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

Machining economics problems usually contain highly nonlinear equations which may present difficulties for some nonlinear programming algorithms. An earlier article by Duffuaa et al. [1] compared the performance of several nonlinear 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. †Jae Chul Choi is Research Associate at The University of Iowa, where he earlier received his M.S. and Ph.D. degrees in Industrial and Management Engineering. ‡Dennis L. Bricker is Associate Professor of Industrial Engineering at The University of Iowa. He received B.S. and M.S. degrees in Mathematics from the University of Illinois, and M.S. and Ph.D. degrees in Industrial Engineering and Management Sciences from Northwestern University. Please address correspondence to the address above or (e-mail) [email protected].