Optimization Modeling with Spreadsheets / Edition 2

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Thoroughly updated to reflect the latest topical and technical advances in the field, Optimization Modeling with Spreadsheets, Third Edition continues to focus on solving real-world optimization problems through the creation of mathematical models and the use of spreadsheets for analysis. Developed and extensively classroom-tested, the book features a systematic approach that equips readers with the skills needed to apply optimization tools effectively without the need to rely on specialized algorithms. This introductory book on optimization (mathematical programming) includes coverage on linear programming, nonlinear programming, integer programming, and heuristic programming, with an emphasis on model building using Excel's freely available Solver. The focus on model building (rather than algorithms) is one feature that makes this book distinctive as most books devote more space to algorithmic details than to formulation principles. These days, however, it is not necessary to know a great deal about algorithms in order to apply optimization tools, especially when relying on the spreadsheet as a solution platform. This emphasis on spreadsheets is another feature that makes this book distinctive. Few books devoted to optimization pay much attention to spreadsheet implementation of optimization principles, and most books that emphasize model building ignore spreadsheets entirely. To address the capabilities of sensitivity analysis, the use of Excel's Sensitivity Toolkit is employed. In addition, the book's past use of the Risk Solver Platform for Education (RSPE) is not completely abandoned as the author does include instructions on the use of RSPE for solving optimization problems within an appendix.

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Product Details

  • ISBN-13: 9780470928639
  • Publisher: Wiley
  • Publication date: 5/24/2011
  • Edition number: 2
  • Pages: 432
  • Sales rank: 758,131
  • Product dimensions: 6.20 (w) x 9.30 (h) x 1.10 (d)

Meet the Author

Kenneth R. Baker, PhD, is Nathaniel Leverone Professor of Management at the Tuck School of Business and Adjunct Professor of Engineering at Dartmouth College. A Fellow of the Institute for Operations Research and the Management Sciences (INFORMS), Dr. Baker has published extensively in his area of research interest, which include mathematical modeling, spreadsheet engineering, and scheduling. He is coauthor of Principles of Sequencing and Scheduling and Management Science: The Art of Modeling with Spreadsheets, Third Edition, both published by Wiley.

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Table of Contents


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 Playoff Scheduling.

6.7 Solving a Large-Scale Set Partitioning Problem.

6.8 The Algorithm for Solving Integer Programs.

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.

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.


1. Optimization Software and Supplement Files.

2. Graphical Method for Linear Programming.

3. The Simplex Method.

4. Stochastic Programming.


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