A genetic algorithm for crop rotation
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/73380 |
Resumo: | In the last few years, crop rotation has gained attention due to its economic, environmental and social importance which explains why it can be highly beneficial for farmers. This paper presents a mathematical model for the Crop Rotation Problem (CRP) that was adapted from literature for this highly complex combinatorial problem. The CRP is devised to find a vegetable planting program that takes into account green fertilization restrictions, the set-aside period, planting restrictions for neighboring lots and for crop sequencing, demand constraints, while, at the same time, maximizing the profitability of the planted area. The main aim of this study is to develop a genetic algorithm and test it in a real context. The genetic algorithm involves a constructive heuristic to build the initial population and the operators of crossover, mutation, migration and elitism. The computational experiment was performed for a medium dimension real planting area with 16 lots, considering 29 crops of 10 different botanical families and a two-year planting rotation. Results showed that the algorithm determined feasible solutions in a reasonable computational time, thus proving its efficacy for dealing with this practical application. |
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A genetic algorithm for crop rotationCrop rotationGenetic algorithmOptimizationComplex combinatorial problemComputational experimentComputational timeConstructive heuristicCrop sequencingFeasible solutionInitial populationPlanted areasCropsMathematical modelsProfitabilityGenetic algorithmsIn the last few years, crop rotation has gained attention due to its economic, environmental and social importance which explains why it can be highly beneficial for farmers. This paper presents a mathematical model for the Crop Rotation Problem (CRP) that was adapted from literature for this highly complex combinatorial problem. The CRP is devised to find a vegetable planting program that takes into account green fertilization restrictions, the set-aside period, planting restrictions for neighboring lots and for crop sequencing, demand constraints, while, at the same time, maximizing the profitability of the planted area. The main aim of this study is to develop a genetic algorithm and test it in a real context. The genetic algorithm involves a constructive heuristic to build the initial population and the operators of crossover, mutation, migration and elitism. The computational experiment was performed for a medium dimension real planting area with 16 lots, considering 29 crops of 10 different botanical families and a two-year planting rotation. Results showed that the algorithm determined feasible solutions in a reasonable computational time, thus proving its efficacy for dealing with this practical application.Curso de Biometria Departamento de Bioestatística IB, UNESP, 18618-970, Botucatu, SPDepartamento de Bioestatística IB, UNESP, 18618-970, Botucatu, SPCIO-FCUL ISEG - UTL, 1200-781 LisboaCurso de Biometria Departamento de Bioestatística IB, UNESP, 18618-970, Botucatu, SPDepartamento de Bioestatística IB, UNESP, 18618-970, Botucatu, SPUniversidade Estadual Paulista (Unesp)ISEG - UTLFilho, Angelo Aliano [UNESP]De Oliveira Florentino, Helenice [UNESP]Pato, Margarida Vaz2014-05-27T11:26:51Z2014-05-27T11:26:51Z2012-06-13info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject454-457ICORES 2012 - Proceedings of the 1st International Conference on Operations Research and Enterprise Systems, p. 454-457.http://hdl.handle.net/11449/733802-s2.0-84861987917Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengICORES 2012 - Proceedings of the 1st International Conference on Operations Research and Enterprise Systemsinfo:eu-repo/semantics/openAccess2021-10-23T21:41:32Zoai:repositorio.unesp.br:11449/73380Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:17:36.354360Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A genetic algorithm for crop rotation |
title |
A genetic algorithm for crop rotation |
spellingShingle |
A genetic algorithm for crop rotation Filho, Angelo Aliano [UNESP] Crop rotation Genetic algorithm Optimization Complex combinatorial problem Computational experiment Computational time Constructive heuristic Crop sequencing Feasible solution Initial population Planted areas Crops Mathematical models Profitability Genetic algorithms |
title_short |
A genetic algorithm for crop rotation |
title_full |
A genetic algorithm for crop rotation |
title_fullStr |
A genetic algorithm for crop rotation |
title_full_unstemmed |
A genetic algorithm for crop rotation |
title_sort |
A genetic algorithm for crop rotation |
author |
Filho, Angelo Aliano [UNESP] |
author_facet |
Filho, Angelo Aliano [UNESP] De Oliveira Florentino, Helenice [UNESP] Pato, Margarida Vaz |
author_role |
author |
author2 |
De Oliveira Florentino, Helenice [UNESP] Pato, Margarida Vaz |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) ISEG - UTL |
dc.contributor.author.fl_str_mv |
Filho, Angelo Aliano [UNESP] De Oliveira Florentino, Helenice [UNESP] Pato, Margarida Vaz |
dc.subject.por.fl_str_mv |
Crop rotation Genetic algorithm Optimization Complex combinatorial problem Computational experiment Computational time Constructive heuristic Crop sequencing Feasible solution Initial population Planted areas Crops Mathematical models Profitability Genetic algorithms |
topic |
Crop rotation Genetic algorithm Optimization Complex combinatorial problem Computational experiment Computational time Constructive heuristic Crop sequencing Feasible solution Initial population Planted areas Crops Mathematical models Profitability Genetic algorithms |
description |
In the last few years, crop rotation has gained attention due to its economic, environmental and social importance which explains why it can be highly beneficial for farmers. This paper presents a mathematical model for the Crop Rotation Problem (CRP) that was adapted from literature for this highly complex combinatorial problem. The CRP is devised to find a vegetable planting program that takes into account green fertilization restrictions, the set-aside period, planting restrictions for neighboring lots and for crop sequencing, demand constraints, while, at the same time, maximizing the profitability of the planted area. The main aim of this study is to develop a genetic algorithm and test it in a real context. The genetic algorithm involves a constructive heuristic to build the initial population and the operators of crossover, mutation, migration and elitism. The computational experiment was performed for a medium dimension real planting area with 16 lots, considering 29 crops of 10 different botanical families and a two-year planting rotation. Results showed that the algorithm determined feasible solutions in a reasonable computational time, thus proving its efficacy for dealing with this practical application. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-06-13 2014-05-27T11:26:51Z 2014-05-27T11:26:51Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
ICORES 2012 - Proceedings of the 1st International Conference on Operations Research and Enterprise Systems, p. 454-457. http://hdl.handle.net/11449/73380 2-s2.0-84861987917 |
identifier_str_mv |
ICORES 2012 - Proceedings of the 1st International Conference on Operations Research and Enterprise Systems, p. 454-457. 2-s2.0-84861987917 |
url |
http://hdl.handle.net/11449/73380 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
ICORES 2012 - Proceedings of the 1st International Conference on Operations Research and Enterprise Systems |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
454-457 |
dc.source.none.fl_str_mv |
Scopus 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_ |
1808128917646606336 |