History of Linear programming

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In mathematics, there are mathematical models that require solving through determination of best outcomes, and these are normally in the form of linear relationships. Linear programming or linear optimization is the technique that mathematicians use to determine these outcomes. In a more technical term, linear programming deals with linear functions that are subject to linear inequality and linear equality constraints. The technique optimizes the linear objective functions to come up with best possible solutions. ChvatalVasek (1983), defines programming as “…the problem of make the most of a linear function issue to a finite sum of linear limits.” The best example that applies in production is, taking the limitations of labor and materials; the next step is to find out the optimum production level that can lead to maximum profits. In real life situations, linear programming is a tool put to use every single day in most organizations especially in allocating resources. Linear programming comes in because in a normal situation these real life systems and choices have multiple variables, sometimes dozens. In many institutions and organizations, linear programming is integrating in to become a standard tool for every organization. From its discovery in the 1930s, the method has been in use helping to solve many day-to-day problems. This essay tries to find out in detail the origin and history of this technique. Looking at various books, articles, and research reports among other available materials on the subject below is a detailed explanation of the history of linear programming.

Origin of linear programming

Although the current programming technique that is in use now started in 1939, there is evidence of similar method being in use, in 1762. This was by a mathematician known as Lagrange who used tractable optimization to solve basic equality constraints. Subsequent methods and discoveries took place such as Gauss in 1820 using linear systems to solve equations, however; the method is known as Gaussian elimination (Charnes, Cooper, Lewin, &Seiford, 1994).

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