Optimality-based bound contraction with multiparametric disaggregation for the global optimization of mixed-integer bilinear problems

Pedro Castro () and Ignacio Grossmann

Journal of Global Optimization, 2014, vol. 59, issue 2, 277-306

Abstract: 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). Copyright Springer Science+Business Media New York 2014

Keywords: Global optimization; Mixed integer nonlinear programming; Mixed integer linear programming; Scheduling; Hydroelectric system (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10898-014-0162-6

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