An outer-approximation algorithm for the solution of multiparametric MINLP problems Academic Article

abstract

• 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., construct-ing 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. 1998 Elsevier Science Ltd. All rights reserved.

authors

• Pistikopoulos, Stratos

published proceedings

• COMPUTERS & CHEMICAL ENGINEERING

author list (cited authors)

• Dua, V., & Pistikopoulos, E. N.

citation count

• 21

publication date

• March 1998

publisher

• Elsevier  Publisher

keywords

• Mixed Integer Nonlinear Programming
• Parametric Optimization
• Process Synthesis
• Uncertainty

Digital Object Identifier (DOI)

• 10.1016/S0098-1354(98)00189-6

start page

• S955

end page

• S958

volume

• 22

issue

• SUPPL.1

URL

• http://dx.doi.org/10.1016/s0098-1354(98)00189-6