Practical Management Science, 6th Edition
By Wayne L. Winston, S. Christian Albright
Contents
Preface xiii
CHAPTER 1 Introduction to Modeling 1
1.1 Introduction 3
1.2 A Capital Budgeting 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
Case 17.1 Subway Token Hoarding 17-57