Mathematical models for optimizing production chain planning in salmon farming

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...

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Autores principales:	Bravo, F., Durán, G., Lucena, A., Marenco, J., Morán, D., Weintraub, A.
Formato:	JOUR
Materias:	Integer programming Planning Production chain Salmon farming Minimizing costs Mixed integer linear programming Processing plants Production planning IS Fish Linear programming Mathematical models Water Seawater
Acceso en línea:	http://hdl.handle.net/20.500.12110/paper_09696016_v20_n5_p731_Bravo
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	todo:paper_09696016_v20_n5_p731_Bravo
record_format	dspace
spelling	todo:paper_09696016_v20_n5_p731_Bravo2023-10-03T15:55:23Z Mathematical models for optimizing production chain planning in salmon farming Bravo, F. Durán, G. Lucena, A. Marenco, J. Morán, D. Weintraub, A. Integer programming Planning Production chain Salmon farming Minimizing costs Mixed integer linear programming Processing plants Production chain Production planning IS Salmon farming Fish Integer programming Linear programming Mathematical models Planning Water Seawater 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. © 2013 International Federation of Operational Research Societies. Fil:Durán, G. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Marenco, J. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_09696016_v20_n5_p731_Bravo
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	Integer programming Planning Production chain Salmon farming Minimizing costs Mixed integer linear programming Processing plants Production chain Production planning IS Salmon farming Fish Integer programming Linear programming Mathematical models Planning Water Seawater
spellingShingle	Integer programming Planning Production chain Salmon farming Minimizing costs Mixed integer linear programming Processing plants Production chain Production planning IS Salmon farming Fish Integer programming Linear programming Mathematical models Planning Water Seawater Bravo, F. Durán, G. Lucena, A. Marenco, J. Morán, D. Weintraub, A. Mathematical models for optimizing production chain planning in salmon farming
topic_facet	Integer programming Planning Production chain Salmon farming Minimizing costs Mixed integer linear programming Processing plants Production chain Production planning IS Salmon farming Fish Integer programming Linear programming Mathematical models Planning Water Seawater
description	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. © 2013 International Federation of Operational Research Societies.
format	JOUR
author	Bravo, F. Durán, G. Lucena, A. Marenco, J. Morán, D. Weintraub, A.
author_facet	Bravo, F. Durán, G. Lucena, A. Marenco, J. Morán, D. Weintraub, A.
author_sort	Bravo, F.
title	Mathematical models for optimizing production chain planning in salmon farming
title_short	Mathematical models for optimizing production chain planning in salmon farming
title_full	Mathematical models for optimizing production chain planning in salmon farming
title_fullStr	Mathematical models for optimizing production chain planning in salmon farming
title_full_unstemmed	Mathematical models for optimizing production chain planning in salmon farming
title_sort	mathematical models for optimizing production chain planning in salmon farming
url	http://hdl.handle.net/20.500.12110/paper_09696016_v20_n5_p731_Bravo
work_keys_str_mv	AT bravof mathematicalmodelsforoptimizingproductionchainplanninginsalmonfarming AT durang mathematicalmodelsforoptimizingproductionchainplanninginsalmonfarming AT lucenaa mathematicalmodelsforoptimizingproductionchainplanninginsalmonfarming AT marencoj mathematicalmodelsforoptimizingproductionchainplanninginsalmonfarming AT morand mathematicalmodelsforoptimizingproductionchainplanninginsalmonfarming AT weintrauba mathematicalmodelsforoptimizingproductionchainplanninginsalmonfarming
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Mathematical models for optimizing production chain planning in salmon farming

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