EXPERIENCE WITH INTERIOR-POINT OPTIMIZATION SOFTWARE FOR A FUEL PLANNING APPLICATION

The Karmarkar interior point algorithm has made it possible to solve l arge-scale decision problems that previously could not be solved in re asonable time, or were too large to be solved at all, in this paper, w e present the results obtained from using the KORBX(R) - Advanced Math ematical Programming System (KMPS), which uses Karmarkar's interior po int algorithm, to solve a number of linear optimization problems arisi ng from a long-term fuel planning problem. Comparison with the results obtained using software based on the simplex method demonstrates the drastic improvements in solution time for the interior point method, e specially with increase in problem sizes. This confirms earlier compar isons of interior point and simplex methods. The paper includes prelim inary ideas and results on ways to combine the interior point and simp lex methods in order to benefit from the superior speed performance of the former, and the warm-start and hot-start capabilities of the latt er methods.