LPP has a unique optimal solution
LPP is infeasible.
LPP is unbounded
LPP has multiple optimal solutions
LPP has no solution
Given: The given LPP is as follows: Maximize
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 plane to visualize the problem:
Plot the lines and .
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 and axes, as both variables are non-negative .
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:
- The origin
- The intersection of -axis and the first constraint
- The intersection of -axis and the first constraint
- The intersection of the two constraints
Now, we calculate z for each corner point:
The max. value of occurs at point , where
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.
For the reaction:
The following kinetic data were obtained for three different experiments performed at the same temperature:
The total order and order in [B] for the reaction are respectively:
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.