Solutions Manual to Accompany
An Introduction To
Management Science
Quantitative Approaches
To Decision Making
Thirteenth Edition
David R. Anderson
University of Cincinnati
Dennis J. Sweeney
University of Cincinnati
Thomas A. Williams
Rochester Institute of Technology
Jeffrey D. Camm
University of Cincinnati
R. Kipp Martin
University of Chicago
SOUTH-WESTERN
CENGAGE LearningTM
,Contents
Preface
Chapter
1. Introduction
2. An Introduction to Linear Programming
3. Linear Programming: Sensitivity Analysis and Interpretation of Solution
4. Linear Programming Applications in Marketing, Finance and Operations Management
5. Advanced Linear Programming Applications
6. Distribution and Network Models
7. Integer Linear Programming
8. Nonlinear Optimization Models
9. Project Scheduling: PERT/CPM
10. Inventory Models
11. Waiting Line Models
12. Simulation
13. Decision Analysis
14. Multicriteria Decision Problems
15. Forecasting
16. Markov Processes
17. Linear Programming: The Simplex Method
18. Simplex-Based Sensitivity Analysis and Duality
19. Solution Procedures for Transportation and Assignment Problems
20. Minimal Spanning Tree
21. Dynamic Programming
Appendix A: Building Spreadsheet Models
,Preface
The purpose of An Introduction to Management Science is to provide students with a sound
conceptual understanding of the role management science pays in the decision-making
process. The text emphasizes the application of management science by using problem
situations to introduce each of the management science concepts and techniques. The book
has been specifically designed to meet the needs of nonmathematicians who are studying
business and economics.
The Solutions Manual furnishes assistance by identifying learning objectives and providing
detailed solutions for all exercises in the text.
Note: The solutions to the case problems are included in the Solutions to Case Problems
Manual.
Acknowledgements
We would like to provide a special acknowledgement to Catherine J. Williams for her
efforts in preparing the Solutions Manual. We are also indebted to our acquisitions editor
Charles E. McCormick, Jr. and our developmental editor Maggie Kubale for their support
during the preparation of this manual.
David R. Anderson
Dennis J. Sweeney
Thomas A. Williams
Jeffrey D. Camm
R. Kipp Martin
, Chapter 21
Dynamic Programming
Learning Objectives
1. Understand the basics of dynamic programming and its approach to problem solving.
2. Learn the general dynamic programming notation.
3. Be able to use the dynamic programming approach to solve problems such as the shortest route
problem, the knapsack problem and production and inventory control problems.
4. Understand the following terms:
stages
state variables
principle of optimality
stage transformation function
return function
knapsack problem
21 - 1
© 2010 Cengage Learning. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
An Introduction To
Management Science
Quantitative Approaches
To Decision Making
Thirteenth Edition
David R. Anderson
University of Cincinnati
Dennis J. Sweeney
University of Cincinnati
Thomas A. Williams
Rochester Institute of Technology
Jeffrey D. Camm
University of Cincinnati
R. Kipp Martin
University of Chicago
SOUTH-WESTERN
CENGAGE LearningTM
,Contents
Preface
Chapter
1. Introduction
2. An Introduction to Linear Programming
3. Linear Programming: Sensitivity Analysis and Interpretation of Solution
4. Linear Programming Applications in Marketing, Finance and Operations Management
5. Advanced Linear Programming Applications
6. Distribution and Network Models
7. Integer Linear Programming
8. Nonlinear Optimization Models
9. Project Scheduling: PERT/CPM
10. Inventory Models
11. Waiting Line Models
12. Simulation
13. Decision Analysis
14. Multicriteria Decision Problems
15. Forecasting
16. Markov Processes
17. Linear Programming: The Simplex Method
18. Simplex-Based Sensitivity Analysis and Duality
19. Solution Procedures for Transportation and Assignment Problems
20. Minimal Spanning Tree
21. Dynamic Programming
Appendix A: Building Spreadsheet Models
,Preface
The purpose of An Introduction to Management Science is to provide students with a sound
conceptual understanding of the role management science pays in the decision-making
process. The text emphasizes the application of management science by using problem
situations to introduce each of the management science concepts and techniques. The book
has been specifically designed to meet the needs of nonmathematicians who are studying
business and economics.
The Solutions Manual furnishes assistance by identifying learning objectives and providing
detailed solutions for all exercises in the text.
Note: The solutions to the case problems are included in the Solutions to Case Problems
Manual.
Acknowledgements
We would like to provide a special acknowledgement to Catherine J. Williams for her
efforts in preparing the Solutions Manual. We are also indebted to our acquisitions editor
Charles E. McCormick, Jr. and our developmental editor Maggie Kubale for their support
during the preparation of this manual.
David R. Anderson
Dennis J. Sweeney
Thomas A. Williams
Jeffrey D. Camm
R. Kipp Martin
, Chapter 21
Dynamic Programming
Learning Objectives
1. Understand the basics of dynamic programming and its approach to problem solving.
2. Learn the general dynamic programming notation.
3. Be able to use the dynamic programming approach to solve problems such as the shortest route
problem, the knapsack problem and production and inventory control problems.
4. Understand the following terms:
stages
state variables
principle of optimality
stage transformation function
return function
knapsack problem
21 - 1
© 2010 Cengage Learning. All Rights Reserved.
May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.
