Mathematical models for optimizing production chain planning in salmon farming
Author
dc.contributor.author
Bravo, Fernanda
Author
dc.contributor.author
Durán Maggiolo, Guillermo
es_CL
Author
dc.contributor.author
Lucena, Abilio
es_CL
Author
dc.contributor.author
Marenco, Javier
es_CL
Author
dc.contributor.author
Morán, Diego
es_CL
Author
dc.contributor.author
Weintraub Pohorille, Andrés
es_CL
Admission date
dc.date.accessioned
2014-01-28T19:26:46Z
Available date
dc.date.available
2014-01-28T19:26:46Z
Publication date
dc.date.issued
2013
Cita de ítem
dc.identifier.citation
Intl. Trans. in Op. Res 20 (2013) 731–766
en_US
Identifier
dc.identifier.other
DOI: 10.1111/itor.12022
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/126315
General note
dc.description
Artículo de publicación ISI
en_US
Abstract
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.