关键词: 海上救援, 无人艇, 基地群布局, 均衡机制, 协同优化

Abstract: To reduce reliance on highly procedural and hazardous maritime search and rescue missions on the traditional operation paradigm of rescue commissioners, and to enhance marine search and rescue efficiency. A collaborative optimization method of coastal base cluster deployment and emergency resource configuration with unmanned surface vehicle (USV) assistance is proposed. A nonlinear survival probability-response time function is built to quantify USV response effectiveness, and by accounting for spatial variations in maritime incident probabilities, a measurement index is proposed to evaluate rescue base cluster deployment and heterogeneous USV configurations schemes. Building upon this foundation, a collaborative optimization model of coastal base cluster deployment and emergency resource configuration with USV assistance is developed to maximize rescue effectiveness across the covered area, subject to constraints including investment budgets, USV endurance limits, and communication range requirements. An adaptive large neighborhood search algorithm incorporating a cyclic augmentation correction strategy is developed to address the model's nonlinear characteristics for efficient solution of the optimization problem. Comparative experiments with varying-scale test cases demonstrate that the proposed algorithm significantly outperforms exact solutions obtained by linearization methods in terms of both optimization accuracy and solution efficiency, while maintaining excellent stability. Case study results demonstrate that the proposed method can optimize the deployment of rescue base cluster and the configuration of heterogeneous USV under the efficiency-equilibrium mechanism, thereby improving the spatial imbalance of maritime rescue effectiveness. Additional experiments verify the method’s extensibility to dynamic maritime risk scenarios, demonstrating its capability to generate appropriate contingency strategies.

Key words: marine rescue, unmanned surface vessel, base cluster deployment, efficiency-equilibrium mechanism, collaborative optimization