Also Av a i l a b l e ! @RISK Version Spreadsheet Modeling and Decision Analysis, Fifth Edition Cliff Ragsdale, Virginia Polytechnic Institute and State University The customized @RISK Version of Ragsdale’s text is available through Thomson Custom Solutions! If you teach your course with an emphasis on @RISK, the customized version of Spreadsheet Modeling and Decision Analysis, Fifth Edition may be perfect for you! This alternate version includes the student edition of The Decision Tools and StatTools Suite software from Palisade Corporation and incorporates @RISK software in chapters 11, 12, 14, and 15 Visit www.textchoice.com today to order the customized @RISK version of Ragsdale’s text, which you’ll find listed in the Decision Sciences section of Business and Economics when you log on to TextChoice Thomson Custom Solutions Choices That Make Sense PostScript Picture textchoice_logo2_gray copy.eps Spreadsheet Modeling & Decision Analysis 5e A Practical Introduction to Management Science Cliff T Ragsdale Virginia Polytechnic Institute and State University In memory of those who were killed and injured in the noble pursuit of education here at Virginia Tech on April 16, 2007 Spreadsheet Modeling & Decision Analysis: A Practical Introduction to Management Science, Fifth Edition Cliff T Ragsdale VP/Editorial Director: Jack W Calhoun Editor-in-Chief: Alex von Rosenberg Senior Acquisitions Editor: Charles McCormick, Jr Senior Developmental Editor: Alice Denny Senior Marketing Manager: Larry Qualls Senior Marketing Coordinator: Angela Glassmeyer COPYRIGHT © 2008 Thomson South-Western, a part of The Thomson Corporation Thomson, the Star logo, and South-Western are trademarks used herein under license Printed in the United States of America 10 09 08 07 Student Edition: ISBN 0-324-65664-5 Student Edition with CD: ISBN 0-324-65663-7 Instructor’s Edition: ISBN 0-324-37769-X Instructor’s Edition with CD: ISBN 0-324-65666-1 Associate Content Project Manager: Scott Dillon Manager of Technology, Editorial: Vicky True Technology Project Manager: John Rich Senior Manufacturing Coordinator: Diane Gibbons Printer: Louiseville Gagne Canada Art Director: Stacy Jenkins Shirley Cover and Internal Designer: cmiller design Cover Images: © Getty Images and © Veer.com Production House: ICC Macmillan Inc ALL RIGHTS RESERVED No part of this work covered by the copyright hereon may be reproduced or used in any form or by any means—graphic, electronic, or mechanical, including photocopying, recording, taping, Web distribution or information storage and retrieval systems, or in any other manner—without the written permission of the publisher For permission to use material from this text or product, submit a request online at http://www.thomsonrights.com Library of Congress Control Number: 2007932214 For more information about our products, contact us at: Thomson Learning Academic Resource Center 1-800-423-0563 Thomson Higher Education 5191 Natorp Boulevard Mason, OH 45040 USA 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 OR/MS course This simultaneously develops students’ skills with a standard tool of today’s business world and opens their eyes to how a variety of OR/MS techniques can be used in this modeling environment Spreadsheets also capture students’ interest and add a new relevance to OR/MS, as they see how it can be applied with popular commercial software being used in the business world Spreadsheet Modeling & Decision Analysis provides an introduction to the most commonly used OR/MS 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 introductory computer courses should have no trouble using this text Step-by-step instructions and screen shots are provided for each example, and software tips are included throughout the text as needed What’s New in the Revised Fifth Edition? This revised version of Spreadsheet Modeling & Decision Analysis updates the ﬁfth edition to be compatible with Microsoft® Ofﬁce 2007 Changes in the revised ﬁfth edition of Spreadsheet Modeling & Decision Analysis from the fourth edition include: • New cases for every chapter of the book • A new interactive graphical tool featured in Chapters and to help students understand how changes in various linear programming model coefﬁcients affect the feasible region and optimal solution • A new version of Crystal Ball with enhanced modeling and analysis capabilities (Chapter 12) • New coverage of Crystal Ball’s Distribution Gallery Tool, correlation tools, and efﬁcient frontier calculation using OptQuest • New coverage of Crystal Ball’s tornado diagrams and spider charts applied in decision analysis (Chapter 15) • Microsoft® Ofﬁce Project 2007 Win32 English 60-Day Direct Trial • Expanded discussion of the use of array formulas in project management models (Chapter 14) • Numerous new and revised end-of-chapter problems throughout iii iv Preface Innovative Features Aside from its strong spreadsheet orientation, the revised ﬁfth edition of Spreadsheet Modeling & Decision Analysis contains several other unique features that distinguish it from traditional OR/MS texts • Algebraic formulations and spreadsheets are used side-by-side to help develop conceptual thinking skills • Step-by-step instructions and numerous annotated screen shots make examples easy to follow and understand • Emphasis is placed on model formulation and interpretation rather than on algorithms • 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 on a data disk bundled with the text • A unique and accessible chapter covering discriminant analysis is provided • Sections entitled “The World of Management Science” show how each topic has been applied in a real company • Excel add-ins and templates are provided to support: decision trees, sensitivity analysis, discriminant analysis, queuing, simulation, and project management Organization The table of contents for Spreadsheet Modeling & Decision Analysis is laid out in a fairly traditional format, but topics may be covered in a variety of ways The text begins with an overview of OR/MS in Chapter Chapters through cover various topics in deterministic modeling techniques: linear programming, sensitivity analysis, networks, integer programming, goal programming and multiple objective optimization, and nonlinear and evolutionary programming Chapters through 11 cover predictive modeling and forecasting techniques: regression analysis, discriminant analysis, and time series analysis Chapters 12 and 13 cover stochastic modeling techniques: simulation (using Crystal Ball) and queuing theory Coverage of simulation using the inherent capabilities of Excel alone is available on the textbook’s Web site, www.thomsonedu.com/ decisionsciences/ragsdale Chapters 14 and 15 cover project management and decision theory, respectively After completing Chapter 1, a quick refresher on spreadsheet fundamentals (entering and copying formulas, basic formatting and editing, etc.) is always a good idea Suggestions for the Excel review may be found at Thomson South-Western’s Decision Sciences Web site 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 New copies of the textbook include three CDs The student CD includes Premium Solver™ for Education, several other add-ins, and data ﬁles for examples, cases and problems within the text The other CDs provide a time-limited trial edition of Microsoft ® Project Instructions for accessing a time-limited full version of Crystal Ball® appear on a card included in this edition Preface v As noted on the front end-sheet of the Instructor’s Edition, the 5e of Spreadsheet Modeling & Decision Analysis will be available in an @RISK version that comes with a student edition of The Decision Tools Suite This product is being handled through Thomson CUSTOM and the @RISK version will not include the Crystal Ball software Several excellent ancillaries for the instructor accompany the revised edition of Spreadsheet Modeling & Decision Analysis All instructor ancillaries are provided on CDROMs Included in this convenient format are: • Instructor’s Manual The Instructor’s Manual, prepared by the author, contains solutions to all the text problems and cases • Test Bank The Test Bank, prepared by Alan Olinsky of Bryant University, includes multiple choice, true/false, and short answer problems 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 Instructors who adopt the text for their classes may call the Thomson Learning Academic Resource Center at 1-800-423-0563 to request the Instructor’s Resource CD (ISBN: 0-324-31261-X) and the ExamView testing software (ISBN 0-324-31273-3) Acknowledgments I thank the following colleagues who made important contributions to the development and completion of this book The reviewers for the ﬁfth edition were: Layek Abdel-Malek, New Jersey Institute of Technology Ajay Aggarwal, Millsaps College Aydin Alptekinoglu, University of Florida Leonard Asimow, Robert Morris University Tom Bramorski, University of Wisconsin-Whitewater John Callister, Cornell University Moula Cherikh, Virginia State University Steve Comer, The Citadel David L Eldredge, Murray State University Ronald Farina, University of Denver Konstantinos Georgatos, John Jay College Michael Gorman, University of Dayton Deborah Hanson, University of Great Falls Duncan Holthausen, North Carolina State University Mark Isken, Oakland University PingSun Leung, University of Hawaii at Manoa Mary McKenry, University of Miami Anuj Mehrotra, University of Miami Stephen Morris, University of San Francisco Manuel Nunez, University of Connecticut Alan Olinsky, Bryant University John Olson, University of St Thomas Mark Parker, Carroll College vi Preface Tom Reiland, North Carolina State University Thomas J Schriber, University of Michigan Bryan Schurle, Kansas State University John Seydel, Arkansas State University Peter Shenkin, John Jay College of Criminal Justice Stan Spurlock, Mississippi State University Donald E Stout, Jr., Saint Martin’s College Ahmad Syamil, Arkansas State University Pandu R Tadikamalla, University of Pittsburgh Shahram Taj, University of Detroit Mercy Danny Taylor, University of Nevada G Ulferts, University of Detroit Mercy Tim Walters, University of Denver Larry White, Prairie View A&M University Barry A Wray, University of North Carolina-Wilmington I also thank Alan Olinsky of Bryant University for preparing the test bank that accompanies this book David Ashley also provided many of the summary articles found in “The World of Management Science” feature throughout the text and created the queuing template used in Chapter 14 Mike Middleton, University of San Francisco, once again provided the TreePlan decision tree add-in found in Chapter 16 Jack Yurkiewicz, Pace University, contributed several of the cases found throughout the text A special word of thanks goes to all students and instructors who have used previous editions of this book and provided many valuable comments and suggestions for making it better I also thank the wonderful SMDA team at Thomson Business and Economics: Charles McCormick, Jr., Senior Acquisitions Editor; Maggie Kubale, Developmental Editor; Scott Dillon, Associate Content Project Manager; and John Rich, Technology Project Editor I also extend my gratitude to Decisioneering, Inc http://www.decisioneering.com for providing the Crystal Ball software that accompanies this book and to Dan Fylstra and the crew at Frontline Systems http://www.solver.com for bringing the power of optimization to the world of spreadsheets Once again, I thank my dear wife, Kathy, for her unending patience, support, encouragement, and love (You’re still the one.) This book is dedicated to our sons, Thomas, Patrick, and Daniel I 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 OR/MS as much as I and that you ﬁnd this book to be very interesting and helpful If you ﬁnd creative 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: crags@vt.edu Brief Contents Introduction to Modeling and Decision Analysis Introduction to Optimization and Linear Programming 17 Modeling and Solving LP Problems in a Spreadsheet 45 Sensitivity Analysis and the Simplex Method 136 Network Modeling 177 Integer Linear Programming 232 Goal Programming and Multiple Objective Optimization 296 Nonlinear Programming & Evolutionary Optimization 339 Regression Analysis 409 10 Discriminant Analysis 459 11 Time Series Forecasting 485 12 Introduction to Simulation Using Crystal Ball 559 13 Queuing Theory 641 14 Project Management 673 15 Decision Analysis 724 Index 801 vii Contents Introduction to Modeling and Decision Analysis Introduction The Modeling Approach to Decision Making Characteristics and Benefits of Modeling Mathematical Models Categories of Mathematical Models The Problem-Solving Process Anchoring and Framing Effects Good Decisions vs Good Outcomes 11 Summary 11 References 12 The World of Management Science 12 Questions and Problems 14 Case 14 Introduction to Optimization and Linear Programming 17 Introduction 17 Applications of Mathematical Optimization 17 Characteristics of Optimization Problems 18 Expressing Optimization Problems Mathematically 19 Decisions 19 Constraints 19 Objective 20 Mathematical Programming Techniques 20 An Example LP Problem 21 Formulating LP Models 21 Steps in Formulating an LP Model 21 Summary of the LP Model for the Example Problem 23 The General Form of an LP Model 23 Solving LP Problems: An Intuitive Approach 24 Solving LP Problems: A Graphical Approach 25 Plotting the First Constraint 26 Plotting the Second Constraint 26 Plotting the Third Constraint 27 The Feasible Region 28 Plotting the Objective Function 29 Finding the Optimal Solution Using Level Curves 30 Finding the Optimal Solution by Enumerating the Corner Points 32 Summary of Graphical Solution to LP Problems 32 Understanding How Things Change 33 Special Conditions in LP Models 34 Alternate Optimal Solutions 34 Infeasibility 38 Summary 39 viii Redundant Constraints 35 Unbounded Solutions 37 806 Index Evolutionary optimization (Continued) run on press (ROP) and preprinted advertising insert scheduling case, 407–408 Tour de France case, 404–405 U.S presidential election campaign stops case, 405–406 Wella Corporation bill of materials (BOM) case, 406–407 economic order quantity (EOQ) models, 344–350 evolutionary algorithms, 380–382 descriptions, 380–382 vs genetic algorithms (GAs), 380–382 generalized reduced gradient (GRG) algorithm, 341–342 vs linear programming (LP), 339 optimization, existing ﬁnancial spreadsheets, 365–368 overviews and summaries, 339, 389 permutation issues, 387 problems, 339–342, 350–404 descriptions, 339–341 Rappaport Communications Company location, 350–355 review questions and problems, 390–404 SafetyTrans nonlinear network ﬂow, 355–360 solution strategies, 341–342 stock market return, 382–385 stock portfolio selection, 368–376 TMC Corporation project selection, 360–365 Wolverine Manufacturing Company Traveling Salesperson Problem (TSP), 385–389 reference resources, 389 sensitivity analyses, 376–379 See also Sensitivity analysis and Simplex Method concepts descriptions, 376–377 gradient reductions, 379 Lagrange multipliers, 378–379 solution strategies, 341–344 descriptions, 341–342 optimal solutions, local vs global, 342–344 Solver options and, 343–390 starting points, 341–342, 344 step sizes/movement amounts, 341–342 EVPI See Expected value of perfect information (EVPI) Examples See Cases and examples; Problems Expected monetary value (EMV), 733–735 Expected regret/expected opportunity loss (EOL), 735 Expected value of perfect information (EVPI), 738–739 Exponential smoothing, 494–498, 511–514 double (Holt’s method), 511–514 techniques, 494–498 Extrapolation, 426, 486 See also Predictions descriptions, 426 models, 486 Extreme points, 29 F FDA example See Food and Drug Administration (FDA) drug testing procedures example Feasibility, 25–26, 28–29, 159–161 feasible regions, 25–26, 28–29 feasible solutions, 159–161 Federal Express, 12–13 Federal Reserve, 276 Fiasco in Florida/U.S Presidential election (2000) case, 456 Financial planning decision problem, 18 Finite populations, 654–658 Finite queue length, 652–654 First constraint, 26 Fit, 413–414, 419–421, 487 See also Regression analysis concepts best ﬁt deﬁnitions, 413–414 error, 414–415 error sum of squares (ESS), 414 estimation, 414–415 evaluations, 419–421 goodness of ﬁt, 487 method of least squares, 414 overﬁt, 433 residuals, 414 TREND( ) function, 419–421 Fitzsimmons, James A., 478 Fixed-charge/ﬁxed cost problem, 254–260 Flow (network) problems, 177–208 See also Network modeling concepts deﬁnitions, 177 equipment replacement, 189–192 generalized network ﬂow, 194–200 maximal ﬂow problem, 201–204 minimal spanning tree, 177, 208–210 shortest path, 184–189 special considerations, 205–208 transportation/assignment, 193 transshipment, 177–184 Flury, B., 477 Flystra, D., 389 Food and Drug Administration (FDA) drug testing procedures example, 710–711 Food stamp program-diet planning case, 335–337 Forecasting (time series) concepts, 485–558 See also Time series forecasting concepts Foreign exchange (FX) market case, 132–133 Forgione, G., 12 Formatting, conditional, 688 Forms, 23–24 Fortune Software spreadsheet wars case, 799–800 Forward pass, 677–680 Foxridge Investment Group rental property income case, 638–640 Framing and anchoring effects, 9–10 decision trees, 9–10 descriptions, Franz Edelman Awards, Frazza, S., 617 Freeland, J., 275 Friends of the Manatees non-proﬁt organization case, 134–135 Frontiers, efﬁcient, 611 Frontline Systems, Inc., 45 Fuel Management and Allocation Model example, 163 Function, objective See Objective function Fundamental concepts, 1–16 See also under individual concepts anchoring and framing effects, 9–10 decision trees, 10 descriptions, cases and examples, 2, 12–16 Hong Kong Terminals (HIT) example, John Deere Company example, Motorola example, Patrick’s paradox case, 14–16 Waste Management example, decision analysis fundamentals, 724–800 decisions vs outcomes, 11 management science, 1–2 Index modeling approaches, 1–7 beneﬁts, 3–4 characteristics, 3–4 descriptions, 1–3 mental models, models, computer, models, mathematical, 3–7 See also Mathematical models models, physical, models, scale, models, spreadsheet, models, valid, models, visual, operations research/management science (OR/MS), 12–13 optimization and linear programming (LP) fundamentals, 17–44 See also Optimization and linear programming (LP) fundamentals overviews and summaries, 1–3, 11 problems, 7–9 problem solving processes, 7–9 review questions and problems, 4, 8, 12, 14 reference resources, 4, 8, 12 FX market case See Foreign exchange (FX) market case Fysco Foods strategic planning case, 557–558 G Gale, J., 784 Gallwey, Timothy, 563 Gantt charts, 688–691 Gardner, E., 545 GAs See Genetic algorithms (GAs) Gass, S., 321, 784 General forms, 23–24 General integer variables, 253 Generalized network ﬂow problem, 194–200 Generalized reduced gradient (GRG) algorithm, 341–342 Generators, random number See Random number generators (RNG) Genetic algorithms (GAs), 380–382 Georgeoff, D., 545 Georgia Public Service Commission case, 457–458 Gido, J., 710 Gilliam, R., 663 Glassey, R., 210 Global vs local optimal solutions, 342–344 Glover, F., 210 Goal constraint, 298–299 See also Constraint and constraint variables Goal programming (GP) and multiple optimization concepts, 296–338 cases and examples, 297, 309–322, 335–338 Blackstone Mining Company example, 309–321 Caro-Life sales territory planning case, 337–338 land developer balanced objectives example, 297 resort hotel and convention center seasonal proﬁts example, 297–306 Truck Transport Company terminal relocation example, 321–322 United States Department of Agriculture (USDA) diet planning-food stamp program case, 335–337 goal programming (GP), 296–307 balanced objectives, 297 constraint, goal, 298–299 constraint, hard, 299 constraint, soft, 297 807 descriptions, 296–297 examples, 297–306 MINIMAX, 307 preemptive goal programming (GP), 307 solution comparisons, 307 target values, 299 underachievement vs overachievement, 299 variables, deviational, 299 iterative solution procedures, 296 multiple objective linear programming (MOLP) problem, 307–321 descriptions, 307–309, 320 examples, 309–321 MINIMAX, 309, 316–321 non-corner point feasible solutions, 307–309 Pareto optimal, 320 steps, 307–309, 320 overviews and summaries, 296, 321 problems, 307–335 multiple objective linear programming (MOLP) problem, 307–321 review questions and problems, 322–335 reference resources, 321 Solver options and solution strategies, 304–306, 311–314 Golenko, G., 710 Golfer’s Link (TGL) supply chain management (SCM) case, 130–131 Goodness of ﬁt, 487 See also Fit Gore, Al, 456 Gould, F., 39, 162 GP concepts See Goal programming (GP) and multiple optimization concepts Gradient reductions, 379 Graham, William W., 211 Graphical solution approaches, 25–34 See also Optimization and linear programming (LP) fundamentals change relationships, 33–34 descriptions, 25–26, 32–33 optimal solutions, 30–31 via corner point enumeration, 31 via level curves, 30–31 plotting techniques, 26–30 constraint, ﬁrst, 26 constraint, second, 26–27 constraint, third, 27–28 feasible regions, 25–26, 28–29 objective function, 29–30 steps, 32–33 GRG algorithm See Generalized reduced gradient (GRG) algorithm Gross, D., 663 Gross, George, 390 Grossman, T., 12 Gupta, V., 210 H Hall, George Sand, 390 Hall, R., 12, 663 Hallmark Cards discard trimming example, 785–786 Hamilton & Jacobs (H&J) case, 227–228 Hamzawi, S., 617 Hand, D J., 477 Hang-on or give-up case, 796–797 Hansen, P., 210 Hard constraint, 299 See also Constraint and constraint variables Harris, C., 663 808 Index Hassler, Stephen M., 211 Hastie, R., 12, 784 Headings, reports, 140 Health insurance problem, 563–565 Heian, B., 784 Helmer, Mark C., 322 Hickle, R., 617 Hierarchy process, analytic See Analytic hierarchy process (AHP) Hilah, S., 113 Hirsch, Fred, 638 HIT example See Hong Kong Terminals (HIT) example H&J case See Hamilton & Jacobs (H&J) case Holland, J H., 389 Holt-Winter’s method, 514–522 additive seasonal effects, 514–518 multiplicative seasonal effects, 518–522 Holt’s method, double exponential smoothing, 511–514 Hong Kong Terminals (HIT) example, Hotel and convention center seasonal proﬁts example, 297–306 Howard, R A., 784 Hube, Karen, 586 Hungry Dawg Restaurants corporate health insurance problem, 563–565 Hyperplanes, 430–431 I Ignizio, J., 321 Ikura, Yoshiro, 390 ILP concepts See Integer linear programming (ILP) concepts Imagination Toy Corporation (ITC) case, 722–723 Immediate predecessors, 675 Immediate successors, 675 Independent variables, 5–6 binary, 440 one, 433–434 three, 438–439 two, 434–437 Infeasibility, 38–39, 161 descriptions, 38–39 infeasible solutions, 161 Inﬂation, R2 statistic, 436–437 See also R2 statistic INFORMS See Institute For Operations Research and the Management Sciences (INFORMS) Initialization techniques, 503 Input cells, 5–6 Institute For Operations Research and the Management Sciences (INFORMS), Insurance ratings, 459 Integer linear programming (ILP) concepts, 232–295 binary variables, 253–254 See also Variables descriptions, 253–254 vs general integer, 253 integer, 253–254 logical conditions, 253–254 bounds, 235–236 branch-and-bound (B&B) algorithm, 238, 268–275 bounding processes, 272–274 branch-and-bound trees, 274 branching variables, 269–271 deﬁnitions, 238 descriptions, 268–269, 274 steps, 275 cases and examples, 276, 291–295 Maryland National Bank (MNB) example, 276 MasterDebt lockbox case, 293–294 Montreal snow removal case, 294–295 Old Dominion Power (ODP) power dispatching case, 292–293 State of Virginia timber harvest case, 291–292 integrality conditions, 232–233 continuous variables, 233 descriptions, 232–233 integer values, 233 linear programming (LP) relaxation, 233–235 optimal integer solutions, 233 logical conditions, 253–254 nonlinearities, 258 See also Nonlinear programming (NLP) and evolutionary optimization concepts overviews and summaries, 232, 274–275 problems, 233–268, 276–291 Air-Express employee scheduling, 243–249 B&G Construction contract award, 262–268 CRT Technologies capital budgeting, 249–253 descriptions, 243 minimum order-purchase size, 261 quantity discount, 261–262 relaxed, 233–235 Remington Manufacturing ﬁxed-charge/ﬁxed cost, 254–260 rebounding, 236–239 reference resources, 275–276 Solver options and solutions strategies, 240–243 stopping rules, 239 Integer values, 233 Integer variables, 253–254 See also Variables binary, 253–254 general, 253 Integrality conditions, 232–233 Interarrival times, 645 Intervals, 423–426 conﬁdence, 425–426 prediction, 423–425 Introductory concepts See Fundamental concepts; Overviews and summaries Intuitive solution approaches, 24–25 Inventory control problem, 595–611 Inventory planning and production problem, 85–90 Investment (retirement) problem, 67–72 ITC case See Imagination Toy Corporation (ITC) case Iterative solution procedures, 296 J Jarvis, J., 39, 162 Jockey concept, 662 John Deere Company example, Jones, Lawrence, 711 K K-group discriminant analysis (DA) problem, 469–477 Kamm Industries carpet manufacturing case, 175–176 Kaplan, A., 617 Keen, Peter F W., 13 Keeney, R., 785 Kellogg Company example, 113 Kelton, W., 617 Kendall notation, 647 Keown, A., 321 Index Khoshevis, B., 617 Kimes, Sheryl E., 478 Klingman, D., 210 Kolesar, P., 389, 663 Krajewski, L J., 447 Kutner, M., 447 Kwak, N K., 322, 711 L La Quinta Motor Inns example, 478 Labe, R., 477 Lagrange multipliers, 378–379 LAN Problem See Local area network (LAN) problem Land developer balanced objectives example, 297 Langevin, A., 294 Lanzenaurer, C., 113 Lasdon, L., 389 Law, A., 617 Leaves, 740 See also Trees, decision Less-than-truckload (LTL) segments, 211 Level curves, 30–31 Levy, F A., 710 Lighner Construction activity-time correlation example, 673–674 Lightner, C., 276 Limits Report, 151–152 LINDO, 45 Line graph techniques, 488 Linear programming (LP) problem spreadsheet-based modeling and solving concepts, 45–135 cases and examples, 113, 130–135 Baldwin Enterprises foreign exchange (FX) market case, 132–133 Friends of the Manatees non-proﬁt organization case, 134–135 The Golfer’s Link (TGL) supply chain management (SCM) case, 130–131 Kellogg Company example, 113 Wolverine Manufacturing retirement fund case, 133–134 implementation steps, 46–47 integer linear programming (ILP), 232–295 See also Integer linear programming (ILP) concepts macro-related issues, 48, 112 make vs buy decisions, 63–67 vs nonlinear programming (NLP), 339–408 See also Nonlinear programming (NLP) and evolutionary optimization concepts optimization and linear programming (LP) fundamentals, 17–44 See also Optimization and linear programming (LP) fundamentals overviews and summaries, 45, 112–113 problems, 48–51, 67–112, 114–130 Agri-Pro blending, 78–84 Blue Ridge Hot Tubs, 48–51 Retirement Planning Services, Inc investment, 67–72 review questions and problems, 114–130 Steak & Burger data envelopment analysis (DEA), 102–112 Taco-Viva multi-period cash ﬂow, 91–102 Tropicsun transportation, 72–78 Upton Corporation production and inventory planning, 85–90 reference resources, 113 Solver options and solutions strategies, 45–46, 51–61 solver tools, 45–46 CPLEX, 45 in Excel, 45–46 Frontline Systems, Inc., 45 LINDO, 45 809 in Lotus 1-2-3, 45–46 MathPro, 45 MPSX, 45 in Quattro Pro, 45–46 spreadsheet design goals and guidelines, 61–62 VBA macro-related issues, 112 Linear regression analysis, simple, 412–413 See also Regression analysis concepts descriptions, 412–413 true regression, 413, 426 Linear trend models, 523–526 See also Trend models Lippman, Stephen, 797 Literature references See Reference resources Local area network (LAN) problem, 208–210 Local vs global optimal solutions, 342–344 Location problem, 350–355 Lockbox case, 293–294 Loﬂin, C., 163 Logical conditions, 253–254 Logistics decision problem, 18 Loosening, constraint, 39 See also Constraint and constraint variables Lotus 1-2-3, solver tools, 45–46 Lower bounds, 276 LP concepts See Linear programming (LP) problem spreadsheetbased modeling and solving concepts LTL segments See Less-than-truckload (LTL) segments M M/D/1 model, 661 M/G/1 model, 658–661 M/M/s model, 648–658 examples, 648–652 ﬁnite populations, 654–658 ﬁnite queue length, 652–654 Mabert, Vincent A., 545 Macro-related issues, 48, 112 MAD See Mean absolute deviation (MAD) Magnolia Inns acquisition problem, 725–726 Mahalanobis distance measures, 474–475 Major Electric Company (MEC) case, 230–231 Makridakis, S., 545 Management fraud detection case, 481–482 Management science, 1–2, 12–13 descriptions, 1–2 operations research/management science (OR/MS), 12–13 Mann, L., 663 Mantel, S., 710 Manufacturing-related decision problem, 17–18 MAPE See Mean absolute percent error (MAPE) MapQuest, 18 Marcus, A., 617 Markland, Robert E., 275–276 Markowitz, H., 389 Maryland National Bank (MNB) example, 276 MasterDebt lockbox case, 293–294 Mathematical models, 3–7 See also Fundamental concepts categories, 6–7 descriptive, 6–7 predictive, 6–7 prescriptive, 6–7 cells, 5–6 descriptions, input, 5–6 810 Index Mathematical models (Continued) output, 5–6 descriptions, 3–6 examples, 5–6 proﬁt equations, 5–6 variables, 5–6 See also Variables dependent, 5–6 independent, 5–6 Mathematical optimization See Optimization and linear programming (LP) fundamentals Mathematical programming (MP) techniques, 17, 20–21 See also Optimization and linear programming (LP) fundamentals MathPro, 45 Maximal ﬂow problem, 201–204 Maximax decision rules, 729–730 Maximin decision rules, 730–731 Maximization-related concepts, 38, 276 maximization vs minimization problems, 276 objective function maximization, 38 See also Objective function MCC problem See Millennium Computer Corporation (MCC) inventory control problem McCardle, Kevin, 797 McKay, A., 113 MDA See Multiple discriminant analysis (MDA) Mean absolute deviation (MAD), 487 Mean absolute percent error (MAPE), 487 Mean square error (MSE), 487 MEC case See Major Electric Company (MEC) case Memoryless property, 645 Mental models, Meredith, J., 710 Merkhofer, M W., 785 META example See Model for evaluating technology alternative (META) example Method of least squares, 414 Mi (big M) values, 256–257 Microsoft Project, 707–710 See also Program Evaluation and Review Technique (PERT); Project management concepts Middleton, Michael, 750 Millennium Computer Corporation (MCC) inventory control problem, 595–611 Minimal spanning tree problem, 177, 208–210 MINIMAX, 307, 309, 316–321 Minimax decision rules, 731–733 Minimization-related concepts, 38, 276 minimization vs maximization problem, 276 objective function minimization, 38 See also Objective function Minimum cost network ﬂow problems, 179–180 Minimum order-purchase size problem, 261 MNB example See Maryland National Bank (MNB) example Model for evaluating technology alternative (META) example, 617–618 Model selection parameters, 433–439 See also Regression analysis concepts best models, 437 descriptions, 433 independent variables, 433–439 See also Variables one, 433–434 three, 438–439 two, 434–437 multicollinearity, 437–438 overﬁt, 433 R2 statistic, 436–437 adjusted, 437 See also R2 statistic inﬂations, 436–437 Modeling approaches See Approaches, modeling Modeling (spreadsheet) and decision analysis concepts See also under individual concepts cases and examples See Cases and examples Crystal Ball simulation concepts, 559–640 decision analysis fundamentals, 724–800 discriminant analysis (DA) concepts, 459–484 fundamental concepts, 1–16 goal programming (GP) and multiple optimization concepts, 296–338 integer linear programming (ILP) concepts, 232–295 linear programming (LP) problem spreadsheet-based modeling and solving concepts, 45–135 network modeling concepts, 177–231 nonlinear programming (NLP) and evolutionary optimization concepts, 339–408 optimization and linear programming (LP) fundamentals, 17–44 overviews and summaries See Overviews and summaries problems See Problems project management concepts, 673–723 queuing theory, 641–672 reference resources See Reference resources regression analysis concepts, 409–458 sensitivity analysis and Simplex Method concepts, 136–176 time series forecasting concepts, 485–558 MOLP problem See Multiple objective linear programming (MOLP) problem Mont Blanc, 173 Montgomery, D., 447 Montreal snow removal case, 294–295 Moore, L., 389 Morrison, D F., 477 Motorola, Motorola example, Movement amounts/step sizes, 341–342 Moving averages, 488–494, 508–511 double, 508–511 techniques, 488–491 weighted, 492–494 MP See Mathematical programming (MP) techniques MPSX, 45 MSE See Mean square error (MSE) Multi-period cash ﬂow problem, 91–102 Multicollinearity, 437–438 Multicriteria decisions problem, 772–777 Multicriteria scoring model, 773–777 Multiple discriminant analysis (MDA), 471–477 Multiple objective linear programming (MOLP) problem, 307–321 See also Goal programming (GP) and multiple optimization concepts descriptions, 307–309, 320 examples, 309–321 MINIMAX, 309, 316–321 non-corner point feasible solutions, 307–309 Pareto optimal, 320 steps, 307–309, 320 Multiple optimization and goal programming (GP) concepts, 296–338 cases and examples, 297, 309–322, 335–338 Blackstone Mining Company example, 309–321 Caro-Life sales territory planning case, 337–338 land developer balanced objectives example, 297 resort hotel and convention center seasonal proﬁts example, 297–306 Index Truck Transport Company terminal relocation example, 321–322 United States Department of Agriculture (USDA) diet planning-food stamp program case, 335–337 goal programming (GP), 296–307 balanced objectives, 297 constraint, goal, 298–299 constraint, hard, 299 descriptions, 296–297 examples, 297–306 MINIMAX, 307 preemptive goal programming (GP), 307 solution comparisons, 307 target values, 299 underachievement vs overachievement, 299 variables, deviational, 299 iterative solution procedures, 296 multiple objective linear programming (MOLP) problem, 307–321 descriptions, 307–309, 320 examples, 309–321 MINIMAX, 309, 316–321 non-corner point feasible solutions, 307–309 Pareto optimal, 320 steps, 307–309, 320 overviews and summaries, 296, 321 problems, 307–321 multiple objective linear programming (MOLP), 307–321 review questions and problems, 322–335 reference resources, 162, 321 Solver options and solution strategies, 304–306, 311–314 Multiple regression, 430–433 See also Regression analysis concepts descriptions, 430–431 examples, 431–433 planes, 430–431 descriptions, 430–431 hyperplanes, 430–431 Multiplicative seasonal effects Holt-Winter’s method, 518–522 techniques, 504–507 Multipliers, Lagrange, 378–379 Multistage decision problem, 750–754 Murdick, R., 545 N National Airlines Fuel Management and Allocation Model example, 163 National Center for Drug Analysis drug testing procedures example, 710–711 Nauss, Robert M., 275–276 Negative numbers vs positive numbers, 178 Nemhauser, G., 276 Net present value (NPV), 249–253 Neter, J., 447 Network deﬁnitions, 177 Network ﬂow problems, 177–208 See also Network modeling concepts deﬁnitions, 177 equipment replacement, 189–192 generalized, 194–200 maximal ﬂow, 201–204 minimal spanning tree, 177, 208–210 SafetyTrans nonlinear network ﬂow, 355–360 shortest path, 184–189 special considerations, 205–208 811 transportation/assignment, 193 transshipment, 177–184 Network modeling concepts, 177–231 cases and examples, 211, 227–231 Hamilton & Jacobs (H&J) case, 227–228 Major Electric Company (MEC) case, 230–231 Old Dominion Energy (ODE), Inc case, 228–229 US Express case, 229–230 Yellow Freight System, Inc example, 211 deﬁnitions, 177 network ﬂow problems, 177–208 deﬁnitions, 177 equipment replacement, 189–192 generalized, 194–200 maximal ﬂow, 201–204 minimal spanning tree, 177, 208–210 shortest path, 184–189 special considerations, 205–208 transportation/assignment, 193 transshipment, 177–184 overviews and summaries, 177, 210 problems, 177–208, 212–226 network ﬂow, 177–208 review questions and problems, 212–226 reference resources, 210 Solver options and solution strategies, 182–201 Network (project) creation, 674–677 See also Project management concepts activity-on-arc (AOA), 675–677 activity-on-node (AON), 674–677 descriptions, 674–677 predecessors, immediate, 675 successors, immediate, 675 New River Land Trust example, 297 Newspaper advertising insert scheduling case, 407–408 NLP concepts See Nonlinear programming (NLP) and evolutionary optimization concepts Nodes, 178, 739–748 See also Trees, decision event, 740, 744–748 terminal, 740 Non-corner point feasible solutions, 307–309 Non-proﬁt organization case, 134–135 Nonbinding constraint, 139 See also Constraint and constraint variables Nonlinear network ﬂow problem, 355–360 Nonlinear programming (NLP) and evolutionary optimization concepts, 339–408 alldifferent constraint, 387 cases and examples, 389–390, 404–408 newspaper advertising insert scheduling case, 407–408 Paciﬁc Gas and Electric power generation example, 389–390 run on press (ROP) and preprinted advertising insert scheduling case, 407–408 Tour de France case, 404–405 U.S presidential election campaign stops case, 405–406 Wella Corporation bill of materials (BOM) case, 406–407 economic order quantity (EOQ) models, 344–350 evolutionary algorithms, 380–382 descriptions, 380–382 vs genetic algorithms (GAs), 380–382 generalized reduced gradient (GRG) algorithm, 341–342 vs linear programming (LP), 339 optimization, existing ﬁnancial spreadsheets, 365–368 overviews and summaries, 339, 389 permutation issues, 387 812 Index Nonlinear programming (NLP) (Continued) problems, 339–342, 350–404 descriptions, 339–341 Rappaport Communications Company location, 350–355 review questions and problems, 390–404 SafetyTrans nonlinear network ﬂow, 355–360 solution strategies, 341–342 stock market return, 382–385 stock portfolio selection, 368–376 TMC Corporation project selection, 360–365 Wolverine Manufacturing Company Traveling Salesperson Problem (TSP), 385–389 reference resources, 389 sensitivity analyses, 376–379 See also Sensitivity analysis and Simplex Method concepts descriptions, 376–377 gradient reductions, 379 Lagrange multipliers, 378–379 solution strategies, 341–344 descriptions, 341–342 optimal solutions, local vs global, 342–344 Solver options and, 343–390 starting points, 341–342, 344 step sizes/movement amounts, 341–342 Nonlinear regression, 442–446 See also Regression analysis concepts Nonlinear relationship expressions via linear models, 442–446 Nonprobabilistic methods, 729–733 See also Decision analysis fundamentals decision rules, 728–733 descriptions, 728 maximax, 729–730 maximin, 730–731 minimax, 731–733 descriptions, 729 vs probabilistic methods, 733–738 See also Probabilistic methods Nonstationary vs stationary times series, 486 See also Time series forecasting concepts Northwest Petroleum Company problem, 201–204 NPV See Net present value (NPV) O Objective function, 20–38, 150–151 coefﬁcients, 24, 150–151 descriptions, 22–24 linear programming (LP) models, 22–24 minimization vs maximization, 38 optimization, 20 plotting techniques, 29–30 Objectives, balanced, 297 Ohio National Bank example, 409–411 Old Dominion Energy (ODE) case, 228–229 Old Dominion Power (ODP) power dispatching case, 292–293 Olson, D., 617 One independent variable, 433–434 See also Variables Operations research/management science (OR/MS), 12–13 Optimal solutions, 30–35, 233, 342–344 alternate, 34–35 integer, 233 local vs global, 342–344 via corner point enumeration, 31 via level curves, 30–31 Optimization and linear programming (LP) fundamentals, 17–44 cases and examples, 18, 21–23, 44 Blue Ridge Hot Tubs case, 44 Blue ridge Hot Tubs example, 21–23 linear programming (LP) model example, 21–23 goal programming (GP) and multiple optimization concepts, 296–338 See also Goal programming (GP) and multiple optimization concepts linear programming (LP) models, 17, 20–39 coefﬁcients, objective functions, 24 descriptions, 17, 20–21 examples, 21– 23 formulation, 21–23 general forms, 23–24 mathematical programming (MP) techniques, 17, 20–21 objective function, 22–24 solution approaches, graphical, 25–34 See also Graphical solution approaches solution approaches, intuitive, 24–25 special conditions, 34–39 See also Special conditions, linear programming (LP) models steps, 21–23 variables, constraint, 22 variables, decision, 22–23 optimization and optimization problems, 17–20 applications, 17–18 characteristics, 18–19 deﬁnitions, 17 descriptions, 17–18 examples, 18–19 ﬁnancial planning decisions, 18 logistics decisions, 18 manufacturing-related decisions, 17–18 mathematical expressions, 19–20 mathematical programming (MP) techniques, 17, 20–21 objective functions, 20 product mix determination, 17 routing decisions, 18 ubiquitousness, 18 variables, constraint, 19–20 variables, decision, 19 overviews and summaries, 17, 39 problems, 17–20, 39–43 optimization, 17–20 review questions and problems, 39–43 reference resources, 39 OptQuest, 601–616 OR/MS See Operations research/management science (OR/MS) Oscar Anderson/Trice-Mikehouse-Loopers (OTAML) case, 481–482 Osyk, B., 477 Outcomes vs decisions, 11 Output cells, 5–6 Overachievement vs underachievement, 299 Overﬁt, 433 Overviews and summaries See also under individual concepts Crystal Ball simulation concepts, 559, 616–617 decision analysis fundamentals, 724, 783–784 discriminant analysis (DA) concepts, 459–460, 477 fundamental concepts, 1–3, 11 goal programming (GP) and multiple optimization concepts, 296, 321 integer linear programming (ILP) concepts, 232, 274–275 linear programming (LP) problem spreadsheet-based modeling and solving concepts, 45, 112–113 Index network modeling concepts, 177, 210 nonlinear programming (NLP) and evolutionary optimization concepts, 339, 389 optimization and linear programming (LP) fundamentals, 17, 39 project management concepts, 673, 710 queuing theory, 641, 663 regression analysis concepts, 409, 446–447 sensitivity analysis and Simplex Method concepts, 136, 162 time series forecasting concepts, 485–486, 544–545 P Paciﬁc Gas and Electric power generation example, 389–390 Pairwise comparisons, 777–779 Parameters, population See Population parameters Pareto optimal, 320 Parker Brothers, 173 Parket Sisters pen and pencil manufacturing case, 173–175 Passes, 677–682 See also Critical Path Method (CPM) backward, 677, 680–682 forward, 677–680 Patrick’s paradox case, 14–16 Payoff matrix, 726–728 PB Chemical Corporation case, 554–555 PCA problem See Piedmont Commuter Airlines (PCA) reservation management problem Peck, E., 447 Peck, Ken, 211 Peiser, R., 276 Pen and pencil manufacturing case, 173–175 Pentel, 173 Permutation issues, 387 Personal ﬁnancial planning example, 586 PERT See Program Evaluation and Review Technique (PERT) Pervaiz, A., 477 Phillips, C., 710 Phillips, D., 210 Phillips, N., 210 Physical models, Piedmont Commuter Airlines (PCA) reservation management problem, 587–595 Pindyck, R., 545 Planes, 430–431 descriptions, 430–431 vs hyperplanes, 430–431 Plotting techniques, 26–30, 411 See also Graphical solution approaches constraint, 26–28 See also Constraint and constraint variables ﬁrst, 26 second, 26–27 third, 27–28 feasible regions, 25–26, 28–29 objective function, 29–30 scatter plots, 411 Police force case, 671 Polynomial regression, 441–446 See also Regression analysis concepts descriptions, 441–442 vs nonlinear regression, 446 nonlinear relationship expressions via linear models and, 442–446 Ponder, Ron J., 12–13 Population parameters, 413, 426–430, 440–441 See also Regression analysis concepts accuracy, 430 analysis of variance (ANOVA), 427 813 assumptions, 427–428 deﬁnitions, 413 descriptions, 426–427, 440–441 statistical tests, 413, 426–430, 440–441 Portfolio selection problem, 368–376 Positive numbers vs negative numbers, 178 Postrel, V., 18 Powell, Warren B., 211 Power dispatching case, 292–293 Power generation example, 389–390 Predecessors, immediate, 675 Predictions, 6–7, 422–426, 439–440 See also Regression analysis concepts descriptions, 422–423, 439–440 extrapolation, 426 intervals, 423–426 conﬁdence, 425–426 prediction, 423–425 predictive models, 6–7 standard error, 423 time series forecasting concepts, 485–558 See also Time series forecasting concepts Preemptive goal programming (GP), 307 See also Goal programming (GP) and multiple optimization concepts Preprinted advertising insert scheduling case, 407–408 Prescriptive models, 6–7 Presidential election cases, 405–406, 456 2000 election, 456 campaign stops, 405–406 Probabilistic methods, 733–738 See also Decision analysis fundamentals decision rules expected monetary value (EMV), 733–735 expected regret/expected opportunity loss (EOL), 735 descriptions, 733 vs nonprobabilistic methods, 729–733 See also Nonprobabilistic methods sensitivity analyses, 736–737, 754–760 See also Sensitivity analysis and Simplex Method concepts Probabilities, conditional, 763–765 Problem solving processes, 7–9 Problems See also under individual concepts ACME Manufacturing, 460–469 Agri-Pro blending, 78–84 Air-Express employee scheduling, 243–249 American Car Association (ACA), 184–189 Bavarian Motor Company (BMC), 177–184 B&G Construction contract award, 262–268 Blue Ridge Hot Tubs, 48–51, 137–138 Coal Bank Hollow Recycling, 194–200 Compu-Train, 189–193 CRT Technologies capital budgeting, 249–253 equipment replacement, 189–192 Hungry Dawg Restaurants corporate health insurance, 563–565 K-group discriminant analysis (DA), 469–477 local area network (LAN), 208–210 Magnolia Inns acquisition, 725–726 maximal ﬂow problem, 201–204 maximization vs minimization, 276 Millennium Computer Corporation (MCC) inventory control, 595–611 minimum order-purchase size, 261 multicriteria decisions, 772–777 814 Index Problems (Continued) multiple objective linear programming (MOLP), 307–321 multistage decision, 750–754 network ﬂow, 177–208 deﬁnitions, 177 equipment replacement, 189–192 generalized, 194–200 maximal ﬂow, 201–204 minimal spanning tree, 177, 208–210 SafetyTrans nonlinear network ﬂow, 355–360 shortest path, 184–189 special considerations, 205–208 transportation/assignment, 193 transshipment, 177–184 Northwest Petroleum Company, 201–204 Piedmont Commuter Airlines (PCA) reservation management, 587–595 quantity discount, 261–262 Rappaport Communications Company location, 350–355 relaxed, 233–235 Remington Manufacturing ﬁxed-charge/ﬁxed cost, 254–260 Retirement Planning Services, Inc investment, 67–72 review questions and problems Crystal Ball simulation concepts, 618–632 decision analysis fundamentals, 786–796 discriminant analysis (DA) concepts, 478–481 evolutionary optimization and nonlinear programming (NLP) concepts, 390–404 fundamental concepts, 4, 8, 12, 14 goal programming (GP) and multiple optimization concepts, 322–335 linear programming (LP) problem spreadsheet-based modeling and solving concepts, 114–130 network modeling concepts, 212–226 optimization and linear programming (LP) fundamentals, 39–43 project management concepts, 711–720 queuing theory, 665–670 regression analysis concepts, 414–416, 448–454 sensitivity analysis and Simplex Method concepts, 164–173 time series forecasting concepts, 546–554 SafetyTrans nonlinear network ﬂow, 355–360 speciﬁc concepts Crystal Ball simulation concepts, 563–565, 587–611, 618–632 decision analysis fundamentals, 725, 750–754, 786–796 discriminant analysis (DA) concepts, 460–481 fundamental concepts, 4, 8, 12, 14 goal programming (GP) and multiple optimization concepts, 307–321 integer linear programming (ILP) concepts, 233–268, 276–291 linear programming (LP) problem spreadsheet-based modeling and solving concepts, 48–51, 67–112, 114–130 network modeling concepts, 177–208, 212–226 nonlinear programming (NLP) and evolutionary optimization concepts, 339–342, 350–404 optimization and linear programming (LP) fundamentals, 17–20, 39–43 project management concepts, 711–720 queuing theory, 665–670 regression analysis concepts, 414–416, 448–454 sensitivity analysis and Simplex Method concepts, 164–173 time series forecasting concepts, 546–554 Steak & Burger data envelopment analysis (DEA), 102–112 stock market return, 382–385 stock portfolio optimization, 611–616 stock portfolio selection, 368–376 Taco-Viva multi-period cash ﬂow, 91–102 TMC Corporation project selection, 360–365 Tropicsun transportation, 72–78 two-group discriminant analysis (DA), 460–469 Upton Corporation production and inventory planning, 85–90 Windstar Aerospace Company, 208–210 Wolverine Manufacturing Company Traveling Salesperson Problem (TSP), 385–389 Product mix determinations, 17 Production and inventory planning problem, 85–90 Proﬁt equations, 5–6 Program Evaluation and Review Technique (PERT), 673, 699–710 See also Project management concepts deﬁnitions, 673 descriptions, 699–702 Microsoft Project, 707–710 network simulations, 702–707 Project crashing, 691–698 Project management concepts, 673–723 cases and examples, 673–674, 710–711, 720–723 Crossroad Academy enrollment case, 720–722 drug testing procedures example, 710–711 Imagination Toy Corporation (ITC) case, 722–723 Lighner Construction activity-time correlation example, 673–674 World Trade Center cleanup case, 722 Critical Path Method (CPM), 673, 677–699 array formulas, 685–688 backward pass, 677, 680–682 circular references, 686 conditional formatting, 688 critical path determination, 682–684 deﬁnitions, 673 descriptions, 677–678 forward pass, 677–680 Gantt charts, 688–691 project crashing, 691–698 slack, 683–684 spreadsheet examples, 685–688 time zero, 678 network creation, 674–677 activity-on-arc (AOA), 675–677 activity-on-node (AON), 674–677 descriptions, 674–677 predecessors, immediate, 675 successors, immediate, 675 overviews and summaries, 673, 710 problems, review questions and problems, 711–720 Program Evaluation and Review Technique (PERT), 673, 699–710 deﬁnitions, 673 descriptions, 699–702 Microsoft Project, 707–710 network simulations, 702–707 reference resources, 710 Solver options and solution strategies, 691–698 Project selection problem, 360–365 Purchase size-minimum order problem, 261 Q Quadratic trend models, 526–528 See also Trend models Quantity discount problem, 261–262 Quattro Pro, solver tools, 45–46 Questions and problems See Problems Index Queuing theory, 641–672 balk concept, 662 cases and examples, 663–665, 671–672 Bank of Boston wait watchers example, 663–665 police force case, 671 Vacation, Inc (VI) call center stafﬁng case, 671–672 jockey concept, 662 Kendall notation, 647 M/D/1 model, 661 M/G/1 model, 658–661 M/M/s model, 648–658 examples, 648–652 ﬁnite populations, 654–658 ﬁnite queue length, 652–654 overviews and summaries, 641, 663 problems, review questions and problems, 665–670 queue simulations, 662–663 queuing models, 641–642, 647–648 characteristics, 647 descriptions, 647–648 objectives, 641–642 queuing systems, 641–647 characteristics, 643–647 conﬁgurations, 642–643 memoryless property, 645 model objectives, 641–642 rates, arrival, 644–645 rates, service, 645–647 times, interarrival, 645 times, queue, 645 times, service, 645 reference resources, 663 renege concept, 662 steady-state assumption, 662–663 transient periods, 662 Quinn, P., 663 R R2 statistic, 421–422 See also Regression analysis concepts adjusted, 437 as coefﬁcient of determination, 421–422 descriptions, 421–422 inﬂations, 436–437 sum of squares, 421–422 See also Sum of squares regression sum of squares (RSS), 421–422 total sum of squares (TSS), 421 Raffray, Andre-Francois, 11 Ragsdale, C., 710 Raiffa, H., 785 Random number generators (RNG), 565–570 assumption cells, 566 descriptions, 566–569 variables, discrete vs continuous, 569–570 Random/unsystematic variation, 411–412 Random variables, 559–560 See also Variables Rappaport Communications Company location problem, 350–355 Rates, 644–647 arrival, 644–645 service, 645–647 Readings See Reference resources Rebounding, 236–239 Reduced cost interpretations, 147–149 Redundant constraint, 34–36 See also Constraint and constraint variables 815 Reeves, C R., 389 Reference resources, 275–276 See also under individual concepts Crystal Ball simulation concepts, 617 decision analysis fundamentals, 784–785 discriminant analysis (DA) concepts, 477 fundamental concepts, 4, 8, 12 goal programming (GP) and multiple optimization concepts, 321 linear programming (LP) problem spreadsheet-based modeling and solving concepts, 113 network modeling concepts, 210 nonlinear programming (NLP) and evolutionary optimization concepts, 389 optimization and linear programming (LP) fundamentals, 39 project management concepts, 710 queuing theory, 663 regression analysis concepts, 447 sensitivity analysis and Simplex Method concepts, 162 time series forecasting concepts, 545 References, circular, 686 Regions, feasible See Feasibility Regression analysis concepts, 409–458 binary independent variables, 440 cases and examples, 409–411, 431–433, 447, 454–458 diamonds are forever case, 454–455 ﬁasco in Florida/U.S Presidential election (2000) case, 456 Georgia Public Service Commission case, 457–458 Ohio National Bank example, 409–411 sales-advertising relationship example, 409–411 ﬁt, 413–414, 419–421 best ﬁt deﬁnitions, 413–414 error sum of squares (ESS), 414 estimation error, 414 evaluations, 419–421 method of least squares, 414 residuals, 414 TREND( ) function, 419–421 model selection parameters, 433–439 best models, 437 descriptions, 433 independent variables, one, 433–434 independent variables, three, 438–439 independent variables, two, 434–437 multicollinearity, 437–438 overﬁt, 433 R2 statistic, adjusted, 437 R2 statistic, inﬂations, 436–437 multiple regression, 430–433 descriptions, 430–431 examples, 431–433 hyperplanes, 430–431 planes, 430–431 overviews and summaries, 409, 446–447 polynomial regression, 441–446 descriptions, 441–442 vs nonlinear regression, 446 nonlinear relationship expressions via linear models, 442–446 population parameters, 413, 426–430, 440–441 accuracy, 430 analysis of variance (ANOVA), 427 assumptions, 427–428 deﬁnitions, 413 descriptions, 426–427, 440–441 statistical tests, 413, 426–430, 440–441 predictions, 422–426, 439–440 See also Predictions 816 Index Regression analysis concepts (Continued) descriptions, 422–423, 439–440 extrapolation, 426 intervals, conﬁdence, 425–426 intervals, prediction, 423–425 standard error, 423 problems, 414–416, 448–454 review questions and problems, 448–454 Solver options and solution strategies, 414–416 R2 statistic, 421–422 as coefﬁcient of determination, 421–422 descriptions, 421–422 sum of squares, regression sum of squares (RSS), 421–422 sum of squares, total sum of squares (TSS), 421 reference resources, 447 regression models, 411–412 descriptions, 411–412 error terms and, 411 unsystematic/random variation, 411–412 scatter diagrams/scatter plots, 411 seasonal regression models, 534–544 simple linear regression analysis, 412–413 descriptions, 412–413 true regression, 413, 426 solutions, 414–418 via regression tool, 417–418 via Solver, 414–417 trend models, 522–523, 528 See also Trend models Regression models, 411–412 Regression sum of squares (RSS), 421–422 See also Sum of squares Regression tool, 417–418 Reid, R., 545 Reidwyl, H., 477 Relaxation, 233–235 Relaxed problem, 233–235 Relocation example, 321–322 Remington Manufacturing ﬁxed-charge/ﬁxed cost problem, 254–260 Renege concept, 662 Rental property income case, 638–640 Reports, 138–152 Answer Report, 138–150 headings, 140 Limits Report, 151–152 Sensitivity Report, 140–152 Reservation management problem, 587–595 Residuals, 414 Resort hotel and convention center seasonal proﬁts example, 297–306 Retirement fund case, 133–134 Retirement Planning Services, Inc investment problem, 67–72 Review questions and problems See Problems Risk analyses, 559–563 See also Crystal Ball simulation concepts best-case/worst-case analyses, 561–563 importance, 560 methods, 560–563 random variables, 559–560 simulation techniques, 562–563 uncertainty, 563 what-if analyses, 562 Ritzman, L P., 447 RNG See Random number generators (RNG) Rogers, Ray, 638 Roll backs, 740–742 See also Trees, decision ROP case See Run on press (ROP) and preprinted advertising insert scheduling case Roush, W., 113 Routing decision problem, 18 RSS See Regression sum of squares (RSS) Rubin, D., 162 Rubinfeld, D., 545 Rules, 239, 728–735 decision, 728–735 descriptions, 728 expected monetary value (EMV), 733–735 expected regret/expected opportunity loss (EOL), 735 maximax, 729–730 maximin, 730–731 minimax, 731–733 stopping, 239 Run on press (ROP) and preprinted advertising insert scheduling case, 407–408 Russell, R., 617 S SafetyTrans nonlinear network ﬂow problem, 355–360 Sales-advertising relationship example, 409–411 Sales territory planning case, 337–338 Sampling uncertainty, 581–585 Scale models, Scatter diagrams/scatter plots, 411 Schaffer, 173 Scheduling problem, 243–249 Schindler, S., 276 Schmidt, C., 39, 162 Schneiderjans, Marc J., 322 Schonberger, R., 710 Schrage, M., 12 Science, management See Management science SCM case See Supply chain management (SCM) case Scoring, 773–777, 781–783 determinations, 781–782 models, 773–777, 783 multicriteria, 773–777 Seasonal effects, 500–507, 514–522 additive, 500–503, 514–518 multiplicative, 504–507, 518–522 Seasonal indices, 529–534 Seasonal models, 498–499, 528, 534–544 Seasonal proﬁts example, 297–306 Second constraint, 26–27 See also Constraint and constraint variables Selected readings See Reference resources Semmels, T., 276 Sensitivity analysis and Simplex Method concepts, 136–176 cases and examples, 163, 173–176 Kamm Industries carpet manufacturing case, 175–176 National Airlines Fuel Management and Allocation Model example, 163 Parket Sisters pen and pencil manufacturing case, 173–175 nonlinear programming (NLP) and evolutionary optimization, 376–379 See also Nonlinear programming (NLP) and evolutionary optimization concepts overviews and summaries, 136, 162 probabilistic methods, 736–737, 754–760 See also Probabilistic methods problems, 164–173 Blue Ridge Hot Tubs, 137–138 review questions and problems, 164–173 reference resources, 162 Index sensitivity analyses, 136–137 alternate optimal solutions, 143 approaches, 136–137 coefﬁcients, constraint, 149–150 coefﬁcients, objective function, 150–151 constancy, 142 constraint, binding, 139 constraint, nonbinding, 139 degeneracy, 151 purposes, 136–137 reduced cost interpretations, 147–149 reports, Answer Report, 138–150 reports, headings, 140 reports, Limits Report, 151–152 reports, Sensitivity Report, 140–152 Sensitivity Assistant Add-in, 152–158 shadow prices, 143–147 Solver Tables, 152–158 Spider Tables, 152–158 Simplex Method, 158–162 constraint, equality, 158–159 descriptions, 158 solutions, best, 162 solutions, feasible, 159–161 solutions, infeasible, 161 variables, basic, 160 variables, slack, 158–159 variables, structural, 159 Solver options and solution strategies, 138–158 Sensitivity Assistant Add-in, 152–158 Sensitivity Report, 140–152 Series forecasting concepts See Time series forecasting concepts Service rates, 645–647 Service times, 645 Shadow prices, 143–147 Shell West, 163 Shetty, C., 389 Shogan, A., 39, 113, 162 Shortest path problem, 184–189 Side constraint, 188 See also Constraint and constraint variables Simple linear regression analysis, 412–413 See also Regression analysis concepts descriptions, 412–413 true regression, 413, 426 Simplex Method and sensitivity analysis concepts, 136–176 cases and examples, 163, 173–176 Kamm Industries carpet manufacturing case, 175–176 National Airlines Fuel Management and Allocation Model example, 163 Parket Sisters pen and pencil manufacturing case, 173–175 nonlinear programming (NLP) and evolutionary optimization, 376–379 See also Nonlinear programming (NLP) and evolutionary optimization concepts overviews and summaries, 136, 162 problems, 164–173 Blue Ridge Hot Tubs, 137–138 review questions and problems, 164–173 reference resources, 162 sensitivity analyses, 136–137 alternate optimal solutions, 143 approaches, 136–137 coefﬁcients, constraint, 149–150 coefﬁcients, objective function, 150–151 constancy, 142 817 constraint, binding, 139 constraint, nonbinding, 139 degeneracy, 151 purposes, 136–137 reduced cost interpretations, 147–149 reports, Answer Report, 138–150 reports, headings, 140 reports, Limits Report, 151–152 reports, Sensitivity Report, 140–152 Sensitivity Assistant Add-in, 152–158 shadow prices, 143–147 Solver Tables, 152–158 Spider Tables, 152–158 Simplex Method, 158–162 constraint, equality, 158–159 solutions, best, 162 solutions, feasible, 159–161 solutions, infeasible, 161 variables, basic, 160 variables, slack, 158–159 variables, structural, 159 Solver options and solution strategies, 138–158 Simulation (Crystal Ball) concepts, 559–640 additional uses, 586 base case models, 565 cases and examples, 586, 617–618, 632–640 death and taxes case, 633–634 Foxridge Investment Group rental property income case, 638–640 personal ﬁnancial planning example, 586 Sound’s Alive Company speaker market case, 634–638 sustainable retirement withdrawal case, 632–633 U.S Postal Service model for evaluating technology alternative (META) example, 617–618 conﬁdence levels, 582–585 data analyses, 578–581 efﬁcient frontiers, 611 graph and statistic incorporation, 581 model preparation techniques, 570–576 OptQuest, 601–616 overviews and summaries, 559, 616–617 problems, 563–565, 587–611, 618–632 Hungry Dawg Restaurants corporate health insurance, 563–565 Millennium Computer Corporation (MCC) inventory control, 595–611 Piedmont Commuter Airlines (PCA) reservation management, 587–595 review questions and problems, 618–632 stock portfolio optimization, 611–616 random number generators (RNG), 565–570 assumption cells, 566 deﬁnitions, 565 descriptions, 566–569 variables, discrete vs continuous, 569–570 reference resources, 617 risk analyses, 559–563 best-case/worst-case analyses, 561–563 importance, 560 methods, 560–563 random variables, 559–560 simulation techniques, 562–563 uncertainty, 563 what-if analyses, 562 running techniques, 576–578 818 Index Simulation (Crystal Ball) concepts (Continued) sampling uncertainty, 581–585 starting techniques, 565–566 Simulations, queue, 662–663 See also Queuing theory Sinkey, J F., 477 Skinner, D., 785 Slack, 683–684 Slack variables, 158–159 Smith, S., 389 Smoothing, exponential, 494–498, 511–514 double (Holt’s method), 511–514 techniques, 494–498 Snow removal case, 294–295 Solution approaches, 24–34 graphical, 25–34 See also Graphical solution approaches intuitive, 24–25 optimal See Optimal solutions Solver options and solution strategies See also under individual concepts Decision analysis fundamentals, 755–760 descriptions, 414–417 goal programming (GP) and multiple optimization concepts, 304–306, 311–314 integer linear programming (ILP) concepts, 240–243 linear programming (LP) problem spreadsheet-based modeling and solving concepts, 45–46, 51–61 network modeling concepts, 182–201 nonlinear programming (NLP) and evolutionary optimization concepts, 343–390 Project management concepts, 691–698 Sensitivity analysis and Simplex Method concepts, 138–158 solver-related tools, 45–46 CPLEX, 45 in Excel, 45–46 Frontline Systems, Inc., 45 LINDO, 45 in Lotus 1-2-3, 45–46 MathPro, 45 MPSX, 45 in Quattro Pro, 45–46 Solver Tables, 152–158 time series forecasting concepts, 494–498 Sonntag, C., 12 Sound’s Alive Company speaker market case, 634–638 Spanning tree (minimal) problem, 177, 208–210 Sparse transportation/assignment problems, 192 Speaker market case, 634–638 Special conditions, linear programming (LP) models, 34–39 alternate optimal solutions, 34–35 constraint, 34–36, 39 See also Constraint and constraint variables loosening, 39 redundant, 34–36 descriptions, 34 infeasibility, 38–39 objective functions, minimization vs maximization, 38 unbounded solutions, 34, 37–38 Spider, 152–158, 755–760 charts, 755–760 Tables, 152–158 Spreadsheet modeling and decision analysis concepts See also under individual concepts cases and examples See Cases and examples Crystal Ball simulation concepts, 559–640 decision analysis fundamentals, 724–800 discriminant analysis (DA) concepts, 459–484 fundamental concepts, 1–16 goal programming (GP) and multiple optimization concepts, 296–338 integer linear programming (ILP) concepts, 232–295 linear programming (LP) problem spreadsheet-based modeling and solving concepts, 45–135 network modeling concepts, 177–231 nonlinear programming (NLP) and evolutionary optimization concepts, 339–408 optimization and linear programming (LP) fundamentals, 17–44 overviews and summaries See Overviews and summaries problems See Problems project management concepts, 673–723 queuing theory, 641–672 reference resources See Reference resources regression analysis concepts, 409–458 sensitivity analysis and Simplex Method concepts, 136–176 time series forecasting concepts, 485–558 Spreadsheet wars case, 799–800 Sprint Corp., 12–13 Standard error, 423 Starting points, 341–342, 344 State of Virginia timber harvest case, 291–292 Stationary data, 500–507, 514–522 See also Time series forecasting concepts seasonal effects, additive, 500–503, 514–518 Holt-Winter’s method, 514–518 techniques, 500–503 seasonal effects, multiplicative, 504–507, 518–522 Holt-Winter’s method, 518–522 techniques, 504–507 Stationary models, 487–488 Stationary vs nonstationary times series, 486 See also Time series forecasting concepts Steady-state assumption, 662–663 Steak & Burger data envelopment analysis (DEA) problem, 102–112 Step sizes/movement amounts, 341–342 Steuer, R., 321 Stock market problems, 368–376, 382–385, 611–616 market return, 382–385 portfolio optimization problem, 611–616 portfolio selection, 368–376 Stopping rules, 239 Stowe, J., 276 Strategic planning case, 557–558 Structural variables, 159 Study questions and problems See Problems Suboptimality tolerances, 239–243 Subramanian, R., 113 Successors, immediate, 675 Suggested readings See Reference resources Sum of squares, 414, 421–422 error sum of squares (ESS), 414 regression sum of squares (RSS), 421–422 total sum of squares (TSS), 421 Summary concepts See Fundamental concepts; Overviews and summaries Supply chain management (SCM) case, 130–131 Supply nodes, 178 Sustainable retirement withdrawal case, 632–633 SYSNET system, 211 Systems, queuing, 641–647 See also Queuing theory characteristics, 643–647 conﬁgurations, 642–643 Index memoryless property, 645 model objectives, 641–642 rates, arrival, 644–645 rates, service, 645–647 times, 644–645 descriptions, 644–645 interarrival, 645 queue, 645 service, 645 T Taco-Viva multi-period cash ﬂow problem, 91–102 Tarrows, Pearson, Foster and Zuligar (TPF&G) cost of living adjustment (COAL), 555–557 Tavakoli, A., 276 Taylor, B., 321, 389 Terminal nodes, 740 Terminal relocation example, 321–322 Territory planning case, 337–338 TGL case See The Golfer’s Link (TGL) supply chain management (SCM) case The Golfer’s Link (TGL) supply chain management (SCM) case, 130–131 Third constraint, 27–28 See also Constraint and constraint variables Three independent variables, 438–439 See also Variables Timber harvest case, 291–292 Time-activity correlation example, 673–674 Time series forecasting concepts, 485–558 accuracy measures, 486–487 descriptions, 486–487 goodness of ﬁt, 487 See also Fit mean absolute deviation (MAD), 487 mean absolute percent error (MAPE), 487 mean square error (MSE), 487 cases and examples, 545, 554–558 Chemical Bank example, 545 Fysco Foods strategic planning case, 557–558 PB Chemical Corporation case, 554–555 Tarrows, Pearson, Foster and Zuligar (TPF&G) cost of living adjustment (COAL) case, 555–557 deﬁnitions, 485 exponential smoothing, 494–498, 511–514 double (Holt’s method), 511–514 techniques, 494–498 extrapolation models, 486 forecast combining techniques, 544 importance, 486 initialization, 503 line graph techniques, 488 methods, 486 moving averages, 488–494, 508–511 double, 508–511 techniques, 488–491 weighted, 492–494 overviews and summaries, 485–486, 544–545 problems, review questions and problems, 546–554 reference resources, 545 seasonal indices, 529–534 seasonal models, 498–499, 528, 534–544 examples, 498–499, 528, 534–544 regression, 534–544 Solver options and solution strategies, 494–498, 504–507 stationary data, 500–507, 514–522 819 additive seasonal effects, Holt-Winter’s method, 514–518 additive seasonal effects, techniques, 500–503 multiplicative seasonal effects, Holt-Winter’s method, 518–522 multiplicative seasonal effects, techniques, 504–507 stationary models, 487–488 stationary vs nonstationary times series, 486 trend models, 507–508, 522–528 examples, 507–508 linear, 523–526 quadratic, 526–528 regression, 522–523, 528 TREND( ) function, 525 See also TREND( ) function Time zero, 678 Times, 644–645 descriptions, 644–645 interarrival, 645 queue, 645 service, 645 TMC Corporation project selection problem, 360–365 Tools, solver See Solver options and solution strategies Tornado charts, 755–760 Total sum of squares (TSS), 421 See also Sum of squares Tour de France case, 404–405 TPF&G case See Tarrows, Pearson, Foster and Zuligar (TPF&G) cost of living adjustment (COAL) Transient periods, 662 Transportation/assignment problem, 193 Transshipment nodes, 178 Transshipment problem, 177–184 Traveling Salesperson Problem (TSP), 385–389 TreePlan add-in, 742–750, 771–772 Trees, decision, 9–10, 739–750 See also Decision analysis fundamentals branches, event, 740, 743–744 descriptions, 9–10, 739–740 leaves, 740 nodes, 739–748 decision, 739–740 event, 740, 744–748 terminal, 740 roll backs, 740–742 TreePlan add-in, 742–750, 771–772 TREND( ) function, 419–421, 525 See also Fit Trend models, 507–508, 522–528 See also Time series forecasting concepts examples, 507–508 linear, 523–526 quadratic, 526–528 regression, 522–523, 528 Tropicsun transportation problem, 72–78 Truck Transport Company terminal relocation example, 321–322 True regression, 413, 426 See also Regression analysis concepts TSP See Traveling Salesperson Problem (TSP) TSS See Total sum of squares (TSS) Two-group discriminant analysis (DA) problem, 460–469 Two independent variables, 434–437 U Ubiquitousness, 18 Unbounded solutions, 34, 37–38 Uncertainty, 563, 581–585 descriptions, 563 sampling, 581–585 Underachievement vs overachievement, 299 820 Index United States Department of Agriculture (USDA) diet planningfood stamp program case, 335–337 Unsystematic/random variation, 411–412 UPS, 18 Upton Corporation production and inventory planning, 85–90 U.S Food and Drug Administration (FDA) drug testing procedures example, 710–711 U.S Postal Service model for evaluating technology alternative (META) example, 617–618 U.S presidential election cases, 405–406, 456 2000 election, 456 campaign stops, 405–406 US Express case, 229–230 USDA See United States Department of Agriculture (USDA) diet planning-food stamp program case Utility functions and theory, 766–773 V Vacation, Inc (VI) call center stafﬁng case, 671–672 Valid models, Van Gogh, Vincent, 11 Variables See also under individual concepts basic, 160 binary, 253–254 branching, 269–271 constraint, 19–20, 22 ﬁrst, 26 plotting techniques, 26–30 second, 26–27 third, 27–28 continuous, 233, 569–570 decision, 19, 22–23 dependent, 5–6 descriptions, 5–6 See also Mathematical models deviational, 299 discrete, 569–570 discrete/categorical, 459 general integer, 253 independent, 5–6 binary, 440 one, 433–434 three, 438–439 two, 434–437 integer, 253–254 random, 559–560 slack, 158–159 structural, 159 Variance, analysis of variance (ANOVA), 427 VBA macro-related issues, 112 Vemuganti, R., 210 VI case See Vacation, Inc (VI) call center stafﬁng case Vigus, B., 113 Visual models, 3, descriptions, problem solving processes, Vollman, T., 389, 617 W Wagner, H., 162 Wait watchers example, 663–665 Wasserman, W., 447 Waste Management example, Waterman, 173 Watson, H., 617 Weatherford, Larry, 638 Weighted moving averages, 492–494 See also Moving averages Weights, criterion, 782–783 Welker, R B., 477 Wella Corporation bill of materials (BOM) case, 406–407 Wendell, R., 210 Wenstop, F., 785 West Virginia Land Trust example, 297 What-if analyses, 562 See also Risk analyses Wheelwright, S., 545 Whinston, Andrew B., 13 Whybark, C., 389, 617 Williams, H., 113 Wilson, J., 12 Windstar Aerospace Company problem, 208–210 Winston, W., 39, 162 Wolsey, L., 276 Wolverine Manufacturing Company cases and problems, 133–134, 385–389 retirement fund case, 133–134 Traveling Salesperson Problem (TSP), 385–389 Wood, K., 113 World Trade Center cleanup case, 722 Worst-case/best-case analyses, 561–563 Y Yellow Freight System, Inc example, 211 Yurkiewicz, Jack, 173–174, 634, 799–800 Z Zahedi, F., 785 Zero, time, 678 [...]... Questions and Problems 711 Cases 720 15 Decision Analysis 724 Introduction 724 Good Decisions vs Good Outcomes 724 Characteristics of Decision Problems 725 An Example 725 The Payoff Matrix 726 Decision Alternatives 727 States of Nature 727 The Payoff Values 727 Decision Rules 728 Nonprobabilistic Methods 729 The Maximax Decision Rule 729 Decision Rule 731 The Maximin Decision Rule 730 The Minimax Regret... This page intentionally left blank 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 let’s begin by discussing exactly what this title means By the very nature of life, all of us must continually make decisions that we hope will solve problems and lead to increased... Benefits of Modeling 3 everyone who uses a spreadsheet today for model building and decision making is a practitioner of management science—whether they realize it or not 1.1 The Modeling Approach to Decision Making The idea of using models in problem solving
and decision analysis is really not new, and certainly is not tied to the use of computers At some point, all of us have used a modeling approach... Regret 735 Sensitivity Analysis 736 The Expected Value of Perfect Information 738 Decision Trees 739 Rolling Back a Decision Tree 740 Using TreePlan 742 Adding Branches 743 Adding Event Nodes 744 Adding the Cash Flows 748 Determining the Payoffs and EMVs 748 Other Features 749 Multistage Decision Problems 750 A Multistage Decision Tree 751 Developing A Risk Proﬁle 753 Sensitivity Analysis 754 Spider Charts... aspects of a decision problem might require the use of judgment and intuition However, it is important to realize that human cognition is often ﬂawed and can lead to incorrect judgments and irrational decisions Errors in human judgment often arise because of what psychologists term anchoring and framing effects associated with decision problems 10 Chapter 1 Introduction to Modeling
and Decision Analysis. .. did you make a bad decision? Certainly not Unforeseeable circumstances beyond your control caused you to experience a bad outcome, but it would be unfair to say that you made a bad decision Good decisions sometimes result in bad outcomes See Figure 1.6 for the story of another good decision having a bad outcome The modeling techniques presented in this book can help you make good decisions, but cannot... Introduction to Modeling
and Decision Analysis Questions and Problems 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 CASE 1.1 What is meant by the term decision analysis? Deﬁne the term computer model What is the difference between a spreadsheet model and a computer model? Deﬁne the term management science What is the relationship between management science and spreadsheet modeling? ... beneﬁts of modeling in general First, the models mentioned earlier are usually simpliﬁed versions of the object or decision problem they represent To study the aerodynamics of a car design, we do not need to build the entire car complete with engine and stereo Such components have little or no effect on aerodynamics So, although a model is often 4 Chapter 1 Introduction to Modeling
and Decision Analysis. .. effects and may exhibit irrationality in decision making due to framing effects As a result, it is best to use computer models to do what they are best at (i.e., modeling structurable portions of a decision problem) and let the human brain do what it is best at (i.e., dealing with the unstructurable portion of a decision problem) FIGURE 1.5 Payoffs $1,500 Alternative A Decision tree for framing effects Initial... the insights needed to make a decision In any event, the insight gained from the modeling process ultimately leads to better decision making 1.3 Mathematical Models As mentioned earlier, the modeling techniques in this book differ quite a bit from scale models of cars and planes, or visual models of production plants The models we will build use mathematics to describe a decision problem We use the term