A main feature of the generalized simplex method is that it solves LP problems by iterations. The process of
moving from an extreme point to another extreme point is one iteration of computation. The generalized simplex
method involves lots of computations, which make the computer an essential tool for solving LP problems.
Another important feature of this method is that this process is to update only one variable at a time. It
will not change variables simultaneously. Variables are classified into two kinds: basic variables and non-basic
variables [8]. The basic variable has a unique solution and its solution is referred to as the basic solution. The
non-basic variables are set to zero. The basic and non-basic variables should be updated when it moves from
one corner point to the next. In general, the simplex iterations move along the edges of the solution space. It
means that this method does not cut through the solution space.
For both the maximization and the minimization problems, the steps of the simplex method can be summarized
as follows:
Step 1: Convert the inequalities into standard form.
Step 2: Set up the initial simplex tableau and determine a starting basic feasible solution.
Step 3: Swap the non-basic variables with basic variables repeatedly until finding out the optimum solution.