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The fact that all linear programming issues share a linear relationship between their goals is a key similarity. In order to find the best solution to a problem, the issues consider a linear relationship (Roos, Terlaky & Vial, 2006). Depending on the kind of linear relationship they examine, linear programming issues can take many different shapes. Problem restrictions and non-negative variables make up a typical form linear programming problem. Only one kind of relationship can be handled by this kind of linear programming. The linear issue is a duality because it might have an upper bound. The purpose of a linear program is to minimize or maximize a linear objection function of a group of real or continuous variables. A major assumption made in such a scenario is one of proportionality where any variable that contributes to the eventual decision is always relative to its value (Roos et al., 2006). Linear programming also assumes that the objective function makes independent contributions. There is also the aspect of divisibility where all variables are assumed to be continuous, and thus the decision variables are capable of being fractional in nature. Linear programing models also make the assumption of certainty of constancy of the objective function.
At the work place, the production process require a fixed amount of raw materials, labor and storage space. It is always necessary to balance these constraints for the purpose of meeting the objective of maximizing profits. Excel includes a special set of commands that handle all forms of linear problems. With excel, it is very easy to maximize the profits because it allows the accountants at the work place to choose an optimal budget for the work force, raw materials and other constraints.
Roos, C., Terlaky, T., & Vial, J. (2006). Interior Point Methods for Linear Optimization, (2nd Ed). Chicago: Springer-Verlag.
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