• Vinit Kumar

The linear programming problems discussed so far are concerned with two variables ,the solution of which can be found out easily by the graphical method. But most real life problems when formulated as an LP model involve more than two variables and manay constraints. Thus, there is a need for a method other than the graphical method. The most popular non-graphical method of solving an LPP is called the simplex method. This method developed by George B. Dantzig in 1947, is applicable to any problem that can be formulated in terms of linear objective function subject to a set of linear constraints. There are no theoretical restrictions placed on the number of decision variables or constraints. The development of computers has further made it easy for the simplex method to solve large scale LP problem very quickly. The concept of simplex method is similar to the graphical method. For LP problems with several variables, the optimal solution lies at a corner point of the many-faced, multi-dimensional figure, called an n-dimensional polyhedron. The simplex method examines the corner points in a systematic manner. It is a computational routine of repeating the same set of steps over and over until an optimal solution is reached. For this reason, it is known as an iterative method. As we move from one iteration to the other, the method improves the value o the objective function and achieves optimal solution in a finite number of iterations.

Simplex algorithm

"Simplex algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.324-327, May 2019, Available :http://www.jetir.org/papers/JETIR1907Q96.pdf

"Simplex algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp324-327, May 2019, Available at : http://www.jetir.org/papers/JETIR1907Q96.pdf