Review of Mixed-Integer Nonlinear and Generalized Disjunctive Programming Methods

This work presents a review of the main deterministic mixed‐integer nonlinear programming (MINLP) solution methods for problems with convex and nonconvex functions. An overview for deriving MINLP formulations through generalized disjunctive programming (GDP), which is an alternative higher‐level rep...

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Vydané v:Chemie ingenieur technik Ročník 86; číslo 7; s. 991 - 1012
Hlavní autori: Trespalacios, Francisco, Grossmann, Ignacio E.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Weinheim WILEY-VCH Verlag 01.07.2014
WILEY‐VCH Verlag
Predmet:
Design engineering
Generalized disjunctive programming
Mathematical models
Mixed-integer nonlinear programming
Nonlinear programming
Nonlinearity
Optimization
Programming
Representations
Scheduling
ISSN:0009-286X, 1522-2640
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Shrnutí:This work presents a review of the main deterministic mixed‐integer nonlinear programming (MINLP) solution methods for problems with convex and nonconvex functions. An overview for deriving MINLP formulations through generalized disjunctive programming (GDP), which is an alternative higher‐level representation of MINLP problems, is also presented. A review of solution methods for GDP problems is provided. Some relevant applications of MINLP and GDP in process systems engineering are described in this work. The optimal design, planning, and scheduling of chemical processes requires the use of mixed‐integer nonlinear programming (MINLP) or generalized disjunctive programming (GDP) models to optimize discrete and continuous variables. Here, applications of MINLP and GDP are described, and a review of deterministic methods to solve these problems is provided.
Bibliografia:Center for Advanced Process Decision-making (CAPD)
ArticleID:CITE201400037
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content type line 23
ISSN:0009-286X
1522-2640
DOI:10.1002/cite.201400037