Question 1

When formulating a linear programming spreadsheet model,there is one target (objective)cell that contains the value of the objective function.

Accepted Answer

A) True 
 B)False

Question 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.

Accepted Answer

A) True 
 B)False

Question 3

If a constraint has the equation   ,then the constraint line passes through the points (0,20)and (30,0):

Accepted Answer

A) True 
 B)False

Question 4

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 5

If a manufacturing process takes 3 hours per unit of x and 5 hours per unit of y and a maximum of 100 hours of manufacturing process time are available,then an algebraic formulation of this constraint is:

Accepted Answer

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

Question 6

All optimization problems have:

Accepted Answer

A) an objective function and decision variables 
B) an objective function and constraints 
C) decision variables and constraints 
D) an objective function,decision variables and constraints 
A) an objective function and decision variables 
B) an objective function and constraints 
C) decision variables and constraints 
D) an objective function,decision variables and constraints

Question 7

Linear programming models have three important properties.They are:

Accepted Answer

A) optimality,additivity and sensitivity 
B) optimality,linearity and divisibility 
C) divisibility,linearity and nonnegativity 
D) proportionality,additivity and divisibility 
A) optimality,additivity and sensitivity 
B) optimality,linearity and divisibility 
C) divisibility,linearity and nonnegativity 
D) proportionality,additivity and divisibility

Question 8

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

Accepted Answer

A) True 
 B)False

Question 9

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 10

It helps to ensure that Solver can find a solution to a linear programming problem if the model is well-scaled; that is,all of the numbers are of roughly the same magnitude.

Accepted Answer

A) True 
 B)False

Question 11

All linear programming problems should have a unique solution,if they can be solved.

Accepted Answer

A) True 
 B)False

Question 12

One of the things that you can do with linear programming and a spreadsheet model is to develop a user interface to make it easier for someone who is not an expert in using linear programming.The output can be a report that explains the optimal policy in non-technical terms.The type of system being described is called a (n):

Accepted Answer

A) expert system 
B) decision support system 
C) linear programming support system 
D) production planning system 
A) expert system 
B) decision support system 
C) linear programming support system 
D) production planning system

Question 13

The additivity property of LP models implies that the sum of the contributions from the various activities to a particular constraint equals the total contribution to that constraint.

Accepted Answer

A) True 
 B)False

Question 14

What are the decision variables in this problem?

Accepted Answer

The decision variables are how

Question 15

What are the constraints in this problem?

Accepted Answer

x11 + x12+ x13 = 5000 (Ohio Va

Question 16

There is often more than one objective in linear programming problems

Accepted Answer

A) True 
 B)False

Question 17

The divisibility property of linear programming means that a solution can have both:

Accepted Answer

A) integer and noninteger levels of an activity 
B) linear and nonlinear relationships 
C) positive and negative values 
D) revenue and cost information in the model 
A) integer and noninteger levels of an activity 
B) linear and nonlinear relationships 
C) positive and negative values 
D) revenue and cost information in the model

Question 18

Write out an algebraic expression for the objective function in this problem.

Accepted Answer

The answer of Write out an algebraic expression for the...

Question 19

If a solution to an LP problem satisfies all of the constraints,then is must be feasible.

Accepted Answer

A) True 
 B)False

Question 20

Consider the following linear programming problem: Maximize   Subject to       The above linear programming problem:

Accepted Answer

A) has only one optimal solution 
B) has more than one optimal solution 
C) exhibits infeasibility 
D) exhibits unboundedness 
A) has only one optimal solution 
B) has more than one optimal solution 
C) exhibits infeasibility 
D) exhibits unboundedness

When formulating a linear programming spreadsheet model,there is one target (objective)cell that contains the value of the objective function.

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

If a constraint has the equation ,then the constraint line passes through the points (0,20)and (30,0):

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:

If a manufacturing process takes 3 hours per unit of x and 5 hours per unit of y and a maximum of 100 hours of manufacturing process time are available,then an algebraic formulation of this constraint is:

All optimization problems have:

Linear programming models have three important properties.They are:

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

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.

It helps to ensure that Solver can find a solution to a linear programming problem if the model is well-scaled; that is,all of the numbers are of roughly the same magnitude.

All linear programming problems should have a unique solution,if they can be solved.

The additivity property of LP models implies that the sum of the contributions from the various activities to a particular constraint equals the total contribution to that constraint.

What are the decision variables in this problem?

What are the constraints in this problem?

There is often more than one objective in linear programming problems

The divisibility property of linear programming means that a solution can have both:

Write out an algebraic expression for the objective function in this problem.

If a solution to an LP problem satisfies all of the constraints,then is must be feasible.

Consider the following linear programming problem: Maximize Subject to The above linear programming problem:

Introduction to Data Analysis and Decision Making

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 Modeling: Applications

Introduction to Simulation Modeling

Simulation Models

Filters

Exam 13: Introduction to Optimization Modeling

When formulating a linear programming spreadsheet model,there is one target (objective)cell that contains the value of the objective function.

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

If a constraint has the equation ,then the constraint line passes through the points (0,20)and (30,0):

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:

If a manufacturing process takes 3 hours per unit of x and 5 hours per unit of y and a maximum of 100 hours of manufacturing process time are available,then an algebraic formulation of this constraint is:

All optimization problems have:

Linear programming models have three important properties.They are:

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

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.

It helps to ensure that Solver can find a solution to a linear programming problem if the model is well-scaled; that is,all of the numbers are of roughly the same magnitude.

All linear programming problems should have a unique solution,if they can be solved.

The additivity property of LP models implies that the sum of the contributions from the various activities to a particular constraint equals the total contribution to that constraint.

What are the decision variables in this problem?

What are the constraints in this problem?

There is often more than one objective in linear programming problems

The divisibility property of linear programming means that a solution can have both:

Write out an algebraic expression for the objective function in this problem.

If a solution to an LP problem satisfies all of the constraints,then is must be feasible.

Consider the following linear programming problem: Maximize Subject to The above linear programming problem:

Introduction to Data Analysis and Decision Making

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 Modeling: Applications

Introduction to Simulation Modeling

Simulation Models

Filters