Optimization of multi-aircraft landing scheduling based on machine learning with quantum annealing under uncertainty conditions on single and multiple runs

The object of this study is multi-aircraft landing scheduling on single and multiple runways, which is an important aspect of modern air traffic management systems. The main problems solved in this research are the complexity of scheduling optimization due to limited runway capacity, the need to maintain a safe distance between aircraft, and the uncertainty of estimated time of arrival (ETA) which is often influenced by external factors such as weather and air traffic density. To overcome these challenges, this research proposes a hybrid approach between Long short-term memory-gradient boosting with the quantum annealing method. the results show that this approach is able to significantly improve the performance of the scheduling system, with an accuracy of 0.93, a precision of 0.91, a recall of 0.90, and an F1 score of 0.91. These values are higher than the model without quantum annealing, which only achieved an accuracy of 0.87, a precision of 0.85, a recall of 0.83, and an F1 score of 0.84. This improvement can be explained by the ability of LSTM-gradient boosting to predict ETA deviation more accurately, as well as the effectiveness of quantum annealing in solving the quadratic unconstrained binary optimization (QUBO) formulation efficiently. The unique feature of this research lies in the application of a hybrid model that combines the power of machine learning and quantum computing, achieving a balance between predictive accuracy and optimization efficiency. These research findings can be applied to air traffic scheduling systems at airports with single or multiple runways. Their implementation has the potential to improve operational efficiency, reduce delays, and enhance flight safety through more precise and adaptive landing time management