Exact and heuristic methods to solve a bi-objective problem of sustainable cultivation
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , |
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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Repositório Institucional da UNESP |
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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 |
|
_version_ |
1808129293374455808 |