An evolutionary multi-objective optimization system for earthworks
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/38251 |
Resumo: | Earthworks involve the levelling or shaping of a target area through the moving or processing of the ground surface. Most construction projects require earthworks, which are heavily dependent on mechanical equipment (e.g., excavators, trucks and compactors). Often, earthworks are the most costly and time-consuming component of infrastructure constructions (e.g., road, railway and airports) and current pressure for higher productivity and safety highlights the need to optimize earthworks, which is a nontrivial task. Most previous attempts at tackling this problem focus on single-objective optimization of partial processes or aspects of earthworks, overlooking the advantages of a multi-objective and global optimization. This work describes a novel optimization system based on an evolutionary multi-objective approach, capable of globally optimizing several objectives simultaneously and dynamically. The proposed system views an earthwork construction as a production line, where the goal is to optimize resources under two crucial criteria (costs and duration) and focus the evolutionary search (non-dominated sorting genetic algorithm-II) on compaction allocation, using linear programming to distribute the remaining equipment (e.g., excavators). Several experiments were held using real-world data from a Portuguese construction site, showing that the proposed system is quite competitive when compared with current manual earthwork equipment allocation. |
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An evolutionary multi-objective optimization system for earthworksEarthworksEvolutionary computationMulti-objective optimizationArtificial intelligenceEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e InformáticaEngenharia e Tecnologia::Engenharia CivilScience & TechnologyEarthworks involve the levelling or shaping of a target area through the moving or processing of the ground surface. Most construction projects require earthworks, which are heavily dependent on mechanical equipment (e.g., excavators, trucks and compactors). Often, earthworks are the most costly and time-consuming component of infrastructure constructions (e.g., road, railway and airports) and current pressure for higher productivity and safety highlights the need to optimize earthworks, which is a nontrivial task. Most previous attempts at tackling this problem focus on single-objective optimization of partial processes or aspects of earthworks, overlooking the advantages of a multi-objective and global optimization. This work describes a novel optimization system based on an evolutionary multi-objective approach, capable of globally optimizing several objectives simultaneously and dynamically. The proposed system views an earthwork construction as a production line, where the goal is to optimize resources under two crucial criteria (costs and duration) and focus the evolutionary search (non-dominated sorting genetic algorithm-II) on compaction allocation, using linear programming to distribute the remaining equipment (e.g., excavators). Several experiments were held using real-world data from a Portuguese construction site, showing that the proposed system is quite competitive when compared with current manual earthwork equipment allocation.The authors wish to thank FCT for the financial support under the doctoral Grant SFRH/BD/71501/2010, as well as the construction company that kindly provided the real-world data. Also, we wish to thank Olaf Mersmann for kindly providing the R code for the SMS-EMOA algorithm.ElsevierUniversidade do MinhoParente, ManuelCortez, PauloCorreia, A. Gomes2015-112015-11-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/38251engParente, M., Cortez, P., & Correia, A. G. (2015). An evolutionary multi-objective optimization system for earthworks. Expert Systems with Applications, 42(19), 6674-6685. doi: 10.1016/j.eswa.2015.04.0510957-417410.1016/j.eswa.2015.04.051The original publication is available at: http://authors.elsevier.com/sd/article/S0957417415002936info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:27:25Zoai:repositorium.sdum.uminho.pt:1822/38251Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:21:59.711142Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
An evolutionary multi-objective optimization system for earthworks |
title |
An evolutionary multi-objective optimization system for earthworks |
spellingShingle |
An evolutionary multi-objective optimization system for earthworks Parente, Manuel Earthworks Evolutionary computation Multi-objective optimization Artificial intelligence Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática Engenharia e Tecnologia::Engenharia Civil Science & Technology |
title_short |
An evolutionary multi-objective optimization system for earthworks |
title_full |
An evolutionary multi-objective optimization system for earthworks |
title_fullStr |
An evolutionary multi-objective optimization system for earthworks |
title_full_unstemmed |
An evolutionary multi-objective optimization system for earthworks |
title_sort |
An evolutionary multi-objective optimization system for earthworks |
author |
Parente, Manuel |
author_facet |
Parente, Manuel Cortez, Paulo Correia, A. Gomes |
author_role |
author |
author2 |
Cortez, Paulo Correia, A. Gomes |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Parente, Manuel Cortez, Paulo Correia, A. Gomes |
dc.subject.por.fl_str_mv |
Earthworks Evolutionary computation Multi-objective optimization Artificial intelligence Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática Engenharia e Tecnologia::Engenharia Civil Science & Technology |
topic |
Earthworks Evolutionary computation Multi-objective optimization Artificial intelligence Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática Engenharia e Tecnologia::Engenharia Civil Science & Technology |
description |
Earthworks involve the levelling or shaping of a target area through the moving or processing of the ground surface. Most construction projects require earthworks, which are heavily dependent on mechanical equipment (e.g., excavators, trucks and compactors). Often, earthworks are the most costly and time-consuming component of infrastructure constructions (e.g., road, railway and airports) and current pressure for higher productivity and safety highlights the need to optimize earthworks, which is a nontrivial task. Most previous attempts at tackling this problem focus on single-objective optimization of partial processes or aspects of earthworks, overlooking the advantages of a multi-objective and global optimization. This work describes a novel optimization system based on an evolutionary multi-objective approach, capable of globally optimizing several objectives simultaneously and dynamically. The proposed system views an earthwork construction as a production line, where the goal is to optimize resources under two crucial criteria (costs and duration) and focus the evolutionary search (non-dominated sorting genetic algorithm-II) on compaction allocation, using linear programming to distribute the remaining equipment (e.g., excavators). Several experiments were held using real-world data from a Portuguese construction site, showing that the proposed system is quite competitive when compared with current manual earthwork equipment allocation. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-11 2015-11-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/38251 |
url |
http://hdl.handle.net/1822/38251 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Parente, M., Cortez, P., & Correia, A. G. (2015). An evolutionary multi-objective optimization system for earthworks. Expert Systems with Applications, 42(19), 6674-6685. doi: 10.1016/j.eswa.2015.04.051 0957-4174 10.1016/j.eswa.2015.04.051 The original publication is available at: http://authors.elsevier.com/sd/article/S0957417415002936 |
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 |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799132688892821504 |