Revisión de técnicas de análisis de decisión multicriterio (múltiple criteria decisión analysis –MCDA) como soporte a problemas complejos: pronósticos de demanda
This article presents a review of the literature based on multiple criteria analysis techniques as a support for business decision-making of SMEs entrepreneurs, since it is of great interest to the research project developed by the group New Technologies, Labor and Management in terms of innovation...
- Autores:
-
Acosta Ríos, Mario Fernando
Díaz Pacheco, Raúl Antonio
Anaya Salazar, Ángela Patricia
- Tipo de recurso:
- Fecha de publicación:
- 2009
- Institución:
- Universidad de San Buenaventura
- Repositorio:
- Repositorio USB
- Idioma:
- spa
- OAI Identifier:
- oai:bibliotecadigital.usb.edu.co:10819/5127
- Acceso en línea:
- http://hdl.handle.net/10819/5127
- Palabra clave:
- Análisis de Decisión Multicriteria (MCDA)
Estado del arte pronósticos
Pronósticos de demanda
Algoritmos genéticos
Redes neuronales artificiales
Demand predictions
Artificial neural networks
Genetic algorithms
State of the art predictions
Multiple Criteria Decision Analysis (MCDA)
Toma de decisiones
Pyme
Competitividad
- Rights
- License
- Atribución-NoComercial-SinDerivadas 2.5 Colombia
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Revisión de técnicas de análisis de decisión multicriterio (múltiple criteria decisión analysis –MCDA) como soporte a problemas complejos: pronósticos de demanda |
| dc.title.alternative.spa.fl_str_mv |
Review of techniques of multiple criteria decision analysis (MCDA) as a support to complex problems: Demand predictions |
| title |
Revisión de técnicas de análisis de decisión multicriterio (múltiple criteria decisión analysis –MCDA) como soporte a problemas complejos: pronósticos de demanda |
| spellingShingle |
Revisión de técnicas de análisis de decisión multicriterio (múltiple criteria decisión analysis –MCDA) como soporte a problemas complejos: pronósticos de demanda Análisis de Decisión Multicriteria (MCDA) Estado del arte pronósticos Pronósticos de demanda Algoritmos genéticos Redes neuronales artificiales Demand predictions Artificial neural networks Genetic algorithms State of the art predictions Multiple Criteria Decision Analysis (MCDA) Toma de decisiones Pyme Competitividad |
| title_short |
Revisión de técnicas de análisis de decisión multicriterio (múltiple criteria decisión analysis –MCDA) como soporte a problemas complejos: pronósticos de demanda |
| title_full |
Revisión de técnicas de análisis de decisión multicriterio (múltiple criteria decisión analysis –MCDA) como soporte a problemas complejos: pronósticos de demanda |
| title_fullStr |
Revisión de técnicas de análisis de decisión multicriterio (múltiple criteria decisión analysis –MCDA) como soporte a problemas complejos: pronósticos de demanda |
| title_full_unstemmed |
Revisión de técnicas de análisis de decisión multicriterio (múltiple criteria decisión analysis –MCDA) como soporte a problemas complejos: pronósticos de demanda |
| title_sort |
Revisión de técnicas de análisis de decisión multicriterio (múltiple criteria decisión analysis –MCDA) como soporte a problemas complejos: pronósticos de demanda |
| dc.creator.fl_str_mv |
Acosta Ríos, Mario Fernando Díaz Pacheco, Raúl Antonio Anaya Salazar, Ángela Patricia |
| dc.contributor.author.none.fl_str_mv |
Acosta Ríos, Mario Fernando Díaz Pacheco, Raúl Antonio Anaya Salazar, Ángela Patricia |
| dc.subject.spa.fl_str_mv |
Análisis de Decisión Multicriteria (MCDA) Estado del arte pronósticos Pronósticos de demanda Algoritmos genéticos Redes neuronales artificiales Demand predictions Artificial neural networks Genetic algorithms State of the art predictions Multiple Criteria Decision Analysis (MCDA) |
| topic |
Análisis de Decisión Multicriteria (MCDA) Estado del arte pronósticos Pronósticos de demanda Algoritmos genéticos Redes neuronales artificiales Demand predictions Artificial neural networks Genetic algorithms State of the art predictions Multiple Criteria Decision Analysis (MCDA) Toma de decisiones Pyme Competitividad |
| dc.subject.lemb.spa.fl_str_mv |
Toma de decisiones Pyme Competitividad |
| description |
This article presents a review of the literature based on multiple criteria analysis techniques as a support for business decision-making of SMEs entrepreneurs, since it is of great interest to the research project developed by the group New Technologies, Labor and Management in terms of innovation and social capital. The emphasis was on the issue of demand predictions because if the variability and uncertainty that they cause in the organization can be reduced, the complexity of decision-making related to the different organizational areas will be reduced as well. Given its importance, some literature was reviewed from its origins to the advanced techniques used today in the pattern of data behavior. These developments are more related to the implementation of these aspects in the business sector to improve competitiveness from effective strategic decisions made in uncertainty scenarios like the current ones, than to the edge of knowledge. |
| publishDate |
2009 |
| dc.date.issued.none.fl_str_mv |
2009-07 |
| dc.date.accessioned.none.fl_str_mv |
2017-11-22T02:08:18Z |
| dc.date.available.none.fl_str_mv |
2017-11-22T02:08:18Z |
| dc.date.submitted.none.fl_str_mv |
2017-11-17 |
| dc.type.spa.fl_str_mv |
Artículo de revista |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.spa.spa.fl_str_mv |
Artículo |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.hasVersion.spa.fl_str_mv |
info:eu-repo/semantics/published |
| dc.identifier.issn.none.fl_str_mv |
1794-192X 2256-3202 (en línea) |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/10819/5127 |
| identifier_str_mv |
1794-192X 2256-3202 (en línea) |
| url |
http://hdl.handle.net/10819/5127 |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.ispartofseries.none.fl_str_mv |
Revista Científica Guillermo de Ockham;Vol. 07, No 2. Julio-Diciembre 2009 |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.cc.spa.fl_str_mv |
Atribución-NoComercial-SinDerivadas 2.5 Colombia |
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http://creativecommons.org/licenses/by-nc-nd/2.5/co/ |
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Atribución-NoComercial-SinDerivadas 2.5 Colombia http://creativecommons.org/licenses/by-nc-nd/2.5/co/ http://purl.org/coar/access_right/c_abf2 |
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pdf |
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91 - 110 páginas |
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Recurso en linea |
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application/pdf |
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Universidad de San Buenaventura - Cali |
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Documento USB |
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Documentos USB |
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Cali |
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Revista Científica Guillermo de Ockham |
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– Adam, E. y Ebert, R. (1991). Administración de la producción y de las operaciones. México, D.F.: Ed. Prentice Hall. – Altay , G. y Erdal, E. (1998). Multicriteria inventory classification using a genetic algorithm. European Journal of Operational Research. Vol. 105, pp. 29-37. – Armstrong, J.S. y Yokuma, J.T. (1995). Beyond accuracy comparison of criteria used to select forecasting methods. International Journal of Forecasting. Vol. 11, No. 4. pp. 591-597. – _______; Collopy, F. y Yokum J.T. (2005). Decomposition by causal forces: a procedure for forecasting complex time series. International Journal of Forecasting. Vol. 21, No. 1. pp. 25-36. – Basheer , I.A y Hajmeer, M. (2000). Artificial Neural Networks: fundamentals, computing, design and application. Journal of Mivrobiological Methods. Vol 43 pp 3-31. – Bermúdez , J.D.; Segura, J.V. y Verchera, E.A. (2006). Decision support system methodology for forecasting of time series based on soft computing. Computational Statistics & Data Analysis. Vol. 51, No. 1, pp. 177-191. – Bovas , A. y Johannes, L. (1986). Forecast functions implied by autoregressive integrated moving average models and other related forecast procedures. International statistical review. Vol. 54, No. 1. pp. 51-66. – Buffa , E. y Sarin, R. (1995). Administración de la producción y de las operaciones. México, D.F.: Ed. Limusa. – Bunn , D.W. y Vassilopoulos, A.I. (1999). Comparison of seasonal estimation methods in multi-item shortterm forecasting. International Journal of Forecasting. Vol. 15, No. 4, pp. 431-443. – Clemen , R.T. (1989). Combining forecasts: A review and annotated bibliography. International Journal of Forecasting. Vol. 5, No. 4. pp. 559-583. – Coll, V. y Blasco, O.M. (2006). Evaluación de la eficiencia mediante el análisis envolvente de datos. Universidad de Valencia. – Collopy , F. y Armstrong, J.S. (1992). Expert opinions about extrapolation and the mystery of the overlooked discontinuities. International Journal of Forecasting. Vol. 8, No. 4, pp. 575-582. – Croston , J.D. (1972). Forecasting and stock control for intermittent demands. Operational Research Quarterly (1970-1977). Vol. 23, No. 3, pp. 289-303. – Chatfield , C. y Prothero, D.L. (1973). Box-Jenkins seasonal forecasting: problems in a case-study. Journal of the Royal Statistical Society. Series A (General). Vol. 36, No. 3, pp. 295-336. – Chen, Y. (2006). Multiple Criteria Decision Analysis: Classification Problems and Solutions. Department of Systems Design Engineering. Tesis doctoral. University of Waterloo. Canadá. – Cheng , Ch. y Wang , J.Ch. (2008). Forecasting the number of outpatient visits using a new fuzzy time series based on weighted-transitional matrix. Expert Systems with Applications. Vol. 34, No. 4, pp. 2.568-2.575. – De Menezes, L.M.; Bunn, D.W. y Taylor , J.W. (2000). Review of guidelines for the use of combined forecasts. European Journal of Operational Research. Vol. 120, No. 1, pp. 190-204. – De Moya , A. y Niño Vásquez, L.F. (2006). Representación y clasificación de datos geoespaciales usando redes neuronales. Colombia: Universidad Nacional de Colombia, Laboratorio de Sistemas Inteligentes. – Domínguez , J.A. et al. (1995). Dirección de operaciones. Aspectos tácticos y operativos en la producción y los servicios. Madrid: Editorial Mc Graw Hill. – Fildes , R. (1989). Evaluation of aggregate and individual forecast method selection rules. Mamagement Science. Vol. 35, No. 9, pp. 1056-1065. – Gardner , Jr.E.S. (2006). Exponential smoothing: The state of the art-Part II. International Journal of Forecasting. Vol. 22, No. 4, pp. 637-666. – Gascón, F. et al. (2007). On macroeconomic characteristics of pharmaceutical generics and the potential for manufacturing and consumption under fuzzy conditions. Artificial Intelligence in Medicine. Vol. 41, No. 3, pp. 223-235. – Green , K.C. y Armstrong, J.S. (2007). Structured analogies for forecasting. International Journal of Forecasting. Vol. 23, No. 3, pp. 365-376. – Guoqiang , Z.B. et al. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting. Vol. 14, pp. 35-62. – Heejoon , K. (1986). Univariate ARIMA Forecasts of Defined Variables. Journal of Business & Economic Statistics. Vol. 4, No. 1, pp. 81-86. – Hilera , J.R. y Martínez , V.J. (1995). Redes neuronales artificiales: Fundamentos, modelos y aplicaciones. Editorial Rama. – Howard , A y Eaves , C. (2002). Forecasting for the Ordering and Stock-Holding of consumable spare parts. Tesis doctoral. Department of Management Science. Lancaster University. – Huarng , K. y Yu, T.H. (2006). The application of neural networks to forecast fuzzy time series. Physica A: Statistical mechanics and its applications. Vol. 363, No. 2, pp. 481-491. – Jung , R.C. y Tremayne , A.R. (2006). Coherent forecasting in integer time series models. International Journal of Forecasting. Vol 22, No. 2, pp. 223-238. – Korpela, J. y Tuominenb, M. (1996). Inventory forecasting with a multiple criteria decision tool. International Journal of Production Economics. Vol. 45, No. 1-3, pp. 159-168. – Kumar , M. (2005). Combining Forecasts using Clústering. Rutgers Center for Operational Research. Rutgers University. New Yersey. – Lawrence , M. et al. (2006). Judgmental forecasting: A review of progress over the last 25 years. International Journal of Forecasting. Vol 22, No. 3, pp. 493-518. – Levén , E y Segerstedt , A. (2004). Inventory control with a modified Croston procedure and Erlang distribution. International Journal of Production Economics. Vol. 90, No. 3, pp. 361-367. – Makridakis , S.; Michele y Moser , Claus. (1978). Accuracy of Forecasting: An empirical investigation. Journal of the Royal Statistical Society. Series A (General). Vol. 142, No. 2, pp. 97-145. – Martínez , D. (2004). Redes neuronales artificiales y mapas autoorganizados. Sistemas Expertos e Inteligencia Artificial. 3° I.T.I.G. Universidad de Burgos. – O’Brien Pallas, L. et al. (2001). Forecasting models for human resources in health care. Journal of Advanced Nursing. Vol. 33, No. 1, pp. 120-129. – Ramanathan , R. (2003). An Introduction to Data Envelopment Analysis: A tool for performance measurement. New Delhi: Sage Publications. – Ranaweera , D.K. et al. (1996). Fuzzy logic for short term load forecasting. International Journal of Electrical Power & Energy Systems. Vol. 18, No. 4, pp. 215-222. – Rojapadhye , M. y Ben Ghalia, M. (2001). Forecasting uncertain hotel room demand. Information Sciences. Vol. 132, No. 1-4. – Royes , G. F. y Bastos , R.C. (2005). Uncertainty analysis in political forecasting. Decision Support Systems. Vol. 42, No. 1, pp. 25-35. – Saaty , T.L. (1980). The analytic hierarchy process. New York: Editorial McGraw-Hill. – Sanders , N.R. y Gramanb, G.A. (2009). Quantifying costs of forecast errors: A case study of the warehouse environment. Omega. Vol. 37, No. 1, pp. 116-125. – Seetha , H. y Saravanan, R. (2007). Short term electric load prediction using fuzzy BP. Journal of Computing and Information Technology. Vol. 3, pp. 267-282. – Segura , J.V. y Vercher, E. (2000). A spreadsheet modeling approach to the holt winters optimal forecasting. European Journal of Operational Research. Vol. 131, No. 2, pp. 375-388. – Shyi -Ming , C y Chia -Ching, H. (2004). A new method to forecast enrollments using fuzzy time series. International Journal of Applied Science and Engineering. Vol. 2, pp. 234-244. – Singh , S.R. (2007). A simple method of forecasting based on fuzzy time series. Applied Mathematics and Computation. Vol. 188, No. 1, pp. 472-484. – Syntetos , A.A. y Boylanb, J.E. (2006). On the stock control performance of intermittent demand estimators. International Journal of Production Economics. Vol. 103, No. 1, pp. 36-47. – Teunter, R. y Sani, B. (2008). On the bias of Croston’s forecasting method. European Journal of Operational Research. En impression. – Thompson , P. A. y Robert B. M. (1986). A bayesian approach to forecasting from univariate time series models. Journal of Business & Economic Statistics. Vol. 4, No. 4, pp. 427-436. – Valarezo, A. y Quezada, D. (2007). Antecedentes y funcionamiento de redes neuronales artificiales. Sistemas informáticos y computación. Universidad Técnica Particular de Loja. Ecuador. – Wang, W. (2007). An adaptive predictor for dynamic system forecasting. Mechanical Systems and Signal Processing. Vol. 21, No. 2, pp. 809-823. – Warner , B. y Misra, M. Understanding Neural Networks as Statistical Tools. American Statistical Association. Vol 50, No. 4, pp. 284-293. – Weiss, A.A. y Andersen, A.P. (1984). Estimating time series models using the relevant forecast evaluation criterion. Journal of the Royal Statistical Society. Series A (General). Vol. 147, No. 3, pp. 484-487. – Winkler , Robert L. y Makrida - kis , Spyros. (1983). The combination of forecasts. Journal of the Royal Statistical Society. Series A (General). Vol. 146, No. 2, pp. 150-157. – Zou, H y Yang, Y. (2004). Combining time series models for forecasting. International Journal of Forecasting. Vol. 20, pp. 69-84. – Zotteria , G.; Kalchschmi - dt, M. y Caniato , F. (2005). The impact of aggregation level on forecasting performance. International Journal of Production Economics. Vol. 93, pp. 479-491. |
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Comunidad Científica y AcadémicaAcosta Ríos, Mario Fernando96cb760a-0a95-4c4c-a56e-9514f41f3872-1Díaz Pacheco, Raúl Antoniode07e4ac-112d-4c62-bb4d-e7f887e470db-1Anaya Salazar, Ángela Patricia136def5d-c0b7-479d-b43f-236ab1fd08d9-12017-11-22T02:08:18Z2017-11-22T02:08:18Z2009-072017-11-17This article presents a review of the literature based on multiple criteria analysis techniques as a support for business decision-making of SMEs entrepreneurs, since it is of great interest to the research project developed by the group New Technologies, Labor and Management in terms of innovation and social capital. The emphasis was on the issue of demand predictions because if the variability and uncertainty that they cause in the organization can be reduced, the complexity of decision-making related to the different organizational areas will be reduced as well. Given its importance, some literature was reviewed from its origins to the advanced techniques used today in the pattern of data behavior. These developments are more related to the implementation of these aspects in the business sector to improve competitiveness from effective strategic decisions made in uncertainty scenarios like the current ones, than to the edge of knowledge.El artículo presenta una revisión de la literatura orientada a las técnicas de análisis multicriterio como soporte para toma de decisiones empresariales orientadas a los empresarios PyME, por ser de interés para el proyecto de investigación que desarrolla el Grupo Nuevas Tecnologías, Trabajo y Gestión en innovación y capital social. Se enfatizó el aspecto de pronósticos de demanda debido a que si se logra disminuir la variabilidad e incertidumbre que generan en la organización se disminuirá la complejidad de la toma de decisiones relacionadas con las diferentes áreas organizacionales. Dada su importancia se revisó literatura desde sus orígenes hasta técnicas avanzadas utilizadas hoy según el patrón de comportamiento de los datos. Estos avances se relacionan más con la aplicación de estos aspectos en el sector empresarial, para el mejoramiento de la competitividad a partir de decisiones estratégicas eficaces en panoramas de incertidumbre como los actuales, que con la frontera del conocimiento.Universidad de San Buenaventura - Calipdf91 - 110 páginasRecurso en lineaapplication/pdf1794-192X2256-3202 (en línea)http://hdl.handle.net/10819/5127spaUniversidad de San Buenaventura - CaliDocumento USBDocumentos USBCaliRevista Científica Guillermo de Ockham;Vol. 07, No 2. Julio-Diciembre 2009Atribución-NoComercial-SinDerivadas 2.5 ColombiaPor medio de este formato manifiesto mi voluntad de AUTORIZAR a la Universidad de San Buenaventura, Sede Bogotá, Seccionales Medellín, Cali y Cartagena, la difusión en texto completo de manera gratuita y por tiempo indefinido en la Biblioteca Digital Universidad de San Buenaventura, el documento académico-investigativo objeto de la presente autorización, con fines estrictamente educativos, científicos y culturales, en los términos establecidos en la Ley 23 de 1982, Ley 44 de 1993, Decisión Andina 351 de 1993, Decreto 460 de 1995 y demás normas generales sobre derechos de autor. Como autor manifiesto que el presente documento académico-investigativo es original y se realiza sin violar o usurpar derechos de autor de terceros, por lo tanto, la obra es de mi exclusiva autora y poseo la titularidad sobre la misma. La Universidad de San Buenaventura no será responsable de ninguna utilización indebida del documento por parte de terceros y será exclusivamente mi responsabilidad atender personalmente cualquier reclamación que pueda presentarse a la Universidad. Autorizo a la Biblioteca Digital de la Universidad de San Buenaventura convertir el documento al formato que el repositorio lo requiera (impreso, digital, electrónico o cualquier otro conocido o por conocer) o con fines de preservación digital. Esta autorización no implica renuncia a la facultad que tengo de publicar posteriormente la obra, en forma total o parcial, por lo cual podrá, dando aviso por escrito con no menos de un mes de antelación, solicitar que el documento deje de estar disponible para el público en la Biblioteca Digital de la Universidad de San Buenaventura, así mismo, cuando se requiera por razones legales y/o reglas del editor de una revista.http://creativecommons.org/licenses/by-nc-nd/2.5/co/http://purl.org/coar/access_right/c_abf2Revista Científica Guillermo de Ockham– Adam, E. y Ebert, R. (1991). Administración de la producción y de las operaciones. México, D.F.: Ed. Prentice Hall. – Altay , G. y Erdal, E. (1998). Multicriteria inventory classification using a genetic algorithm. European Journal of Operational Research. Vol. 105, pp. 29-37. – Armstrong, J.S. y Yokuma, J.T. (1995). Beyond accuracy comparison of criteria used to select forecasting methods. International Journal of Forecasting. Vol. 11, No. 4. pp. 591-597. – _______; Collopy, F. y Yokum J.T. (2005). Decomposition by causal forces: a procedure for forecasting complex time series. International Journal of Forecasting. Vol. 21, No. 1. pp. 25-36. – Basheer , I.A y Hajmeer, M. (2000). Artificial Neural Networks: fundamentals, computing, design and application. Journal of Mivrobiological Methods. Vol 43 pp 3-31. – Bermúdez , J.D.; Segura, J.V. y Verchera, E.A. (2006). Decision support system methodology for forecasting of time series based on soft computing. Computational Statistics & Data Analysis. Vol. 51, No. 1, pp. 177-191. – Bovas , A. y Johannes, L. (1986). Forecast functions implied by autoregressive integrated moving average models and other related forecast procedures. International statistical review. Vol. 54, No. 1. pp. 51-66. – Buffa , E. y Sarin, R. (1995). Administración de la producción y de las operaciones. México, D.F.: Ed. Limusa. – Bunn , D.W. y Vassilopoulos, A.I. (1999). Comparison of seasonal estimation methods in multi-item shortterm forecasting. International Journal of Forecasting. Vol. 15, No. 4, pp. 431-443. – Clemen , R.T. (1989). Combining forecasts: A review and annotated bibliography. International Journal of Forecasting. Vol. 5, No. 4. pp. 559-583. – Coll, V. y Blasco, O.M. (2006). Evaluación de la eficiencia mediante el análisis envolvente de datos. Universidad de Valencia. – Collopy , F. y Armstrong, J.S. (1992). Expert opinions about extrapolation and the mystery of the overlooked discontinuities. International Journal of Forecasting. Vol. 8, No. 4, pp. 575-582. – Croston , J.D. (1972). Forecasting and stock control for intermittent demands. Operational Research Quarterly (1970-1977). Vol. 23, No. 3, pp. 289-303. – Chatfield , C. y Prothero, D.L. (1973). Box-Jenkins seasonal forecasting: problems in a case-study. Journal of the Royal Statistical Society. Series A (General). Vol. 36, No. 3, pp. 295-336. – Chen, Y. (2006). Multiple Criteria Decision Analysis: Classification Problems and Solutions. Department of Systems Design Engineering. Tesis doctoral. University of Waterloo. Canadá. – Cheng , Ch. y Wang , J.Ch. (2008). Forecasting the number of outpatient visits using a new fuzzy time series based on weighted-transitional matrix. Expert Systems with Applications. Vol. 34, No. 4, pp. 2.568-2.575. – De Menezes, L.M.; Bunn, D.W. y Taylor , J.W. (2000). Review of guidelines for the use of combined forecasts. European Journal of Operational Research. Vol. 120, No. 1, pp. 190-204. – De Moya , A. y Niño Vásquez, L.F. (2006). Representación y clasificación de datos geoespaciales usando redes neuronales. Colombia: Universidad Nacional de Colombia, Laboratorio de Sistemas Inteligentes. – Domínguez , J.A. et al. (1995). Dirección de operaciones. Aspectos tácticos y operativos en la producción y los servicios. Madrid: Editorial Mc Graw Hill. – Fildes , R. (1989). 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The impact of aggregation level on forecasting performance. International Journal of Production Economics. Vol. 93, pp. 479-491.Universidad de San Buenaventura - CaliCali, Hemeroteca 3er. pisoBiblioteca Digital Universidad de San BuenaventuraAnálisis de Decisión Multicriteria (MCDA)Estado del arte pronósticosPronósticos de demandaAlgoritmos genéticosRedes neuronales artificialesDemand predictionsArtificial neural networksGenetic algorithmsState of the art predictionsMultiple Criteria Decision Analysis (MCDA)Toma de decisionesPymeCompetitividadRevisión de técnicas de análisis de decisión multicriterio (múltiple criteria decisión analysis –MCDA) como soporte a problemas complejos: pronósticos de demandaReview of techniques of multiple criteria decision analysis (MCDA) as a support to complex problems: Demand predictionsArtículo de revistaArtículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedhttp://purl.org/coar/resource_type/c_2df8fbb1PublicationORIGINAL547-1372-1-PB.pdf547-1372-1-PB.pdfapplication/pdf564718https://bibliotecadigital.usb.edu.co/bitstreams/95ecd0bc-a547-4fe6-ba93-a46bbe53b8ba/download5d336f01cac05a3c0268b9b3bf3447bfMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-82071https://bibliotecadigital.usb.edu.co/bitstreams/caf77449-091b-4e46-8155-9b43725c257d/download0c7b7184e7583ec671a5d9e43f0939c0MD52TEXT547-1372-1-PB.pdf.txt547-1372-1-PB.pdf.txtExtracted texttext/plain67964https://bibliotecadigital.usb.edu.co/bitstreams/fb6599b5-c99c-4e2f-9dfa-cb7c84817695/download259a39dbcfea236ce3f8cbdec6dcc5b4MD53THUMBNAIL547-1372-1-PB.pdf.jpg547-1372-1-PB.pdf.jpgGenerated Thumbnailimage/jpeg14148https://bibliotecadigital.usb.edu.co/bitstreams/ddd8eb02-1800-42dd-801f-2ef94e91b785/downloadbc23282bf16d3883423659590549332aMD5410819/5127oai:bibliotecadigital.usb.edu.co:10819/51272023-04-12 16:47:24.531http://creativecommons.org/licenses/by-nc-nd/2.5/co/https://bibliotecadigital.usb.edu.coRepositorio Institucional Universidad de San Buenaventura Colombiabdigital@metabiblioteca.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 |