1. Introduction to Modeling
1.1. Introduction
1.2. A Capital Budgeting Example
1.3. Modeling Versus Models
1.4. A Seven-Step Modeling Process
1.5. A Great Source for Management Science Applications: Interfaces
1.6. Why Study Management Science?
1.7. Software Included with this Book
1.8. Conclusion
2. Introductory Spreadsheet Modeling
2.1. Introduction
2.2. Basic Spreadsheet Modeling: Concepts and Best Practices
2.3. Cost Projections
2.4. Breakeven Analysis
2.5. Ordering with Quantity Discounts and Demand Uncertainty
2.6. Estimating the Relationship between Price and Demand
2.7. Decisions Involving the Time Value of Money
2.8. Conclusion
Appendix: Tips for Editing and Documenting Spreadsheets
3. Introduction to Optimization Modeling
3.1. Introduction
3.2. Introduction to Optimization
3.3. A Two-Variable Product Mix Model
3.4. Sensitivity Analysis
3.5. Properties of Linear Models
3.6. Infeasibility and Unboundedness
3.7. A Larger Product Mix Model
3.8. A Multiperiod Production Model
3.9. A Comparison of Algebraic and Spreadsheet Models
3.10. A Decision Support System
3.11. Conclusion
Appendix: Information on Optimization Software
4. Linear Programming Models
4.1. Introduction
4.2. Advertising Models
4.3. Employee Scheduling Models
4.4. Aggregate Planning Models
4.5. Blending Models
4.6. Production Process Models
4.7. Financial Models
4.8. Data Envelopment Analysis (DEA)
4.9. Conclusion
5. Network Models
5.1. Introduction
5.2. Transportation Models
5.3. Assignment Models
5.4. Other Logistics Models
5.5. Shortest Path Models
5.6. Network Models in the Airline Industry
5.7. Conclusion
6. Optimization Models with Integer Variables
6.1. Introduction
6.2. Overview of Optimization with Integer Variables
6.3. Capital Budgeting Models
6.4. Fixed-Cost Models
6.5. Set Covering Models and Location–Assignment Models
6.6. Cutting Stock Models
6.7. Conclusion
7. Nonlinear Optimization Models
7.1. Introduction
7.2. Basic Ideas of Nonlinear Optimization
7.3. Pricing Models
7.4. Advertising Response and Selection Models
7.5. Facility Location Models
7.6. Models for Rating Sports Teams
7.7. Portfolio Optimization Models
7.8. Estimating the Beta of a Stock
7.9. Conclusion
8. Evolutionary Solver: An Alternative Optimization Procedure
8.1. Introduction
8.2. Introduction to Genetic Algorithms
8.3. Introduction to Evolutionary Solver
8.4. Nonlinear Pricing Models
8.5. Combinatorial Models
8.6. Fitting an S-Shaped Curve
8.7. Portfolio Optimization
8.8. Optimal Permutation Models
8.9. Conclusion
9. Decision Making Under Uncertainty
9.1. Introduction
9.2. Elements of Decision Analysis
9.3. Single-Stage Decision Problems
9.4. The PrecisionTree Add-In
9.5. Multistage Decision Problems
9.6. The Role of Risk Aversion
9.7. Conclusion
10. Introduction to Simulation Modeling
10.1. Introduction
10.2. Probability Distributions for Input Variables
10.3. Simulation and the Flaw of Averages
10.4. Simulation with Built-In Excel Tools
10.5. Introduction to @RISK
10.6. The Effects of Input Distributions on Results
10.7. Conclusion
Appendix: Learning More about @RISK
11. Simulation Models
11.1. Introduction
11.2. Operations Models
11.3. Financial Models
11.4. Marketing Models
11.5. Simulating Games of Chance
11.6. Conclusion
Appendix: Other Palisade Tools for Simulation
12. Queueing Models
12.1. Introduction
12.2. Elements of Queueing Models
12.3. The Exponential Distribution
12.4. Important Queueing Relationships
12.5. Analytic Steady-State Queueing Models
12.6. Queueing Simulation Models
12.7. Conclusion
13. Regression and Forecasting Models
13.1. Introduction
13.2. Overview of Regression Models
13.3. Simple Regression Models
13.4. Multiple Regression Models
13.5. Overview of Time Series Models
13.6. Moving Averages Models
13.7. Exponential Smoothing Models
13.8. Conclusion
14. Data Mining
14.1. Introduction
14.2. Classification Methods
14.3. Clustering Methods
14.4. Conclusion
Online Chapters
15. Project Management
15.1. Introduction
15.2. The Basic CPM Model
15.3. Modeling Allocation of Resources
15.4. Models with Uncertain Activity Times
15.5. A Brief Look at Microsoft Project
15.6. Conclusion
16. Multiobjective Decision Making
16.1. Introduction
16.2. Goal Programming
16.3. Pareto Optimality and Trade-off Curves
16.4. The Analytic Hierarchy Process (AHP)
16.5. Conclusion
17. Inventory and Supply Chain Models
17.1. Introduction
17.2. Categories of Inventory and Supply Chain Models
17.3. Types of Costs in Inventory and Supply Chain Models
17.4. Economic Order Quantity (EOQ) Models
17.5. Probabilistic Inventory Models
17.6. Ordering Simulation Models
17.7. Supply Chain Models
17.8. Conclusion