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An MILP-MINLP decomposition method for the global optimization of a source based model of the multiperiod blending problem

Irene Lotero(iloteroherranz***at***gmail.com)
Francisco Trespalacios(ftrespal***at***andrew.cmu.edu)
Ignacio E. Grossmann(grossmann***at***cmu.edu)
Dimitri J. Papageorgiou(dimitri.j.papageorgiou***at***exxonmobil.com)
Myun-Seok Cheon(myun-seok.cheon***at***exxonmobil.com)

Abstract: The multiperiod blending problem involves binary variables and bilinear terms, yielding a nonconvex MINLP. In this work we present two major contributions for the global solution of the problem. The rst one is an alternative formulation of the problem. This formulation makes use of redundant constraints that improve the MILP relaxation of the MINLP. The second contribution is an algorithm that decomposes the MINLP model into two levels. The rst level, or master problem, is an MILP relaxation of the original MINLP. The second level, or subproblem, is a smaller MINLP in which some of the binary variables of the original problem are xed. The results show that the new formulation can be solved faster than alternative models, and that the decomposition method can solve the problems faster than state of the art general purpose solvers.

Keywords: Muliperiod blending; Pooling; MINLP; Global optimization

Category 1: Applications -- OR and Management Sciences (Scheduling )

Category 2: Applications -- Science and Engineering (Chemical Engineering )

Citation: Submitted for Publication to Computers and Chemical Engineering

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Entry Submitted: 04/14/2015
Entry Accepted: 04/15/2015
Entry Last Modified: 04/14/2015

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