New strategies for flexibility analysis and design under uncertainty

Autores
Raspanti, Claudia Gabriela; Bandoni, Jose Alberto; Biegler, L.
Año de publicación
2000
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Process flexibility and design under uncertainty have been researched extensively in the literature. Problem formulations for flexibility include nested optimization problems and these can often be refined by substituting the optimality conditions for these nested problems. However, these reformulations are highly constrained and can be expensive to solve. In this paper we extend algorithms to solve these reformulated NLP problem under uncertainty by introducing two contributions to this approach. These are the use of a Constraint Aggregation function (KS function) and Smoothing Functions. We begin with basic properties of KS function. Next, we review a class of parametric smooth functions, used to replace the complementarity conditions of the KKT conditions with a well-behaved, smoothed nonlinear equality constraint. In this paper we apply the previous strategies to two specific problems: i) the'worst case algorithm', that assesses design under uncertainty and, ii) the flexibility index and feasibility test formulations. In the first case, two new algorithms are derived, one of them being single level optimization problem. Next using similar ideas, both flexibility index and feasibility test are reformulated leading to a single non linear programming problem instead of a mixed integer non linear programming one. The new formulations are demonstrated on five different example problems where a CPU time reduction of more than 70 and 80% is demonstrated.
Fil: Raspanti, Claudia Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Bandoni, Jose Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Biegler, L.. Carnegie Mellon University; Estados Unidos
Materia
Uncertainty
Design
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/101422

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spelling New strategies for flexibility analysis and design under uncertaintyRaspanti, Claudia GabrielaBandoni, Jose AlbertoBiegler, L.UncertaintyDesignhttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2Process flexibility and design under uncertainty have been researched extensively in the literature. Problem formulations for flexibility include nested optimization problems and these can often be refined by substituting the optimality conditions for these nested problems. However, these reformulations are highly constrained and can be expensive to solve. In this paper we extend algorithms to solve these reformulated NLP problem under uncertainty by introducing two contributions to this approach. These are the use of a Constraint Aggregation function (KS function) and Smoothing Functions. We begin with basic properties of KS function. Next, we review a class of parametric smooth functions, used to replace the complementarity conditions of the KKT conditions with a well-behaved, smoothed nonlinear equality constraint. In this paper we apply the previous strategies to two specific problems: i) the'worst case algorithm', that assesses design under uncertainty and, ii) the flexibility index and feasibility test formulations. In the first case, two new algorithms are derived, one of them being single level optimization problem. Next using similar ideas, both flexibility index and feasibility test are reformulated leading to a single non linear programming problem instead of a mixed integer non linear programming one. The new formulations are demonstrated on five different example problems where a CPU time reduction of more than 70 and 80% is demonstrated.Fil: Raspanti, Claudia Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Bandoni, Jose Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Biegler, L.. Carnegie Mellon University; Estados UnidosPergamon-Elsevier Science Ltd2000-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/101422Raspanti, Claudia Gabriela; Bandoni, Jose Alberto; Biegler, L.; New strategies for flexibility analysis and design under uncertainty; Pergamon-Elsevier Science Ltd; Computers and Chemical Engineering; 24; 9-10; 10-2000; 2193-22090098-1354CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0098135400005913info:eu-repo/semantics/altIdentifier/doi/10.1016/S0098-1354(00)00591-3info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:45:52Zoai:ri.conicet.gov.ar:11336/101422instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:45:52.701CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv New strategies for flexibility analysis and design under uncertainty
title New strategies for flexibility analysis and design under uncertainty
spellingShingle New strategies for flexibility analysis and design under uncertainty
Raspanti, Claudia Gabriela
Uncertainty
Design
title_short New strategies for flexibility analysis and design under uncertainty
title_full New strategies for flexibility analysis and design under uncertainty
title_fullStr New strategies for flexibility analysis and design under uncertainty
title_full_unstemmed New strategies for flexibility analysis and design under uncertainty
title_sort New strategies for flexibility analysis and design under uncertainty
dc.creator.none.fl_str_mv Raspanti, Claudia Gabriela
Bandoni, Jose Alberto
Biegler, L.
author Raspanti, Claudia Gabriela
author_facet Raspanti, Claudia Gabriela
Bandoni, Jose Alberto
Biegler, L.
author_role author
author2 Bandoni, Jose Alberto
Biegler, L.
author2_role author
author
dc.subject.none.fl_str_mv Uncertainty
Design
topic Uncertainty
Design
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Process flexibility and design under uncertainty have been researched extensively in the literature. Problem formulations for flexibility include nested optimization problems and these can often be refined by substituting the optimality conditions for these nested problems. However, these reformulations are highly constrained and can be expensive to solve. In this paper we extend algorithms to solve these reformulated NLP problem under uncertainty by introducing two contributions to this approach. These are the use of a Constraint Aggregation function (KS function) and Smoothing Functions. We begin with basic properties of KS function. Next, we review a class of parametric smooth functions, used to replace the complementarity conditions of the KKT conditions with a well-behaved, smoothed nonlinear equality constraint. In this paper we apply the previous strategies to two specific problems: i) the'worst case algorithm', that assesses design under uncertainty and, ii) the flexibility index and feasibility test formulations. In the first case, two new algorithms are derived, one of them being single level optimization problem. Next using similar ideas, both flexibility index and feasibility test are reformulated leading to a single non linear programming problem instead of a mixed integer non linear programming one. The new formulations are demonstrated on five different example problems where a CPU time reduction of more than 70 and 80% is demonstrated.
Fil: Raspanti, Claudia Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Bandoni, Jose Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Biegler, L.. Carnegie Mellon University; Estados Unidos
description Process flexibility and design under uncertainty have been researched extensively in the literature. Problem formulations for flexibility include nested optimization problems and these can often be refined by substituting the optimality conditions for these nested problems. However, these reformulations are highly constrained and can be expensive to solve. In this paper we extend algorithms to solve these reformulated NLP problem under uncertainty by introducing two contributions to this approach. These are the use of a Constraint Aggregation function (KS function) and Smoothing Functions. We begin with basic properties of KS function. Next, we review a class of parametric smooth functions, used to replace the complementarity conditions of the KKT conditions with a well-behaved, smoothed nonlinear equality constraint. In this paper we apply the previous strategies to two specific problems: i) the'worst case algorithm', that assesses design under uncertainty and, ii) the flexibility index and feasibility test formulations. In the first case, two new algorithms are derived, one of them being single level optimization problem. Next using similar ideas, both flexibility index and feasibility test are reformulated leading to a single non linear programming problem instead of a mixed integer non linear programming one. The new formulations are demonstrated on five different example problems where a CPU time reduction of more than 70 and 80% is demonstrated.
publishDate 2000
dc.date.none.fl_str_mv 2000-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/101422
Raspanti, Claudia Gabriela; Bandoni, Jose Alberto; Biegler, L.; New strategies for flexibility analysis and design under uncertainty; Pergamon-Elsevier Science Ltd; Computers and Chemical Engineering; 24; 9-10; 10-2000; 2193-2209
0098-1354
CONICET Digital
CONICET
url http://hdl.handle.net/11336/101422
identifier_str_mv Raspanti, Claudia Gabriela; Bandoni, Jose Alberto; Biegler, L.; New strategies for flexibility analysis and design under uncertainty; Pergamon-Elsevier Science Ltd; Computers and Chemical Engineering; 24; 9-10; 10-2000; 2193-2209
0098-1354
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0098135400005913
info:eu-repo/semantics/altIdentifier/doi/10.1016/S0098-1354(00)00591-3
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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