Inteligencia artificial aplicada en la gestión de proyectos
Palabras clave:
inteligencia artificial, gestión de proyectos, aprendizaje automático, técnicas híbridas, optimización
Resumen
La inteligencia artificial (IA) transforma la gestión de proyectos al optimizar procesos y mejorar la toma de decisiones. Este estudio analiza modelos de IA aplicados a la administración de proyectos mediante una revisión sistemática de literatura en Scopus, Web of Science e IEEE Xplore (2013-2024). La investigación, cualitativa y exploratoria, clasificó modelos como aprendizaje automático, redes neuronales, lógica difusa y algoritmos genéticos según su utilidad en las fases del proyecto. Los resultados destacan el aprendizaje automático y las redes neuronales por su capacidad predictiva, y los sistemas híbridos (neuro-difusos, máquinas de soporte vectorial) por su eficacia en costos y riesgos. Se identifican limitaciones en la calidad de datos y la especialización técnica. Se concluye que los sistemas híbridos de IA son clave para abordar la complejidad organizacional.
Citas
Abbasianjahromi, H., & Rajaie, H. (2012). Developing a project portfolio selection model for contractor firms considering the risk factor. Journal of Civil Engineering and Management, 18(6), 879–889. https://doi.org/10.3846/13923730.2012.734856
Abdelaziz, A. Y., Kamh, M. Z., Mekhamer, S. F., & Badr, M. A. L. (2008). A hybrid HNN-QP approach for dynamic economic dispatch problem. Electric Power Systems Research, 78(10), 1784–1788. https://doi.org/10.1016/j.epsr.2008.03.011
Abraham, A. (2005). Adaptation of Fuzzy Inference System Using Neural Learning. http://ajith.softcomputing.net
Amer, F., & Golparvar-Fard, M. (2019). Formalizing Construction Sequencing Knowledge and Mining Company-Specific Best Practices from Past Project Schedules. Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019, 215–223. https://doi.org/10.1061/9780784482421.028
Anguita, D., Ghio, A., Greco, N., Oneto, L., & Ridella, S. (2010). Model Selection for Support Vector Machines: Advantages and Disadvantages of the Machine Learning Theory. IEEE.
Arslan, O., & Yetik, O. (2011). ANN based optimization of supercritical ORC-Binary geothermal power plant: Simav case study. Applied Thermal Engineering, 31(17–18), 3922–3928. https://doi.org/10.1016/j.applthermaleng.2011.07.041
Auria, L., Berlin, R. A. M., & Moro, R. A. (2008a). Support Vector Machines (SVM) as a Technique for Solvency Analysis.
Auria, L., Berlin, R. A. M., & Moro, R. A. (2008b). Support Vector Machines (SVM) as a Technique for Solvency Analysis.
Aziz, R. F., Hafez, S. M., & Abuel-Magd, Y. R. (2014). Smart optimization for mega construction projects using artificial intelligence. Alexandria Engineering Journal, 53(3), 591–606. https://doi.org/10.1016/j.aej.2014.05.003
Bhattacharyya, R., Kumar, P., & Kar, S. (2011a). Fuzzy R&D portfolio selection of interdependent projects. Computers and Mathematics with Applications, 62(10), 3850–3870. https://doi.org/10.1016/j.camwa.2011.09.036
Bhattacharyya, R., Kumar, P., & Kar, S. (2011b). Fuzzy R&D portfolio selection of interdependent projects. Computers and Mathematics with Applications, 62(10), 3857–3870. https://doi.org/10.1016/j.camwa.2011.09.036
Bhoskar, T., Kulkarni, O. K., Kulkarni, N. K., Patekar, S. L., Kakandikar, G. M., & Nandedkar, V. M. (2015). Genetic Algorithm and its Applications to Mechanical Engineering: A Review. Materials Today: Proceedings, 2(4–5), 2624–2630. https://doi.org/10.1016/j.matpr.2015.07.219
Boejko, W., Hejducki, Z., & Wodecki, M. (2012). Applying metaheuristic strategies in construction projects management. Journal of Civil Engineering and Management, 18(5), 621–630. https://doi.org/10.3846/13923730.2012.719837
Bottani, E., Montanari, R., Rinaldi, M., & Vignali, G. (2015). Intelligent algorithms for warehouse management. Intelligent Systems Reference Library, 87, 645–667. https://doi.org/10.1007/978-3-319-17906-3_25
Braswell, G. (2013). Artificial Intelligence Comes of Age in Oil and Gas. Journal of Petroleum Technology, 65(01), 50–57.
Brunner, F. (2019). Artificial Intelligence for Games Seminar Report Mastering the game of Go with deep neural networks and tree search (Silver et al., 2016).
Carmona Suárez, E. J. (2016). Tutorial sobre Máquinas de Vectores Soporte (SVM). https://www.researchgate.net/publication/263817587_Tutorial_sobre_Maquinas_de_Vectores_Soporte_SVM
Castillo, J. M., Cortes, C., González, J., & Benito, A. (2009). Prospecting The Future with AI. International Journal of Artificial Intelligence and Interactive Multimedia, 1(2), 1–53.
Chakravarthy, V. S. (2019). Networks that Learn. In Demystifying the Brain. Springer.
Chapelle, O., & Vapnik, V. (n.d.). Model Selection for Support Vector Machines.
Chapelle, O., & Vapnik, V. (2000). Model Selection for Support Vector Machines. In INFOR~ATIO~ AND CONTROL (Vol. 8).
Cheng, M., Lien, C., Tsai, , H., & Chen, , H. (2012). Artificial intelligence approaches to dynamic project success assessment taxonomic. Life Science Journal, 9, 5156–5163.
Cheng, M., Tsai, H., & Sudjono, E. (2011). Evaluating subcontractor performance using evolutionary fuzzy hybrid neural network. International Journal of Project Management, 29(3), 349–356. https://doi.org/10.1016/j.ijproman.2010.03.005
Cheng, M., Tsai, H.-C., & Sudjono, E. (2009). Evolutionary fuzzy hybrid neural network for conceptual cost estimates in construction projects.
Cheng, M., Wibowo, D., Prayogo, D., & Roy, A. F. (2015). Predicting productivity loss caused by change orders using the evolutionary fuzzy support vector machine inference model. Journal of Civil Engineering and Management, 21(7), 881–892. https://doi.org/10.3846/13923730.2014.893922
Cheng, M. Y., Tsai, H. C., & Hsieh, W. S. (2009). Web-based conceptual cost estimates for construction projects using Evolutionary Fuzzy Neural Inference Model. Automation in Construction, 18(2), 164–172. https://doi.org/10.1016/j.autcon.2008.07.001
Cheng, T., & Yan, R. Z. (2009). Integrating messy genetic algorithms and simulation to optimize resource utilization. Computer-Aided Civil and Infrastructure Engineering, 24(6), 401–415. https://doi.org/10.1111/j.1467-8667.2008.00588.x
Chou, J., Cheng, M., & Wu, Yu. (2013). Improving classification accuracy of project dispute resolution using hybrid artificial intelligence and support vector machine models. Expert Systems with Applications, 40(6), 2263–2274. https://doi.org/10.1016/j.eswa.2012.10.036
Chou, J. S., Cheng, M. Y., Wu, Y. W., & Pham, A. D. (2014). Optimizing parameters of support vector machine using fast messy genetic algorithm for dispute classification. Expert Systems with Applications, 41(8), 3955–3964. https://doi.org/10.1016/j.eswa.2013.12.035
Cordón, O. (2011). A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems. In International Journal of Approximate Reasoning (Vol. 52, Issue 6, pp. 894–913). https://doi.org/10.1016/j.ijar.2011.03.004
Davison, A. C.., & Hinkley, D. V.. (2014). Bootstrap methods and their application. Cambridge University Press.
Day, R., Zydallis, J., & Lamont, G. (2002). Analysis of Fine Granularity and Building Block Sizes in the Parallel Fast Messy GA.
De la Rosa, J. J. G., Pérez, A. A., Palomares Salas, J. C., Ramiro Leo, J. G., & Muñoz, A. M. (2011). A novel inference method for local wind conditions using genetic fuzzy systems. Renewable Energy, 36(6), 1747–1753. https://doi.org/10.1016/j.renene.2010.12.017
Deng, L., & Yu, D. (2014). Deep learning: Methods and applications. In Foundations and Trends in Signal Processing (Vol. 7, Issues 3–4, pp. 197–387). Now Publishers Inc. https://doi.org/10.1561/2000000039
Ebrahimnejad, S., Mousavi, S. M., & Seyrafianpour, H. (2010). Risk identification and assessment for build–operate–transfer projects: A fuzzy multi attribute decision making model. Expert Systems with Applications, 37(1), 575–586. https://doi.org/10.1016/j.eswa.2009.05.037
Egbu, C. (2008). Knowledge Mapping Techniques Within The Construction Industry:An Exploratory Study. 48–58.
Egbu, C., & Suresh, S. (2008). Knowledge Mapping Techniques Within The Construction Industry: An Exploratory Study. CIB W102-Information and Knowledge Management in Buildings, 48–57.
Esogbue, A. 0, & Murrell, J. A. (1993). A Fuzzy Adaptive Controller Using Reinforcement Learning Neural Networks.
Fahim, A. M., Salem, A. M., Torkey, F. A., & Ramadan, M. A. (2006). Efficient enhanced k-means clustering algorithm. Journal of Zhejiang University: Science, 7(10), 1626–1633. https://doi.org/10.1631/jzus.2006.A1626
Fogel A, Hsu HC, Shapiro AF, Nelson-Goens GC, Secrist C. Effects of normal and perturbed social play on the duration and amplitude of different types of infant smiles. Dev Psychol. 2006 May;42(3):459-473. doi: 10.1037/0012-1649.42.3.459. PMID: 16756438.
Ganesan, T., Vasant, P., & Elamvazuthi, I. (2014). Hopfield neural networks approach for design optimization of hybrid power systems with multiple renewable energy sources in a fuzzy environment. Journal of Intelligent & Fuzzy Systems, 26, 2143–2154. https://doi.org/10.3233/IFS-130889
García, V. R., Pupo, I. P., Villavicencio, N., Piñero, P. Y., & Beovides, S. (2016). Experiences by using genetic algorithms in project scheduling. Revista Cubana de Ciencias Informáticas, 10. http://rcci.uci.cuPág.71-86Editorial”EdicionesFuturo”
Gupta, M. M., Bukovsky, I., Homma, N., Solo, A. M. G., & Hou, Z. G. (2012). Fundamentals of higher order neural networks for modeling and simulation. In Artificial Higher Order Neural Networks for Modeling and Simulation (pp. 103–133). IGI Global. https://doi.org/10.4018/978-1-4666-2175-6.ch006
Hashemi, H., Mousavi, S. M., & Mojtahedi, S. M. H. (2011). Bootstrap Technique for Risk Analysis with Interval Numbers in Bridge Construction Projects. Journal of Construction Engineering and Management, 137(8), 600–608. https://doi.org/10.1061/(asce)co.1943-7862.0000344
Hashemi, H., Mousavi, S. M., Tavakkoli-Moghaddam, R., & Gholipour, Y. (2013). Compromise Ranking Approach with Bootstrap Confidence Intervals for Risk Assessment in Port Management Projects. Journal of Management in Engineering, 29(4), 334–344. https://doi.org/10.1061/(asce)me.1943-5479.0000167
Heiss-Czedik, D. (1997). An Introduction to Genetic Algorithms.,” Artificial Life (Vol. 3).
Herrera, F. (2008). Genetic fuzzy systems: Taxonomy, current research trends and prospects. Evolutionary Intelligence, 1(1), 27–46. https://doi.org/10.1007/s12065-007-0001-5
Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. science. Science, 313(5786), 502–504. https://doi.org/10.1126/science.1129198
Hon, H. W. (2019). A Brief History of Intelligence. In ACM (Ed.), International Conference on Multimodal Interaction (pp. 1–1).
Ioannou, S. V., Raouzaiou, A. T., Tzouvaras, V. A., Mailis, T. P., Karpouzis, K. C., & Kollias, S. D. (2005). Emotion recognition through facial expression analysis based on a neurofuzzy network. Neural Networks, 18(4), 423–435. https://doi.org/10.1016/j.neunet.2005.03.004
Ishibuchi, H. (2004). Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases. Fuzzy Sets and Systems, 141(1), 161–162. https://doi.org/10.1016/s0165-0114(03)00262-8
Jackson, P. (2019). Introduction to artificial intelligence (Tercera Edición). Courier Dover Publications.
Jeffries, C. (1995). Tracking, code recognition, and memory management with high order neural networks. 2492, 964–973. http://proceedings.spiedigitallibrary.org/
Kandpal, A. P. K., & Mehta, B. A. (2019). Comparative Study between Multiplicative Neuron and Spiking Neuron Model. IEEE.
Kanungo, T., Member, S., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002). An Efficient k-Means Clustering Algorithm: Analysis and Implementation.
Kar, S., Das, S., & Ghosh, P. K. (2014). Applications of neuro fuzzy systems: A brief review and future outline. In Applied Soft Computing Journal (Vol. 15, pp. 243–259). https://doi.org/10.1016/j.asoc.2013.10.014
Kargupta, H. (1995). SEARCH, Polynomial Complexity, And The Fast Messy Genetic Algorithm. www.manaraa.com
Kisi, O. (2014). Modeling solar radiation of Mediterranean region in Turkey by using fuzzy genetic approach. Energy, 64, 429–436. https://doi.org/10.1016/j.energy.2013.10.009
Ko, C. H., Cheng, M. Y., & Wu, T. K. (2007). Evaluating sub-contractors’ performance using EFNIM. Automation in Construction, 16(4), 525–530. https://doi.org/10.1016/j.autcon.2006.09.005
Kobbacy, K. A. H. (2012). Application of Artificial Intelligence in maintenance modelling and management. IFAC Proceedings Volumes (IFAC-PapersOnline), 45(31), 54–59. https://doi.org/10.3182/20121122-2-ES-4026.00046
Kumar, M., Husian, M., Upreti, N., & Gupta, D. (2010). GENETIC ALGORITHM: REVIEW AND APPLICATION. https://ssrn.com/abstract=3529843
Lendaris, G., Mathia, K., & Sacks, R. (1999). Linear Hopfield networks and constrained optimization. IEEE Trans. Knowl. Data Eng, 1, 114–118.
Lewis, F. L., & Vrabie, D. (2009). Reinforcement learning and adaptive dynamic programming for feedback control. IEEE Circuits and Systems Magazine, 9(3), 32–50. https://doi.org/10.1109/MCAS.2009.933854
Leyva - Vázquez, M. Y., Pérez Teruel, K., Febles Estrada, A., & Gulín González, J. (2013). Mapas cognitivos difusos para la selección de proyectos de tecnologías de la información. Contaduría y Administración, 58(4), 95–117. https://doi.org/10.1016/s0186-1042(13)71235-x
Luo, J., Meng, Q., & Cai, Y. (2018). Analysis of the Impact of Artificial Intelligence Application on the Development of Accounting Industry. Open Journal of Business and Management, 06(04), 850–856. https://doi.org/10.4236/ojbm.2018.64063
MacKinnon, J. G. (2006). Bootstrap methods in econometrics. Economic Record, 82(SPEC. ISS. 1). https://doi.org/10.1111/j.1475-4932.2006.00328.x
Mijwil, M. (2021). Artificial Neural Networks Advantages and Disadvantages. Mesopotamian Journal of Big Data, 2021, 29–31. https://doi.org/10.58496/mjbd/2021/006
Nápoles, G., & Bello, R. (2014). Algoritmo para mejorar la convergencia en Mapas Cognitivos Difusos Sigmoidales. Universidad Central “Marta Abreu” de Las Villas .
Obayashi, M., Nishida, ,T., Kuremoto, T., Kobayashi, K., & Feng, L. B. (2010). A reinforcement learning system embedded agent with neural networkbased multi-valued pattern memory structure. ICCAS 2010 - International Conference on Control, Automation and Systems, 176–181.
Prieto, B. (2019). Impacts of Artificial Intelligence on Management of Large Complex Projects. In PM World Journal Complexity in Large Engineering &: Vol. VIII. www.pmworldlibrary.net
Ricalde, L. J., Cruz, B., Sanchez, E., Ricalde, L. J., Cruz, B. J., & Sánchez, E. N. (2010). Neural Control for Wind Turbine with a Permanent Magnet Synchronous Generator Control Neuronal Recurrente de Alto Orden para Turbinas de Viento con Generador Síncrono de Imán Permanente. 14(2), 133–143. https://www.researchgate.net/publication/262613566
Rodriguez-Repiso, L., Setchi, R., & Salmeron, J. L. (2007). Modelling IT projects success with Fuzzy Cognitive Maps. Expert Systems with Applications, 32(2), 543–559. https://doi.org/10.1016/j.eswa.2006.01.032
Rodríguez -Cevallos, M., José Andrade-Albán, M., Carlos Maldonado Palacios, R., & Alfonso Cobos-Cevallos, C. (2020). Obtener el cromosoma mejor adaptado implementation of a genetic algorithm using a computer application based on neural and evolutionary computing to obtain the best adapted chromosome. https://orcid.org/0000-0001-8409-0530
Rutkowski Leszek and Cpałka, K. and N. R. and P. A. and S. R. (2012). Neuro-fuzzy SystemsNeuro-fuzzy systems (NFS). In R. A. Meyers (Ed.), Computational Complexity: Theory, Techniques, and Applications (pp. 2069–2081). Springer New York. https://doi.org/10.1007/978-1-4614-1800-9_131
Sonmez, R. (2008). Parametric Range Estimating of Building Costs Using Regression Models and Bootstrap. Journal of Construction Engineering and Management, 134(12), 1011–1016. https://doi.org/10.1061/ASCE0733-93642008134:121011
Stylios, C. D., & Groumpos, P. P. (2004). Modeling Complex Systems Using Fuzzy Cognitive Maps. IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans., 34(1), 155–162. https://doi.org/10.1109/TSMCA.2003.818878
Sulehria, H. K., & Zhang, , Y. (2007). Hopfield Neural Networks — A Survey. In Engineering (pp. 125–130).
Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning: An Introduction,” IEEE Transactions on Neural Networks. 9(5), 1054–1054.
Tiwari, M. K., & Chatterjee, C. (2010). Development of an accurate and reliable hourly flood forecasting model using wavelet-bootstrap-ANN (WBANN) hybrid approach. Journal of Hydrology, 394(3–4), 458–470. https://doi.org/10.1016/j.jhydrol.2010.10.001
Tsakonas, A., & Gabrys, B. (2013). A fuzzy evolutionary framework for combining ensembles. Applied Soft Computing Journal, 13(4), 1800–1812. https://doi.org/10.1016/j.asoc.2012.12.027
TutorialPoint.com. (2024). Artificial Intelligence - Fuzzy Logic Systems.” [Online]. . Https://Www.Tutorialspoint.Com/Articles/Index.Php.
Umit, H. (2000). Digital business strategies in blockchain ecosystems : transformational design and future of global business. Contributions to Management Science.
Wauters, M., & Vanhoucke, M. (2014). Support Vector Machine Regression for project control forecasting. Automation in Construction, 47, 92–106. https://doi.org/10.1016/j.autcon.2014.07.014
Waziri, B. S., Bala, K., & Bustani, S. A. (2017). Artificial Neural Networks in Construction Engineering and Management. International Journal of Architecture, Engineering and Construction, 6(1). https://doi.org/10.7492/ijaec.2017.006
Wehrens, R., Putter, H., & Buydens, M. C. (2000). The bootstrap: a tutorial. In Chemometrics and Intelligent Laboratory Systems (Vol. 54). www.elsevier.comrlocaterchemometrics
Whitley, D., Beveridge, J., & GuerraSalcedo J. (1997). Messy Genetic Algorithms for Subset Feature Selection. Department of Computer Science, 568–575.
Yacim, J. A., & Boshoff, D. G. B. (2018). Impact of artificial neural networks training algorithms on accurate prediction of property values. Journal of Real Estate Research, 40(3), 375–418. https://doi.org/10.1080/10835547.2018.12091505
Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. In International Journal of Forecasting (Vol. 14).
Zhou, Y., Zhao, S., Wang, X., & Liu, W. (2018). Deep learning model and its application in big data. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10918 LNCS, 795–806. https://doi.org/10.1007/978-3-319-91797-9_55
Abdelaziz, A. Y., Kamh, M. Z., Mekhamer, S. F., & Badr, M. A. L. (2008). A hybrid HNN-QP approach for dynamic economic dispatch problem. Electric Power Systems Research, 78(10), 1784–1788. https://doi.org/10.1016/j.epsr.2008.03.011
Abraham, A. (2005). Adaptation of Fuzzy Inference System Using Neural Learning. http://ajith.softcomputing.net
Amer, F., & Golparvar-Fard, M. (2019). Formalizing Construction Sequencing Knowledge and Mining Company-Specific Best Practices from Past Project Schedules. Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019, 215–223. https://doi.org/10.1061/9780784482421.028
Anguita, D., Ghio, A., Greco, N., Oneto, L., & Ridella, S. (2010). Model Selection for Support Vector Machines: Advantages and Disadvantages of the Machine Learning Theory. IEEE.
Arslan, O., & Yetik, O. (2011). ANN based optimization of supercritical ORC-Binary geothermal power plant: Simav case study. Applied Thermal Engineering, 31(17–18), 3922–3928. https://doi.org/10.1016/j.applthermaleng.2011.07.041
Auria, L., Berlin, R. A. M., & Moro, R. A. (2008a). Support Vector Machines (SVM) as a Technique for Solvency Analysis.
Auria, L., Berlin, R. A. M., & Moro, R. A. (2008b). Support Vector Machines (SVM) as a Technique for Solvency Analysis.
Aziz, R. F., Hafez, S. M., & Abuel-Magd, Y. R. (2014). Smart optimization for mega construction projects using artificial intelligence. Alexandria Engineering Journal, 53(3), 591–606. https://doi.org/10.1016/j.aej.2014.05.003
Bhattacharyya, R., Kumar, P., & Kar, S. (2011a). Fuzzy R&D portfolio selection of interdependent projects. Computers and Mathematics with Applications, 62(10), 3850–3870. https://doi.org/10.1016/j.camwa.2011.09.036
Bhattacharyya, R., Kumar, P., & Kar, S. (2011b). Fuzzy R&D portfolio selection of interdependent projects. Computers and Mathematics with Applications, 62(10), 3857–3870. https://doi.org/10.1016/j.camwa.2011.09.036
Bhoskar, T., Kulkarni, O. K., Kulkarni, N. K., Patekar, S. L., Kakandikar, G. M., & Nandedkar, V. M. (2015). Genetic Algorithm and its Applications to Mechanical Engineering: A Review. Materials Today: Proceedings, 2(4–5), 2624–2630. https://doi.org/10.1016/j.matpr.2015.07.219
Boejko, W., Hejducki, Z., & Wodecki, M. (2012). Applying metaheuristic strategies in construction projects management. Journal of Civil Engineering and Management, 18(5), 621–630. https://doi.org/10.3846/13923730.2012.719837
Bottani, E., Montanari, R., Rinaldi, M., & Vignali, G. (2015). Intelligent algorithms for warehouse management. Intelligent Systems Reference Library, 87, 645–667. https://doi.org/10.1007/978-3-319-17906-3_25
Braswell, G. (2013). Artificial Intelligence Comes of Age in Oil and Gas. Journal of Petroleum Technology, 65(01), 50–57.
Brunner, F. (2019). Artificial Intelligence for Games Seminar Report Mastering the game of Go with deep neural networks and tree search (Silver et al., 2016).
Carmona Suárez, E. J. (2016). Tutorial sobre Máquinas de Vectores Soporte (SVM). https://www.researchgate.net/publication/263817587_Tutorial_sobre_Maquinas_de_Vectores_Soporte_SVM
Castillo, J. M., Cortes, C., González, J., & Benito, A. (2009). Prospecting The Future with AI. International Journal of Artificial Intelligence and Interactive Multimedia, 1(2), 1–53.
Chakravarthy, V. S. (2019). Networks that Learn. In Demystifying the Brain. Springer.
Chapelle, O., & Vapnik, V. (n.d.). Model Selection for Support Vector Machines.
Chapelle, O., & Vapnik, V. (2000). Model Selection for Support Vector Machines. In INFOR~ATIO~ AND CONTROL (Vol. 8).
Cheng, M., Lien, C., Tsai, , H., & Chen, , H. (2012). Artificial intelligence approaches to dynamic project success assessment taxonomic. Life Science Journal, 9, 5156–5163.
Cheng, M., Tsai, H., & Sudjono, E. (2011). Evaluating subcontractor performance using evolutionary fuzzy hybrid neural network. International Journal of Project Management, 29(3), 349–356. https://doi.org/10.1016/j.ijproman.2010.03.005
Cheng, M., Tsai, H.-C., & Sudjono, E. (2009). Evolutionary fuzzy hybrid neural network for conceptual cost estimates in construction projects.
Cheng, M., Wibowo, D., Prayogo, D., & Roy, A. F. (2015). Predicting productivity loss caused by change orders using the evolutionary fuzzy support vector machine inference model. Journal of Civil Engineering and Management, 21(7), 881–892. https://doi.org/10.3846/13923730.2014.893922
Cheng, M. Y., Tsai, H. C., & Hsieh, W. S. (2009). Web-based conceptual cost estimates for construction projects using Evolutionary Fuzzy Neural Inference Model. Automation in Construction, 18(2), 164–172. https://doi.org/10.1016/j.autcon.2008.07.001
Cheng, T., & Yan, R. Z. (2009). Integrating messy genetic algorithms and simulation to optimize resource utilization. Computer-Aided Civil and Infrastructure Engineering, 24(6), 401–415. https://doi.org/10.1111/j.1467-8667.2008.00588.x
Chou, J., Cheng, M., & Wu, Yu. (2013). Improving classification accuracy of project dispute resolution using hybrid artificial intelligence and support vector machine models. Expert Systems with Applications, 40(6), 2263–2274. https://doi.org/10.1016/j.eswa.2012.10.036
Chou, J. S., Cheng, M. Y., Wu, Y. W., & Pham, A. D. (2014). Optimizing parameters of support vector machine using fast messy genetic algorithm for dispute classification. Expert Systems with Applications, 41(8), 3955–3964. https://doi.org/10.1016/j.eswa.2013.12.035
Cordón, O. (2011). A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems. In International Journal of Approximate Reasoning (Vol. 52, Issue 6, pp. 894–913). https://doi.org/10.1016/j.ijar.2011.03.004
Davison, A. C.., & Hinkley, D. V.. (2014). Bootstrap methods and their application. Cambridge University Press.
Day, R., Zydallis, J., & Lamont, G. (2002). Analysis of Fine Granularity and Building Block Sizes in the Parallel Fast Messy GA.
De la Rosa, J. J. G., Pérez, A. A., Palomares Salas, J. C., Ramiro Leo, J. G., & Muñoz, A. M. (2011). A novel inference method for local wind conditions using genetic fuzzy systems. Renewable Energy, 36(6), 1747–1753. https://doi.org/10.1016/j.renene.2010.12.017
Deng, L., & Yu, D. (2014). Deep learning: Methods and applications. In Foundations and Trends in Signal Processing (Vol. 7, Issues 3–4, pp. 197–387). Now Publishers Inc. https://doi.org/10.1561/2000000039
Ebrahimnejad, S., Mousavi, S. M., & Seyrafianpour, H. (2010). Risk identification and assessment for build–operate–transfer projects: A fuzzy multi attribute decision making model. Expert Systems with Applications, 37(1), 575–586. https://doi.org/10.1016/j.eswa.2009.05.037
Egbu, C. (2008). Knowledge Mapping Techniques Within The Construction Industry:An Exploratory Study. 48–58.
Egbu, C., & Suresh, S. (2008). Knowledge Mapping Techniques Within The Construction Industry: An Exploratory Study. CIB W102-Information and Knowledge Management in Buildings, 48–57.
Esogbue, A. 0, & Murrell, J. A. (1993). A Fuzzy Adaptive Controller Using Reinforcement Learning Neural Networks.
Fahim, A. M., Salem, A. M., Torkey, F. A., & Ramadan, M. A. (2006). Efficient enhanced k-means clustering algorithm. Journal of Zhejiang University: Science, 7(10), 1626–1633. https://doi.org/10.1631/jzus.2006.A1626
Fogel A, Hsu HC, Shapiro AF, Nelson-Goens GC, Secrist C. Effects of normal and perturbed social play on the duration and amplitude of different types of infant smiles. Dev Psychol. 2006 May;42(3):459-473. doi: 10.1037/0012-1649.42.3.459. PMID: 16756438.
Ganesan, T., Vasant, P., & Elamvazuthi, I. (2014). Hopfield neural networks approach for design optimization of hybrid power systems with multiple renewable energy sources in a fuzzy environment. Journal of Intelligent & Fuzzy Systems, 26, 2143–2154. https://doi.org/10.3233/IFS-130889
García, V. R., Pupo, I. P., Villavicencio, N., Piñero, P. Y., & Beovides, S. (2016). Experiences by using genetic algorithms in project scheduling. Revista Cubana de Ciencias Informáticas, 10. http://rcci.uci.cuPág.71-86Editorial”EdicionesFuturo”
Gupta, M. M., Bukovsky, I., Homma, N., Solo, A. M. G., & Hou, Z. G. (2012). Fundamentals of higher order neural networks for modeling and simulation. In Artificial Higher Order Neural Networks for Modeling and Simulation (pp. 103–133). IGI Global. https://doi.org/10.4018/978-1-4666-2175-6.ch006
Hashemi, H., Mousavi, S. M., & Mojtahedi, S. M. H. (2011). Bootstrap Technique for Risk Analysis with Interval Numbers in Bridge Construction Projects. Journal of Construction Engineering and Management, 137(8), 600–608. https://doi.org/10.1061/(asce)co.1943-7862.0000344
Hashemi, H., Mousavi, S. M., Tavakkoli-Moghaddam, R., & Gholipour, Y. (2013). Compromise Ranking Approach with Bootstrap Confidence Intervals for Risk Assessment in Port Management Projects. Journal of Management in Engineering, 29(4), 334–344. https://doi.org/10.1061/(asce)me.1943-5479.0000167
Heiss-Czedik, D. (1997). An Introduction to Genetic Algorithms.,” Artificial Life (Vol. 3).
Herrera, F. (2008). Genetic fuzzy systems: Taxonomy, current research trends and prospects. Evolutionary Intelligence, 1(1), 27–46. https://doi.org/10.1007/s12065-007-0001-5
Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. science. Science, 313(5786), 502–504. https://doi.org/10.1126/science.1129198
Hon, H. W. (2019). A Brief History of Intelligence. In ACM (Ed.), International Conference on Multimodal Interaction (pp. 1–1).
Ioannou, S. V., Raouzaiou, A. T., Tzouvaras, V. A., Mailis, T. P., Karpouzis, K. C., & Kollias, S. D. (2005). Emotion recognition through facial expression analysis based on a neurofuzzy network. Neural Networks, 18(4), 423–435. https://doi.org/10.1016/j.neunet.2005.03.004
Ishibuchi, H. (2004). Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases. Fuzzy Sets and Systems, 141(1), 161–162. https://doi.org/10.1016/s0165-0114(03)00262-8
Jackson, P. (2019). Introduction to artificial intelligence (Tercera Edición). Courier Dover Publications.
Jeffries, C. (1995). Tracking, code recognition, and memory management with high order neural networks. 2492, 964–973. http://proceedings.spiedigitallibrary.org/
Kandpal, A. P. K., & Mehta, B. A. (2019). Comparative Study between Multiplicative Neuron and Spiking Neuron Model. IEEE.
Kanungo, T., Member, S., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002). An Efficient k-Means Clustering Algorithm: Analysis and Implementation.
Kar, S., Das, S., & Ghosh, P. K. (2014). Applications of neuro fuzzy systems: A brief review and future outline. In Applied Soft Computing Journal (Vol. 15, pp. 243–259). https://doi.org/10.1016/j.asoc.2013.10.014
Kargupta, H. (1995). SEARCH, Polynomial Complexity, And The Fast Messy Genetic Algorithm. www.manaraa.com
Kisi, O. (2014). Modeling solar radiation of Mediterranean region in Turkey by using fuzzy genetic approach. Energy, 64, 429–436. https://doi.org/10.1016/j.energy.2013.10.009
Ko, C. H., Cheng, M. Y., & Wu, T. K. (2007). Evaluating sub-contractors’ performance using EFNIM. Automation in Construction, 16(4), 525–530. https://doi.org/10.1016/j.autcon.2006.09.005
Kobbacy, K. A. H. (2012). Application of Artificial Intelligence in maintenance modelling and management. IFAC Proceedings Volumes (IFAC-PapersOnline), 45(31), 54–59. https://doi.org/10.3182/20121122-2-ES-4026.00046
Kumar, M., Husian, M., Upreti, N., & Gupta, D. (2010). GENETIC ALGORITHM: REVIEW AND APPLICATION. https://ssrn.com/abstract=3529843
Lendaris, G., Mathia, K., & Sacks, R. (1999). Linear Hopfield networks and constrained optimization. IEEE Trans. Knowl. Data Eng, 1, 114–118.
Lewis, F. L., & Vrabie, D. (2009). Reinforcement learning and adaptive dynamic programming for feedback control. IEEE Circuits and Systems Magazine, 9(3), 32–50. https://doi.org/10.1109/MCAS.2009.933854
Leyva - Vázquez, M. Y., Pérez Teruel, K., Febles Estrada, A., & Gulín González, J. (2013). Mapas cognitivos difusos para la selección de proyectos de tecnologías de la información. Contaduría y Administración, 58(4), 95–117. https://doi.org/10.1016/s0186-1042(13)71235-x
Luo, J., Meng, Q., & Cai, Y. (2018). Analysis of the Impact of Artificial Intelligence Application on the Development of Accounting Industry. Open Journal of Business and Management, 06(04), 850–856. https://doi.org/10.4236/ojbm.2018.64063
MacKinnon, J. G. (2006). Bootstrap methods in econometrics. Economic Record, 82(SPEC. ISS. 1). https://doi.org/10.1111/j.1475-4932.2006.00328.x
Mijwil, M. (2021). Artificial Neural Networks Advantages and Disadvantages. Mesopotamian Journal of Big Data, 2021, 29–31. https://doi.org/10.58496/mjbd/2021/006
Nápoles, G., & Bello, R. (2014). Algoritmo para mejorar la convergencia en Mapas Cognitivos Difusos Sigmoidales. Universidad Central “Marta Abreu” de Las Villas .
Obayashi, M., Nishida, ,T., Kuremoto, T., Kobayashi, K., & Feng, L. B. (2010). A reinforcement learning system embedded agent with neural networkbased multi-valued pattern memory structure. ICCAS 2010 - International Conference on Control, Automation and Systems, 176–181.
Prieto, B. (2019). Impacts of Artificial Intelligence on Management of Large Complex Projects. In PM World Journal Complexity in Large Engineering &: Vol. VIII. www.pmworldlibrary.net
Ricalde, L. J., Cruz, B., Sanchez, E., Ricalde, L. J., Cruz, B. J., & Sánchez, E. N. (2010). Neural Control for Wind Turbine with a Permanent Magnet Synchronous Generator Control Neuronal Recurrente de Alto Orden para Turbinas de Viento con Generador Síncrono de Imán Permanente. 14(2), 133–143. https://www.researchgate.net/publication/262613566
Rodriguez-Repiso, L., Setchi, R., & Salmeron, J. L. (2007). Modelling IT projects success with Fuzzy Cognitive Maps. Expert Systems with Applications, 32(2), 543–559. https://doi.org/10.1016/j.eswa.2006.01.032
Rodríguez -Cevallos, M., José Andrade-Albán, M., Carlos Maldonado Palacios, R., & Alfonso Cobos-Cevallos, C. (2020). Obtener el cromosoma mejor adaptado implementation of a genetic algorithm using a computer application based on neural and evolutionary computing to obtain the best adapted chromosome. https://orcid.org/0000-0001-8409-0530
Rutkowski Leszek and Cpałka, K. and N. R. and P. A. and S. R. (2012). Neuro-fuzzy SystemsNeuro-fuzzy systems (NFS). In R. A. Meyers (Ed.), Computational Complexity: Theory, Techniques, and Applications (pp. 2069–2081). Springer New York. https://doi.org/10.1007/978-1-4614-1800-9_131
Sonmez, R. (2008). Parametric Range Estimating of Building Costs Using Regression Models and Bootstrap. Journal of Construction Engineering and Management, 134(12), 1011–1016. https://doi.org/10.1061/ASCE0733-93642008134:121011
Stylios, C. D., & Groumpos, P. P. (2004). Modeling Complex Systems Using Fuzzy Cognitive Maps. IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans., 34(1), 155–162. https://doi.org/10.1109/TSMCA.2003.818878
Sulehria, H. K., & Zhang, , Y. (2007). Hopfield Neural Networks — A Survey. In Engineering (pp. 125–130).
Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning: An Introduction,” IEEE Transactions on Neural Networks. 9(5), 1054–1054.
Tiwari, M. K., & Chatterjee, C. (2010). Development of an accurate and reliable hourly flood forecasting model using wavelet-bootstrap-ANN (WBANN) hybrid approach. Journal of Hydrology, 394(3–4), 458–470. https://doi.org/10.1016/j.jhydrol.2010.10.001
Tsakonas, A., & Gabrys, B. (2013). A fuzzy evolutionary framework for combining ensembles. Applied Soft Computing Journal, 13(4), 1800–1812. https://doi.org/10.1016/j.asoc.2012.12.027
TutorialPoint.com. (2024). Artificial Intelligence - Fuzzy Logic Systems.” [Online]. . Https://Www.Tutorialspoint.Com/Articles/Index.Php.
Umit, H. (2000). Digital business strategies in blockchain ecosystems : transformational design and future of global business. Contributions to Management Science.
Wauters, M., & Vanhoucke, M. (2014). Support Vector Machine Regression for project control forecasting. Automation in Construction, 47, 92–106. https://doi.org/10.1016/j.autcon.2014.07.014
Waziri, B. S., Bala, K., & Bustani, S. A. (2017). Artificial Neural Networks in Construction Engineering and Management. International Journal of Architecture, Engineering and Construction, 6(1). https://doi.org/10.7492/ijaec.2017.006
Wehrens, R., Putter, H., & Buydens, M. C. (2000). The bootstrap: a tutorial. In Chemometrics and Intelligent Laboratory Systems (Vol. 54). www.elsevier.comrlocaterchemometrics
Whitley, D., Beveridge, J., & GuerraSalcedo J. (1997). Messy Genetic Algorithms for Subset Feature Selection. Department of Computer Science, 568–575.
Yacim, J. A., & Boshoff, D. G. B. (2018). Impact of artificial neural networks training algorithms on accurate prediction of property values. Journal of Real Estate Research, 40(3), 375–418. https://doi.org/10.1080/10835547.2018.12091505
Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. In International Journal of Forecasting (Vol. 14).
Zhou, Y., Zhao, S., Wang, X., & Liu, W. (2018). Deep learning model and its application in big data. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10918 LNCS, 795–806. https://doi.org/10.1007/978-3-319-91797-9_55
Publicado
2025-09-30
Cómo citar
Béjar Tinoco, V., Madrigal Moreno, F., & Madrigal Moreno, S. (2025). Inteligencia artificial aplicada en la gestión de proyectos. Revista Venezolana De Gerencia, 30(112), 1743-1761. https://doi.org/10.52080/rvgluz.30.112.3
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EN LA MIRA: Tecnología y digitalización organizacional
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