What is the optimal cost represented by objective function

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Reference no: EM13842966 , Length:

1. Consider the following LP problem, in which X and Y denote the number of units of products X and Y to produce, respectively:

Maximize profit = $4X + $5Y

Subject to the constraints

X + 2Y 5- 10 (labor available, in hours)

6X + 6Y 5- 36 (material available, in pounds) 8X + 4Y s 40 (storage available, in square feet) X, Y 0 (nonnegativity)

Variable Cells

 

 

Final

Reduced

Objective

Allowable

Allowable

Cell

Name

Value

Cost

Coefficient

Increase

Decrease

WU

Solution value X

2.00

0 00

4.00

1.00

1.50

SC$4

Solution value Y

4.00

0 00

5.00

3.00

1.00

Constraints

Cell

Name

Final Value

Shadow Price

Constraint R.N. Side

Allowable Increase

Allowable Decrease

5DS7

Labor

10 00

1 .00

10

2.00

2.00

SOS8

Material

36.00

0.50

36.00

4.00

6.00

W39

Storage

32.00

0 .00

40.00

1E+30

8.00

Calculate and explain what happens to the optimal solution for each of the following situations. Each question is independent of the other questions.

(a) You acquire 2 additional pounds of material.

(b) You acquire 1.5 additional hours of labor.

(c) You give up 1 hour of labor and get 1.5 pounds of material.

(d) The profit contributions for both products X and Y are changed to $4.75 each.

(e) You decide to introduce a new product that has a profit contribution of $2. Each unit of this prod-uct will use 1 hour of labor, 1 pound of material, and 2 square feet of storage space.

2. The Tiger Catering Company is trying to determine the most economical combination of sandwiches to make for a tennis club. The club has asked Tiger to provide 70 sandwiches in a variety to include tuna, tuna and cheese, ham, ham and cheese, and cheese. The club has specified a minimum of 10 each of tuna and ham and 12 each of tuna/cheese and ham/ cheese. Tiger makes the sandwiches using the fol¬lowing resources: bread, tuna, ham, cheese, mayon¬naise, mustard, lettuce, tomato, packaging material, and labor hours.

 

Tuna

Tuna/Ch

Ham

Ham/Ch

Cheese

 

 

Number to make

10 00

30 00

10.00

12 00

8.00

Cost

$2.42

$2 12

S3.35

S3 02

52 36

$176.42

 

Constraints

 

 

 

 

 

 

 

 

Bread (slices)

2

2

2

2

2

140.00

a

140

Tuna (oz.)

4

3

 

 

 

130.00

<=

130

Ham (or.)

 

 

4

3

 

76.00

<=

100

 Cheese (oz.)

 

1

 

1

4

74.00

<=

80

Mayo (oz.)

1.2

0.9

0.5

0.5

0.5

54.00

<=

72

 Mustard (oz.)

 

 

0.2

0.2

 

4.40

<=

8

 Lettuce (or)

0.25

0.25

0.25

0.25

0.25

17.50

<=

20

 Tomato (oz.)

0.5

0.5

0.5

0.5

0.5

35.00

<=

40

 Package (unit)

1

1

1

1

1

70.00

<=

72

 Labor (hrs)

0.08

0.08

0.08

0.08

0.08

5.60

<=

8

 Min total

1

1

1

1

1

70.00

>=

70

Min Tuna

1

 

 

 

 

10.00

>=

10

Min Tuna/Ch

 

1

 

 

 

30.00

›=

12

Min Ham

 

 

1

 

 

10 00

>=

10

Min Ham/Ch

 

 

 

1

 

12.00

>=

12







LHS

Sign

RHS

The objective funcntion coefficients in the screenshots refer to unit cost per item. Each of the following questions is independent of the others.

(a) What is the optimal cost represented by the objective function and what is the optimal sandwich-making plan?

(b) Which constraints are binding?

(c) What is the range over which the cost for cheese sandwiches could vary without changing the production plan?

(d) What is the range over which the quantity of tuna could vary without changing the combina¬tion of binding constraints?

(e) Does this Sensitivity Report indicate the presence of multiple optimal solutions? How do you know?

(f) After the sandwiches are made, how many labor hours remain?

3 Consider the Tiger Catering problem the impact on the sandwich-making plan and total cost? If it is possible to compute the new cost or sandwich-making plan, do so.

Variable Cells

Cell

Name

Final
Value

Reduced
Cost

Objective
Coefficient

Allowable
Increase

Allowable Decrease

$B$4

Number of units Oak tables

3.00

0.00

75.00

0.00

1E+30

$C$4

Number of units Oak chairs

51.67

0.00

35.00

1E+30

0.00

$D$4

Number of units Cherry tables

3.00

0.00

90.00

0.00

1E+30

$E$4

Number of units Cherry chairs

85.56

0.00

60.00

1E+30

0.00

$F$4

Number of units Pine tables

42.26

0.00

45.00

88.33

0.00

$G$4

Number of units Pine chairs

33.08

0.00

20.00

0.00

13.25

Constraints

Cell

Name

Final
Value

Shadow
Price

Constraint
R.H. Side

Allowable
Increase

Allowable Decrease

$G$7

Labor hours

1000.00

10.00

1000.00

373.30

37.21

$G$8

Oak (pounds)

2150.00

0.00

2150.00

318.93

1250.00

$G$9

Cherry (pounds)

3800.00

0.00

3800.00

223 25

2239.78

$G$10

Pine (pounds)

8500.00

0.00

8500.00

1488.33

5039.50

$G$11

Min oak tables

3.00

0.00

3.00

6.25

2.35

$G$12

Min cherry tables

3.00

0.00

3.00

11.33

1.20

$G$13

Min oak chairs

51.67

0.00

10.00

41.67

1E+30

$G$14

Min cherry chairs

85.56

0.00

10.00

75.56

1E+30

$G$15

Min pine chairs

33.08

0.00

5.00

28.08

1E+30

(a) The unit cost for tuna sandwiches decreases by $0.30.
(b) The unit cost for tuna and cheese sandwiches increases to $2.40.
(c) The unit cost for ham sandwiches increases to $3.75.
(d) The unit cost for ham and cheese sandwiches decreases by $0.70.
(e) The club does not want any more than 12 ham sandwiches.
(f) The unit cost for cheese sandwiches decreases to $2.05.

4) Consider the Tiger Catering problem. For each of the following situations, what would be the impact on the sandwich-making plan and total cost? If it is possible to compute the new cost or sandwich-making plan, do so.

(a) The quantity of tuna available decreases to 120 ounces.
(b) The quantity of ham available increases to 115 ounces.
(c) The quantity of cheese available decreases to 72 ounces.
(d) Tiger is required to deliver a minimum of 13 tuna sandwiches.
(e) Tiger is required to deliver only a minimum of 10 tuna and cheese sandwiches.
(t) Tiger is asked to bring a minimum of only 66 sandwiches.

Verified Expert

This task provides a clear working procedure on linear programming problem on sandwich making situations. It is infeasible because there is no common feasible region. The solution is unbounded since the objective is to maximize and the feasible region is unbounded.

Reference no: EM13842966

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