Receding Horizon Optimization for Dynamic Joint Scheduling of Taxiways and Gates

In this study, a joint scheduling problem of taxiways and gates are considered where both the flight taxiing time and the gate idle time are optimized in the objective function. The problem is formulated as a nonlinear mathematical programming optimization problem which is NP-hard. A modified version of a particle swarm algorithm base on receding horizon is applied to solve it. A novel matrix coding scheme is designed to convert a particle position vector into the priority sequence of gates for each flight while a taxiing path planning heuristic algorithm based on conflict avoidance is proposed to allocate taxiway paths for all incoming and outgoing flights. In order to improve the search capability of PSO, an opposition-based learning search strategy is introduced to exploit more latitude of search space and solve the global minimum localization problem. The algorithm is tested based on the simulated operational data from the actual airport. Experimental results reveal that the proposed algorithm is effective in solving the problem.