Explain how to find alternate optimal solutions in workforce$-$scheduling

models.

Why are binary variables needed for project selection models?

Explain, using examples, how to use binary variables to model various logical

constraints.

Why are nonlinear optimization models generally harder to model than linear

optimization models?

State some practical issues that must be considered when solving nonlinear

optimization models.

Explain the traveling salesperson problem. List two other applications of TSP

problem.

Chapter $15$

Explain why what$-$if analysis is important to apply to optimization models.

What information does the Solver Sensitivity Report provide for a linear

model? Explain how to interpret and use the Solver Sensitivity Report for a

linear model.

How does the Sensitivity Report differ for nonlinear optimization models?

Describe the differences found in the Solver Sensitivity Report for models that

have bounded variables.

Explain why integer optimization models do not produce Solver Sensitivity

Reports.

How can you conduct what$-$if analyses for integer optimization models?

Explain the value of data visualization in communicating information contained

in Solver reports.

What information in Solver reports can be visualized using Excel charts? What

types of charts are useful?

What should you look for in models to ensure that sensitivity information is

interpreted correctly?

How can you use Excel features to ensure the correct interpretation of

sensitivity analysis?

Chapter $16$

List the three things that must be specified in formulating a decision problem

from among a small set of alternatives with uncertain consequences.

Explain the structure of a payoff table.

State the three types of strategies that can be used for decision involving

uncertainty when no probabilities can be estimated for the outcomes.

Explain how each of these strategies differ for minimizing and maximizing

payoffs.

Explain how to make an optimal tradeoff between two conflicting objectives,

when one is gnod and the other is bad.

How does the average payoff strategy differ from the expected value

strategy?

Explain the issues involved in using an expected value strategy for one$-$time

decisions.

Explain the structure and components of decision trees.

Describe the process of "rolling back" a decision tree to find the best decision

path.

Explain what a risk profile is and how to find it$.$

Define the terms value of information, perfect information, and expected

value of perfect information.

What is the expected value of sample information?

What is utility theory, and how does it help to understand decision making?

How does a risk$-$averse utility function compare to a risk$-$neutral utility

function?