多Agent的航空器滑行策略优化

快速发展的民航事业导致很多机场容量不足。为缓解大型机场交通拥堵的现状,研究了航空器滑行策略优化问题。滑行路径优化是指在特定的时间段内,根据机场资源信息和地面运行管理系统对进离场航空器在跑道和停机位之间的距离进行优化管理。通过深入剖析机场地面的网络结构,综合考虑滑行冲突、地面运行规则等因素,提出了多Agent滑行策略优化方法,该方法提升了机场资源利用率;基于地面网络链路结构的概念,建立了航空器滑行策略优化模型;结合多Agent的基本理论,设计了跑道出口选择概率函数和多Agent系统滑行路径优化结构,以寻求航空器的最优滑行路径。以国内某大型机场的实际情况为研究背景进行了航空器滑行策略实验,结果表明,与以往的算法相比,多Agent滑行策略优化方法的效果更为显著。设置跑道口的速度和同一交叉口航空器的最小间隔距离,通过对跑道出口的选择和Agent间的交互协商,航空器能够对原滑行路径进行有效调整,并缩短其在机场场面上的滑行时间。与最短路径算法相比,多Agent滑行策略方法在航空器的总滑行距离、航空器在滑行道上的密度以及平均等待时间方面的优化效果更好,且其对滑行道资源的分配更合理。其中,航空器在节点处的平均等待时间减少了8.26%。所提策略可有效缓解机场交通拥堵的现状,提高场面运行效率,对减少航空器延误和保障机场的运营安全具有重要意义。     

On-line decision support for take-off runway scheduling with uncertain taxi times at London Heathrow airport

This paper addresses the challenge of building an automated decision support methodology to tackle the complex problem faced every day by runway controllers at London Heathrow Airport. Aircraft taxi from stands to holding areas at the end of the take-off runway where they wait in queues for permission to take off. A runway controller attempts to find the best order for aircraft to take off. Sequence-dependent separation rules that depend upon aircraft size, departure route and speed group ensure that this is not a simple problem to solve. Take-off time slots on some aircraft and the need to avoid excessive delay for any aircraft make this an even more complicated problem. Making this decision at the holding area helps to avoid the problems of unpredictable push-back and taxi times, but introduces a number of complex spatial constraints that would not otherwise exist. The holding area allows some flexibility for interchange of aircraft between queues, but this is limited by its physical layout. These physical constraints are not usually included in academic models of the departure problem. However, any decision support system to support the take-off runway controller must include them. We show, in this paper, that a decision support system could help the controllers to significantly improve the departure sequence at busy times of the day, by considering the taxiing aircraft in addition to those already at the holding area. However, undertaking this re-introduces the issue of taxi time uncertainty, the effect of which we explicitly measure in these experiments. Empirical results are presented for experiments using real data from different times of the day, showing how the performance of the system varies depending upon the volume of traffic and the accuracy of the provided taxi time estimations. We conclude that the development of a good taxi time prediction system is key to maximising the benefits, although benefits can be observed even without this.     

基于改进A^*算法的机场场面滑行路径优化

为了缓解机场场面交通拥挤状况,提高空侧交通运行效率和安全性,以优化进离场航班的滑行路径为目标,通过分析机场场面滑行路径优化问题,构建了机场场面滑行路径优化模型,并提出基于变步长的滑动时间窗方法和A*算法相结合的模型求解算法。以北京首都国际机场为例进行算例分析,与未考虑冲突的路径优化方案相比较,上述方法得到的总滑行时间节省121秒,通过算法实现对滑行冲突的智能识别与解脱,可为繁忙机场的调度工作提供了决策支持。     

An efficient hybrid particle swarm optimization algorithm in a rolling horizon framework for the aircraft landing problem

This paper proposes a hybrid particle swarm optimization algorithm in a rolling horizon framework to solve the aircraft landing problem (ALP). ALP is an important optimization problem in air traffic control and is well known as NP-hard. The problem consists of allocating the arriving aircrafts to runways at an airport and assigning a landing time to each aircraft. Each aircraft has an optimum target landing time determined based on its most fuel-efficient airspeed and a deviation from it incurs a penalty which is proportional to the amount of deviation. The landing time of each aircraft is constrained within a specified time window and must satisfy minimum separation time requirement with its preceding aircrafts. The objective is to minimize the total penalty cost due to deviation of landing times of aircrafts from the respective target landing times. The performance of the proposed algorithm is evaluated on a set of benchmark instances involving upto 500 aircrafts and 5 runways. Computational results reveal that the proposed algorithm is effective in solving the problem in short computational time.     

时间有色佩特里网在航班进离港中应用

为了解决目前和以后越来越严重的空中交通拥挤问题,文中对终端区空中交通流量管理中航班进离港部分连续航班动态飞机排序问题进行研究.提出了基于时间有色佩特里网(TCPN)的航班进离港的调度方法,用MATLAB软件中的M函数编写了算法程序,然后进行了仿真.仿真结果表明,该方法选用的算法是合理的,而且可以明显的减少航班总的延误,优化效率高,算法简单可行,而且更符合实际的要求.该方法在一定程度上可以帮助空管人员进行调度决策,从而更加快速有序地对飞机进离港进行调度,对目前空管优化调度,减少延误等情况的处理具有一定的参考研究价值.     

Methods for Operations Planning in Airport Decision Support Systems

Simulation and decision support tools can help airport ground controllers to improve surface operations and safety, leading to enhancements in the process of traffic flow management. In this paper, two planning approaches for automatically finding the best routes and sequences for demanded operations are proposed and analyzed. These approaches are integrated into a general decision support system architecture. The problem addressed is the global management of departure operations, moving aircraft along airport taxiways between gate positions and runways. Two global optimization approaches have been developed together with a suitable problem representation: a modified time-space flow algorithm and a genetic algorithm, both aimed at minimizing the total ground delay. The capability and performance of these planning techniques have been illustrated on simulated samples of ground operations at Madrid Barajas International Airport.This work was funded by CICYT (TIC2002-04491-C02-02) and CAM (07T/0034/2003 1)     

终端区航班排序的遗传算法

为提高空域利用率和航班调度效率,建立了以航班延误总时间为目标函数的规划模型,利用遗传算法中的交叉和变异运算,针对多跑道航班排序问题设计了编码方式和遗传算子,进行了仿真计算,并与先到先服务算法进行了对比研究.同时,结合空中交通管制中的实际情况,引入了一些约束条件.以三跑道为例,仿真结果表明:与先到先服务算法相比,设计的遗传算法大大降低了时间延误,明显提高了航班调度效率.     

基于IBA-LSSVM强迫选择模型的民用飞机着陆滑行仪表灯常亮故障关联检测

民用飞机着陆滑行仪表灯是故障检测系统的重要组成部分，因民用飞机在地面滑行过程中，电气接口易受到电磁干扰，引起着陆滑行仪表灯常亮故障，在飞行过程中增加了安全隐患。为此，提出引入IBA-LSSVM强迫选择模型的民用飞机着陆滑行仪表灯常亮故障关联检测方法。分析仪表灯常亮故障产生的原因，利用Python编程数据采集飞机起飞、近进、降落、地面滑行时的照明信号及电气交联数据，通过控制逻辑，建立数据关联检测逻辑关系数据库。利用自适应多普勒补偿方法改进蝙蝠算法（IBA），检测着陆滑行灯常亮故障特征，在强迫选择部分，构建IBA-LSSVM模型，将分布式博弈和线性输出调节理论相结合，抵消外部干扰，完成着陆滑行灯常亮故障检测。Matlab仿真测试结果表明：所提方法的故障识别率均在95%以上，故障识别准确率可以达到97.3%，故障识别时间低于2ms，可有效识别故障数据，降低飞行安全隐患。     

An integrated aircraft routing,crew scheduling and flight retiming model

In the integrated aircraft routing, crew scheduling and flight retiming problem, a minimum-cost set of aircraft routes and crew pairings must be constructed while choosing a departure time for each flight leg within a given time window. Linking constraints ensure that the same schedule is chosen for both the aircraft routes and the crew pairings, and impose minimum connection times for crews that depend on aircraft connections and departure times. We propose a compact formulation of the problem and a Benders decomposition method with a dynamic constraint generation procedure to solve it. Computational experiments performed on test instances provided by two major airlines show that allowing some flexibility on the departure times within an integrated model yields significant cost savings while ensuring the feasibility of the resulting aircraft routes and crew pairings.     

求解机场终端区飞机着陆调度问题的遗传算法

空中管制员需为到达的飞机安排跑道并计算着陆时间,以飞机空中延误最小为出发点研究了多跑道的飞机着陆调度问题,约束条件为每架飞机的着陆时间应落在规定的时间窗内及相邻两架飞机应满足最小时间间隔。针对该问题设计了一种遗传算法对问题进行求解,其中染色体由飞机排序链表和跑道链表组成,相应的交叉和变异算子也做了改进设计。仿真实验用数据库OR-Library中的实例验证了该算法的有效性。     

一类无冲突轨迹规划方案

在新一代空中交通管理自动化系统实施方案中,航迹规划模块在基于航迹的航空器运行过程中扮演着重要角色.为了在大流量、高密度和小间隔条件下获取多航空器无冲突轨迹,针对不同的航路空间分布结构,基于航路冲突点保护区竞争机制,构建了空域多航空器极大代数耦合模型和多航空器冲突预调配模型,根据管制间隔约束建立了模型输入、状态和输出之间的约束关系.采用调整航空器过冲突点时刻和初始放行时刻两种策略,提出了一种多航空器轨迹规划优化模型.算例分析表明,所提出的多航空器轨迹规划模型可行有效.     

基于干扰估计的非对称运动下飞机刹车系统模型预测控制

针对飞机在非对称运动下的双侧机轮协调控制问题, 提出一种基于滑模干扰估计的模型预测控制方法. 首先, 通过对飞机制动过程横纵方向力矩机理分析并分别考虑左右机轮对刹车性能的影响, 建立全面刻画系统动态的地面滑跑动力学模型. 在此基础上, 设计滑模观测器对侧风干扰进行实时估计, 利用补偿机制实现对侧风扰动的有效抑制. 此外, 提出基于前轮荷载状态门限特征和结合系数阈值范围特征的分析方法, 解决切换跑道环境辨识问题. 设计非线性模型预测算法, 实现飞机纵向防滑刹车和横向跑道纠偏的协调控制. 最后, 在侧风干扰、跑道切换以及不对称着陆等情况下进行仿真实验, 验证了所提出的控制策略能够有效提升刹车系统的防滑效率及纠偏性能.     

Modeling and analysis of departure routine in air traffic control based on Petri nets

Departure routine is essential part in the air traffic control and must be formally designed to avoid potential hazards and to verify proper functioning of the underlying processes. This paper addresses the Petri net approach to formally model the departure routine of the aircraft which ensures the organized flow of air traffic during departure. First, the high-level design of the system is presented by identifying key objects involved in departure routine, and then, its detailed model is presented. Moreover, the verification of the underlying methodology has been made using coverability tree. The proposed model is verified to be safe (bounded), potentially reversible and deadlock free which ensures reliability of the system and guarantees the efficient and controlled communication between the aircraft and local and ground controllers.     

弹道导弹和航空兵联合突击机场跑道的仿真建模方法

采用弹道导弹和航空兵对机场跑道实施联合突击是一种能够取得较好压制效果的作战运用方式.为了对整个作战过程进行客观分析,需要对其进行整体性的仿真.首先,分析了联合突击中的任务分工和交战行动过程.然后,建立了进行整体性仿真建模的框架,分析了其中的模型种类.其次,根据仿真建模框架,对其中的弹道导弹突击机场、机场跑道修复、空战、地空对抗、空地突击机场等作战过程及压制效果进行判断等,分别建立了模型算法.最后,设计了一个算例来验证仿真建模方法的有效性.     

航班滑行路径规划和停机位分配联合优化

针对机场的航班滑行路径规划和停机位分配的联合优化调度问题,构建基于冲突回避的滑行道与停机位联合调度模型,并提出改进的自适应差分进化算法求解问题。以最小化航班的滑行时间和旅客转机的行走时间为优化目标,建立非线性混合整数规划联合调度模型。设计考虑了滑行冲突的路径规划算法,完成航班的滑行路径分配,并通过自适应动态调整差分进化算法参数,引入个体位置边界的扰动策略,进一步改善了算法的搜索性能。对国内某枢纽机场的仿真结果表明,所提出的算法可以有效避免滑行冲突,缩短滑行时间,是能够兼顾旅客服务质量和航班场面滑行效率的有效方法。     

Data mining modeling on the environmental impact of airport deicing activities

This paper presents a statistical analysis on the environmental impact of airport deicing activities at Dallas-Fort Worth (D/FW) International Airport. The focus of this paper is on aircraft deicing, which typically uses a spray of aircraft deicing and anti-icing fluids (ADAF). ADAF has a high concentration of ethylene/propylene/diethylene glycol, which shears off airfoil surfaces during takeoff and drips to the runways during taxiing. A significant portion of the glycol runs off and mixes with the airport’s receiving waters during heavy deicing periods, resulting in bacterial growth that causes an increase in chemical oxygen demand (COD) and a subsequent reduction in dissolved oxygen (DO) in the receiving waters. In this study, statistical methods for data mining were employed to evaluate the impact of airport deicing activities on COD and DO in the receiving waters immediately surrounding D/FW Airport. In particular, decision tree models were developed to determine important explanatory variables for predicting levels of COD and DO in the airport’s waterways. The decision tree modeling and analysis of COD determined north–south wind, glycol usage at a specific deicing pad, and monitoring site to be significant explanatory variables. The impact of glycol usage on DO was apparent as every decision tree at least one group with a median DO below 4.0 mg/l, and these low-DO groups were associated with high glycol usage. These results are crucial to D/FW Airport in their goal to minimize the potential adverse impact of deicing activities on the water quality in waterways proximate to the airport. The advantages of data-driven modeling and analysis are its cost-effectiveness due to its potential to be implemented without making major changes in physical systems, ease of application, and usefulness in making future management decisions.     

A Dynamic Transportation Model for the Stockholm Area: Implementation Issues Regarding Departure Time Choice and OD-pair Reduction

Road traffic congestion is an increasing problem in urban areas. Building new roads often attracts latent demand and turns parts of the city into building sites for several years. Policy measures that stimulate more effective use of the existing network, such as variable road pricing, are therefore becoming increasingly popular among policy makers and citizens. These measures are often aimed at changing the temporal distribution of traffic. Yet transportation models taking departure time choice into account are rare. This paper describes the implementation of an urban transportation application for Stockholm, which includes departure time choice, mode choice and time dependent network assignment. Through iterations between demand and supply the objective of the transportation model is to forecast effects of congestion charges, intelligent transport systems and infrastructure investments on departure time choice. The complexity of large-scale departure time choice modelling and dynamic traffic assignment is high, which results in very long run times. Therefore, research on how to increase model efficiency is needed. This paper describes choices made in the implementation for a more efficient model.     

基于满意度准则的机场终端区容量利用和流量分配的多目标优化模型研究

为优化跑道容量和定位点空中交通流量,将机场终端区的进离港定位点和跑道看作一个系统;在 服从整个系统容量限制的前提下,引入了满意度准则,通过分析跑道容量和定位点流量的相互影响,建立了 多目标优化模型．算例研究表明,通过对进离港航班流量需求的协调分配,给定时段内的所有航班均得到有 效调度．整体航班需求排队队列减少了约10%,容量利用满意度为0.75,在机场终端区实现了跑道容量的有 效利用和定位点流量的合理分配．     

人工鱼群—粒子群混合算法优化进港航班排序

针对空中交通管理中的进港航班排序问题, 提出了人工鱼群—粒子群混合算法(AFPSO)这一航班排序算法来优化进港航班排序, 使时段内进港航班队列总延误时间最少。算法结合了基本人工鱼群算法(AFSA)和基本粒子群算法(PSO)各自的优点, 先以AFSA在全局寻找满意的解域, 再以PSO算法在这些解域中进行快速的局部搜索获得精确解, 最终使算法提高收敛速度和搜索精度。仿真结果表明, 在单跑道和双跑道情况下, AFPSO算法使得航班队列总延误时间比FCFS调度方法减少了20. 9%和34. 4%, 比基本AFSA减少了3. 2%和3. 5%。算法得到的满意解能够为自动化空中交通管理提供实时支持。     

基于背压路由算法的离港飞机滑行路径优化

针对现有优化算法仅从最短路径或最小滑行时间的角度出发,忽略了航空公司满意度和滑行道负载率对滑行道调度影响的问题,提出了基于背压路由的离港滑行路径优化算法．算法首先将离港滑行路径优化问题等价转化为网络拓扑结构中的路由搜索问题,然后利用背压路由算法求解具有最大航空满意度和最小滑行道负载率的滑行路径．对国内某枢纽机场实际运行数据的仿真结果表明,运用该算法获得的滑行路径在保证跑道及滑行道效用的同时,能够有效减少飞机滑行时间,改善机场拥塞问题,缓解机场容量与需求的矛盾,提高机场运行效率,为离港航班滑行路径优化提供了新的研究思路．