Exact and heuristic methods to solve a bi-objective problem of sustainable cultivation

Detalhes bibliográficos
Autor(a) principal: Aliano Filho, Angelo
Data de Publicação: 2019
Outros Autores: Oliveira Florentino, Helenice de [UNESP], Pato, Margarida Vaz, Poltroniere, Sonia Cristina [UNESP], Silva Costa, Joao Fernando da
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/s10479-019-03468-9
http://hdl.handle.net/11449/209498
Resumo: This work proposes a binary nonlinear bi-objective optimization model for the problem of planning the sustainable cultivation of crops. The solution to the problem is a planting schedule for crops to be cultivated in predefined plots, in order to minimize the possibility of pest proliferation and maximize the profit of this process. Biological constraints were also considered. Exact methods, based on the nonlinear model and on a linearization of that model were proposed to generate Pareto optimal solutions for the problem of sustainable cultivation, along with a metaheuristic approach for the problem based on a genetic algorithm and on constructive heuristics. The methods were tested using semi-randomly generated instances to simulate real situations. According to the experimental results, the exact methodologies performed favorably for small and medium size instances. The heuristic method was able to potentially determine Pareto optimal solutions of good quality, in a reduced computational time, even for high dimension instances. Therefore, the mathematical models and the methods proposed may support a powerful methodology for this complex decision-making problem.
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spelling Exact and heuristic methods to solve a bi-objective problem of sustainable cultivationMulti-objective optimizationGenetic algorithmConstructive heuristics and sustainabilityThis work proposes a binary nonlinear bi-objective optimization model for the problem of planning the sustainable cultivation of crops. The solution to the problem is a planting schedule for crops to be cultivated in predefined plots, in order to minimize the possibility of pest proliferation and maximize the profit of this process. Biological constraints were also considered. Exact methods, based on the nonlinear model and on a linearization of that model were proposed to generate Pareto optimal solutions for the problem of sustainable cultivation, along with a metaheuristic approach for the problem based on a genetic algorithm and on constructive heuristics. The methods were tested using semi-randomly generated instances to simulate real situations. According to the experimental results, the exact methodologies performed favorably for small and medium size instances. The heuristic method was able to potentially determine Pareto optimal solutions of good quality, in a reduced computational time, even for high dimension instances. Therefore, the mathematical models and the methods proposed may support a powerful methodology for this complex decision-making problem.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Federal Technological University of ParanaFundacAo para a Ciencia e a Tecnologia, PortugalResearch Fund of ISEGUniv Tecnol Fed Parana, Dept Acad Matemat, Apucarana, BrazilUniv Estadual Paulista, Inst Biociencias Botucatu, Botucatu, SP, BrazilUniv Lisbon, ISEG, Lisbon, PortugalUniv Lisbon, CMAFcIO, Lisbon, PortugalUniv Estadual Paulista, Dept Matemat, Bauru, SP, BrazilUniv Tecnol Fed Parana, Apucarana, BrazilUniv Estadual Paulista, Inst Biociencias Botucatu, Botucatu, SP, BrazilUniv Estadual Paulista, Dept Matemat, Bauru, SP, BrazilFAPESP: 2014/01604-0FAPESP: 2014/04353-8FAPESP: 2013/07375-0CNPq: 302454/2016-0FundacAo para a Ciencia e a Tecnologia, Portugal: UID/MAT/04561/2013FundacAo para a Ciencia e a Tecnologia, Portugal: UID/Multi/00491/2013CNPq: 303267/2011-9SpringerUniv Tecnol Fed ParanaUniversidade Estadual Paulista (Unesp)Univ LisbonAliano Filho, AngeloOliveira Florentino, Helenice de [UNESP]Pato, Margarida VazPoltroniere, Sonia Cristina [UNESP]Silva Costa, Joao Fernando da2021-06-25T12:20:23Z2021-06-25T12:20:23Z2019-11-14info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article30http://dx.doi.org/10.1007/s10479-019-03468-9Annals Of Operations Research. Dordrecht: Springer, 30 p., 2019.0254-5330http://hdl.handle.net/11449/20949810.1007/s10479-019-03468-9WOS:000574637500001Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAnnals Of Operations Researchinfo:eu-repo/semantics/openAccess2024-04-29T14:59:43Zoai:repositorio.unesp.br:11449/209498Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:10:13.485889Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Exact and heuristic methods to solve a bi-objective problem of sustainable cultivation
title Exact and heuristic methods to solve a bi-objective problem of sustainable cultivation
spellingShingle Exact and heuristic methods to solve a bi-objective problem of sustainable cultivation
Aliano Filho, Angelo
Multi-objective optimization
Genetic algorithm
Constructive heuristics and sustainability
title_short Exact and heuristic methods to solve a bi-objective problem of sustainable cultivation
title_full Exact and heuristic methods to solve a bi-objective problem of sustainable cultivation
title_fullStr Exact and heuristic methods to solve a bi-objective problem of sustainable cultivation
title_full_unstemmed Exact and heuristic methods to solve a bi-objective problem of sustainable cultivation
title_sort Exact and heuristic methods to solve a bi-objective problem of sustainable cultivation
author Aliano Filho, Angelo
author_facet Aliano Filho, Angelo
Oliveira Florentino, Helenice de [UNESP]
Pato, Margarida Vaz
Poltroniere, Sonia Cristina [UNESP]
Silva Costa, Joao Fernando da
author_role author
author2 Oliveira Florentino, Helenice de [UNESP]
Pato, Margarida Vaz
Poltroniere, Sonia Cristina [UNESP]
Silva Costa, Joao Fernando da
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Univ Tecnol Fed Parana
Universidade Estadual Paulista (Unesp)
Univ Lisbon
dc.contributor.author.fl_str_mv Aliano Filho, Angelo
Oliveira Florentino, Helenice de [UNESP]
Pato, Margarida Vaz
Poltroniere, Sonia Cristina [UNESP]
Silva Costa, Joao Fernando da
dc.subject.por.fl_str_mv Multi-objective optimization
Genetic algorithm
Constructive heuristics and sustainability
topic Multi-objective optimization
Genetic algorithm
Constructive heuristics and sustainability
description This work proposes a binary nonlinear bi-objective optimization model for the problem of planning the sustainable cultivation of crops. The solution to the problem is a planting schedule for crops to be cultivated in predefined plots, in order to minimize the possibility of pest proliferation and maximize the profit of this process. Biological constraints were also considered. Exact methods, based on the nonlinear model and on a linearization of that model were proposed to generate Pareto optimal solutions for the problem of sustainable cultivation, along with a metaheuristic approach for the problem based on a genetic algorithm and on constructive heuristics. The methods were tested using semi-randomly generated instances to simulate real situations. According to the experimental results, the exact methodologies performed favorably for small and medium size instances. The heuristic method was able to potentially determine Pareto optimal solutions of good quality, in a reduced computational time, even for high dimension instances. Therefore, the mathematical models and the methods proposed may support a powerful methodology for this complex decision-making problem.
publishDate 2019
dc.date.none.fl_str_mv 2019-11-14
2021-06-25T12:20:23Z
2021-06-25T12:20:23Z
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://dx.doi.org/10.1007/s10479-019-03468-9
Annals Of Operations Research. Dordrecht: Springer, 30 p., 2019.
0254-5330
http://hdl.handle.net/11449/209498
10.1007/s10479-019-03468-9
WOS:000574637500001
url http://dx.doi.org/10.1007/s10479-019-03468-9
http://hdl.handle.net/11449/209498
identifier_str_mv Annals Of Operations Research. Dordrecht: Springer, 30 p., 2019.
0254-5330
10.1007/s10479-019-03468-9
WOS:000574637500001
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Annals Of Operations Research
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 30
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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