Pricing is a significant step in the simplex algorithm where an improving
nonbasic variable is selected in order to enter the basis. This step is
crucial and can dictate the total execution time. In this paper, we perform a
computational study in which the pricing operation is computed with eight
different pivoting rules: (i) Bland?s Rule, (ii) Dantzig?s Rule, (iii)
Greatest Increment Method, (iv) Least Recently Considered Method, (v) Partial
Pricing Rule, (vi) Queue Rule, (vii) Stack Rule, and (viii) Steepest Edge
Rule; and incorporate them with the revised simplex algorithm. All pivoting
rules have been implemented in MATLAB. The test sets used in the
computational study are a set of randomly generated optimal sparse and dense
LPs and a set of benchmark LPs (Netliboptimal, Kennington,
Netlib-infeasible).