COMPARISON THE METAHEURISTIC SIMULATED ANNEALING AND INTEGER LINEAR PROGRAMMING FOR SOLVING THE FOREST HARVEST SCHEDULING WITH ADJACENCY CONSTRAINTS

Detalhes bibliográficos
Autor(a) principal: Gomide, Lucas Rezende
Data de Publicação: 2013
Outros Autores: Arce, Júlio Eduardo, Silva, Arinei Carlos Lindbeck da
Tipo de documento: Artigo
Idioma: por
Título da fonte: Ciência Florestal (Online)
Texto Completo: https://periodicos.ufsm.br/cienciaflorestal/article/view/9289
Resumo: http://dx.doi.org/10.5902/198050989289The impacts on the landscape after forest harvesting in reforestation are visible, but the cutting is a necessary process to ensure a sustained yield and introduce new technologies. An alternative of control is to use the adjacency constraints in the mathematical models. Thus, the aim of the study was to assess the ability of the metaheuristic SA to solve mathematical models with adjacency constraints type URM, and to check its action with the increasing of the problem complexity. The study was conducted in a forest project containing 52 stands, and created 8 scenarios, where the Johnson and Scheurmann (1977) model I was used as reference. The adjacency constraint type URM was used to control the cutting of adjacent stands. The models were solved by the ILP and metaheuristic SA, which was sued 100 times per scenario. The results showed that the scenario 8 has consumed 137.530 seconds via PLI, which represented 2.023,09 times more than the average time processing of the SA metaheuristic (67,98 seconds). The best solutions were 4.71% (scenario 1) to 11.40% (scenario 8) far from the optimal (ILP). The metaheuristic SA is capable to solve the forest problem, meeting the targets in the most cases. The increasing of complexity produced a higher deviation from the optimal. Concludes that the metaheuristic SA should not be processed a single time, because there are hazards in obtain inferior solutions, but doing it is recommended to increase the stop criterion.
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spelling COMPARISON THE METAHEURISTIC SIMULATED ANNEALING AND INTEGER LINEAR PROGRAMMING FOR SOLVING THE FOREST HARVEST SCHEDULING WITH ADJACENCY CONSTRAINTSComparação entre a meta-heurística simulated annealing e a programação linear inteira no agendamento da colheita florestal com restrições de adjacênciaartificial intelligenceinteger linear programmingforest harvestinteligência artificialprogramação linear inteiracolheita florestalhttp://dx.doi.org/10.5902/198050989289The impacts on the landscape after forest harvesting in reforestation are visible, but the cutting is a necessary process to ensure a sustained yield and introduce new technologies. An alternative of control is to use the adjacency constraints in the mathematical models. Thus, the aim of the study was to assess the ability of the metaheuristic SA to solve mathematical models with adjacency constraints type URM, and to check its action with the increasing of the problem complexity. The study was conducted in a forest project containing 52 stands, and created 8 scenarios, where the Johnson and Scheurmann (1977) model I was used as reference. The adjacency constraint type URM was used to control the cutting of adjacent stands. The models were solved by the ILP and metaheuristic SA, which was sued 100 times per scenario. The results showed that the scenario 8 has consumed 137.530 seconds via PLI, which represented 2.023,09 times more than the average time processing of the SA metaheuristic (67,98 seconds). The best solutions were 4.71% (scenario 1) to 11.40% (scenario 8) far from the optimal (ILP). The metaheuristic SA is capable to solve the forest problem, meeting the targets in the most cases. The increasing of complexity produced a higher deviation from the optimal. Concludes that the metaheuristic SA should not be processed a single time, because there are hazards in obtain inferior solutions, but doing it is recommended to increase the stop criterion.http://dx.doi.org/10.5902/198050989289Os impactos gerados na paisagem após a colheita florestal em reflorestamentos são visíveis, porém, o corte raso é um processo necessário para garantir uma produção sustentada e introduzir novas tecnologias. Uma alternativa de controle é utilizar restrições de adjacência nos modelos matemáticos. Assim, o objetivo do estudo foi avaliar a capacidade da meta-heurística SA na resolução de modelos matemáticos com restrições de adjacência do tipo URM, e observar sua ação com o aumento da complexidade do problema. O estudo foi conduzido em um projeto florestal contendo 52 talhões, sendo criados 8 cenários, onde o modelo I de Johnson e Scheurmann (1977) foi usado como referência. A restrição de adjacência do tipo URM foi usada para controlar o corte de talhões adjacentes. Os modelos foram resolvidos pela PLI e meta-heurística SA, no qual foi processada 100 vezes/cenário. Os resultados mostraram que o cenário 8 consumiu 137.530 segundos via PLI, gastando um tempo de 2.023,09 vezes a mais que o tempo médio de processamento da meta-heurística SA (67,98 segundos). As melhores soluções ficaram 4,71 % (cenário 1) a 11,40 % (cenário 8) distante do ótimo (PLI). A meta-heurística SA é capaz de resolver o problema florestal, atendendo às metas na maioria das vezes. O aumento da complexidade produz um maior desvio em relação ao ótimo. Conclui-se que a meta-heurística SA não deve ser processada uma única vez, pois há riscos de se obter soluções inferiores, caso seja feita, deve-se aumentar o tempo de parada.Universidade Federal de Santa Maria2013-06-28info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.ufsm.br/cienciaflorestal/article/view/928910.5902/198050989289Ciência Florestal; Vol. 23 No. 2 (2013); 449-460Ciência Florestal; v. 23 n. 2 (2013); 449-4601980-50980103-9954reponame:Ciência Florestal (Online)instname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMporhttps://periodicos.ufsm.br/cienciaflorestal/article/view/9289/pdf_1Gomide, Lucas RezendeArce, Júlio EduardoSilva, Arinei Carlos Lindbeck dainfo:eu-repo/semantics/openAccess2017-04-17T17:50:57Zoai:ojs.pkp.sfu.ca:article/9289Revistahttp://www.ufsm.br/cienciaflorestal/ONGhttps://old.scielo.br/oai/scielo-oai.php||cienciaflorestal@ufsm.br|| cienciaflorestal@gmail.com|| cf@smail.ufsm.br1980-50980103-9954opendoar:2017-04-17T17:50:57Ciência Florestal (Online) - Universidade Federal de Santa Maria (UFSM)false
dc.title.none.fl_str_mv COMPARISON THE METAHEURISTIC SIMULATED ANNEALING AND INTEGER LINEAR PROGRAMMING FOR SOLVING THE FOREST HARVEST SCHEDULING WITH ADJACENCY CONSTRAINTS
Comparação entre a meta-heurística simulated annealing e a programação linear inteira no agendamento da colheita florestal com restrições de adjacência
title COMPARISON THE METAHEURISTIC SIMULATED ANNEALING AND INTEGER LINEAR PROGRAMMING FOR SOLVING THE FOREST HARVEST SCHEDULING WITH ADJACENCY CONSTRAINTS
spellingShingle COMPARISON THE METAHEURISTIC SIMULATED ANNEALING AND INTEGER LINEAR PROGRAMMING FOR SOLVING THE FOREST HARVEST SCHEDULING WITH ADJACENCY CONSTRAINTS
Gomide, Lucas Rezende
artificial intelligence
integer linear programming
forest harvest
inteligência artificial
programação linear inteira
colheita florestal
title_short COMPARISON THE METAHEURISTIC SIMULATED ANNEALING AND INTEGER LINEAR PROGRAMMING FOR SOLVING THE FOREST HARVEST SCHEDULING WITH ADJACENCY CONSTRAINTS
title_full COMPARISON THE METAHEURISTIC SIMULATED ANNEALING AND INTEGER LINEAR PROGRAMMING FOR SOLVING THE FOREST HARVEST SCHEDULING WITH ADJACENCY CONSTRAINTS
title_fullStr COMPARISON THE METAHEURISTIC SIMULATED ANNEALING AND INTEGER LINEAR PROGRAMMING FOR SOLVING THE FOREST HARVEST SCHEDULING WITH ADJACENCY CONSTRAINTS
title_full_unstemmed COMPARISON THE METAHEURISTIC SIMULATED ANNEALING AND INTEGER LINEAR PROGRAMMING FOR SOLVING THE FOREST HARVEST SCHEDULING WITH ADJACENCY CONSTRAINTS
title_sort COMPARISON THE METAHEURISTIC SIMULATED ANNEALING AND INTEGER LINEAR PROGRAMMING FOR SOLVING THE FOREST HARVEST SCHEDULING WITH ADJACENCY CONSTRAINTS
author Gomide, Lucas Rezende
author_facet Gomide, Lucas Rezende
Arce, Júlio Eduardo
Silva, Arinei Carlos Lindbeck da
author_role author
author2 Arce, Júlio Eduardo
Silva, Arinei Carlos Lindbeck da
author2_role author
author
dc.contributor.author.fl_str_mv Gomide, Lucas Rezende
Arce, Júlio Eduardo
Silva, Arinei Carlos Lindbeck da
dc.subject.por.fl_str_mv artificial intelligence
integer linear programming
forest harvest
inteligência artificial
programação linear inteira
colheita florestal
topic artificial intelligence
integer linear programming
forest harvest
inteligência artificial
programação linear inteira
colheita florestal
description http://dx.doi.org/10.5902/198050989289The impacts on the landscape after forest harvesting in reforestation are visible, but the cutting is a necessary process to ensure a sustained yield and introduce new technologies. An alternative of control is to use the adjacency constraints in the mathematical models. Thus, the aim of the study was to assess the ability of the metaheuristic SA to solve mathematical models with adjacency constraints type URM, and to check its action with the increasing of the problem complexity. The study was conducted in a forest project containing 52 stands, and created 8 scenarios, where the Johnson and Scheurmann (1977) model I was used as reference. The adjacency constraint type URM was used to control the cutting of adjacent stands. The models were solved by the ILP and metaheuristic SA, which was sued 100 times per scenario. The results showed that the scenario 8 has consumed 137.530 seconds via PLI, which represented 2.023,09 times more than the average time processing of the SA metaheuristic (67,98 seconds). The best solutions were 4.71% (scenario 1) to 11.40% (scenario 8) far from the optimal (ILP). The metaheuristic SA is capable to solve the forest problem, meeting the targets in the most cases. The increasing of complexity produced a higher deviation from the optimal. Concludes that the metaheuristic SA should not be processed a single time, because there are hazards in obtain inferior solutions, but doing it is recommended to increase the stop criterion.
publishDate 2013
dc.date.none.fl_str_mv 2013-06-28
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.ufsm.br/cienciaflorestal/article/view/9289
10.5902/198050989289
url https://periodicos.ufsm.br/cienciaflorestal/article/view/9289
identifier_str_mv 10.5902/198050989289
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://periodicos.ufsm.br/cienciaflorestal/article/view/9289/pdf_1
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal de Santa Maria
publisher.none.fl_str_mv Universidade Federal de Santa Maria
dc.source.none.fl_str_mv Ciência Florestal; Vol. 23 No. 2 (2013); 449-460
Ciência Florestal; v. 23 n. 2 (2013); 449-460
1980-5098
0103-9954
reponame:Ciência Florestal (Online)
instname:Universidade Federal de Santa Maria (UFSM)
instacron:UFSM
instname_str Universidade Federal de Santa Maria (UFSM)
instacron_str UFSM
institution UFSM
reponame_str Ciência Florestal (Online)
collection Ciência Florestal (Online)
repository.name.fl_str_mv Ciência Florestal (Online) - Universidade Federal de Santa Maria (UFSM)
repository.mail.fl_str_mv ||cienciaflorestal@ufsm.br|| cienciaflorestal@gmail.com|| cf@smail.ufsm.br
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