Sequencing landing aircraft process to minimize schedule length

2010

This paper studies the problem of sequencing aircraft take-off and landing operations at congested airports. We introduce and analyze alternative detailed formulations and solution algorithms for scheduling arrival and departure times of the aircrafts, such that the delay with respect to the scheduled times is minimized. The aircraft scheduling problem (ASP) is viewed as an extension of the job shop scheduling problem with additional real-world constraints and formulated by using alternative graphs. Two alternative formulations model the required time separation among aircrafts in air segments and runways according to safety regulations and differ for the level of detail used to represent the holding circles. Scheduling rules, heuristic and exact methods are implemented and tested on practical size instances of the Fiumicino airport, the busiest airport in Italy. We show that two versions of an innovative branch and bound algorithm are always able to find good solutions in a few seconds and often improve the best solution computed by the scheduling heuristics. Optimality is proved in less than two minutes for more than half of the instances.

2008

This work contributes to the development of microscopic traffic performance models in the airport. It enhances the existing models and develops new ones. An important contribution of this research is the empirical work, i.e. estimating models using statistically rigorous methods and microscopic data collected from real traffic. With the ever increasing congestion at airports around the world, studies into ways of maximizing infrastructures capacity and minimizing delay costs while meeting the goals of the airlines are necessary. The methodology applied for the calculation of runway capacity start from the traffic data elaboration of the Naples International Airport: we have been determined this aim from the airline pattern in the above airport. The hourly capacity is calculated as the inverse of the headway of consecutive aircraft operation; this is drawn with an average of the time headway in the "critical periods". The determination of the "capacity periods" it happens in three phases: in the first one we are drawn by the sample the stationary periods; in the second we are considered, of these, only those with the lowest averages time headway, these are called "critical" periods; subsequently we are examined only that (critical periods) had time length less than the 60 minutes and that have an average of time headway that it is almost attested around to a constant value. The stationary periods, as it says the same definition, are characterized by the relatives constant time headway, in other words there aren't meaningful phenomenon of increase or diminution of the traffic flow. The critical periods are static periods that are found on the traffic flow curve defined "critic", this curve we have obtained to envelop some values that mark the lower limit of the diagram defined time length vs. averages of the static periods. Defined the critical periods we have been determined the experimental headway curves and we compared with those theoretical. Of such experimental curves we have been considered the characteristics values: the average and the standard deviation. In this way we have determined a matrix of the average time headway both for operations flight that for type of airplane. The following step has been that to define and to implement the analytical model. We suggest a mathematic algorithm that is able to optimize, a posteriori, the flight operation under the delay restraint. The algorithm determines the best sequence of aircrafts that minimizes the delays and it maximizes the runway capacity. The proposed methodology, even if determine an evident improvement of the runway capacity, in the respect of thresholds of acceptable average delays (constrain), represents an initial methodological phase that will desirably conclude in the determination of a "dynamic" model, that is able to assist the inspectors' job in way real-time, assigning, opportunely, an excellent sequence of the successions of flight operations.

Intelligent Decision Technologies, 2017

In typical cases, air traffic controllers make the schedule for arrivals and departures of aircraft on a runway based on the pre prepared schedule, where aircraft that are scheduled first are served first (a.k.a. First Come First Served-FCFS rule). In practice, it often happens that two or more aircraft are scheduled at the same time, and since only one can be served at a time, the others have to be shifted from service at a later time. Hence, the main issue stands at selecting the aircraft that have to be shifted, as, in general, their delay is correlated to excessive expense based on various factors, such as number of passengers, type of aircraft, precedence, ambient pollution, etc. For schedules with a large number of aircraft, deciding manually-which aircraft should be shifted based on FCFS sequence becomes quite complex. Consequently, in this paper, we present a genetic based algorithm to solve the problem of aircraft sequencing in a runway within a computation time of dozens of seconds by using a computing device with standard processing and memory capabilities. The results of the proposed algorithm are compared against three state of the art algorithms on existing test sets that in total consist of 527 instances. For 49.34% of instances, the proposed algorithm finds the optimal solutions, while its results are also quite competitive for difficult instances when compared against a state of the art solution based on the tabu search meta-heuristic. The computational results show that the proposed approach can be easily adapted and fine-tuned for application in practice.

1990

TA time-advance optimization scheduling method TMA Traffic Management Advisor randomness of the arrival times of aircraft in the terminal area. CPS must, therefore, be applied to individual groups of aircraft as we have done here, or the algorithm's performance index must be rewritten from that given in appendix A, so that it minimizes the sum of the scheduled flight times instead.

2008

Due to a dramatic increase in air traffic around the globe, the tasks for air traffic controllers have increased multifold. This work aims to develop an air traffic control to prioritize landing sequences assigned to planes when, unexpectedly, a large number of planes approach the airfield. Two alternatives are proposed: one is based on rule-based expert system and another on artificial neural networks. The author shows that each of the models helps to optimize the prioritization of overall landing requests, with exception only to a situation of a larger number of emergencies. Further, a combination of these approaches is discussed to show that it does help in minimizing time to handle landing sequences during emergencies.

2014

Due to an anticipated increase in air traffic during the next decade, air traffic control in busy airports is one of the main challenges confronting the controllers in the near future. Since the runway is often a bottleneck in an airport system, there is a great interest in optimizing the use of the runway. The most important factors in aircraft landing modeling are time and cost. For this reason, Aircraft Landing Scheduling Problem (ASLP) is a typical hard multi-constraint optimization problem and finding its efficient solution would be very difficult. So in real applications finding the best solution is not the most important issue and providing a feasible landing schedule in an acceptable time would be the preferred requirement. In this study a three objectives formulation of the problem proposed as a mathematical programming model on a runway in static mode. Problem is solved by multi-objective genetic algorithm (NSGA-II) and multi-objective Particle Swarm Optimization Algorithm...

In this percent decade's, airport scheduling operation are most essential for aircraft landing and takeoff. The radar range control systems act as the brain for the aircraft scheduling operations. Arrival runways are a critical resource in the air traffic system. Arrival delays have a great impact on airline operations and cost. Radar system is to communicate with all the aircrafts within the 200 nautical miles (370 km). In this paper the technique describes the execution time and penalty cost of the each aircrafts. Throughout, we discuss how our formulations can be utilized to model a number of issues (aircraft selection, precedence restrictions, restricting the number of landings and takeoffs in a given time period, runway workload balancing) commonly encountered in practice. Existing techniques does not considered the timing factor, so based on the time factor penalty cost is very high. Many of the techniques are used to reducing the penalty cost whenever possible to landing and takeoff operations are done for the emergency flights. These experiment shows whenever flights landing on the runway at that time no congestion on that particular path, if it's occur then its seems to be problem. In order to eradicate these problem, neural network and Genetic Algorithms are utilized to eradicate the congestion occur in the runway and also our proposed technique reduced the penalty cost to be charged.

In a scenario characterized by a continuous growth of air transportation demand, the runways of large airports serve hundreds of aircraft every day. Aircraft sequencing is a challenging problem that aims to increase runway capacity in order to reduce delays as well as the workload of air traffic controllers.

This paper examines the Aircraft Sequencing Problem (ASP) over multiple runways, under mixed mode operations with the objective of minimizing the total weighted tardiness of aircraft landings and departures simultaneously. The ASP can be modeled as a parallel machine scheduling problem with unequal ready-times, target times and deadlines. Furthermore, sequence-dependent separation times on each runway are considered to prevent the dangers associated with wake-vortex effects. Due to the problem being NP-hard, greedy heuristics and metaheuristics are applied in this paper to obtain solutions in reasonable computation times. The algorithms' solutions are compared to optimal solutions and their performances are evaluated in terms of solution quality and CPU time.

2020

This work presents an analysis of arrival sequencing at Stockholm Arlanda airport. Thesequencing of arrivals is very important part of air traffic control management and assuressafe space and time distancing of arriving aircraft. In this work we use historical flight datafrom Opensky Network database. The historical flight data contains the information about allthe arrivals of the year 2018. The aim of this work is to propose the key performanceindicators (KPIs) for evaluation of the arrival sequencing at Stockholm Arlanda airport. Thethree KPIs we are considering in this work are the minimum time to final, spacing deviationand sequence pressure. We choose data subsets of different size representing different trafficsituations. We visualize the results and summarize them in tables which assures better clarityfor the comparison of the same KPIs for different data subsets. In addition, we demonstratehow the proposed KPIs can be used for evaluation of optimization results from related ...