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An outer-approximation algorithm for the solution of multiparametric MINLP problems
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DOI：10.1016/S0098-1354(98)00189-6.png)
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Process synthesis problems involving uncertainty can be mathematically represented as multiparametric mixed integer nonlinear programming(mp-MINLP) models. In this paper, we present an outer-approximation algorithm for the solution of such mp-MINLPs, described by convex process models, linear in the vectors of binary variables and uncertain parameters. The algorithm follows decomposition principles, i.e., constructing a converging sequence of valid upper and lower bounds through the solution of parametric primal and master subproblems. The solution is characterized in different sub-domains of the uncertain parameter space by (i) linear parametric profiles, and (ii) the corresponding integer solutions. (C) 1998 Elsevier Science Ltd. All rights reserved.
Keyword:
process synthesis
uncertainty
mixed integer nonlinear programming
parametric optimization
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A multiparametric programming approach for linear process engineering problems under uncertainty
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