Regdevelopment of decision support system for emergency response operations in port
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Published:
Dec 1, 2024
Keywords:
decision support system, emergency situation, port, intelligent agent, fuzzy set, knowledge base, personoid
Main Article Content
Abstract
The aim of the study is to develop the architecture of a decision support system for managing emergencies in a port and the main algorithms for its functioning. To achieve the research goal, abstract intelligent agents-personoids-were used. To enhance the description of domain knowledge and simplify the execution of the reasoning procedure, attribute classes were described using fuzzy sets. The results include a formal description of the environment's states through object attributes represented as fuzzy sets; a generalized algorithm for organizing the interaction between the decision-maker and the decision support system in the event of a fire on a tanker at the port's oil terminal; and the implementation of a planning system based on network planning and management methods
Article Details
How to Cite
Khussein, Y. (2024). Regdevelopment of decision support system for emergency response operations in port. Herald of the Odessa National Maritime University, (74), 103-120. https://doi.org/10.47049/2226-1893-2024-3-103-120
Section
Ensuring the safety of navigation
References
1. Bologna S., Gadomski A.M. (1996). MINDES program managerial intelligent node of decision-support for emergency supervision. In Proce- edings of the GEMINI (Global Emergency Management International Network Initiative) Meeting, ENEA Press, Rome.
2. Almukhlifi A., Taji K. (2020). «A framework for intelligent decision support systems in emergency response operations.» International Journal of Disaster Risk Reduction, Vol. 50, Р. 101812. DOI: 10.1016/j.ijdrr.2020.101812.
3. Jiang X., Lu X. (2021). An intelligent decision support system for emer- gency management: integrating machine learning and optimization // Journal of Decision Systems. Vol. 30. No. 2. Р. 120-135. DOI: 10.1080/ 12460125.2021.1879875.
4. Moya J., Pérez M., Herrera, F. (2021). Intelligent decision support systems for large-scale emergencies: A survey.. Information Fusion, Vol. 67Р. 31-47. DOI: 10.1016/j.inffus.2020.08.010.
5. Zhu Y., Gao Q., Zhang, J. (2019). Towards a real-time decision support system for emergency management: A framework and key technologies. IEEE Access, Vol. 7, Р. 165282-165296. DOI: 10.1109/ACCESS. 2019.
2952159.
6. González-Ruiz C., Martínez-Almela A. (2019). Advanced decision support systems for emergency management: A review of recent developments and future directions.. IEEE Transactions on Systems, Man, and Cybernetics: Systems. Vol. 49. No. 8 Р. 1601-1614. DOI: 10.1109/TSMC.2019. 2902490.
7. Gadomski A.M., Di Costanzo G. (1996). Intelligent decision support system for industrial accident management. In Proceedings of 8th European Simulation Symposium, A.G. Bruzzone and J.H. Kerckhoffs (Eds.), SCS International Press, Genoa, Р. 137-141.
8. Gadomski A.M., Bologna S., Di Costanzo G., Perini A., Schaerf M. Towards intelligent decision support systems for emergency managers: the IDA approach // International Journal of Risk Assessment and Management, 2001. Vol. 2. No.3/4. Р. 224-242. DOI: 10.1504/IJRAM.2001.001507.
9. Smith M. and Carver S. (2022). Agent-based modeling for emergency management: A new approach to simulate human behavior in disaster scenarios //Journal of Artificial Societies and Social Simulation, Vol. 25. No. 1. DOI: 10.18564/jasss.4697.
10. Balducelli C., Bologna S., Di Costanzo G., Gadomski A.M., Vicoli G. (1995). Computer aided training for cooperating emergency managers: some results of the MUSTER project. In Proceedings of the MEMbrain Conference on International Aspects of Emergency Management and Environmental Technology, H. Drager (Ed.), A/Q Quasar Consultants Press, Norway, Oslo, Р. 433-444.
11. Aamodt A., Plaza E. (1994). Case-based reasoning: foundational issues, methodological variations and system approaches. Artificial Intelligence Communications, J., IOS Press. Vol. 7. No. 1. Р. 39-59.
12. Quatrani T. (1998). Visual Modelling with Rational Rose and UML. Addison-Wesley Longman Inc, Reading, Massachusetts.
13. Kim S., Park J. (2020). Enhanced decision-making framework for emer- gency management using multi-agent systems //. Journal of Computational Science. Vol. 41. Р. 101082. DOI: 10.1016/j.jocs.2020.101082
2. Almukhlifi A., Taji K. (2020). «A framework for intelligent decision support systems in emergency response operations.» International Journal of Disaster Risk Reduction, Vol. 50, Р. 101812. DOI: 10.1016/j.ijdrr.2020.101812.
3. Jiang X., Lu X. (2021). An intelligent decision support system for emer- gency management: integrating machine learning and optimization // Journal of Decision Systems. Vol. 30. No. 2. Р. 120-135. DOI: 10.1080/ 12460125.2021.1879875.
4. Moya J., Pérez M., Herrera, F. (2021). Intelligent decision support systems for large-scale emergencies: A survey.. Information Fusion, Vol. 67Р. 31-47. DOI: 10.1016/j.inffus.2020.08.010.
5. Zhu Y., Gao Q., Zhang, J. (2019). Towards a real-time decision support system for emergency management: A framework and key technologies. IEEE Access, Vol. 7, Р. 165282-165296. DOI: 10.1109/ACCESS. 2019.
2952159.
6. González-Ruiz C., Martínez-Almela A. (2019). Advanced decision support systems for emergency management: A review of recent developments and future directions.. IEEE Transactions on Systems, Man, and Cybernetics: Systems. Vol. 49. No. 8 Р. 1601-1614. DOI: 10.1109/TSMC.2019. 2902490.
7. Gadomski A.M., Di Costanzo G. (1996). Intelligent decision support system for industrial accident management. In Proceedings of 8th European Simulation Symposium, A.G. Bruzzone and J.H. Kerckhoffs (Eds.), SCS International Press, Genoa, Р. 137-141.
8. Gadomski A.M., Bologna S., Di Costanzo G., Perini A., Schaerf M. Towards intelligent decision support systems for emergency managers: the IDA approach // International Journal of Risk Assessment and Management, 2001. Vol. 2. No.3/4. Р. 224-242. DOI: 10.1504/IJRAM.2001.001507.
9. Smith M. and Carver S. (2022). Agent-based modeling for emergency management: A new approach to simulate human behavior in disaster scenarios //Journal of Artificial Societies and Social Simulation, Vol. 25. No. 1. DOI: 10.18564/jasss.4697.
10. Balducelli C., Bologna S., Di Costanzo G., Gadomski A.M., Vicoli G. (1995). Computer aided training for cooperating emergency managers: some results of the MUSTER project. In Proceedings of the MEMbrain Conference on International Aspects of Emergency Management and Environmental Technology, H. Drager (Ed.), A/Q Quasar Consultants Press, Norway, Oslo, Р. 433-444.
11. Aamodt A., Plaza E. (1994). Case-based reasoning: foundational issues, methodological variations and system approaches. Artificial Intelligence Communications, J., IOS Press. Vol. 7. No. 1. Р. 39-59.
12. Quatrani T. (1998). Visual Modelling with Rational Rose and UML. Addison-Wesley Longman Inc, Reading, Massachusetts.
13. Kim S., Park J. (2020). Enhanced decision-making framework for emer- gency management using multi-agent systems //. Journal of Computational Science. Vol. 41. Р. 101082. DOI: 10.1016/j.jocs.2020.101082