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Definition of Linear Programming  - The common LPP says for optimizing (maximizing / minimizing) a linear function of variables known as 'Objective function' subject to a set of linear equations and / or inequalities known as 'Constraints' or 'Restrictions'. 

Linear programming known as LP, or linear optimization is a mathematical approach and method for determining a solution to achieve the best outcome and results such as maximum profit and lowest cost in a known mathematical model for some list of needs and requirements represented such as linear relationships. Linear programming method is a particular specific case of mathematical programming or mathematical optimization.

More elaborate, linear programming is a unique technique for optimization of a linear objective function, it is subject to linear equality & linear inequality constraints. Its feasible region is a polyhedron that is a set described as the intersection of finitely many half spaces, each of which is considered by a linear inequality. Its objective function is a real-valued affine function considered on this polyhedron. A linear programming algorithm gives a point in the polyhedron where the function has the smallest or largest value if such point exists.

Linear Programming is a part of Operation Research homework help and also considered in Mathematics assignments as well. It requires more skilled knowledge before solving any type of questions. Specialized linear programming tutors are avail here at TutorsGlobe for your help. Get solved questions anytime from anywhere, just one click away.

Benefits of Linear Programming Techniques

1. It provides assistance in making the optimum utilization of useful resources.
2. The quality of decisions can also be enhanced by linear programming techniques.
3. Provides practical or feasible solutions.
4. In production processes, highlighting of bottlenecks is the highly important advantage of this technique.

Disadvantages of Linear Programming

Some disadvantages are linked with linear programming techniques

1. In some cases, constraints and objective functions are not linear. Usually, in actual life situations concerning business and industrial problems constraints are not linearly related to variables.
2. There is no assurance of getting integer valued answers. For instance, in determining how many men and machines would be needed to do a particular job, rounding off the solution to the closest integer will not give a best possible solution. Integer programming handles such problems.
3. Linear programming model does not take into concern the result of time and uncertainty. Therefore the model should be defined in such a way that every change due to internal or external factors can be incorporated.
4. At times large scale problems cannot be resolved with linear programming methods even though the computer facility is available. Such difficulty may be removed by decaying the major problem into various small problems and then solving them individually.
5. Parameters seem in the model are supposed to be constant. But, in actual life situations they are neither deterministic nor constant.
6. Linear programming deals with only solo objective, while in real life situation problems come across with various different objectives. Goal programming and multi-objective programming handles with such problems.