Deck 2: An Introduction to Linear Programming

Question 1

The improvement in the value of the objective function per unit increase in a right-hand side is the

A) sensitivity value.
B) dual price.
C) constraint coefficient.
D) slack value.

Answer 1

B

Question 2

A redundant constraint is a binding constraint.

Answer 2

False

Question 3

Only binding constraints form the shape (boundaries) of the feasible region.

Answer 3

False

Question 4

In a linear programming problem, the objective function and the constraints must be linear functions of the decision variables.

Question 5

All linear programming problems have all of the following properties EXCEPT

A) a linear objective function that is to be maximized or minimized.
B) a set of linear constraints.
C) alternative optimal solutions.
D) variables that are all restricted to nonnegative values.

Question 6

The maximization or minimization of a quantity is the

A) goal of management science.
B) decision for decision analysis.
C) constraint of operations research.
D) objective of linear programming.

Question 7

In a feasible problem, an equal-to constraint cannot be nonbinding.

Question 8

To find the optimal solution to a linear programming problem using the graphical method

A) find the feasible point that is the farthest away from the origin.
B) find the feasible point that is at the highest location.
C) find the feasible point that is closest to the origin.
D) None of the alternatives is correct.

Question 9

The constraint 5x₁ -2x₂ $\le$ 0 passes through the point (20, 50).

Question 10

A solution that satisfies all the constraints of a linear programming problem except the nonnegativity constraints is called

A) optimal.
B) feasible.
C) infeasible.
D) semi-feasible.

Question 11

Which of the following is a valid objective function for a linear programming problem?

A) Max 5xy
B) Min 4x + 3y + (2/3)z
C) Max 5x² + 6y²
D) Min (x₁ + x₂)/x₃

Question 12

Increasing the right-hand side of a nonbinding constraint will not cause a change in the optimal solution.

Question 13

As long as the slope of the objective function stays between the slopes of the binding constraints

A) the value of the objective function won't change.
B) there will be alternative optimal solutions.
C) the values of the dual variables won't change.
D) there will be no slack in the solution.

Question 14

Which of the following statements is NOT true?

A) A feasible solution satisfies all constraints.
B) An optimal solution satisfies all constraints.
C) An infeasible solution violates all constraints.
D) A feasible solution point does not have to lie on the boundary of the feasible region.

Question 15

Slack

A) is the difference between the left and right sides of a constraint.
B) is the amount by which the left side of a $\le$ constraint is smaller than the right side.
C) is the amount by which the left side of a $\ge$ constraint is larger than the right side.
D) exists for each variable in a linear programming problem.

Question 16

Which of the following special cases does not require reformulation of the problem in order to obtain a solution?

A) alternate optimality
B) infeasibility
C) unboundedness
D) each case requires a reformulation.

Question 17

All of the following statements about a redundant constraint are correct EXCEPT

A) A redundant constraint does not affect the optimal solution.
B) A redundant constraint does not affect the feasible region.
C) Recognizing a redundant constraint is easy with the graphical solution method.
D) At the optimal solution, a redundant constraint will have zero slack.

Question 18

Whenever all the constraints in a linear program are expressed as equalities, the linear program is said to be written in

A) standard form.
B) bounded form.
C) feasible form.
D) alternative form.

Question 19

A constraint that does not affect the feasible region is a

A) non-negativity constraint.
B) redundant constraint.
C) standard constraint.
D) slack constraint.

Question 20

Decision variables

A) tell how much or how many of something to produce, invest, purchase, hire, etc.
B) represent the values of the constraints.
C) measure the objective function.
D) must exist for each constraint.

Question 21

A range of optimality is applicable only if the other coefficient remains at its original value.

Question 22

An unbounded feasible region might not result in an unbounded solution for a minimization or maximization problem.

Question 23

Find the complete optimal solution to this linear programming problem.

Question 24

Find the complete optimal solution to this linear programming problem.

Question 25

It is possible to have exactly two optimal solutions to a linear programming problem.

Question 26

The Sanders Garden Shop mixes two types of grass seed into a blend. Each type of grass has been rated (per pound) according to its shade tolerance, ability to stand up to traffic, and drought resistance, as shown in the table. Type A seed costs $1 and Type B seed costs $2. If the blend needs to score at least 300 points for shade tolerance, 400 points for traffic resistance, and 750 points for drought resistance, how many pounds of each seed should be in the blend? Which targets will be exceeded? How much will the blend cost?

Question 27

For the following linear programming problem, determine the optimal solution by the graphical solution method

Question 28

Decision variables limit the degree to which the objective in a linear programming problem is satisfied.

Question 29

The standard form of a linear programming problem will have the same solution as the original problem.

Question 30

The point (3, 2) is feasible for the constraint 2x₁ + 6x₂ $\le$ 30.

Question 31

A linear programming problem can be both unbounded and infeasible.

Question 32

Because the dual price represents the improvement in the value of the optimal solution per unit increase in right-hand-side, a dual price cannot be negative.

Question 33

Alternative optimal solutions occur when there is no feasible solution to the problem.

Question 34

No matter what value it has, each objective function line is parallel to every other objective function line in a problem.

Question 35

An optimal solution to a linear programming problem can be found at an extreme point of the feasible region for the problem.

Question 36

Find the complete optimal solution to this linear programming problem.

Question 37

The constraint 2x₁ F-x₂ = 0 passes through the point (200,100).

Question 38

An infeasible problem is one in which the objective function can be increased to infinity.

Question 39

Because surplus variables represent the amount by which the solution exceeds a minimum target, they are given positive coefficients in the objective function.

Question 40

Find the complete optimal solution to this linear programming problem.

Question 41

Given the following linear program:
Solve the problem graphically. How many extreme points exist for this problem?

Question 42

Solve the following linear program by the graphical method.

Question 43

Solve the following linear program graphically.

Question 44

Does the following linear programming problem exhibit infeasibility, unboundedness, or alternate optimal solutions? Explain.

Question 45

Does the following linear programming problem exhibit infeasibility, unboundedness, or alternate optimal solutions? Explain.

Question 46

Muir Manufacturing produces two popular grades of commercial carpeting among its many other products. In the coming production period, Muir needs to decide how many rolls of each grade should be produced in order to maximize profit. Each roll of Grade X carpet uses 50 units of synthetic fiber, requires 25 hours of production time, and needs 20 units of foam backing. Each roll of Grade Y carpet uses 40 units of synthetic fiber, requires 28 hours of production time, and needs 15 units of foam backing.
The profit per roll of Grade X carpet is $200 and the profit per roll of Grade Y carpet is $160. In the coming production period, Muir has 3000 units of synthetic fiber available for use. Workers have been scheduled to provide at least 1800 hours of production time (overtime is a possibility). The company has 1500 units of foam backing available for use.
Develop and solve a linear programming model for this problem.