A multi-objective resource reallocation framework for disruption management in long-range maritime search and rescue

• Proposes a multi-objective disruption management framework for LRMSAR. • Integrates stakeholder satisfaction, workload balance, and cost efficiency. • Formulates a realistic ILP model with organizational, technical, and environmental constraints. • Introduces an enhanced evolutionary algorithm with adaptive operators for rapid convergence. • Demonstrates effectiveness via a Bohai Sea capsized vessel case study. Long-range maritime search and rescue operations face critical challenges due to coastal distance, unpredictable weather, and limited resources. Disturbances such as resource failures or adverse conditions often render initial resource allocations ineffective, necessitating robust reallocation strategies. To address this, we propose a novel disruption management framework that employs a multi-objective reallocation model jointly optimizing operational efficiency, stakeholder equity, and plan stability. To solve this model, we introduce an improved Non-dominated Sorting Genetic Algorithm. Experimental results show that the proposed algorithm achieves approximately 98% reduction in Inverted Generational Distance compared to the classic algorithm, significantly improving optimization quality. In the Bohai Sea fishing boat capsizing case, our reallocation strategy successfully limited the increase in victim dissatisfaction to only 0.23. The reallocation results keep the victim dissatisfaction deviation and resources budget are both controlled rightly, demonstrating the framework’s superior adaptability and practical value. This research provides decision-makers with a robust, data-driven tool for managing disruptions in maritime emergency operations.