Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Díaz Pacheco, Raúl Antonio"'
Autor:
Díaz Pacheco, Raúl Antonio1 radiazpa@unal.edu.co, Benedito, Ernest2
Publikováno v:
Cogent Business & Management. 2024, Vol. 11 Issue 1, p1-18. 18p.
Publikováno v:
Logistics (2305-6290); Sep2024, Vol. 8 Issue 3, p63, 19p
Publikováno v:
Revista Guillermo de Ockham, Vol 7, Iss 2 (2009)
Revista 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). 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.
Universidad de San Buenaventura-Cali
Cali, Hemeroteca 3er. piso
Biblioteca Digital Universidad de San Buenaventura
Repositorio USB
Universidad de San Buenaventura
instacron:Universidad de San Buenaventura
Revista 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). 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.
Universidad de San Buenaventura-Cali
Cali, Hemeroteca 3er. piso
Biblioteca Digital Universidad de San Buenaventura
Repositorio USB
Universidad de San Buenaventura
instacron:Universidad de San Buenaventura
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 Technolo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::4cd6c95b003699c8f762a67268a7c8d8
https://revistas.usbbog.edu.co/index.php/GuillermoOckham/article/view/547
https://revistas.usbbog.edu.co/index.php/GuillermoOckham/article/view/547
Publikováno v:
Repositorio UN
Universidad Nacional de Colombia
instacron:Universidad Nacional de Colombia
Universidad Nacional de Colombia
instacron:Universidad Nacional de Colombia
La Educación Ambiental (EA) es una herramienta esencial para que todos los seres humanos logren ampliar sus conocimientos sobre el medio ambiente y adquieran conciencia de su entorno, además puedan realizar cambios en sus valores, conductas, estilo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::0c3cbe3aac922adbfbe6712ab917da3b
https://repositorio.unal.edu.co/handle/unal/60829
https://repositorio.unal.edu.co/handle/unal/60829
Autor:
Masso Sanjuán, Olga Lucía
Publikováno v:
Repositorio UN
Universidad Nacional de Colombia
instacron:Universidad Nacional de Colombia
Universidad Nacional de Colombia
instacron:Universidad Nacional de Colombia
Este trabajo es resultado de la presentación y desarrollo de una propuesta pedagógica que tiene como propósito facilitar y mejorar significativamente el aprendizaje del álgebra en grado 8º en la institución Educativa Rafael Navia Varón de la c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::152239d68b38ed3e524d30ea6180bec7
http://bdigital.unal.edu.co/12715/
http://bdigital.unal.edu.co/12715/