Optimization Modeling with Spreadsheets / Edition 2

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Overview

Updated with Microsoft® Office Excel®'s freely available Solver throughout, this Third Edition successfully bridges the gap between the theory and applications of model building for optimization analysis.
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 to equip 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) and on spreadsheets makes this book distinctive in this area.

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Product Details

  • ISBN-13: 9780470928639
  • Publisher: Wiley
  • Publication date: 5/24/2011
  • Edition number: 2
  • Pages: 432
  • Sales rank: 930,983
  • 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 a 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 Balance Equations

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 HCUs

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 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 Budget Allocation

9.6 Two-Dimensional Location

9.7 Line Balancing

9.8 Group Assignment

Appendices

1. Supplemental Files and Software

2. Graphical Methods for Linear Programming

3. The Simplex Method

4. Using Analytic Solver Platform for Education (Online)

5. Using OpenSolver (Online)

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