Question:

A toy company manufactures two types of dolls,A and B.Market tests and available resources have indicate that the combined production lebel should not exceed 1200 dolls per week and the demand for dolls of type B is at most half of that for dolls of type A.Further the production level of dolls of type A can exceed three times the production of dolls of other type by at most 600units.If the company makes profit of Rs12 and Rs16 per doll respectively on dolls A and B,how many of each should be produced weekly in order to maximize the profit?

Updated On: Sep 21, 2023
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Solution and Explanation

Let x and y be the number of dolls of type A and B respectively that are produced per week.

The given problem can be formulated as follows.

 

Maximize
Z=12x+16y…………..(1)

Subject to the constraints,
x+y≤1200…………....(2)
y≤\(\frac{x}{2}\)\(\Rightarrow\)x≥2y………………..(3) 
x-3y≤600....(4)
x,y≥0...(5)

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

The corner points are A(600,0),B(1050,150),and C(800,400).
The value of Z at these points are as follows.

Corner pointZ=12x+16y 
A(600,0)7200 
B(1050,150)15000 
C(800,400)16000\(\rightarrow\)Maximum


The maximum value of Z is 16000 at(800,400).

Thus,800 and 400 dolls of type A and type B should be produced respectively to get the maximum profit of Rs16000.

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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.