Stochastic forestry harvest planning under soil compaction conditions

Autores
Rossit, Daniel Alejandro; Pais, Cristóbal; Weintraub, Andrés; Broz, Diego Ricardo; Frutos, Mariano; Tohmé, Fernando Abel
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Matemática de Bahía Blanca; Argentina.
Fil: Rossit, Daniel Alejandro. Universidad Nacional del Sur. Instituto de Matemática de Bahía Blanca; Argentina.
Fil: Rossit, Daniel Alejandro. Universidad Nacional del Sur. Departamento de Ingeniería (Bahía Blanca); Argentina.
Fil: Pais, Cristóbal. University of California Berkeley. Industrial Engineering and Operations Research; United States.
Fil: Weintraub, Andrés. Universidad de Chile. Departamento de Ingeniería Industrial; Chile.
Fil: Broz, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Broz, Diego Ricardo. Universidad Nacional de Misiones. Facultad de Ciencias Forestales; Argentina.
Fil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina.
Fil: Frutos, Mariano. Universidad Nacional del Sur. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina.
Fil: Frutos, Mariano. Universidad Nacional del Sur. Departamento de Ingeniería (Bahía Blanca); Argentina.
Fil: Tohmé, Fernando Abel. Universidad Nacional del Sur. Instituto de Matemática de Bahía Blanca; Argentina.
Fil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Matemática de Bahía Blanca; Argentina.
Fil: Tohmé, Fernando Abel. Universidad Nacional del Sur. Departamento de Economía; Argentina.
We present a study of annual forestry harvesting planning considering the risk of compaction generated by the transit of heavy forestry machinery. Soil compaction is a problem that occurs when the soil loses its natural resistance to resist the movement of machinery, causing the soil to be compacted in excess. This compaction generates unwanted effects on both the ecosystem and its economic sustainability. Therefore, when the risk of compaction is considerable, harvest operations must be stopped, complicating the annual plan and incurring in excessive costs to alleviate the situation. To incorporate the risk of compaction into the planning process, it is necessary to incorporate the analysis of the soil's hydrological balance, which combines the effect of rainfall and potential evapotranspiration. This requires analyzing the uncertainty of rainfall regimes, for which we propose a stochastic model under different scenarios. This stochastic model yields better results than the current deterministic methods used by lumber companies. Initially, the model is solved analyzing monthly scenarios. Then, we change to a biweekly model that provides a better representation of the dynamics of the system. While this improves the performance of the model, this new formulation increases the number of scenarios of the stochastic model. To address this complexity, we apply the Progressive Hedging method, which decomposes the problem in scenarios, yielding high-quality solutions in reasonable time.
Materia
Soil compaction
Forest harvest planning
Sustainable management
Stochastic modeling
Rainfall regime
Progressive hedging
Nivel de accesibilidad
acceso abierto
Condiciones de uso
Atribución-NoComercial-CompartirIgual 4.0 Internacional
Repositorio
Repositorio Institucional Digital de la Universidad Nacional de Misiones (UNaM)
Institución
Universidad Nacional de Misiones
OAI Identificador
oai:rid.unam.edu.ar:20.500.12219/4988

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spelling Stochastic forestry harvest planning under soil compaction conditionsRossit, Daniel AlejandroPais, CristóbalWeintraub, AndrésBroz, Diego RicardoFrutos, MarianoTohmé, Fernando AbelSoil compactionForest harvest planningSustainable managementStochastic modelingRainfall regimeProgressive hedgingFil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Matemática de Bahía Blanca; Argentina.Fil: Rossit, Daniel Alejandro. Universidad Nacional del Sur. Instituto de Matemática de Bahía Blanca; Argentina.Fil: Rossit, Daniel Alejandro. Universidad Nacional del Sur. Departamento de Ingeniería (Bahía Blanca); Argentina.Fil: Pais, Cristóbal. University of California Berkeley. Industrial Engineering and Operations Research; United States.Fil: Weintraub, Andrés. Universidad de Chile. Departamento de Ingeniería Industrial; Chile.Fil: Broz, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Broz, Diego Ricardo. Universidad Nacional de Misiones. Facultad de Ciencias Forestales; Argentina.Fil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina.Fil: Frutos, Mariano. Universidad Nacional del Sur. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina.Fil: Frutos, Mariano. Universidad Nacional del Sur. Departamento de Ingeniería (Bahía Blanca); Argentina.Fil: Tohmé, Fernando Abel. Universidad Nacional del Sur. Instituto de Matemática de Bahía Blanca; Argentina.Fil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Matemática de Bahía Blanca; Argentina.Fil: Tohmé, Fernando Abel. Universidad Nacional del Sur. Departamento de Economía; Argentina.We present a study of annual forestry harvesting planning considering the risk of compaction generated by the transit of heavy forestry machinery. Soil compaction is a problem that occurs when the soil loses its natural resistance to resist the movement of machinery, causing the soil to be compacted in excess. This compaction generates unwanted effects on both the ecosystem and its economic sustainability. Therefore, when the risk of compaction is considerable, harvest operations must be stopped, complicating the annual plan and incurring in excessive costs to alleviate the situation. To incorporate the risk of compaction into the planning process, it is necessary to incorporate the analysis of the soil's hydrological balance, which combines the effect of rainfall and potential evapotranspiration. This requires analyzing the uncertainty of rainfall regimes, for which we propose a stochastic model under different scenarios. This stochastic model yields better results than the current deterministic methods used by lumber companies. Initially, the model is solved analyzing monthly scenarios. Then, we change to a biweekly model that provides a better representation of the dynamics of the system. While this improves the performance of the model, this new formulation increases the number of scenarios of the stochastic model. To address this complexity, we apply the Progressive Hedging method, which decomposes the problem in scenarios, yielding high-quality solutions in reasonable time.Journal of Environmental Management2021-06-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdf1.722 MBhttps://hdl.handle.net/20.500.12219/4988enginfo:eu-repo/semantics/altIdentifier/urn/https://www.sciencedirect.com/science/article/pii/S0301479721012196info:eu-repo/semantics/openAccessAtribución-NoComercial-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-sa/4.0/reponame:Repositorio Institucional Digital de la Universidad Nacional de Misiones (UNaM)instname:Universidad Nacional de Misiones2025-09-29T15:02:30Zoai:rid.unam.edu.ar:20.500.12219/4988instacron:UNAMInstitucionalhttps://rid.unam.edu.ar/Universidad públicahttps://www.unam.edu.ar/https://rid.unam.edu.ar/oai/rsnrdArgentinaopendoar:2025-09-29 15:02:30.847Repositorio Institucional Digital de la Universidad Nacional de Misiones (UNaM) - Universidad Nacional de Misionesfalse
dc.title.none.fl_str_mv Stochastic forestry harvest planning under soil compaction conditions
title Stochastic forestry harvest planning under soil compaction conditions
spellingShingle Stochastic forestry harvest planning under soil compaction conditions
Rossit, Daniel Alejandro
Soil compaction
Forest harvest planning
Sustainable management
Stochastic modeling
Rainfall regime
Progressive hedging
title_short Stochastic forestry harvest planning under soil compaction conditions
title_full Stochastic forestry harvest planning under soil compaction conditions
title_fullStr Stochastic forestry harvest planning under soil compaction conditions
title_full_unstemmed Stochastic forestry harvest planning under soil compaction conditions
title_sort Stochastic forestry harvest planning under soil compaction conditions
dc.creator.none.fl_str_mv Rossit, Daniel Alejandro
Pais, Cristóbal
Weintraub, Andrés
Broz, Diego Ricardo
Frutos, Mariano
Tohmé, Fernando Abel
author Rossit, Daniel Alejandro
author_facet Rossit, Daniel Alejandro
Pais, Cristóbal
Weintraub, Andrés
Broz, Diego Ricardo
Frutos, Mariano
Tohmé, Fernando Abel
author_role author
author2 Pais, Cristóbal
Weintraub, Andrés
Broz, Diego Ricardo
Frutos, Mariano
Tohmé, Fernando Abel
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Soil compaction
Forest harvest planning
Sustainable management
Stochastic modeling
Rainfall regime
Progressive hedging
topic Soil compaction
Forest harvest planning
Sustainable management
Stochastic modeling
Rainfall regime
Progressive hedging
dc.description.none.fl_txt_mv Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Matemática de Bahía Blanca; Argentina.
Fil: Rossit, Daniel Alejandro. Universidad Nacional del Sur. Instituto de Matemática de Bahía Blanca; Argentina.
Fil: Rossit, Daniel Alejandro. Universidad Nacional del Sur. Departamento de Ingeniería (Bahía Blanca); Argentina.
Fil: Pais, Cristóbal. University of California Berkeley. Industrial Engineering and Operations Research; United States.
Fil: Weintraub, Andrés. Universidad de Chile. Departamento de Ingeniería Industrial; Chile.
Fil: Broz, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Broz, Diego Ricardo. Universidad Nacional de Misiones. Facultad de Ciencias Forestales; Argentina.
Fil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina.
Fil: Frutos, Mariano. Universidad Nacional del Sur. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina.
Fil: Frutos, Mariano. Universidad Nacional del Sur. Departamento de Ingeniería (Bahía Blanca); Argentina.
Fil: Tohmé, Fernando Abel. Universidad Nacional del Sur. Instituto de Matemática de Bahía Blanca; Argentina.
Fil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Matemática de Bahía Blanca; Argentina.
Fil: Tohmé, Fernando Abel. Universidad Nacional del Sur. Departamento de Economía; Argentina.
We present a study of annual forestry harvesting planning considering the risk of compaction generated by the transit of heavy forestry machinery. Soil compaction is a problem that occurs when the soil loses its natural resistance to resist the movement of machinery, causing the soil to be compacted in excess. This compaction generates unwanted effects on both the ecosystem and its economic sustainability. Therefore, when the risk of compaction is considerable, harvest operations must be stopped, complicating the annual plan and incurring in excessive costs to alleviate the situation. To incorporate the risk of compaction into the planning process, it is necessary to incorporate the analysis of the soil's hydrological balance, which combines the effect of rainfall and potential evapotranspiration. This requires analyzing the uncertainty of rainfall regimes, for which we propose a stochastic model under different scenarios. This stochastic model yields better results than the current deterministic methods used by lumber companies. Initially, the model is solved analyzing monthly scenarios. Then, we change to a biweekly model that provides a better representation of the dynamics of the system. While this improves the performance of the model, this new formulation increases the number of scenarios of the stochastic model. To address this complexity, we apply the Progressive Hedging method, which decomposes the problem in scenarios, yielding high-quality solutions in reasonable time.
description Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Matemática de Bahía Blanca; Argentina.
publishDate 2021
dc.date.none.fl_str_mv 2021-06-06
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 https://hdl.handle.net/20.500.12219/4988
url https://hdl.handle.net/20.500.12219/4988
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/urn/https://www.sciencedirect.com/science/article/pii/S0301479721012196
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
Atribución-NoComercial-CompartirIgual 4.0 Internacional
http://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv Atribución-NoComercial-CompartirIgual 4.0 Internacional
http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.format.none.fl_str_mv application/pdf
application/pdf
1.722 MB
dc.publisher.none.fl_str_mv Journal of Environmental Management
publisher.none.fl_str_mv Journal of Environmental Management
dc.source.none.fl_str_mv reponame:Repositorio Institucional Digital de la Universidad Nacional de Misiones (UNaM)
instname:Universidad Nacional de Misiones
reponame_str Repositorio Institucional Digital de la Universidad Nacional de Misiones (UNaM)
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instname_str Universidad Nacional de Misiones
repository.name.fl_str_mv Repositorio Institucional Digital de la Universidad Nacional de Misiones (UNaM) - Universidad Nacional de Misiones
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