Question 1

Which of the following make linear programming (LP) a popular tool for data scientists?

Accepted Answer

A)  LP structure allows practitioners to achieve goals despite limited resources. 
B)  LP models are easy to formulate. 
C)  LP models are easy to solve. 
D)  All of the above 
A)  LP structure allows practitioners to achieve goals despite limited resources. 
B)  LP models are easy to formulate. 
C)  LP models are easy to solve. 
D)  All of the above A

Question 2

Which of the following is not a major step when formulating linear programming models?

Accepted Answer

A)  Defining decision variables 
B)  Defining the value of decision variables 
C)  Formulating the objective function 
D)  Identifying the set of constraints 
E)  Identifying the set of non-negativity constraints 
A)  Defining decision variables 
B)  Defining the value of decision variables 
C)  Formulating the objective function 
D)  Identifying the set of constraints 
E)  Identifying the set of non-negativity constraints B

Question 3

A maximization objective function could be used by the modeler in any of the following situations except:

Accepted Answer

A)  When trying to optimize profit. 
B)  When trying to reach production levels. 
C)  When trying to improve customer satisfaction. 
D)  When trying to optimize cost. 
A)  When trying to optimize profit. 
B)  When trying to reach production levels. 
C)  When trying to improve customer satisfaction. 
D)  When trying to optimize cost. D

Question 4

Linear programming is considered to be one of the most commonly used techniques for prescriptive analytics in business organizations.

Accepted Answer

A) True 
 B)False

Question 5

Which of the following situations is associated with a non-binding constraint in the solution of the linear programming model?

Accepted Answer

A)  A non-binding constraint always has no slack (slack = 0). 
B)  A non-binding constraint always has slack (slack ? 0). 
C)  A non-binding constraint sometimes has negative slack (slack ? 0). 
D)  None of the above 
A)  A non-binding constraint always has no slack (slack = 0). 
B)  A non-binding constraint always has slack (slack ? 0). 
C)  A non-binding constraint sometimes has negative slack (slack ? 0). 
D)  None of the above

Question 6

Which of the following is a situation in which ? (i.e., the greater-than-or-equal-to sign) must be used?

Accepted Answer

A)  When a constraint represents the usage of available labor hours 
B)  When a constraint indicates that the manager wants to produce at least the ordered amount for a certain product 
C)  When a constraint indicates that the manager should not exceed the machine hours available 
D)  All of the above 
A)  When a constraint represents the usage of available labor hours 
B)  When a constraint indicates that the manager wants to produce at least the ordered amount for a certain product 
C)  When a constraint indicates that the manager should not exceed the machine hours available 
D)  All of the above

Question 7

Which of the following describes a situation in which linear programming models may result in unbounded solutions?

Accepted Answer

A)  The model has many restrictions, and the constraints do not have a common feasible region. 
B)  The model has few restrictions, and the feasible region is not bounded by the model constraints. 
C)  The model has unrealistic goals, and the objective function cannot be maximized. 
D)  All of the above 
A)  The model has many restrictions, and the constraints do not have a common feasible region. 
B)  The model has few restrictions, and the feasible region is not bounded by the model constraints. 
C)  The model has unrealistic goals, and the objective function cannot be maximized. 
D)  All of the above

Question 8

In the case of maximization problems, the line representing the objective function is moved upward and parallel to itself until reaching the first point of contact with the area of feasible solution.

Accepted Answer

A) True 
 B)False

Question 9

Which of the following is not a suggested step when formulating an objective function?

Accepted Answer

A)  Identifying a set of non-negativity constraints 
B)  Defining whether the goal is to maximize or minimize the objective function 
C)  Identifying contribution coefficients 
D)  Creating the equation for the objective function 
A)  Identifying a set of non-negativity constraints 
B)  Defining whether the goal is to maximize or minimize the objective function 
C)  Identifying contribution coefficients 
D)  Creating the equation for the objective function

Question 10

Which of the following situations is associated with a binding constraint in the solution of the linear programming model?

Accepted Answer

A)  A binding constraint always has no slack (slack = 0). 
B)  A binding constraint always has slack (slack ? 0). 
C)  A binding constraint sometimes has negative slack (slack ? 0). 
D)  None of the above 
A)  A binding constraint always has no slack (slack = 0). 
B)  A binding constraint always has slack (slack ? 0). 
C)  A binding constraint sometimes has negative slack (slack ? 0). 
D)  None of the above

Question 11

The proportionality feature of linear programming (LP) models assumes all of the following except that:

Accepted Answer

A)  The contribution of decision variables to the objective function is proportional. 
B)  The contribution of decision variables to each resource is proportional. 
C)  The contribution of each decision variable can be added to obtain the overall value of the objective function. 
D)  All of the above are features of proportionality in LP. 
A)  The contribution of decision variables to the objective function is proportional. 
B)  The contribution of decision variables to each resource is proportional. 
C)  The contribution of each decision variable can be added to obtain the overall value of the objective function. 
D)  All of the above are features of proportionality in LP.

Question 12

Linear programming models have multiple solutions when the slope of the iso-profit line of the objective function differs from the slopes of each line representing any binding constraint.

Accepted Answer

A) True 
 B)False

Question 13

Which of the following is a business situation in which linear programming (LP) models can be used appropriately?

Accepted Answer

A)  A manager of a production line wants to minimize the total production and inventory cost while meeting the sales demands for products. 
B)  A financial analyst wants to maximize the return on an investment with a limited budget for stocks and bonds. 
C)  A marketing manager wants to maximize the customer lifetime value with a limited advertising budget. 
D)  A logistics manager wants to minimize the total transportation cost for products and raw materials while meeting customer demands for those products. 
E)  All of the above business situations can be modeled with LP models. 
A)  A manager of a production line wants to minimize the total production and inventory cost while meeting the sales demands for products. 
B)  A financial analyst wants to maximize the return on an investment with a limited budget for stocks and bonds. 
C)  A marketing manager wants to maximize the customer lifetime value with a limited advertising budget. 
D)  A logistics manager wants to minimize the total transportation cost for products and raw materials while meeting customer demands for those products. 
E)  All of the above business situations can be modeled with LP models.

Question 14

Which of the following is assumed when considering the additivity feature of linear programming (LP) models?

Accepted Answer

A)  The contribution of decision variables to the objective function is proportional. 
B)  The contribution of decision variables to each resource is proportional. 
C)  The contribution of each decision variable can be added to obtain the overall value of the objective function. 
D)  All of the above are features of proportionality in LP. 
A)  The contribution of decision variables to the objective function is proportional. 
B)  The contribution of decision variables to each resource is proportional. 
C)  The contribution of each decision variable can be added to obtain the overall value of the objective function. 
D)  All of the above are features of proportionality in LP.

Question 15

Which of the following is a situation in which ? (i.e., the less-than-or equal-to sign) must be used?

Accepted Answer

A)  When a constraint represents the usage of available labor hours 
B)  When a constraint indicates that the manager wants to produce at least the ordered amount for a certain product 
C)  When a constraint indicates that the manager should not exceed the machine hours available 
D)  All of the above 
A)  When a constraint represents the usage of available labor hours 
B)  When a constraint indicates that the manager wants to produce at least the ordered amount for a certain product 
C)  When a constraint indicates that the manager should not exceed the machine hours available 
D)  All of the above

Question 16

Why it is important for a modeler to follow a step-by-step methodology when formulating linear programming models?

Accepted Answer

A)  The modeler can avoid the complexity of the models. 
B)  The modeler can implement a "model less, iterate more" approach to improve the efficiency of the optimization algorithms. 
C)  The modeler can use the formulated model multiple times with newly calculated input parameters. 
D)  The modeler can fine-tune the model accordingly. 
A)  The modeler can avoid the complexity of the models. 
B)  The modeler can implement a "model less, iterate more" approach to improve the efficiency of the optimization algorithms. 
C)  The modeler can use the formulated model multiple times with newly calculated input parameters. 
D)  The modeler can fine-tune the model accordingly.

Question 17

Which of the following describes a situation in which linear programming models result in infeasible solutions?

Accepted Answer

A)  The model has many restrictions, and the constraints do not have a common feasible region. 
B)  The model has unrealistic goals, and the objective function cannot be maximized. 
C)  The model has unrealistic goals, and the objective function cannot be minimized. 
D)  All of the above 
A)  The model has many restrictions, and the constraints do not have a common feasible region. 
B)  The model has unrealistic goals, and the objective function cannot be maximized. 
C)  The model has unrealistic goals, and the objective function cannot be minimized. 
D)  All of the above

Question 18

Following a step-by-step approach to formulate and solve linear programming models helps practitioners to avoid complexity.

Accepted Answer

A) True 
 B)False

Question 19

The additivity feature of linear programming models assumes that the usage of each constraint can be calculated by adding the individual usage of the constraint by each decision variable.

Accepted Answer

A) True 
 B)False

Question 20

Linear programming models with infeasible solutions often arise in practice because management expectations are too high.

Accepted Answer

A) True 
 B)False

Which of the following make linear programming (LP) a popular tool for data scientists?

Which of the following is not a major step when formulating linear programming models?

A maximization objective function could be used by the modeler in any of the following situations except:

Linear programming is considered to be one of the most commonly used techniques for prescriptive analytics in business organizations.

Which of the following situations is associated with a non-binding constraint in the solution of the linear programming model?

Which of the following is a situation in which ? (i.e., the greater-than-or-equal-to sign) must be used?

Which of the following describes a situation in which linear programming models may result in unbounded solutions?

In the case of maximization problems, the line representing the objective function is moved upward and parallel to itself until reaching the first point of contact with the area of feasible solution.

Which of the following is not a suggested step when formulating an objective function?

Which of the following situations is associated with a binding constraint in the solution of the linear programming model?

The proportionality feature of linear programming (LP) models assumes all of the following except that:

Linear programming models have multiple solutions when the slope of the iso-profit line of the objective function differs from the slopes of each line representing any binding constraint.

Which of the following is a business situation in which linear programming (LP) models can be used appropriately?

Which of the following is assumed when considering the additivity feature of linear programming (LP) models?

Which of the following is a situation in which ? (i.e., the less-than-or equal-to sign) must be used?

Why it is important for a modeler to follow a step-by-step methodology when formulating linear programming models?

Which of the following describes a situation in which linear programming models result in infeasible solutions?

Following a step-by-step approach to formulate and solve linear programming models helps practitioners to avoid complexity.

The additivity feature of linear programming models assumes that the usage of each constraint can be calculated by adding the individual usage of the constraint by each decision variable.

Linear programming models with infeasible solutions often arise in practice because management expectations are too high.

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Business Analytics With Linear Programming

Business Analytics With Nonlinear Programming

Business Analytics With Goal Programming

Business Analytics With Integer Programming

Business Analytics With Shipment Models

Marketing Analytics With Linear Programming

Marketing Analytics With Multiple Goals

Business Analytics With Simulation

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Exam 2: Introduction to Linear Programming

Which of the following make linear programming (LP) a popular tool for data scientists?

Which of the following is not a major step when formulating linear programming models?

A maximization objective function could be used by the modeler in any of the following situations except:

Linear programming is considered to be one of the most commonly used techniques for prescriptive analytics in business organizations.

Which of the following situations is associated with a non-binding constraint in the solution of the linear programming model?

Which of the following is a situation in which ? (i.e., the greater-than-or-equal-to sign) must be used?

Which of the following describes a situation in which linear programming models may result in unbounded solutions?

In the case of maximization problems, the line representing the objective function is moved upward and parallel to itself until reaching the first point of contact with the area of feasible solution.

Which of the following is not a suggested step when formulating an objective function?

Which of the following situations is associated with a binding constraint in the solution of the linear programming model?

The proportionality feature of linear programming (LP) models assumes all of the following except that:

Linear programming models have multiple solutions when the slope of the iso-profit line of the objective function differs from the slopes of each line representing any binding constraint.

Which of the following is a business situation in which linear programming (LP) models can be used appropriately?

Which of the following is assumed when considering the additivity feature of linear programming (LP) models?

Which of the following is a situation in which ? (i.e., the less-than-or equal-to sign) must be used?

Why it is important for a modeler to follow a step-by-step methodology when formulating linear programming models?

Which of the following describes a situation in which linear programming models result in infeasible solutions?

Following a step-by-step approach to formulate and solve linear programming models helps practitioners to avoid complexity.

The additivity feature of linear programming models assumes that the usage of each constraint can be calculated by adding the individual usage of the constraint by each decision variable.

Linear programming models with infeasible solutions often arise in practice because management expectations are too high.

Business Analytics With Management Science

Business Analytics With Linear Programming

Business Analytics With Nonlinear Programming

Business Analytics With Goal Programming

Business Analytics With Integer Programming

Business Analytics With Shipment Models

Marketing Analytics With Linear Programming

Marketing Analytics With Multiple Goals

Business Analytics With Simulation

Filters