An Introducon to - Decision Making, 15e David Anderson, Dennis Sw...

An Introduc�on to Management Science Quan�ta�ve Approaches to Decision Making, 15e David Anderson, Dennis Sweeney, Thomas Williams et al (Solu�ons Manual All Chapters, 100% Original Verified, A+ Grade) Solution Of CH 7 Is Missing Part 1: Solutions Manual: Page 1-421 Part 2: Case Solutions: Page 422-517 1 / 4

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Chapter 1 Introduction Learning Objectives

• Develop a general understanding of the management science/operations research approach to decision

making.

• Realize that quantitative applications begin with a problem situation.
• Obtain a brief introduction to quantitative techniques and their frequency of use in practice.
• Understand that managerial problem situations have both quantitative and qualitative considerations

that are important in the decision making process.

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

models).

• Identify the step-by-step procedure that is used in most quantitative approaches to decision making.
• Learn about basic models of cost, revenue, and profit and be able to compute the breakeven point.
• 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:

modelinfeasible solution objective function management science constraintoperations research deterministic model fixed cost stochastic model variable cost feasible solution breakeven point

Solutions:

Part 1: Solutions Manual 2 / 4

Chapter 1

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• Management science and operations research, terms used almost interchangeably, are broad

disciplines that employ scientific methodology in managerial decision making or problem 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.

• Define the problem

Identify the alternatives

Determine the criteria

Evaluate the alternatives

Choose an alternative

For further discussion see section 1.3

• See section 1.2.

• A quantitative approach should be considered because the problem is large, complex, important,

new and repetitive.

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

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

• Let d = distance

m = miles per gallon c = cost per gallon,

Total Cost =2d c m







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

• 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)

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Introduction

1 - 3 Profit:

Labor Hours: 5/unit for x

2/ unit for y $10/unit for x $ 5/ unit for y 40 labor-hour capacity Uncontrollable Inputs Production Quantities x and y Co ntrollable Input Projected Profit and check on production time constraint Output Max 10 x + 5 y s.t.10x y+5 40 x y

0  Mathematical Mo del

• x = 0, y = 20 Profit = $100

(Solution by trial-and-error)

• Deterministic - all uncontrollable inputs are fixed and known.

• If a = 3, x = 13 1/3 and profit = 133

If a = 4, x = 10 and profit = 100 If a = 5, x = 8 and profit = 80 If a = 6, x = 6 2/3 and profit = 67

Since a is unknown, the actual values of x and profit are not known with certainty.

• a. Total Units Received = x + y

• Total Cost = 0.20x +0.25y

• x + y = 5000

• x  4000 Kansas City Constraint

y  3000 Minneapolis Constraint

• Min 0.20x + 0.25y

s.t.x + y = 5000 x  4000 y  3000

x, y  0

• a. at $20 d = 800 - 10(20) = 600
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