Performance of four meta-heuristics to solve a forestry production planning problem

Detalhes bibliográficos
Autor(a) principal: Magalhães, Emanuelly Canabrava
Data de Publicação: 2020
Outros Autores: Araújo Júnior, Carlos Alberto, Roca, Francisco Conesa, Sousa, Mylla Vyctória Coutinho
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.
id UFMG-1_378a85548a1254d3cf5964b42cc57371
oai_identifier_str oai:periodicos.ufmg.br:article/15891
network_acronym_str UFMG-1
network_name_str Caderno de Ciências Agrárias (Online)
repository_id_str
spelling 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

Registros relacionados