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.</p
Abstract. Mixed Integer Nonlinear Programming (MINLP) problems present two main challenges: the inte...
We survey recent progress in applying disjunctive programming theory for the effective solution of m...
We survey recent progress in applying disjunctive programming theory for the effective solution of m...
<p>This work presents a review of the main deterministic mixed-integer nonlinear programming (MINLP)...
In this chapter we present some of the applications of MINLP and generalized disjunctive programming...
This work presents a review of the applications of mixed-integer nonlinear programming (MINLP) in pr...
Abstract. This paper has as a major objective to present a unified overview and derivation of mixed-...
Generalized disjunctive programming (GDP) is an extension of the disjunctive programming paradigm de...
Generalized disjunctive programming (GDP) is an extension of the disjunctive pro-gramming paradigm d...
Abstract. Many optimization problems involve integer and continuous variables that can be modeled as...
<p>In this work, we propose an algorithmic approach to improve mixed-integer models that are origina...
Many optimization problems require the modelling of discrete and continuous variables, giving rise t...
<p>Many optimization problems require the modelling of discrete and continuous variables, giving ris...
This paper provides a survey of recent progress and software for solving mixed integer nonlinear pr...
Generalized disjunctive programming (GDP) is an extension of the disjunctive programming paradigm de...
Abstract. Mixed Integer Nonlinear Programming (MINLP) problems present two main challenges: the inte...
We survey recent progress in applying disjunctive programming theory for the effective solution of m...
We survey recent progress in applying disjunctive programming theory for the effective solution of m...
<p>This work presents a review of the main deterministic mixed-integer nonlinear programming (MINLP)...
In this chapter we present some of the applications of MINLP and generalized disjunctive programming...
This work presents a review of the applications of mixed-integer nonlinear programming (MINLP) in pr...
Abstract. This paper has as a major objective to present a unified overview and derivation of mixed-...
Generalized disjunctive programming (GDP) is an extension of the disjunctive programming paradigm de...
Generalized disjunctive programming (GDP) is an extension of the disjunctive pro-gramming paradigm d...
Abstract. Many optimization problems involve integer and continuous variables that can be modeled as...
<p>In this work, we propose an algorithmic approach to improve mixed-integer models that are origina...
Many optimization problems require the modelling of discrete and continuous variables, giving rise t...
<p>Many optimization problems require the modelling of discrete and continuous variables, giving ris...
This paper provides a survey of recent progress and software for solving mixed integer nonlinear pr...
Generalized disjunctive programming (GDP) is an extension of the disjunctive programming paradigm de...
Abstract. Mixed Integer Nonlinear Programming (MINLP) problems present two main challenges: the inte...
We survey recent progress in applying disjunctive programming theory for the effective solution of m...
We survey recent progress in applying disjunctive programming theory for the effective solution of m...