简介
This text takes an active-learning approach, providing numerous examples and problems so students can practice extensively with a concept before moving on. Four types of problems - skill-building, skill-extending, modeling, and cases are graded within sections and chapters to help instructors assign homework. Another important feature is the way that the text integrates modeling into all functional areas of business: finance, marketing, operations management using real examples and real data. The text emphasizes modeling over algebraic formulations and memorization of particular models. Shell files are also provided so that instructors can give students as much or as little information as they need.
目录
Table Of Contents:
Introduction to Modeling 1(22)
Introduction 3(1)
A Waiting-Line Example 4(3)
Modeling Versus Models 7(1)
The Seven-Step Modeling Process 8(6)
A Successful Management Science Application 14(3)
Why Study Management Science? 17(1)
Software Included in This Book 18(2)
Conclusion 20(3)
Introduction to Spreadsheet Modeling 23(48)
Introduction 24(1)
Basic Spreadsheet Modeling: Concepts and Best Practices 24(5)
Cost Projections 29(4)
Breakeven Analysis 33(9)
Ordering with Quantity Discounts and Demand Uncertainty 42(5)
Estimating the Relationship Between Price and Demand 47(10)
Decisions Involving the Time Value of Money 57(6)
Conclusion 63(8)
Appendix: Tips for Editing and Documenting Spreadsheets 67(4)
Introduction to Optimization Modeling 71(60)
Introduction 72(1)
Introduction to Optimization 73(1)
A Two-Variable Model 74(11)
Sensitivity Analysis 85(8)
Properties of Linear Models 93(3)
Infeasibility and Unboundedness 96(2)
A Product Mix Model 98(10)
A Multiperiod Production Model 108(10)
A Comparison of Algebraic and Spreadsheet Models 118(1)
A Decision Support System 118(2)
Conclusion 120(11)
Appendix: Information on Solvers 126(1)
Case 3.1 Shelby Shelving 127(2)
Case 3.2 Sonoma Valley Wines 129(2)
Linear Programming Models 131(90)
Introduction 132(1)
Advertising Models 133(9)
Workforce Scheduling Models 142(8)
Aggregate Planning Models 150(10)
Blending Models 160(7)
Production Process Models 167(6)
Financial Models 173(11)
Data Envelopment Analysis (DEA) 184(7)
Conclusion 191(30)
Case 4.1 Amarco, Inc. 207(3)
Case 4.2 American Office Systems, Inc. 210(5)
Case 4.3 Lakefield Corporation's Oil Trading Desk 215(5)
Case 4.4 Foreign Currency Trading 220(1)
Network Models 221(64)
Introduction 222(1)
Transportation Models 223(12)
Assignment Models 235(6)
Minimum Cost Network Flow Models 241(9)
Shortest Path Models 250(10)
Other Network Models 260(13)
Conclusion 273(12)
Case 5.1 International Textile Company, Ltd. 280(2)
Case 5.2 Optimized Motor Carrier Selection at Westvaco 282(3)
Optimization Models with Integer Variables 285(60)
Introduction 286(1)
Overview of Optimization with Integer Variables 287(3)
Capital Budgeting Models 290(7)
Fixed-Cost Models 297(12)
Set-Covering and Location-Assignment Models 309(18)
Cutting Stock Models 327(4)
Conclusion 331(14)
Case 6.1 Giant Motor Company 341(2)
Case 6.2 Selecting Telecommunication Carriers to Obtain Volume Discounts 343(2)
Nonlinear Optimization Models 345(66)
Introduction 346(1)
Basic Ideas of Nonlinear Optimization 347(4)
Pricing Models 351(17)
Advertising Response and Selection Models 368(10)
Facility Location Models 378(4)
Models for Rating Sports Teams 382(5)
Portfolio Optimization Models 387(9)
Estimating the Beta of a Stock 396(5)
Conclusion 401(10)
Case 7.1 GMS Stock Hedging 407(2)
Case 7.2 Durham Asset Management 409(2)
Evolutionary Solver: An Alternative Optimization Procedure 411(52)
Introduction 412(3)
Introduction to Genetic Algorithms 415(1)
Introduction to Evolutionary Solver 416(6)
Nonlinear Pricing Models 422(6)
Combinatorial Models 428(9)
Fitting an S-Shaped Curve 437(5)
Portfolio Optimization 442(2)
Cluster Analysis 444(6)
Discriminant Analysis 450(4)
The Traveling Salesperson Problem 454(4)
Conclusion 458(5)
Case 8.1 Assigning MBA Students to Teams 462(1)
Multiobjective Decision Making 463(40)
Introduction 464(1)
Goal Programming 465(10)
Pareto Optimality and Trade-Off Curves 475(9)
The Analytic Hierarchy Process (AHP) 484(10)
Conclusion 494(9)
Case 9.1 Play Time Toy Company 499(4)
Decision Making Under Uncertainty 503(76)
Introduction 505(1)
Elements of a Decision Analysis 506(16)
The PrecisionTree Add-In 522(12)
Bayes' Rule 534(4)
Multistage Decision Problems 538(16)
Incorporating Attitudes Toward Risk 554(7)
Conclusion 561(18)
Case 10.1 Jogger Shoe Company 574(1)
Case 10.2 Westhouser Paper Company 575(1)
Case 10.3 Biotechnical Engineering 576(3)
Introduction to Simulation Modeling 579(72)
Introduction 581(1)
Real Applications of Simulation 582(1)
Probability Distributions for Input Variables 583(20)
Simulation with Built-In Excel Tools 603(12)
Introduction to @RISK 615(17)
The Effects of Input Distributions on Results 632(8)
Conclusion 640(11)
Appendix: Creating Histograms with Excel Tools 646(3)
Case 11.1 Ski Jacket Production 649(1)
Case 11.2 Ebony Bath Soap 650(1)
Simulation Models 651(86)
Introduction 653(1)
Operations Models 653(19)
Financial Models 672(23)
Marketing Models 695(13)
Simulating Games of Chance 708(6)
Using TopRank with @RISK for Powerful Modeling 714(8)
Conclusion 722(15)
Case 12.1 College Fund Investment 732(1)
Case 12.2 Bond Investment Strategy 733(1)
Case 12.3 Financials at Carco 734(3)
Inventory Models 737(56)
Introduction 739(1)
Categories of Inventory Models 740(1)
Types of Costs in Inventory Models 741(2)
Economic Order Quantity (EOQ) Models 743(16)
Probabilistic Inventory Models 759(14)
Ordering Simulation Models 773(5)
Supply Chain Models 778(6)
Conclusion 784(9)
Case 13.1 Subway Token Hoarding 792(1)
Queueing Models 793(68)
Introduction 795(1)
Elements of Queueing Models 796(3)
The Exponential Distribution 799(5)
Important Queueing Relationships 804(3)
Analytical Steady-State Queueing Models 807(23)
Approximating Short-Run Behavior Analytically 830(5)
Queueing Simulation Models 835(17)
Conclusion 852(9)
Case 14.1 Catalog Company Phone Orders 857(2)
Case 14.2 Pacific National Bank 859(2)
Project Management 861(42)
Introduction 863(2)
The Basic CPM Model 865(9)
Modeling Allocation of Resources 874(16)
Models with Uncertain Activity Times 890(6)
A Brief Look at Microsoft Project 896(3)
Conclusion 899(4)
Regression and Forecasting Models 903(63)
Introduction 904(1)
Overview of Regression Models 905(4)
Simple Regression Models 909(12)
Multiple Regression Models 921(11)
Overview of Time Series Models 932(5)
Moving Averages Models 937(5)
Exponential Smoothing Models 942(16)
Conclusion 958(8)
Case 16.1 Demand for French Bread at Howie's 964(1)
Case 16.2 Forecasting Overhead at Wagner Printers 964(1)
Case 16.3 Arrivals at the Credit Union 965(1)
References 966(3)
Index 969
Introduction to Modeling 1(22)
Introduction 3(1)
A Waiting-Line Example 4(3)
Modeling Versus Models 7(1)
The Seven-Step Modeling Process 8(6)
A Successful Management Science Application 14(3)
Why Study Management Science? 17(1)
Software Included in This Book 18(2)
Conclusion 20(3)
Introduction to Spreadsheet Modeling 23(48)
Introduction 24(1)
Basic Spreadsheet Modeling: Concepts and Best Practices 24(5)
Cost Projections 29(4)
Breakeven Analysis 33(9)
Ordering with Quantity Discounts and Demand Uncertainty 42(5)
Estimating the Relationship Between Price and Demand 47(10)
Decisions Involving the Time Value of Money 57(6)
Conclusion 63(8)
Appendix: Tips for Editing and Documenting Spreadsheets 67(4)
Introduction to Optimization Modeling 71(60)
Introduction 72(1)
Introduction to Optimization 73(1)
A Two-Variable Model 74(11)
Sensitivity Analysis 85(8)
Properties of Linear Models 93(3)
Infeasibility and Unboundedness 96(2)
A Product Mix Model 98(10)
A Multiperiod Production Model 108(10)
A Comparison of Algebraic and Spreadsheet Models 118(1)
A Decision Support System 118(2)
Conclusion 120(11)
Appendix: Information on Solvers 126(1)
Case 3.1 Shelby Shelving 127(2)
Case 3.2 Sonoma Valley Wines 129(2)
Linear Programming Models 131(90)
Introduction 132(1)
Advertising Models 133(9)
Workforce Scheduling Models 142(8)
Aggregate Planning Models 150(10)
Blending Models 160(7)
Production Process Models 167(6)
Financial Models 173(11)
Data Envelopment Analysis (DEA) 184(7)
Conclusion 191(30)
Case 4.1 Amarco, Inc. 207(3)
Case 4.2 American Office Systems, Inc. 210(5)
Case 4.3 Lakefield Corporation's Oil Trading Desk 215(5)
Case 4.4 Foreign Currency Trading 220(1)
Network Models 221(64)
Introduction 222(1)
Transportation Models 223(12)
Assignment Models 235(6)
Minimum Cost Network Flow Models 241(9)
Shortest Path Models 250(10)
Other Network Models 260(13)
Conclusion 273(12)
Case 5.1 International Textile Company, Ltd. 280(2)
Case 5.2 Optimized Motor Carrier Selection at Westvaco 282(3)
Optimization Models with Integer Variables 285(60)
Introduction 286(1)
Overview of Optimization with Integer Variables 287(3)
Capital Budgeting Models 290(7)
Fixed-Cost Models 297(12)
Set-Covering and Location-Assignment Models 309(18)
Cutting Stock Models 327(4)
Conclusion 331(14)
Case 6.1 Giant Motor Company 341(2)
Case 6.2 Selecting Telecommunication Carriers to Obtain Volume Discounts 343(2)
Nonlinear Optimization Models 345(66)
Introduction 346(1)
Basic Ideas of Nonlinear Optimization 347(4)
Pricing Models 351(17)
Advertising Response and Selection Models 368(10)
Facility Location Models 378(4)
Models for Rating Sports Teams 382(5)
Portfolio Optimization Models 387(9)
Estimating the Beta of a Stock 396(5)
Conclusion 401(10)
Case 7.1 GMS Stock Hedging 407(2)
Case 7.2 Durham Asset Management 409(2)
Evolutionary Solver: An Alternative Optimization Procedure 411(52)
Introduction 412(3)
Introduction to Genetic Algorithms 415(1)
Introduction to Evolutionary Solver 416(6)
Nonlinear Pricing Models 422(6)
Combinatorial Models 428(9)
Fitting an S-Shaped Curve 437(5)
Portfolio Optimization 442(2)
Cluster Analysis 444(6)
Discriminant Analysis 450(4)
The Traveling Salesperson Problem 454(4)
Conclusion 458(5)
Case 8.1 Assigning MBA Students to Teams 462(1)
Multiobjective Decision Making 463(40)
Introduction 464(1)
Goal Programming 465(10)
Pareto Optimality and Trade-Off Curves 475(9)
The Analytic Hierarchy Process (AHP) 484(10)
Conclusion 494(9)
Case 9.1 Play Time Toy Company 499(4)
Decision Making Under Uncertainty 503(76)
Introduction 505(1)
Elements of a Decision Analysis 506(16)
The PrecisionTree Add-In 522(12)
Bayes' Rule 534(4)
Multistage Decision Problems 538(16)
Incorporating Attitudes Toward Risk 554(7)
Conclusion 561(18)
Case 10.1 Jogger Shoe Company 574(1)
Case 10.2 Westhouser Paper Company 575(1)
Case 10.3 Biotechnical Engineering 576(3)
Introduction to Simulation Modeling 579(72)
Introduction 581(1)
Real Applications of Simulation 582(1)
Probability Distributions for Input Variables 583(20)
Simulation with Built-In Excel Tools 603(12)
Introduction to @RISK 615(17)
The Effects of Input Distributions on Results 632(8)
Conclusion 640(11)
Appendix: Creating Histograms with Excel Tools 646(3)
Case 11.1 Ski Jacket Production 649(1)
Case 11.2 Ebony Bath Soap 650(1)
Simulation Models 651(86)
Introduction 653(1)
Operations Models 653(19)
Financial Models 672(23)
Marketing Models 695(13)
Simulating Games of Chance 708(6)
Using TopRank with @RISK for Powerful Modeling 714(8)
Conclusion 722(15)
Case 12.1 College Fund Investment 732(1)
Case 12.2 Bond Investment Strategy 733(1)
Case 12.3 Financials at Carco 734(3)
Inventory Models 737(56)
Introduction 739(1)
Categories of Inventory Models 740(1)
Types of Costs in Inventory Models 741(2)
Economic Order Quantity (EOQ) Models 743(16)
Probabilistic Inventory Models 759(14)
Ordering Simulation Models 773(5)
Supply Chain Models 778(6)
Conclusion 784(9)
Case 13.1 Subway Token Hoarding 792(1)
Queueing Models 793(68)
Introduction 795(1)
Elements of Queueing Models 796(3)
The Exponential Distribution 799(5)
Important Queueing Relationships 804(3)
Analytical Steady-State Queueing Models 807(23)
Approximating Short-Run Behavior Analytically 830(5)
Queueing Simulation Models 835(17)
Conclusion 852(9)
Case 14.1 Catalog Company Phone Orders 857(2)
Case 14.2 Pacific National Bank 859(2)
Project Management 861(42)
Introduction 863(2)
The Basic CPM Model 865(9)
Modeling Allocation of Resources 874(16)
Models with Uncertain Activity Times 890(6)
A Brief Look at Microsoft Project 896(3)
Conclusion 899(4)
Regression and Forecasting Models 903(63)
Introduction 904(1)
Overview of Regression Models 905(4)
Simple Regression Models 909(12)
Multiple Regression Models 921(11)
Overview of Time Series Models 932(5)
Moving Averages Models 937(5)
Exponential Smoothing Models 942(16)
Conclusion 958(8)
Case 16.1 Demand for French Bread at Howie's 964(1)
Case 16.2 Forecasting Overhead at Wagner Printers 964(1)
Case 16.3 Arrivals at the Credit Union 965(1)
References 966(3)
Index 969
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