航班协同调度仿真系统的设计与实现

发布时间：2018-04-24 16:41

本文选题：滑行道调度 + 航班着陆调度　；参考：《中国民航大学》2017年硕士论文

【摘要】：近年来,我国航空产业的发展依旧保持着强劲的势头。然而随着飞行流量的增加,航班调度的工作量和难度不断增大,繁忙机场拥堵现象也越来越严重。目前,我国在机场终端区航班调度方面仍采用人工调度的方式,管制员根据经验调度航班,具有随机性和不确定性,容易使空域得不到充分利用。因此,机场终端区自动化调度研究就具有重大意义。本文在国内外相关研究的基础上,首先针对现有机场滑行道调度算法效率偏低的问题,提出了一种引入A*算法的协同蚁群算法。算法首先采用A*算法得到的滑行道调度结果作为蚁群启发式信息;然后使用带有启发式信息的蚁群对航班进行调度排序,排序过程中检测冲突,利用冲突解决算法根据航班优先级处理航班冲突,通过蚁群间通信对信息素进行更新迭代,最终得出航班最优调度结果。实验结果表明,与现有滑行道调度算法相比,本文提出的算法收敛速度较快,航班调度效率较高,可以对繁忙机场滑行道调度提供决策支持。关于目前滑行道调度模型局限性的问题,本文将航班着陆调度问题和滑行道调度问题通过共享资源跑道结合起来,将空中网络与地面网络相结合,统一两个问题的描述方式,在滑行道调度模型的基础上融合航班着陆调度经典模型,实现着陆与滑行航班相结合的协同调度模型。同时利用上述协同蚁群算法对协同调度模型进行了验证实验,结果表明进离场航班协同调度的可行性与优越性。最后,在如上研究工作的基础上设计了航班协同调度仿真系统。系统的主要功能包括以下几点:1)系统能通过更新版本自由添加航班调度算法。2)系统通过读取航班计划数据和机场网络布局数据,根据选定的航班调度算法,可以得到最优的航班调度方案。3)系统能展示算法每次迭代的最优值并画出变化折线图。4)系统能展示最优调度结果。
[Abstract]:In recent years, the development of China's aviation industry is still maintaining a strong momentum. However, with the increase of flight flow, the workload and difficulty of flight scheduling are increasing, and congestion in busy airports is becoming more and more serious. At present, our country still uses the manual dispatch method in the airport terminal area flight scheduling, the controller dispatches the flight according to the experience, has the randomness and the uncertainty, easily causes the airspace not to be fully utilized. Therefore, the research of airport terminal automation dispatching is of great significance. Based on the domestic and foreign research, this paper first proposes a cooperative ant colony algorithm based on A* algorithm to solve the problem of low efficiency of the existing taxiway scheduling algorithm. The algorithm firstly uses the taxiway scheduling result obtained by the A* algorithm as the ant colony heuristic information, and then uses the ant colony with heuristic information to sort the flight and detect the conflicts during the sorting process. The conflict resolution algorithm is used to deal with the flight conflicts according to the flight priority, and the pheromone is updated by means of ant colony communication. Finally, the optimal flight scheduling results are obtained. The experimental results show that compared with the existing taxiway scheduling algorithms, the proposed algorithm has faster convergence speed and higher flight scheduling efficiency, and can provide decision support for taxiway scheduling in busy airports. With regard to the limitation of taxiway scheduling model at present, this paper combines the flight landing scheduling problem and taxiway scheduling problem through the shared resource runway, combines the air network with the ground network, and unifies the description of the two problems. Based on the taxiway scheduling model, the classical flight landing scheduling model is integrated to realize the cooperative scheduling model of landing and taxiing flight. At the same time, the cooperative ant colony algorithm is used to verify the cooperative scheduling model. The results show the feasibility and superiority of the cooperative scheduling of inbound and outbound flights. Finally, a flight coordination scheduling simulation system is designed based on the above research work. The main functions of the system include the following points: 1) the system can freely add the flight scheduling algorithm. 2) the system can read the flight plan data and the airport network layout data, according to the selected flight scheduling algorithm. The optimal flight scheduling scheme. 3) the system can show the optimal value of each iteration of the algorithm and draw the variable line diagram. 4) the system can display the optimal scheduling results.
【学位授予单位】：中国民航大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F562;TP301.6

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