Chapter 16
Simulation
Learning Objectives
1.
Understand what simulation is and how it aids in the analysis of a problem.
2.
Learn why simulation is a significant problem-solving tool.
3.
Understand the difference between static and dynamic simulation.
4.

Chapter 13
Project Scheduling: PERT/CPM
Learning Objectives
1.
Understand the role and application of PERT/CPM for project scheduling.
2.
Learn how to define a project in terms of activities such that a network can be used to describe the
project.
3.
Know

Chapter 8
Linear Programming: Sensitivity Analysis
and Interpretation of Solution
Learning Objectives
1.
Understand what happens in graphical solutions when coefficients of the objective function change.
2.
Be able to interpret the range for an objective

Chapter 9
Linear Programming Applications in
Marketing, Finance, and Operations
Management
Learning Objectives
1.
Learn about applications of linear programming that have been encountered in practice.
2.
Develop an appreciation for the diversity of proble

Chapter 4
Decision Analysis
Learning Objectives
1.
Learn how to describe a problem situation in terms of decisions to be made, chance events and
consequences.
2.
Be able to analyze a simple decision analysis problem from both a payoff table and decision t

Appendix A
Building Spreadsheet Models
Learning Objectives
1.
Learn the basic fundamentals of spreadsheet modeling.
2.
Learn how to create and save a spreadsheet model in Microsoft Excel.
3.
Learn to use functions that are useful in quantitative modeling.

Chapter 5
Utility and Game Theory
Learning Objectives
1.
Know what is meant by utility.
2.
Understand why utility is a better criterion than monetary value in some decision making situations.
3.
Know how to develop a utility function for money.
4.
Learn a

CHAPTER 7
15. a.
D
600
Optimal Solution
(300,400)
500
400
z = 10,560
300
(540,252)
200
100
0
b.
100
200
300
400
500
600
S
700 x
Similar to part (a): the same feasible region with a different objective function. The
optimal solution occurs at (708, 0) with

Chapter 3
Probability Distributions
Learning Objectives
1.
Understand the concepts of a random variable and a probability distribution.
2.
Be able to distinguish between discrete and continuous random variables.
3.
Be able to compute and interpret the exp

Chapter 6
Forecasting
Learning Objectives
1.
Understand that the long-run success of an organization is often closely related to how well
management is able to predict future aspects of the operation.
2.
Know the various components of a time series.
3.
Be

Chapter 12
Advanced Optimization Applications
Learning Objectives
1.
Learn about applications of more advanced optimization models that are solved in practice.
2.
Develop an appreciation for the diversity of problems that can be modeled as optimization
pr

Chapter 11
Integer Linear Programming
Learning Objectives
1.
Be able to recognize the types of situations where integer linear programming problem formulations
are desirable.
2.
Know the difference between all-integer and mixed integer linear programming

Chapter 7
An Introduction to Linear Programming
Learning Objectives
1.
Obtain an overview of the kinds of problems linear programming has been used to solve.
2.
Learn how to develop linear programming models for simple problems.
3.
Be able to identify the

Chapter 17
Markov Processes
Learning Objectives
1.
Learn about the types of problems that can be modeled as Markov processes.
2.
Understand the Markov process approach to the market share or brand loyalty problem.
3.
Be able to set up and use the transiti

Chapter 15
Waiting Line Models
Learning Objectives
1.
Be able to identify where waiting line problems occur and realize why it is important to study these
problems.
2.
Know the difference between single-channel and multiple-channel waiting lines.
3.
Under

Chapter 14
Inventory Models
Learning Objectives
1.
Learn where inventory costs occur and why it is important for managers to make good inventory
policy decisions.
2.
Learn the economic order quantity (EOQ) model.
3.
Know how to develop total cost models f

Chapter 10
Distribution and Network Models
Learning Objectives
1.
Understand the usefulness of using optimization for supply chain problems.
2.
Be able to identify the special features of the transportation problem.
3.
Become familiar with the types of pr

Chapter 2
Introduction to Probability
Learning Objectives
1.
Obtain an understanding of the role probability information plays in the decision making process.
2.
Understand probability as a numerical measure of the likelihood of occurrence.
3.
Be able to

Chapter 1
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 introducti

Chapter 2
Introduction to Probability
Case Problem: Hamilton County Judges
The data in the table provides the basis for the analysis. We provide notes as a guide to answering
questions 1 through 5.
1.
The conditional probabilities of cases being appealed

Chapter 7
Introduction to Linear Programming
Case Problem 1: Workload Balancing
1.
Model
DI-910
DI-950
Production Rate
(minutes per printer)
Line 1
Line 2
3
4
6
2
Profit Contribution ($)
42
87
Capacity: 8 hours 60 minutes/hour = 480 minutes per day
Let
D1

Chapter 8
Linear Programming: Sensitivity Analysis and Interpretation
of Solution
Case Problem 1: Product Mix
Note to Instructor: The difference between relevant and sunk costs is critical. The cost of the shipment of
nuts is a sunk cost. Practice in appl

Chapter 10
Distribution and Network Models
Case Problem 1: Solutions Plus
1.
This case can be formulated as a transportation problem with the Cincinnati and Oakland production
facilities as the origins. The locations of the railway stations are the destin

Chapter 3
Probability Distributions
Case Problem: Specialty Toys
1.
Information provided by the forecaster
.05
.90
10,000
20,000
At x = 30,000,
z
x 30, 000 20, 000
1.96
30, 000 20, 000
5102
1.96
Normal distribution 20, 000
2.
5102
@ 15,000
15, 000 20,

Chapter 4
Decision Analysis
Case Problem 1: Property Purchase Strategy
The decision tree for the Oceanview decision problem is shown below. Note that the final outcome of
whether the zoning change is approved or rejected by the voters occurs after Oceanvi

Chapter 9
Linear Programming Applications in Marketing, Finance, and
Operations Management
Case Problem 1: Planning an Advertising Campaign
The decision variables are as follows:
T1 = number of television advertisements with rating of 90 and 4000 new cust

Chapter 15
Waiting Line Models
Case Problem 1: Regional Airlines
1.
Single-Server Waiting Line Analysis
The analysis that follows is based upon the assumptions of Poisson arrivals and exponential
service times. With one call every 3.75 minutes, we have an

Chapter 13
Project Scheduling: PERT/CPM
Case Problem: R.C. Coleman
1.
R.C. Coleman's Project Network
D
A
Start
C
B
E
F
G
Activity
A
B
C
D
E
F
G
H
I
J
K
Activity
A
B
C
D
E
F
G
H
I
J
K
I
Earliest
Start
0
0
9
13
13
23
13
29
29
35
39
K
H
J
Expected Time
6
9
4

Chapter 14
Inventory Models
Case Problem 1: Wagner Fabricating Company
1.
2.
Holding Cost
Cost of capital
Taxes/Insurance (24,000/600,000)
Shrinkage (9,000/600,000)
Warehouse overhead (15,000/600,000)
Annual rate
Ordering Cost
2 hours at $28.00
Other expe

Chapter 16
Simulation
Case Problem 1: Tri-State Corporation
With the specific financial analysis data input into cells D3:D8, the formulas used to develop the portfolio
projection worksheet are shown below. The rows are copied to extend the worksheet to t