Optimization Modeling with Spreadsheets, 2nd Edition

Chapter 1. Introduction to Spreadsheet Models for Optimization.

1.1 Elements of Model.

1.2 Spreadsheet Models.

1.3 A Hierarchy for Analysis.

1.4 Optimization Software.

1.5 Using Solver.

Chapter 2. Linear Programming: Allocation, Covering and Blending Models.

2.1 Linear Models.

2.2 Allocation Models.

2.3 Covering Models.

2.4 Blending Models.

2.5 Modeling Errors in Linear Programming.

Chapter 3. Linear Programming Network Models.

3.1 The Transportation Model.

3.2 The Assignment Model.

3.3 The Transshipment Model.

3.4 Features of Special Network Models.

3.5 Building Network Models with Yields.

3.6 General Network Models with Yields.

3.7 General Network Models with Transformed Flows.

Chapter 4. Sensitivity Analysis in Linear Programs.

4.1 Parameter Analysis in the Transportation Example

4.2 Parameter Analysis in the Allocation Example.

4.3 The Sensitivity Report and the Transportation Example.

4.4 The Sensitivity Report and the Allocation Example.

4.5 Degeneracy and Alternative Optima.

4.6 Patterns in Linear Programming Solutions.

Chapter 5. Linear Programming: Data Envelopment Analysis.

5.1 A Graphical Perspective on DEA.

5.2 An Algebraic Perspective on DEA.

5.3 A Spreadsheet Model for DEA.

5.4 Indexing.

5.5 Finding Reference Sets and HSUs.

5.6 Assumptions and Limitations of DEA.

Chapter 6. Integer Programming: Binary Choice Models.

6.1 Using Solver with Integer Requirements.

6.2 The Capital Budgeting Problem.

6.3 Set Covering.

6.4 Set Packing.

6.5 Set Partitioning.

6.6 Solving a Large-Scale Set Partitioning Problem.

Chapter 7. Integer Programming: Logical Constraints.

7.1 Simple Logical Constraints: Contingency and Exclusivity.

7.2 Linking Constraints: The Fixed Cost Problem.

7.3 Linking Constraints: The Threshold Level Problem.

7.4 Linking Constraints: The Facility Location Model.

7.5 Disjunctive Constraints: The Machine Sequencing Problem.

7.6 Tour and Subset Constraints: The Traveling Salesperson Problem.

7.7 The Algorithm for Solving Integer Programs.

Chapter 8. Nonlinear Programming.

8.1 One-Variable Models.

8.2 Local Optima and the Search for an Optimum.

8.3 Two-Variable Models.

8.4 Nonlinear Models with Constraints.

8.5 Linearizations.

Chapter 9. Heuristic Solutions with the Evolutionary Solver.

9.1 Features of the Evolutionary Solver.

9.2 An Illustrative Example: Nonlinear Regression.

9.3 The Machine-Sequencing Problem Revisited.

9.4 The Traveling Salesperson Problem Revisited.

9.5 Multi-Machine Scheduling.

9.6 Two-Dimensional Location.

9.7 Line Balancing.

Appendices.

1. Optimization Software and Supplement Files.

2. Graphical Method for Linear Programming.

3. The Simplex Method.

4. Stochastic Programming.

Index.