Question:

Consider the following Linear Programming Problem(LPP):Maximize \(z=60x_1+50x_2\) subject to \(x_1+2x_2≤40 3x_1+2x_2≤60 x_1,x_2≥0\).Then, the ______.

Updated On: May 29, 2024
  • LPP has a unique optimal solution 

  • LPP is infeasible.

  • LPP is unbounded

  • LPP has multiple optimal solutions

  • LPP has no solution 

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The Correct Option is A

Solution and Explanation

Given that:

The given LPP is as follows: Maximize \(z = 60x_1 + 50x_2\) 

Subject to the constraints: x_1 + 2x_2 ≤ 40  , 3x_1 + 2x_2 ≤ 60 ,   x_1, x_2 ≥ 0

We can graph the feasible region defined by these constraints in the\( x_1, x_2\) plane to visualize the problem:

Plot the lines \(x_1 + 2x_2 = 40\) and \(3x_1 + 2x_2 = 60\).

Then,

Shade the region below both lines (since they are inequalities, the feasible region is below the lines).

Then,

The feasible region is bounded by the \(x_1\) and \(x_2\) axes, as both variables are non-negative \((x_1, x_2 ≥ 0)\).

The feasible region will look like a triangular area in the first quadrant.

Now, we need to find the optimal solution. To do that, we evaluate the objective function (z = 60x_1 + 50x_2) at each corner point (vertex) of the feasible region, as there are only a finite number of corner points.

Corner points of the feasible region:

\((0, 0) \)- The origin

\((0, 20)\) - The intersection of \(x_1\)-axis and the first constraint

\((20, 0)\) - The intersection of \(x_2\)-axis and the first constraint

\((10, 15) \)- The intersection of the two constraints

Now, we calculate z for each corner point:

\(z(0, 0) = 60 × 0 + 50  0 = 0\)

\(z(0, 20) = 60 × 0 + 50 × 20 = 1000\)

\(z(20, 0) = 60 × 20 + 50 × 0 = 1200\)

\(z(10, 15) = 60 × 10 + 50 × 15 = 1350\)

The max. value of \(z\) occurs at point \((10, 15)\), where \(z = 1350.\)

Now, since the objective function has a unique maximum value at a specific point, and the feasible region is bounded (as shown by the graph), we can conclude that the LPP has a unique optimal solution.  (_Ans.)

 

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Concepts Used:

Linear Programming

Linear programming is a mathematical technique for increasing the efficiency and effectiveness of operations under specific constraints. The main determination of linear programming is to optimize or minimize a numerical value. It is built of linear functions with linear equations or inequalities restricting variables.

Characteristics of Linear Programming:

  • Decision Variables: This is the first step that will determine the output. It provides the final solution to the problem.
  • Constraints: The mathematical form in which drawbacks are expressed, regarding the resource.
  • Data: They are placeholders for known numbers to make writing complex models simple. They are constituted by upper-case letters.
  • Objective Functions: Mathematically, the objective function should be quantitatively defined.
  • Linearity: The function's relation between two or more variables must be straight. It indicates that the variable's degree is one.
  • Finiteness: Input and output numbers must be finite and infinite. The best solution is not possible if the function consists infinite components.
  • Non-negativity: The value of the variable should be either positive (+ve) or 0. It can't be a negative (-ve) number.