Sobre el enfoque híbrido de Inteligencia Computacional y Apoyo a la Toma de Decisiones para la administración de recursos financieros
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mar 31, 2023
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Efraín Solares
http://orcid.org/0000-0003-1310-8638
Victor de-León-Gómez
http://orcid.org/0000-0001-7250-5073
Juana María Hernández
http://orcid.org/0000-0002-7385-298X
http://orcid.org/0000-0003-1310-8638
Victor de-León-Gómez
http://orcid.org/0000-0001-7250-5073
Juana María Hernández
http://orcid.org/0000-0002-7385-298X
Resumen
El uso óptimo de recursos financieros constituye uno de los factores más importantes del éxito de las organizaciones. Éstas suelen basarse en la experiencia empírica de los miembros de su alta dirección para tomar decisiones respecto a la distribución de recursos financieros. Sin embargo, este proceso ignora los avances tecnológicos y de modelado que han revolucionado globalmente la toma de decisiones de alto nivel.
Este trabajo identifica las ventajas y limitaciones de los principales enfoques que intentan optimizar los recursos financieros de las organizaciones, resalta las problemáticas actuales a las que se enfrentan las organizaciones al realizar esta actividad, y describe el uso de la inteligencia computacional puede superar tales limitaciones.
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Como citar
SOLARES, Efraín; DE-LEÓN-GÓMEZ, Victor; HERNÁNDEZ, Juana María.
Sobre el enfoque híbrido de Inteligencia Computacional y Apoyo a la Toma de Decisiones para la administración de recursos financieros.
CIENCIA ergo-sum, [S.l.], v. 31, n. 2, mar. 2023.
ISSN 2395-8782.
Disponible en: <https://cienciaergosum.uaemex.mx/article/view/17881>. Fecha de acceso: 29 mayo 2023
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Esta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial-SinObrasDerivadas 4.0.
Citas
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Barbati, M., Greco, S., Kadziński, M., & Słowiński, R. (2018). Optimization of multiple satisfaction levels in portfolio decision analysis. Omega, 78, 192-204.
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De Almeida, A. T. & Duarte, M. (2011). A multi-criteria decision model for selecting project portfolio with consideration being given to a new concept for synergies. Pesquisa Operacional, 31(2), 301-318.
Doerner, K.F., Gutjahr, W.J., Hartl, R.F., Strauss, C., Stummer, C. (2006): “Pareto ant colony optimization with ILP preprocessing in multiobjective project portfolio selection”, European Journal of Operational Research 171 (3), 830-841.
Espin, E., Téllez, G., Fernández, E., Marx-Gómez, J., and Lecich, M. I. (2006). Com- pensatory Logic: A fuzzy normative model for decision making. Investigación Operativa, 27, 188-197.
Fernández, E. (2003). “Análisis de la Decisión para evaluar y formar carteras de proyectos de I&D: un enfoque crítico y propositivo”, Gestión y Política Pública, vol. 12, no.1,pp. 43-79
Fernández, E., Figueira, J. R., & Navarro, J. (2020). Interval-based extensions of two outranking methods for multi-criteria ordinal classification. Omega, 95, 102065.
Figueira, J. R., Mousseau, V., & Roy, B. (2016). ELECTRE methods. In Multiple criteria decision analysis (pp. 155-185). Springer, New York, NY.
Gunasekaran, A., Sharif, A. M., Zhu, Q., Dou, Y., & Sarkis, J. (2010). A portfolio‐based analysis for green supplier management using the analytical network process. Supply Chain Management: an international journal.
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Kumar, G., Jain, S., & Singh, U. P. (2020). Stock market forecasting using computational intelligence: A survey. Archives of Computational Methods in Engineering, 1-33.
Larios, J. A. C., & Martínez, N. S. (2018). Aplicación teórica de un modelo de análisis predictivo para desarrollar estrategias competitivas en MiPYMES. Red Internacional de Investigadores en Competitividad, 10(1).
Larsen, P. M. (1980). Industrial applications of fuzzy logic control. International Journal of Man-Machine Studies, 12(1), 3-10.
Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International journal of man-machine studies, 7(1), 1-13.
Mavrotas, G., & Pechak, O. (2013). Combining mathematical programming and Monte Carlo simulation to deal with uncertainty in energy project portfolio selection. In Assessment and simulation tools for sustainable energy systems (pp. 333-356). Springer, London.
Mavrotas, G., Diakoulaki, D., & Caloghirou, Y. (2006). Project prioritization under policy restrictions. A combination of MCDA with 0–1 programming. European Journal of Operational Research, 171(1), 296-308.
Mavrotas, G., Diakoulaki, D., & Kourentzis, A. (2008). Selection among ranked projects under segmentation, policy and logical constraints. European Journal of Operational Research, 187(1), 177-192.
Pawlak, Z., Grzymala-Busse, J., Slowinski, R., & Ziarko, W. (1995). Rough sets. Communications of the ACM, 38(11), 88-95.
Picos., J. C. (2017). Modelado Lógico de Relaciones de Preferencia Básicas a partir de Argumentos (Disertación de tesis doctoral).
Relich, M., & Pawlewski, P. (2017). A fuzzy weighted average approach for selecting portfolio of new product development projects. Neurocomputing, 231, 19-27.
Roy, B. (1990). The outranking approach and the foundations of ELECTRE methods. In Readings in multiple criteria decision aid (pp. 155-183). Springer, Berlin, Heidelberg.
Roy, B., Figueira, J. R., & Almeida-Dias, J. (2014). Discriminating thresholds as a tool to cope with imperfect knowledge in multiple criteria decision aiding: Theoretical results and practical issues. Omega, 43, 9-20.
Saaty, T. L. (1990). How to make a decision: the analytic hierarchy process. European journal of operational research, 48(1), 9-26.
Stummer, C., & Heidenberger, K. (2003). Interactive R&D portfolio analysis with project interdependencies and time profiles of multiple objectives. IEEE Transactions on Engineering Management, 50(2), 175-183.
Szilágyi, I., Sebestyén, Z., & Tóth, T. (2020). Project Ranking in Petroleum Exploration. The Engineering Economist, 65(1), 66-87.
Vetschera, R., & De Almeida, A. T. (2012). A PROMETHEE-based approach to portfolio selection problems. Computers & Operations Research, 39(5), 1010-1020.
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
Barbati, M., Greco, S., Kadziński, M., & Słowiński, R. (2018). Optimization of multiple satisfaction levels in portfolio decision analysis. Omega, 78, 192-204.
Carazo, A.F., Gomez, T., Molina, J., Hernandez-Diaz, J., Guerrero, F., Caballero, R. (2010): “Solving a Comprehensive Model for Multi-Objective Project Portfolio Selection”, Computers and Operations Research 37 (4), 630-639.
De Almeida, A. T. & Duarte, M. (2011). A multi-criteria decision model for selecting project portfolio with consideration being given to a new concept for synergies. Pesquisa Operacional, 31(2), 301-318.
Doerner, K.F., Gutjahr, W.J., Hartl, R.F., Strauss, C., Stummer, C. (2006): “Pareto ant colony optimization with ILP preprocessing in multiobjective project portfolio selection”, European Journal of Operational Research 171 (3), 830-841.
Espin, E., Téllez, G., Fernández, E., Marx-Gómez, J., and Lecich, M. I. (2006). Com- pensatory Logic: A fuzzy normative model for decision making. Investigación Operativa, 27, 188-197.
Fernández, E. (2003). “Análisis de la Decisión para evaluar y formar carteras de proyectos de I&D: un enfoque crítico y propositivo”, Gestión y Política Pública, vol. 12, no.1,pp. 43-79
Fernández, E., Figueira, J. R., & Navarro, J. (2020). Interval-based extensions of two outranking methods for multi-criteria ordinal classification. Omega, 95, 102065.
Figueira, J. R., Mousseau, V., & Roy, B. (2016). ELECTRE methods. In Multiple criteria decision analysis (pp. 155-185). Springer, New York, NY.
Gunasekaran, A., Sharif, A. M., Zhu, Q., Dou, Y., & Sarkis, J. (2010). A portfolio‐based analysis for green supplier management using the analytical network process. Supply Chain Management: an international journal.
Kleinmuntz, D. (2011). Portfolio Decision Analysis: Improved methods for resource allocation, chapter Foreword.
Kumar, G., Jain, S., & Singh, U. P. (2020). Stock market forecasting using computational intelligence: A survey. Archives of Computational Methods in Engineering, 1-33.
Larios, J. A. C., & Martínez, N. S. (2018). Aplicación teórica de un modelo de análisis predictivo para desarrollar estrategias competitivas en MiPYMES. Red Internacional de Investigadores en Competitividad, 10(1).
Larsen, P. M. (1980). Industrial applications of fuzzy logic control. International Journal of Man-Machine Studies, 12(1), 3-10.
Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International journal of man-machine studies, 7(1), 1-13.
Mavrotas, G., & Pechak, O. (2013). Combining mathematical programming and Monte Carlo simulation to deal with uncertainty in energy project portfolio selection. In Assessment and simulation tools for sustainable energy systems (pp. 333-356). Springer, London.
Mavrotas, G., Diakoulaki, D., & Caloghirou, Y. (2006). Project prioritization under policy restrictions. A combination of MCDA with 0–1 programming. European Journal of Operational Research, 171(1), 296-308.
Mavrotas, G., Diakoulaki, D., & Kourentzis, A. (2008). Selection among ranked projects under segmentation, policy and logical constraints. European Journal of Operational Research, 187(1), 177-192.
Pawlak, Z., Grzymala-Busse, J., Slowinski, R., & Ziarko, W. (1995). Rough sets. Communications of the ACM, 38(11), 88-95.
Picos., J. C. (2017). Modelado Lógico de Relaciones de Preferencia Básicas a partir de Argumentos (Disertación de tesis doctoral).
Relich, M., & Pawlewski, P. (2017). A fuzzy weighted average approach for selecting portfolio of new product development projects. Neurocomputing, 231, 19-27.
Roy, B. (1990). The outranking approach and the foundations of ELECTRE methods. In Readings in multiple criteria decision aid (pp. 155-183). Springer, Berlin, Heidelberg.
Roy, B., Figueira, J. R., & Almeida-Dias, J. (2014). Discriminating thresholds as a tool to cope with imperfect knowledge in multiple criteria decision aiding: Theoretical results and practical issues. Omega, 43, 9-20.
Saaty, T. L. (1990). How to make a decision: the analytic hierarchy process. European journal of operational research, 48(1), 9-26.
Stummer, C., & Heidenberger, K. (2003). Interactive R&D portfolio analysis with project interdependencies and time profiles of multiple objectives. IEEE Transactions on Engineering Management, 50(2), 175-183.
Szilágyi, I., Sebestyén, Z., & Tóth, T. (2020). Project Ranking in Petroleum Exploration. The Engineering Economist, 65(1), 66-87.
Vetschera, R., & De Almeida, A. T. (2012). A PROMETHEE-based approach to portfolio selection problems. Computers & Operations Research, 39(5), 1010-1020.
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.