| Aircraft sequencing problem of terminal area is one of important contents of Air Traffic Flow Management (ATFM). Because of the uncertainty factors and the non-real time information exchange, it will result in congestion in terminal area. The aim of landing aircrafts sequencing is to schedule a reasonable landing sequence and time on the premise of ensuring safety, and to reduce the burden of controlling officer.About flights scheduling problem, this thesis establishes a static model targeting minimum delay cost on the basis of president flights scheduling theories. Considering the dynamic nature of waiting queue, the dynamic model based on scroll window which is triggered by period and event is built. Then, we proposed an improved particle swarm optimization algorithm which is based on population initialization optimization and mutation strategy. To take fairness into account, maximum position shift is introduced to the model.In the improved PSO, penalty function is designed to handle the maximum position shift.In order to speed up the algorithm converges and to reduce the number of iterations, we design the adaptive inertia weight to update particles at different levels. To overcome the weakness that PSO is easy to fall into the local extreme value, swarm initialization optimization strategy is proposed. When the algorithm crunches to a standstill, we adopt variation, crossover, and selection operation. This operation can help the particle jump out of the local extreme points.Last, we use actual flight data to analyze and verify the model and algorithm, and compare the ranking result with FCFS and basic PSO. The study cases show that the proposed model aimed at the minimum delay cost on the basis of maximum position shift is feasible. The landing sequence which is obtained by using PSO to solve this model can decrease the total delay cost, which provides a solution for aircraft sequencing problem and the solution will be reference for similar problems. |
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