IACA algorithm parameters.

The continuous growth in air traffic at civil airports has placed significant economic pressure on surface operations. Consequently, strategic adjustments to departure schedules and optimization of taxiing routes have become essential to reduce operational costs. This study proposes a two-stage optimization framework aimed at minimizing surface operation expenditures. In the first stage, a dynamic pushback slot control (DPSC) strategy is employed to regulate departure sequences. The second stage enables preplanning of taxi routes for both arriving and departing aircraft by optimizing the taxiway control threshold, thereby refining the pushback slots for departing flights. To support route planning, multiple taxiing configurations are generated for different departure intervals. To improve solution quality and mitigate ground conflicts, an improved ant colony algorithm (IACA) incorporating a negative feedback mechanism is developed. Experimental results show that, compared to a baseline scenario without departure control, the proposed framework reduces taxiing costs by 17.8%, yielding an optimized total cost of USD 8,163.44. Furthermore, relative to strategies without the negative feedback mechanism, the proposed approach achieves an average cost saving of USD 1,412.71. These results demonstrate that the proposed framework provides superior economic benefits while simultaneously improving operational safety and efficiency.