An Intelligent Decision Algorithm for the Generation of Maritime Search and Rescue Emergency Response Plans

Maritime search and rescue (SAR) decisions are the most important part of maritime SAR operations. In the process of making maritime SAR decisions, a key factor affecting efficiency and success rate is how to quickly respond to accidents and develop an emergency response plan. At present, maritime SAR emergency response plans are still mostly obtained through a combination of drift prediction models and SAR experience. There is a lack of SAR resource scheduling and SAR task assignment. The primary purpose of this paper is to explore the possibility of using an intelligent decision-making algorithm to formulate maritime SAR emergency response plans so as to produce results more scientifically. First, the relevant research areas and research data are briefly introduced, and the main mathematical models involved in the optimal search theory are expounded. Next, key technologies involved in the process of maritime SAR emergency response plan generation, including the determination of search area, the scheduling of SAR resources, the allocation of search tasks, and the planning of search routes, are analyzed in detail. Two optimization model algorithms, namely the SAR resource scheduling model based on genetic simulated annealing algorithm (GSAA) and the regional task allocation algorithm based on space-time characteristics, are proposed as approaches to solving the problem of resource scheduling and task allocation. Finally, the effectiveness and optimization of the proposed algorithms are verified by analyzing the emergency response of a real case which occurred in the Bohai Sea and comparing the different schemes. Through the algorithm proposed in this paper, the efficiency of maritime SAR operations can be effectively improved and the loss of life and property can be reduced.