Solving push-out conflicts in apron taxiways of airports by a network-based simulation

At busy airports, push-out conflicts occur simultaneously in apron taxiways and affect gate assignment operations in apron control. In current manual operations, it is not easy for apron controllers to consider and solve such conflicts in an exact and efficient manner, due to the uncertainties involved in the operations, the difficulties in efficiently controlling and solving push-out conflicts, and also the fact that too many more important factors should be considered in these real-time operations. At most airports today, a buffer time between the use of gates is introduced in gate assignment operations to reduce the impact of push-out conflicts to the delay of aircraft getting into, or leaving from, a gate. From the standpoint of gate assignment operations in apron control, this study analyzes aircraft ground movements and conflicts in apron taxiways. For coping with increasing air traffic demand and minimizing delays caused by inefficient scheduling of aircraft ground movements, and their effect on the gate assignment operations, a network-based model is proposed to perform simulation to support the analysis of the effect of aircraft ground movements in apron taxiways to gate assignment operations. By the proposed method, the delay characteristics of aircraft ground movements in apron taxiways and the activities on gates can be analyzed and studied in detail. Furthermore, the results of the simulation are applied to solve push-out conflicts and minimize delays to aircraft on gates in apron control. Computational results of experiments are also presented and discussed.     

Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment

Gate is a key resource in the airport, which can realize rapid and safe docking, ensure the effective connection between flights and improve the capacity and service efficiency of airport. The minimum walking distances of passengers, the minimum idle time variance of each gate, the minimum number of flights at parking apron and the most reasonable utilization of large gates are selected as the optimization objectives, then an efficient multi-objective optimization model of gate assignment problem is proposed in this paper. Then an improved adaptive particle swarm optimization(DOADAPO) algorithm based on making full use of the advantages of Alpha-stable distribution and dynamic fractional calculus is deeply studied. The dynamic fractional calculus with memory characteristic is used to reflect the trajectory information of particle updating in order to improve the convergence speed. The Alpha-stable distribution theory is used to replace the uniform distribution in order to escape from the local minima in a certain probability and improve the global search ability. Next, the DOADAPO algorithm is used to solve the constructed multi-objective optimization model of gate assignment in order to fast and effectively assign the gates to different flights in different time. Finally, the actual flight data in one domestic airport is used to verify the effectiveness of the proposed method. The experiment results show that the DOADAPO algorithm can improve the convergence speed and enhance the local search ability and global search ability, and the multi-objective optimization model of gate assignment can improve the comprehensive service of gate assignment. It can effectively provide a valuable reference for assigning the gates in hub airport.     

Multi-commodity flow network model of the flight gate assignment problem

The flight gate assignment problem is encountered by gate managers at an airport on a periodic basis. This assignment should be made so as to balance carrier efficiency and passenger comfort, while providing buffers for unexpected events that cause assignment disruptions. In this paper, a binary integer multi-commodity gate flow network model is presented with the objective of minimizing the fuel burn cost of aircraft taxi by type and expected passenger discomfort for “tight” connections as a function of inter-gate distance and connection time. This approach is shown to be computationally efficient within a decomposition approach for large problem instances. A numerical application of this approach is given for the gating of Continental Airlines at George W. Bush Intercontinental Airport in Houston (IAH).     

A knowledge-based airport gate assignment system integrated with mathematical programming

Airport gate assignment is the process of selecting and allocating aircraft to gates to create an assignment schedule, and it is one of the major functions of airport operations. With the increase of passenger traffic volumes and the number of flights, the complexity of this task and the factors to be considered have increased significantly, and efficient gate utilization has received considerable attention. This paper proposes a knowledge-based airport gate assignment system integrated with mathematical programming techniques to provide a solution that satisfies both static and dynamic situations within a reasonable computing time. A partial parallel assignment is introduced, which considers a group of aircraft and looks at all the available gates and then does the gate assignments by optimizing a multi-objective function. For the validation of the proposed approach, an example is used as a case study, and a prototype system with various functions has been developed in a microcomputer environment.     

Multi-commodity flow network model of the flight gate assignment problem

The flight gate assignment problem is encountered by gate managers at an airport on a periodic basis. This assignment should be made so as to balance carrier efficiency and passenger comfort, while providing buffers for unexpected events that cause assignment disruptions. In this paper, a binary integer multi-commodity gate flow network model is presented with the objective of minimizing the fuel burn cost of aircraft taxi by type and expected passenger discomfort for “tight” connections as a function of inter-gate distance and connection time. This approach is shown to be computationally efficient within a decomposition approach for large problem instances. A numerical application of this approach is given for the gating of Continental Airlines at George W. Bush Intercontinental Airport in Houston (IAH).     

基于松弛算法的停机位分配优化方法

针对机场近机位资源紧缺及实际航班到离港时间偏离计划时间对停机位分配所造成的扰动，提出了在同机位相邻航班间加入缓冲时间的停机位分配调度方法。首先，建立了以机位空闲时间、远机位占用时间最小为目标的鲁棒性停机位分配模型；然后，设计了一种基于双目标的拉格朗日松弛优化算法，并使用次梯度算法求解拉格朗日松弛算法中的对偶问题。基于国内某枢纽机场运行数据的仿真结果表明，所提方法的优化方案和原始机位分配方案相比，机位使用量和机位空闲时间分别降低了15.79%、7.56%，机位占用率提高了18.72%，并且冲突率降低到3.57%，达到了有效提高停机位利用率与鲁棒性的目的。     

舰载机甲板停机位分配问题研究

舰栽机甲板停机位分配问题关系到航母飞行甲板作业的效率,合理的停机位分配对于提高甲板作业效率十分重要。对舰载机停机位分配问题进行了详细研究,以舰载机移动路径、加油路径以及挂弹路径总和最小为目标函数,建立了甲板停机位分配模型,利用贪心算法并结合模拟退火算法对模型进行了求解,仿真结果表明,该模型对停机位的分配合理。     

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。     

多机场地面等待策略数学模型的研究

为有效地解决空中交通拥挤问题,研究了空中交通流量管理方法中的地面等待策略。通过地面等待来调节空中交通网络的流量,减少延误时间,从而减少经济损失,提高机场和空域资源的利用率。在空中交通流量管理的各种方法中,地面等待策略是一种比较有效的方法。针对多机场地面等待问题,提出一种以地面等待成本和延迟成本为目标函数的方法,并建立相应的数学模型,为实际的流量管理提供理论方法和依据。通过对机场数据的仿真计算,证明了该模型的有效性和可行性。     

复合类别航站楼分配问题的改进和声搜索算法

随着航空运输业的蓬勃发展,如何在硬件条件受限的情况下尽量提高机场的运行效率来满足日益增长的航班起降需求,日益受到关注.为了对机场航站楼登机门分配问题进一步优化,提出一种考虑登机门复合类别的航站楼分配问题,并建立数学模型,描述在航线类别、班机型号以及最短停靠间隔对于登机门选取的约束下,带有临时停机坪辅助的登机门分配优化问题.在模型经过精确算法验证的基础上,为适应登机门问题特性并求解中大规模问题,首次引进和声搜索算法,增加复杂约束条件,对编码解码、初始解产生以及寻优过程进行改进,提出一种更高效的改进和声搜索算法对模型进行求解.通过使用Lingo软件和Matlab软件对中小规模算例分别进行精确求解和智能算法求解,对比表明所提出智能算法的有效性、全局搜索能力以及求解效率.再通过对大规模问题的求解,表明所提出算法在现有条件下能够减小转机旅客的总转机路程,取得了较好的效果.     

Robust assignment of airport gates with operational safety constraints

This paper reviews existing approaches to the airport gate assignment problem (AGAP) and presents an optimization model for the problem considering operational safety constraints. The main objective is to minimize the dispersion of gate idle time periods (to get robust optimization) while ensuring appropriate matching between the size of each aircraft and its assigned gate type and avoiding the potential hazard caused by gate apron operational conflict. Genetic algorithm is adopted to solve the problem. An illustrative example is given to show the effectiveness and efficiency of the algorithm. The algorithm performance is further demonstrated using data of a terminal from Beijing Capital International Airport (PEK).     

基于改进火力分配模型的综合防空火力智能优化分配

针对综合防空火力分配中,传统火力分配模型没有全面考虑火力通道射击效能因素,且在火力资源相对充足的情况下火力资源易浪费和易延误战机的问题,将射击有利度、飞临时间与威胁度等因素结合,构建一种新的综合防空火力分配模型.基于此模型,针对来袭目标、火力节点以及制导节点3者之间的火力优化匹配问题,提出一种基于深度优先搜索预处理的多种群并行布谷鸟搜索算法(PMPCSA).该方法采用Prolog智能规划语言构建目标-火力节点-制导节点匹配规则库,在规则库中利用深度优先搜索快速生成可行的目标-火力节点-制导节点的匹配预处理方案;采用多种群并行布谷鸟搜索算法,对得到的可行分配空间进行搜索,通过引入多种群并行优化搜索,不同种群赋予不同控制参数的思想,兼顾算法的全局探索和局部开发能力,可有效提高算法寻优效率.最后,通过仿真结果验证所建综合防空火力分配模型的优势性,同时表明,所提出的PMPCSA算法能有效地平衡全局探索与局部开发,在保证较高收敛速度的同时,提高全局探索能力.     

The over-constrained airport gate assignment problem

In this paper, we study the over-constrained airport gate assignment problem where the objectives are to minimize the number of ungated flights and total walking distances or connection times. We first use a greedy algorithm to minimize ungated flights. Exchange moves are employed to facilitate the use of heuristics. Simulated annealing and a hybrid of simulated annealing and tabu search are used. Experimental results are good and exceed those previously obtained.     

Integration control for traffic corridors considering guidance information

The problem of designing integration traffic strategies for traffic corridors with the use of ramp metering, speed limit, and route guidance is considered in this paper. As an improvement to the previous work, the presented approach has the following five features： 1） modeling traffic flow to analyze traffic characteristics under the influence of variable speed limit, on-ramp metering and guidance information; 2） building a hierarchy model to realize the integration design of traffic control and route guidance in traffic corridors; 3） devising a multi-class analytical dynamic traffic assignment （DTA） model for traffic corridors, where not only the route choice process will be different for each user-class, but also the traffic flow operations are user-class specific because the travel time characteristic for each user-class is considered; 4） predicting route choice probabilities adaptively with real-time traffic conditions and route choice behaviors corresponding to variant users, rather than assuming as pre-determined; and 5） suggesting a numerical solution algorithm of the hierarchy model presented in this paper based on the modified algorithm of iterative optimization assignment （IOA）. Preliminary numerical test demonstrates the potential of the developed model and algorithm for integration corridor control.     

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)     

一种改进的自适应遗传算法求解专家分配问题

基金项目管理中，专家分配问题的研究具有很现实的意义。在解决专家分配问题上做过一些基础性的工作，提出了使用遗传算法及一种信息素指导变异的新算法求解该问题。实验证明，遗传算法是一种可行的途径，并且信息素指导下的启发式变异操作，可以加速算法向最优解搜索。但是，这两种方法都存在局部搜索能力差的问题，在算法运行的中后期会出现大量的冗余迭代。鉴于此，提出一种信息素指导下的自适应变异方法求解专家分配问题。实验证明，新算法具有更强的收敛能力和局部搜索能力。     

Multi-objective adaptive large neighborhood search for distributed reentrant permutation flow shop scheduling

Factory management plays an important role in improving the productivity and quality of service in the production process. In particular, the distributed permutation flow shop scheduling problem with multiple factories is considered a priority factor in the factory automation. This study proposes a novel model of the developed distributed scheduling by supplementing the reentrant characteristic into the model of distributed reentrant permutation flow shop (DRPFS) scheduling. This problem is described as a given set of jobs with a number of reentrant layers is processed in the factories, which compromises a set of machines, with the same properties. The aim of the study is to determine the number of factory needs to be used, jobs assignment to certain factory and sequence of job assigned to the factory in order to simultaneously satisfy three objectives of minimizing makespan, total cost and average tardiness. To do this, a novel multi-objective adaptive large neighborhood search (MOALNS) algorithm is developed for finding the near optimal solutions based on the Pareto front. Various destroy and repair operators are presented to balance between intensification and diversification of searching process. The numerical examples of computational experiments are carried out to validate the proposed model. The analytical results on the performance of proposed algorithm are checked and compared with the existing methods to validate the effectiveness and robustness of the proposed potential algorithm in handling the DRPFS problem.     

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。     

Mathematical modeling and heuristic approaches to flexible job shop scheduling problems

Scheduling for the flexible job shop is very important in both fields of production management and combinatorial optimization. However, it is quite difficult to achieve an optimal solution to this problem in medium and actual size problem with traditional optimization approaches owing to the high computational complexity. For solving the realistic case with more than two jobs, two types of approaches have been used: hierarchical approaches and integrated approaches. In hierarchical approaches assignment of operations to machines and the sequencing of operations on the resources or machines are treated separately, i.e., assignment and sequencing are considered independently, where in integrated approaches, assignment and sequencing are not differentiated. In this paper, a mathematical model and heuristic approaches for flexible job shop scheduling problems (FJSP) are considered. Mathematical model is used to achieve optimal solution for small size problems. Since FJSP is NP-hard problem, two heuristics approaches involve of integrated and hierarchical approaches are developed to solve the real size problems. Six different hybrid searching structures depending on used searching approach and heuristics are presented in this paper. Numerical experiments are used to evaluate the performance of the developed algorithms. It is concluded that, the hierarchical algorithms have better performance than integrated algorithms and the algorithm which use tabu search and simulated annealing heuristics for assignment and sequencing problems consecutively is more suitable than the other algorithms. Also the numerical experiments validate the quality of the proposed algorithms.     

The use of meta-heuristics for airport gate assignment

Improper assignment of gates may result in flight delays, inefficient use of the resource, customer’s dissatisfaction. A typical metropolitan airport handles hundreds of flights a day. Solving the gate assignment problem (GAP) to optimality is often impractical. Meta-heuristics have recently been proposed to generate good solutions within a reasonable timeframe. In this work, we attempt to assess the performance of three meta-heuristics, namely, genetic algorithm (GA), tabu search (TS), simulated annealing (SA) and a hybrid approach based on SA and TS. Flight data from Incheon International Airport are collected to carry out the computational comparison. Although the literature has documented these algorithms, this work may be a first attempt to evaluate their performance using a set of realistic flight data.