Question

Explain the concept and computational steps of the simplex method for solving linear programming problems. How would you identify whether an optimal solution to a problem obtained using the simplex algorithm is unique or not?

03 Aug 2025
Answer :
Word Count : 1184
Linear programming is one of the most widely used techniques in operations research for optimizing the allocation of limited resources to achieve a particular objective, such as minimizing cost or maximizing profit. The simplex method, developed by George Dantzig in 1947, is the most popular algorithm for solving linear programming problems (LPPs). It is an iterative procedure that moves from one feasible solution to another while improving the value of the objective function at each step until an optimal solution is obtained. The method is based on the idea that if an optimal solution exists, it lies at a vertex, or corner point, of the feasible region formed by the constraints of the linear program. The general form of a linear programming problem can be expressed as: maximize or minimize $Z = c_1x_1 + c_2x_2 + \cdots + c_nx_n$, subject to a set of linear constraints such as $a_{11}x_1 + a_{12}x_2 + \cdots + a_{1n}x_n \leq b_1$, and so on, with non-negativity restrictions $x_i \geq 0$. To apply the simplex method, the problem is usually transformed into standard form. In this form, the problem is typically set as a maximization problem with all constraints expressed as equalities by introducing slack, surplus, or artificial variables, and all decision variables required to be non-negative. The computational procedure of the simplex method begins with setting up the initial simplex tableau. The first step is to convert inequalities into equalities ______ _______ ___ ______ ____.
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