TUNING OF THE METAHEURISTIC VARIABLE NEIGHBORHOOD SEARCH FOR A FOREST PLANNING PROBLEM

Detalhes bibliográficos
Autor(a) principal: Araújo Júnior, Carlos Alberto
Data de Publicação: 2018
Outros Autores: Mendes, João Batista, Assis, Adriana Leandra de, Cabacinha, Christian Dias, Stocks, Jonathan James, Silva, Liniker Fernandes da, Leite, Helio Garcia
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Cerne (Online)
Texto Completo: https://cerne.ufla.br/site/index.php/CERNE/article/view/1869
Resumo: In forest science it is important evaluate new technologies from computational science. This work aimed to test a different kind of metaheuristic called Variable Neighborhood Search in a forest planning problem. The management total area has 4.210 ha distributed in 120 stands in ages between 1 and 6 years old and site index since 22 m to 31 m. The problem was modelled considering the maximization of the net present value subject to the restrictions: annual cut volume between 140.000 m³ and 160.000 m³, harvester ages equal to 5, 6 or 7 years, and the impossibility of division of the management unity at harvester time. It was evaluated different settings for the Variable Neighborhood Search, varying the quantity of neighbours, the neighbourhood structure and number or generations. 30 repetitions were performed for each setting. The results were compared to the one obtained from integer linear programming and linear programming. The integer linear programming considered the best solution obtained after 1 hour of processing. The best setting to the Variable Neighborhood Search was 100 neighbours, a neighbourhood structure with changes in 1%, 2%, 3% and 4% of prescriptions and 500 iterations. The results shown by the Variable Neighborhood Search was 2,77% worse than one obtained by the integer linear programming with 1 hours of processing, and 2,84% worse than the linear programming. It is possible to conclude that the presented metaheuristic can be used in resolution of forest scheduling problem.
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spelling TUNING OF THE METAHEURISTIC VARIABLE NEIGHBORHOOD SEARCH FOR A FOREST PLANNING PROBLEMMETAHEURÍSTICA VARIABLE NEIGHBORHOOD SEARCH APLICADA PARA UM PROBLEMA DE PLANEJAMENTO FLORESTALOperational ResearchArtificial IntelligenceForest ManagementPesquisa OperacionalInteligência ArtificialManejo FlorestalIn forest science it is important evaluate new technologies from computational science. This work aimed to test a different kind of metaheuristic called Variable Neighborhood Search in a forest planning problem. The management total area has 4.210 ha distributed in 120 stands in ages between 1 and 6 years old and site index since 22 m to 31 m. The problem was modelled considering the maximization of the net present value subject to the restrictions: annual cut volume between 140.000 m³ and 160.000 m³, harvester ages equal to 5, 6 or 7 years, and the impossibility of division of the management unity at harvester time. It was evaluated different settings for the Variable Neighborhood Search, varying the quantity of neighbours, the neighbourhood structure and number or generations. 30 repetitions were performed for each setting. The results were compared to the one obtained from integer linear programming and linear programming. The integer linear programming considered the best solution obtained after 1 hour of processing. The best setting to the Variable Neighborhood Search was 100 neighbours, a neighbourhood structure with changes in 1%, 2%, 3% and 4% of prescriptions and 500 iterations. The results shown by the Variable Neighborhood Search was 2,77% worse than one obtained by the integer linear programming with 1 hours of processing, and 2,84% worse than the linear programming. It is possible to conclude that the presented metaheuristic can be used in resolution of forest scheduling problem.Dada a importância de se avaliar novas tecnologias para otimização do planejamento florestal, objetivou-se introduzir a metaheurística Variable Neighborhood Search na resolução de um problema de ordenamento da produção florestal. Considerou-se uma área manejada de 4.210 ha, contendo 120 talhões com idades entre 1 e 6 anos e índice de sítio variando entre 22 m e 31 m, em uma idade-índice de 72 meses. O problema foi modelado com o objetivo de se maximizar o valor presente líquido global do empreendimento e considerou como restrições uma demanda anual entre 140.000 m³ e 160.000 m³, colheita nas idades de 5, 6 e 7 anos e a imposição do não fracionamento dos talhões no momento do corte. Foram avaliadas diferentes configurações da metaheurística, variando-se a quantidade de vizinhos, a estrutura de vizinhança e a quantidade de gerações. Para cada parametrização foram avaliadas 30 repetições. Os resultados foram comparados com aqueles obtidos utilizando-se programação linear e programação linear inteira. Para a programação inteira considerou-se a melhor solução após 1 hora de processamento. A melhor configuração considerou 100 vizinhos, uma estrutura de vizinhança com alterações em 1%, 2%, 3% e 4% das prescrições e 500 gerações. Os resultados apresentados pela metaheurística Variable Neighborhood Search foram 2,77% inferiores à solução da programação inteira obtida após 1 hora de processamento e 2,84% inferior à programação linear. Conclui-se que a metaheurística apresentada pode ser utilizada para resolução de problemas de ordenamento florestal, podendo gerar bons resultados em comparação à programação inteira no caso de instâncias NP-difíceis.CERNECERNE2018-10-16info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://cerne.ufla.br/site/index.php/CERNE/article/view/1869CERNE; Vol. 24 No. 3 (2018); 259-268CERNE; v. 24 n. 3 (2018); 259-2682317-63420104-7760reponame:Cerne (Online)instname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://cerne.ufla.br/site/index.php/CERNE/article/view/1869/1087Copyright (c) 2018 CERNEinfo:eu-repo/semantics/openAccessAraújo Júnior, Carlos AlbertoMendes, João BatistaAssis, Adriana Leandra deCabacinha, Christian DiasStocks, Jonathan JamesSilva, Liniker Fernandes daLeite, Helio Garcia2019-06-05T14:06:30Zoai:cerne.ufla.br:article/1869Revistahttps://cerne.ufla.br/site/index.php/CERNEPUBhttps://cerne.ufla.br/site/index.php/CERNE/oaicerne@dcf.ufla.br||cerne@dcf.ufla.br2317-63420104-7760opendoar:2024-05-21T19:54:37.538002Cerne (Online) - Universidade Federal de Lavras (UFLA)true
dc.title.none.fl_str_mv TUNING OF THE METAHEURISTIC VARIABLE NEIGHBORHOOD SEARCH FOR A FOREST PLANNING PROBLEM
METAHEURÍSTICA VARIABLE NEIGHBORHOOD SEARCH APLICADA PARA UM PROBLEMA DE PLANEJAMENTO FLORESTAL
title TUNING OF THE METAHEURISTIC VARIABLE NEIGHBORHOOD SEARCH FOR A FOREST PLANNING PROBLEM
spellingShingle TUNING OF THE METAHEURISTIC VARIABLE NEIGHBORHOOD SEARCH FOR A FOREST PLANNING PROBLEM
Araújo Júnior, Carlos Alberto
Operational Research
Artificial Intelligence
Forest Management
Pesquisa Operacional
Inteligência Artificial
Manejo Florestal
title_short TUNING OF THE METAHEURISTIC VARIABLE NEIGHBORHOOD SEARCH FOR A FOREST PLANNING PROBLEM
title_full TUNING OF THE METAHEURISTIC VARIABLE NEIGHBORHOOD SEARCH FOR A FOREST PLANNING PROBLEM
title_fullStr TUNING OF THE METAHEURISTIC VARIABLE NEIGHBORHOOD SEARCH FOR A FOREST PLANNING PROBLEM
title_full_unstemmed TUNING OF THE METAHEURISTIC VARIABLE NEIGHBORHOOD SEARCH FOR A FOREST PLANNING PROBLEM
title_sort TUNING OF THE METAHEURISTIC VARIABLE NEIGHBORHOOD SEARCH FOR A FOREST PLANNING PROBLEM
author Araújo Júnior, Carlos Alberto
author_facet Araújo Júnior, Carlos Alberto
Mendes, João Batista
Assis, Adriana Leandra de
Cabacinha, Christian Dias
Stocks, Jonathan James
Silva, Liniker Fernandes da
Leite, Helio Garcia
author_role author
author2 Mendes, João Batista
Assis, Adriana Leandra de
Cabacinha, Christian Dias
Stocks, Jonathan James
Silva, Liniker Fernandes da
Leite, Helio Garcia
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Araújo Júnior, Carlos Alberto
Mendes, João Batista
Assis, Adriana Leandra de
Cabacinha, Christian Dias
Stocks, Jonathan James
Silva, Liniker Fernandes da
Leite, Helio Garcia
dc.subject.por.fl_str_mv Operational Research
Artificial Intelligence
Forest Management
Pesquisa Operacional
Inteligência Artificial
Manejo Florestal
topic Operational Research
Artificial Intelligence
Forest Management
Pesquisa Operacional
Inteligência Artificial
Manejo Florestal
description In forest science it is important evaluate new technologies from computational science. This work aimed to test a different kind of metaheuristic called Variable Neighborhood Search in a forest planning problem. The management total area has 4.210 ha distributed in 120 stands in ages between 1 and 6 years old and site index since 22 m to 31 m. The problem was modelled considering the maximization of the net present value subject to the restrictions: annual cut volume between 140.000 m³ and 160.000 m³, harvester ages equal to 5, 6 or 7 years, and the impossibility of division of the management unity at harvester time. It was evaluated different settings for the Variable Neighborhood Search, varying the quantity of neighbours, the neighbourhood structure and number or generations. 30 repetitions were performed for each setting. The results were compared to the one obtained from integer linear programming and linear programming. The integer linear programming considered the best solution obtained after 1 hour of processing. The best setting to the Variable Neighborhood Search was 100 neighbours, a neighbourhood structure with changes in 1%, 2%, 3% and 4% of prescriptions and 500 iterations. The results shown by the Variable Neighborhood Search was 2,77% worse than one obtained by the integer linear programming with 1 hours of processing, and 2,84% worse than the linear programming. It is possible to conclude that the presented metaheuristic can be used in resolution of forest scheduling problem.
publishDate 2018
dc.date.none.fl_str_mv 2018-10-16
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://cerne.ufla.br/site/index.php/CERNE/article/view/1869
url https://cerne.ufla.br/site/index.php/CERNE/article/view/1869
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://cerne.ufla.br/site/index.php/CERNE/article/view/1869/1087
dc.rights.driver.fl_str_mv Copyright (c) 2018 CERNE
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2018 CERNE
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv CERNE
CERNE
publisher.none.fl_str_mv CERNE
CERNE
dc.source.none.fl_str_mv CERNE; Vol. 24 No. 3 (2018); 259-268
CERNE; v. 24 n. 3 (2018); 259-268
2317-6342
0104-7760
reponame:Cerne (Online)
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Cerne (Online)
collection Cerne (Online)
repository.name.fl_str_mv Cerne (Online) - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv cerne@dcf.ufla.br||cerne@dcf.ufla.br
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