Collaborative Scheduling of Mass Rescue Operations at Sea

The continued growth of the global cruise market has posed a major challenge to maritime search and rescue, as available rescue resources are limited compared to the scale of passenger ship disasters. To bridge the gap between the number of passengers in distress and the availability of rescue resources, this paper develops a two-stage Mass Rescue Operation (MRO) decision support model (MRO model) to fully utilize the available multiple rescue resources. Based on the combinatorial optimization theory, in the MRO model we consider the rescue capacity of multiple rescue resources and the synergy between them, the accident types and the marine environment conditions to optimize two objectives (rescue time and number of rescue resources dispatched) in two stages. In the first stage, the objective is to minimize the rescue time by Classical Selection Sort Algorithm. In the second stage, the rescue time and the number of rescue resources are simultaneously optimized by Simulated Annealing Arithmetic (SAA) integrated with Genetic Algorithm (GA). Furthermore, considering the actual role of helicopters in MROs, the MRO model is enhanced to schedule helicopters mandatorily or non-mandatorily. Finally, the MRO model was verified by simulating accidents in the Taiwan Strait. The simulation results show that compared with the first stage, the rescue time in the second stage model is saved by up to 16.18% and the number of rescue resources is reduced by up to 45.16%.