Performance of four meta-heuristics to solve a forestry production planning problem
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Caderno de Ciências Agrárias (Online) |
Texto Completo: | https://periodicos.ufmg.br/index.php/ccaufmg/article/view/15891 |
Resumo: | The use of artificial intelligence as a tool to aid in the planning of forest production has gained more and more space. Highlighting the metaheuristics, due to the ability to generate optimal solutions for a given optimization problem in a short time, without great computational effort. The present study aims to evaluate the performance of the metaheuristics Genetic Algorithm, Simulated Annealing, Variable Neighborhood Search and Clonal Selection Algorithm applied in a model of regulation of forest production. It was considered a planning horizon of 16 years, in which the model aims to maximize the Net Present Value (NPV), having as restrictions age of cut between 5 and 7 years and minimum and maximum logging demand of 140,000 and 160,000 m3, respectively. Different combinations of configurations were considered for each of the metaheuristics, 30-second processing time and 30 replicates for each configuration, all processing being performed in MeP - Metaheuristics for forest Planning software. The Simulated Annealing metaheuristic obtained the best results when compared to the others, reaching the minimum and maximum demand demanded in all tested configurations, in contrast, the Genetic Algorithm was the one with the worst performance. Thus, the capacity to use metaheuristics as a tool for forest planning is observed. |
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Performance of four meta-heuristics to solve a forestry production planning problemPerformance de quatro metaheurísticas para solução de um problema de planejamento da produção florestalInteligência artificalSilviculturaManejo florestalPesquisa operacionalArtificial intelligenceForestryForest managementOperational researchThe use of artificial intelligence as a tool to aid in the planning of forest production has gained more and more space. Highlighting the metaheuristics, due to the ability to generate optimal solutions for a given optimization problem in a short time, without great computational effort. The present study aims to evaluate the performance of the metaheuristics Genetic Algorithm, Simulated Annealing, Variable Neighborhood Search and Clonal Selection Algorithm applied in a model of regulation of forest production. It was considered a planning horizon of 16 years, in which the model aims to maximize the Net Present Value (NPV), having as restrictions age of cut between 5 and 7 years and minimum and maximum logging demand of 140,000 and 160,000 m3, respectively. Different combinations of configurations were considered for each of the metaheuristics, 30-second processing time and 30 replicates for each configuration, all processing being performed in MeP - Metaheuristics for forest Planning software. The Simulated Annealing metaheuristic obtained the best results when compared to the others, reaching the minimum and maximum demand demanded in all tested configurations, in contrast, the Genetic Algorithm was the one with the worst performance. Thus, the capacity to use metaheuristics as a tool for forest planning is observed.O uso da inteligência artificial como ferramenta de auxílio ao planejamento da produção florestal tem ganhado cada vez mais espaço. Destacando-se as metaheurísticas, em função da capacidade de gerar soluções ótimas para determinado problema de otimização em um curto espaço de tempo, sem grande esforço computacional. Pensando nisso, o presente estudo objetiva avaliar o desempenho das metaheurísticas Algoritmo Genético, Simulated Annealing, Variable Neighbourhood Search e Clonal Selection Algorithm aplicadas em um modelo de regulação da produção florestal. Foi considerado um horizonte de planejamento de 16 anos, no qual o modelo apresenta como objetivo a maximização do Valor Presente Líquido (VPL), tendo como restrições idade de corte entre 5 e 7 anos e demanda mínima e máxima madeireira de 140.000 e 160.000 m3, respectivamente. Considerou-se diferentes combinações de configurações para cada uma das metaheurísticas, tempo de processamento de 30 segundos e 30 repetições para cada configuração, sendo todo o processamento realizado no software MeP - Metaheuristics for Forest Planning. A metaheurística Simulated Annealing obteve os melhores resultados quando comparada as demais, atingindo a demanda mínima e máxima exigida em todas as configurações testadas, em contrapartida, o Algoritmo Genético foi o de pior desempenho. Assim, observa-se a capacidade de uso da metaheurística como ferramenta de planejamento florestal.lUniversidade Federal de Minas Gerais2020-02-29info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdftext/htmlhttps://periodicos.ufmg.br/index.php/ccaufmg/article/view/1589110.35699/2447-6218.2020.15891Agrarian Sciences Journal; Vol. 12 (2020); 1-5Caderno de Ciências Agrárias; v. 12 (2020); 1-52447-62181984-6738reponame:Caderno de Ciências Agrárias (Online)instname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGenghttps://periodicos.ufmg.br/index.php/ccaufmg/article/view/15891/16384https://periodicos.ufmg.br/index.php/ccaufmg/article/view/15891/16385Copyright (c) 2020 Caderno de Ciências Agráriashttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessMagalhães, Emanuelly CanabravaAraújo Júnior, Carlos AlbertoRoca, Francisco ConesaSousa, Mylla Vyctória Coutinho 2022-07-28T15:48:54Zoai:periodicos.ufmg.br:article/15891Revistahttps://periodicos.ufmg.br/index.php/ccaufmgPUBhttps://periodicos.ufmg.br/index.php/ccaufmg/oaiccaufmg@ica.ufmg.br2447-62181984-6738opendoar:2022-07-28T15:48:54Caderno de Ciências Agrárias (Online) - Universidade Federal de Minas Gerais (UFMG)false |
dc.title.none.fl_str_mv |
Performance of four meta-heuristics to solve a forestry production planning problem Performance de quatro metaheurísticas para solução de um problema de planejamento da produção florestal |
title |
Performance of four meta-heuristics to solve a forestry production planning problem |
spellingShingle |
Performance of four meta-heuristics to solve a forestry production planning problem Magalhães, Emanuelly Canabrava Inteligência artifical Silvicultura Manejo florestal Pesquisa operacional Artificial intelligence Forestry Forest management Operational research |
title_short |
Performance of four meta-heuristics to solve a forestry production planning problem |
title_full |
Performance of four meta-heuristics to solve a forestry production planning problem |
title_fullStr |
Performance of four meta-heuristics to solve a forestry production planning problem |
title_full_unstemmed |
Performance of four meta-heuristics to solve a forestry production planning problem |
title_sort |
Performance of four meta-heuristics to solve a forestry production planning problem |
author |
Magalhães, Emanuelly Canabrava |
author_facet |
Magalhães, Emanuelly Canabrava Araújo Júnior, Carlos Alberto Roca, Francisco Conesa Sousa, Mylla Vyctória Coutinho |
author_role |
author |
author2 |
Araújo Júnior, Carlos Alberto Roca, Francisco Conesa Sousa, Mylla Vyctória Coutinho |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Magalhães, Emanuelly Canabrava Araújo Júnior, Carlos Alberto Roca, Francisco Conesa Sousa, Mylla Vyctória Coutinho |
dc.subject.por.fl_str_mv |
Inteligência artifical Silvicultura Manejo florestal Pesquisa operacional Artificial intelligence Forestry Forest management Operational research |
topic |
Inteligência artifical Silvicultura Manejo florestal Pesquisa operacional Artificial intelligence Forestry Forest management Operational research |
description |
The use of artificial intelligence as a tool to aid in the planning of forest production has gained more and more space. Highlighting the metaheuristics, due to the ability to generate optimal solutions for a given optimization problem in a short time, without great computational effort. The present study aims to evaluate the performance of the metaheuristics Genetic Algorithm, Simulated Annealing, Variable Neighborhood Search and Clonal Selection Algorithm applied in a model of regulation of forest production. It was considered a planning horizon of 16 years, in which the model aims to maximize the Net Present Value (NPV), having as restrictions age of cut between 5 and 7 years and minimum and maximum logging demand of 140,000 and 160,000 m3, respectively. Different combinations of configurations were considered for each of the metaheuristics, 30-second processing time and 30 replicates for each configuration, all processing being performed in MeP - Metaheuristics for forest Planning software. The Simulated Annealing metaheuristic obtained the best results when compared to the others, reaching the minimum and maximum demand demanded in all tested configurations, in contrast, the Genetic Algorithm was the one with the worst performance. Thus, the capacity to use metaheuristics as a tool for forest planning is observed. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-02-29 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.ufmg.br/index.php/ccaufmg/article/view/15891 10.35699/2447-6218.2020.15891 |
url |
https://periodicos.ufmg.br/index.php/ccaufmg/article/view/15891 |
identifier_str_mv |
10.35699/2447-6218.2020.15891 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://periodicos.ufmg.br/index.php/ccaufmg/article/view/15891/16384 https://periodicos.ufmg.br/index.php/ccaufmg/article/view/15891/16385 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2020 Caderno de Ciências Agrárias https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2020 Caderno de Ciências Agrárias https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf text/html |
dc.publisher.none.fl_str_mv |
Universidade Federal de Minas Gerais |
publisher.none.fl_str_mv |
Universidade Federal de Minas Gerais |
dc.source.none.fl_str_mv |
Agrarian Sciences Journal; Vol. 12 (2020); 1-5 Caderno de Ciências Agrárias; v. 12 (2020); 1-5 2447-6218 1984-6738 reponame:Caderno de Ciências Agrárias (Online) instname:Universidade Federal de Minas Gerais (UFMG) instacron:UFMG |
instname_str |
Universidade Federal de Minas Gerais (UFMG) |
instacron_str |
UFMG |
institution |
UFMG |
reponame_str |
Caderno de Ciências Agrárias (Online) |
collection |
Caderno de Ciências Agrárias (Online) |
repository.name.fl_str_mv |
Caderno de Ciências Agrárias (Online) - Universidade Federal de Minas Gerais (UFMG) |
repository.mail.fl_str_mv |
ccaufmg@ica.ufmg.br |
_version_ |
1797042443480203264 |