Comparing the performances of two techniques for the optimization under parametric uncertainty of the simultaneous design and planning of a multiproduct batch plant

Autores
Durand, Guillermo; Moreno, M. Susana; Mele, Fernando D.; Montagna, Jorge Marcelo; Bandoni, Alberto
Año de publicación
2013
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
This paper addresses the comparison between two techniques for the optimization under parametric uncertainty of multiproduct batch plants integrating design and production planning decisions. This problem has been conceived as a two-stage stochastic mixed integer linear programming (MILP) in which the first-stage decisions consist of design variables that allow determining the batch plant structure, and the second-stage decisions consist of production planning continuous variables in a multiperiod context. The objective function maximizes the expected net present value. In the first solving approach, the problem has been tackled through mathematical programming considering a discrete set of scenarios. In the second solving approach, the multi-scenario MILP problem has been reformulated by adopting a simulation-based optimization scheme to accommodate the variables belonging to different management levels. Advantages and disadvantages of both approaches are demonstrated through a case study. Results allow concluding that a simulation-based optimization strategy may be a suitable technique to afford two-stage stochastic programming problems.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Uncertainty
Two-stage stochastic programming
Simulation-based optimization
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/94333

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spelling Comparing the performances of two techniques for the optimization under parametric uncertainty of the simultaneous design and planning of a multiproduct batch plantDurand, GuillermoMoreno, M. SusanaMele, Fernando D.Montagna, Jorge MarceloBandoni, AlbertoCiencias InformáticasUncertaintyTwo-stage stochastic programmingSimulation-based optimizationThis paper addresses the comparison between two techniques for the optimization under parametric uncertainty of multiproduct batch plants integrating design and production planning decisions. This problem has been conceived as a two-stage stochastic mixed integer linear programming (MILP) in which the first-stage decisions consist of design variables that allow determining the batch plant structure, and the second-stage decisions consist of production planning continuous variables in a multiperiod context. The objective function maximizes the expected net present value. In the first solving approach, the problem has been tackled through mathematical programming considering a discrete set of scenarios. In the second solving approach, the multi-scenario MILP problem has been reformulated by adopting a simulation-based optimization scheme to accommodate the variables belonging to different management levels. Advantages and disadvantages of both approaches are demonstrated through a case study. Results allow concluding that a simulation-based optimization strategy may be a suitable technique to afford two-stage stochastic programming problems.Sociedad Argentina de Informática e Investigación Operativa2013-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf94-104http://sedici.unlp.edu.ar/handle/10915/94333enginfo:eu-repo/semantics/altIdentifier/issn/2313-9102info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:51:53Zoai:sedici.unlp.edu.ar:10915/94333Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:51:54.352SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Comparing the performances of two techniques for the optimization under parametric uncertainty of the simultaneous design and planning of a multiproduct batch plant
title Comparing the performances of two techniques for the optimization under parametric uncertainty of the simultaneous design and planning of a multiproduct batch plant
spellingShingle Comparing the performances of two techniques for the optimization under parametric uncertainty of the simultaneous design and planning of a multiproduct batch plant
Durand, Guillermo
Ciencias Informáticas
Uncertainty
Two-stage stochastic programming
Simulation-based optimization
title_short Comparing the performances of two techniques for the optimization under parametric uncertainty of the simultaneous design and planning of a multiproduct batch plant
title_full Comparing the performances of two techniques for the optimization under parametric uncertainty of the simultaneous design and planning of a multiproduct batch plant
title_fullStr Comparing the performances of two techniques for the optimization under parametric uncertainty of the simultaneous design and planning of a multiproduct batch plant
title_full_unstemmed Comparing the performances of two techniques for the optimization under parametric uncertainty of the simultaneous design and planning of a multiproduct batch plant
title_sort Comparing the performances of two techniques for the optimization under parametric uncertainty of the simultaneous design and planning of a multiproduct batch plant
dc.creator.none.fl_str_mv Durand, Guillermo
Moreno, M. Susana
Mele, Fernando D.
Montagna, Jorge Marcelo
Bandoni, Alberto
author Durand, Guillermo
author_facet Durand, Guillermo
Moreno, M. Susana
Mele, Fernando D.
Montagna, Jorge Marcelo
Bandoni, Alberto
author_role author
author2 Moreno, M. Susana
Mele, Fernando D.
Montagna, Jorge Marcelo
Bandoni, Alberto
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Uncertainty
Two-stage stochastic programming
Simulation-based optimization
topic Ciencias Informáticas
Uncertainty
Two-stage stochastic programming
Simulation-based optimization
dc.description.none.fl_txt_mv This paper addresses the comparison between two techniques for the optimization under parametric uncertainty of multiproduct batch plants integrating design and production planning decisions. This problem has been conceived as a two-stage stochastic mixed integer linear programming (MILP) in which the first-stage decisions consist of design variables that allow determining the batch plant structure, and the second-stage decisions consist of production planning continuous variables in a multiperiod context. The objective function maximizes the expected net present value. In the first solving approach, the problem has been tackled through mathematical programming considering a discrete set of scenarios. In the second solving approach, the multi-scenario MILP problem has been reformulated by adopting a simulation-based optimization scheme to accommodate the variables belonging to different management levels. Advantages and disadvantages of both approaches are demonstrated through a case study. Results allow concluding that a simulation-based optimization strategy may be a suitable technique to afford two-stage stochastic programming problems.
Sociedad Argentina de Informática e Investigación Operativa
description This paper addresses the comparison between two techniques for the optimization under parametric uncertainty of multiproduct batch plants integrating design and production planning decisions. This problem has been conceived as a two-stage stochastic mixed integer linear programming (MILP) in which the first-stage decisions consist of design variables that allow determining the batch plant structure, and the second-stage decisions consist of production planning continuous variables in a multiperiod context. The objective function maximizes the expected net present value. In the first solving approach, the problem has been tackled through mathematical programming considering a discrete set of scenarios. In the second solving approach, the multi-scenario MILP problem has been reformulated by adopting a simulation-based optimization scheme to accommodate the variables belonging to different management levels. Advantages and disadvantages of both approaches are demonstrated through a case study. Results allow concluding that a simulation-based optimization strategy may be a suitable technique to afford two-stage stochastic programming problems.
publishDate 2013
dc.date.none.fl_str_mv 2013-09
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
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dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/94333
url http://sedici.unlp.edu.ar/handle/10915/94333
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/2313-9102
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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