Uncertainty Inclusive Runway Balancing Using Convolutional Neural Network

and arrival aircraft delay. The delay prediction for runway balancing optimization is obtained by a neural network, onlywithoutanyadditionalsimulations.Developinganaccuratesimulationmodelunderanuncertainenvironmentis difficult, but the proposed neural network model can estimate the average delay without modeling uncertainty explicitly. In this paper, the effectiveness of the proposed method is validated through numerical simulations. First, simulationsareusedtogeneratethedata,whicharethenusedtotraintheneuralnetwork.Next,therunwaybalancing problem is solved via simulated annealing using the delay predicted by the neural network. The simulation result shows that the proposed approach outperforms the simulation-based method under an uncertainty environment. Therefore, the neural network is shown to accurately estimate the delay under the uncertainty environment, which makes the proposed neural-network-based method applicable to objective function calculations for optimization.