A Multi-Objective Optimization Scheduling Method for Airport Runway and Taxiway Systems Oriented Towards Air-Ground Coordination

objectives the deviation between actual take-off time and the Calculated Take-Off Time (CTOT) assigned by the air traffic flow management system, the total aircraft taxi time, and runway utilization efficiency. In addition, multiple practical operational constraints are incorporated to better reflect real-world scenarios. To efficiently solve this NP-hard problem, a hybrid intelligent optimization framework is designed and implemented. The proposed framework integrates a Sparrow Search Algorithm enhanced by the Sine Cosine Algorithm (SSA-SCA) with the Non-dominated Sorting Genetic Algorithm II (NSGA-II), forming the SSA-SCA-NSGA-II approach. This hybrid strategy effectively balances global exploration and local exploitation, thereby ensuring good convergence and distribution of the obtained Pareto solution set. A case study based on departure operations during a typical peak period (07:20–09:20) at Beijing Daxing International Airport is conducted for validation. The results demonstrate that the proposed method achieves an effective trade-off among multiple objectives, improving CTOT compliance by 13.8% on average, reducing average taxi time by 11.2%, and enhancing runway load balancing by 3.2%. These findings confirm that the proposed approach provides a valuable theoretical framework and methodological reference for air–ground collaborative scheduling and intelligent decision-making at multi-runway airports.