Optimization of forest management strategies using clustering method and mathematical programming: A case study in Misiones, Argentina

Autores
Dussel, María Emilia; Piedra Jimenez, Frank; Novas, Juan M.; Rodríguez, María Analía
Año de publicación
2024
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In this study, a novel decision-making approach is proposed for the forest management planning process. Even in small-scale cases of study, the relationship between the dataset size and the complexity of mathematical optimization models (in terms of constraints and variables) is factorial, resulting in exponential increases in computational complexity. Thus, while acknowledging large size and realistic data is crucial to account for reasonable conclusions, it is also a challenge itself. Hence, a procedure is proposed to approach this strategic problem.First, random data is generated to assume an ongoing forest inventory. Second, data is processed applying three successive grouping steps to enhance the utilization of large datasets. Within this stage, clustering techniques are applied using the Scikit-learn library for a large group of stands with several characteristics. Last, a mathematical framework is presented, rooted in Generalized Disjunctive Programming (GDP) and reformulated as a Mixed Integer Linear Programming (MILP) model, to address optimal forest management strategy to maximize the net present value (NPV). The MILP model is implemented in Pyomo library in Python and solved using GAMS-CPlex. The feasibility of the proposed model is assessed using data obtained from the Desarrollo Foresto Industrial web page of the Secretaría de Agricultura, Ganadería y Pesca (Ministerio de Economía de la República Argentina). Computational analysis demonstrates the versatility of the framework as a decision-making tool, highlighting its ability to generate diverse and viable solutions for forest management.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Forestry Planning
Generalized Disjunctive Programming
Clustering Method
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:10915/177462

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spelling Optimization of forest management strategies using clustering method and mathematical programming: A case study in Misiones, ArgentinaDussel, María EmiliaPiedra Jimenez, FrankNovas, Juan M.Rodríguez, María AnalíaCiencias InformáticasForestry PlanningGeneralized Disjunctive ProgrammingClustering MethodIn this study, a novel decision-making approach is proposed for the forest management planning process. Even in small-scale cases of study, the relationship between the dataset size and the complexity of mathematical optimization models (in terms of constraints and variables) is factorial, resulting in exponential increases in computational complexity. Thus, while acknowledging large size and realistic data is crucial to account for reasonable conclusions, it is also a challenge itself. Hence, a procedure is proposed to approach this strategic problem.First, random data is generated to assume an ongoing forest inventory. Second, data is processed applying three successive grouping steps to enhance the utilization of large datasets. Within this stage, clustering techniques are applied using the Scikit-learn library for a large group of stands with several characteristics. Last, a mathematical framework is presented, rooted in Generalized Disjunctive Programming (GDP) and reformulated as a Mixed Integer Linear Programming (MILP) model, to address optimal forest management strategy to maximize the net present value (NPV). The MILP model is implemented in Pyomo library in Python and solved using GAMS-CPlex. The feasibility of the proposed model is assessed using data obtained from the Desarrollo Foresto Industrial web page of the Secretaría de Agricultura, Ganadería y Pesca (Ministerio de Economía de la República Argentina). Computational analysis demonstrates the versatility of the framework as a decision-making tool, highlighting its ability to generate diverse and viable solutions for forest management.Sociedad Argentina de Informática e Investigación Operativa2024-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf321-334http://sedici.unlp.edu.ar/handle/10915/177462enginfo:eu-repo/semantics/altIdentifier/url/https://revistas.unlp.edu.ar/JAIIO/article/view/18023info:eu-repo/semantics/altIdentifier/issn/2451-7496info: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:UNLP2025-09-03T11:19:36Zoai:sedici.unlp.edu.ar:10915/177462Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:19:37.072SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Optimization of forest management strategies using clustering method and mathematical programming: A case study in Misiones, Argentina
title Optimization of forest management strategies using clustering method and mathematical programming: A case study in Misiones, Argentina
spellingShingle Optimization of forest management strategies using clustering method and mathematical programming: A case study in Misiones, Argentina
Dussel, María Emilia
Ciencias Informáticas
Forestry Planning
Generalized Disjunctive Programming
Clustering Method
title_short Optimization of forest management strategies using clustering method and mathematical programming: A case study in Misiones, Argentina
title_full Optimization of forest management strategies using clustering method and mathematical programming: A case study in Misiones, Argentina
title_fullStr Optimization of forest management strategies using clustering method and mathematical programming: A case study in Misiones, Argentina
title_full_unstemmed Optimization of forest management strategies using clustering method and mathematical programming: A case study in Misiones, Argentina
title_sort Optimization of forest management strategies using clustering method and mathematical programming: A case study in Misiones, Argentina
dc.creator.none.fl_str_mv Dussel, María Emilia
Piedra Jimenez, Frank
Novas, Juan M.
Rodríguez, María Analía
author Dussel, María Emilia
author_facet Dussel, María Emilia
Piedra Jimenez, Frank
Novas, Juan M.
Rodríguez, María Analía
author_role author
author2 Piedra Jimenez, Frank
Novas, Juan M.
Rodríguez, María Analía
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Forestry Planning
Generalized Disjunctive Programming
Clustering Method
topic Ciencias Informáticas
Forestry Planning
Generalized Disjunctive Programming
Clustering Method
dc.description.none.fl_txt_mv In this study, a novel decision-making approach is proposed for the forest management planning process. Even in small-scale cases of study, the relationship between the dataset size and the complexity of mathematical optimization models (in terms of constraints and variables) is factorial, resulting in exponential increases in computational complexity. Thus, while acknowledging large size and realistic data is crucial to account for reasonable conclusions, it is also a challenge itself. Hence, a procedure is proposed to approach this strategic problem.First, random data is generated to assume an ongoing forest inventory. Second, data is processed applying three successive grouping steps to enhance the utilization of large datasets. Within this stage, clustering techniques are applied using the Scikit-learn library for a large group of stands with several characteristics. Last, a mathematical framework is presented, rooted in Generalized Disjunctive Programming (GDP) and reformulated as a Mixed Integer Linear Programming (MILP) model, to address optimal forest management strategy to maximize the net present value (NPV). The MILP model is implemented in Pyomo library in Python and solved using GAMS-CPlex. The feasibility of the proposed model is assessed using data obtained from the Desarrollo Foresto Industrial web page of the Secretaría de Agricultura, Ganadería y Pesca (Ministerio de Economía de la República Argentina). Computational analysis demonstrates the versatility of the framework as a decision-making tool, highlighting its ability to generate diverse and viable solutions for forest management.
Sociedad Argentina de Informática e Investigación Operativa
description In this study, a novel decision-making approach is proposed for the forest management planning process. Even in small-scale cases of study, the relationship between the dataset size and the complexity of mathematical optimization models (in terms of constraints and variables) is factorial, resulting in exponential increases in computational complexity. Thus, while acknowledging large size and realistic data is crucial to account for reasonable conclusions, it is also a challenge itself. Hence, a procedure is proposed to approach this strategic problem.First, random data is generated to assume an ongoing forest inventory. Second, data is processed applying three successive grouping steps to enhance the utilization of large datasets. Within this stage, clustering techniques are applied using the Scikit-learn library for a large group of stands with several characteristics. Last, a mathematical framework is presented, rooted in Generalized Disjunctive Programming (GDP) and reformulated as a Mixed Integer Linear Programming (MILP) model, to address optimal forest management strategy to maximize the net present value (NPV). The MILP model is implemented in Pyomo library in Python and solved using GAMS-CPlex. The feasibility of the proposed model is assessed using data obtained from the Desarrollo Foresto Industrial web page of the Secretaría de Agricultura, Ganadería y Pesca (Ministerio de Economía de la República Argentina). Computational analysis demonstrates the versatility of the framework as a decision-making tool, highlighting its ability to generate diverse and viable solutions for forest management.
publishDate 2024
dc.date.none.fl_str_mv 2024-08
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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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