Author:
Muts Pavlo,Nowak Ivo,Hendrix Eligius M. T.
Abstract
AbstractMost industrial optimization problems are sparse and can be formulated as block-separable mixed-integer nonlinear programming (MINLP) problems, defined by linking low-dimensional sub-problems by (linear) coupling constraints. This paper investigates the potential of using decomposition and a novel multiobjective-based column and cut generation approach for solving nonconvex block-separable MINLPs, based on the so-called resource-constrained reformulation. Based on this approach, two decomposition-based inner- and outer-refinement algorithms are presented and preliminary numerical results with nonconvex MINLP instances are reported.
Funder
Hochschule für Angewandte Wissenschaften Hamburg (HAW Hamburg)
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Control and Optimization,Mechanical Engineering,Aerospace Engineering,Civil and Structural Engineering,Software
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