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

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...

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Autores principales: Dussel, María Emilia, Piedra Jimenez, Frank, Novas, Juan M., Rodríguez, María Analía
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2024
Materias:
Ciencias Informáticas
Forestry Planning
Generalized Disjunctive Programming
Clustering Method
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/177462
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SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-177462
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spelling I19-R120-10915-1774622025-05-08T17:41:52Z http://sedici.unlp.edu.ar/handle/10915/177462 Optimization of forest management strategies using clustering method and mathematical programming: A case study in Misiones, Argentina Dussel, María Emilia Piedra Jimenez, Frank Novas, Juan M. Rodríguez, María Analía 2024-08 2024 2025-03-17T16:15:59Z en Ciencias Informáticas Forestry Planning Generalized Disjunctive Programming Clustering Method 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 Objeto de conferencia Objeto de conferencia http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf 321-334
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Forestry Planning
Generalized Disjunctive Programming
Clustering Method
spellingShingle Ciencias Informáticas
Forestry Planning
Generalized Disjunctive Programming
Clustering Method
Dussel, María Emilia
Piedra Jimenez, Frank
Novas, Juan M.
Rodríguez, María Analía
Optimization of forest management strategies using clustering method and mathematical programming: A case study in Misiones, Argentina
topic_facet Ciencias Informáticas
Forestry Planning
Generalized Disjunctive Programming
Clustering Method
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.
format Objeto de conferencia
Objeto de conferencia
author Dussel, María Emilia
Piedra Jimenez, Frank
Novas, Juan M.
Rodríguez, María Analía
author_facet Dussel, María Emilia
Piedra Jimenez, Frank
Novas, Juan M.
Rodríguez, María Analía
author_sort Dussel, María Emilia
title Optimization of forest management strategies using clustering method and mathematical programming: A case study in Misiones, Argentina
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
publishDate 2024
url http://sedici.unlp.edu.ar/handle/10915/177462
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