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Solutions Manual to Accompany An Introduction To Management Science Quantitative Approaches To Decision Making Thirteenth Edition David R. Anderson $10.98
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Solutions Manual to Accompany An Introduction To Management Science Quantitative Approaches To Decision Making Thirteenth Edition David R. Anderson
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Solutions Manual to Accompany An Introduction To Management Science Quantitative Approaches To Decision Making Thirteenth Edition David R. Anderson

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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


3-1
© 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.

,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




Chapter 1
3-2
© 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.

,Introduction

Learning Objectives
1. Develop a general understanding of the management science/operations research approach to decision
making.

2. Realize that quantitative applications begin with a problem situation.

3. Obtain a brief introduction to quantitative techniques and their frequency of use in practice.

4. Understand that managerial problem situations have both quantitative and qualitative considerations
that are important in the decision making process.

5. Learn about models in terms of what they are and why they are useful (the emphasis is on mathematical
models).

6. Identify the step-by-step procedure that is used in most quantitative approaches to decision making.

7. Learn about basic models of cost, revenue, and profit and be able to compute the breakeven point.

8. Obtain an introduction to the use of computer software packages such as Microsoft Excel in applying
quantitative methods to decision making.

9. Understand the following terms:

model infeasible solution
objective function management science
constraint operations research
deterministic model fixed cost
stochastic model variable cost
feasible solution breakeven point




Solutions:

1. Management science and operations research, terms used almost interchangeably, are broad
disciplines that employ scientific methodology in managerial decision making or problem
3-3
© 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.

, solving. Drawing upon a variety of disciplines (behavioral, mathematical, etc.), management
science and operations research combine quantitative and qualitative considerations in order to
establish policies and decisions that are in the best interest of the organization.

2. Define the problem

Identify the alternatives

Determine the criteria

Evaluate the alternatives

Choose an alternative

For further discussion see section 1.3

3. See section 1.2.

4. A quantitative approach should be considered because the problem is large, complex, important,
new and repetitive.

5. Models usually have time, cost, and risk advantages over experimenting with actual situations.

6. Model (a) may be quicker to formulate, easier to solve, and/or more easily understood.

7. Let d = distance
m = miles per gallon
c = cost per gallon,

 2d 
Total Cost =  c
 m 

We must be willing to treat m and c as known and not subject to variation.

8. a. Maximize 10x + 5y
s.t.
5x + 2y  40
x  0, y  0

b. Controllable inputs: x and y
Uncontrollable inputs: profit (10,5), labor hours (5,2) and labor-hour availability (40)




c.




3-4
© 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.

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