Table Of ContentOverview of Applications by Discipline
ECONOMICS
Estimating sensitivity of demand to price 47–56, Assessing a utility function 554–556
352–356 Estimating demand for products 632–638,
Pricing problems 352–366, 422–427 649–650, 764–771, 965
Estimating cost of power 363–366 Subway token hoarding 792
FINANCE AND ACCOUNTING
Collecting on delinquent credit accounts 14–16 Stock hedging 407–408
Cost projections 29–33 Asset management 409–410
Finding a breakeven point 33–41 New product development 503–504, 574, 673–676,
Calculating NPV 57–62 715–722
Calculating NPVfor production capacity Bidding for a government contract 513–518,
decision 58–62 523–533, 653–657
Portfolio management 173–178, 345–346, Investing with risk aversion 557–560
387–394, 442–444, 689–691 Land purchasing decision 575
Pension fund management 178–182 Risk analysis 582–583
Financial planning 210–214, 676–681, Liquidity risk management 651–653
734–735 Estimating warranty costs 657–661
Arbitrage opportunities in oil pricing 215–219 Retirement planning 681–685
Currency trading 220 Modeling stock prices 685–686
Capital budgeting 290–295 Pricing options 686–689, 691–693
Estimating stock betas 396–401 Investing for college 732
Hedging risk with put options 407–408 Bond investment 733
HUMAN RESOURCES AND HEALTH CARE
Fighting HIV/AIDS 23–24 Selecting a job 484–492
DEAin the hospital industry 184–189 Selecting a health care plan 519–521
Salesforce allocation problems 454–456 Drug testing for athletes 535–538, 539–542
Assigning MBAstudents to teams 462
MARKETING
Determining an advertising schedule 133–141, Classifying subscribers of the WSJ 450–453
373–376, 465–471, 480–483 New product marketing 543–552
Estimating an advertising response function 369–373 Valuing a customer 695–699
Retail pricing 422–427 Reducing churn 699–703
Estimating a sales response function 437–441 Estimating market share 703–706
Cluster analysis of large cities 445–449 Estimating sales from promotions 703–706
MISCELLANEOUS
Investment in U.S. Space Systems 285–286
Prioritizing projects at NASA 463–464
Biotechnical engineering 576–577
OPERATIONS MANAGEMENT
Queueing problems 4–7, 796–850 Airline hub location 309–314
Ordering problems (newsboy) 25–28, 604–613, Locating plants and warehouses 314–325,
617–631, 632–639, 649, 760–763 378–381
Ordering with quantity discounts 42–46, 747–748 Cutting stock problems 327–330
Manufacturing operations 71–72 Plant expansion and retooling 341–342
Choosing an optimal diet 75–92 Telephone call processing 343, 857–858
Product mix problems 98–107, 127–130, 168–172, Railway planning 411–412
298–307 Loading a gas truck 429–432
Production scheduling 108–117, 150–159, Traveling salesperson problem 454–457
432–437, 650 Determining trade–off between profit
Production, inventory management 131–132, and pollution 477–479
499–501, 661–666 Airline boarding strategies 579–580
Scheduling workers 142–148 Deming’s funnel experiment 667–671
Aggregate planning 150–159 Global supply chain decisions 737–738
Gasoline, oil blending 161–166, 207–209 Economic order quantity models 743–758
Logistics problems 221–222, 223–234, 241–249 Ordering decisions with demand uncertainty
Assigning workers to jobs 235–236 764–771, 773–778
Assigning school buses to routes 237–240 Production planning in fashion industry 779–784
Finding a shortest route 250–254 Reducing work in progress 793–794
Equipment replacement 254–258 Operations at banks 859–860
Airline crew scheduling 260–265 Scheduling multiple projects 861–862, 885–889
Airline flight scheduling 265–271 Project scheduling with CPM 865–885, 890–895
Aircraft maintenance 272 Forecasting problems 903–904, 910–919, 921–929,
Global manufacturing and distribution 280–281 938–941, 944–956, 964–965
Motor carrier selection 282–284
SPORTS AND GAMES
Rating NFLteams 382–386
Playing craps 708–710
NCAAbasketball tournament 710–713
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R
EVISED
3
RD
Practical Management Science
E
DITION
Wayne L.Winston
Kelley School ofBusiness,Indiana University
S. Christian Albright
Kelley School ofBusiness,Indiana University
With Cases by
Mark Broadie
Graduate School ofBusiness,Columbia University
Lawrence L. Lapin
San Jose State University
William D.Whisler
California State University,Hayward
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Practical Management Science, ©2009, 2007South-Western, a part of Cengage Learning
RevisedThird Edition
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Wayne L. Winston, hereon may be reproduced or used in any form or by any means—graphic,
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Printed in the United States of America
1 2 3 4 5 6 7 12 11 10 09 08
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.
About the Authors
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. Since then he has been teaching in the
Operations & Decision Technologies Department in the Kelley
School of Businessat Indiana University. He has taught courses in
management science, computer simulation, and statistics to all
levels of business students: undergraduates, MBAs, and doctoral
students. He has also taught courses on database analysis to the
U.S. Army. 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 Management
Science, Data Analysis and Decision Making, Data Analysis for Managers, Spreadsheet
Modeling and Applications, andVBAfor Modelers.He jointly developed StatTools,a
statistical add-in for Excel, with the Palisade Corporation. His current interests are in
spreadsheet modeling and the development of VBAapplications in Excel, as well as Web
programming with Microsoft’s .NETtechnology.
On the personal side, Chris has been married to his wonderful wife Mary for
37years. They have one son, Sam, who is currently finishing a law degree at Penn Law
School. Chris has many interests outside the academic area. They include activities with
his family (especially traveling with Mary), going to cultural events at Indiana University,
playing golf and tennis, running and power walking, and reading. And although he earns
his livelihood from statistics and management science, his realpassion is for playing
classical music on the piano.
Wayne L.Winstonis Professor of Operations & Decision
Technologies in the Kelley School of Business at Indiana
University, where he has taught since 1975. Wayne received his
B.S. degree in Mathematics from MITand his Ph.D. degree in
Operations Research from Yale. He has written the successful
textbooksOperations Research: Applications and Algorithms,
Mathematical Programming: Applications and Algorithms,
Simulation Modeling Using @RISK, Data Analysis and
DecisionMaking, andFinancial Models Using Simulation and
Optimization. Wayne has published over 20 articles in leading
journals and has won many teaching awards, including the schoolwide MBAaward four
times. He has taught classes at Microsoft, GM, Ford, Eli Lilly, Bristol-Myers Squibb,
Arthur Andersen, Roche, PriceWaterhouseCoopers, and NCR. His current interest is in
showing how spreadsheet models can be used to solve business problems in all
disciplines, particularly in finance 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.
Brief Contents
Preface xi
1 Introduction to Modeling 1
2 Introduction to Spreadsheet Modeling 23
3 Introduction to Optimization Modeling 71
4 Linear Programming Models 131
5 Network Models 221
6 Optimization Models with Integer Variables 285
7 Nonlinear Optimization Models 345
8 Evolutionary Solver: An Alternative Optimization Procedure 411
9 Multiobjective Decision Making 463
10 Decision Making Under Uncertainty 503
11 Introduction to Simulation Modeling 579
12 Simulation Models 651
13 Inventory Models 737
14 Queueing Models 793
15 Project Management 861
16 Regression and Forecasting Models 903
References 966
Index 969
v
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