Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 An Introduction to Management Science Quantitative Approaches to Decision Making Fifteenth Edition David R Anderson Dennis J Sweeney University of Cincinnati Thomas A Williams University of Cincinnati Jeffrey D Camm Rochester Institute of Technology James J Cochran Wake Forest University University of Alabama Michael J Fry Jeffrey W Ohlmann University of Cincinnati University of Iowa Australia Brazil Mexico Singapore United Kingdom United States ● ● ● ● ● Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it Multi-media study-tools such as videos, games, flashcards, crossword puzzles, and more allow you to review and check your understanding of key concepts to help you prepare for quizzes and exams The MindTap eReader is fully optimized for the iPad, provides note-taking and highlighting capabilities, and features an online text-to-speech application that vocalizes the content - providing a fun reading experience Flashcards — use the MindTap eReader’s pre-made flashcards or make your own Then print the cards and get to work CengageNOW Users Achieve Higher Grades Treatment Control Final Course Grades “I love the check your work option Really, when you’re having a hard time figuring out an answer, sometimes working backwards is the best way to understand conceptually what you’re doing wrong.” Brad Duncan University of Utah “[I liked]…the read-a-loud option with the ebook… This helped when first starting a chapter and then when studying for tests.” Jennifer Loughren Student, Northeast Iowa Community College ENGAGED WITH YOU | cengage.com Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it This is an electronic version of the print textbook Due to electronic rights restrictions, some third party content may be suppressed Editorial review has deemed that any suppressed content does not materially affect the overall learning experience The publisher reserves the right to remove content from this title at any time if subsequent rights restrictions require it For valuable information on pricing, previous editions, changes to current editions, and alternate formats, please visit www.cengage.com/highered to search by ISBN#, author, title, or keyword for materials in your areas of interest Important Notice: Media content referenced within the product description or the product text may not be available in the eBook version Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it An Introduction to Management Science: Quantitative Approaches to Decision Making, Fifteenth Edition David R Anderson, Dennis J Sweeney, Thomas A Williams, Jeffrey D Camm, James J Cochran, Michael J Fry, Jeffrey W Ohlmann Senior Vice President, Higher Ed Product, Content, and Market Development: Erin Joyner © 2019, 2016 Cengage Learning, Inc Unless otherwise noted, all content is © Cengage ALL RIGHTS RESERVED No part of this work covered by the copyright herein may be reproduced or distributed in any form or by any means, except as permitted by U.S copyright law, without the prior written permission of the copyright owner Vice President, B&E, 4-LTR, and Support Programs: Mike Schenk For product information and technology assistance, contact us at Cengage Customer & Sales Support, 1-800-354-9706 Senior Product Team Manager: Joe Sabatino For permission to use material from this text or product, submit all Senior Product Manager: Aaron Arnsparger requests online at www.cengage.com/permissions Further permissions questions can be emailed to Content Developer: Anne Merrill permissionrequest@cengage.com Product Assistant: Renee Schnee Senior Digital Content Designer: Brandon Foltz Library of Congress Control Number: 2017953024 Senior Marketing Manager: Nathan Anderson Content Project Manager: D Jean Buttrom Production Service: MPS Limited Senior Art Director: Michelle Kunkler Cover and Text Designer: Beckmeyer Design Cover Image: iStockPhoto.com/tawanlubfah Intellectual Property  Analyst: Reba Frederics  Project Manager: Nick Barrows ISBN: 978-1-337-40652-9 Cengage 20 Channel Center Street Boston, MA 02210 USA Cengage is a leading provider of customized learning solutions with employees residing in nearly 40 different countries and sales in more than 125 countries around the world Find your local representative at www.cengage.com Cengage products are represented in Canada by Nelson Education, Ltd To learn more about Cengage platforms and services, visit www.cengage.com To register or access your online learning solution or purchase materials for your course, visit www.cengagebrain.com Printed in the United States of America Print Number: 01   Print Year: 2017 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it Dedication To My Parents Ray and Ilene Anderson DRA To My Parents James and Gladys Sweeney DJS To My Parents Phil and Ann Williams TAW To My Parents Randall and Jeannine Camm JDC To My Wife Teresa JJC To My Parents Mike and Cynthia Fry MJF To My Parents Willis and Phyllis Ohlmann JWO Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it Brief Contents Preface  xxi About the Authors  xxv Chapter Introduction  Chapter An Introduction to Linear Programming  27 Chapter Linear Programming: Sensitivity Analysis and Interpretation of Solution  84 Chapter Linear Programming Applications in Marketing, Finance, and Operations Management  139 Chapter Advanced Linear Programming Applications  195 Chapter Distribution and Network Models  234 Chapter Integer Linear Programming  291 Chapter Nonlinear Optimization Models  336 Chapter Project Scheduling: PERT/CPM  381 Chapter 10 Inventory Models  417 Chapter 11 Waiting Line Models  461 Chapter 12 Simulation  497 Chapter 13 Decision Analysis  543 Chapter 14 Multicriteria Decisions  613 Chapter 15 Time Series Analysis and Forecasting  654 Chapter 16 Markov Processes  On Website Chapter 17 Linear Programming: Simplex Method  On Website Chapter 18 Simplex-Based Sensitivity Analysis and Duality  On Website Chapter 19 Solution Procedures for Transportation and Assignment Problems  On Website Chapter 20 Minimal Spanning Tree  On Website Chapter 21 Dynamic Programming  On Website Appendices 711 Appendix A Building Spreadsheet Models  712 Appendix B Areas for the Standard Normal Distribution  741 Appendix C Values of e2l  743 Appendix D References and Bibliography  744 Appendix E Self-Test Solutions and Answers to Even-Numbered Exercises  On Website Index 747 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net 748 Index Bounding property, 298 Branches, 547 Branch probabilities, 568–572 Breakeven analysis, 14–15 using Excel for, 24–26 Breakeven point, 15 determining, using Goal Seek tool, 25–26 C Canonical form for minimization problem, 18-12–18-14 Capacitated transshipment problem, 245–247 Capital budgeting, 299–300 Carslon Rezidoe Hotel Group, 204 Categorical variables, 679–680 Centers for Disease Control and Prevention (CDC), 344–345 Central limit theorem, 396n Central Planning Engine (CPE), 60 Chance events, 545 Chance nodes, 545 Citibank, 462 Clp (COIN-OR linear programming), 46 Coal allocation case problem, 189–191 College softball recruiting, 601 Colon notation, 720 Combined-constraint graph, 35, 37, 40, 41 Computer output, interpretation of, 47–48, 94–96 Computer solutions all-integer linear program, 298 Electronic Communications problem, 107–110 goal programming, 624–626 linear programming problems, 46–48, 52–53 minimal spanning tree problem, 20-4 sensitivity analysis, 94–100 Concave functions, 342–343 Conditional constraints, 314 Conditional probability, 570 Conjoint analysis, 309–310 Consequence nodes, 545 Consequences, 545 Conservative approach, 548–549 Consistency, 636–637 Consistency ratio, 636–637 Constant demand rate, 418, 419 Constant supply rate, 427 Constrained nonlinear optimization problems, 339–341 Constraint coefficients, 102–103 Constraints, 7, 29, 30–31, 34–35, 51–52 conditional, 314 corequisite, 314 equality, 17-21–17-22 goal programming, 615–616, 621 greater-than-or-equal-to, 17-17–17-21 k out of n alternatives, 313–314 multiple-choice, 313 mutually exclusive, 313 nonnegativity, 32 redundant, 45 Continuous probability distribution, 502, 506n, 537–539 Continuous review inventory system, 445 Controllable inputs, 8, 499 Convex functions, 342–343 Corequisite constraints, 314 Corporate Average Fuel Economy (CAFE) compliance, 373–375 Costs capital, 419 fixed, 13 goodwill, 430–431 holding, 419–420 marginal, 14 ordering, 420 relevant, 97 setup, 427 sunk, 97 transaction, 370–373 variable, 13 Cost-volume models, 13–14 COUNTIF function, 722–724 County Beverage Drive-Thru case problem, 535–537 Crashing activity times, 400–402 linear programming, 402–404 Crash testing vehicles, 312 Critical activities, 384, 388 Critical chain project management, 398 Critical path, 383–384 algorithm, 389 determining, 385–389 uncertain activity times, 394–395 Critical path method (CPM), 382, 383–390 Cumulative probabilities, 416 Custom discrete distribution, 540 Customer order allocation model, 314–315 CVS Corporation, 418 Cyclical pattern, 660–662 D Damping factor, 696 Danaos Corporation, 260 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net 749 Index Dantzig, George, Data, Data envelopment analysis (DEA), 196–203 linear programming model, 198–203 overview of, 197–198 summary of approach, 203 Data preparation, 9–10 Daugherty Porta-Vac project, 391–398 Decision, Decision alternatives, 545 Decision analysis, 16, 544 baseball, 566 Bayes’ theorem, 568–572 branch probabilities, 568–572 conditional probabilities, 570 conservative approach, 548–549 decision strategy, 562–564 decision trees, 546–547, 560–562, 565–566, 568–572 expected value approach, 550–554, 557–558 hepatitis B treatment, 572 influence diagrams, 545–546, 559–560 joint probabilities, 570 minimax regret approach, 549–550 optimistic approach, 548 posterior probabilities, 559, 570 with probabilities, 550–554 problem formulation, 545–547 risk analysis, 554–555 risk profile, 554–555, 559, 565, 567 with sample information, 559–568 sensitivity analysis, 555–558 states of nature, 556, 558 utility functions, 577–581 utility theory, 572–581 without probabilities, 547–550 Decision making defined, payoff tables, 546 problem solving and, process, 3–5 quantitative analysis, 4–6 Decision nodes, 545 Decision problems multicriteria, single-criterion, Decision science, Decision strategy, 562–564 Decision trees, 546–547, 551–552, 560–562, 565–566 Analytic Solver, 602–612 branches, 602–604 branch probabilities, 568–572 chance nodes, 604–605 probabilities and payoffs, 605–607 Decision variables, 8, 30, 21-7 Definitional variables, 214 Degeneracy, 17-29–17-30, 19-11–19-15 Delta Air Lines, 17-2 Dependent variables, 675 Deterministic model, 9, 436 Deviation variables, 616 Discrete-event simulation, 514, 522–523 Discrete probability distribution, 501–502, 539–540 Discrete uniform distribution, 539 Distribution models, 16 Distribution system design problems, 302–305 Divisibility, 33 Duality, 18-12–18-18 primal problems, 18-16–18-18 primary solutions, 18-16 Dual problems, 18-12, 18-15 Dual values, 92–93, 96–97, 104–105, 344 Dual variables, 18-12 economic interpretation of, 18-14–18-15 Duke Energy, 189–191 Dummy columns, 19-20 Dummy destination, 19-16 Dummy origin, 240, 19-16 Dummy variables, 679, 684 Dynamic programming, 21-2 applications, 21-9 knapsack problem, 21-9–21-15 notation, 21-6–21-9 production and inventory control problem, 21-15–21-19 shortest-route problem, 21-2–21-7 stages, 21-6 Dynamic simulation models, 514 E Earliest finish time, 385 Earliest start time, 385 Economic analysis, of waiting lines, 476–478 Economic order quantity (EOC) model, 418–426 assumptions, 426 constant demand rate, 418, 419 Excel solution, 425–426 how-much-to-order decision, 422–423 optimal order quantity, 459 quantity discounts, 434–436 sensitivity analysis, 424–425 when-to-order decision, 423–424 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net 750 Index Economic production lot size model, 427–430 total cost model, 427–429 EDS, 20-5 Efficient frontier, 350–351 Eisner, Mark, 17 Electronic Communications problem, 105–110 Elementary row operations, 17-11–17-13 Energy Education, Inc., 253 Equality constraints, 17-21–17-22 Erlang, A.K., 462 Error Checking button, 730–731 Evaluate Formulas button, 730 Excel absolute cell references, 731–733 basic workbook operations, 714–716 breakeven analysis using, 24–26 cells, 716–717 colon notation, 720 creating, saving, and opening files, 715–716 data analysis tools, 695–703 EOQ model, 425–426 financial planning, 191–194 forecasting with, 695–703 Forecast Sheet, 703–709 formulas, 716–717 functions, using, 717–721 Goal Seek tool, 25–26 matrix inversion, 16-24 modeling functions, 722–725 models, auditing, 728–732 overview of, 712–714 principles of building good models, 725–728 random variables generation, 502–506 reference, 716–717 relative cell references, 728 scoring models, 652–653 simulations, 501, 506–507 waiting line models, 469–470 workbooks, 712 Excel Solver, 47 assignment problem, 288–290 integer linear programs, 331–334 linear programming, 78–82 nonlinear problems, 375–378 sensitivity analysis, 133–136 transportation problems, 284–286 transshipment problem, 286–288 Expected activity times, 382–390 Expected time, 392 Expected utility (EU), 576 Expected value (EV), 220, 550–551, 557–558 of perfect information, 553–554 of sample information, 568 without perfect information, 553 Expected value approach, 550–554 Expected value of perfect information (EVPI), 553–554 Expected value of sample information (EVSI), 568 Expert Choice, 641 Exponential probability distribution, 465, 538 Exponential smoothing, 672–675, 695–697 Exponential utility function, 580–581, 609–612 Extreme points, 45–46, 47 EZ Trailers, Inc case problem, 651–652 F Fantasy sports, 16-14–16-15 Feasibility, 11, 18-9, 18-10–18-11 Feasible regions/solutions, 35–38, 42–43, 50 Federal Communications Commission (FCC), 217 Financial applications, linear programming, 146–153 Financial planning, 149–153 Excel, 191–194 Finite calling populations, 483–486 First-come, first-served (FCFC), 465 Fixed cost, 13 Fixed cost problem, 300–302 Flow capacity, 257 Ford Motor Company, 312 Forecast error, 664–666 mean, 664–665 mean absolute, 665–666, 667–668 mean absolute percentage, 666, 667–668, 670–671 mean squared, 665–666 Forecasting, 16, 655–656 accuracy, 663–668, 669–671, 671–672 energy needs in utility industry, 655 with Excel data analysis tools, 695–703 Excel Forecast Sheet, 703–709 exponential smoothing, 672–675, 695–697 food and beverage sales, 693–694 linear trend projection, 675–679 lost sales, 694–695 method selection, 662–663 moving averages, 668–671, 675, 695 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net 751 Index new product adoption, 356–360 seasonality, 679–684, 700–703 time series patterns, 656–663 trend projection, 697–700 weighted moving averages, 671–672, 675 Forward pass, 386 Four Corners case problem, 532–534 Functions, Excel for modeling, 722–725 using, 717–721 Fundamental matrix, 16-11–16-12 G Game theory, 216–226 market share, competing for, 216–219 mixed strategy solutions, 219–226 payoff tables, 216–218 pure strategy solutions, 219 two-person, zero-sum games, 216 Gamma distribution, 538 GE Capital, 28–29 General Accountability Office (GAO), 16-2 General Electric (GE), 149–150 General linear programming model assignment problem, 252 shortest route between, 256 transportation problem, 241 transshipment problem, 247 General notation, 59 linear programming, 57–58 GE Plastics (GEP), 100 Global maximum, 342 Global minimum, 342 Global optimum, 342–344 Goal constraints, 621 Goal equations, 615–616, 622–623 Goal programming, 16, 614–621 complex problems, 621–626 computer solution, 624–626 constraints, 615–616, 621 deviation variables, 616 goal equations, 615–616, 622–623 graphical solution, 617–620 model, 620 objective functions, 616–617, 623–624 preemptive priorities, 615, 616–617 Goal Seek tool (Excel), 25–26 Goodwill costs, 430–431 Google, 217 Graphical sensitivity analysis, 86–93 Graphical solutions all-integer linear program, 296 goal programming, 617–620 LP Relaxation, 295–296 maximization problems, 33–45 minimization problems, 50, 51 Graphing, lines, 41–43 Greater-than-or-equal-to constraints, 17-17–17-21 Greedy algorithm, 20–25 H Harbor Dunes Golf Course case problem, 534–535 Harmonic average, 374 Hart Venture Capital (HVC) case problem, 77–78 Hepatitis B treatment, 572 Heracles General Cement Company, 12–13 Heuristics, 19-3 Hewlitt Corporation, 191–194 Hierarchy, 631 Hinds County Realty Partners, Inc case problem, 20-7–20-8 Holding costs, 419–420 Horizontal time series pattern, 656–657 Hospital-acquired infections, 498 How-much-to-order decision, 422–423, 443 Hungarian method, 19-17–19-22 Hypothetical composites, 197–198 I IBM, 60 Iconic models, IF function, 722–724 Incoming arc, 19-6 Incremental analysis, 437–438 Independent variables, 675 Index funds, 345–349 India, tea production and distribution in, 111 Individual Retirement Accounts (IRAs), 209–210 Infeasibility, 11, 54–56, 17-26–17-27, 17-31 Infinite calling population, 483 Influence diagrams, 545–546, 559–560 Inputs controllable, uncontrollable, Integer linear programming, 16, 292–293 0-1 integer variables, modeling flexibility of, 313–315 0-1 variables, applications involving, 298–312 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net 752 Index Integer linear programming (Continued) all-integer linear program, 293, 295–298 bank location, 305–309 capital budgeting, 299–300 distribution system design, 302–305 Excel solution, 331–334 fixed cost problem, 300–302 graphical solutions, 295 integer solutions, rounding to obtain, 295–296 LINGO, 334–335 model types, 293–294 product design and market share optimization, 309–312 Integer solutions, rounding to obtain, 295–296 Integer uniform distribution, 539 Interarrival times, 514–519 Intercontinental Hotels, 337 Inventory management, 418 Inventory models, 16, 418 deterministic, 436 economic order quantity model, 418–426, 434–436 economic production lot size model, 427–430 at Microsoft, 445 order-quantity, reorder point, 441–444, 448 periodic review model, 445–448 with planned shortages, 430–434 probabilistic demand, 436–441 single-period, 436–441 Inventory position, 423–424 Inventory simulation, 510–514 Inventory system continuous review, 445 periodic review, 445–448 Investment strategy case problem, 132 Iterations, 17-10 J Joint probabilities, 570 K Kellogg Company, 163 Kendall notation, 478 Ketron Management Science, 314–315 Kimpton Hotels, 85 Knapsack problem, 21-9–21-15 k out of n alternatives constraint, 313–314 L Latest finish time, 386–388, 395 Latest start time, 385, 387, 395 LAVA, 12 Lawsuit defense strategy, 599 Lead time, 424 Lead-time demand, 424, 426 Lead-time demand distribution, 442, 443 Linear functions, 32 Linear program, 32 Linear programming, 15–16, 28 alternative optimal solutions, 53–54 applications, 28, 140 asset allocation, 209–216 assignment problem, 248–253, 288–290 blending problems, 166–171 computer solutions, 46–48, 52–53 constraints, 29, 30–31, 34–35, 45, 51–52 for crashing, 402–404 data envelopment analysis, 196–203 degeneracy, 17-29–17-30 duality, 18-12–18-18 Excel Solver, 78–82 extreme points, 45–46, 47 feasible regions, 35–38, 42–43, 50 financial applications, 146–153 financial planning, 149–153, 191–194 game theory, 216–226 general notation, 57–58, 59 graphical solution procedure, 33–45, 50, 51 infeasibility, 54–56, 17-26–17-27, 17-31 integer see Integer linear programming LINGO, 82–83 make-or-buy decisions, 153–156 marketing applications, 140–146 marketing research, 143–146 mathematical models, 32, 33 maximal flow problem, 257–260 maximization problems, 29–33 media selection, 140–143 minimization problems, 48–53 models, 32 network flow problems, 235 operations management applications, 153–171 optimal solution, 17-16 optimal solutions, 40–41, 45–46, 17-28–17-29 portfolio models, 209–216 portfolio selection, 146–149 problem formulation, 29–32 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net 753 Index production and inventory application, 260–263 production scheduling, 157–163 profit lines, 38–40 revenue management, 203–209 right-hand-side values, 18-6–18-12 sensitivity analysis, 85–110 shortest-route problem, 253–256 simplex method, 2, 17-2–17-31 slack variables, 44–45 solution points, 33–35 special cases, 53–56 standard form, 44 supply chain models, 235–248 surplus variables, 50–52 transportation problems, 235–241, 284–286 transshipment problems, 242–248, 286–288 unbounded, 56–57 unboundedness, 17-27–17-28, 17-31 workforce assignment, 163–166 Linear trend projection, 675–679 Lines, graphing, 41–43 LINGO, 47 integer linear programs, 334–335 linear programming, 82–83 nonlinear problems, 378–380 sensitivity analysis, 136–138 Little’s flow equations, 475 Local maximum, 341–344 Local minimum, 342–344 Local optimum, 341–344 Log-normal distribution, 537 Lost sales forecasting, 694–695 LP Relaxation, 293–294 graphical solution, 295 using to establish bounds, 296–297 M Machine repair problem, 484–486 Make-or-buy decisions, 153–156 Management science history of, techniques, 15–17 Management Science in Action analytic hierarchy process, 640 asset allocation and variable annuities, 216 assigning consultants to clients, 253 ATM waiting times, 462 benefit of health care services, 16-2 communication network design, 20-5 customer order allocation model, 314–315 decision analysis, 552, 566, 572 determining optimal production quantities, 100 efficiency evaluation, 93 fleet assignment, 17-2 forecasting demand, 663 forecasting energy needs in utility industry, 655 game theory, 217 hospital revenue bond, 390 impact of operations research on everyday living, 17 inventory management, 418 inventory models at Microsoft, 445 linear programming for supply chain operations, 60 marketing planning model, 140 natural resource management, 544 optimization for setting prices on Priceline, 85 optimization of production, inventory, and distribution, 163 optimizing transport of oil rig crews, 292–293 personal financial goal optimization, 209–210 product sourcing heuristic, 248 project management, 398 quantitative analysis and supply chain management, 12–13 reducing patient infections in the ICU, 498 retail pricing optimization, 337 revenue management, 204 revenue management at AT&T Park, 2–3 risk reduction from pandemics using nonlinear optimization, 344–345 route optimization at UPS, 241 sales and operations planning, 157 scheduling of automobile crash tests, 312 scheduling the Virginia Court of Appeals, 294 service efficiency evaluation, 196–197 shale oil production forecasting, 360 shortest paths for containerships, 260 solar energy investment decisions, 149–150 tea production and distribution in India, 111 timber harvesting model, 28 voting machine allocation, 486 Managerial utopia, 56 Marathon Oil Company, 28, 140 Marginal cost, 14 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net 754 Index Marginal revenue, 14 Marketing applications, 140–146 Marketing planning model, 140 Marketing research, 143–146 Market share, 216–219 Market share analysis, 16-2–16-9 Markov chains with stationary transition probabilities, 16-2 Markov decision processes, 16-9 Markov process models, 16, 16-2 absorbing state, 16-11 accounts receivable analysis, 16-10–16-14 fantasy sports, 16-14–16-15 fundamental matrix, 16-11–16-12 market share analysis, 16-2–16-9 matrix notation, 16-21 matrix operations, 16-21–16-23 memoryless property, 16-9 state of the system, 16-3 state probabilities, 16-5–16-7 steady-state probabilities, 16-7–16-9 transition probabilities, 16-3–16-4 trials of the process, 16-3 Markowitz portfolio model, 349–351 Marks, Daniel, 663 Mathematical models, 7, 32, 33 Matrix notation, 16-21 Matrix operations, 16-21–16-23 with Excel, 16-24 inverse, 16-23, 16-24 multiplication, 16-22–16-23 transpose, 16-21–16-22 Maximal flow problem, 257–260 optimal solution, 259 Maximization objective function, 240 Maximization problems, 29–33 Electronic Communications problem, 105–110 graphical solution procedure, 33–45 MeadWestvaco Corporation, 28 Mean absolute error (MAE), 665–668 Mean absolute percentage error (MAPE), 666, 667–668, 670–671 Mean forecast error (MFE), 664–665 Mean squared error (MSE), 665–668 Media selection, 140–143 Memoryless property, 16-9 M/G/1 model, 479–480 M/G/k model, 481–483 Microsoft, 444, 445 Microsoft Excel See Excel Microsoft Project, 390 Minimal spanning tree algorithm, 20-2–20-5 Minimax regret approach, 549–550 Minimization problems, 48–53, 17-24–17-26 Minimum cost method, 19-3–19-6, 19-16 Minimum ratio test, 17-10 MINVERSE function, 16-12 Mixed-integer linear program, 293–294 Mixed strategy solutions, 219–226 M/M/1 model, 483–486 Model development, 7–9 Modeling, 29–32 Models, See also specific models analog, cost-volume, 13–14 deterministic, distribution, 16 iconic, integer linear programming, 293–294 inventory, 16 Markov process, 16 mathematical, network, 16 portfolio, 209–216, 349–351 probabilistic, profit-volume, 14 revenue-volume, 14 scoring, 626–630 stochastic, supply chain, 235–248 Model solution, 10–12 MODI (modified distribution) method, 19-6–19-7, 19-11–19-12 Most probable time, 392, 393 Moving averages, 668–671, 675, 695 weighted, 671–672, 675 Multicriteria decisions, 4, 614 analytic hierarchy process, 630–641 goal programming, 614–626 scoring models, 626–630 Multiple-choice constraints, 313 Multiple-server waiting line model with Poisson arrivals and exponential service times, 470–474 with Poisson arrivals, arbitrary services times, and no waiting line, 481–483 Mutual funds, portfolio of, 210 Mutually exclusive constraints, 313 N Nationwide Car Rental, 440–441 Natural resource management, 544 Neiman Marcus, 437–440 Net evaluation index, 19-7–19-8 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net 755 Index Net evaluation row, 17-8–17-9 Network flow problems, 235 Network models, 16 Networks, 236 New product adoption forecasting, 356–360 Nodes, 236, 545 chance, 545 consequence, 545 decision, 545 shortest route between, 253–256 transshipment, 242 Nonlinear optimization models, 337 blending problems, 352–356 constrained problem, 339–341 dual values, 344 Excel Solver, 375–378 forecasting new product adoption, 356–360 index fund construction, 345–349 LINGO, 378–380 local and global optima, 341–344 Markowitz portfolio model, 349–351 pandemic risk reduction using, 344–345 pooling problem, 352–356 production application, 338–345 unconstrained problem, 338–339 Nonlinear programming, 16 Nonnegativity constraints, 32 Normal distribution, 396n, 397, 416, 537 standard, 737–738 O Objective function, 7–8 dual values, 92–93, 96–97 goal programming, 616–617, 623–624 maximization, 240 slope, 88–90 Objective function coefficients, 18-2–18-6, 87–91 Office Equipment, Inc (OEI) case problem, 495–496 Oil drilling, 552 Operating characteristics, 462 Operations management applications blending problems, 166–171 linear programming, 153–171 make-or-buy decisions, 153–156 production scheduling, 157–163 workforce assignment, 163–166 Operations research, 2, 17 Operations Research in Ship Management (ORISMA) tool, 260 Opportunity loss, 549, 550, 554, 19-21–19-22 Optimal lot size (Q*) formula, 460 Optimal order quantity (Q) formula, 459 Optimal solutions, 10, 40–41, 45–46, 19-6–19-14 alternative, 53–54, 17-28–17-29 Optimistic approach, 548 Optimistic time, 392, 393 Ordering costs, 420 Order-quantity, reorder point model, 441–444, 448 how-much-to-order decision, 443 when-to-order decision, 443–444 ORION, 241 Outgoing arc, 19-6 P Pairwise comparison matrix, 633–634 Pairwise comparisons, 632–633, 637–639 Pandemics, risk reduction from, 344–345 Parameters, 498 Path, 383–384 Payoffs, 546 Payoff tables, 216–218, 546 Perfect information, expected value of, 553–554 Performance Analysis Corporation, 93 Periodic review model more complex, 448 with probabilistic demand, 445–448 Personal financial goal optimization, 209–210 PERT/CPM, 16, 382 contributions of, 389 crashing problem, 402–404 critical path procedure, 383–390 expected activity times, 382–390 Pessimistic time, 392, 393 Petrobras, 292–293 Pipelining, 398 Pittsburgh Supercomputing Center (PSC), 17 Pivot columns, 17-11 Pivot elements, 17-11 Pivot rows, 17-11 Planned shortages, 430–434 Poisson probability distribution, 463–464, 466–474 Pooling problems, 352–356 Portfolio models, 209–216 conservative, 210–213 Markowitz, 349–351 moderate risk, 213–214 mutual funds, 210 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net 756 Index Portfolio optimization, with transaction costs, 370–373 Portfolio selection, 146–149 Posterior probabilities, 559, 570 Postoptimality analysis See Sensitivity analysis Postrel, Virginia, 17 Preemptive priorities, 615, 616–617 Priceline, 85 Price-setting optimization, 85 Primal problems, 18-12, 18-15, 18-16–18-18 Primal solutions, 18-16 Principle of optimality, 21-2 Priority ranking, 639–640 Prior probability, 559 Probabilistic demand order-quantity, reorder point model with, 441–444 periodic review model with, 445–448 single-period inventory model with, 436–441 Probabilistic models, Probabilities decision making with, 550–554 decision making without, 547–550 Probability distribution, 498 continuous, 502, 506n, 537–539 discrete, 501–502, 539–540 exponential, 465, 538 Poisson, 463–464 for random variables, 537–550 uniform, 502, 515, 538 Problem formulation, in decision analysis, 545–547 Problem solving decision making and, defined, process, 3–4 Process design case problem, 21-24–21-25 Procter & Gamble, 248 Product design and market share optimization problem, 309–312 Production and inventory application, 260–263 Production and inventory control problem, 21-15–21-19 Production application, 338–345 Production lot size model, 460 Production model, 9–10, 11 Production scheduling, 157–163 with changeover costs, 329 Production strategy case problem, 76–77 Product mix case problem, 131–132 Product sourcing heuristic, 248 Profit lines, 38–40 Profit models, Profit-volume models, 14 Program evaluation and review technique (PERT), 382 Project completion, variability in, 395–398 Project management, critical chain, 398 Project network, 383, 384 Project scheduling, 16, 382 based on expected activity times, 382–390 time-cost trade-offs, 399–404 uncertain activity times, 391–399 Property purchase strategy, 597–599 Proportionality, 33 Prudential Financial, 216 Pure strategy solutions, 219 Q Qualitative analysis, 5, Quantitative analysis, 6–12 data preparation, 9–10 decision making and, 4–6 implementation, 12 model development, 7–9 model solution, 10–12 report generation, 12 Quantity discounts, 434–436 Queue, 462 Queue discipline, 465 Queueing theory, 462 R RAND functions, 509 Random variables, 498 cumulative probabilities for normally distributed, 416 generating values, with Excel, 502–506 probability distributions for, 537–550 probability distributions to represent, 501–502, 506n Range of feasibility, 18-9, 18-10–18-11 Range of optimality, 87, 18-2–18-6 R C Coleman, 414–415 Redundant constraint, 45 Regional Airlines case problem, 494–495 Regional Healthcare Ecosystem Analyst (RHEA), 17 Regression analysis, 675–679 Regret, 549, 554 Relative cell references, 728 Relevant cost, 97 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net 757 Index Reorder point, 424, 426 Report generation, 12 Retail pricing optimization, 337 Return function, 21-8 Revenue management, 203–209, 337 Revenue-volume models, 14 Right-hand sides, 91–93 Right-hand-side values, 18-6–18-12 Risk analysis, 554–555 Risk avoider, 575, 579–580, 581 Risk-neutral, 579–580 Risk profile, 554–555, 559, 565, 567 Risk taker, 579–580, 581, 577–578 River City Fire Department (RCFD) case problem, 458–459 Rob’s Market (RM), 600–601 Roth IRAs, 209–210 Route capacities, 240 Route minimums, 240 S Saaty, Thomas L., 630 Safety stock, 444 Sales and operations planning, 157 Sample information decision analysis with, 559–568 efficiency of, 568 expected value of, 568 San Francisco Giants, 2–3 Schneider’s Sweet Shop case problem, 185–186 Scientific management revolution, Scoring models, 626–630 Excel, 652–653 Seasonality, 679–684 based on monthly data, 684 with trend, 682–684, 701–703 without trend, 679–682, 700–701 Seasonal patterns, 660 Seasongood & Mayer, 390 Semiconductor supply chains, 60 Sensitivity analysis, 85–86 cautionary note about, 315 computer solutions, 94–100 constraint coefficients, changes in, 102–103 decision analysis, 555–558 dual values, 92–93, 96–97, 104–105 Electronic Communications problem, 105–110 EOQ model, 424–425 Excel Solver, 133–136 graphical, 86–93 introduction to, 86 limitations of classical, 100–105 LINGO, 136–138 objective function coefficients, 87–91, 18-2–18-6 range of optimality, 18-2–18-6 right-hand sides, 91–93 right-hand-side values, 18-6–18-12 with simplex tableau, 18-2–18-12 simultaneous changes, 90–91, 101–102 Service time distribution, 464–465 Setup costs, 427 700-MGz frequency bandwidth, 217 Shale oil production forecasting, 360 Shortages, planned, 430–434 Shortest-route problem, 253–256 dynamic programming, 21-2–21-7 general linear programming model, 256 optimal solution, 256 principle of optimality, 21-2 Show Formulas button, 730 Simon, Herbert A., Simple linear regression, 675–676 Simplex method, 2, 17-2 algebraic overview of, 17-2–17-6 basic feasible solutions, 17-4–17-6 basic solutions, 17-3–17-4 degeneracy, 17-29–17-30 infeasibility, 17-26–17-27, 17-31 iterations, 17-10, 17-13–17-14 minimization problems, 17-24–17-26 optimal solutions, 17-28–17-29 right-hand-side values, 18-6–18-12 summary of, 17-16–17-17 tableau form, 17-6, 17-17–17-24 transportation, 19-2–19-17 unboundedness, 17-27–17-28, 17-31 Simplex tableau, 17-7–17-9, 19-17 basic feasible solutions, 17-14–17-16 calculating next, 17-11–17-17 elementary row operations, 17-11–17-13 improving solution, 17-9–17-11 iteration, 17-10 iterations, 17-13–17-14, 17-20–17-21 net evaluation row, 17-7–17-9 optimal solution, 17-16 pivot elements, 17-11 range of feasibility, 18-9, 18-10–18-11 sensitivity analysis, 18-2–18-12 units columns/vectors, 17-8 Simulation, 16, 399, 498–499 advantages and disadvantages, 524 Analytic Solver, 540–542 considerations, 523–524 discrete-event, 514–515, 522–523 dynamic, 514 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net 758 Index Simulation (Continued) Excel solution, 501, 506–507 inventory, 510–514 output, measuring and analyzing, 507–509 probability distributions, 501–502 random variables, 501–506 Sanotronics problem, 509 static, 514 steps for conducting, 525 validation, 523–524 verification, 523–524 waiting line, 514–523 what-if analysis, 499–500 Simultaneous changes, 90–91, 101–102 Single-criterion decision problems, Single-period inventory model, with probabilistic demand, 436–441 Single-server waiting line, 463 with Poisson arrivals and arbitrary service times, 479–480 with Poisson arrivals and exponential service times, 466–470 Slack, 388 Slack variables, 44–45 Slope-intercept form, 39 Smart phones, Solar energy investment decisions, 149–150 Solution model, 10–12 optimal, 10 Solution points, 33–35, 617 Solutions Plus case problem, 281–282 Spanning tree, 20-2 minimal spanning tree algorithm, 20-2–20-5 Spreadsheet models See also Excel auditing, 728–732 building, 712–732 principles of good, 725–728 Stages, of dynamic programming, 21-6 Stage transformation function, 21-8–21-9 Standard form, 44 Standard normal distribution, 737–738 State of the system, 16-3 State probability, 16-5–16-7 State variables, 21-8 Static simulation models, 514 Stationary time series, 657 Steady-state operation, 465–466 Steady-state probabilities, 16-7–16-9 Stepping-stone method, 19-8–19-11 Stochastic models, Stock-outs, 430 Sunk cost, 97 Supply chain design case problem, 282–284 Supply chain models, 235–248 production and inventory application, 260–263 transportation problem, 235–241 transshipment problem, 242–248 Supply chain operations, 60 Supply chain optimization and planning (SCOP), 12–13 Supply chains, 235 Surplus variables, 50–52 Synthetization, 635–636 System constraints, 621 T Tableau form, 17-6, 17-17–17-24 artificial variables, 17-18–17-20 equality constraints, 17-21–17-22 greater-than-or-equal-to constraints, 17-17–17-21 negative right-hand-side values, eliminating, 17-22 summary of steps to create, 17-22–17-24 Taylor, Frederic W., Textbook publishing case problem, 327–328 Textile mill scheduling case problem, 186–187 Timber harvesting model, 28 Time-cost trade-offs, 399–404 Time series, 656 Time series analysis, 655–656 Time series patterns, 656–663 cyclical, 660–662 horizontal, 656–657 seasonal, 660 stationary, 657 trend, 657–659 trend and seasonal, 660 Time series plot, 656 Tornado diagrams, 559 Total cost model, 427–429 Trace Dependents button, 729–730 Trace Precedents button, 729–730 Transaction costs, portfolio optimization with, 370–373 Transient period, 466 Transition probabilities, 16-3–16-4 Transportation problems, 235–241 Excel solution, 284–286 general linear programming model, 241 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net 759 Index maximization objective function, 240 minimum cost method, 19-3–19-6 optimal solution, 238–239 problem variations, 240 route capacities or route minimums, 240 simplex method, 19-2–19-17 total supply not equal to total demand, 240 unacceptable routes, 240 Transportation simplex method, 19-2–19-17 degeneracy, 19-11–19-14 initial feasible solution, 19-3–19-6, 19-7, 19-16 minimum cost method, 19-16 MODI method, 19-11–19-12 net evaluation index, 19-7–19-8 optimal solutions, 19-6–19-14 problem variations, 19-16 stepping-stone method, 19-8–19-11 summary of, 19-14–19-16 Transportation tableau, 19-2–19-3, 19-17 Transshipment problems, 242–248 capacitated, 245–247 Excel solution, 286–288 general linear programming model, 247 optimal solution, 244–245, 247 problem variations, 245–247 Trend and seasonal pattern, 660 Trend patterns, 657–659 Trend projection, 697–700 Trial-and-error approach, 10–11 Trials of the process, 16-3 Triangular distribution, 538–539 Truck leasing strategy case problem, 133 Two-person, zero-sum games, 216–217, 225–226 Utility functions, 577–580 exponential, 580–581, 609–612 money, 579 Utility industry forecasting, 655 Utility theory, 572–581 U W Unacceptable routes, 240 Unboundedness, 17-27–17-28, 17-31, 56–57 Uncertain activity times, 391–399 Uncertain variables, 498 Unconstrained nonlinear optimization problems, 338–339 Uncontrollable inputs, Uniform probability distribution, 502, 515, 538 Unit columns, 17-8 Unit vectors, 17-8 UPS, 241 U.S Air Force, 398 Utility, 572 expected, 576 Wagner Fabricating Company case problem, 457–458 Waiting line models, 462 blocked customers, 481–483 distribution of arrivals, 463–464 distribution of service times, 464–465 economic analysis, 476–478 Excel solution, 469–470 finite calling populations, 483–486 general relationships for, 475–476 improving waiting line operation, 468–469 infinite calling populations, 483 Kendall notation, 478 managers’ use of, 468 V Validation, 523–524 Variable annuities, 216 Variable cost, 13 Variables artificial, 17-18–17-20, 17-27 binary, 292 categorical, 679–680 decision, 8, 21-7 definitional, 214 dependent, 675 deviation, 616 dual, 18-12 dummy, 679, 684 independent, 675 random, 498, 501–506, 537–550 slack, 44–45 state, 21-8 surplus, 50–52 uncertain, 498 Variance, 392 Variance equation, 392n Verification, 523–524 Verizon Wireless, 217 Vestel Electronics, 157 Virginia Court of Appeals, 294 VLOOKUP function, 724–725 Volume models, 13–14 Voting machine allocation, 486 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net 760 Index Waiting line models (Continued) M/G/1 model, 479–480 M/G/k model, 481–483 M/M/1 model, 483–486 multiple-server, 470–474, 481–483 operating characteristics, 462, 466–468, 471–474, 475 Poisson probability distribution, 466–474, 479–483 queue discipline, 465 single-server, 463, 466–470, 479–480 steady-state operation, 465–466 structure of, 463–466 Waiting line simulation, 514–523 Warner Robins Air Logistics Center (WR-ALC), 398 Watch Window, 731–732 Weighted moving averages, 671–672, 675 What-if analysis, 499–500 base-case scenario, 499–500 best-case scenario, 500 worst-case scenario, 500 When-to-order decision, 423–424, 443–444 Workbooks, 712 Workforce assignment, 163–166 Workforce scheduling, 187–188 Workload balancing case problem, 75–76 Worst-case scenario, 500 Y Yeager National Bank (YNB) case problem, 328–329 Yield management, 85 Z 0-1 linear integer program, 294 applications, 298–312 bank location, 305–309 capital budgeting, 299–300 conditional and corequisite constraints, 314 distribution system design, 302–305 fixed cost problem, 300–302 k out of n alternatives constraint, 313–314 modeling flexibility, 313–315 multiple-choice and mutually exclusive constraints, 313 product design and market share optimization, 309–312 Zero-sum games, 216–217, 225–226 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it www.downloadslide.net Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part WCN 02-200-203 Copyright 2019 Cengage Learning All Rights Reserved May not be copied, scanned, or duplicated, in whole or in part Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s) Editorial review has deemed that any suppressed content does not materially affect the overall learning experience Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it ... edition of Management Science and want to explain them and why they were made Many changes have been made throughout the text in response to suggestions from instructors and students While we cannot... any time if subsequent rights restrictions require it Dedication To My Parents Ray and Ilene Anderson DRA To My Parents James and Gladys Sweeney DJS To My Parents Phil and Ann Williams TAW To. .. management science in their organizations An Introduction to Management Science: Quantiative Approaches to Decision Making, 15e is applications oriented and continues to use the problem-scenario approach