of t, 487. See also Fit
mean absolute deviation (MAD), 487
mean absolute percent error (MAPE), 487
mean square error (MSE), 4
Chapter 6
Integer Linear Programming
6.0 Introduction
When some or all of the decision variables in an LP problem are restricted to assuming
only integer values, the resulting problem is referred to as an integer linear programming (ILP) problem. Many pra
Chapter 12
Introduction to Simulation
Using Crystal Ball
12.0 Introduction
Chapter 1 discussed how the calculations in a spreadsheet can be viewed as a mathematical model that denes a functional relationship between various input variables (or
independent
Chapter 4
Sensitivity Analysis
and the Simplex Method
4.0 Introduction
In Chapters 2 and 3, we studied how to formulate and solve LP models for a variety
of decision problems. However, formulating and solving an LP model does not necessarily mean that the
Chapter 2
Introduction to Optimization
and Linear Programming
2.0 Introduction
Our world is lled with limited resources. The amount of oil we can pump out of the
earth is limited. The amount of land available for garbage dumps and hazardous waste is
limit
Chapter 7
Goal Programming and Multiple
Objective Optimization
7.0 Introduction
Chapter 6 discussed the modeling techniques that apply to optimization problems that
require integer solutions. This chapter presents two other modeling techniques that
are so
Chapter 14
Project Management
14.0 Introduction
At some point, almost every manager assumes responsibility for the completion of some
type of project. The project might be relatively simplesuch as planning a company
picnic or producing an employee newslet
Chapter 15
Decision Analysis
15.0 Introduction
The previous chapters in this book describe a variety of modeling techniques that can
help managers gain insight and understanding about the decision problems they face.
But models do not make decisionspeople
Chapter 5
Network Modeling
5.0 Introduction
A number of practical decision problems in business fall into a category known as
network ow problems. These problems share a common characteristicthey can be
described or displayed in a graphical form known as
Chapter 8
Nonlinear Programming &
Evolutionary Optimization
8.0 Introduction
Up to this point in our study of optimization, we have considered only mathematical
programming models in which the objective function and constraints are linear functions
of the
Chapter 3
Modeling and Solving
LP Problems in a Spreadsheet
3.0 Introduction
Chapter 2 discussed how to formulate linear programming (LP) problems and how to
solve simple, two-variable LP problems graphically. As you might expect, very few realworld LP pr
Chapter 10
Discriminant Analysis
10.0 Introduction
Discriminant analysis (DA) is a statistical technique that uses the information available
in a set of independent variables to predict the value of a discrete, or categorical, dependent variable. Typicall
Chapter 9
Regression Analysis
9.0 Introduction
Regression analysis is a modeling technique for analyzing the relationship between a
continuous (real-valued) dependent variable Y and one or more independent variables
X1, X2, . . . , Xk. The goal in regress
Chapter 11
Time Series Forecasting
11.0 Introduction
A time series is a set of observations on a quantitative variable collected over time. For
example, every night the evening news reports the closing value of the Dow Jones
Industrial Average. These clos