Effectiveness of a geometric programming algorithm for optimization of machining economics models

Machining economics problems usually contain highly non-linear equations which may present difficulties for some non-linear programming algorithms. An earlier article by Duffuaa et al. [1] compared the performance of several non-linear programming algorithms, including a geometric programming algorithm, applied to five machining economics problems. Those authors concluded that the Generalized Reduced Gradient (GRG) algorithm is the most suitable method for solving such problems. In this paper, we point out shortcomings in that conclusion and demonstrate the effectiveness of the Geometric Programming technique in such problems compared with the results of GRG which were presented.