DOI:
https://doi.org/10.14483/22484728.9872Publicado:
2014-11-26Número:
Vol. 8 Núm. 2 (2014)Sección:
Visión InvestigadoraBenchmarking among artificial intelligence techniques applied to forecast
Palabras clave:
Demand forecasting, Genetic algorithms, Artificial neural networks, Forecasting methods (es).Descargas
Resumen (es)
The article is about creating a space for multiple tests of demand forecasting techniques, this space is a software development where besides to testing the algorithms on the same database, these code routines can be compared with each other, this tool allows generate forecasts to be usable in decision making on purchases of Distribution Companies. Besides comparing forecasting some simple techniques like Moving Average (MM) and Last Period with other techniques such as Artificial Neural Networks (ARN) and genetic algorithms (GA), the comparison is made taking into account the error criteria of generated forecasts and the processing time of the methods. Throughout the article explains the design, development and implementation of the above methods and their integration with the tool.
Referencias
L. Mora, ”Gesti´on Log´ıstica Integral, Gestion Log´ıstica - FESC”, Bogot´a: editorial ECOE, 2008, Capitulo 2 pp.118-150
El Control y Planificaci´on de los Inventarios Unidad IV, Universidad Jose Carlos Mariategui, pp. 29–54, Julio 2005, [En l´ınea] Disponible en: http://www.ujcm.edu.pe/bv/links/cur comercial/LogisticaEmpresarial-11.pdf
T. Foster, “Forecasting, Demand Planning in a Difficult Economy, Global Logistics & Supply Chain Strategies”, portal: Supply Chain Brain, pp 1-4, December 2008. [En l´ınea]. Disponible en: http://www.supplychainbrain.com/content/technolo
gy-solutions/forecasting-demand-planning/single-article-
page/article/forecasting-demand-planning-in-adifficult-
economy/
“Mercados Melexa”, portal Melexa-Sonepar SAS, Julio 2013 [En l´ınea]. Disponible en: http://www.melexa.com/mercados.
M. Becerra, “Enterprise Resource Planning (ERP) - presentaci´on BAAN”, Mind de Colombia, Bogot´a, pp. 2-15, Colombia, Diciembre 2008,
T. Fucci, “El grafico abc como tecnica de gesti´on de inventarios”, Universidad Nacional de Lujan, Buenos Aires, Argentina, pp. 1-6, Junio 1999.
Free-Logistics, the free supply chain portal, “ ABC analysis Pareto principle ”, Free-Logistics, [En l´ınea]. Disponible en: http://www.freelogistics.com/index.php?option=com content&view=article&id=378:abc-analysis-paretoprinciple&
catid=67&Itemid=51.
C. Rojas, “La desviaci´on est´andar y los modelos normales”,
Universidad Aut´onoma de Chile, pp.5-24, Octubre 2011
M. Oliveros, “Tema: Pron´osticos”, Universidad de los Andes, Bogot´a, Colombia, pp. 10–21, Septiembre 2007.
A. De Pe˜na, P. Truyol, and G. S. Ags, “Algoritmos gen´eticos”, Universidad Carlos III, Madrid, Espa˜na, pp.1-8, Enero 2007.
I. Mart´ınez, ”Introducci´on a las Redes Neuronales”, Universidad Complutense de Madrid, trabajo doctoral, pp. 1–19, Junio 2005.
A. M. Pedro Larra˜naga, I˜naki Inza, “Tema 8. Redes Neuronales,” in Redes Neuronales, U. del P. Vasco, p. 12-17. Ed. 2011.
M. A. V. Reyes, C. Y. M´arquez, and L. P. S. Fern´andez, “Algoritmo Backpropagation para Redes Neuronales: conceptos y aplicaciones,” INSTITUTO POLIT´ECNICO NACIONAL, 2006.
J. A. P. Ortiz, “Modelos predictivos basados en Redes
Neuronales recurrentes de tiempo discreto”, Tesis Doctoral, Universidad de Alicante, Espa˜na, pp. 35-63, Julio 2002.
G. Colmenares, “CAP´ITULO 2 TLU(s) MULTICAPAS,” RedULA, Universidad de los Andes Venezuela, pp. 40–78. Noviembre 2004.
J. Casellas, “Ap´endice A: Metodolog´ıa para la evaluaci´on del modelo de pron´ostico meteorol´ogico”, Tesis Doctorales en Red. p. 3. Enero 2005.