Question

In: Statistics and Probability

USE EXCEL. While we assume the principle of certainty, in reality, the coefficients of constraints or...

USE EXCEL. While we assume the principle of certainty, in reality, the coefficients of constraints or the objective function in a LP are subject to changes. Dried cranberries calories is assumed to be 3.08. However, this value can change for different batches of dried cranberries. Assume that dried cranberries calories is a uniform random variable in the interval [2.4, 3.4]. Generate 30 random numbers in the interval [2.4, 3.4]. For each random number generated solve the LP once considering the random number to be the dried cranberries calories. Out of 30 times that you solved the LP problem, what percentage of times were you not able to find an optimum solution due to infeasibility? Create a 95% confidence interval for this percentage. Note that the calculated infeasibility percentage is p_bar. What does this practice tell you about LP limitations? Make sure you include all related information such as test statistic in your answer.

Healthy Snacks Co. produces snack mixes. Recently, the company has decided to introduce a new snack mix that has peanuts, raisins, pretzels, dries cranberries, sunflower seeds and pistachios. Each bag of the new snack is designed in order to hold 250 grams of the snack. The company has decided to market the new product with a emphasis on its health benefits. After consulting nutritionists, Healthy Snacks decides to mix the ingredients so that the snack has the following specifications:

1 - Total calories <= 800  

2 - Fat <= 20

3 - Sodium <= 200

4 - Potassium <= 700

5 - Carb <= 140

6 - Protein >= 10

7 - Vitamin A >= 3

8 - Vitamin B6 >= 25

9 - Vitamin C >= 2

10 - Iron >= 15

11 - Calcium >= 5

Table below shows the amount of each nutrition in each snack's ingredient per 1 gram:

peanut raisin pretzel dried cranberries sunflower seed pistachio
Calories 5.67 3.00 1.00 3.08 5.84 5.62
Fat (g) 0.49 0.00 0.01 0.01 0.51 0.06
Sodium (mg) 0.18 0.12 3.32 0.03 0.09 0.01
Potassium (mg) 7.05 7.49 0.36 0.40 6.45 10.25
Carb (g) 0.16 0.79 0.21 0.83 0.20 0.28
Protein (g) 0.26 0.03 0.03 0.00 0.21 0.20
Vitamin A (%) 0.00 0.00 0.00 0.00 0.01 0.08
Vitamin B-6 (%) 0.17 0.12 0.00 0.00 0.68 0.85
Vitamin C (%) 0.00 0.02 0.00 0.00 0.02 0.09
Iron (%) 0.25 0.09 0.07 0.03 0.29 0.21
Calcium (%) 0.09 0.05 0.00 0.00 0.07 0.10

Table below shows the cost of each ingredients per 1 gram:

peanut raisin pretzel dried cranberries sunflower seed pistachio
cost per 1 gram ($) $ 0.005 $ 0.020 $ 0.013 $                     0.013 $                  0.022 $     0.031

Per marketing department research, each bag of snack should have at least 10 grams of each ingredient.

Solutions

Expert Solution

Problem can be initialized with all zero values for quantity of the six ingredients as

Ingradient peanut raisin pretzel dried cranberries sunflower seed pistachio
Variable Name X1 X2 X3 X4 X5 X6
Initialization 0 0 0 0 0 0
Minimum Quantity Constraints
Variable Quanity X1 X2 X3 X4 X5 X6
Inequality >= >= >= >= >= >=
Value 10 10 10 10 10 10
Nutritional Value (Health Benefits) Constraints
Variable Quanity 5.67(X1)+3(X2)+(X3)+3.08(X4)
+5.84(X5)+5.62(X6)
0.49(X1)+0.01(X3)+0.01(X4)
+0.51(X5)+0.06(X6)
0.18(X1)+0.12(X2)+3.32(X3)
+0.03(X4)+0.09(X5)+0.01(X6)
7.05(X1)+7.49(X2)+0.36(X3)
+0.4(X4)+6.45(X5)+10.25(X6)
0.16(X1)+0.79(X2)+0.21(X3)
+0.83(X4)+0.2(X5)+0.28(X6)
0.26(X1)+0.03(X2)+0.03(X3)
+0.21(X5)+0.2(X6)
Initial Value 0 0 0 0 0 0
Inequality <= <= <= <= <= >=
Value 800 20 200 700 140 10
Variable Quanity 0.01(X5)+0.08(X6) 0.17(X1)+0.12(X2)+0.68(X5)
+0.85(X6)
0.02(X2)+0.02(X5)+0.09(X6) 0.25(X1)+0.09(X2)+0.07(X3)+
0.03(X4)+0.29(X5)+0.21(X6)
0.09(X1)+0.05(X2)+0.07(X5)+
0.1(X6)
Initial Value 0 0 0 0 0
Inequality >= >= >= >= >=
Value 3 25 2 15 5
Total Quantity Constraint
Total Quantity X1+X2+X3+X4+X5+X6 = 250
Initial Value 0
Cost Optimization Function
Total Cost Function 0.005(X1)+0.02(X2)+0.013(X3)
+0.013(X4)+0.022(X5)+0.031(X6)
Cost 0

The Solver can be initialized with all constraints outlined above as

which then gives the optimized solution as


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