Question:

A fruit grower can use two types of fertilizer in his garden, brand P and brand Q. The amounts(in kg) of nitrogen, phosphoric acid, potash and chlorine in a bag of each brand are given in the table. Tests indicate that the garden needs at least 240kg of phosphoric acid at least 270kg of potash and at most 310kg of chlorine. If the grower wants to minimize the amount of nitrogen added to the garden, how many bags of each brand should be used? What is the minimum amount of nitrogen added in the garden?
 

kg per bag

 Brand PBrand Q

Nitrogen

Phosphoric acid

Potash

Chlorine

3

1

3

1.5

3.5

2

1.5

2

Updated On: Oct 4, 2023
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Solution and Explanation

Let the fruit grower use x bags of brand P and y bags of brand Q.

The problem can be formulated as follows.

Minimize Z=3x+3.5y...(1)

Subject to the constraints,
x+2y \(\geq\) 240...(2)
x+0.5y \(\geq\) 90....(3)
1.5x+2y \(\leq\) 310....(4)
x,y \(\geq\) 0...(5)

The feasible region is determined by the system of constraints as follows.

 A (240,0), B (140,50), and C (20,140)

The corner points are A (240,0), B (140,50), and C (20,140)
The values of Z at these corner points are as follows.

Corner Pointz = 3x + 3.5y 
A (140, 50)595 
B (20, 140)550 
C (40, 100)470\(\rightarrow\) Minimum

The maximum value of Z is 470 at (40,100).
Thus, 40 bags of brand P and 100 bags of brand Q should be added to the garden to minimize the amount of nitrogen.
The minimum amount of nitrogen added to the garden is 470kg.

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Concepts Used:

Linear Programming Problems

The Linear Programming Problems (LPP) is a problem that is concerned with finding the optimal value of the given linear function. The optimal value can be either maximum value or minimum value. Here, the given linear function is considered an objective function. The objective function can contain several variables, which are subjected to the conditions and it has to satisfy the set of linear inequalities called linear constraints.

Linear Programming Simplex Method

Step 1: Establish a given problem. (i.e.,) write the inequality constraints and objective function.

Step 2: Convert the given inequalities to equations by adding the slack variable to each inequality expression.

Step 3: Create the initial simplex tableau. Write the objective function at the bottom row. Here, each inequality constraint appears in its own row. Now, we can represent the problem in the form of an augmented matrix, which is called the initial simplex tableau.

Step 4: Identify the greatest negative entry in the bottom row, which helps to identify the pivot column. The greatest negative entry in the bottom row defines the largest coefficient in the objective function, which will help us to increase the value of the objective function as fastest as possible.

Step 5: Compute the quotients. To calculate the quotient, we need to divide the entries in the far right column by the entries in the first column, excluding the bottom row. The smallest quotient identifies the row. The row identified in this step and the element identified in the step will be taken as the pivot element.

Step 6: Carry out pivoting to make all other entries in column is zero.

Step 7: If there are no negative entries in the bottom row, end the process. Otherwise, start from step 4.

Step 8: Finally, determine the solution associated with the final simplex tableau.