Improving Air Mobility for Pre-Disaster Planning with Neural Network Accelerated Genetic Algorithm
This work addresses air mobility challenges for emergency planners during weather disasters, though it is incremental as it combines existing methods.
The paper tackled optimizing airport evacuation flight schedules for pre-disaster scenarios by proposing a neural network-accelerated genetic algorithm, resulting in comparable performance with reduced computational overhead and faster convergence.
Weather disaster related emergency operations pose a great challenge to air mobility in both aircraft and airport operations, especially when the impact is gradually approaching. We propose an optimized framework for adjusting airport operational schedules for such pre-disaster scenarios. We first, aggregate operational data from multiple airports and then determine the optimal count of evacuation flights to maximize the impacted airport's outgoing capacity without impeding regular air traffic. We then propose a novel Neural Network (NN) accelerated Genetic Algorithm(GA) for evacuation planning. Our experiments show that integration yielded comparable results but with smaller computational overhead. We find that the utilization of a NN enhances the efficiency of a GA, facilitating more rapid convergence even when operating with a reduced population size. This effectiveness persists even when the model is trained on data from airports different from those under test.