In this first module, you read an introduction to management science. Connect what you have with your own experience.
If you are already in a management role:
- How have you used Management Science/Quantitative Methods to approach assigned projects and/or challenges at work?
- Can you identify ways the course may help you improve your approach to managerial decisions?
If you are not in a management role:
- How do you see the methods covered in this course helping you in your current job or anticipated future employment?
Please be sure to clarify which question(s) you are answering by providing some background about your current/past employment and then answering the question(s).
Please respond to the discussion question with 1 original post and at least two substantial replies to other students. A substantial reply is considered a post which moves our discussion forward and deepens our understanding of the material. You may wish to post a probing question (i.e. How would your model apply in ____ context? What would happen if we changed ______?) or by adding new information (i.e. This is similar to _____ because ______). Posts which simply state “Way to go Bob!” or “I thought the same thing.” do not deepen our understanding and will not earn full credit. You are expected to frequently review this discussion forum.
and
M1 Journal Assignment
For your first journal entry, please share what background you bring to this course. Please include relevant work experience. Please also share both formal and informal math background (e.g. courses taken, such as statistics, and workplace training). Please also share any questions you have about what has been covered so far.
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cengage.com/mindtap
Fit your coursework
into your hectic life.
Make the most of your time by learning
your way. Access the resources you need to
succeed wherever, whenever.
• �Get more from your time online with an easy-to-follow
five-step�learning�path.
• �Stay�focused�with�an�all-in-one-place,�integrated�
presentation�of�course�content.
• �Get�the�free�MindTap�Mobile�App�and�learn�
wherever you are.
Break limitations. Create your
own�potential,�and�be�unstoppable�
with�MindTap.
MINDTAP. POWERED BY YOU.
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Practical Management Science
Wayne L. Winston
Kelley School of Business, Indiana University
S. Christian Albright
Kelley School of Business, Indiana University
6th
Edition
Australia ● Brazil ● Mexico ● Singapore ● United Kingdom ● United States
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some third party content may be suppressed. Editorial review has deemed that any suppressed
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Practical Management Science,
Sixth Edition
Wayne L. Winston,
S. Christian Albright
Senior Vice President: Erin Joyner
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Economics: Mike Schenk
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Library of Congress Control Number: 2017947975
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To Mary, my wonderful wife, best friend, and constant companion
And to our Welsh Corgi, Bryn, who still just wants to play ball S.C.A.
To my wonderful family
Vivian, Jennifer, and Gregory W.L.W.
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S. Christian Albright got his B.S. degree in Mathematics from
Stanford in 1968 and his Ph.D. degree in Operations Research
from Stanford in 1972. Until his retirement in 2011, he taught in
the Operations & Decision Technologies Department in the Kelley
School of Business at Indiana University. His teaching included
courses in management science, computer simulation, and statis-
tics to all levels of business students: undergraduates, MBAs, and
doctoral students. He has published over 20 articles in leading
operations research journals in the area of applied probability,
and he has authored several books, including Practical Manage-
ment Science, Data Analysis and Decision Making, Data Analysis for Managers, Spread-
sheet Modeling and Applications, and VBA for Modelers. He jointly developed StatTools,
a statistical add-in for Excel, with the Palisade Corporation. In “retirement,” he continues
to revise his books, and he has developed a commercial product, ExcelNow!, an extension
of the Excel tutorial that accompanies this book.
On the personal side, Chris has been married to his wonderful wife Mary for
46 years. They have a special family in Philadelphia: their son Sam, his wife Lindsay,
and their two sons, Teddy and Archer. Chris has many interests outside the academic
area. They include activities with his family (especially traveling with Mary), going to
cultural events, power walking, and reading. And although he earns his livelihood from
statistics and management science, his real passion is for playing classical music on the
piano.
Wayne L. Winston is Professor Emeritus of Decision
Sciences at the Kelley School of Business at Indiana University
and is now a Professor of Decision and Information Sciences
at the Bauer College at the University of Houston. Winston
received his B.S. degree in Mathematics from MIT and his
Ph.D. degree in Operations Research from Yale. He has written
the successful textbooks Operations Research: Applications
and Algorithms, Mathematical Programming: Applications
and Algorithms, Simulation Modeling with @RiSk, Practical
Management Science, Data Analysis for Managers, Spreadsheet
Modeling and Applications, Mathletics, Data Analysis and Business Modeling with
Excel 2013, Marketing Analytics, and Financial Models Using Simulation and
Optimization. Winston has published over 20 articles in leading journals and has won
more than 45 teaching awards, including the school-wide MBA award six times. His
current interest is in showing how spreadsheet models can be used to solve business
problems in all disciplines, particularly in finance, sports, and marketing.
Wayne enjoys swimming and basketball, and his passion for trivia won him an
appearance several years ago on the television game show Jeopardy, where he won two
games. He is married to the lovely and talented Vivian. They have two children, Gregory
and Jennifer.
About the Authors
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vii
Preface xiii
1 Introduction to Modeling 1
2 Introduction to Spreadsheet Modeling 19
3 Introduction to Optimization Modeling 71
4 Linear Programming Models 135
5 Network Models 219
6 Optimization Models with Integer Variables 277
7 Nonlinear Optimization Models 339
8 Evolutionary Solver: An Alternative Optimization Procedure 407
9 Decision Making under Uncertainty 457
10 Introduction to Simulation Modeling 515
11 Simulation Models 589
12 Queueing Models 667
13 Regression and Forecasting Models 715
14 Data Mining 771
References 809
Index 815
MindTap Chapters
15 Project Management 15-1
16 Multiobjective Decision Making 16-1
17 Inventory and Supply Chain Models 17-1
Brief Contents
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ix
Preface xiii
CHAPTER 1 Introduction to Modeling 1
1.1 Introduction 3
1.2 A Capital Budgeting Example 3
1.3 Modeling versus Models 6
1.4 A Seven-Step Modeling Process 7
1.5 A Great Source for Management Science
Applications: Interfaces 13
1.6 Why Study Management Science? 13
1.7 Software Included with This Book 15
1.8 Conclusion 17
CHAPTER 2 Introduction to Spreadsheet
Modeling 19
2.1 Introduction 20
2.2 Basic Spreadsheet Modeling:
Concepts and Best Practices 21
2.3 Cost Projections 25
2.4 Breakeven Analysis 31
2.5 Ordering with Quantity Discounts
and Demand Uncertainty 39
2.6 Estimating the Relationship between
Price and Demand 44
2.7 Decisions Involving the Time Value of
Money 54
2.8 Conclusion 59
Appendix Tips for Editing and
Documenting Spreadsheets 64
Case 2.1 Project Selection at Ewing Natural
Gas 66
Case 2.2 New Product Introduction at eTech 68
CHAPTER 3 Introduction to Optimization
Modeling 71
3.1 Introduction 72
3.2 Introduction to Optimization 73
3.3 A Two-Variable Product Mix Model 75
Contents
3.4 Sensitivity Analysis 87
3.5 Properties of Linear Models 97
3.6 Infeasibility and Unboundedness 100
3.7 A Larger Product Mix Model 103
3.8 A Multiperiod Production Model 111
3.9 A Comparison of Algebraic
and Spreadsheet Models 120
3.10 A Decision Support System 121
3.11 Conclusion 123
Appendix Information on Optimization Software 130
Case 3.1 Shelby Shelving 132
CHAPTER 4 Linear Programming Models 135
4.1 Introduction 136
4.2 Advertising Models 137
4.3 Employee Scheduling Models 147
4.4 Aggregate Planning Models 155
4.5 Blending Models 166
4.6 Production Process Models 174
4.7 Financial Models 179
4.8 Data Envelopment Analysis (DEA) 191
4.9 Conclusion 198
Case 4.1 Blending Aviation Gasoline at Jansen
Gas 214
Case 4.2 Delinquent Accounts at GE Capital 216
Case 4.3 Foreign Currency Trading 217
CHAPTER 5 Network Models 219
5.1 Introduction 220
5.2 Transportation Models 221
5.3 Assignment Models 233
5.4 Other Logistics Models 240
5.5 Shortest Path Models 249
5.6 Network Models in the Airline Industry 258
5.7 Conclusion 267
Case 5.1 Optimized Motor Carrier Selection at
Westvaco 274
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CHAPTER 9 Decision Making under
Uncertainty 457
9.1 Introduction 458
9.2 Elements of Decision Analysis 460
9.3 Single-Stage Decision Problems 467
9.4 The PrecisionTree Add-In 471
9.5 Multistage Decision Problems 474
9.6 The Role of Risk Aversion 492
9.7 Conclusion 499
Case 9.1 Jogger Shoe Company 510
Case 9.2 Westhouser Paper Company 511
Case 9.3 Electronic Timing System for
Olympics 512
Case 9.4 Developing a Helicopter Component
for the Army 513
CHAPTER 10 Introduction to Simulation
Modeling 515
10.1 Introduction 516
10.2 Probability Distributions for Input
Variables 518
10.3 Simulation and the Flaw of Averages 537
10.4 Simulation with Built-in Excel Tools 540
10.5 Introduction to @RISK 551
10.6 The Effects of Input Distributions on
Results 568
10.7 Conclusion 577
Appendix Learning More About @RISK 583
Case 10.1 Ski Iacket Production 584
Case 10.2 Ebony Bath Soap 585
Case 10.3 Advertising Effectiveness 586
Case 10.4 New Project Introduction at eTech 588
CHAPTER 11 Simulation Models 589
11.1 Introduction 591
11.2 Operations Models 591
11.3 Financial Models 607
11.4 Marketing Models 631
11.5 Simulating Games of Chance 646
11.6 Conclusion 652
Appendix Other Palisade Tools for Simulation 662
x Contents
CHAPTER 6 Optimization Models with Integer
Variables 277
6.1 Introduction 278
6.2 Overview of Optimization with Integer
Variables 279
6.3 Capital Budgeting Models 283
6.4 Fixed-Cost Models 290
6.5 Set-Covering and Location-Assignment
Models 303
6.6 Cutting Stock Models 320
6.7 Conclusion 324
Case 6.1 Giant Motor Company 334
Case 6.2 Selecting Telecommunication Carriers to
Obtain Volume Discounts 336
Case 6.3 Project Selection at Ewing Natural Gas 337
CHAPTER 7 Nonlinear Optimization Models 339
7.1 Introduction 340
7.2 Basic Ideas of Nonlinear Optimization 341
7.3 Pricing Models 347
7.4 Advertising Response and Selection Models 365
7.5 Facility Location Models 374
7.6 Models for Rating Sports Teams 378
7.7 Portfolio Optimization Models 384
7.8 Estimating the Beta of a Stock 394
7.9 Conclusion 398
Case 7.1 GMS Stock Hedging 405
CHAPTER 8 Evolutionary Solver: An Alternative
Optimization Procedure 407
8.1 Introduction 408
8.2 Introduction to Genetic Algorithms 411
8.3 Introduction to Evolutionary Solver 412
8.4 Nonlinear Pricing Models 417
8.5 Combinatorial Models 424
8.6 Fitting an S-Shaped Curve 435
8.7 Portfolio Optimization 439
8.8 Optimal Permutation Models 442
8.9 Conclusion 449
Case 8.1 Assigning MBA Students to Teams 454
Case 8.2 Project Selection at Ewing Natural Gas 455
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Contents xi
Case 11.1 College Fund Investment 664
Case 11.2 Bond Investment Strategy 665
Case 11.3 Project Selection Ewing Natural Gas 666
CHAPTER 12 Queueing Models 667
12.1 Introduction 668
12.2 Elements of Queueing Models 670
12.3 The Exponential Distribution 673
12.4 Important Queueing Relationships 678
12.5 Analytic Steady-State Queueing Models 680
12.6 Queueing Simulation Models 699
12.7 Conclusion 709
Case 12.1 Catalog Company Phone Orders 713
CHAPTER 13 Regression and Forecasting Models 715
13.1 Introduction 716
13.2 Overview of Regression Models 717
13.3 Simple Regression Models 721
13.4 Multiple Regression Models 734
13.5 Overview of Time Series Models 745
13.6 Moving Averages Models 746
13.7 Exponential Smoothing Models 751
13.8 Conclusion 762
Case 13.1 Demand for French Bread at Howie’s
Bakery 768
Case 13.2 Forecasting Overhead at Wagner
Printers 769
Case 13.3 Arrivals at the Credit Union 770
CHAPTER 14 Data Mining 771
14.1 Introduction 772
14.2 Classification Methods 774
14.3 Clustering Methods 795
14.4 Conclusion 806
Case 14.1 Houston Area Survey 808
References 809
Index 815
MindTap Chapters
CHAPTER 15 Project Management 15-1
15.1 Introduction 15-2
15.2 The Basic CPM Model 15-4
15.3 Modeling Allocation of Resources 15-14
15.4 Models with Uncertain Activity Times 15-30
15.5 A Brief Look at Microsoft Project 15-35
15.6 Conclusion 15-39
CHAPTER 16 Multiobjective Decision Making 16-1
16.1 Introduction 16-2
16.2 Goal Programming 16-3
16.3 Pareto Optimality and Trade-Off Curves 16-12
16.4 The Analytic Hierarchy Process (AHP) 16-20
16.5 Conclusion 16-25
CHAPTER 17 Inventory and Supply Chain Models 17-1
17.1 Introduction 17-2
17.2 Categories of Inventory and Supply Chain
Models 17-3
17.3 Types of Costs in Inventory and Supply Chain
Models 17-5
17.4 Economic Order Quantity (EOQ) Models 17-6
17.5 Probabilistic Inventory Models 17-21
17.6 Ordering Simulation Models 17-34
17.7 Supply Chain Models 17-40
17.8 Conclusion 17-50
Case 17.1 Subway Token Hoarding 17-57
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xiii
Practical Management Science provides a spreadsheet-
based, example-driven approach to management
science. Our initial objective in writing the book was to
reverse negative attitudes about the course by making
the subject relevant to students. We intended to do this
by imparting valuable modeling skills that students can
appreciate and take with them into their careers. We are
very gratified by the success of previous editions.
The book has exceeded our initial objectives. We are
especially pleased to hear about the success of the book
at many other colleges and universities around the
world. The acceptance and excitement that has been
generated has motivated us to revise the book and make
the current edition even better.
When we wrote the first edition, management
science courses were regarded as irrelevant or
uninteresting to many business students, and the use of
spreadsheets in management science was in its early
stages of development. Much has changed since the
first edition was published in 1996, and we believe that
these changes are for the better. We have learned a lot
about the best practices of spreadsheet modeling for
clarity and communication. We have also developed
better ways of teaching the materials, and we
understand more about where students tend to
have difficulty with the concepts. Finally, we
have had the opportunity to teach this material at
several Fortune 500 companies (including Eli Lilly,
PricewaterhouseCoopers, General Motors, Tomkins,
Microsoft, and Intel). These companies, through their
enthusiastic support, have further enhanced the
realism of the examples included in this book.
Our objective in writing the first edition was very
simple—we wanted to make management science
relevant and practical to students and professionals.
This book continues to distinguish itself in the market
in four fundamental ways:
■ Teach by Example. The best way to learn
modeling concepts is by working through
examples and solving an abundance of problems.
This active learning approach is not new, but our
text has more fully developed this approach than
any book in the field. The feedback we have
received from many of you has confirmed the
success of this pedagogical approach for
management science.
■ Integrate Modeling with Finance, Marketing,
and Operations Management. We integrate
modeling into all functional areas of business.
This is an important feature because the majority
of business students major in finance and
marketing. Almost all competing textbooks
emphasize operations management–related
examples. Although these examples are
important, and many are included in the book,
the application of modeling to problems in
finance and marketing is too important to ignore.
Throughout the book, we use real examples from
all functional areas of business to illustrate the
power of spreadsheet modeling to all of these
areas. At Indiana University, this led to the
development of two advanced MBA electives
in finance and marketing that built upon the
content in this book.
■ Teach Modeling, Not Just Models. Poor attitudes
among students in past management science
courses can be attributed to the way in which they
were taught: emphasis on algebraic formulations
and memorization of models. Students gain more
insight into the power of management science by
developing skills in modeling. Throughout the
book, we stress the logic associated with model
development, and we discuss solutions in this
context. Because real problems and real models
often include limitations or alternatives, we
include several “Modeling Issues” sections to
discuss these important matters. Finally, we
include “Modeling Problems” in most chapters to
help develop these skills.
■ Provide Numerous Problems and Cases.
Whereas all textbooks contain problem sets for
students to practice, we have carefully and
judiciously crafted the problems and cases
contained in this book. Each chapter contains
four types of problems: easier Level A Problems,
more difficult Level B Problems, Modeling
Problems, and Cases. Most of the problems
following sections of chapters ask students to
extend the examples in the preceding section.
The end-of-chapter problems then ask students
to explore new models. Selected solutions are
available to students through MindTap and are
Preface
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xiv Preface
denoted by the second-color numbering of the
problem. Solutions for all of the problems and
cases are provided to adopting instructors. In
addition, shell files (templates) are available for
many of the problems for adopting instructors.
The shell files contain the basic structure of the
problem with the relevant formulas omitted. By
adding or omitting hints in individual solutions,
instructors can tailor these shell files to best meet
the specific needs of students.
New to the Sixth Edition
The immediate reason for the sixth …
Im currently a senior at Massachusetts Maritime, and I have not had a position to be in a management role where I was able to utilize any Management Science/Quantitative Methods to approach any assigned projects.
My work experience so far will be best described as applying excel applications in my last internship. Using an A to Z systematic approach I had access to follow the SOP for the corporation which was already perfectly organized.
In my anticipated future employment, I predict that I will see these different types of course methods help me in my daily management activities. This type of course material arms managers with the ability to use mathematics and statistics in combination with quantitative techniques for most management strategies. It also arms managers with the ability to use computer models for maximum output at minimum input. This is the best option to save money and time. In today’s world of management, MSQM is seen at the forefront with its success in forecasting projections for management preplanning. I believe that I will be utilizing the many shortcuts that MSQM provides in my daily management protocol.
Specifically, it will most likely provide avenues to use math based solutions to directly manage the how and when to order certain products. In addition to this, MSQM is a direct precursor to queuing theory. In my future realm of management, I predict that most of the management systems that I will use will already be digitalized to meet predetermined standards. Information systems and workplace management will have to work in harmony to establish conctrete systematic formulas and programs with the main objective to organize data. I believe that as a future manager it will be my duty to make this type of system usable and accessible to all levels and employees within my workforce. With many of the MSQM systems and programs already intact, it will be the human managers that must be able to determine alternative solutions. For the future, management will responsible for determining the best alternative solutions for specific need based problems. Management will have to base their answers to such situations by using MSQM in all aspects to the proper approach and delivery of the computerized and calibrated solution.
I am
not in
a management role:
· How do you see the methods covered in this course helping you in your current job or anticipated future employment?
My current job is in the banking sector doing a variety of activities related to account inactivity and state reporting. Each state regulates how the bank should handle accounts that have not been active for a predetermined amount of time and then requires each financial agency to report annually the total accounts due in a given reporting year based on the last date the customer performed a transaction.
Although I couldn’t see using the methods would help a ton in my current role, I could see the methods being used in the branches where there is more sales activity. Being able to project the costs of new financial products through the use of a spreadsheet model would allow upper-level sponsors to support the product quicker and include funding to ensure its longevity. What if scenarios could also be addressed through the use of a sensitivity analysis within Excel that addresses input values that predict future outcomes.