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dc.contributor.author
Bravo, Fernanda
dc.contributor.author
Duran, Guillermo Alfredo
dc.contributor.author
Lucena, Abilio
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Marenco, Javier
dc.contributor.author
Moran, Diego
dc.contributor.author
Weintraub, Andres
dc.date.available
2017-04-27T17:17:49Z
dc.date.issued
2013
dc.identifier.citation
Bravo, Fernanda; Duran, Guillermo Alfredo; Lucena, Abilio; Marenco, Javier; Moran, Diego; et al.; Mathematical models for optimizing production chain planning in salmon farming; Wiley; International Transactions In Operational Research; 20; 5; -1-2013; 731-766
dc.identifier.issn
0969-6016
dc.identifier.uri
http://hdl.handle.net/11336/15803
dc.description.abstract
The salmon farming production chain is structured in four consecutive phases: freshwater, seawater, plant processing, and distribution and marketing. The phases interact in a pull manner, freshwater stocks fish to meet seawater's demand, seawater produces to meet plant processing biomass demand, and the processing plant produces to satisfy consumers' demand. Freshwater planning decisions are in regard to which freshwater center the fish should be located depending on the state of development of the fish. The goal is to satisfy seawater's demand while minimizing costs. In the seawater phase, the fish are first placed in seawater centers, and then sent to the processing plant as they approach suitable harvest conditions. The goal of seawater is to maximize harvested biomass while satisfying processing plant's demand. This paper presents two mixed-integer linear programming models—one for the freshwater phase and another for the seawater phase. These models are designed in such a way that the production planning is well integrated and more efficient and incorporates the requirements of the farm operator's freshwater and seawater units (biological, economic, and health-related constraints) ensuring that production in both phases is better coordinated. The development of the two models was based on the farming operations of one of the main producer farms in Chile. Preliminary evaluations of the models indicate that they not only succeed in enforcing constraints that are difficult to be met by manual planning but also led to more effective results in terms of the objectives set out.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Wiley
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Mathematical Programming
dc.subject
Integer Programming
dc.subject
Models
dc.subject.classification
Matemática Aplicada
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Matemáticas
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CIENCIAS NATURALES Y EXACTAS
dc.title
Mathematical models for optimizing production chain planning in salmon farming
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2017-04-24T14:49:05Z
dc.journal.volume
20
dc.journal.number
5
dc.journal.pagination
731-766
dc.journal.pais
Estados Unidos
dc.journal.ciudad
Hoboken
dc.description.fil
Fil: Bravo, Fernanda. Massachusetts Institute of Technology; Estados Unidos. Universidad de Chile; Chile
dc.description.fil
Fil: Duran, Guillermo Alfredo. Universidad de Chile; Chile. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Lucena, Abilio. Universidade Federal do Rio de Janeiro; Brasil
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Fil: Marenco, Javier. Universidad Nacional de General Sarmiento. Instituto de Ciencias; Argentina
dc.description.fil
Fil: Moran, Diego. Universidad de Chile; Chile. Georgia Institute Of Techology; Estados Unidos
dc.description.fil
Fil: Weintraub, Andres. Universidad de Chile; Chile
dc.journal.title
International Transactions In Operational Research
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1111/itor.12022
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1111/itor.12022/abstract
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