I need someone who can answer these questions i need excel file and word file
5.7. Ken and Larry, Inc., supplies its ice cream parlors with three flavors of ice cream: chocolate, vanilla, and banana. Due to extremely hot weather and a high demand for its products, the company has run short of its supply of ingredients: milk, sugar, and cream. Hence, they will not be able to fill all the orders received from their retail outlets, the ice cream parlors. Due to these circumstances, the company has decided to choose the amount of each flavor to produce that will maximize total profit, given the constraints on the supply of the basic ingredients.
The chocolate, vanilla, and banana flavors generate, respectively, $1.00, $0.90, and $0.95 of profit per gallon sold. The company has only 200 gallons of milk, 150 pounds of sugar, and 60 gallons of cream left in its inventory. The linear programming formulation for this problem is shown below in algebraic form.
Let
C = Gallons of chocolate ice cream produced
V = Gallons of vanilla ice cream produced
B = Gallons of banana ice cream produced
Maximize Profit = 1.00C + 0.90V + 0.95B
subject to
Milk: 0.45C + 0.50V + 0.40B ≤ 200 gallons
Sugar: 0.50C + 0.40V + 0.40B ≤ 150 pounds
Cream: 0.10C + 0.15V + 0.20B ≤ 60 gallons
and
C≥0 V≥0 B≥0
This problem was solved using Solver. The spreadsheet (already solved) and the sensitivity report are shown below. (Note: The numbers in the sensitivity report for the milk constraint are missing on purpose, since you will be asked to fill in these numbers in part f.)
For each of the following parts, answer the question as specifically and completely as possible without solving the problem again with Solver. Note: Each part is independent (i.e., any change made to the model in one part does not apply to any other parts).
Variable Cells 

Cell 
Name 
Final Value 
Reduced Cost 
Objective Coefficient 
Allowable Increase 
Allowable Decrease 
$B$10 
Gallons Produced Chocolate 
0 
−0.0375 
1 
0.0375 
1E + 30 
$C$10 
Gallons Produced Vanilla 
300 
0 
0.9 
0.05 
0.0125 
$D$10 
Gallons Produced Banana 
75 
0 
0.95 
0.0214 
0.05 
Constraints 

Cell 
Name 
Final Value 
Shadow Price 
Constraint R. H. Side 
Allowable Increase 
Allowable Decrease 
$E$5 
Milk Used 

$E$6 
Sugar Used 
150 
1.875 
150 
10 
30 
$E$7 
Cream Used 
60 
1 
60 
15 
3.75 
5.12. Consider a resourceallocation problem having the following data:
Resource 
Resource Usage per Unit of Each Activity 
Amount of Resource Available 

1 
2 

1 
1 
3 
8 
2 
1 
1 
4 
Unit profit 
$1 
$2 
The objective is to determine the number of units of each activity to undertake so as to maximize the total profit.
5.19.* Reconsider Problem 5.12. Now suppose that all of the parameters are uncertain, with ranges of uncertainty as given in the table below. Use the procedure for robust optimization with independent parameters to find the solution that maximizes profit when the solution also is guaranteed to be feasible.
Resource 
Resource Usage per Unit of Each Activity 
Amount of Resource Available 

1 
2 

1 
0.9–1.1 
2.7–3.3 
7.2–8.8 
2 
0.8–1.2 
0.7–1.3 
3.5–4.5 
Unit Profit 
$0.90–$1.10 
$1.75–$2.25 
5.18. Reconsider the example illustrating the use of robust optimization that was presented in Section 5.7. Wyndor management now feels that there is uncertainty in all of the parameters of the problem—the unit profit per door and window (P_{D} and P_{W}), the hours of production time used for each door or window produced across the three plants (HD_{1}, H_{W}_{2}, H_{D}_{3}, and H_{W}_{3}) Page 196and the three righthandsides representing the hours available at each plant (RHS_{1}, RHS_{2}, and RHS_{3}). The original estimates along with the ranges of uncertainty are shown in the table below. Apply the procedure for robust optimization with independent parameters to find the solution that maximizes profit when the solution also is guaranteed to be feasible.
Parameter 
Original Estimate 
Range of Uncertainty 
P_{D} 
$300 
$250–$350 
P_{W} 
$500 
$400–$600 
HD_{1} 
1 
0.9–1.1 
HW_{2} 
2 
1.6–2.4 
HD_{3} 
3 
2.5–3.5 
HW_{3} 
2 
1.8–2.2 
RHS_{1} 
4 
3.5–4.5 
RHS_{2} 
12 
11–13 
RHS_{3} 
18 
16–20 
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