Test Bank For Practical Management Science 6th Edition by Wayne L. Winston Chapter 1_14

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Test Bank For Practical Management Science 6th Edition by Wayne L. Winston Chapter 1_14 Contents Preface xiii CHAPTER 1 Introduction to Modeling 1 1.1 Introduction 3 1.2 A Capital Budgeti... ng Example 3 1.3 Modeling versus Models 6 1.4 A Seven-Step Modeling Process 7 1.5 A Great Source for Management Science Applications: Interfaces 13 1.6 Why Study Management Science? 13 1.7 Software Included with This Book 15 1.8 Conclusion 17 CHAPTER 2 Introduction to Spreadsheet Modeling 19 2.1 Introduction 20 2.2 Basic Spreadsheet Modeling: Concepts and Best Practices 21 2.3 Cost Projections 25 2.4 Breakeven Analysis 31 2.5 Ordering with Quantity Discounts and Demand Uncertainty 39 2.6 Estimating the Relationship between Price and Demand 44 2.7 Decisions Involving the Time Value of Money 54 2.8 Conclusion 59 Appendix Tips for Editing and Documenting Spreadsheets 64 Case 2.1 Project Selection at Ewing Natural Gas 66 Case 2.2 New Product Introduction at eTech 68 CHAPTER 3 Introduction to Optimization Modeling 71 3.1 Introduction 72 3.2 Introduction to Optimization 73 3.3 A Two-Variable Product Mix Model 75 3.4 Sensitivity Analysis 87 3.5 Properties of Linear Models 97 3.6 Infeasibility and Unboundedness 100 3.7 A Larger Product Mix Model 103 3.8 A Multiperiod Production Model 111 3.9 A Comparison of Algebraic and Spreadsheet Models 120 3.10 A Decision Support System 121 3.11 Conclusion 123 Appendix Information on Optimization Software 130 Case 3.1 Shelby Shelving 132 Chapter 4 Linear Programming Models 135 4.1 Introduction 136 4.2 Advertising Models 137 4.3 Employee Scheduling Models 147 4.4 Aggregate Planning Models 155 4.5 Blending Models 166 4.6 Production Process Models 174 4.7 Financial Models 179 4.8 Data Envelopment Analysis (Dea) 191 4.9 Conclusion 198 Case 4.1 Blending Aviation Gasoline at Jansen Gas 214 CASE 4.2 Delinquent Accounts at GE Capital 216 CASE 4.3 Foreign Currency Trading 217 CHAPTER 5 Network Models 219 5.1 Introduction 220 5.2 Transportation Models 221 5.3 Assignment Models 233 5.4 Other Logistics Models 240 5.5 Shortest Path Models 249 5.6 Network Models in the Airline Industry 258 5.7 Conclusion 267 Case 5.1 Optimized Motor Carrier Selection at Westvaco 274 Chapter 6 Optimization Models with Integer Variables 277 6.1 Introduction 278 6.2 Overview of Optimization with Integer Variables 279 6.3 Capital Budgeting Models 283 6.4 Fixed-Cost Models 290 6.5 Set-Covering and Location-Assignment Models 303 6.6 Cutting Stock Models 320 6.7 Conclusion 324 Case 6.1 Giant Motor Company 334 Case 6.2 Selecting Telecommunication Carriers to Obtain Volume Discounts 336 Case 6.3 Project Selection at Ewing Natural Gas 337 Chapter 7 Nonlinear Optimization Models 339 7.1 Introduction 340 7.2 Basic Ideas of Nonlinear Optimization 341 7.3 Pricing Models 347 7.4 Advertising Response and Selection Models 365 7.5 Facility Location Models 374 7.6 Models for Rating Sports Teams 378 7.7 Portfolio Optimization Models 384 7.8 Estimating the Beta of a Stock 394 7.9 Conclusion 398 Case 7.1 Gms Stock Hedging 405 Chapter 8 Evolutionary Solver: An Alternative Optimization Procedure 407 8.1 Introduction 408 8.2 Introduction to Genetic Algorithms 411 8.3 Introduction to Evolutionary Solver 412 8.4 Nonlinear Pricing Models 417 8.5 Combinatorial Models 424 8.6 Fitting an S-Shaped Curve 435 8.7 Portfolio Optimization 439 8.8 Optimal Permutation Models 442 8.9 Conclusion 449 Case 8.1 Assigning Mba Students to Teams 454 Case 8.2 Project Selection at Ewing Natural Gas 455 Chapter 9 Decision Making under Uncertainty 457 9.1 Introduction 458 9.2 Elements of Decision Analysis 460 9.3 Single-Stage Decision Problems 467 9.4 The PrecisionTree Add-In 471 9.5 Multistage Decision Problems 474 9.6 The Role of Risk Aversion 492 9.7 Conclusion 499 CASE 9.1 Jogger Shoe Company 510 CASE 9.2 Westhouser Paper Company 511 CASE 9.3 Electronic Timing System for Olympics 512 CASE 9.4 Developing a Helicopter Component for the Army 513 Chapter 10 Introduction to Simulation Modeling 515 10.1 Introduction 516 10.2 Probability Distributions for Input Variables 518 10.3 Simulation and the Flaw of Averages 537 10.4 Simulation with Built-in Excel Tools 540 10.5 Introduction to @RISK 551 10.6 The Effects of Input Distributions on Results 568 10.7 Conclusion 577 Appendix Learning More About @Risk 583 CASE 10.1 Ski Iacket Production 584 CASE 10.2 Ebony Bath Soap 585 CASE 10.3 Advertising Effectiveness 586 CASE 10.4 New Project Introduction at eTech 588 Chapter 11 Simulation Models 589 11.1 Introduction 591 11.2 Operations Models 591 11.3 Financial Models 607 11.4 Marketing Models 631 11.5 Simulating Games of Chance 646 11.6 Conclusion 652 Appendix Other Palisade Tools for Simulation 662 CASE 11.1 College Fund Investment 664 CASE 11.2 Bond Investment Strategy 665 CASE 11.3 Project Selection Ewing Natural Gas 666 Chapter 12 Queueing Models 667 12.1 Introduction 668 12.2 Elements of Queueing Models 670 12.3 The Exponential Distribution 673 12.4 Important Queueing Relationships 678 12.5 Analytic Steady-State Queueing Models 680 12.6 Queueing Simulation Models 699 12.7 Conclusion 709 Case 12.1 Catalog Company Phone Orders 713 Chapter 13 Regression and Forecasting Models 715 13.1 Introduction 716 13.2 Overview of Regression Models 717 13.3 Simple Regression Models 721 13.4 Multiple Regression Models 734 13.5 Overview of Time Series Models 745 13.6 Moving Averages Models 746 13.7 Exponential Smoothing Models 751 13.8 Conclusion 762 Case 13.1 Demand for French Bread at Howie’s Bakery 768 Case 13.2 Forecasting Overhead at Wagner Printers 769 Case 13.3 Arrivals at the Credit Union 770 Chapter 14 Data Mining 771 14.1 Introduction 772 14.2 Classification Methods 774 14.3 Clustering Methods 795 14.4 Conclusion 806 Case 14.1 Houston Area Survey 808 References 809 Index 815 MindTap Chapters Chapter 15 Project Management 15-1 15.1 Introduction 15-2 15.2 The Basic CPM Model 15-4 15.3 Modeling Allocation of Resources 15-14 15.4 Models with Uncertain Activity Times 15-30 15.5 A Brief Look at Microsoft Project 15-35 15.6 Conclusion 15-39 Chapter 16 Multiobjective Decision Making 16-1 16.1 Introduction 16-2 16.2 Goal Programming 16-3 16.3 Pareto Optimality and Trade-Off Curves 16-12 16.4 The Analytic Hierarchy Process (AHP) 16-20 16.5 Conclusion 16-25 Chapter 17 Inventory and Supply Chain Models 17-1 17.1 Introduction 17-2 17.2 Categories of Inventory and Supply Chain Models 17-3 17.3 Types of Costs in Inventory and Supply Chain Models 17-5 17.4 Economic Order Quantity (EOQ) Models 17-6 17.5 Probabilistic Inventory Models 17-21 17.6 Ordering Simulation Models 17-34 17.7 Supply Chain Models 17-40 17.8 Conclusion 17-50 [Show More]

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