Artificial intelligence techniques for optimizing the efficiency in public works contracting selection processes
Descripción del Articulo
The present article proposes to design a model that provides a generic architecture which acts autonomously in public works contracting selection processes, in order to generate an automated decision criterion in the event of a tie. For the Simplified Tender selection process, in case of a tie, it i...
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| Formato: | artículo |
| Fecha de Publicación: | 2018 |
| Institución: | Universidad de Lima |
| Repositorio: | Revistas - Universidad de Lima |
| Lenguaje: | español |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/2951 |
| Enlace del recurso: | https://revistas.ulima.edu.pe/index.php/Interfases/article/view/2951 |
| Nivel de acceso: | acceso abierto |
| Materia: | artificial neural networks decision-making risk redes neuronales artificiales toma de decisiones riesgo |
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Artificial intelligence techniques for optimizing the efficiency in public works contracting selection processesTécnicas de inteligencia artificial para optimizar la eficiencia del procedimiento de selección para la contratación de obras públicasSolís-Villanueva, Reinerartificial neural networksdecision-makingriskredes neuronales artificialestoma de decisionesriesgoThe present article proposes to design a model that provides a generic architecture which acts autonomously in public works contracting selection processes, in order to generate an automated decision criterion in the event of a tie. For the Simplified Tender selection process, in case of a tie, it is proposed to choose the bidder by means of an electronic lottery based on a controlled randomization system of encryption and transformation. For the Public Bidding selection process, in the event of a tie, the bidder is chosen by means of a predicted compliance index according to the behavior of the companies when executing similar infrastructure projects. To this end, a model that predicts the probability of success or failure of the bidder to execute a project before initiating it is generated, using artificial neural networks as an analysis tool. This paper reviews the common characteristics of artificial neural networks.El presente artículo propone el diseño de un modelo que proporciona una arquitectura genérica que actúa en forma autónoma en los procedimientos de selección en la contratación de obras públicas, generando un criterio de decisión autómata en caso de empate. Para el procedimiento de selección Adjudicación Simplificada, en caso de empate, se propone la elección del postor mediante un sorteo electrónico basado en un sistema de aleatoriedad controlada de encriptación y transformación. Para el procedimiento de selección Licitación Pública, en caso de empate se propone la elección del postor mediante un índice de cumplimiento pronosticado de acuerdo con el comportamiento de las empresas en la ejecución de proyectos de infraestructura similares. Con este fin se genera un modelo que realiza la predicción de la probabilidad de éxito o fracaso del postor de ejecutar el proyecto antes de iniciarlo, usando redes neuronales artificiales como herramienta de análisis. En el presente documento se revisan las características comunes de las redes neuronales artificiales.Universidad de Lima2018-12-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.ulima.edu.pe/index.php/Interfases/article/view/295110.26439/interfases2018.n011.2951Interfases; No. 011 (2018); 13-42Interfases; Núm. 011 (2018); 13-42Interfases; n. 011 (2018); 13-421993-491210.26439/interfases2018.n011reponame:Revistas - Universidad de Limainstname:Universidad de Limainstacron:ULIMAspahttps://revistas.ulima.edu.pe/index.php/Interfases/article/view/2951/3183Copyright (c) 2019 Interfacesinfo:eu-repo/semantics/openAccessoai:ojs.pkp.sfu.ca:article/29512023-07-24T13:32:31Z |
| dc.title.none.fl_str_mv |
Artificial intelligence techniques for optimizing the efficiency in public works contracting selection processes Técnicas de inteligencia artificial para optimizar la eficiencia del procedimiento de selección para la contratación de obras públicas |
| title |
Artificial intelligence techniques for optimizing the efficiency in public works contracting selection processes |
| spellingShingle |
Artificial intelligence techniques for optimizing the efficiency in public works contracting selection processes Solís-Villanueva, Reiner artificial neural networks decision-making risk redes neuronales artificiales toma de decisiones riesgo |
| title_short |
Artificial intelligence techniques for optimizing the efficiency in public works contracting selection processes |
| title_full |
Artificial intelligence techniques for optimizing the efficiency in public works contracting selection processes |
| title_fullStr |
Artificial intelligence techniques for optimizing the efficiency in public works contracting selection processes |
| title_full_unstemmed |
Artificial intelligence techniques for optimizing the efficiency in public works contracting selection processes |
| title_sort |
Artificial intelligence techniques for optimizing the efficiency in public works contracting selection processes |
| dc.creator.none.fl_str_mv |
Solís-Villanueva, Reiner |
| author |
Solís-Villanueva, Reiner |
| author_facet |
Solís-Villanueva, Reiner |
| author_role |
author |
| dc.subject.none.fl_str_mv |
artificial neural networks decision-making risk redes neuronales artificiales toma de decisiones riesgo |
| topic |
artificial neural networks decision-making risk redes neuronales artificiales toma de decisiones riesgo |
| description |
The present article proposes to design a model that provides a generic architecture which acts autonomously in public works contracting selection processes, in order to generate an automated decision criterion in the event of a tie. For the Simplified Tender selection process, in case of a tie, it is proposed to choose the bidder by means of an electronic lottery based on a controlled randomization system of encryption and transformation. For the Public Bidding selection process, in the event of a tie, the bidder is chosen by means of a predicted compliance index according to the behavior of the companies when executing similar infrastructure projects. To this end, a model that predicts the probability of success or failure of the bidder to execute a project before initiating it is generated, using artificial neural networks as an analysis tool. This paper reviews the common characteristics of artificial neural networks. |
| publishDate |
2018 |
| dc.date.none.fl_str_mv |
2018-12-03 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
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article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
https://revistas.ulima.edu.pe/index.php/Interfases/article/view/2951 10.26439/interfases2018.n011.2951 |
| url |
https://revistas.ulima.edu.pe/index.php/Interfases/article/view/2951 |
| identifier_str_mv |
10.26439/interfases2018.n011.2951 |
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spa |
| language |
spa |
| dc.relation.none.fl_str_mv |
https://revistas.ulima.edu.pe/index.php/Interfases/article/view/2951/3183 |
| dc.rights.none.fl_str_mv |
Copyright (c) 2019 Interfaces info:eu-repo/semantics/openAccess |
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Copyright (c) 2019 Interfaces |
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openAccess |
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application/pdf |
| dc.publisher.none.fl_str_mv |
Universidad de Lima |
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Universidad de Lima |
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Interfases; No. 011 (2018); 13-42 Interfases; Núm. 011 (2018); 13-42 Interfases; n. 011 (2018); 13-42 1993-4912 10.26439/interfases2018.n011 reponame:Revistas - Universidad de Lima instname:Universidad de Lima instacron:ULIMA |
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Universidad de Lima |
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ULIMA |
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ULIMA |
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Revistas - Universidad de Lima |
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13.905282 |
Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
