Accident black spot clustering oriented maritime search and rescue resource (MSAR) allocation and optimization Provisionally Accepted

Yang Sun¹ Hou ChengYang¹ Xinqiang Chen^2* Yanhao Wang³ Lihao Dai⁴ QinYou Hu¹

• ¹Merchant Marine College, Shanghai Maritime University, China
• ²Institute of Logistics Science and Engineering, Shanghai Maritime University, China
• ³China Construction Harbour and Channel Engineering Bureau Group, China
• ⁴Shanghai Harbour Engineering Design & Research Institute, China

In the realm of maritime search and rescue (MSAR) operations, the ship scheduling problem holds great significance. To mitigate the impact of accidental black spots on MSAR planning, this paper proposes an integrated approach for emergency resource allocation. The approach encompasses three main steps: identifying accident black spots, assessing high-risk areas, and optimizing the outcomes through a synergistic combination of an optimization algorithm and reinforcement learning. In the initial step, the paper introduces the iterative self-organizing data analysis technology (ISODATA) for identifying accident spots at sea. A comparative analysis is conducted with other clustering algorithms, highlighting the superiority of ISODATA in effectively conducting dense clustering. This can effectively carry out dense clustering, instead of the situation where the data spots are too dispersed or obvious anomalies that affect the clustering. Furthermore, this approach incorporates entropy weighting to reassess the significance of accident spots by considering both the distance and the frequency of accidents. This integrated approach enhances the allocation of search and rescue forces, ensuring more efficient resource utilization. To address the MSAR vessel scheduling problem at sea, the paper employs the non-dominated sorting genetic algorithm II combined with reinforcement learning (NSGAII-RL). Comparative evaluations against other optimization algorithms reveal that the proposed approach can save a minimum of 7% in search and rescue time, leading to enhanced stability and improved efficiency in large-scale MSAR operations. Overall, the integrated approach presented in this paper offers a robust solution to the ship scheduling problem in maritime search and rescue operations. Its effectiveness is demonstrated through improved resource allocation, enhanced timeliness, and higher efficiency in responding to maritime accidents.