An introduction to management science quantitive approaches to decision making 14e by anderson

www.downloadslide.net www.downloadslide.net An Introduction to Management Science: to Quantitative Approaches Decision Making 14e Copyright 2016 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 2016 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 An Introduction to Management Science: to Quantitative Approaches Decision Making 14e Thomas A Williams David R Anderson Rochester Institute of Technology Dennis J Sweeney University of Cincinnati University of Cincinnati Jeffrey D Camm University of Cincinnati Michael J Fry University of Cincinnati Jeffrey W Ohlmann James J Cochran University of Iowa University of Alabama Australia     Brazil     Mexico     Singapore     United Kingdom     United States ● ● ● ● ● Copyright 2016 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 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 Copyright 2016 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 An Introduction to Management Science: Quantitative Approaches to Decision Making, Fourteenth Edition David R Anderson, Dennis J Sweeney, Thomas A Williams, Jeffrey D Camm, James J Cochran, Michael J Fry, Jeffrey W Ohlmann Vice President, General Manager, Science, Math and Quantitative Business: Balraj Kalsi Product Director: Joe Sabatino © 2016, 2012 Cengage Learning WCN: 02-200-203 ALL RIGHTS RESERVED No part of this work covered by the copyright herein may be reproduced, transmitted, stored, or used in any form or by any means graphic, electronic, or mechanical, including but not limited to photocopying, recording, scanning, digitizing, taping, Web distribution, information networks, or information storage and retrieval systems, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without the prior written permission of the publisher Product Manager: Aaron Arnsparger Sr Content Developer: Maggie Kubale Content Coordinator: Eileen Corcoran Sr Product Assistant: Brad Sullender Marketing Manager: Heather Mooney Content Project Manager: Jana Lewis Media Developer: Chris Valentine Manufacturing Planner: Ron Montgomery Production Service: MPS Limited Sr Art Director: Stacy Shirley Internal Designer: Mike Stratton/Chris Miller Design Cover Designer: Beckmeyer Design Cover Image: iStockphoto.com/alienforce For product information and technology assistance, contact us at Cengage Learning Customer & Sales Support, 1-800-354-9706 For permission to use material from this text or product, submit all requests online at www.cengage.com/permissions Further permissions questions can be emailed to permissionrequest@cengage.com Unless otherwise noted, all items â Cengage Learningđ Library of Congress Control Number: 2014947009 ISBN: 978-1-111-82361-0 Cengage Learning 20 Channel Center Street Boston, MA 02210 USA Intellectual Property Analyst: Christina Ciaramella Project Manager: Betsy Hathaway Cengage Learning is a leading provider of customized learning solutions with office locations around the globe, including Singapore, the United Kingdom, Australia, Mexico, Brazil, and Japan Locate your local office at: www.cengage.com/global Cengage Learning products are represented in Canada by Nelson Education, Ltd To learn more about Cengage Learning Solutions, visit www.cengage.com Purchase any of our products at your local college store or at our preferred online store www.cengagebrain.com Printed in the United States of America Print Number: 01  Print Year: 2014 Copyright 2016 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 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 2016 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 2016 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 Brief Contents Preface  xxi About the Authors  xxv Chapter Introduction  Chapter An Introduction to Linear Programming  30 Chapter Linear Programming: Sensitivity Analysis and Interpretation of Solution  94 Chapter Linear Programming Applications in Marketing, Finance, and Operations Management  154 Chapter Advanced Linear Programming Applications  216 Chapter Distribution and Network Models  258 Chapter Integer Linear Programming  320 Chapter Nonlinear Optimization Models  369 Chapter Project Scheduling: PERT/CPM  418 Chapter 10 Inventory Models  457 Chapter 11 Waiting Line Models 506 Chapter 12 Simulation  547 Chapter 13 Decision Analysis  610 Chapter 14 Multicriteria Decisions  689 Chapter 15 Time Series Analysis and Forecasting  733 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 Appendixes  787 Appendix A Building Spreadsheet Models  788 Appendix B Areas for the Standard Normal Distribution  815 Appendix C Values of e2l  817 Appendix D References and Bibliography  819 Appendix E Self-Test Solutions and Answers to Even-Numbered Problems  821 Index  863 Copyright 2016 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 2016 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 2016 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 Index “Note: entries with n indicate notes Chapters 16 through 21 are found on the accompanying website and are indicated by the chapter number followed by the page number (i.e 17-5).” A ACCO Brands Co., 743 Accounts receivable analysis, 16-11, 16-16 absorbing states, 16-12–16-13 fundamental matrix, 16-12–16-15 total balance method, 16-11 Activities, project scheduling (PERT/ CPM), 419–420 Activity times, 443 accurate, 429n crashing, 437n, 438–442 estimates, 430–431 expected, 430, 432 project scheduling, 429, 429n, 434 uncertain, 430–431, 432n Additivity, 36n Advertising campaign case problem, 204–205 Advertising media See Media selection All-integer linear programming, 321–322, 347 computer solutions, 324, 327–328 graphical solutions, 324–327 Alternative optimal solutions, 59-60, 17-31–17-32 American Airlines, 2–3, 225–226 American Red Cross, 217–218 American Skandia, 238 Analog models, Analytic hierarchy process (AHP), 17, 690, 708–709 consistency ratio, 714–715 environmentally sustainable transportation routes, 718–719 graphical representations, 709 pairwise comparisons, 710–714, 716–717 priority development, 709–710, 714, 717 priority ranking, 717–718 software packages, 719n synthesization, 713, 716 Analytic Solver Platform (ASP), 603–609, 672–682 Arcs, 261 Arrival rates, 509 Artificial variables, 17-20–17-24, 17-34 Asset allocation, 231, 238 Assignment problems, 274–279, 291, 19-2 Excel solution, 317–319 Hungarian method, 19-18–19-22, 19-24 maximization objectives, 19-22–19-23 opportunity loss, 19-23–19-24 optimal solution, 276–277 problem variations, 277 unacceptable assignments, 19-24 AT&T Park (San Francisco), Automatic teller machines (ATMs), waiting times, 507–508 B Backorders, 471–475 Backward pass, 424 Baseball decision analysis, 635 Basic feasible solution, 17-4–17-7, 17-20–17-21 Basic solutions, 17-4 Bass, Frank, 391 Bass forecasting model, 391–395, 396n Bayes’ theorem, 638, 641 Bellman, Richard, 21-2 Beta probability distribution, 431 Better Fitness, Inc (BFI), 85–86 Bickel, J Eric, 635 Binary expansion, 343n Binary variables, 321 Blending problems, 184–189 case problem, 205–206 Copyright 2016 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 866 Index Blending problems (Continued ) optimal solution, 188–189 pooling, 386–390 Bombardier Flexjet, 370–371 Bounding property, 326–327 Branches in decision trees, 614–615 probabilities, 638–641, 677–679 Breakeven analysis, 15–16 Excel and, 26–29 Breakeven point, 16 C Canonical form, 18-13–18-14 Capacitated transportation problem, 265 Capacity planning, 549 Capital, cost of, 460 Capital budgeting problems, 328–329 Carlson Department Store, 777–778 Categorical variables, 761–762 Central Planning Engine (CPE), 67 Citibank, 507–508 Coal allocation case problem, 209–211 Combined-constraint graph, 41 Concave functions, 375–376 Conditional constraints, 345 Conditional probability, 638–640 Conjoint analysis, 340–341 Constant demand rate, 459 Constant supply rate, 468 Constrained nonlinear optimization problems, 371–375 Constraint line, 38–39 Constraints, 8, 32–35 coefficients, 114 equality, 17-23, 18-8 goal, 697n greater-than-or-equal-to, 17-19–17-20, 18-7 right-hand sides, 102–104, 18-6 system, 697n Consultant assignments, 278–279 Containership travel routes, 287 Continuous review inventory system, 488 Contour lines, 374 Controllable inputs, 9, 548 Convex functions, 376–377 Cook County Hospital, 568 Corequisite constraints, 345 Corporate Average Fuel Economy (CAFE) regulations, 378, 410 County Beverage Drive-Thru, Inc case problem, 595–597 Crashing, 428n, 437n, 438–442 Credit card payment case problem, 361–362 Critical activities, 422, 426n Critical chain project management (CCPM), 437 Critical path method (CPM), 419, 422, 424, 426n, 427–428, 432, 438 Cumulative probabilities, 435 Excel and, 455–456 Customer order allocation model, 346 CVS, 458 Cycle service levels, 488n Cycle time, 465 D Danaos Co., 287 Dantzig, George, 2, 32n, 17-3 Darby Co., 311–312 Data envelopment analysis (DEA), 217–225 optimal solution, 223–224 Data preparation, 10–11 Decision analysis, 17, 611, 653 baseball, 635 chance events, 612 chance nodes, 613 consequence nodes, 613 consequences, 612 conservative approach, 616 decision alternatives, 612, 617 decision nodes, 613 decision strategy, 632 decision trees, 614–615, 619–620, 629–631, 633–636 expected utility (EU), 646–648 expected value approach, 618–619 expected value of perfect information (EVPI), 621–622 expected value of sample information (EVSI), 637 Hepatitis B treatment, 642 influence diagrams, 613, 628 minimax regret approach, 616–617 models, 7–10 Copyright 2016 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 867 Index Decision analysis (Continued ) natural resources management, 611–612 oil exploration, 621–622 opportunity loss, 616 optimistic approach, 615 payoff tables, 613 perfect information, 621 posterior probabilities, 628 probabilities, 624–627 probabilities, without, 615 problem formation, 612 qualitative analysis, 6–7 quantitative analysis, 5–10 regret, 616 risk analysis, 622–623 risk avoiders, 645, 650 risk-neutral, 651 risk profile, 622–623, 636–637 risk takers, 648, 650 sample information, 627–629, 638 sensitivity analysis, 622–627 states of nature, 612, 627 utility function for money, 650 utility theory, 642–646, 649–652 Decision making, 3–5 mathematical models, 7–8 quantitative analysis, 6–7 simulation, 548–549 Decision Sciences Institute (DSI), 19 Decision strategies, 632 Decision trees, 612, 614–615, 619–620, 629–631, 633–636 Analytic Solver Platform (ASP), 672–682 branches, 614–615, 638–641, 677–679 exponential utility functions, 680–681 TreePlan, 683–688 Decision variables, 9, 11, 64 Decomposition methods, 370 Definitional variables, 236n Degeneracy, 112n , 17-28, 17-32, 17-33–17-34, 19-12–19-13, 19-16 Delta Air Lines, 17-2 Deterministic inventory models, 458 Deterministic models, 10 Deviation variables, 692 Digital Imaging (DI), 84 DIRECTV, 395–396 Discrete-event simulation models, 568 Distribution and network models, 17 Distribution channels, 118 Distribution system design problems, 333–335 computer solutions, 336 graphical solutions, 334 Divisibility, 36n Drillinginfo, 620 Dual prices, 18-6–18-9 Dual problems, 18-13, 18-15–18-17 optimal solution, 18-15 Dual values, 103–104, 108, 378 nonintuitive, 114–117 Dual variables, 18-14, 18-16–18-20 Duke Energy, 734 Duncan Industries Ltd (DIL), 124 Dynamic programming, 95, 371, 21-2 divide and conquer solution strategy, 21-10n knapsack problem, 21-10–21-16 notation, 21-6–21-8, 21-10 production and inventory control problem, 21-16–21-20 shortest-route problem, 21-2–21-6 stages, 21-6–21-10 Dynamic simulation models, 568 E Earliest finish times, 424–425, 433 Earliest start times, 433 Economic order quantity (EOQ) model, 461, 466–467 constant demand rate, 459 Excel solution, 466–467 holding costs, 460 order quantity, 463, 504 quantity discounts, 476–478 sensitivity analysis, 465 Economic production lot size model, 470–471 constant supply rate, 468 total cost model, 469, 471 Edmonton Folk Festival, 343–344 EDS, 20-5 Efficiency evaluation, 104 Efficient frontier, 385 Eisner, Mark, 18 Copyright 2016 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 868 Index Electronic communications problem, 118–123 Elementary row operations, 17-12–17-13, 17-17 Energy Education, Inc (EEI), 278–279 Environmental compliance pricing, 378 Environmentally sustainable transportation routes, 718–719 Equality constraints, 17-23 Erlang, A K., 507 Evaluating efficiency, 104 Excel break even analysis, 26–29 cumulative probabilities, 455–456 exponential smoothing, 780–781 forecasting, 778–786 integer linear programs, 364–367 linear programming, 89–91 linear trend projection, 780–783 matrix inversion, 16-26–16-27 moving averages, 778–780 scoring models, 731–732 seasonality, 783–786 TreePlan, 683–688 trend patterns, 785–786 waiting-line models, 515–516 Excel Solver, 53, 63, 105n assignment problems, 317–319 electronic communications problem, 120 financial planning, 212–215 integer linear programs, 364–367 linear programming, 91–93 nonlinear optimization problems, 414–417 sensitivity analysis, 149–151 transportation problem, 312–314 transshipment problem, 314–317 Expected time, 430, 432 Expected values, 243 Expert Choice (software), 719n Exponential probability distribution, 510 Exponential smoothing, 753–756 Excel and, 780–781 smoothing constants, 753–756 spreadsheets, 756n Exponential utility functions, 651–652, 680–682 Extreme points, 50–52 F Fantasy sports, 16-16 Feasibility studies, 429 Feasible regions, 40–42, 51–52 Feasible solutions, 39–40, 48, 249 Federal Communications Commission (FCC), 238–239 Financial applications Excel, 212–215 linear programming, 162, 165 optimal solution, 169 portfolio selection, 162–165 Financial planning, 165–169 case problem, 592–593 optimal solution, 168 Financial portfolio theory, 165n Finite calling populations, 530–533 Fixed costs, 14, 329–330 Flight reservations case problem, 543–544 Flow capacity, 283 Forecast error, 744–745, 747–748 mean absolute deviation (MAD), 746 mean absolute error (MAE), 745, 752 mean absolute percentage error (MAPE), 746, 752 mean forecast error (MFE), 745 mean squared error (MSE), 746, 752 Forecasting, 17, 391–395 accuracy of, 743–745, 747–749, 752–753, 756 case problem, 776–778 categorical variables, 761–762 Excel and, 778–786 exponential smoothing, 753–756 linear trend projection, 757–760 lost sales, 777–778 moving averages, 749–751 moving averages, weighted, 752–753 naïve, 744–745, 747 new product adoption, 391–396 qualitative, 734 quantitative, 734 restaurant sales, 776–777 seasonal, 761–767 selection of, 742–743 strategic, 743 time series methods, 734–742 time series plots, 735, 737, 742, 751, 757, 762, 765, 767 Copyright 2016 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 869 Index Forward pass, 424 Freight cars assignment, 266 G GamesaEólica, 718–719 Game theory, 238–240 FCC auction, 239 mixed strategy solutions, 242–249 pure strategy solutions, 241–242 Gantt, Henry L., 419n Gantt Chart, 419n General Electric (GE), 165–166 General Motors, 378 GE Plastics (GEP), 112 Global manufacturing, 112 Global maximum, 375 Global minimum, 375 Global optimum, 375 Goal constraints, 697n Goal programming, 17, 690, 719–720 complex problems, 698–700 computer solutions, 701–703 constraints for, 691–693, 697n deviation variables, 692 goal equations, 691–693, 699–700 graphical solutions, 694–697 objective functions, 693, 700–701 preemptive priorities, 691, 693 target values, 691 Golf tee time sales, 593–595 Goodwill costs, 472 Graphical sensitivity analysis, 97 Graphing lines, 46–48 Greedy algorithm, 20-5n H Harbor Dunes Gold Course case problem, 593–595 Harmonic average, 410–412 Harrah’s Cherokee Casino & Hotel, 226 Hart Venture Capital (HVC), 86–87 Hepatitis B treatment decision analysis, 642 Heracles General Cement Co., 13 Heuristics, 273, 19-2–19-3 Hewlett-Packard, 458 Holding costs, 460, 462, 468 total cost model, 470 Horizontal patterns, 735 Hospital revenue bonds, 428 Hungarian method, 19-18, 19-24 dummy columns, 19-22 dummy rows, 19-22 matrix reduction, 19-19–19-21 Hydro Inasa, 718–719 Hypothetical composites, 219, 224 I IBM, 66–67, 458 Ice cream blending case problem, 205–206 Iconic models, Immediate predecessors, 420 Implementation, 13 Incremental analysis, 480 Index funds, 378–379, 383 Industrial chemicals case problem, 309–310 Infeasibility, 60–62, 63n, 249, 17-28–17-30, 17-34n Infinite calling population, 530 Influence diagrams, 613, 628 Institute for Operations Research and the Management Sciences (INFORMS), 19 Insurance companies, 238 Integer linear programming, 17, 321–322 331–332, 335, 343n applications, 348 computer solutions, 327 conjoint analysis, 340–341 Excel solution, 364–367 LINGO, 327, 368 optimal solution, 343 sensitivity analysis, 347 share of choices problem, 342–343 software packages, 348 0-1 linear integer programs, 323, 328, Integer variables, 321 Intel Co., 549 Intensive care unit simulation, 568 Interarrival times, 569–578 Internet sales case problem, 363–364 Inventory models, 17, 458 assumptions, 467 backorders, 471–475 constant demand rate, 459 Copyright 2016 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 870 Index Inventory models (Continued ) constant supply rate, 468 cost of capital, 460 cycle time, 465 deterministic, 458, 478 economic order quantity (EOQ), 459, 460n, 461, 465, 467 economic production lot size, 468–471 Excel solution, 466–467, 567 goodwill cost, 472 holding costs, 460, 462, 468, 470 lot size, 468 multiperiod order-quantity, reorder point, 484 ordering cost, 460, 468 order quantity, 463–464, 485 probabilistic, 458, 478–485, 488–492 quantity discounts, 476 reorder point, 464, 486 safety stock, 487 setup costs, 468 shortages/stock-outs, 471 single-period, 478–483 total cost models, 469, 472, 492 weekly review system, 458 Inventory position, 464, 488 Inventory simulation, 563–567 Investment opportunities case problem, 86–87 Investment strategy case problem, 147–148 Iterations, 17-11, 17-15, 17-22–17-23, 17-28 J Jeppesen Sanderson, Inc., 174 Joint probabilities, 640 K Kellogg Co., 181 Kellogg Planning System (KPS), 181 Kendall, D G., 525 Kendall notation, 525 Ketron Management Science, 346–347 Kimpton Hotels, 95 Knapsack problem, 21-10–21-16 Koopmans, Tjalling, 32n K out of n alternatives constraints, 345 L Latest finish times, 424–425, 433 Latest start times, 433 Lawsuit defense strategy case problem, 671–672 Lead-time demand, 464 Lead-time demand distribution, 486 Linear functions, 36 Linear programming, 17, 155 alternative optimal solutions, 59–60, 17-31–17-32 applications, 31–32, 155 assignment problems, 274–279, 291, 19-2, 19-18–19-24 basic feasible solutions, 17-4–17-7, 17-21 blending problems, 184–189 computer solutions, 52–54, 58–59, 108–111 constraints, 32, 17-19–17-20, 17-23–17-24 crashing, 441–442 data envelopment analysis (DEA), 217–225 decision-making, 189 degeneracy, 17-28, 17-32–17-34 duality, 18-18–18-20 dual prices, 18-6–18-9 dual problems, 18-13, 18-15, 18-17 dual variables, 18-14, 18-16–18-17 Excel, 89–91 Excel Solver, 53, 63, 91–93 extreme points, 50–52 feasible regions, 40–42, 51–52 financial applications, 162 financial planning, 165–169 game theory, 238–249 goal programming, 693–697, 719–720 graphical solutions, 37–41, 46–49, 55–56, 65, 97–102 infeasibility, 60–62, 17-28–17-30 LINGO, 52, 63, 87–89 make-or-buy decisions, 169–173 marketing research, 159–161 maximizing quantity, 32 media selection, 156–158, 159n minimizing quantity, 32, 54 models, 36, 66 Copyright 2016 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 871 Index Linear programming (Continued ) negative right-hand-side values, 17-24–17-25 network flow problems, 259 notation, 64–65 operations management applications, 169–170 optimal solution, 11, 45–46, 50–51, 96, 17-18, 18-15 portfolio models, 231–237 primal problems, 18-13, 18-15, 18-17–18-19 primal solutions, 18-17 production scheduling, 173–180 revenue management, 225–231 right-hand sides, 18-6 sensitivity analysis, 95–97, 18-2 shortest-route problem, 279–283 simplex method, 17-2–17-4, 17-18–17-19, 17-28, 17-34–17-35, 18-3, 18-6, 18-8 solution points, 37, 45–46 standard form, 49 supply chains, 259–264, 273n surplus variables, 57 tableau form, 17-7, 17-25–17-26 transportation models, 265–287 transportation problems, 259–264, 290, 19-2–19-9, 19-11–19-18 transportation simplex method, 19-2 transshipment problem, 272 unboundedness, 62–63, 17-30–17-31 workforce assignment, 180–184 Linear trend projection, 757–760, 764–765 Excel and, 782–783 LINGO, 52, 63 electronic communications problem, 120 integer linear programs, 327, 368 linear programming, 87–89 nonlinear optimization problems, 412–414 sensitivity analysis, 151–153 Solver, 327 Little, John D C., 521 Little’s flow equations, 521–522 Local maximum, 375 Local minimum, 375 Local optimum, 375 Location problems, 338–340 Lot size, 468 LP relaxation, 322–323 bounds, 326–327 graphical solutions, 325–327 M Machine repair problem, 532–533 Make-or-buy decisions, 169–173 case problem, 501–503 Management science history of, methods of, techniques, 17–18 Marathon Oil Co., 155 Marginal costs, 14–15 Marginal revenue, 15 Marketing planning models, 155 Marketing research, 159–161 Market share analysis, 16-3–16-10, 16-16 Markov chains with stationary transition probabilities, 16-2 Markov decision processes, 16-10n Markov process models, 18, 16-2, 16-16 absorbing states, 16-12–16-13 accounts receivable analysis, 16-11–16-14 case problem, 16-22–16-23 fantasy football, 16-16 first-order, 16-10n fundamental matrix, 16-12–16-15 higher-order, 16-10n memoryless property, 16-10n probabilities, 16-2–16-12 state of the system, 16-3 state probabilities, 16-5–16-8 steady-state probabilities, 16-8–16-9 transition probabilities, 16-3–16-4, 16-9–16-10, 16-12, 16-14–16-15 trials of the process, 16-3 Markowitz, Harry, 231n, 383 Markowitz mean-variance portfolio model, 370, 383–385, 396 Master problems, 370 Mathematical models, 7–9, 19, 36 Mathematical programming models, 36n Matrix inversion, 16-26 Excel and, 16-26–16-27 Copyright 2016 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 872 Index Matrix multiplication, 16-24–16-26 Matrix notation, 16-23–16-24 Matrix operations, 16-24–16-26 Matrix reduction, 19-19–19-21 Matrix transpose, 16-24 Maximal flow problem, 283–286, 291 optimal solution, 285–286 Maximin strategies, 244 Maximization objective function, 264 Maximization objectives, 19-22–19-23 Maximization problems, 32–35, 17-30–17-31, 18-6, 18-13 graphical solutions, 48 MeadWestvaco Co., 31 quantitative analysis, 31 Media selection, 156–158, 159n Microsoft, 487, 488n Microsoft Project, 428n Minimal spanning tree algorithm, 20-2–20-4, 20-5n Minimax strategies, 241, 247 Minimization problems, 54–56, 17-26–17-27, 17-34, 18-8, 18-14 graphical solutions, 56–57 Minimum cost method, 19-3, 19-5–19-6, 19-17 Mixed-integer linear programming, 321, 323, 347 graphical solutions, 346 Mixed strategy solutions, 242–249 Model development, 7–12 Modeling, 33 Model solution, 7, 11–12 Modified distribution method (MODI), 19-7, 19-11–19-12, 19-15 Monte Carlo simulation, 562n Morningstar Asset Allocator, 238 Most probable time, 430 Moving averages, 749–751 Excel and, 778–780 weighted, 752–753 Multicriteria decision problems, analytic hierarchy process (AHP), 708–718 goal programming, 690–693, 698 scoring models, 704–707 Multiperiod order-quantity, reorder point inventory models, 484 Multiple-choice constraints, 344–345 Multiple linear regression, 761 Multiple-server waiting line, 516–521 Kendall notation, 525 Poisson probability distribution, 528–530 Mutual funds, 231–232, 378–380 Mutually exclusive constraints, 345 N Nationwide Car Rental, 482–483 Neiman Marcus, 479–481 Net evaluation index, 19-8–19-9 Net evaluation rows, 17-9–17-11 Network flow problems, 259, 290 Network graphs, 260 Noninteger solutions, 325 Nonintuitive dual values, 114–117 Nonlinear optimization problems, 370, 396 constrained, 372–375 dual values, 378 Excel Solver, 414–417 global optimal solutions, 375, 377 lawsuit defense strategy case problem, 671–672 LINGO, 412–414 local optimal solutions, 375, 377 unconstrained, 371–373 Nonlinear programming, 17 Nonnegativity constraints, 35 Normal distribution, 435n North American Product Sourcing Study, 273 Notation, 64–65 O Objective function, Objective function coefficients, 97–102, 105n, 18-2–18-3, 18-5–18-6, 18-20 Office Equipment, Inc (OEI) case problem, 544–546 Ohio Edison, 611 Oil exploration decision analysis, 620 Opaque marketing, 95 Operating characteristics, 507, 511–513 Operations management applications, 169–170 Copyright 2016 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 873 Index Operations research, 18–19 Operations Research in Ship Management (ORISMA), 287 Opportunity loss, 622n, 19-23–19-24 Optimal lot size formula, 504 Optimal primal solutions, 18-17–18-18 Optimal production quantities, 112 Optimistic time, 430 Ordering cost, 460, 462, 468 Order-picking system case problem, 454–455 Order quantity, 463, 485 P Pairwise comparisons, 710 consistency ratio, 714–715 matrix, 711–713, 717 priority ranking, 717–718 synthesization, 713, 716 Pareto optimality, 104 Paths, 422 Payoff tables, 613 Performance Analysis Co., 104 Performance evaluation, 218–225 Performance feedback, 217–218 Periodic review inventory system, 488, 491 PERT/CPM, 419–428, 429n, 430, 441, 443 Pessimistic time, 430 Petrobras, 321–322 Pfizer, 562 Phytopharm, 582 Pipelining, 437 Pivot columns, 17-12 Pivot elements, 17-12 Pivot rows, 17-12 Poisson probability distribution, 509–511, 516, 522, 525, 527–529 Pole-centric manufacturing, 112 Pooling problems, 386–391 Portfolio models, 231, 238, 378–382 conservative, 232–234 Markowitz mean-variance portfolio model, 383–385 moderate risk, 234–237 optimal solution, 236–237 Portfolio optimization case problem, 407–410 Portfolio selection, 162–165 optimal solution, 163–165 Postoptimality analysis, 95, 123 Preprocessing routines, 236n Priceline.com, 95 Price setting, 95 Primal problems, 18-13, 18-15–18-17 finding the dual, 18-18–18-19 optimal solution, 18-15 Principle of optimality, 21-2 Probabilistic inventory models, 458 Probabilistic models, 10 Problem formulation, 33–35 Problem-solving, 3–5 dynamic programming, 21-2, 21-10n, 21-20–21-21 quantitative analysis, 6–7 Process design case problem, 21-26–21-27 Proctor & Gamble (P&G), 273 Product design and market share optimization problems, 340 Production and inventory, 287–291, 21-16–21-20 optimal solution, 290 simulation models, 562 Production lot size model, 504–505 Production planning, 174, 181 Production scheduling, 173–181 case problem, 206–208, 362–363, 730–731 Production strategy case problem, 85–86 Product mix case problem, 146–147 Product Sourcing Heuristic (PSH), 273 Profit-volume models, 15 Program evaluation and review technique (PERT), 419–427, 430–437 Project network, 420–422, 428–429, 433, 443 Project scheduling (PERT/CPM), 17, 419–437 Property purchase strategy case problem, 670–671 Proportionality, 36n Pure strategy solutions, 241–242, 249n Q Qualitative analysis, Quantitative analysis, 2, 5–7 data preparation, 10–11 mathematical models, 7–11 Copyright 2016 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 874 Index Quantitative analysis (Continued) model development, 12 report generation, 12–13 results, 13 supply chain management, 13 Quantity discounts, 476–478 Queueing models, 17, 507 Queueing theory, 507 R Random numbers, 554–556 Random variables, 455–456 Range of feasibility, 108, 18-9–18-13, 18-20 Range of optimality, 97, 18-2–18-3, 18-5–18-6 R C Coleman case problem, 454–455 Reduced cost, 107 Redundant constraints, 50 Regional Airlines case problem, 543–544 Regression analysis, 757–760 dependent variables, 758, 761 independent variables, 758, 761 multiple linear regression, 761 simple linear regression, 758 software packages, 761n Relevant costs, 108 Reorder point, 464, 486 Report generation, 12, 26 Return function, 21-9 Revenue management, 2–3, 95, 225–231 optimal solution, 229–230 Right-hand sides, 102–104 Risk analysis, 550 River City Fire Dept., 503 Rounding, 325–326 Route capacities, 264–265 Route minimums, 264–265 S Saaty, Thomas L., 708 Saddle points, 241 Safety stock, 487 Sales, lost, 777–778 Sales forecasts, 743 Sample information, 627–629 San Francisco Giants, 2–3 Saudi Basic Industries Co (SABIC), 112n Scheduling case problem, 25–26 Scheduling flights/crews, 370–371 Scoring models, 690, 704–707 Excel and, 731–732 Seasonality, 761–767 Excel and, 783–785 Seasongood & Mayer, 428 Sensitivity analysis, 95–97, 104, 347, 465, 624 computer solutions, 105–108, 112, 123 constraint coefficients, changes in, 114 decision analysis, 622–627 economic order quantity (EOQ), 465 Excel Solver, 149–151 graphical solutions, 97–102 LINGO, 151–153 nonintuitive dual values, 114–117 range of optimality, 18-2, 18-20 simplex tableau, 18-2–18-5 simulation, 582 simultaneous changes, 113–114 Service contracts case problem, 544–546 Service levels, 488n Service rate, 510 Service times, 510, 570–574, 576–578 Setup costs, 329, 468 Shadow price, 105n Share of choices problems, 342–343 Shortages/stock-outs, 471 Shortest-route problem, 279–283, 287, 291, 21-2–21-6 optimal solution, 281–282 Simplex method, 17-2–17-4, 17-18–17-19, 17-34 alternative optimal solutions, 17-32 basic feasible solutions, 17-4–17-9, 17-11–17-14 basic solutions, 17-4 basic variables, 17-4 degeneracy, 17-32–17-34 iterations, 17-11, 17-15, 17-22 minimization problems, 17-26–17-27 nonbasic variables, 17-4 optimal solution, 17-18, 17-23 unboundedness, 17-30–17-31 Simplex tableau, 17-5–17-12, 17-21, 17-23, 17-26–17-27 basic feasible solutions, 17-22 basic feasible solutions, new, 17-13–17-16 dual prices, 18-7–18-8 Copyright 2016 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 875 Index Simplex tableau (Continued ) elementary row operations, 17-12–17-13, 17-17 optimal solution, 18-17 pivot elements, 17-12 range of feasibility, 18-9–18-13 sensitivity analysis, 18-2–18-5 unit columns/vectors, 17-8 variables, new, 17-9–17-11, 17-16–17-17 Simulation, 17, 437n, 548–550 advantages of, 581 Analytic Solver Platform (ASP), 603–609 computer solutions, 579–580 controllable inputs, 548 direct labor cost, 552, 559 disadvantages of, 581 discrete-event, 568 dynamic, 568 Excel solution, 560, 567, 575, 578, 597–603 first-year demand, 553–554, 557, 559 interarrival times, 569–578 inventory, 563–567 models, 548, 558, 561 Monte Carlo simulation, 562n parts cost, 552, 557, 559 random numbers, 554, 556, 558 risk analysis, 581 sensitivity analysis, 582 service times, 570–578 spreadsheets, 558, 560, 562n–563n, 566–567, 575, 578–580 static, 568 uncertain inputs, 548 validation, 580–581 verification, 580–581 waiting lines, 568–579 what-if analysis, 551 Simulation experiments, 548 Simultaneous changes, 101, 113–114 Single-criterion decision problems, Single-period inventory models, 478–483 Single-server waiting line, 508–516 constant service times, 527 Kendall notation, 525 Poisson probability distribution, 525–527 Slack time, 426, 434 Slack variables, 49, 18-7 Slope-intercept form, 44 Solar energy investment decisions, 165–166 Solution points, 37, 45–46 Solutions Plus, 309–310 Solver, integer linear programs, 327 Spanning tree, 20-2 Stanley, Russ, State variables, 21-8, 21-10 Static simulation models, 568 Stationary time series, 735–736 Steady-state operation, 511 Stepping-stone method, 19-8–19-11, 19-14–19-16 Stochastic models, 10 Subproblems, 370 Sunk costs, 108 Supply chain model case problem, 311–312 Supply chain models, 66–67, 259 production and inventory, 287–290 transportation, 259–264 Supply chain optimization and planning (SCOP), 13 Surplus variables, 57–58 Synthesization, 713, 716 System constraints, 697n T Tableau form, 17-5, 17-7, 17-25 minimization problems, 17-27 slack variables, 17-19 Taylor, Frederic W., Tea production, 124 Textbook publishing case problem, 360–361 Textile mill scheduling case problem, 206–208 Timber harvesting model, 31 Time-cost relationship, 437, 440 Time series analysis, 734–735 dummy variables, 764–767 forecasting, 743–766 horizontal patterns, 735, 742, 767 seasonality, 761–765, 767 stationary time series, 735–736 trend-cycle effects, 741 trend patterns, 738–741, 764–765, 767 Time series plots, 735, 742, 751, 757, 762, 765, 767 Transient period, 511 Copyright 2016 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 876 Index Transportation problems, 208, 259–264, 290, 19-2 assignment problems, 274–279, 19-18–19-24 capacitated, 265 Excel solution, 312–314 maximal flow problem, 283–286 route capacities, 264–265 route minimums, 264–265 shortest-route problem, 279–283, 287 simplex method, 19-2–19-9, 19-11–19-18 transshipment nodes, 266–273 unacceptable routes, 265 Transportation simplex method, 19-2–19-7, 19-17 degeneracy, 19-12–19-13, 19-16 dummy destinations, 19-18 dummy origins, 19-18 heuristics, 19-2–19-3 incoming arcs, 19-7 iterations, 19-7 modified distribution method (MODI), 19-7, 19-11–19-12, 19-15 net evaluation index, 19-8–19-9 optimal solution, 19-12 outgoing arcs, 19-7 stepping-stone method, 19-8–19-11, 19-14–19-16 Transportation tableau, 19-2–19-6, 19-14 Transshipment nodes, 266–273 Transshipment problems, 266, 287–288 Excel solution, 314–317 optimal solution, 269–271 TreePlan, 683–688 Trend-cycle effects, 741 Trend patterns, 738 cyclical patterns, 741 Excel and, 785–786 linear, 738–739 nonlinear, 739 seasonal, 740–741 Trend projection, Excel and, 780–781 Trial-and-error approach, 11–12 Tri-State Co case problem, 592–593 Truck leasing strategy case problem, 148–149 Two-person zero-sum games, 238–242, 248–249 Type-I service levels, 488n U Unacceptable routes, 265 Unboundedness, 62–63, 17-30–17-31 Uncertain activity times, 430–432 Uncertain inputs, 548 Unconstrained nonlinear optimization problems, 371–373 Uncontrollable inputs, 9–10 Union Pacific (UP), 266 U.S Air Force, 437 U.S Environmental Protection Agency (EPA), 21-21 Utility industry, forecasting, 734 Utility theory, 642–650 Utilization factors, 513 V Variability in project completion time, 434–435 Variable annuities, 238 Variable costs, 14, 329 Venture capital case problem, 86–87 Vintage Restaurant, 776–777 Virginia Court of Appeals, 323 Visual Basic for Applications (VBA), 218 Visual CAFE, 378 Volume variables, 14 Voting machine allocation, 534 W Wagner Fabricating Co case problem, 501–503 Waiting cost, 523–524 Waiting-line models, 17, 513 blocked customers, 528–530 economic analysis, 523–524 Excel solution, 515–516 exponential probability distribution, 510 finite calling populations, 530–533 first-come first served (FCFS), 511 Kendall notation, 525 multiple-server, 516–521, 528–530 operating characteristics, 507, 511–515, 517–520, 522–523, 528, 531–532 Poisson probability distribution, 509–511, 516, 522, 526–527 simulation, 568–579 Copyright 2016 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 877 Index Waiting-line models (Continued ) single-server, 508–516, 525–527 steady-state operation, 511 structure of, 508 waiting cost, 523–524 Warner Robins Air Logistics Center (WR-ALC), 437 What-if analysis, 550 base-case scenarios, 551 best-case scenarios, 551 simulation, 552 worst-case scenarios, 551 Workforce assignment, 180–184 case problem, 208–209 optimal solution, 182–184 Workload balancing case problem, 84–85 Y Yield management, 95 Z 0-1 linear integer programs, 323, 347 capital budgeting, 328–329 distribution system design, 332–337 fixed cost, 329–332 location problems, 337–340 modeling, 344 product design and market share optimization, 340–343 Zero-sum games, 238–242 Copyright 2016 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 2016 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 ... likely the ensuing quantitative analysis will make an important contribution to the decision- making process To successfully apply quantitative analysis to decision making, the management scientist... are operations research and ? ?decision science Today, many use the terms management science, operations research, and decision science interchangeably The scientific management revolution of the... only by studying the assumptions and methods of management science A manager can increase decision- making effectiveness by learning more about quantitative methodology and by better understanding