基于滚动时域的遗传-免疫算法优化航班着陆调度

genetic-immune algorithm based on receding horizon for aircraft landing scheduling problems

摘要:

航班调度一直是空中交通管制（ATC）中的一个复杂而具有重要意义的任务，而航班着陆问题(ALS)是其中的核心问题.航班着陆调度是NP-hard问题，具有规模大、约束条件多的特点.因此，为了有效合理地解决航班着陆问题，本文提出了基于滚动时域的遗传-免疫算法（RHC_HGIA）优化航班着陆调度.RHC_HGIA主要从两个方面解决航班着陆调度问题，一方面根据设定的滚动时域长度与大小选择需要进行优化的待降落航班；另一方面对选择的待降落航班使用遗传-免疫算法进行优化并确定其实际着陆时间.经过优化后的航班组成新的航班降落序列，从该序列中选择实际着陆时间在给定时域范围内的航班进行着陆.重新设置滚动时域长度，选择待降落航班进行优化，直到所有待着陆航班都已着陆为止.本文仿真实验以某机场一天内的20架待着陆航班数据为基础，在机场管制仿真系统中进行模拟仿真.仿真实验表明，与传统航班着陆调度算法(FCFS)相比，经过RHC_HGIA算法优化后的待着陆航班的额外成本有明显的降低.

Abstract:

Flight scheduling has been a complex and key task for the air traffic control (ATC), and aircraft landing scheduling (ALS) problem is one of the core issues. ALS is a NP-hard problem with a large scale and multi-constraints characteristics. Thus, in order to solve the flight landing problem effectively and rationally, a flight landing scheduling algorithm based on receding horizon and genetic-immune algorithm (RHC_HGIA) is proposed. RHC_HGIA solves the problem of flight landing by two aspects mainly, one is that selecting the flights that are waiting to land and need to be optimized based on the receding horizon length and size which have been set; on the other hand, optimizing The selected flights which are waiting to land by using genetic-immune algorithm and determining actual landing time of them. the flights that have been optimized form a new flight landing sequence, selecting the flights from the sequence that the actual landing time of them in the field within a given time range to land. Then resetting receding horizon length and re-selecting the flights to be optimized until all pending landings have landed so far. In this paper, simulation is conducted in the airport control simulation system on the base of an airport of 20 flights to be landing of one day. Simulation results show that, RHC_HGIA algorithm can solve ALS problem preferably, and comparing with traditional flights landing scheduling algorithm(FCFS), the extra costs of flight is reduced much more.