Sobre el enfoque híbrido de inteligencia computacional y apoyo a la toma de decisiones para la administración de recursos financieros
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abr 26, 2024
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https://doi.org/10.30878/ces.v31n0a35
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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
Se identifican las ventajas y limitaciones de los enfoques para optimizar los recursos financieros, además de resaltar las problemáticas actuales a las que se enfrentan las organizaciones cuando realizan esta actividad. El principal resultado es la descripción de un enfoque híbrido que incorpora el uso de la inteligencia computacional en la toma de decisiones financieras y que permite ayudar a superar tales limitaciones. El uso óptimo de recursos financieros constituye uno de los factores más importantes del éxito de las organizaciones. Sin embargo, muchas organizaciones suelen basarse en la experiencia empírica de su alta dirección para decidir cómo distribuir sus recursos financieros, pero ignoran los avances tecnológicos y de modelado que han revolucionado globalmente la toma de decisiones de este tipo.
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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, abr. 2024.
ISSN 2395-8782.
Disponible en: <https://cienciaergosum.uaemex.mx/article/view/17881>. Fecha de acceso: 25 jun. 2025
doi: https://doi.org/10.30878/ces.v31n0a35.
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Esta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial-SinObrasDerivadas 4.0.
Citas
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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.
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, 15(4), 306-319.
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., & 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, 12(1), 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). New York: Springer.
Kumar, G., Jain, S., & Singh, U. P. (2020). Stock market forecasting using computational intelligence: A survey. Archives of Computational Methods in Engineering, 28, 1069-1101.
Larios, J. A. C. y 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.
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). London: Springer.
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 (tesis de doctorado).
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). Berlin: Springer.
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.
Salo, A., Keisler, J., & Morton, A. (2010). Portfolio decision analysis: improved methods for resource allocation. 10.1007/978-1-4419-9943-6.
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.
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, 15(4), 306-319.