Design of Self-regulating Planning Model

Purpose: This research aims to develop a dynamic and self-regulated application that considers demand forecasts, based on linear regression as a basic algorithm for machine learning. Methodology: This research uses aggregate planning and machine learning along with inventory policies through the sol...

Full description

Autores:
Espitia Rincon, Maria Paula
Sanabria Martínez, David Alejandro
Abril Juzga, Kevin Alberto
Santos Hernández, Andrés Felipe
Tipo de recurso:
Part of book
Fecha de publicación:
2019
Institución:
Escuela Colombiana de Ingeniería Julio Garavito
Repositorio:
Repositorio Institucional ECI
Idioma:
spa
OAI Identifier:
oai:repositorio.escuelaing.edu.co:001/1855
Acceso en línea:
https://repositorio.escuelaing.edu.co/handle/001/1855
Palabra clave:
Aprendizaje automático (Inteligencia artificial) - Modelos matemáticos
Regresión lineal
Análisis de regresión
Programación lineal
Machine learning - Mathematical models
Linear Programming
Linear Regression,
Aggregate Planning
Cost Minimization
Rights
openAccess
License
http://purl.org/coar/access_right/c_abf2
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dc.title.spa.fl_str_mv Design of Self-regulating Planning Model
title Design of Self-regulating Planning Model
spellingShingle Design of Self-regulating Planning Model
Aprendizaje automático (Inteligencia artificial) - Modelos matemáticos
Regresión lineal
Análisis de regresión
Programación lineal
Machine learning - Mathematical models
Linear Programming
Linear Regression,
Aggregate Planning
Cost Minimization
title_short Design of Self-regulating Planning Model
title_full Design of Self-regulating Planning Model
title_fullStr Design of Self-regulating Planning Model
title_full_unstemmed Design of Self-regulating Planning Model
title_sort Design of Self-regulating Planning Model
dc.creator.fl_str_mv Espitia Rincon, Maria Paula
Sanabria Martínez, David Alejandro
Abril Juzga, Kevin Alberto
Santos Hernández, Andrés Felipe
dc.contributor.author.none.fl_str_mv Espitia Rincon, Maria Paula
Sanabria Martínez, David Alejandro
Abril Juzga, Kevin Alberto
Santos Hernández, Andrés Felipe
dc.contributor.researchgroup.spa.fl_str_mv Manufactura y Servicios
dc.subject.armarc.spa.fl_str_mv Aprendizaje automático (Inteligencia artificial) - Modelos matemáticos
Regresión lineal
Análisis de regresión
Programación lineal
topic Aprendizaje automático (Inteligencia artificial) - Modelos matemáticos
Regresión lineal
Análisis de regresión
Programación lineal
Machine learning - Mathematical models
Linear Programming
Linear Regression,
Aggregate Planning
Cost Minimization
dc.subject.armarc.eng.fl_str_mv Machine learning - Mathematical models
dc.subject.proposal.eng.fl_str_mv Linear Programming
Linear Regression,
Aggregate Planning
Cost Minimization
description Purpose: This research aims to develop a dynamic and self-regulated application that considers demand forecasts, based on linear regression as a basic algorithm for machine learning. Methodology: This research uses aggregate planning and machine learning along with inventory policies through the solver excel tool to make optimal decisions at the distribution center to reduce costs and guarantee the level of service. Findings: The findings after this study pertain to planning supply tactics in real-time, self-regulation of information in real-time and optimization of the frequency of the supply. Originality: An application capable of being updated in real-time by updating data by the planning director, which will show the optimal aggregate planning and the indicators of the costs associated with the picking operation of a company with 12000 SKU's (Stock Keeping Unit), in which a retail trade of 65 stores is carried out.
publishDate 2019
dc.date.issued.none.fl_str_mv 2019
dc.date.accessioned.none.fl_str_mv 2021-11-24T17:51:20Z
dc.date.available.none.fl_str_mv 2021-11-24T17:51:20Z
dc.type.spa.fl_str_mv Capítulo - Parte de Libro
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
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dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/bookPart
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format http://purl.org/coar/resource_type/c_3248
status_str publishedVersion
dc.identifier.isbn.none.fl_str_mv 9789585233300
dc.identifier.uri.none.fl_str_mv https://repositorio.escuelaing.edu.co/handle/001/1855
identifier_str_mv 9789585233300
url https://repositorio.escuelaing.edu.co/handle/001/1855
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.indexed.spa.fl_str_mv N/A
dc.relation.ispartofbook.eng.fl_str_mv Artificial Intelligence and Digital Transformation in Supply Chain Management
dc.relation.references.spa.fl_str_mv Alonso, Martínez, Dorado,Páez, Lota, 2018. National logistics survey 2018, Bogotá: www.puntoaparte.com.co.
Aldana, P., 2014. The cross docking as an important tool in the chain, Bogotá: University of Nueva Granada.
Aldana, P., 2014. The cross docking as an important tool in the chain, Bogotá: University of Nueva Granada.
Anon., 2017. ingenieriaindustrialonline. [Online] Available at: https://www.ingenieriaindustrialonline.com/herramientas-para-el-ingeniero-industrial/producci%C3%B3n/planeacion-agregada-mediante-programacion-lineal/ [Accessed 01 May 2019].
Borissova, D., 2008. Bibliography. Cybernetics and information technologies, 8(2), pp. 102-103.
Chopra, M., 2008. Bibliography. En: L. M. C. Castillo, ed. Supply Chain management. Naucalpan de luárez (Mexico state): Pearson Education, pp. 56-57.
Chopra, M., 2008. Bibliography. En: L. M. C. Castillo, ed. Supply Chain management. Naucalpan de luárez (Mexico state): Pearson Education, pp. 56-57.
Columbus, 2018. 10 Ways Machine Learning Is Revolutionizing Supply Chain Management, New York: Forbes.
Dinero, 2015. Competencia ragulacion farmacias. [Online] Available at: https://www.dinero.com/edicion-impresa/negocios/articulo/competencia-regulacion-farmacias/215331 [Accessed 01 May 2019].
Dinero, 2019.Accelerated expansion plan in Farmatodo, Bogotá: s.n.
Espectador, E., 2016. El Espectador. [Online] Available at: https://www.elespectador.com/noticias/economia/colombia-hay-menos-3000-droguerias-de-barrioarticulo-654947 [Accessed 01 May 2019].
Fernández, I. A., 2011. Production and consumption: 49(1), pp. 179-191.
Gandhi, R., 2018. towards data science. [Online] Available at: https://towardsdatascience.com/introduction-to-machine-learning-algorithms-linear-regression14c4e325882a [Accessed 25 April 2019].
Gholamian, M.-M., 2015. Comprehensive fuzzy multi-objective multi-product multisite. 134(42), pp. 585-607.
Granja, A.-L., 2014. An optimization-based on a simulation approach to patient admission. Journal of Biomedical Informatics, Issue 52, pp. 427-437.
Julian, D., 2016. Designing Machine Learning Systems with Python. 1 ed. Birmingham B3 2PB: Packt Publishing Ltd.
Pereira, J., 2018. BigData mazine. [Online] Available at: https://bigdatamagazine.es/utilizacion-de-big-data-y-machine-learning-en-la-industria-4-0 [Accessed 01 May 2019].
Rüssmann, L., 2015. Bibliography. En: I. 2. A. r. r. The Boston Consulting Group, ed. The Future of Productivity and Growth in Manufacturing Industries. Boston: The Boston Consulting Group, p. 5.
Souza, C., 2018. Direct stockpile scheduling: Mathematical formulation •. 85(204), pp. 296-301.
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dc.format.extent.spa.fl_str_mv 33 páginas.
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dc.publisher.spa.fl_str_mv Artificial Intelligence and Digital Transformation in Supply Chain Management: Innovative Approaches for Supply Chains. Proceedings of the Hamburg International Conference of Logistics (HICL)
dc.publisher.place.spa.fl_str_mv Berlín
dc.source.spa.fl_str_mv https://www.econstor.eu/handle/10419/209383?locale=en
institution Escuela Colombiana de Ingeniería Julio Garavito
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spelling Espitia Rincon, Maria Paulaf989e555322c3c97f467203e08dfd514600Sanabria Martínez, David Alejandrodf9773ad3720a378b8280e3407b39ec3600Abril Juzga, Kevin Alberto6a68bfe0a477592f3478475909eb6839600Santos Hernández, Andrés Felipe76f7bdc11663a0365bc102ce36f71500600Manufactura y Servicios2021-11-24T17:51:20Z2021-11-24T17:51:20Z20199789585233300https://repositorio.escuelaing.edu.co/handle/001/1855Purpose: This research aims to develop a dynamic and self-regulated application that considers demand forecasts, based on linear regression as a basic algorithm for machine learning. Methodology: This research uses aggregate planning and machine learning along with inventory policies through the solver excel tool to make optimal decisions at the distribution center to reduce costs and guarantee the level of service. Findings: The findings after this study pertain to planning supply tactics in real-time, self-regulation of information in real-time and optimization of the frequency of the supply. Originality: An application capable of being updated in real-time by updating data by the planning director, which will show the optimal aggregate planning and the indicators of the costs associated with the picking operation of a company with 12000 SKU's (Stock Keeping Unit), in which a retail trade of 65 stores is carried out.Propósito: Esta investigación tiene como objetivo desarrollar una aplicación dinámica y autorregulada que considere los pronósticos de demanda, basados ​​en la regresión lineal como algoritmo básico para el aprendizaje automático. Metodología: Esta investigación utiliza la planificación agregada y el aprendizaje automático junto con las políticas de inventario a través de la herramienta solver excel para tomar decisiones óptimas en el centro de distribución para reducir costos y garantizar el nivel de servicio. Hallazgos: Los hallazgos de este estudio se refieren a la planificación de tácticas de suministro en tiempo real, la autorregulación de la información en tiempo real y la optimización de la frecuencia del suministro. Originalidad: Una aplicación susceptible de ser actualizada en tiempo real mediante la actualización de datos por parte del director de planificación, que mostrará la planificación agregada óptima y los indicadores de los costos asociados a la operación de picking de una empresa con 12000 SKU's (Stock Keeping Unit), en el que se realiza un comercio minorista de 65 tiendas.33 páginas.application/pdfspaArtificial Intelligence and Digital Transformation in Supply Chain Management: Innovative Approaches for Supply Chains. Proceedings of the Hamburg International Conference of Logistics (HICL)Berlínhttps://www.econstor.eu/handle/10419/209383?locale=enDesign of Self-regulating Planning ModelCapítulo - Parte de Libroinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_3248http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/bookParthttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85N/AArtificial Intelligence and Digital Transformation in Supply Chain ManagementAlonso, Martínez, Dorado,Páez, Lota, 2018. National logistics survey 2018, Bogotá: www.puntoaparte.com.co.Aldana, P., 2014. The cross docking as an important tool in the chain, Bogotá: University of Nueva Granada.Aldana, P., 2014. The cross docking as an important tool in the chain, Bogotá: University of Nueva Granada.Anon., 2017. ingenieriaindustrialonline. [Online] Available at: https://www.ingenieriaindustrialonline.com/herramientas-para-el-ingeniero-industrial/producci%C3%B3n/planeacion-agregada-mediante-programacion-lineal/ [Accessed 01 May 2019].Borissova, D., 2008. Bibliography. Cybernetics and information technologies, 8(2), pp. 102-103.Chopra, M., 2008. Bibliography. En: L. M. C. Castillo, ed. Supply Chain management. Naucalpan de luárez (Mexico state): Pearson Education, pp. 56-57.Chopra, M., 2008. Bibliography. En: L. M. C. Castillo, ed. Supply Chain management. Naucalpan de luárez (Mexico state): Pearson Education, pp. 56-57.Columbus, 2018. 10 Ways Machine Learning Is Revolutionizing Supply Chain Management, New York: Forbes.Dinero, 2015. Competencia ragulacion farmacias. [Online] Available at: https://www.dinero.com/edicion-impresa/negocios/articulo/competencia-regulacion-farmacias/215331 [Accessed 01 May 2019].Dinero, 2019.Accelerated expansion plan in Farmatodo, Bogotá: s.n.Espectador, E., 2016. El Espectador. [Online] Available at: https://www.elespectador.com/noticias/economia/colombia-hay-menos-3000-droguerias-de-barrioarticulo-654947 [Accessed 01 May 2019].Fernández, I. A., 2011. Production and consumption: 49(1), pp. 179-191.Gandhi, R., 2018. towards data science. [Online] Available at: https://towardsdatascience.com/introduction-to-machine-learning-algorithms-linear-regression14c4e325882a [Accessed 25 April 2019].Gholamian, M.-M., 2015. Comprehensive fuzzy multi-objective multi-product multisite. 134(42), pp. 585-607.Granja, A.-L., 2014. An optimization-based on a simulation approach to patient admission. Journal of Biomedical Informatics, Issue 52, pp. 427-437.Julian, D., 2016. Designing Machine Learning Systems with Python. 1 ed. Birmingham B3 2PB: Packt Publishing Ltd.Pereira, J., 2018. BigData mazine. [Online] Available at: https://bigdatamagazine.es/utilizacion-de-big-data-y-machine-learning-en-la-industria-4-0 [Accessed 01 May 2019].Rüssmann, L., 2015. Bibliography. En: I. 2. A. r. r. The Boston Consulting Group, ed. The Future of Productivity and Growth in Manufacturing Industries. Boston: The Boston Consulting Group, p. 5.Souza, C., 2018. Direct stockpile scheduling: Mathematical formulation •. 85(204), pp. 296-301.info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Aprendizaje automático (Inteligencia artificial) - Modelos matemáticosRegresión linealAnálisis de regresiónProgramación linealMachine learning - Mathematical modelsLinear ProgrammingLinear Regression,Aggregate PlanningCost MinimizationORIGINALDesign of Self-regulating Planning Model.pdfDesign of Self-regulating Planning Model.pdfArtículo principal.application/pdf1976440https://repositorio.escuelaing.edu.co/bitstream/001/1855/1/Design%20of%20Self-regulating%20Planning%20Model.pdfa5fac5609280fb2c3e548dcd473c11b9MD51open accessDesign of Self-regulating Planning Model.pdfDesign of Self-regulating Planning Model.pdfapplication/pdf1976440https://repositorio.escuelaing.edu.co/bitstream/001/1855/2/Design%20of%20Self-regulating%20Planning%20Model.pdfa5fac5609280fb2c3e548dcd473c11b9MD52open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-81881https://repositorio.escuelaing.edu.co/bitstream/001/1855/3/license.txt5a7ca94c2e5326ee169f979d71d0f06eMD53open accessTEXTDesign of Self-regulating Planning Model.pdf.txtDesign of Self-regulating Planning Model.pdf.txtExtracted texttext/plain45134https://repositorio.escuelaing.edu.co/bitstream/001/1855/4/Design%20of%20Self-regulating%20Planning%20Model.pdf.txtbc6d4d5f9c8239c9f54812ba4bfe198fMD54open accessTHUMBNAILDesign of Self-regulating Planning Model.pdf.jpgDesign of Self-regulating Planning Model.pdf.jpgGenerated Thumbnailimage/jpeg11578https://repositorio.escuelaing.edu.co/bitstream/001/1855/5/Design%20of%20Self-regulating%20Planning%20Model.pdf.jpgba039559011691d70d65d8d5aba72ecaMD55open access001/1855oai:repositorio.escuelaing.edu.co:001/18552022-08-24 13:08:29.116open accessRepositorio Escuela Colombiana de Ingeniería Julio Garavitorepositorio.eci@escuelaing.edu.coU0kgVVNURUQgSEFDRSBQQVJURSBERUwgR1JVUE8gREUgUEFSRVMgRVZBTFVBRE9SRVMgREUgTEEgQ09MRUNDScOTTiAiUEVFUiBSRVZJRVciLCBPTUlUQSBFU1RBIExJQ0VOQ0lBLgoKQXV0b3Jpem8gYSBsYSBFc2N1ZWxhIENvbG9tYmlhbmEgZGUgSW5nZW5pZXLDrWEgSnVsaW8gR2FyYXZpdG8gcGFyYSBwdWJsaWNhciBlbCB0cmFiYWpvIGRlIGdyYWRvLCBhcnTDrWN1bG8sIHZpZGVvLCAKY29uZmVyZW5jaWEsIGxpYnJvLCBpbWFnZW4sIGZvdG9ncmFmw61hLCBhdWRpbywgcHJlc2VudGFjacOzbiB1IG90cm8gKGVuICAgIGFkZWxhbnRlIGRvY3VtZW50bykgcXVlIGVuIGxhIGZlY2hhIAplbnRyZWdvIGVuIGZvcm1hdG8gZGlnaXRhbCwgeSBsZSBwZXJtaXRvIGRlIGZvcm1hIGluZGVmaW5pZGEgcXVlIGxvIHB1YmxpcXVlIGVuIGVsIHJlcG9zaXRvcmlvIGluc3RpdHVjaW9uYWwsIAplbiBsb3MgdMOpcm1pbm9zIGVzdGFibGVjaWRvcyBlbiBsYSBMZXkgMjMgZGUgMTk4MiwgbGEgTGV5IDQ0IGRlIDE5OTMsIHkgZGVtw6FzIGxleWVzIHkganVyaXNwcnVkZW5jaWEgdmlnZW50ZQphbCByZXNwZWN0bywgcGFyYSBmaW5lcyBlZHVjYXRpdm9zIHkgbm8gbHVjcmF0aXZvcy4gRXN0YSBhdXRvcml6YWNpw7NuIGVzIHbDoWxpZGEgcGFyYSBsYXMgZmFjdWx0YWRlcyB5IGRlcmVjaG9zIGRlIAp1c28gc29icmUgbGEgb2JyYSBlbiBmb3JtYXRvIGRpZ2l0YWwsIGVsZWN0csOzbmljbywgdmlydHVhbDsgeSBwYXJhIHVzb3MgZW4gcmVkZXMsIGludGVybmV0LCBleHRyYW5ldCwgeSBjdWFscXVpZXIgCmZvcm1hdG8gbyBtZWRpbyBjb25vY2lkbyBvIHBvciBjb25vY2VyLgpFbiBtaSBjYWxpZGFkIGRlIGF1dG9yLCBleHByZXNvIHF1ZSBlbCBkb2N1bWVudG8gb2JqZXRvIGRlIGxhIHByZXNlbnRlIGF1dG9yaXphY2nDs24gZXMgb3JpZ2luYWwgeSBsbyBlbGFib3LDqSBzaW4gCnF1ZWJyYW50YXIgbmkgc3VwbGFudGFyIGxvcyBkZXJlY2hvcyBkZSBhdXRvciBkZSB0ZXJjZXJvcy4gUG9yIGxvIHRhbnRvLCBlcyBkZSBtaSBleGNsdXNpdmEgYXV0b3LDrWEgeSwgZW4gY29uc2VjdWVuY2lhLCAKdGVuZ28gbGEgdGl0dWxhcmlkYWQgc29icmUgw6lsLiBFbiBjYXNvIGRlIHF1ZWphIG8gYWNjacOzbiBwb3IgcGFydGUgZGUgdW4gdGVyY2VybyByZWZlcmVudGUgYSBsb3MgZGVyZWNob3MgZGUgYXV0b3Igc29icmUgCmVsIGRvY3VtZW50byBlbiBjdWVzdGnDs24sIGFzdW1pcsOpIGxhIHJlc3BvbnNhYmlsaWRhZCB0b3RhbCB5IHNhbGRyw6kgZW4gZGVmZW5zYSBkZSBsb3MgZGVyZWNob3MgYXF1w60gYXV0b3JpemFkb3MuIEVzdG8gCnNpZ25pZmljYSBxdWUsIHBhcmEgdG9kb3MgbG9zIGVmZWN0b3MsIGxhIEVzY3VlbGEgYWN0w7phIGNvbW8gdW4gdGVyY2VybyBkZSBidWVuYSBmZS4KVG9kYSBwZXJzb25hIHF1ZSBjb25zdWx0ZSBlbCBSZXBvc2l0b3JpbyBJbnN0aXR1Y2lvbmFsIGRlIGxhIEVzY3VlbGEsIGVsIENhdMOhbG9nbyBlbiBsw61uZWEgdSBvdHJvIG1lZGlvIGVsZWN0csOzbmljbywgCnBvZHLDoSBjb3BpYXIgYXBhcnRlcyBkZWwgdGV4dG8sIGNvbiBlbCBjb21wcm9taXNvIGRlIGNpdGFyIHNpZW1wcmUgbGEgZnVlbnRlLCBsYSBjdWFsIGluY2x1eWUgZWwgdMOtdHVsbyBkZWwgdHJhYmFqbyB5IGVsIAphdXRvci5Fc3RhIGF1dG9yaXphY2nDs24gbm8gaW1wbGljYSByZW51bmNpYSBhIGxhIGZhY3VsdGFkIHF1ZSB0ZW5nbyBkZSBwdWJsaWNhciB0b3RhbCBvIHBhcmNpYWxtZW50ZSBsYSBvYnJhIGVuIG90cm9zIAptZWRpb3MuRXN0YSBhdXRvcml6YWNpw7NuIGVzdMOhIHJlc3BhbGRhZGEgcG9yIGxhcyBmaXJtYXMgZGVsIChsb3MpIGF1dG9yKGVzKSBkZWwgZG9jdW1lbnRvLiAKU8OtIGF1dG9yaXpvIChhbWJvcykK

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