A multiple objective methodology for sugarcane harvest management with varying maturation periods
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s10479-017-2568-2 http://hdl.handle.net/11449/178988 |
Resumo: | This paper addresses the management of a sugarcane harvest over a multi-year planning period. A methodology to assist the harvest planning of the sugarcane is proposed in order to improve the production of POL (a measure of the amount of sucrose contained in a sugar solution) and the quality of the raw material, considering the constraints imposed by the mill such as the demand per period. An extended goal programming model is proposed for optimizing the harvest plan of the sugarcane so the harvesting point is as close as possible to the ideal, considering the constrained nature of the problem. A genetic algorithm (GA) is developed to tackle the problem in order to solve realistically large problems within an appropriate computational time. A comparative analysis between the GA and an exact method for small instances is also given in order to validate the performance of the developed model and methods. Computational results for medium and large farm instances using GA are also presented in order to demonstrate the capability of the developed method. The computational results illustrate the trade-off between satisfying the conflicting goals of harvesting as closely as possible to the ideal and making optimum use of harvesting equipment with a minimum of movement between farms. They also demonstrate that, whilst harvesting plans for small scale farms can be generated by the exact method, a meta-heuristic GA method is currently required in order to devise plans for medium and large farms. |
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A multiple objective methodology for sugarcane harvest management with varying maturation periodsGenetic algorithmGoal programmingMultiple objective optimizationSugarcane harvest planningThis paper addresses the management of a sugarcane harvest over a multi-year planning period. A methodology to assist the harvest planning of the sugarcane is proposed in order to improve the production of POL (a measure of the amount of sucrose contained in a sugar solution) and the quality of the raw material, considering the constraints imposed by the mill such as the demand per period. An extended goal programming model is proposed for optimizing the harvest plan of the sugarcane so the harvesting point is as close as possible to the ideal, considering the constrained nature of the problem. A genetic algorithm (GA) is developed to tackle the problem in order to solve realistically large problems within an appropriate computational time. A comparative analysis between the GA and an exact method for small instances is also given in order to validate the performance of the developed model and methods. Computational results for medium and large farm instances using GA are also presented in order to demonstrate the capability of the developed method. The computational results illustrate the trade-off between satisfying the conflicting goals of harvesting as closely as possible to the ideal and making optimum use of harvesting equipment with a minimum of movement between farms. They also demonstrate that, whilst harvesting plans for small scale farms can be generated by the exact method, a meta-heuristic GA method is currently required in order to devise plans for medium and large farms.Department of Biostatistics UNESP - Univ Estadual PaulistaDepartment of Mathematics Centre for Operational Research and Logistics University of PortsmouthAcademic Department of Mathematics Federal Technology University of ParanáEnergy in Agriculture FCA UNESP - Univ Estadual PaulistaDepartment of Biostatistics UNESP - Univ Estadual PaulistaEnergy in Agriculture FCA UNESP - Univ Estadual PaulistaUniversidade Estadual Paulista (Unesp)University of PortsmouthFederal Technology University of ParanáFlorentino, Helenice de Oliveira [UNESP]Irawan, ChandraAliano, Angelo FilhoJones, Dylan F.Cantane, Daniela Renata [UNESP]Nervis, Jonis Jecks [UNESP]2018-12-11T17:33:03Z2018-12-11T17:33:03Z2018-08-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article153-177application/pdfhttp://dx.doi.org/10.1007/s10479-017-2568-2Annals of Operations Research, v. 267, n. 1-2, p. 153-177, 2018.1572-93380254-5330http://hdl.handle.net/11449/17898810.1007/s10479-017-2568-22-s2.0-850217529712-s2.0-85021752971.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAnnals of Operations Research0,9430,943info:eu-repo/semantics/openAccess2023-12-03T06:11:06Zoai:repositorio.unesp.br:11449/178988Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:21:30.897323Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A multiple objective methodology for sugarcane harvest management with varying maturation periods |
title |
A multiple objective methodology for sugarcane harvest management with varying maturation periods |
spellingShingle |
A multiple objective methodology for sugarcane harvest management with varying maturation periods Florentino, Helenice de Oliveira [UNESP] Genetic algorithm Goal programming Multiple objective optimization Sugarcane harvest planning |
title_short |
A multiple objective methodology for sugarcane harvest management with varying maturation periods |
title_full |
A multiple objective methodology for sugarcane harvest management with varying maturation periods |
title_fullStr |
A multiple objective methodology for sugarcane harvest management with varying maturation periods |
title_full_unstemmed |
A multiple objective methodology for sugarcane harvest management with varying maturation periods |
title_sort |
A multiple objective methodology for sugarcane harvest management with varying maturation periods |
author |
Florentino, Helenice de Oliveira [UNESP] |
author_facet |
Florentino, Helenice de Oliveira [UNESP] Irawan, Chandra Aliano, Angelo Filho Jones, Dylan F. Cantane, Daniela Renata [UNESP] Nervis, Jonis Jecks [UNESP] |
author_role |
author |
author2 |
Irawan, Chandra Aliano, Angelo Filho Jones, Dylan F. Cantane, Daniela Renata [UNESP] Nervis, Jonis Jecks [UNESP] |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) University of Portsmouth Federal Technology University of Paraná |
dc.contributor.author.fl_str_mv |
Florentino, Helenice de Oliveira [UNESP] Irawan, Chandra Aliano, Angelo Filho Jones, Dylan F. Cantane, Daniela Renata [UNESP] Nervis, Jonis Jecks [UNESP] |
dc.subject.por.fl_str_mv |
Genetic algorithm Goal programming Multiple objective optimization Sugarcane harvest planning |
topic |
Genetic algorithm Goal programming Multiple objective optimization Sugarcane harvest planning |
description |
This paper addresses the management of a sugarcane harvest over a multi-year planning period. A methodology to assist the harvest planning of the sugarcane is proposed in order to improve the production of POL (a measure of the amount of sucrose contained in a sugar solution) and the quality of the raw material, considering the constraints imposed by the mill such as the demand per period. An extended goal programming model is proposed for optimizing the harvest plan of the sugarcane so the harvesting point is as close as possible to the ideal, considering the constrained nature of the problem. A genetic algorithm (GA) is developed to tackle the problem in order to solve realistically large problems within an appropriate computational time. A comparative analysis between the GA and an exact method for small instances is also given in order to validate the performance of the developed model and methods. Computational results for medium and large farm instances using GA are also presented in order to demonstrate the capability of the developed method. The computational results illustrate the trade-off between satisfying the conflicting goals of harvesting as closely as possible to the ideal and making optimum use of harvesting equipment with a minimum of movement between farms. They also demonstrate that, whilst harvesting plans for small scale farms can be generated by the exact method, a meta-heuristic GA method is currently required in order to devise plans for medium and large farms. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-12-11T17:33:03Z 2018-12-11T17:33:03Z 2018-08-01 |
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/s10479-017-2568-2 Annals of Operations Research, v. 267, n. 1-2, p. 153-177, 2018. 1572-9338 0254-5330 http://hdl.handle.net/11449/178988 10.1007/s10479-017-2568-2 2-s2.0-85021752971 2-s2.0-85021752971.pdf |
url |
http://dx.doi.org/10.1007/s10479-017-2568-2 http://hdl.handle.net/11449/178988 |
identifier_str_mv |
Annals of Operations Research, v. 267, n. 1-2, p. 153-177, 2018. 1572-9338 0254-5330 10.1007/s10479-017-2568-2 2-s2.0-85021752971 2-s2.0-85021752971.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Annals of Operations Research 0,943 0,943 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
153-177 application/pdf |
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_ |
1808129057287569408 |