Fuzzy Logic in Selection of Maritime Search and Rescue Units
Source abstract
Abstract
The article discusses methods of ships assessment when determining their suitability for search and rescue action (SAR) at sea. Selection of the most preferable ships is one of the action planning elements. Due to various construction and equipment the civilian ships can only perform rescue task to a certain degree. According to the Multi-Criteria Decision Analysis (MCDA), many parameters and data have to be compared in order to create a ranking of vessels ordered according to the coordinator’s preferences. When data are missing, incomplete or uncertain, a similar effect can be obtained using fuzzy logic. The author discussed the nature of the criteria, evaluation methods and presented a simulation of a ship study using fuzzy logic. The author developed fuzzy rules and presented the principle of operation of the controller. The article deals with the main principles of a decision support system (DSS) for the selection of ships in SAR operations.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Maritime Navigation and Safety · Multi-Criteria Decision Making
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Affiliations
Faculty of Navigation, Maritime University of Szczecin, Wały Chrobrego 1-2, 70-500 Szczecin, Poland
References (19)
Show source-provided references
1. European Maritime Safety Agency (2021). Annual Overview of Marine Casualties and Incidents 2020, European Maritime Safety Agency. Available online: http://www.emsa.europa.eu/accident-investigation-publications/annual-overview.html.
2. IMO (2019). International Aeronautical and Maritime Search and Rescue Manual, International Maritime Organization.
3. Analysis of parameters influencing the suitability of the surface unit for search and rescue operation at sea
4. Wielgosz, M., and Malyszko, M. (2021). Multi-Criteria Selection of Surface Units for SAR Operations at Sea Supported by AIS Data. Remote Sens., 13.10.3390/rs13163151 ↗
5. Bayesian methods in reliability of search and rescue action
6. Search and rescue optimization algorithm: A new optimization methodfor solving constrained engineering optimization problems10.1016/j.eswa.2020.113698 ↗
7. Wykorzystanie systemu AIS do selekcji i doboru jednostek nieratowniczych do akcji SAR (Using the AIS system to select non-professional rescue units for SAR operations)
8. SARPlan: A decision support system for Canadian Search and Rescue Operations10.1016/j.ejor.2003.10.029 ↗
9. A dynamic multi-objective location-allocation model for search and rescue assets10.1016/j.ejor.2020.06.003 ↗
10. A maritime search and rescue location analysis considering multiple criteria, with simulated demand
11. Optimizing Search and Rescue Personnel Allocation in Disaster Emergency Response using Fuzzy Logic10.14716/ijtech.v10i7.3709 ↗
12. Intelligent UAV Map Generation and Discrete Path Planning for Search and Rescue Operations
13. Hydrogen energy storage method selection using fuzzy axiomatic design and analytic hierarchy process10.1016/j.ijhydene.2019.11.130 ↗
14. Achiri, L., Guida, R., and Iervolino, P. (2018, January 10–13). SAR and AIS Fusion for Maritime Surveillance. Proceedings of the IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI), Palermo, Italy. Available online: https://ieeexplore.ieee.org/document/8548352.10.1109/RTSI.2018.8548352 ↗
15. Ship Detection with Spectral Analysis of Synthetic Aperture Radar: A Comparison of New and Well-Known Algorithms10.3390/rs70505416 ↗
16. Figueira, J., Greco, S., and Ehrgott, M. (2005). PROMETHEE methods. Multiple Criteria Decision Analysis: State of the Art Surveys, Springer.10.1007/b100605 ↗
17. Fuzzy sets10.1016/S0019-9958(65)90241-X ↗
18. Application of fuzzy algorithms for control of simple dynamic plant
19. Łachwa, A. (2001). Rozmyty Świat Zbiorów, Liczb, Relacji, Faktów, Reguł i Decyzji, EXIT.