关键词: 岛屿, 海空协同, 海上救助, 选址, 优化

Abstract: The Nansha Islands, situated at the southernmost end of China’s maritime border within the South China Sea, one of China’s three marginal seas, require an independent rescue support system due to their distance from the mainland. Rich in resources, the Nansha Islands are vital for the development of China’s offshore fisheries. As competition for marine resources in the South China Sea intensifies and China’s political policies and global dynamics evolve, establishing maritime search and rescue bases and dynamic duty stations in the Nansha Islands holds significant theoretical and practical importance, including shortening rescue times, promptly responding to emergencies, protecting China’s maritime rights, and ensuring the safety of fishermen. Given the unique background of the site selection of rescue base in the Nansha Islands, this paper primarily addresses two challenges: how to establish maritime rescue duty points to enhance their coverage of fishing vessels, and how to determine the number and locations of rescue bases to minimize investment costs.
Analyzing the issues reveals the dilemma we confront: Firstly, there is a matter of determining the number and locations of maritime rescue bases. Establishing one rescue base may reduce construction costs, but if the distance between the maritime duty points and the base is too great, it will inevitably inflate operational expenses. Secondly, there is a question of the quantity of maritime rescue duty points and helicopters. A shortage of rescue facilities will diminish rescue efficiency, while equipping more search and rescue facilities will escalate investment costs. Hence, an optimization model is devised to achieve maximum rescue coverage and the shortest rescue time within the constraints of limited rescue facilities and investment. Based on concepts from particle swarm optimization and evolutionary algorithms, a multi-objective P-MOEA algorithm tailored to this problem is designed. The fishing vessel locations are put into the fishery information database, which is developed and designed using GIS technology. Subsequently, the K-means clustering algorithm is applied to determine the locations of maritime rescue duty points. The results obtained are then put into the established integer optimization model to derive the search and rescue base locations and facility configuration plan. Finally, a cost-effectiveness method is employed to select the most economically viable solution.
This paper focuses on optimizing the construction of the fishery rescue system in the Nansha Islands in the South China Sea, using it as an application example for analysis and verification. Firstly, there are seven islands in the Nansha sea area qualified to establish fishery rescue bases. These islands have docks and landing sites for search and rescue helicopters. The paper considers these islands as candidate rescue bases, selecting several of them to establish maritime search and rescue bases. By interpolating data obtained from the South China Sea fishery resources survey results of the Academy of Fisheries Sciences, a fishery information database application platform is designed based on ArcGIS software. This platform projects the distribution positions of fishing vessels and island coordinates in the Nansha area. By using the K-means algorithm the positions of maritime rescue duty points at Nansha Islands are clustered. All these above data are then put into the established integer planning model to calculate the final site selection and search and rescue facility configuration scheme for the Nansha Islands through the P-MOEA algorithm. The results indicate that two maritime rescue bases are established in the Nansha Islands, namely, Huayang Island and Meiji Island. There are a total of seven maritime search and rescue duty points. Three of these duty points are based on Huayang Island, while the four are based on Meiji Island. Moreover, each rescue base is equipped with large search and rescue helicopter, and each duty point is equipped with suitable rescue ships to promptly respond to needs. To verify the performance of the proposed algorithm, it is compared with traditional evolutionary algorithms by increasing the number of fishing boats by 10%, 25%, and 50%, respectively. The algorithm population size is set to 1000, and the number of iterations is 1000. After running for 20 times, the examples’ Pareto frontier solutions show that the proposed algorithm in this paper outperforms traditional evolutionary algorithms in terms of Pareto front optimization. Furthermore, the cost improvement, uniformity, and diversity of the Pareto optimal solutions achieved by P-MOEA algorithm in this article surpass those of traditional evolutionary algorithms. In terms of the cost improvement index, the P-MOEA algorithm produces a more cost-effective result in total investment for the same rescue coverage rate. In terms of diversity metrics, the algorithm proposed in this paper generates a greater quantity of Pareto frontier solutions. Furthermore, in terms of the uniformity index, the algorithm proposed in this paper exhibits a broader distribution of Pareto frontier solutions. The aforementioned comparative results demonstrate that the algorithm proposed in this paper can be applied to optimize the site selection of offshore fishing rescue base and the allocation of rescue facilities. It also exhibits outstanding performance.
This study provides an effective analytical approach for selecting rescue base locations and scientifically allocating rescue resources in China’s offshore islands. However, there are still some limitations in this article. The research focuses on operational fishing vessels, yet in reality, rescue operations are also required for passing transport vessels, necessitating a comprehensive consideration. This will make the analysis process more complex, and determining how to model and optimize this problem will be the next step in further research.

Key words: islands, sea-air cooperation, marine rescue, site selection, optimization