Maritime search and rescue resource location–allocation–scheduling under uncertain demand and time-varying locations

Abstract

Minimizing casualties and property losses from maritime emergencies requires a timely and reliable search and rescue (SAR) logistics network. Unlike conventional emergency logistics, maritime incidents are not only stochastic but also involve demand locations that evolve over time under ocean environmental influences. To address these characteristics, we develop a two-stage dynamic location–allocation–scheduling (2S-DLAS) model for maritime SAR operations under uncertain demand and time-varying demand locations. The model minimizes total rescue cost by jointly optimizing rescue base locations, the allocation of multiple SAR resource types, and the coordinated routing and task assignment of SAR platforms. It explicitly captures the dynamic coupling between travel time and ocean conditions and incorporates navigational restrictions into accessibility constraints. To solve the model, we use a Sample Average Approximation (SAA) framework and design an Improved Adaptive Large Neighborhood Search (IALNS) algorithm that integrates cross-stage nested neighborhoods to improve solution quality and computational efficiency. Numerical experiments based on real SAR cases from the South China Sea demonstrate the effectiveness of the proposed model and algorithm. Sensitivity analysis further provides managerial insights on strategic SAR deployment and multi-platform coordination, supporting evidence-based planning in maritime emergency response systems.