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Branch-and-cut algorithmic framework for 0-1 mixed-integer convex nonlinear programs / Hao Wu in Industrial & engineering chemistry research, Vol. 48 N° 20 (Octobre 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 20 (Octobre 2009) . - pp. 9119–9127 Titre : Branch-and-cut algorithmic framework for 0-1 mixed-integer convex nonlinear programs Type de document : texte imprimé Auteurs : Hao Wu, Auteur ; Hao Wen, Auteur ; Yushan Zhu, Auteur Année de publication : 2010 Article en page(s) : pp. 9119–9127 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Branch-and-cut  algorithmic  framework  Mixed-Integer  Nonlinear  Optimizer  Nonlinear  programming Résumé : With widespread use of mixed-integer modeling for continuous and discontinuous nonlinear chemical processes, a robust mixed-integer nonlinear optimization algorithm is strongly demanded for the design and operation of chemical processes that achieve greater profits and also satisfy environmental and safety constraints. In this article, an extensive evaluation of a branch-and-cut algorithmic framework for 0−1 mixed-integer convex nonlinear programs, called MINO for Mixed-Integer Nonlinear Optimizer, is presented. The numerical performances of MINO are tested against four sets of medium-sized practical application problems from the fields of operations research and chemical engineering. The effects of the nonlinear programming (NLP) solvers, the cut generating and maintaining strategy, the node selection method, and the frequency of cut generation are examined, in order to handle MINLP problem with different mathematical structures. Preliminary computational results show that MINO exhibits a capability for handling practical MINLP problems that is comparable to that of the commercial solvers SBB and DICOPT, and it shows better performance for some problems for which either commercial solver encounters convergence problem within a CPU time limit of 6 h. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie9001074 [article] Branch-and-cut algorithmic framework for 0-1 mixed-integer convex nonlinear programs [texte imprimé] / Hao Wu, Auteur ; Hao Wen, Auteur ; Yushan Zhu, Auteur . - 2010 . - pp. 9119–9127. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 20 (Octobre 2009) . - pp. 9119–9127 Mots-clés : Branch-and-cut  algorithmic  framework  Mixed-Integer  Nonlinear  Optimizer  Nonlinear  programming Résumé : With widespread use of mixed-integer modeling for continuous and discontinuous nonlinear chemical processes, a robust mixed-integer nonlinear optimization algorithm is strongly demanded for the design and operation of chemical processes that achieve greater profits and also satisfy environmental and safety constraints. In this article, an extensive evaluation of a branch-and-cut algorithmic framework for 0−1 mixed-integer convex nonlinear programs, called MINO for Mixed-Integer Nonlinear Optimizer, is presented. The numerical performances of MINO are tested against four sets of medium-sized practical application problems from the fields of operations research and chemical engineering. The effects of the nonlinear programming (NLP) solvers, the cut generating and maintaining strategy, the node selection method, and the frequency of cut generation are examined, in order to handle MINLP problem with different mathematical structures. Preliminary computational results show that MINO exhibits a capability for handling practical MINLP problems that is comparable to that of the commercial solvers SBB and DICOPT, and it shows better performance for some problems for which either commercial solver encounters convergence problem within a CPU time limit of 6 h. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie9001074