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Equip students with a conceptual understanding of management science's role in the decision-making process with Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' market-leading AN INTRODUCTION TO MANAGEMENT SCIENCE: QUANTITATIVE APPROACHES TO DECISION MAKING, 16E. This edition uses a hallmark problem-scenario approach in a new full-color design as the authors introduce each quantitative technique within an application setting. Students learn to apply the management science model to generate solutions and make recommendations for management. Updates clarify concept explanations while new vignettes and problems demonstrate concepts at work. Mathematical methods are presented using graphical solutions with chapter appendices that show the steps for using Microsoft® Office Excel® 365. In addition, WebAssign courseware puts techniques and models into practice with randomized problems from the book and instant feedback as well as problem walk-throughs and step-by-step tutorials. Cover 1 Brief Contents 7 Contents 9 Preface 19 About the Authors 23 Chapter 1: Introduction 29 1.1 Problem Solving and Decision Making 31 1.2 Quantitative Analysis and Decision Making 32 1.3 Quantitative Analysis 34 1.4 Models of Cost, Revenue, and Profit 41 1.5 Management Science Techniques 43 Summary 46 Glossary 46 Problems 47 Case Problem: Scheduling a Youth Soccer League 52 Appendix 1.1: Using Excel for Breakeven Analysis 53 Chapter 2: An Introduction to Linear Programming 57 2.1 A Simple Maximization Problem 59 2.2 Graphical Solution Procedure 63 2.3 Extreme Points and the Optimal Solution 75 2.4 Computer Solution of the Par, Inc., Problem 76 2.5 A Simple Minimization Problem 78 2.6 Special Cases 83 2.7 General Linear Programming Notation 87 Summary 89 Glossary 90 Problems 90 Case Problem 1: Workload Balancing 106 Case Problem 2: Production Strategy 107 Case Problem 3: Hart Venture Capital 108 Appendix 2.1: Solving Linear Programs with Excel Solver 110 Chapter 3: Linear Programming: Sensitivity Analysis and Interpretation of Solution 115 3.1 Introduction to Sensitivity Analysis 117 3.2 Graphical Sensitivity Analysis 117 3.3 Sensitivity Analysis: Computer Solution 125 3.4 Limitations of Classical Sensitivity Analysis 132 3.5 The Electronic Communications Problem 137 Summary 142 Glossary 143 Problems 144 Case Problem 1: Product Mix 164 Case Problem 2: Investment Strategy 165 Appendix 3.1: Sensitivity Analysis with Excel Solver 167 Chapter 4: Linear Programming Applications in Marketing, Finance, and Operations Management 171 4.1 Marketing Applications 172 4.2 Financial Applications 178 4.3 Operations Management Applications 185 Summary 203 Problems 203 Case Problem 1: Planning an Advertising Campaign 216 Case Problem 2: Schneider's Sweet Shop 217 Case Problem 3: Textile Mill Planning 218 Case Problem 4: Workforce Scheduling 219 Case Problem 5: Duke Energy Coal Allocation 221 Appendix 4.1: Excel Solution of Hewlitt Corporation Financial Planning Problem 224 Chapter 5: Advanced Linear Programming Applications 229 5.1 Data Envelopment Analysis 230 5.2 Revenue Management 237 5.3 Portfolio Models and Asset Allocation 241 5.4 Game Theory 249 Summary 258 Glossary 259 Problems 259 Chapter 6: Distribution and Network Models 267 6.1 Supply Chain Models 268 6.2 Assignment Problem 281 6.3 Shortest-Route Problem 286 6.4 Maximal Flow Problem 290 6.5 A Production and Inventory Application 293 Summary 296 Glossary 297 Problems 298 Case Problem 1: Solutions Plus 314 Case Problem 2: Supply Chain Design 315 Appendix 6.1: Excel Solution of Transportation, Transshipment, and Assignment Problems 318 Chapter 7: Integer Linear Programming 325 7.1 Types of Integer Linear Programming Models 327 7.2 Graphical and Computer Solutions for an All-Integer Linear Program 328 7.3 Applications Involving 0-1 Variables 332 7.4 Modeling Flexibility Provided by 0-1 Integer Variables 346 Summary 350 Glossary 350 Problems 351 Case Problem 1: Textbook Publishing 364 Case Problem 2: Yeager National Bank 365 Case Problem 3: Production Scheduling with Changeover Costs 366 Case Problem 4: Applecore Children's Clothing 367 Appendix 7.1: Excel Solution of Integer Linear Programs 369 Chapter 8: Nonlinear Optimization Models 373 8.1 A Production Application - Par, Inc., Revisited 375 8.2 Constructing an Index Fund 382 8.3 Markowitz Portfolio Model 386 8.4 Blending: The Pooling Problem 388 8.5 Forecasting Adoption of a New Product 393 Summary 398 Glossary 398 Problems 399 Case Problem 1: Portfolio Optimization with Transaction Costs 407 Case Problem 2: Cafe Compliance in the Auto Industry 410 Appendix 8.1: Solving Nonlinear Optimization Problems with Excel Solver 413 Chapter 9: Project Scheduling: PERT/CPM 417 9.1 Project Scheduling Based on Expected Activity Times 418 9.2 Project Scheduling Considering Uncertain Activity Times 427 9.3 Considering Time-Cost Trade-Offs 435 Summary 440 Glossary 440 Problems 441 Case Problem 1: R. C. Coleman 451 Appendix 9.1: Finding Cumulative Probabilities for Normally Distributed Random Variables 453 Chapter 10: Inventory Models 455 10.1 Economic Order Quantity (EOQ) Model 456 10.2 Economic Production Lot Size Model 465 10.3 Inventory Model with Planned Shortages 468 10.4 Quantity Discounts for the EOQ Model 472 10.5 Single-Period Inventory Model with Probabilistic Demand 475 10.6 Order-Quantity, Reorder Point Model with Probabilistic Demand 479 10.7 Periodic Review Model with Probabilistic Demand 483 Summary 487 Glossary 487 Problems 488 Case Problem 1: Wagner Fabricating Company 496 Case Problem 2: River City Fire Department 497 Appendix 10.1: Development of the Optimal Order Quantity (Q) Formula for the EOQ Model 499 Appendix 10.2: Development of the Optimal Lot Size (Q*) Formula for the Production Lot Size Model 499 Chapter 11: Waiting Line Models 501 11.1 Structure of a Waiting Line System 503 11.2 Single-Server Waiting Line Model with Poisson Arrivals and Exponential Service Times 506 11.3 Multiple-Server Waiting Line Model with Poisson Arrivals and Exponential Service Times 510 11.4 Some General Relationships for Waiting Line Models 515 11.5 Economic Analysis of Waiting Lines 516 11.6 Kendall's Notation for Classifying Queueing Models 518 11.7 Single-Server Waiting Line Model with Poisson Arrivals and General Service Times 519 11.8 Multiple-Server Model with Poisson Arrivals, General Service Times, and No Waiting Line 521 11.9 Waiting Line Models with Finite Calling Populations 523 Summary 526 Glossary 527 Problems 527 Case Problem 1: Regional Airlines 536 Case Problem 2: Olympus Equipment, Inc. 537 Chapter 12: Simulation 539 12.1 What-If Analysis 541 12.2 Simulation of Sanotronics Problem 543 12.3 Inventory Simulation 552 12.4 Waiting Line Simulation 557 12.5 Simulation Considerations 566 Summary 567 Summary of Steps for Conducting a Simulation Analysis 568 Glossary 568 Problems 569 Case Problem 1: Four Corners 577 Case Problem 2: Harbor Dunes Golf Course 578 Case Problem 3: County Beverage Drive-Thru 580 Appendix 12.1: Common Probability Distributions for Simulation 582 Chapter 13: Decision Analysis 589 13.1 Problem Formulation 591 13.2 Decision Making without Probabilities 593 13.3 Decision Making with Probabilities 596 13.4 Risk Analysis and Sensitivity Analysis 600 13.5 Decision Analysis with Sample Information 605 13.6 Computing Branch Probabilities with Bayes' Theorem 614 13.7 Utility Theory 618 Summary 628 Glossary 628 Problems 630 Case Problem 1: Property Purchase Strategy 645 Case Problem 2: Lawsuit Defense Strategy 646 Case Problem 3: Rob's Market 647 Case Problem 4: College Softball Recruiting 648 Chapter 14: Multicriteria Decisions 651 14.1 Goal Programming: Formulation and Graphical Solution 652 14.2 Goal Programming: Solving More Complex Problems 660 14.3 Scoring Models 665 14.4 Analytic Hierarchy Process 668 14.5 Establishing Priorities Using AHP 670 14.6 Using AHP to Develop an Overall Priority Ranking 677 Summary 679 Glossary 680 Problems 680 Case Problem 1: Banh Trailers, Inc. 690 Appendix 14.1: Scoring Models with Excel 691 Chapter 15: Time Series Analysis and Forecasting 693 15.1 Time Series Patterns 695 15.2 Forecast Accuracy 703 15.3 Moving Averages and Exponential Smoothing 707 15.4 Linear Trend Projection 714 15.5 Seasonality 718 Summary 724 Glossary 724 Problems 725 Case Problem 1: Forecasting Food and Beverage Sales 732 Case Problem 2: Forecasting Lost Sales 733 Appendix 15.1: Forecasting with Excel Data Analysis Tools 735 Appendix 15.2: Using the Excel Forecast Sheet 744 Chapter 16: Markov Processes 751 16.1 Market Share Analysis 752 16.2 Accounts Receivable Analysis 760 Summary 764 Glossary 765 Problems 765 Case Problem 1: Dealer's Absorbing State Probabilities in Blackjack 770 Appendix 16.1: Matrix Notation and Operations 772 Appendix 16.2: Matrix Inversion with Excel 775 Appendices 777 Appendix A: Building Spreadsheet Models 778 Appendix B: Areas for the Standard Normal Distribution 807 Appendix C: Values of e-Lambda 809 Appendix D: References and Bibliography 810 Index 812 WCN:,02-300 WCN: 02-300 Gain a strong understanding of the role of management science in the decision-making process while mastering the latest advantages of Microsoft® Office Excel® 365 with Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams'AN INTRODUCTION TO MANAGEMENT SCIENCE: QUANTITATIVE APPROACHES TO DECISION MAKING, 16E. This market-leading edition uses a proven problem-scenario approach in a new full-color design as the authors introduce each quantitative technique within an application setting. You learn to apply the management science model to generate solutions and make recommendations for management. Updates clarify concept explanations while new vignettes and problems demonstrate concepts at work. All data sets, applications and screen visuals reflect the details of Excel® 365 to prepare you to work with the latest spreadsheet tools. In addition, WebAssign courseware demonstrates techniques with instant feedback, problem walk-throughs and step-by-step tutorials. Gain a strong understanding of the role of management science in the decision-making process while mastering the latest advantages of Microsoft Office Excel 365 with Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' AN INTRODUCTION TO MANAGEMENT SCIENCE: QUANTITATIVE APPROACHES TO DECISION MAKING, 16E. This market-leading edition uses a proven problem-scenario approach in a new full-color design as the authors introduce each quantitative technique within an application setting. You learn to apply the management science model to generate solutions and make recommendations for management. Updates clarify concept explanations while new vignettes and problems demonstrate concepts at work. All data sets, applications and screen visuals reflect the details of Excel 365 to prepare you to work with the latest spreadsheet tools. In addition, WebAssign courseware demonstrates techniques with instant feedback, problem walk-throughs and step-by-step tutorials Helps gain a strong understanding of the role of management science in the decision-making process while mastering the latest advantages of Microsoft Office Excel 365.You learn to apply the management science model to generate solutions and make recommendations for management. Updates clarify concept explanations while new vignettes and problems demonstrate concepts at work.