Implementation of models for decision-making and risk management in autonomous maritime systems
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Published:
Dec 1, 2024
Keywords:
autonomous vessels, risk management, decision-making, Bayesian networks, adaptive strategies, maritime operations
Main Article Content
Abstract
The introduction of autonomous systems in maritime transport is fundamentally changing the approach to shipping, providing increased safety, reduced operating costs, and increased efficiency. At the same time, these innovations pose a number of challenges, especially in the areas of decision-making and risk management. This article explores the integration of advanced models, such as decision trees and Bayesian networks, with robust risk management strategies to effectively deal with the uncertainties and threats that accompany the deployment of autonomous vessels. Real-life cases of implementing these models in practice are analyzed, emphasizing the importance of adaptive strategies and real-time decision support systems to ensure reliable and safe operation of autonomous vessels, contributing to the long- term sustainability of maritime operations.
Article Details
How to Cite
Lohinov, O., Burlachenko, D., Nikitiuk, P., Kucherenko, V., Kotenko, O., & Voloshyn, D. (2024). Implementation of models for decision-making and risk management in autonomous maritime systems. Herald of the Odessa National Maritime University, (74), 86-102. https://doi.org/10.47049/2226-1893-2024-3-86-102
Section
Ensuring the safety of navigation
References
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2. Zhang, D., Yan, X., Yang, Z., & Wang, J. (2013). An accident data-based approach for congestion risk assessment of inland waterways: A Yangtze River case. Journal of Reliability, 288, 176-188
3. Zhao, X., Yuan, H., & Yu, Q. (2021). Autonomous vessels in the Yangtze River: A study on the maritime accidents using data-driven Bayesian networks. Sustainability, 13(9985). https://doi.org/10.3390/su13179985
4. Abilio Ramos, M., Utne, I. B., & Mosleh, A. (2019). Collision avoidance on maritime autonomous surface ships: Operators’ tasks and human failure events. Safety Science, 116, 33-44.
5. Jokioinen, E., Poikonen, J., Jalonen, R., & Saarni, J. (2016). Remote and Autonomous Ships—The Next Steps; AAWA Position Paper. Rolls Royce plc.
6. Goerlandt, F. (2020). Maritime Autonomous Surface Ships from a risk governance perspective: Interpretation and implications. Safety Science, 128, 104758.
7. Wróbel, K., Montewka, J., & Kujala, P. (2017). Towards the assessment of potential impact of unmanned vessels on maritime transportation safety. Reliability Engineering & System Safety, 165, 155-169.
8. Bye, R. J., & Aalberg, A. L. (2018). Maritime navigation accidents and risk indicators: An exploratory statistical analysis using AIS data and accident reports. Reliability Engineering & System Safety, 176, 174-186.
9. Jalonen, R., Tuominen, R., & Wahlström, M. (2017). Safety of Unmanned Ships: Safe Shipping with Autonomous and Remote Controlled Ships. Aalto University.
10. Dghaym, D., Hoang, T. S., Turnock, S. R., Butler, M., Downes, J., & Pritchard, B. (2021). An STPA-based formal composition framework for trustworthy autonomous maritime systems. Safety Science, 136, 105139.
11. Ventikos, N. P., Chmurski, A., & Louzis, K. (2020). A systems-based application for autonomous vessels safety: Hazard identification as a function of increasing autonomy levels. Safety Science, 131, 104919.
12. Sotiralis, P., Ventikos, N. P., Hamann, R., Golyshev, P., & Teixeira, A. P. (2016). Incorporation of human factors into ship collision risk models focusing on human centred design aspects. Reliability Engineering & System Safety, 156, 210-227.
13. Van Dorp, J. R., & Merrick, J. R. W. (2011). On a risk management analysis of oil spill risk using maritime transportation system simulation. Annals of Operations Research, 187, 249-277.
14. Fan, S., Blanco-Davis, E., Yang, Z., Zhang, J., & Yan, X. (2020). Incorporation of human factors into maritime accident analysis using a data- driven Bayesian network. Reliability Engineering & System Safety, 203, 107070.
15. Heij, C., & Knapp, S. (2012). Evaluation of safety and environmental risk at individual ship and company level. Transportation Research Part D: Transport and Environment, 17, 228-236.
16. Zhang, D., Yan, X. P., Yang, Z. L., Wall, A., & Wang, J. (2013). Incorporation of formal safety assessment and Bayesian network in navigational risk estimation of the Yangtze River. Reliability Engineering & System Safety, 118, 93-105.
17. Zhang, D., Yan, X., Yang, Z., & Wang, J. (2012). A subjective approach for evaluating navigational risk of Yangtze River. In Proceedings of the 11th International Probabilistic Safety Assessment and Management Conference and the Annual European Safety and Reliability Conference 2012 (PSAM11 ESREL 2012), Helsinki, Finland, 25-29 June 2012.
18. Tezdogan, T., Demirel, Y. K., Kellett, P., Khorasanchi, M., Incecik, A., & Turan, O. (2015). Full-scale unsteady RANS CFD simulations of ship behaviour and performance in head seas due to slow steaming. Ocean Engineering, 97, 186-206.
19. Korban, D., Melnyk, O., Onishchenko, O., Kurdiuk, S., & Shevchenko, V. (2024). Radar-based detection and recognition methodology of autonomous surface vehicles in challenging marine environment. Scientific Journal of Silesian University of Technology. Series Transport, 122, 111-127. https://doi.org/10.20858/sjsutst.2024.122.7
20. Melnyk, O., Bychkovsky, Y., Onishchenko, O., Onyshchenko, S., & Volianska, Y. (2023). Development the Method of Shipboard Operations Risk Assessment Quality Evaluation Based on Experts Review. Studies in Systems, Decision and Control, 481, 695-710. Springer, Cham. https://doi.org/10.1007/978-3-031-35088-7_40
21. Melnyk, O., Onyshchenko, S., Onishchenko, O., Shcherbina, O., & Vasalatii, N. (2023). Simulation-Based Method for Predicting Changes in the Ship's Seaworthy Condition Under Impact of Various Factors. Studies in Systems, Decision and Control, 481, 653-664. Springer, Cham. https://doi.org/10.1007/978-3-031-35088-7_37
22. Melnyk, O., Onishchenko, O., Onyshchenko, S., Shumylo, O., Volyanskyy, S., Bondar, A., & Cheredarchuk, N. (2023). Application of Fuzzy Controllers in Automatic Ship Motion Control Systems. International Journal of Electrical and Computer Engineering, 13 (4), 3958-3968. https://doi.org/10.11591/ijece.v13i4.pp3948-3957
23. Melnyk, O., Onyshchenko, S., Onishchenko, O., Shumylo, O., Voloshyn, A., Koskina, Y., & Volianska, Y. (2022). Review of Ship Information Security Risks and Safety of Maritime Transportation Issues. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, 16(4), 717-722. https://doi.org/10.12716/1001.16.04.13
24. Melnyk, O., Onishchenko, O., Onyshchenko, S., Voloshyn, A., Kalinichenko, Y., Rossomakha, O., & Naleva, G. (2022). Autonomous Ships Concept and Mathematical Models Application in their Steering Process Control. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, 16(3), 553-559. https://doi.org/10.12716/1001.16.03.18
25. Melnyk, O.M., Volyanskaya, Y.B., Kalinichenko, E.V., Loginov, O.V., Koryakin, K.S., Burlachenko, D.A., & Shenyavsky, G.S. (2022). The use of information technologies in water transport and prospects for their development. Scientific notes of TNU named after Vernadsky.Technical Sciences, 33(72) No. 3, 99-105. https://doi.org/10.32838/2663-5941/2022.3/16
26. Melnyk, O.M., Onishchenko, O.A., Vasalatii, N.V., Koryakin, K.S., Pulia- iev, I.O., & Shenyavskyi, G.S. (2022). Technologies of information interaction in the process of improving the safety of navigation. Scientific notes of TNU named after Vernadsky.Technical Sciences, 33(72) No. 4, 260-265. https://doi.org/10.32838/2663-5941/2022.4/39