Joint Optimization Scheduling Using AHMQDE-ACO for Key Resources in Smart Operations

To address the inefficiencies in airport operations caused by fragmented key resource scheduling, a deeply integrated joint scheduling model with considering the interactions among runways, taxiways and gates is constructed to enhance overall operational efficiency and safety of airport. Then a two-stage hybrid optimization algorithm (AHMQDE-ACO) is developed to overcome the low convergence efficiency in solving the joint scheduling model. In AHMQDE-ACO, a parameter adaptation strategy is designed to dynamically balance global and local search capabilities, and a weighted mutation operator with combining differential evolution with hybrid subpopulation mutation strategies is developed to improve convergence and exploration. In addition, the AHMQDE is integrated with ant colony optimization (ACO) to further enhance path search efficiency. To improve the resolution of taxiway conflicts, a conflict-handling strategy with integrating multi-level priority and time-cost considerations is designed to mitigate taxiway conflicts, while a gate assignment strategy based on aircraft turnaround attributes optimizes gate utilization by aligning gate allocations with operational characteristics. The effectiveness of the AHMQDE-ACO is validated by using real-world operational data from a major hub airport. Experimental results show a 24 % improvement in taxiing efficiency and a 10 % increase in resource allocation efficiency over traditional methods. The proposed approach effectively coordinates dynamic collaboration and feedback among different resources.