Optimizing pre-occurrence maritime search and rescue system: A dynamic location-allocation model with considering spatiotemporal accessibility

Abstract

This study presents a novel approach to optimizing a pre-occurrence search and rescue (SAR) system to enhance both efficiency and cost-effectiveness in maritime emergency. First, we formulate a dynamic location-allocation problem (LAP) for rescue resources (including rescue stations at sea, rescue vessels, and emergency supplies) under multi-period scenarios, simultaneously accounting for seasonal variations in incident demand, rescue capacity, and oceanic conditions. To quantify the impact of these variations on LAP decision-making, we integrate a spatiotemporal SAR accessibility measure, enabling seasonal reallocation of SAR resources across different scenarios. The model aims to achieve two key objectives: maximizing expected accessibility to incidents and minimizing total system costs. To solve this complex problem, we employ a hybrid algorithm that combines k-means clustering with the multi-objective plant growth simulation algorithm (MO-PGSA). A real-life case study in the Bohai Sea, China, validates the effectiveness of the proposed approach, with numerical results demonstrating significant improvements in SAR system performance. The findings offer valuable decision support for strategic SAR planning, enhance resource utilization, and contribute to improved maritime safety management.