Previsão da duração de carregamentos de embarcações PLSV
Resumo
As embarcações Pipe-laying Support Vessel (PLSV) realizam tarefas de interligação submarinas, que necessitam de diversos recursos materiais. Estes recursos são carregados nos navios, e atualmente o planejamento dos carregamentos é resolvido de forma heurística, com taxas de erros altas, em torno de 84%. Com o objetivo de auxiliar neste planejamento operacional, este trabalho propôs a investigação e seleção de diversos modelos de aprendizado de máquina para prever a duração dos carregamentos. Os modelos que apresentaram melhor desempenho na base de teste foram o Gradient Boosting, Regressão Linear e o Stacking Regressor, com um erro percentual médio absoluto de no máximo 36% nos dados de teste.
Referências
Bnamericas (2021). Petrobras to hire brazilian pipelay vessels. Acessado em 20 de junho de 2022.
Branco, P., Torgo, L., and Ribeiro, R. P. (2017). Smogn: a pre-processing approach for imbalanced regression.
Breiman, L. (2001). Random forests. Machine Learning, 45(1):5-32. Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. CoRR, abs/1603.02754.
Dantas, L. (2020). Predicting the acquisition of resistant pathogens in ICUs using machine learning techniques. PhD thesis, Pontifícia Universidade Católica do Rio de Janeiro.
Ferreira, D. (2013). As principais operações das embarcações plsv. Master's thesis.
Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, pages 1189-1232.
Friedman, M. (1940). A comparison of alternative tests of significance for the problem of m rankings. Annals of Mathematical Statistics, 11:86-92.
Gkerekos, C., Lazakis, I., and Theotokatos, G. (2019). Machine learning models for predicting ship main engine fuel oil consumption: A comparative study. Ocean Engineering.
Hall, M. A. (1999). Correlation-based feature selection for machine learning. Technical report.
Haykin, S. (1994). Neural networks: a comprehensive foundation. Prentice Hall PTR.
Jeon, M., Noh, Y., Shin, Y., Lim, O.-K., Lee, I., and Cho, D. S. (2018). Prediction of ship fuel consumption by using an artificial neural network. Journal of Mechanical Science and Technology, 32:5785-5796.
Kim, Y.-R., Jung, M., and Park, J.-B. (2021). Development of a fuel consumption prediction model based on machine learning using ship in-service data. Journal of Marine Science and Engineering, 9:137.
Kononenko, I., Simec, E., and Robnik-Sikonja, M. (2004). Overcoming the myopia of inductive learning algorithms with relieff. Applied Intelligence, 7:39-55.
Mekkaoui, S. E., Benabbou, L., and Berrado, A. (2020). Predicting ships estimated time of arrival based on ais data. Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications.
Peng, Y., Liu, H., Li, X., Huang, J., and Wang, W. (2020). Machine learning method for energy consumption prediction of ships in port considering green ports. Journal of Cleaner Production, 264:121564.
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., and Gulin, A. (2017). Catboost: unbiased boosting with categorical features.
Quinlan, J. R. (1986). Induction of decision trees. MACH. LEARN, 1:81-106.
Schapire, R. E. (2013). Explaining adaboost. In Empirical inference, pages 37-52. Springer.
Stepec, D., Martincic, T., Klein, F., Vladusic, D., and Costa, J. P. (2020). Machine learning based system for vessel turnaround time prediction. 2020 21st IEEE International Conference on Mobile Data Management (MDM), pages 258-263.
Tariq, Z., Aljawad, M. S., Hasan, A., Murtaza, M., Mohammed, E. S., El-Husseiny, A., Alarifi, S. A., Mahmoud, M., and Abdulraheem, A. (2021). A systematic review of data science and machine learning applications to the oil and gas industry. Journal of Petroleum Exploration and Production Technology, 11:4339 - 4374.
Vapnik, V. N. (2000). Methods of Pattern Recognition, pages 123-180. Springer New York, New York, NY.
Branco, P., Torgo, L., and Ribeiro, R. P. (2017). Smogn: a pre-processing approach for imbalanced regression.
Breiman, L. (2001). Random forests. Machine Learning, 45(1):5-32. Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. CoRR, abs/1603.02754.
Dantas, L. (2020). Predicting the acquisition of resistant pathogens in ICUs using machine learning techniques. PhD thesis, Pontifícia Universidade Católica do Rio de Janeiro.
Ferreira, D. (2013). As principais operações das embarcações plsv. Master's thesis.
Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, pages 1189-1232.
Friedman, M. (1940). A comparison of alternative tests of significance for the problem of m rankings. Annals of Mathematical Statistics, 11:86-92.
Gkerekos, C., Lazakis, I., and Theotokatos, G. (2019). Machine learning models for predicting ship main engine fuel oil consumption: A comparative study. Ocean Engineering.
Hall, M. A. (1999). Correlation-based feature selection for machine learning. Technical report.
Haykin, S. (1994). Neural networks: a comprehensive foundation. Prentice Hall PTR.
Jeon, M., Noh, Y., Shin, Y., Lim, O.-K., Lee, I., and Cho, D. S. (2018). Prediction of ship fuel consumption by using an artificial neural network. Journal of Mechanical Science and Technology, 32:5785-5796.
Kim, Y.-R., Jung, M., and Park, J.-B. (2021). Development of a fuel consumption prediction model based on machine learning using ship in-service data. Journal of Marine Science and Engineering, 9:137.
Kononenko, I., Simec, E., and Robnik-Sikonja, M. (2004). Overcoming the myopia of inductive learning algorithms with relieff. Applied Intelligence, 7:39-55.
Mekkaoui, S. E., Benabbou, L., and Berrado, A. (2020). Predicting ships estimated time of arrival based on ais data. Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications.
Peng, Y., Liu, H., Li, X., Huang, J., and Wang, W. (2020). Machine learning method for energy consumption prediction of ships in port considering green ports. Journal of Cleaner Production, 264:121564.
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., and Gulin, A. (2017). Catboost: unbiased boosting with categorical features.
Quinlan, J. R. (1986). Induction of decision trees. MACH. LEARN, 1:81-106.
Schapire, R. E. (2013). Explaining adaboost. In Empirical inference, pages 37-52. Springer.
Stepec, D., Martincic, T., Klein, F., Vladusic, D., and Costa, J. P. (2020). Machine learning based system for vessel turnaround time prediction. 2020 21st IEEE International Conference on Mobile Data Management (MDM), pages 258-263.
Tariq, Z., Aljawad, M. S., Hasan, A., Murtaza, M., Mohammed, E. S., El-Husseiny, A., Alarifi, S. A., Mahmoud, M., and Abdulraheem, A. (2021). A systematic review of data science and machine learning applications to the oil and gas industry. Journal of Petroleum Exploration and Production Technology, 11:4339 - 4374.
Vapnik, V. N. (2000). Methods of Pattern Recognition, pages 123-180. Springer New York, New York, NY.
Publicado
28/11/2022
Como Citar
VENTRIGLIA, Rachel Martins; BASTOS, Leonardo; FIGUEIREDO, Karla; VALLASCO, Marley.
Previsão da duração de carregamentos de embarcações PLSV. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 19. , 2022, Campinas/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 294-305.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2022.227313.
