Question 1

What are the decision variables in this problem?

Accepted Answer

SS = pounds of steel sheeting produced a

Question 2

A Solver's sensitivity report shows sensitivity to objective coefficients and right sides of constraints.​

Accepted Answer

A) True 
 B)False

Question 3

Suppose an objective function has the equation:   .
Then the slope of the objective function line is 2.

Accepted Answer

A) True 
 B)False

Question 4

When formulating a linear programming spreadsheet model, we specify the constraints in a Solver dialog box, since Excel does not show the constraints directly.

Accepted Answer

A) True 
 B)False

Question 5

Reduced costs indicate how much the objective coefficient of a decision variable that is currently 0 or at its upper bound must change before that the value of that variable changes.

Accepted Answer

A) True 
 B)False

Question 6

Linear programming problems can always be formulated algebraically, but not always on a spreadsheet.

Accepted Answer

A) True 
 B)False

Question 7

If an LP model has an unbounded solution, then we must have made a mistake - either we have made an input error or we omitted one or more constraints.

Accepted Answer

A) True 
 B)False

Question 8

The value, such as profit, to be optimized in an optimization model is the objective.

Accepted Answer

A) True 
 B)False

Question 9

Suppose a firm must at least meet minimum expected demands of 60 for product x and 80 of product y. An algebraic formulation of these constraints is:

Accepted Answer

A)    
B)    
C)    
D)    
A)    
B)    
C)    
D)

Question 10

It is often useful to perform sensitivity analysis to see how, or if, the optimal solution to a linear programming problem changes as we change one or more model inputs.

Accepted Answer

A) True 
 B)False

Question 11

A company produces two products. Each product can be produced on either of two machines. The time (in hours) required to produce each product on each machine is shown below:   Each month, 600 hours of time are available on each machine, and that customers are willing to buy up to the quantities of each product at the prices that are shown below:   The company's goal is to maximize the revenue obtained from selling units during the next two months.
-What are the constraints in this problem?

Accepted Answer

Market demand constraints for

Question 12

Every linear programming problem involves optimizing a:

Accepted Answer

A)  linear regression model subject to several linear constraints 
B)  linear function subject to several linear constraints 
C)  linear function subject to several non-linear constraints 
D)  non-linear function subject to several linear constraints 
A)  linear regression model subject to several linear constraints 
B)  linear function subject to several linear constraints 
C)  linear function subject to several non-linear constraints 
D)  non-linear function subject to several linear constraints

Question 13

In some cases, a linear programming problem can be formulated such that the objective can become infinitely large (for a maximization problem) or infinitely small (for a minimization problem). This type of problem is said to be:

Accepted Answer

A)  infeasible 
B)  inconsistent 
C)  unbounded 
D)  redundant 
A)  infeasible 
B)  inconsistent 
C)  unbounded 
D)  redundant

Question 14

A chemical manufacturer produces two products, chemical X and chemical Y. Each product is manufactured by a two-step process that involves blending and mixing in machine A and packaging on machine B. Chemical X provides a $60/unit contribution to profit, while Chemical Y provides a $50 contribution to profit. The processing times for the two products on the mixing machine (A) and the packaging machine (B) are as follows:   For the upcoming two-week period, machine A has available 80 hours and machine B has available 60 hours of processing time. Forecasts of the markets indicate that the manufacturer can expect to sell a maximum of 16 units of chemical X and 18 units of chemical Y.
-What are the decision variables in this problem?

Accepted Answer

x = the amount of ch

Question 15

In general, the complete solution of a linear programming problem involves three stages: formulating the model, invoking Solver to find the optimal solution, and performing sensitivity analysis.

Accepted Answer

A) True 
 B)False

Question 16

There are two primary ways to formulate a linear programming problem: the traditional algebraic way and with spreadsheets.

Accepted Answer

A) True 
 B)False

Question 17

In using Excel® to solve linear programming problems, the decision variable cells represent the:

Accepted Answer

A)  value of the objective function 
B)  constraints 
C)  decision variables 
D)  total cost of the model 
A)  value of the objective function 
B)  constraints 
C)  decision variables 
D)  total cost of the model

Question 18

The solution of a linear programming problem using Excel® typically involves the following three stages:

Accepted Answer

A)  formulating the problem, invoking Solver, and sensitivity analysis 
B)  formulating the problem, graphing the problem, and sensitivity analysis 
C)  designing the decision variable cells, the target cells, and the constraints 
D)  designing the inputs, the decision variable cells, and the outputs 
A)  formulating the problem, invoking Solver, and sensitivity analysis 
B)  formulating the problem, graphing the problem, and sensitivity analysis 
C)  designing the decision variable cells, the target cells, and the constraints 
D)  designing the inputs, the decision variable cells, and the outputs

Question 19

In using Excel® to solve linear programming problems, the objective cell represents the:

Accepted Answer

A)  value of the objective function 
B)  constraints 
C)  decision variables 
D)  total cost of the model 
A)  value of the objective function 
B)  constraints 
C)  decision variables 
D)  total cost of the model

Question 20

What is the objective function in this problem?

Accepted Answer

Minimize 50x11 + 40x

What are the decision variables in this problem?

A Solver's sensitivity report shows sensitivity to objective coefficients and right sides of constraints.

Suppose an objective function has the equation: . Then the slope of the objective function line is 2.

When formulating a linear programming spreadsheet model, we specify the constraints in a Solver dialog box, since Excel does not show the constraints directly.

Reduced costs indicate how much the objective coefficient of a decision variable that is currently 0 or at its upper bound must change before that the value of that variable changes.

Linear programming problems can always be formulated algebraically, but not always on a spreadsheet.

If an LP model has an unbounded solution, then we must have made a mistake - either we have made an input error or we omitted one or more constraints.

The value, such as profit, to be optimized in an optimization model is the objective.

Suppose a firm must at least meet minimum expected demands of 60 for product x and 80 of product y. An algebraic formulation of these constraints is:

It is often useful to perform sensitivity analysis to see how, or if, the optimal solution to a linear programming problem changes as we change one or more model inputs.

Every linear programming problem involves optimizing a:

In some cases, a linear programming problem can be formulated such that the objective can become infinitely large (for a maximization problem) or infinitely small (for a minimization problem). This type of problem is said to be:

In general, the complete solution of a linear programming problem involves three stages: formulating the model, invoking Solver to find the optimal solution, and performing sensitivity analysis.

There are two primary ways to formulate a linear programming problem: the traditional algebraic way and with spreadsheets.

In using Excel^® to solve linear programming problems, the decision variable cells represent the:

The solution of a linear programming problem using Excel^® typically involves the following three stages:

In using Excel^® to solve linear programming problems, the objective cell represents the:

What is the objective function in this problem?

Introduction to Business Analytics

Describing the Distribution of a Single Variable

Finding Relationships Among Variables

Probability and Probability Distributions

Normal, Binomial, Poisson, and Exponential Distributions

Decision Making Under Uncertainty

Sampling and Sampling Distributions

Confidence Interval Estimation

Hypothesis Testing

Regression Analysis: Estimating Relationships

Regression Analysis: Statistical Inference

Time Series Analysis and Forecasting

Optimization Models

Introduction to Simulation Modeling

Simulation Models

Data Mining

Importing Data Into Excel

Analysis of Variance and Experimental Design

Statistical Process Control

Filters

Exam 13: Introduction to Optimization Modeling

What are the decision variables in this problem?

A Solver's sensitivity report shows sensitivity to objective coefficients and right sides of constraints.​

Suppose an objective function has the equation: . Then the slope of the objective function line is 2.

When formulating a linear programming spreadsheet model, we specify the constraints in a Solver dialog box, since Excel does not show the constraints directly.

Reduced costs indicate how much the objective coefficient of a decision variable that is currently 0 or at its upper bound must change before that the value of that variable changes.

Linear programming problems can always be formulated algebraically, but not always on a spreadsheet.

If an LP model has an unbounded solution, then we must have made a mistake - either we have made an input error or we omitted one or more constraints.

The value, such as profit, to be optimized in an optimization model is the objective.

Suppose a firm must at least meet minimum expected demands of 60 for product x and 80 of product y. An algebraic formulation of these constraints is:

It is often useful to perform sensitivity analysis to see how, or if, the optimal solution to a linear programming problem changes as we change one or more model inputs.

Every linear programming problem involves optimizing a:

In some cases, a linear programming problem can be formulated such that the objective can become infinitely large (for a maximization problem) or infinitely small (for a minimization problem). This type of problem is said to be:

In general, the complete solution of a linear programming problem involves three stages: formulating the model, invoking Solver to find the optimal solution, and performing sensitivity analysis.

There are two primary ways to formulate a linear programming problem: the traditional algebraic way and with spreadsheets.

In using Excel® to solve linear programming problems, the decision variable cells represent the:

The solution of a linear programming problem using Excel® typically involves the following three stages:

In using Excel® to solve linear programming problems, the objective cell represents the:

What is the objective function in this problem?

Introduction to Business Analytics

Describing the Distribution of a Single Variable

Finding Relationships Among Variables

Probability and Probability Distributions

Normal, Binomial, Poisson, and Exponential Distributions

Decision Making Under Uncertainty

Sampling and Sampling Distributions

Confidence Interval Estimation

Hypothesis Testing

Regression Analysis: Estimating Relationships

Regression Analysis: Statistical Inference

Time Series Analysis and Forecasting

Optimization Models

Introduction to Simulation Modeling

Simulation Models

Data Mining

Importing Data Into Excel

Analysis of Variance and Experimental Design

Statistical Process Control

Filters

A Solver's sensitivity report shows sensitivity to objective coefficients and right sides of constraints.

In using Excel^® to solve linear programming problems, the decision variable cells represent the:

The solution of a linear programming problem using Excel^® typically involves the following three stages:

In using Excel^® to solve linear programming problems, the objective cell represents the: