Premium
Stochastic mixed integer nonlinear programming using rank filter and ordinal optimization
Author(s) -
Wen Chengtao,
Ydstie B. Erik,
Ma Xiaoyan
Publication year - 2009
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.11901
Subject(s) - rank (graph theory) , benchmark (surveying) , mathematical optimization , filter (signal processing) , ordinal optimization , integer (computer science) , mathematics , nonlinear programming , nonlinear system , algorithm , integer programming , computer science , ordinal regression , statistics , combinatorics , physics , geodesy , quantum mechanics , computer vision , programming language , geography
A rank filter algorithm is developed to cope with the computational‐difficulty in solving stochastic mixed integer nonlinear programming (SMINLP) problems. The proposed approximation method estimates the expected performance values, whose relative rank forms a subset of good solutions with high probability. Suboptimal solutions are obtained by searching the subset using the accurate performances. High‐computational efficiency is achieved, because the accurate performance is limited to a small subset of the search space. Three benchmark problems show that the rank filter algorithm can reduce computational expense by several orders of magnitude without significant loss of precision. The rank filter algorithm presents an efficient approach for solving the large‐scale SMINLP problems that are nonconvex, highly combinatorial, and strongly nonlinear. © 2009 American Institute of Chemical Engineers AIChE J, 2009