Reshma wishes to mix two types of food P and Q in such a way that the vitamin contents of the mixture contain at least 8 units of vitamin A and 11 units of vitamin B.Food P costs Rs.60/kg and food Q costs Rs.80/kg. Food P contains 3 units/kg of vitamin A and 5 units/kg of vitamin B while food Q contains 4units/kg of vitamin A and 2 units/kg of vitamin B. Determine the minimum cost of the mixture.
Let the mixture contain x kg of food P and y kg of food Q.
Therefore, x≥0andy≥0
The given information can be compiled in a table as follows.
Vitamin A (units/kg) | Vitamin B (units/kg) | Cost (Rs/kg) | |
Food P | 3 | 5 | 60 |
Food Q | 4 | 2 | 80 |
Requirement(units/kg) | 8 | 11 |
The mixture must contain at least 8 units of vitamin A and 11 units of vitamin B.
Therefore,
the constraints are 3x+4y≥8 5x+2y≥11
The total cost, Z, of purchasing food is Z=60x+80y
The mathematical foundation of the given problem is
Minimise Z=60x+80y...(1)
subject to the constraints,
3x+4y≥8...(2)
5x+2y≥11...(3)
x,y≥0...(4)
The feasible region determined by the system of constraints is as follows.
It can be seen that the feasible region is unbounded.
The corner points of the feasible region are A(\(\frac{8}{3}\),0),B(2,\(\frac{1}{2}\)), and C(0,\(\frac{11}{2}\)) The values of Z at these corner points are as follows.
Corner point Z=60x+80y A(\(\frac{8}{3}\),0)160→Minimum B(2,\(\frac{1}{2}\))160→Minimum C(0,\(\frac{11}{2}\))440
As the feasible region is unbounded, therefore, 160 may or may not be the minimum value of Z.
For this we graph the inequality,60x+80y<160 or 3x+4y <8, and check whether the resulting half-plane has points in common with the feasible region or not.
It can be seen that the feasible region has no common point with 3x+4y<8
Therefore, the minimum cost of the mixture will be Rs160 at the line segment joining the points (\(\frac{8}{3}\),0)and(2,\(\frac{1}{2}\)).
What is the Planning Process?
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.
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.