Multiobjective tactical planning under uncertainty for air traffic flow and capacity management

Caron, Gaétan Marceau; Savéant, Pierre; Schoenauer, Marc

doi:10.1109/cec.2013.6557746

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…These safety margins should reflect the degree of confidence in the trajectory prediction of the system and defined in function of uncertainty given by our model. [6] gives further details on the problem and the chosen model.…”

Section: Modelmentioning

confidence: 99%

Multiobjective optimization for reducing delays and congestion in air traffic management

Marceau

Savéant

Schoenauer

2013

Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation

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Nowadays, with the increasing traffic density of the European airspace, air traffic management includes the planning and monitoring of the capacity of the network as a way to facilitate the work of the air traffic controllers. This article presents the use of an evolutionary multiobjective algorithm for optimizing a schedule on the time of overflight of the waypoints. The evaluation function is defined as a probabilistic model, which reports the expected delays and the expected congestion giving the schedule. We believe that the robustness of such decision-support tool can only arise from a precise modeling of the uncertainty at a trajectory level and to propagate it to the sector level, allowing us to compute the probability of congestion. This paper gives the outline of the probabilistic model and the way it is used with an evolutionary algorithm in order to optimize the schedule. The proposed approach is tested against two artificial instances where one consists of 300 aircraft and 16 sectors.

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Section: Modelmentioning

confidence: 99%

Multiobjective optimization for reducing delays and congestion in air traffic management

Marceau

Savéant

Schoenauer

2013

Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation

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“…In this section, we briefly present the mathematical model wich is based on [13] and [14]. It consists of two submodels, the flight model and the sector model.…”

Section: The Mathematical Modelmentioning

confidence: 99%

Multi-objective optimization approach for air traffic flow management

et al. 2017

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Abstract. Air traffic has long been a generally high growth sector and all forecasts indicate that this trend will continue at a similar pace for the next twenty years. The regular traffic demand growth has led to congestion at airports and in space. In this paper, we will create in first a probabilistic model which describes the uncertainty of the aircraft's trajectory, and its presence in a sector during a time interval. We define as result, a multi-objective optimization problem whose objective functions are the expected cost of delay and the expected cost of congestion. Then we use the Non-dominated Sorting Genetic Algorithm (NSGA-II) to solve an instance included 21 flights and 1 sector, and is able to provide a good approximation of the Pareto front. The decision-making stage was then performed with the aid of data clustering techniques to reduce the size of the Pareto-optimal set and obtain a smaller representation of the multi-objective design space, thereby making it easier for the decision-maker to find satisfactory and meaningful trade-offs, and to select a preferred final design solution.

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“…A probabilistic model has been introduced to handle the propagation of the uncertainty from the trajectories to the sectors [112]. The prerequisites of this model are the marginal of the initial arrival in the airspace, the policy of the flight management system when trying to stick to the target schedule, and the potential external disruptions.…”

Section: Comparison Of the Modelsmentioning

confidence: 99%

A study of optimization of air traffic network and flight schedules

Sailauov¹

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SouthEast Asia is considered one of the fastest air traffic growing regions in the world. Congested air traffic and weather conditions have thus become major factors in air traffic management. Models have been proposed to manage air traffic flow efficiently and to solve congested airspace problems. In order to manage air traffic flow efficiently, this thesis focused on forecasting air traffic, rescheduling of flights and restructuring an air network. In this research the model for air traffic forecasting was able to forecast the country, city-pair and airport-pair air traffic. Results show that the passenger forecasting for Singapore has dependence not only on that country but on neighbouring countries as well. The research predicts that the passenger movements in Changi airport will increase up to 81.65 million people by year 2023, which is 31.23 % more than that in 2017. Also, the number of passenger aircraft between Singapore and Jakarta city-pair will increase up to 34702 by year 2023, which is 26.6 % more than that in 2017. In addition, the number of passenger aircraft between Changi airport and KLIA airport-pair will be between 31698 and 40311 by year 2023. The model to reschedule flights focuses on solving reduced capacity problems and on reducing delays efficiently. Rescheduling of flights was done operationalizing and satisfying the constraints of delay costs, airport departure and arrival capacities, waypoints' capacities, routes capacity and rerouting options. Results show that in the flights between KLIA and Changi airport, ground delays were reduced by 4.98 min and 3.05 min on average. More importantly, the airborne delays were reduced by 2.70 min and 3.95 min on average. The model made sure none of the flights got delayed longer than a specific time and ensured fairness to all flights. Also, a model was developed to reconstruct the air network. A case study was implemented involving 8 airports. The total route distance and the objective function were reduced by 1.53% and 1.48 % respectively. The results show that restructuring the air network can reduce flights' distances using proper merging methods.

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Multiobjective tactical planning under uncertainty for air traffic flow and capacity management

Cited by 5 publications

References 8 publications

Multiobjective optimization for reducing delays and congestion in air traffic management

Multiobjective optimization for reducing delays and congestion in air traffic management

Multi-objective optimization approach for air traffic flow management

A study of optimization of air traffic network and flight schedules

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