On decomposition and multiobjective-based column and disjunctive cut generation for MINLP
Copyright policy )On decomposition and multiobjective-based column and disjunctive cut generation for MINLP
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.
- University of Malaga Spain
- Hamburg University of Applied Science Germany
- Hochschule für Angewandte Wissenschaften Hamburg Germany
- Wageningen University & Research Netherlands
- Hamburg University of Applied Sciences Germany
Parallel computing, Decomposition, column generation, parallel computing, nonconvex optimization, global optimization, Column generation, 620, 510, Nonconvex optimization, Programación no lineal, Mixed integer programming, Nonlinear programming, Mixed-integer nonlinear programming, decomposition method, Global optimization, Decomposition method, mixed-integer nonlinear programming, Multi-objective and goal programming
Parallel computing, Decomposition, column generation, parallel computing, nonconvex optimization, global optimization, Column generation, 620, 510, Nonconvex optimization, Programación no lineal, Mixed integer programming, Nonlinear programming, Mixed-integer nonlinear programming, decomposition method, Global optimization, Decomposition method, mixed-integer nonlinear programming, Multi-objective and goal programming
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