Graphical Method Of Solving Linear Programming Problems

Graphical Method Of Solving Linear Programming Problems-89
The input base variable in the Simplex method determines towards what new vertex is performed the displacement.In this example, as P1 (corresponding to 'x') enters, the displacement is carried out by the OF-edge to reach the F-vertex, where the Z-function value is calculated. We all have finite resources and time and we want to make the most of them.

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As a manager of a company, you always have finite or limited resources, and top management's expectation is for you to make the most out of it.

From time productivity to capital utilization, land to labor, and from supply chain to production-almost everything you do is to optimize productivity.

You have covered a lot of details about linear programming.

You learned what linear programming is, basic concepts, and terminologies used in LP, LP-problem formulation, solving LP problems using the graphical method, and use cases of the LP problem.

In this tutorial, you are going to learn about linear programming, and the following topics will be covered: Mathematically, linear programming optimizes (minimizes or maximizes) the linear objective of several variables subject to the given conditions/constraints that satisfies a set of linear inequalities.

Graphical Method Of Solving Linear Programming Problems Best Medical School Essays

Linear programming can be applied in planning economic activities such as transportation of goods and services, manufacturing products, optimizing the electric power systems, and network flows.

LP is applicable in all kinds of problems such as economic activities in agriculture, engineering, manufacturing, energy, logistics, and supply chain.

Congratulations, you have made it to the end of this tutorial!

The maximum value of the objective function is 33, and it corresponds to the values x = 3 and y = 12 (G-vertex coordinates).

In Graphical method is necessary to calculate the value of the objective function at each vertex of feasible region, while the Simplex method ends when the optimum value is found.


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