QUESTIONS $-$

Please provide very brief $($clear and concise$)$ responses to each question. Cite any sources in APA format and list them in a reference list at the end of the document.; however, you do not need to adhere to any other APA guidelines.

Chapter $13$

$1.$ What are the properties of linear, integer, and nonlinear optimization models?

$2.$ Suggest two additional generic examples of linear optimization models in the format of Table $13\mathrm{.}1$ based on your work experience, personal interests $($hobbies$,$ etc.$),$ or information found in business articles.

$3.$ Explain the steps in developing a linear optimization model.

$4.$ Classify the following descriptions of constraints as bounds, limitations$,$ requirements, proportional relationships, or balance constraints:

a$.$ Each serving of chili should contain a quarter$-$pound of beef.

b$.$ Customer demand for a cereal is not expected to exceed $800$ boxes during the next month.

c$.$ The amount of cash available to invest in March is equal to the accounts receivable in February plus investment yields due on February $28.$

d$.$ A can of premium nuts should have at least twice as many cashews as peanuts.

e$.$ A warehouse has $3,500$ units available to ship to customers.

f$.$ A call center needs at least $15$ service representatives on Monday morning.

g$.$ An ice cream manufacturer has $40$ dozen fresh eggs at the start of the production shift.

$5.$ What is a feasible solution?

$6.$ What information is provided in the Solver Answer Report?

$7.$ Explain how to graphically visualize linear optimization models with two variables.

Chapter $14$

$1.$ Explain the difference between general integer variables and binary variables.

$2.$ What changes must you make in Solver to solve integer optimization models?

$3.$ Explain how to find alternate optimal solutions in workforce$-$scheduling models.

$4.$ Why are binary variables needed for project selection models?

$5.$ Explain, using examples, how to use binary variables to model various logical constraints.

$6.$ Why are nonlinear optimization models generally harder to model than linear optimization models?

$7.$ State some practical issues that must be considered when solving nonlinear optimization models.

$8.$ Explain the traveling salesperson problem. List two other applications of TSP problem.

Chapter $15$

$1.$ Explain why what$-$if analysis is important to apply to optimization models.

$2.$ 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.

$3.$ How does the Sensitivity Report differ for nonlinear optimization models?

$4.$ Describe the differences found in the Solver Sensitivity Report for models that have bounded variables.

$5.$ Explain why integer optimization models do not produce Solver Sensitivity Reports.

$6.$ How can you conduct what$-$if analyses for integer optimization models?

$7.$ Explain the value of data visualization in communicating information contained in Solver reports.

$8.$ What information in Solver reports can be visualized using Excel charts? What types of charts are useful?

$9.$ What should you look for in models to ensure that sensitivity information is interpreted correctly?

$10.$ How can you use Excel features to ensure the correct interpretation of sensitivity analysis?

Chapter $16$

$1.$ List the three things that must be specified in formulating a decision problem from among a small set of alternatives with uncertain consequences.

$2.$ Explain the structure of a payoff table.

$3.$ State the three types of strategies that can be used for decision involving uncertainty when no probabilities can be estimated for the outcomes.

$4.$ Explain how each of these strategies differ for minimizing and maximizing payoffs.

$5.$ Explain how to make an optimal tradeoff between two conflicting objectives, when one is good and the other is bad.

$6.$ How does the average payoff strategy differ from the expected value strategy?

$7.$ Explain the issues involved in using an expected value strategy for one$-$time decisions.

$8.$ Explain the structure and components of decision trees.

$9.$ Describe the process of $$rolling back$$ a decision tree to find the best decision path.

$10.$Explain what a risk profile is and how to find it$.$

$11.$ Define the terms value of information, perfect information, and expected value of perfect information.

$12.$ What is the expected value of sample information?

$13.$ What is utility theory, and how does it help to understand decision making?

$14.$ How does a risk$-$averse utility function compare to a risk$-$neutral utility function?