Optimality-based bound contraction with multiparametric disaggregation for the global optimization of mixed-integer bilinear problems
We address nonconvex mixed-integer bilinear problems where the main challenge is the computation of a tight upper bound for the objective function to be maximized. This can be obtained by using the recently developed concept of multiparametric disaggregation following the solution of a mixed-integer...
| Autor principal: | |
|---|---|
| Outros Autores: | |
| Formato: | article |
| Idioma: | eng |
| Publicado em: |
2015
|
| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10400.9/2649 |
| País: | Portugal |
| Oai: | oai:repositorio.lneg.pt:10400.9/2649 |
| Resumo: | We address nonconvex mixed-integer bilinear problems where the main challenge is the computation of a tight upper bound for the objective function to be maximized. This can be obtained by using the recently developed concept of multiparametric disaggregation following the solution of a mixed-integer linear relaxation of the bilinear problem. Besides showing that it can provide tighter bounds than a commercial global optimization solver within a given computational time, we propose to also take advantage of the relaxed formulation for contracting the variables domain and further reduce the optimality gap. Through the solution of a real-life case study from a hydroelectric power system, we show that this can be an efficient approach depending on the problem size. The relaxed formulation from multiparametric formulation is provided for a generic numeric representation system featuring a base between 2 (binary) and 10 (decimal). |
|---|