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Spreadsheet Modeling and Decision Analysis 7th edition.pdf
Preface
Brief Contents
Contents
Ch 1: Introduction to Modeling and Decision Analysis
1.0: Introduction
1.1: The Modeling Approach to Decision Making
1.2: Characteristics and Benefits of Modeling
1.3: Mathematical Models
1.4: Categories of Mathematical Models
1.5: Business Analytics and the Problem-Solving Process
1.6: Anchoring and Framing Effects
1.7: Good Decisions vs. Good Outcomes
1.8: Summary
1.9: References
Ch 2: Introduction to Optimization and Linear Programming
2.0: Introduction
2.1: Applications of Mathematical Optimization
2.2: Characteristics of Optimization Problems
2.3: Expressing Optimization Problems Mathematically
2.4: Mathematical Programming Techniques
2.5: An Example LP Problem
2.6: Formulating LP Models
2.7: Summary of the LP Model for the Example Problem
2.8: The General Form of an LP Model
2.9: Solving LP Problems: An Intuitive Approach
2.10: Solving LP Problems: A Graphical Approach
2.11: Special Conditions in LP Models
2.12: Summary
2.13: References
Ch 3: Modeling and Solving LP Problems in a Spreadsheet
3.0: Introduction
3.1: Spreadsheet Solvers
3.2: Solving LP Problems in a Spreadsheet
3.3: The Steps in Implementing an LP Model in a Spreadsheet
3.4: A Spreadsheet Model for the Blue Ridge Hot Tubs Problem
3.5: How Solver Views the Model
3.6: Using Analytic Solver Platform
3.7: Using Excel's Built-In Solver
3.8: Goals and Guidelines for Spreadsheet Design
3.9: Make vs. Buy Decisions
3.10: An Investment Problem
3.11: A Transportation Problem
3.12: A Blending Problem
3.13: A Production and Inventory Planning Problem
3.14: A Multiperiod Cash-Flow Problem
3.15: Data Envelopment Analysis
3.16: Summary
3.17: References
Ch 4: Sensitivity Analysis and the Simplex Method
4.0: Introduction
4.1: The Purpose of Sensitivity Analysis
4.2: Approaches to Sensitivity Analysis
4.3: An Example Problem
4.4: The Answer Report
4.5: The Sensitivity Report
4.6: The Limits Report
4.7: Ad Hoc Sensitivity Analysis
4.8: Robust Optimization
4.9: The Simplex Method
4.10: Summary
4.11: References
Ch 5: Network Modeling
5.0: Introduction
5.1: The Transshipment Problem
5.2: The Shortest Path Problem
5.3: The Equipment Replacement Problem
5.4: Transportation/Assignment Problems
5.5: Generalized Network Flow Problems
5.6: Maximal Flow Problems
5.7: Special Modeling Considerations
5.8: Minimal Spanning Tree Problems
5.9: Summary
5.10: References
Ch 6: Integer Linear Programming
6.0: Introduction
6.1: Integrality Conditions
6.2: Relaxation
6.3: Solving the Relaxed Problem
6.4: Bounds
6.5: Rounding
6.6: Stopping Rules
6.7: Solving ILP Problems Using Solver
6.8: Other ILP Problems
6.9: An Employee Scheduling Problem
6.10: Binary Variables
6.11: A Capital Budgeting Problem
6.12: Binary Variables and Logical Conditions
6.13: The Fixed-Charge Problem
6.14: Minimum Order/Purchase Size
6.15: Quantity Discounts
6.16: A Contract Award Problem
6.17: The Branch-and-Bound Algorithm (Optional)
6.18: Summary
6.19: References
Ch 7: Goal Programming and Multiple Objective Optimization
7.0: Introduction
7.1: Goal Programming
7.2: A Goal Programming Example
7.3: Comments about Goal Programming
7.4: Multiple Objective Optimization
7.5: An MOLP Example
7.6: Comments on MOLP
7.7: Summary
7.8: References
Ch 8: Nonlinear Programming and Evolutionary Optimization
8.0: Introduction
8.1: The Nature of NLP Problems
8.2: Solution Strategies for NLP Problems
8.3: Local vs. Global Optimal Solutions
8.4: Economic Order Quantity Models
8.5: Location Problems
8.6: Nonlinear Network Flow Problem
8.7: Project Selection Problems
8.8: Optimizing Existing Financial Spreadsheet Models
8.9: The Portfolio Selection Problem
8.10: Sensitivity Analysis
8.11: Solver Options for Solving NLPs
8.12: Evolutionary Algorithms
8.13: Forming Fair Teams
8.14: The Traveling Salesperson Problem
8.15: Summary
8.16: References
Ch 9: Regression Analysis
9.0: Introduction
9.1: An Example
9.2: Regression Models
9.3: Simple Linear Regression Analysis
9.4: Defining "Best Fit"
9.5: Solving the Problem Using Solver
9.6: Solving the Problem Using the Regression Tool
9.7: Evaluating the Fit
9.8: The R2 Statistic
9.9: Making Predictions
9.10: Statistical Tests for Population Parameters
9.11: Introduction to Multiple Regression
9.12: A Multiple Regression Example
9.13: Selecting the Model
9.14: Making Predictions
9.15: Binary Independent Variables
9.16: Statistical Tests for the Population Parameters
9.17: Polynomial Regression
9.18: Summary
9.19: References
Ch 10: Data Mining
10.0: Introduction
10.1: Data Mining Overview
10.2: Classification
10.3: Classification Data Partitioning
10.4: Discriminant Analysis
10.5: Logistic Regression
10.6: k-Nearest Neighbor
10.7: Classification Trees
10.8: Neural Networks
10.9: Naive Bayes
10.10: Comments on Classification
10.11: Prediction
10.12: Association Rules (Affinity Analysis)
10.13: Cluster Analysis
10.14: Time Series
10.15: Summary
10.16: References
Ch 11: Time Series Forecasting
11.0: Introduction
11.1: Time Series Methods
11.2: Measuring Accuracy
11.3: Stationary Models
11.4: Moving Averages
11.5: Weighted Moving Averages
11.6: Exponential Smoothing
11.7: Seasonality
11.8: Stationary Data with Additive Seasonal Effects
11.9: Stationary Data with Multiplicative Seasonal Effects
11.10: Trend Models
11.11: Double Moving Average
11.12: Double Exponential Smoothing (Holt's Method)
11.13: Holt-Winter's Method for Additive Seasonal Effects
11.14: Holt-Winter's Method for Multiplicative Seasonal Effects
11.15: Modeling Time Series Trends Using Regression
11.16: Linear Trend Model
11.17: Quadratic Trend Model
11.18: Modeling Seasonality with Regression Models
11.19: Adjusting Trend Predictions with Seasonal Indices
11.20: Seasonal Regression Models
11.21: Combining Forecasts
11.22: Summary
11.23: References
Ch 12: Introduction to Simulation Using Analytic Solver Platform
12.0: Introduction
12.1: Random Variables and Risk
12.2: Why Analyze Risk?
12.3: Methods of Risk Analysis
12.4: A Corporate Health Insurance Example
12.5: Spreadsheet Simulation Using Analytic Solver Platform
12.6: Random Number Generators
12.7: Preparing the Model for Simulation
12.8: Running the Simulation
12.9: Data Analysis
12.10: The Uncertainty of Sampling
12.11: Interactive Simulation
12.12: The Benefits of Simulation
12.13: Additional Uses of Simulation
12.14: A Reservation Management Example
12.15: An Inventory Control Example
12.16: A Project Selection Example
12.17: A Portfolio Optimization Example
12.18: Summary
12.19: References
Ch 13: Queuing Theory
13.0: Introduction
13.1: The Purpose of Queuing Models
13.2: Queuing System Configurations
13.3: Characteristics of Queuing Systems
13.4: Kendall Notation
13.5: Queuing Models
13.6: The M/M/s Model
13.7: The M/M/s Model with Finite Queue Length
13.8: The M/M/s Model with Finite Population
13.9: The M/G/1 Model
13.10: The M/D/1 Model
13.11: Simulating Queues and the Steady-State Assumption
13.12: Summary
13.13: References
Ch 14: Decision Analysis
14.0: Introduction
14.1: Good Decisions vs. Good Outcomes
14.2: Characteristics of Decision Problems
14.3: An Example
14.4: The Payoff Matrix
14.5: Decision Rules
14.6: Nonprobabilistic Methods
14.7: Probabilistic Methods
14.8: The Expected Value of Perfect Information
14.9: Decision Trees
14.10: Creating Decision Trees with Analytic Solver Platform
14.11: Multistage Decision Problems
14.12: Sensitivity Analysis
14.13: Using Sample Information in Decision Making
14.14: Computing Conditional Probabilities
14.15: Utility Theory
14.16: Multicriteria Decision Making
14.17: The Multicriteria Scoring Model
14.18: The Analytic Hierarchy Process
14.19: Summary
14.20: References
Ch 15: Project Management
15.0: Introduction
15.1: An Example
15.2: Creating the Project Network
15.3: CPM: An Overview
15.4: The Forward Pass
15.5: The Backward Pass
15.6: Determining the Critical Path
15.7: Project Management Using Spreadsheets
15.8: Gantt Charts
15.9: Project Crashing
15.10: PERT: An Overview
15.11: Simulating Project Networks
15.12: Microsoft Project
15.13: Summary
15.14: References
Index
Spreadsheet Modeling
& Decision Analysis 7e
A Practical Introduction to Business Analytics
Cliff Ragsdale
Virginia Polytechnic Institute
and State University
In memory of those
here at Virginia Tech on April 16, 2007
who were killed and injured
in the noble pursuit of education
A u s t r a l i a ● B r a z i l ● J a p a n ● K o r e a ● M e x i c o ● S i n g a p o r e ● S p a i n ● U n i t e d K i n g d o m ● U n i t e d S t a t e s
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Spreadsheet Modeling and Decision
Analysis, 7e
Cliff Ragsdale
© 2015, 2012 Cengage Learning
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Preface
Spreadsheets are one of the most popular and ubiquitous software packages on the
planet. Every day, millions of business people use spreadsheet programs to build models
of the decision problems they face as a regular part of their work activities. As a result,
employers look for experience and ability with spreadsheets in the people they recruit.
Spreadsheets have also become the standard vehicle for introducing undergraduate
and graduate students in business and engineering to the concepts and tools covered in
the introductory quantitative analysis course. This simultaneously develops students’
skills with a standard tool of today’s business world and opens their eyes to how a
variety of quantitative analysis techniques can be used in this modeling environment.
Spreadsheets also capture students’ interest and add a new relevance to quantitative
analysis, as they see how it can be applied with popular commercial software being
used in the business world.
Spreadsheet Modeling & Decision Analysis: A Practical Introduction to Business Analytics
provides an introduction to the most commonly used quantitative analysis techniques
and shows how these tools can be implemented using Microsoft® Excel. Prior experience
with Excel is certainly helpful, but is not a requirement for using this text. In general, a
student familiar with computers and the spreadsheet concepts presented in most intro-
ductory computer courses should have no trouble using this text. Step-by-step instruc-
tions and screen shots are provided for each example, and software tips are included
throughout the text as needed.
What’s New in the Seventh Edition?
The most significant changes in the seventh edition of Spreadsheet Modeling & Decision
Analysis are its new focus on business analytics, a new chapter on data mining, and ex-
tensive coverage and use of Analytic Solver Platform for Education by Frontline Sys-
tems, Inc. Analytic Solver Platform for Education is an add-in for Excel that provides
access to analytical tools for performing optimization, simulation, sensitivity analysis,
and decision tree analysis, as well as a variety of tools for data mining. Analytic Solver
Platform for Education makes it easy to run multiple parameterized optimizations and
simulations and apply optimization techniques to simulation models in one integrated,
coherent interface. Analytic Solver Platform also offers amazing interactive simulation
features in which simulation results are automatically updated in real-time whenever
a manual change is made to a spreadsheet. Additionally, when run in its optional
“Guided Mode,” Analytic Solver Platform provides students with over 100 customized
dialogs that provide diagnoses of various model conditions and explain the steps in-
volved in solving problems. Analytic Solver Platform also includes Frontline’s XLMiner
product that offers easy access to a variety of data mining techniques including discri-
minant analysis, logistic regression, neural networks, classification and regression trees,
k-nearest neighbor classification, cluster analysis, and affinity analysis. Analytic Solver
Platform offers numerous other features and, I believe, will transform the way we ap-
proach education in quantitative analysis now and in the future.
Copyright 2013 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
iii
iv
Preface
The most significant changes in the seventh edition of Spreadsheet Modeling & Decision
Analysis from the sixth edition include:
● Microsoft® Office 2013 is featured throughout.
● Data files and software that accompany the book are now available for download
online at www.cengagebrain.com.
● Chapter 1 is re-written from a business analytics perspective and focuses on the use
of quantitative analysis to leverage business opportunities. The new business ana-
lytics perspective is carried on throughout the text.
● Chapter 4 features new enhancements to Analytic Solver Platform that simplify the
creation of spider plots and solver tables.
● Chapter 7 contains new discussion of the triple bottom line perspective as it relates
to multi-criteria optimization.
● Chapter 10 (formerly covering discriminant analysis) now provides a full introduc-
tion to the topic of data mining including descriptions and examples of the major
data mining techniques and the use of XLMiner.
● Several new and revised end-of-chapter problems are incorporated throughout.
Innovative Features
Aside from its strong spreadsheet orientation, the seventh edition of Spreadsheet Mod-
eling & Decision Analysis contains several other unique features that distinguish it from
traditional quantitative analysis texts.
● Algebraic formulations and spreadsheets are used side-by-side to help develop con-
● Step-by-step instructions and numerous annotated screen shots make examples
ceptual thinking skills.
easy to follow and understand.
rithms.
● Emphasis is placed on model formulation and interpretation rather than on algo-
● Realistic examples motivate the discussion of each topic.
● Solutions to example problems are analyzed from a managerial perspective.
● Spreadsheet files for all the examples are provided online.
● A unique and accessible chapter covering data mining is provided.
● Sections entitled “The World of Business Analytics” show how each topic has been
applied in a real company.
Organization
The table of contents for Spreadsheet Modeling & Decision Analysis is laid out in a fairly tra-
ditional format, but topics may be covered in a variety of ways. The text begins with an
overview of business analytics in Chapter 1. Chapters 2 through 8 cover various topics in
deterministic modeling techniques: linear programming, sensitivity analysis, networks,
integer programming, goal programming and multiple objective optimization, and non-
linear and evolutionary programming. Chapters 9 through 11 cover predictive modeling
and forecasting techniques: regression analysis, data mining, and time series analysis.
Chapters 12 and 13 cover stochastic modeling techniques: simulation and queuing
theory. Chapter 14 covers decision analysis and Chapter 15 (available online) provides
an introduction to project management.
Copyright 2013 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
Preface
v
After completing Chapter 1, a quick refresher on spreadsheet fundamentals (enter-
ing and copying formulas, basic formatting and editing, etc.) is always a good idea.
Suggestions for the Excel review may be found at Cengage’s Decision Sciences website.
Following this, an instructor could cover the material on optimization, forecasting, or
simulation, depending on personal preferences. The chapters on queuing and project
management make general references to simulation and, therefore, should follow the
discussion of that topic.
Ancillary Materials
Several excellent ancillaries for the instructor accompany the revised edition of Spread-
sheet Modeling & Decision Analysis. All instructor ancillaries are provided online at
www.cengagebrain.com. Included in this convenient format are:
●
Instructor’s Manual. The Instructor’s Manual, prepared by the author, contains so-
lutions to all the text problems and cases.
● Test Bank. The Test Bank, prepared by Tom Bramorski of the University of
Wisconsin-Whitewater, includes multiple choice, true/false, and short answer prob-
lems for each text chapter. It also includes mini-projects that may be assigned as
take-home assignments. The Test Bank is included as Microsoft® Word files. The
Test Bank also comes separately in a computerized ExamView™ format that allows
instructors to use or modify the questions and create original questions.
● PowerPoint Presentation Slides. PowerPoint presentation slides, prepared by the
author, provide ready-made lecture material for each chapter in the book.
Acknowledgments
I thank the following colleagues who made important contributions to the development
and completion of this book. The reviewers for the seventh edition were:
Arthur Adelberg, Queens College
Jeff Barrows, Columbia College
Mithu Bhattacharya, University of Detroit Mercy
Bob Boylan, Jacksonville University
Timothy Butler, Wayne State University
Johathan P. Caulkins, Carnegie Mellon University Heinz College
Farhad Chowdhury, Mississippi Valley State University
Kevin R. Craig, University of Pittsburgh
Jack Fuller, West Virginia University
Lawrence V. Fulton, Texas State University
Owen P. Hall, Jr., Pepperdine University
Vish Hegde, California State University East Bay
Terrence Hendershott, Haas School of Business, University of California, Berkeley
Martin Markowitz, Rutgers Business School
Frank Montabon, Iowa State University
Alan Olinsky, Bryantt University
John Olson, University of St. Thomas
Hari K. Rajagopalan, School of Business, Francis Marion University
Copyright 2013 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
vi
Preface
Christopher M. Rump, Bowling Green State University
Samuel L. Seaman, Pepperdine University
Maureen Sevigny, Oregon Institute of Technology
Larry E. Shirland, The University of Vermont
Andrew Shogan, Haas School of Business, University of California, Berkeley
Thomas R. Steinhagen, Colorado Christian University
Pandu R. Tadikamalla, University of Pittsburgh, Katz School of Business
Nabil Tamimi, University of Scranton
I also thank Tom Bramorski of the University of Wisconsin-Whitewater for preparing
the test bank that accompanies this book. David Ashley also provided many of the sum-
mary articles found in “The World of Business Analytics” feature throughout the text
and created the queuing template used in Chapter 13. Jack Yurkiewicz, Pace University,
contributed several of the cases found throughout the text. Tallys Yunes, University of
Miami, provided some especially helpful feedback and suggestions for this edition of
the book.
My sincere thanks goes to all students and instructors who have used previous edi-
tions of this book and provided many valuable comments and suggestions for making it
better. I also thank the wonderful SMDA team at Cengage : Aaron Arnsparger, Product
Manager; Maggie Kubale, Content Development; Cliff Kallemeyn, Sr. Content Project
Manager; and Chris Valentine, Media Developer. I feel very fortunate and privileged to
work with each of you.
A very special word of thanks to my friend Dan Fylstra and the crew at Frontline
Systems (http://www.solver.com) for conceiving and creating Analytic Solver Platform
and supporting me so graciously and quickly throughout my revision work on this
book. In my opinion, Analytic Solver Platform is the most significant development in
OR/MS education since the creation of personal computers and the electronic spread-
sheet. (Dan, you get my vote for a lifetime achievement award in analytical modeling
and induction in the OR/MS Hall of Fame!)
Once again, I thank my dear wife, Kathy, for her unending patience, support, encour-
agement, and love. (You will always be the one.) This book is dedicated to our sons,
Thomas, Patrick, and Daniel. I am proud of each one of you and will always be so glad
that God let me be your daddy and the leader of the Ragsdale ragamuffin band.
Final Thoughts
I hope you enjoy the spreadsheet approach to teaching quantitative analysis as much
as I do and that you find this book to be very interesting and helpful. If you find crea-
tive ways to use the techniques in this book or need help applying them, I would love
to hear from you. Also, any comments, questions, suggestions, or constructive criticism
you have concerning this text are always welcome.
Cliff T. Ragsdale
e-mail: Cliff.Ragsdale@vt.edu
Copyright 2013 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
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