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C. Applications of Systems of Linear Inequalities

# C. Applications of Systems of Linear Inequalities

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EXAMPLE 6

Solving Applications of Linear Inequalities

As part of their retirement planning, James and Lily decide to invest up to \$30,000

in two separate investment vehicles. The first is a bond issue paying 9% and the

second is a money market certificate paying 5%. A financial adviser suggests they

invest at least \$10,000 in the certificate and not more than \$15,000 in bonds. What

various amounts can be invested in each?

Solution

Consider the ordered pairs (B, C) where B represents the money invested in bonds

and C the money invested in the certificate. Since they plan to invest no more than

\$30,000, the investment constraint would be B ϩ C Յ 30 (in thousands).

C

B ϭ 15

constraints on each investment would be

40

B Յ 15 and C Ն 10. Since they cannot invest (0, 30) Solution

region

QI

less than zero dollars, the last two constraints

30

are B Ն 0 and C Ն 0.

B ϩ C Յ 30

B Յ 15

μ C Ն 10

BՆ0

CՆ0

20

(15, 15)

C ϭ 10

10

(0, 10)

(15, 10)

10

20

30

40

B

The resulting system is shown in the figure, and indicates solutions will be in the

There is a vertical boundary line at B ϭ 15 with shading to the left (less than)

and a horizontal boundary line at C ϭ 10 with shading above (greater than). After

graphing C ϭ 30 Ϫ B, we see the solution region is a quadrilateral with vertices at

(0, 10), (0, 30), (15, 10), and (15, 15), as shown.

Now try Exercises 61 and 62 ᮣ

C. You’ve just seen how

we can solve applications

using a system of linear

inequalities

From Example 6, any ordered pair in this region or on its boundaries would represent an investment of the form (money in bonds, money in CDs) S (B, C), and would

satisfy all constraints in the system. A natural follow-up question would be—What

combination of (money in bonds, money in CDs) would offer the greatest return? This

would depend on the interest being paid on each investment, and introduces us to a

study of linear programming, which follows soon.

D. Linear Programming

To become as profitable as possible, corporations look for ways to maximize their revenue and minimize their costs, while keeping up with delivery schedules and product

demand. To operate at peak efficiency, plant managers must find ways to maximize

productivity, while minimizing related costs and considering employee welfare, union

agreements, and other factors. Problems where the goal is to maximize or minimize

the value of a given quantity under certain constraints or restrictions are called

programming problems. The quantity we seek to maximize or minimize is called the

objective function. For situations where linear programming is used, the objective

function is given as a linear function in two variables and is denoted f (x, y). A function

in two variables is evaluated in much the same way as a single variable function. To

evaluate f 1x, y2 ϭ 2x ϩ 3y at the point (4, 5), we substitute 4 for x and 5 for y:

f 14, 52 ϭ 2142 ϩ 3152 ϭ 23.

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EXAMPLE 7

Determining Maximum Values

Determine which of the following ordered pairs maximizes the value of

f 1x, y2 ϭ 5x ϩ 4y: (0, 6), (5, 0), (0, 0), or (4, 2).

Solution

Organizing our work in table form gives

Given

Point

(0, 6)

(5, 0)

(0, 0)

(4, 2)

Evaluate

f 1x, y2 ‫ ؍‬5x ؉ 4y

f 10, 62 ϭ 5102 ϩ 4162 ϭ 24

f 15, 02 ϭ 5152 ϩ 4102 ϭ 25

f 10, 02 ϭ 5102 ϩ 4102 ϭ 0

f 14, 22 ϭ 5142 ϩ 4122 ϭ 28

The function f 1x, y2 ϭ 5x ϩ 4y is maximized at (4, 2).

Now try Exercises 51 through 54 ᮣ

When the objective is stated as a linear function in two variables and the constraints are expressed as a system of linear inequalities, we have what is called a linear

programming problem. The systems of inequalities solved earlier produced solution

regions that were either bounded (as in Example 6) or unbounded (as in Example 4).

We interpret the word bounded to mean we can enclose the solution region within a

circle of appropriate size. If we cannot draw a circle around the region because it extends indefinitely in some direction, the region is said to be unbounded. In this study,

we will consider only situations that produce bounded solution regions, meaning the

regions will have three or more vertices. The regions we study will also be convex,

meaning that for any two points in the enclosed region, the line segment between them

is also in the region (Figure 6.50). Under these conditions, it can be shown that the

maximum or minimum values must occur at one of the corner points of the solution

region, also called the feasible region.

Figure 6.50

Convex

Not convex

EXAMPLE 8

Finding the Maximum of an Objective Function

Solution

y

Begin by noting that the solutions must be in QI,

8

since x Ն 0 and y Ն 0. Graph the boundary lines

7

y ϭ Ϫx ϩ 4 and y ϭ Ϫ3x ϩ 6, shading the lower

6

half plane in each case since they are “less than”

5

4

inequalities. This produces the feasible region

(1, 3)

3

shown in lavender. There are four corner points to

Feasible

2

this region: (0, 0), (0, 4), (2, 0), and (1, 3). Three of

region

1

these points are intercepts and can be found quickly.

Ϫ5

Ϫ4

Ϫ3

Ϫ2

Ϫ1

1 2 3

The point (1, 3) was found by solving the

Ϫ1

xϩyϭ4

Ϫ2

. Knowing that the objective

system e

3x ϩ y ϭ 6

function will be maximized at one of the corner points, we test them in the

objective function, using a table to organize our work.

Find the maximum value of the objective function f 1x, y2 ϭ 2x ϩ y given the

xϩyՅ4

3x ϩ y Յ 6

constraints shown: μ

.

xՆ0

yՆ0

4

5

x

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Corner

Point

(0, 0)

(0, 4)

(2, 0)

(1, 3)

Objective Function

f 1x, y2 ‫ ؍‬2x ؉ y

f 10, 02 ϭ 2102 ϩ 102 ϭ 0

f 10, 42 ϭ 2102 ϩ 142 ϭ 4

f 12, 02 ϭ 2122 ϩ 102 ϭ 4

f 11, 32 ϭ 2112 ϩ 132 ϭ 5

The objective function f 1x, y2 ϭ 2x ϩ y is maximized at (1, 3).

Now try Exercises 55 through 58 ᮣ

To help understand why solutions must occur at a vertex, note the objective function f(x, y) is maximized using only (x, y) ordered pairs from the feasible region. If we

let K represent this maximum value, the function from

Figure 6.51

Example 8 becomes K ϭ 2x ϩ y or y ϭ Ϫ2x ϩ K,

y

8

which is a line with slope Ϫ2 and y-intercept K. The

7

table in Example 8 suggests that K should range from 0

6

to 5 and graphing y ϭ Ϫ2x ϩ K for K ϭ 1, K ϭ 3,

5

and K ϭ 5 produces the family of parallel lines shown

4

(1, 3)

in Figure 6.51. Note that values of K larger than 5 will

3

2

cause the line to miss the solution region, and the max1

imum value of 5 occurs where the line intersects the

feasible region at the vertex (1, 3). These observations Ϫ5 Ϫ4 Ϫ3 Ϫ2 Ϫ1

1 2 3 4 5 x

Ϫ1

Kϭ5

lead to the following principles, which we offer withKϭ1

Kϭ3

out a formal proof.

Linear Programming Solutions

1. If the feasible region is convex and bounded, a maximum and a minimum

value exist.

2. If a unique solution exists, it will occur at a vertex of the feasible region.

3. If more than one solution exists, at least one of them occurs at a vertex of the

feasible region with others on a boundary line.

4. If the feasible region is unbounded, a linear programming problem may have

no solutions.

Solving linear programming problems depends in large part on two things:

(1) identifying the objective and the decision variables (what each variable represents

in context), and (2) using the decision variables to write the objective function and

constraint inequalities. This brings us to our five-step approach for solving linear programming applications.

Solving Linear Programming Applications

1. Identify the main objective and the decision variables (descriptive variables

may help) and write the objective function in terms of these variables.

2. Organize all information in a table, with the decision variables and

3. Complete the table using the information given, and write the constraint

inequalities using the decision variables, constraints, and the domain.

4. Graph the constraint inequalities, determine the feasible region, and identify

all corner points.

5. Test these points in the objective function to determine the optimal solution(s).

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EXAMPLE 9

623

Solving an Application of Linear Programming

The owner of a snack food business wants to create two nut mixes for the holiday

season. The regular mix will have 14 oz of peanuts and 4 oz of cashews, while the

deluxe mix will have 12 oz of peanuts and 6 oz of cashews. The owner estimates

he will make a profit of \$3 on the regular mixes and \$4 on the deluxe mixes. How

many of each should be made in order to maximize profit, if only 840 oz of

peanuts and 348 oz of cashews are available?

Solution

Our objective is to maximize profit, and the decision variables could be r to

represent the regular mixes sold, and d for the number of deluxe mixes. This gives

P1r, d2 ϭ \$3r ϩ \$4d as our objective function. The information is organized in

Table 6.1, using the variables r, d, and the constraints to head each column. Since

the mixes are composed of peanuts and cashews, these lead the rows in the table.

Table 6.1

؉

P1r, d2 ‫ ؍‬\$3r

T

Regular

r

\$4 d

T

Deluxe

d

Constraints: Total

Ounces Available

Peanuts

14

12

840

Cashews

4

6

348

After filling in the appropriate values, reading the table from left to right along the

“peanut” row and the “cashew” row, gives the constraint inequalities 14r ϩ 12d Յ 840

and 4r ϩ 6d Յ 348. Realizing we won’t be making negative numbers of mixes, the

remaining constraints are r Ն 0 and d Ն 0. The complete system is

14r ϩ 12d Յ 840

4r ϩ 6d Յ 348

μ

rՆ0

dՆ0

Note once again that the solutions must be in QI, since

r Ն 0 and d Ն 0. Graphing the first two inequalities

using slope-intercept form gives d Յ Ϫ76r ϩ 70 and

d Յ Ϫ23r ϩ 58 producing the feasible region shown

in lavender. The four corner points are (0, 0), (60, 0),

(0, 58), and (24, 42). Three of these points are

intercepts and can be read from a table of values or the

graph itself. The point (24, 42) was found by solving

14r ϩ 12d ϭ 840

the system e

. Knowing the solution

4r ϩ 6d ϭ 348

must occur at one of these points, we test them in

the objective function (Table 6.2).

d

100

90

80

70

60

50

40

30

20

Feasible

region

10

10 20 30 40 50 60 70 80 90 100

r

Table 6.2

Corner

Point

(0, 0)

(60, 0)

Objective Function

P(r, d ) ‫ ؍‬\$3r ؉ \$4d

P10, 02 ϭ \$3102 ϩ \$4102 ϭ \$0

P160, 02 ϭ \$31602 ϩ \$4102 ϭ \$180

(0, 58)

P10, 582 ϭ \$3102 ϩ \$41582 ϭ \$232

(24, 42)

P124, 422 ϭ \$31242 ϩ \$41422 ϭ \$240

Profit will be maximized if 24 boxes of the regular mix and 42 boxes of the deluxe

Now try Exercises 63 through 68

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Linear programming can also be used to minimize an objective function, as in

Example 10.

EXAMPLE 10

Minimizing Costs Using Linear Programming

A beverage producer needs to minimize shipping costs from its two primary plants

in Kansas City (KC) and St. Louis (STL). All wholesale orders within the state are

shipped from one of these plants. An outlet in Macon orders 200 cases of soft

drinks on the same day an order for 240 cases comes from Springfield. The plant in

KC has 300 cases ready to ship and the plant in STL has 200 cases. The cost of

shipping each case to Macon is \$0.50 from KC, and \$0.70 from STL. The cost of

shipping each case to Springfield is \$0.60 from KC, and \$0.65 from STL. How

many cases should be shipped from each warehouse to minimize costs?

Solution

Our objective is to minimize costs, which depends on the number of cases shipped

from each plant. To begin we use the following assignments:

A S cases shipped from KC to Macon

B S cases shipped from KC to Springfield

C S cases shipped from STL to Macon

D S cases shipped from STL to Springfield

From this information, the equation for total cost T is

T ϭ 0.5A ϩ 0.6B ϩ 0.7C ϩ 0.65D,

an equation in four variables. To make the cost equation more manageable, note

since Macon ordered 200 cases, A ϩ C ϭ 200. Similarly, Springfield ordered

240 cases, so B ϩ D ϭ 240. After solving for C and D, respectively, these equations

enable us to substitute for C and D, resulting in an equation with just two variables.

For C ϭ 200 Ϫ A and D ϭ 240 Ϫ B we have

T1A, B2 ϭ 0.5A ϩ 0.6B ϩ 0.71200 Ϫ A2 ϩ 0.651240 Ϫ B2

ϭ 0.5A ϩ 0.6B ϩ 140 Ϫ 0.7A ϩ 156 Ϫ 0.65B

ϭ 296 Ϫ 0.2A Ϫ 0.05B

The constraints involving the KC plant are A ϩ B Յ 300 with A Ն 0, B Ն 0. The

constraints for the STL plant are C ϩ D Յ 200 with C Ն 0, D Ն 0. Since we want

a system in terms of A and B only, we again substitute C ϭ 200 Ϫ A and

D ϭ 240 Ϫ B in all the STL inequalities:

C ϩ D Յ 200

1200 Ϫ A2 ϩ 1240 Ϫ B2 Յ 200

440 Ϫ A Ϫ B Յ 200

240 Յ A ϩ B

STL inequalities

substitute 200 Ϫ A

for C, 240 Ϫ B for D

CՆ0

200 Ϫ A Ն 0

200 Ն A

DՆ0

240 Ϫ B Ն 0

240 Ն B

simplify

result

Combining the new STL constraints with those from KC produces the following

system and solution. All points of intersection were read from the graph or located

using the related system of equations.

400

u

A ϩ B Յ 300

A ϩ B Ն 240

A Յ 200

B Յ 240

AՆ0

BՆ0

B

A ϭ 200

300

B ϭ 240

(60, 240)

200

100

Feasible

region

(200, 100)

(200, 40)

100

200

A

300

400

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To find the minimum cost, we check each vertex in the objective function.

Vertices

Objective Function

T(A, B) ‫ ؍‬296 ؊ 0.2 A ؊ 0.05B

(0, 240)

P10, 2402 ϭ 296 Ϫ 0.2102 Ϫ 0.0512402 ϭ \$284

(60, 240)

P160, 2402 ϭ 296 Ϫ 0.21602 Ϫ 0.0512402 ϭ \$272

(200, 100)

P1200, 1002 ϭ 296 Ϫ 0.212002 Ϫ 0.0511002 ϭ \$251

(200, 40)

P1200, 402 ϭ 296 Ϫ 0.212002 Ϫ 0.051402 ϭ \$254

The minimum cost occurs when A ϭ 200 and B ϭ 100, meaning the producer

should ship the following quantities:

D. You’ve just seen how

we can solve applications

using linear programming

A S cases shipped from KC to Macon ϭ 200

B S cases shipped from KC to Springfield ϭ 100

C S cases shipped from STL to Macon ϭ 0

D S cases shipped from STL to Springfield ϭ 140

Now try Exercises 69 and 70 ᮣ

6.4 EXERCISES

CONCEPTS AND VOCABULARY

Fill in the blank with the appropriate word or phrase. Carefully reread the section if needed.

1. Any line y ϭ mx ϩ b drawn in the coordinate

plane divides the plane into two regions called

.

2. For the line y ϭ mx ϩ b drawn in the coordinate

plane, solutions to y 7 mx ϩ b are found in the

region

the line.

3. The overlapping region of two or more linear

inequalities in a system is called the

region.

4. If a linear programming problem has a unique

solution (x, y), it must be a

of the feasible

region.

5. Suppose two boundary lines in a system of linear

inequalities intersect, but the point of intersection

is not a vertex of the feasible region. Describe how

this is possible.

6. Describe the conditions necessary for a linear

programming problem to have multiple solutions.

(Hint: Consider the diagram in Figure 6.51, and the

slope of the line from the objective function.)

Determine whether the ordered pairs given are solutions.

7. 2x ϩ y 7 3; (0, 0), 13, Ϫ52, 1Ϫ3, Ϫ42, 1Ϫ3, 92

8. 3x Ϫ y 7 5; (0, 0), 14, Ϫ12 , 1Ϫ1, Ϫ52, 11, Ϫ22

9. 4x Ϫ 2y Յ Ϫ8; (0, 0), 1Ϫ3, 52 , 1Ϫ3, Ϫ22 , 1Ϫ1, 12

10. 3x ϩ 5y Ն 15; (0, 0), 13, 52, 1Ϫ1, 62, 17, Ϫ32

Solve the linear inequalities by shading the appropriate

calculator.

11. x ϩ 2y 6 8

12. x Ϫ 3y 7 6

13. 2x Ϫ 3y Ն 9

14. 4x ϩ 5y Ն 15

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Solve the following inequalities using a graphing

facsimile, and comments regarding a test point.

15. 3x ϩ 2y Յ 8

16. 2x ϩ 5y Ն 10

17. 4x Ϫ 5y Ͼ Ϫ20

18. 6x Ϫ 3y Ͻ 18

Determine whether the ordered pairs given are solutions

to the accompanying system.

5y Ϫ x Ն 10

;

5y ϩ 2x Յ Ϫ5

1Ϫ2, 12, 1Ϫ5, Ϫ42, 1Ϫ6, 22, 1Ϫ8, 2.22

19. e

8y ϩ 7x Ն 56

20. • 3y Ϫ 4x Ն Ϫ12

y Ն 4; 11, 52, 14, 62, 18, 52, 15, 32

Solve each system of inequalities by graphing the

solution region. Verify the solution using a test point.

x ϩ 2y Ն 1

21. e

2x Ϫ y Յ Ϫ2

Ϫx ϩ 5y 6 5

22. e

x ϩ 2y Ն 1

3x ϩ y 7 4

23. e

x 7 2y

3x Յ 2y

24. e

y Ն 4x ϩ 3

25. e

2x ϩ y 6 4

2y 7 3x ϩ 6

26. e

x Ϫ 2y 6 Ϫ7

2x ϩ y 7 5

27. e

x 7 Ϫ3y Ϫ 2

x ϩ 3y Յ 6

28. e

2x Ϫ 5y 6 15

3x Ϫ 2y 7 6

5x ϩ 4y Ն 20

29. e

xϪ1Նy

31. e

0.2x 7 Ϫ0.3y Ϫ 1

0.3x ϩ 0.5y Յ 0.6

10x Ϫ 4y Յ 20

30. e

5x Ϫ 2y 7 Ϫ1

32. e

x 7 Ϫ0.4y Ϫ 2.2

x ϩ 0.9y Յ Ϫ1.2

3

x

2

33. •

4y Ն 6x Ϫ 12

3x ϩ 4y 7 12

2

34. •

y 6 x

3

Ϫ2

3

35. μ

1

x ϩ

2

2

1

xϩ yՅ5

2

5

36. μ

5

x Ϫ 2y Ն Ϫ5

6

3

yՅ1

4

2y Ն 3

x Ϫ y Ն Ϫ4

37. • 2x ϩ y Յ 4

x Ն 0, y Ն 0

2x Ϫ y Յ 5

38. • x ϩ 3y Յ 6

x Ն 0, y Ն 0

yՅxϩ3

39. • x ϩ 2y Յ 4

x Ն 0, y Ն 0

4y 6 3x ϩ 12

40. • y Յ x ϩ 1

x Ն 0, y Ն 0

2x ϩ 3y Յ 18

41. • 2x ϩ y Յ 10

x Ն 0, y Ն 0

8x ϩ 5y Յ 40

42. • x ϩ y Յ 7

x Ն 0, y Ն 0

Use a graphing calculator to find the solution region for

include a screen shot or facsimile, and the location of

any points of intersection.

y ϩ 2x 6 8

yϩx 6 6

43. μ

xՆ0

yՆ0

x ϩ 2y 6 10

xϩy 6 7

44. μ

xՆ0

yՆ0

Ϫ2x Ϫ y 7 Ϫ8

Ϫx Ϫ 2y 6 Ϫ7

45. μ

xՆ0

yՆ0

y ϩ 2x Ն 10

2y ϩ x Յ 11

46. μ

xՆ0

yՆ0

Use the equations given to write the system of linear

inequalities represented by each graph.

47.

Ϫ5Ϫ4Ϫ3Ϫ2Ϫ1

Ϫ1

Ϫ2

Ϫ3

Ϫ4

Ϫ5

49.

48.

y

5

4

3

2

1

yϪxϭ1

Ϫ5Ϫ4Ϫ3Ϫ2Ϫ1

Ϫ1

Ϫ2

Ϫ3

Ϫ4

Ϫ5

Ϫ5Ϫ4Ϫ3Ϫ2Ϫ1

Ϫ1

Ϫ2

Ϫ3

Ϫ4

Ϫ5

1 2 3 4 5 x

xϩyϭ3

50.

y

5

4

3

2

1

yϪxϭ1

1 2 3 4 5 x

xϩyϭ3

y

5

4

3

2

1

yϪxϭ1

1 2 3 4 5 x

xϩyϭ3

y

5

4

3

2

1

Ϫ5Ϫ4Ϫ3Ϫ2Ϫ1

Ϫ1

Ϫ2

Ϫ3

Ϫ4

Ϫ5

yϪxϭ1

1 2 3 4 5 x

xϩyϭ3

Determine which of the ordered pairs given produces

the maximum value of f (x, y).

51. f 1x, y2 ϭ 12x ϩ 10y; (0, 0), (0, 8.5), (7, 0), (5, 3)

52. f 1x, y2 ϭ 50x ϩ 45y; (0, 0), (0, 21), (15, 0),

(7.5, 12.5)

Determine which of the ordered pairs given produces

the minimum value of f (x, y).

53. f 1x, y2 ϭ 8x ϩ 15y; (0, 20), (35, 0), (5, 15),

(12, 11)

54. f 1x, y2 ϭ 75x ϩ 80y; (0, 9), (10, 0), (4, 5), (5, 4)

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For Exercises 55 and 56, find the maximum value of the

objective function f (x, y) ‫ ؍‬8x ؉ 5y and where this

value occurs, given the constraints shown.

x ϩ 2y Յ 6

3x ϩ y Յ 8

55. μ

xՆ0

yՆ0

2x ϩ y Յ 7

x ϩ 2y Յ 5

56. μ

xՆ0

yՆ0

For Exercises 57 and 58, find the minimum value of the

objective function f (x, y) ‫ ؍‬36x ؉ 40y and where this

value occurs, given the constraints shown.

3x ϩ 2y Ն 18

3x ϩ 4y Ն 24

57. μ

xՆ0

yՆ0

2x ϩ y Ն 10

x ϩ 4y Ն 3

58. μ

xՆ2

yՆ0

WORKING WITH FORMULAS

Area Formulas

59. The area of a triangle is usually given as

A ϭ 12 BH, where B and H represent the base and

height, respectively. The area of a rectangle can be

stated as A ϭ BH. If the base of both a triangle and

rectangle is equal to 20 in., what are the possible

values for H if the triangle must have an area

greater than 50 in2 and the rectangle must have an

area less than 200 in2?

627

Volume Formulas

60. The volume of a cone is V ϭ 13␲r 2h, where r is the

radius of the base and h is the height. The volume

of a cylinder is V ϭ ␲r 2h. If the radius of both a

cone and cylinder is equal to 10 cm, what are the

possible values for h if the cone must have a

volume greater than 200 cm3 and the volume of

the cylinder must be less than 850 cm3?

APPLICATIONS

Write a system of linear inequalities that models the

information given, then solve. Verify the solution region

using a graphing calculator.

61. Gifts to grandchildren: Grandpa Augustus is

considering how to divide a \$50,000 gift between

his two grandchildren, Julius and Anthony. After

weighing their respective positions in life and

family responsibilities, he decides he must

bequeath at least \$20,000 to Julius, but no more

than \$25,000 to Anthony. Determine the possible

ways that Grandpa can divide the \$50,000.

62. Guns versus butter: Every year, governments

around the world have to make the decision as to

how much of their revenue must be spent on

national defense and domestic improvements (guns

versus butter). Suppose total revenue for these two

needs was \$120 billion, and a government decides

they need to spend at least \$42 billion on butter and

no more than \$80 billion on defense. Determine the

possible amounts that can go toward each need.

Solve the following applications of linear programming.

63. Land/crop allocation: A farmer has 500 acres of

land to plant corn and soybeans. During the last

few years, market prices have been stable and the

farmer anticipates a profit of \$900 per acre on the

corn harvest and \$800 per acre on the soybeans.

The farmer must take into account the time it takes

to plant and harvest each crop, which is 3 hr/acre

for corn and 2 hr/acre for soybeans. If the farmer

has at most 1300 hr to plant, care for, and harvest

each crop, how many acres of each crop should be

planted in order to maximize profits?

64. Coffee blends: The owner of a coffee shop has

decided to introduce two new blends of coffee in

order to attract new customers — a Deluxe Blend

and a Savory Blend. Each pound of the deluxe

blend contains 30% Colombian and 20% Arabian

coffee, while each pound of the savory blend

contains 35% Colombian and 15% Arabian coffee

(the remainder of each is made up of cheap and

plentiful domestic varieties). The profit on the

deluxe blend will be \$1.25 per pound, while the

profit on the savory blend will be \$1.40 per pound.

How many pounds of each should the owner make

in order to maximize profit, if only 455 lb of

Colombian coffee and 250 lb of Arabian coffee are

currently available?

65. Manufacturing screws: A machine shop

manufactures two types of screws — sheet metal

screws and wood screws, using three different

machines. Machine Moe can make a sheet metal

screw in 20 sec and a wood screw in 5 sec.

Machine Larry can make a sheet metal screw in

5 sec and a wood screw in 20 sec. Machine Curly,

the newest machine (nyuk, nyuk) can make a sheet

metal screw in 15 sec and a wood screw in 15 sec.

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CHAPTER 6 Systems of Equations and Inequalities

(Shemp couldn’t get a job because he failed the

math portion of the employment exam.) Each

machine can operate for only 3 hr each day before

shutting down for maintenance. If sheet metal

screws sell for 10 cents and wood screws sell for

12 cents, how many of each type should the

machines be programmed to make in order to

maximize revenue? (Hint: Standardize time units.)

66. Hauling hazardous waste: A waste disposal

company is contracted to haul away some

hazardous waste material. A full container of liquid

waste weighs 800 lb and has a volume of 20 ft3. A

full container of solid waste weighs 600 lb and has

a volume of 30 ft3. The trucks used can carry at

most 10 tons (20,000 lb) and have a carrying

volume of 800 ft3. If the trucking company makes

\$300 for disposing of liquid waste and \$400 for

disposing of solid waste, what is the maximum

revenue per truck that can be generated?

67. Maximizing profit — food service: P. Barrett &

Justin, Inc., is starting up a fast-food restaurant

specializing in peanut butter and jelly sandwiches.

Some of the peanut butter varieties are smooth,

crunchy, reduced fat, and reduced sugar. The jellies

will include those expected and common, as well

as some exotic varieties such as kiwi and mango.

Independent research has determined the two most

popular sandwiches will be the traditional P&J

(smooth peanut butter and grape jelly), and the

uses 2 oz of peanut butter and 3 oz of jelly. The

Double-T uses 4 oz of peanut butter and 5 oz of

jelly. The traditional sandwich will be priced at

\$2.00, and a Double-T at \$3.50. If the restaurant

has 250 oz of smooth peanut butter and 345 oz of

grape jelly on hand for opening day, how many of

each should they make and sell to maximize

revenue?

6–54

68. Maximizing profit — construction materials:

Mooney and Sons produces and sells two varieties

of concrete mixes. The mixes are packaged in 50-lb

bags. Type A is appropriate for finish work, and

contains 20 lb of cement and 30 lb of sand. Type B

is appropriate for foundation and footing work, and

contains 10 lb of cement and 20 lb of sand. The

remaining weight comes from gravel aggregate.

The profit on type A is \$1.20/bag, while the profit

on type B is \$0.90/bag. How many bags of each

should the company make to maximize profit, if

2750 lb of cement and 4500 lb of sand are

currently available?

69. Minimizing transportation costs: Robert’s Las

Vegas Tours needs to drive 375 people and 19,450 lb

of luggage from Salt Lake City, Utah, to Las Vegas,

Nevada, and can charter buses from two companies.

The buses from company X carry 45 passengers and

2750 lb of luggage at a cost of \$1250 per trip.

Company Y offers buses that carry 60 passengers

and 2800 lb of luggage at a cost of \$1350 per trip.

How many buses should be chartered from each

company in order for Robert to minimize the cost?

70. Minimizing shipping costs: An oil company is

trying to minimize shipping costs from its two

primary refineries in Tulsa, Oklahoma, and

Houston, Texas. All orders within the region are

shipped from one of these two refineries. An order

for 220,000 gal comes in from a location in

Colorado, and another for 250,000 gal from a

location in Mississippi. The Tulsa refinery has

320,000 gal ready to ship, while the Houston

refinery has 240,000 gal. The cost of transporting

each gallon to Colorado is \$0.05 from Tulsa and

\$0.075 from Houston. The cost of transporting

each gallon to Mississippi is \$0.06 from Tulsa and

\$0.065 from Houston. How many gallons should

be distributed from each refinery to minimize the

cost of filling both orders?

EXTENDING THE CONCEPT

71. Graph the feasible region formed by the system

xՆ0

yՆ0

μ

. (a) How would you describe this region?

yՅ3

xՅ3

(b) Select random points within the region or on any

boundary line and evaluate the objective function

f 1x, y2 ϭ 4.5x ϩ 7.2y. At what point (x, y) will this

function be maximized? (c) How does this relate to

optimal solutions to a linear programing problem?

72. Find the maximum value of the objective function

f 1x, y2 ϭ 22x ϩ 15y given the constraints

2x ϩ 5y Յ 24

3x ϩ 4y Յ 29

μ x ϩ 6y Յ 26 .

xՆ0

yՆ0

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Making Connections

75. (2.6) The resistance to current flow in copper wire

varies directly as its length and inversely as the

square of its diameter. A wire 8 m long with a

0.004-m diameter has a resistance of 1500 ⍀. Find

the resistance in a wire of like material that is 2.7 m

long with a 0.005-m diameter.

73. (R.4) Find all solutions (real and complex) by

factoring: x3 Ϫ 5x2 ϩ 3x Ϫ 15 ϭ 0.

74. (4.6) Solve the rational inequality. Write your

xϩ2

7 0

x Ϫ9

76. (5.5) Solve for x: Ϫ350 ϭ 211eϪ0.025x Ϫ 450.

MAKING CONNECTIONS

Making Connections: Graphically, Symbollically, Numerically, and Verbally

Eight graphs (a) through (h) are given. Match the characteristics or equations shown in 1 through 16 to one of the

eight graphs

y

(a)

Ϫ4Ϫ3Ϫ2Ϫ1

Ϫ1

Ϫ2

Ϫ3

Ϫ4

Ϫ5

(f)

5

4

3

2

1

Ϫ5Ϫ4Ϫ3Ϫ2Ϫ1

Ϫ1

Ϫ2

Ϫ3

Ϫ4

Ϫ5

1. ____ e

1 2 3 4 5 x

xϩyՅ4

x ϩ 2y Յ 6

b2

4. ____ (0, 0) is a solution

5. ____ e

2x ϩ 3y ϭ 9

Ϫ2x ϩ 3y ϭ Ϫ3

(h)

5

4

3

2

1

Ϫ5Ϫ4Ϫ3Ϫ2Ϫ1

Ϫ1

Ϫ2

Ϫ3

Ϫ4

Ϫ5

xϩyՅ4

x ϩ 2y Ն 6

5

4

3

2

1

Ϫ5Ϫ4Ϫ3Ϫ2Ϫ1

Ϫ1

Ϫ2

Ϫ3

Ϫ4

Ϫ5

1 2 3 4 5 6 x

y

(g)

1 2 3 4 5 6 x

6. ____ e

2x ϩ 3y ϭ 9

9

3

2. ____ •

xϩ yϭ

2

2

3. ____ m1 ϭ m2, b1

y

y

(d)

5

4

3

2

1

Ϫ4Ϫ3Ϫ2Ϫ1

Ϫ1

Ϫ2

Ϫ3

Ϫ4

Ϫ5

1 2 3 4 5 x

5

4

3

2

1

Ϫ4Ϫ3Ϫ2Ϫ1

Ϫ1

Ϫ2

Ϫ3

Ϫ4

Ϫ5

y

(c)

5

4

3

2

1

Ϫ5Ϫ4Ϫ3Ϫ2Ϫ1

Ϫ1

Ϫ2

Ϫ3

Ϫ4

Ϫ5

1 2 3 4 5 6 x

y

(e)

y

(b)

5

4

3

2

1

1 2 3 4 5 x

1 2 3 4 5 x

y

5

4

3

2

1

Ϫ4Ϫ3Ϫ2Ϫ1

Ϫ1

Ϫ2

Ϫ3

Ϫ4

Ϫ5

1 2 3 4 5 6 x

11. ____ consistent, dependent

7. ____ (Ϫ1, 4) is a solution

12. ____ inconsistent system

8. ____ nonlinear system

13. ____ e

xϩyՆ4

x ϩ 2y Յ 6

2x ϩ 3y ϭ 9

x2 ϩ y ϭ 2x ϩ 3

9. ____ e

2x ϩ 3y ϭ 9

2x ϩ 3y ϭ 3

14. ____ e

10. ____ e

xϩyՆ4

x ϩ 2y Ն 6

15. ____ exactly two solutions

16. ____ consistent, independent,

linear system

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CHAPTER 6 Systems of Equations and Inequalities

6–56

SUMMARY AND CONCEPT REVIEW

SECTION 6.1

Linear Systems in Two Variables with Applications

KEY CONCEPTS

• A solution to a linear system in two variables is an ordered pair (x, y) that makes all equations in the system true.

• Since every point on the graph of a line satisfies the equation of that line, a point where two lines intersect must

satisfy both equations and is a solution of the system.

• A system with at least one solution is called a consistent system.

• If the lines have different slopes, there is a unique solution to the system (they intersect at a single point). The

system is called a consistent and independent system.

• If the lines have equal slopes and the same y-intercept, they form identical or coincident lines. Since one line is

right atop the other, they intersect at all points with an infinite number of solutions. The system is called a

consistent and dependent system.

• If the lines have equal slopes but different y-intercepts, they will never intersect. The system has no solution and is

called an inconsistent system.

EXERCISES

Solve each system by graphing manually. Verify your answer by graphing on a graphing calculator. If the system is

inconsistent or dependent, so state.

0.2x ϩ 0.5y ϭ Ϫ1.4

3x Ϫ 2y ϭ 4

2x ϩ y ϭ 2

1. e

2. e

3. e

Ϫx ϩ 3y ϭ 8

x Ϫ 0.3y ϭ 1.4

x Ϫ 2y ϭ 4

Solve using substitution. Indicate whether each system is consistent, inconsistent, or dependent. Write unique

solutions as an ordered pair. Check your answer using a graphing calculator.

x Ϫ 2y ϭ 3

yϭ5Ϫx

yϭ4

4. e

5. e

6. e

x Ϫ 4y ϭ Ϫ1

2x ϩ 2y ϭ 13

0.4x ϩ 0.3y ϭ 1.7

Solve using elimination. Indicate whether each system is consistent, inconsistent, or dependent. Write unique

solutions as an ordered pair. Check your answer using a graphing calculator.

2x Ϫ 4y ϭ 10

2x ϭ 3y ϩ 6

7. e

8. e

3x ϩ 4y ϭ 5

2.4x ϩ 3.6y ϭ 6

9. When it was first constructed in 1968, the John Hancock building in Chicago, Illinois, was the tallest structure in

the world. In 1974, the Willis Tower in Chicago (formerly known as the Sears Tower) became the world’s tallest

structure. The Willis Tower is 323 ft taller than the John Hancock Building, and the sum of their heights is 2577 ft.

How tall is each structure?

10. The manufacturer of a revolutionary automobile spark plug, finds that demand for the plug

can be modeled by the function D(p) ϭ Ϫ0.8p ϩ 110, where D(p) represents the number of

plugs bought (demanded, in tens of thousands) at price p in cents. The supply of these spark

plugs is modeled by S(p) ϭ 0.24p Ϫ 14.8, where S(p) represents the number of plugs

manufactured/supplied (in tens of thousands) at price p. Find the price for market

equilibrium using a graphing calculator.

SECTION 6.2

Linear Systems in Three Variables with Applications

KEY CONCEPTS

• The graph of a linear equation in three variables is a plane.

• Systems in three variables can be solved using substitution and elimination.

• A linear system in three variables has the following possible solution sets:

• If the planes intersect at a point, the system has one unique solution (x, y, z).

• If the planes intersect at a line, the system has linear dependence and the solution (x, y, z) can be written as

linear combinations of a single variable (a parameter).

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