分布式全局调度动态反馈自由飞算法

在诸多空管算法基础上,提出了分割空域航线和时间并压缩的研究范式。单机根据空域航线点阵和时间窗矩阵的环境参数进行全局路径寻优,然后沿全局路径模拟飞行进行局部路径寻优,并将获得的环境参数随时回写空域航线点阵和时间窗矩阵,使得全局优化和局部优化动态反馈并逼近收敛一致。算法突破传统算法难以兼顾分布优化与集中优化的局限。

A New Meta Heuristic Approach for Aircraft Landing Problem

A new meta-heuristic approach is proposed in this paper based on a new composite dispatching rule to tackle the aircraft landing problem(ALP).First,the ALP is modeled as a machine scheduling problem with the objective of minimizing the total penalty,i.e.,total weighted earliness plus total weighted tardiness.Second,a composite dispatching rule,minimized penalty with due dates and set-ups(MPDS),is presented to determine the landing sequence.Then,an efficient heuristic approach is proposed to solve the problem by integrating the MPDS rule and CPLEX solver.In the first stage,the landing sequence is established based on the proposed MPDS rule.In the second stage,landing time is optimized using CPLEX solver.Next,a new meta-heuristic strategy is introduced into the heuristic approach by conducting the local search from the potential landing sequences,which are generated by the proposed MPDS rule.Finally,the performance of the proposed approach is evaluated using a set of benchmark instances taken from the OR library.The results demonstrate the effectiveness and efficiency of the proposed approaches.

Bilevel Programming Model for Joint Scheduling of Arrival and Departure Flights Based on Traffic Scenario

In order to meet the needs of collaborative decision making,considering the different demands of air traffic control units,airlines,airports and passengers in various traffic scenarios,the joint scheduling problem of arrival and departure flights is studied systematically.According to the matching degree of capacity and flow,it is determined that the traffic state of arrival/departure operation in a certain period is peak or off-peak.The demands of all parties in each traffic state are analyzed,and the mathematical models of arrival/departure flight scheduling in each traffic state are established.Aiming at the four kinds of joint operation traffic scenarios of arrival and departure,the corresponding bi-level programming models for joint scheduling of arrival and departure flights are established,respectively,and the elitism genetic algorithm is designed to solve the models.The results show that:Compared with the first-come-firstserved method,in the scenarios of arrival peak&departure off-peak and arrival peak&departure peak,the departure flight equilibrium satisfaction is improved,and the runway occupation time of departure flight flow is reduced by 38.8%.In the scenarios of arrival off-peak&departure off-peak and departure peak&arrival off-peak,the arrival flight equilibrium delay time is significantly reduced,the departure flight equilibrium satisfaction is improved by 77.6%,and the runway occupation time of departure flight flow is reduced by 46.6%.Compared with other four kinds of strategies,the optimal scheduling method can better balance fairness and efficiency,so the scheduling results are more reasonable.

Arrival Pattern Recognition and Prediction Based on Machine Learning

A data-driven method for arrival pattern recognition and prediction is proposed to provide air traffic controllers(ATCOs)with decision support. For arrival pattern recognition,a clustering-based method is proposed to cluster arrival patterns by control intentions. For arrival pattern prediction,two predictors are trained to estimate the most possible command issued by the ATCOs in a particular traffic situation. Training the arrival pattern predictor could be regarded as building an ATCOs simulator. The simulator can assign an appropriate arrival pattern for each arrival aircraft,just like real ATCOs do. Therefore,the simulator is considered to be able to provide effective advice for part of the work of ATCOs. Finally,a case study is carried out and demonstrates that the convolutional neural network(CNN)-based predictor performs better than the radom forest(RF)-based one.