Optimal estimating the project completion time and diagnosing the fault in the project

The main objective of the project management team is to implement the project taking into consideration the Budget, schedule and constraints. In addition, project accomplishment, especially with large projects, requires the project to be correctly envisaged. Earned value (EV) management is a valuabl...

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Autores:
Hajali-Mohamad, M. T.
Mosavi, M. R.
Shahanaghi, K.
Tipo de recurso:
Article of journal
Fecha de publicación:
2016
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/60579
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/60579
http://bdigital.unal.edu.co/58911/
Palabra clave:
62 Ingeniería y operaciones afines / Engineering
Earned Value
Plan Value
ANFIS
PSFS
Neural Network
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
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spelling Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Hajali-Mohamad, M. T.637d2fda-21d0-4dd0-a158-e60c8f55b167300Mosavi, M. R.fbeacbf1-a938-4464-a39c-468fdc530c49300Shahanaghi, K.b5dbf8dc-bbaa-45ea-b2d7-43b5b6b2de323002019-07-02T18:38:33Z2019-07-02T18:38:33Z2016-01-01ISSN: 2346-2183https://repositorio.unal.edu.co/handle/unal/60579http://bdigital.unal.edu.co/58911/The main objective of the project management team is to implement the project taking into consideration the Budget, schedule and constraints. In addition, project accomplishment, especially with large projects, requires the project to be correctly envisaged. Earned value (EV) management is a valuable technique for analyzing and controlling the performance of the project and predicting the total cost before its completion. Thus, fuzzy systems such as Adaptive Network based on the Fuzzy Inference System (ANFIS) and Parallel Structure based on the Fuzzy System (PSFS) are used to predict the project completion time. In this paper, the plan value diagram is used to predict the earn value diagram using three methods. These three methods are based on the PSFS and Neural Networks (NNs), which help with the implementation of the projects in organizations. The results of these three methods decreased the prediction error of the EV diagram by up to 2%.application/pdfspaUniversidad Nacional de Colombia (Sede Medellín). Facultad de Minas.https://revistas.unal.edu.co/index.php/dyna/article/view/44293Universidad Nacional de Colombia Revistas electrónicas UN DynaDynaHajali-Mohamad, M. T. and Mosavi, M. R. and Shahanaghi, K. (2016) Optimal estimating the project completion time and diagnosing the fault in the project. DYNA, 83 (195). pp. 121-127. ISSN 2346-218362 Ingeniería y operaciones afines / EngineeringEarned ValuePlan ValueANFISPSFSNeural NetworkOptimal estimating the project completion time and diagnosing the fault in the projectArtículo de revistainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/ARTORIGINAL44293-284288-2-PB.pdfapplication/pdf2259738https://repositorio.unal.edu.co/bitstream/unal/60579/1/44293-284288-2-PB.pdfbeb4c9a0e8a43cdb710fca5a0da006c4MD51THUMBNAIL44293-284288-2-PB.pdf.jpg44293-284288-2-PB.pdf.jpgGenerated Thumbnailimage/jpeg9084https://repositorio.unal.edu.co/bitstream/unal/60579/2/44293-284288-2-PB.pdf.jpg67797616f6abcae0b3524a10261d6b04MD52unal/60579oai:repositorio.unal.edu.co:unal/605792024-04-14 23:11:08.373Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co
dc.title.spa.fl_str_mv Optimal estimating the project completion time and diagnosing the fault in the project
title Optimal estimating the project completion time and diagnosing the fault in the project
spellingShingle Optimal estimating the project completion time and diagnosing the fault in the project
62 Ingeniería y operaciones afines / Engineering
Earned Value
Plan Value
ANFIS
PSFS
Neural Network
title_short Optimal estimating the project completion time and diagnosing the fault in the project
title_full Optimal estimating the project completion time and diagnosing the fault in the project
title_fullStr Optimal estimating the project completion time and diagnosing the fault in the project
title_full_unstemmed Optimal estimating the project completion time and diagnosing the fault in the project
title_sort Optimal estimating the project completion time and diagnosing the fault in the project
dc.creator.fl_str_mv Hajali-Mohamad, M. T.
Mosavi, M. R.
Shahanaghi, K.
dc.contributor.author.spa.fl_str_mv Hajali-Mohamad, M. T.
Mosavi, M. R.
Shahanaghi, K.
dc.subject.ddc.spa.fl_str_mv 62 Ingeniería y operaciones afines / Engineering
topic 62 Ingeniería y operaciones afines / Engineering
Earned Value
Plan Value
ANFIS
PSFS
Neural Network
dc.subject.proposal.spa.fl_str_mv Earned Value
Plan Value
ANFIS
PSFS
Neural Network
description The main objective of the project management team is to implement the project taking into consideration the Budget, schedule and constraints. In addition, project accomplishment, especially with large projects, requires the project to be correctly envisaged. Earned value (EV) management is a valuable technique for analyzing and controlling the performance of the project and predicting the total cost before its completion. Thus, fuzzy systems such as Adaptive Network based on the Fuzzy Inference System (ANFIS) and Parallel Structure based on the Fuzzy System (PSFS) are used to predict the project completion time. In this paper, the plan value diagram is used to predict the earn value diagram using three methods. These three methods are based on the PSFS and Neural Networks (NNs), which help with the implementation of the projects in organizations. The results of these three methods decreased the prediction error of the EV diagram by up to 2%.
publishDate 2016
dc.date.issued.spa.fl_str_mv 2016-01-01
dc.date.accessioned.spa.fl_str_mv 2019-07-02T18:38:33Z
dc.date.available.spa.fl_str_mv 2019-07-02T18:38:33Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.issn.spa.fl_str_mv ISSN: 2346-2183
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identifier_str_mv ISSN: 2346-2183
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dc.relation.spa.fl_str_mv https://revistas.unal.edu.co/index.php/dyna/article/view/44293
dc.relation.ispartof.spa.fl_str_mv Universidad Nacional de Colombia Revistas electrónicas UN Dyna
Dyna
dc.relation.references.spa.fl_str_mv Hajali-Mohamad, M. T. and Mosavi, M. R. and Shahanaghi, K. (2016) Optimal estimating the project completion time and diagnosing the fault in the project. DYNA, 83 (195). pp. 121-127. ISSN 2346-2183
dc.rights.spa.fl_str_mv Derechos reservados - Universidad Nacional de Colombia
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial 4.0 Internacional
Derechos reservados - Universidad Nacional de Colombia
http://creativecommons.org/licenses/by-nc/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia (Sede Medellín). Facultad de Minas.
institution Universidad Nacional de Colombia
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