Medioni et al 1995a - Revision history

Scipediacontent at 14:08, 21 January 2021

2021-01-21T14:08:58Z

Scipediacontent: Scipediacontent moved page Draft Content 393053577 to Medioni et al 1995a

2020-10-14T12:17:32Z

Scipediacontent moved page Draft Content 393053577 to Medioni et al 1995a

Scipediacontent: Created page with " == Abstract == International audience; The resolution of Air Traffic Control (ATC) conflicts is a constrained optimization problem : the goal is to propose, for a certain nu..."

2020-10-14T12:17:30Z

Created page with " == Abstract == International audience; The resolution of Air Traffic Control (ATC) conflicts is a constrained optimization problem : the goal is to propose, for a certain nu..."

New page

== Abstract ==

International audience; The resolution of Air Traffic Control (ATC) conflicts is a constrained optimization problem : the goal is to propose, for a certain number, n, of aircraft, which might be in conflict in a near future, trajectories that satisfy the separation constraints between aircraft, and minimizes the delays due to the conflict's resolution. The type of conflict resolution trajectories we use allows to split the problem in two steps: first we choose, and freeze, what we call a configuration of the problem, i.e. for each aircraft, the direction in which the aircraft is diverted, and for each pair of aircraft, which of the two aircraft passes first at the crossing point of the two aircraft trajectories. We can then compute the optimal trajectories corresponding to this configuration, by solving a simple linear optimization problem. Thus we can use an Genetic Algorithm, along with a linear optimization algorithm, such as the simplex algorithm: the elements of the population, on which the GA operates, code configurations of the problem, and are evaluated using a linear optimization program.The advantage of this approach is that we get, as well as the fitness of an element of the population, the local optima corresponding to the configuration coded by this element. The GA actually searches for the global optimum among these local optima. We applied this methods to conflicts in which up to 6 aircraft are involved, and obtained really promising results.

Document type: Part of book or chapter of book

== Full document ==
<pdf>Media:Draft_Content_393053577-beopen505-1139-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [https://hal-enac.archives-ouvertes.fr/hal-01004091/file/Durand_AE1995.pdf https://hal-enac.archives-ouvertes.fr/hal-01004091/file/Durand_AE1995.pdf]

← Older revision		Revision as of 14:08, 21 January 2021
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	International audience; The resolution of Air Traffic Control (ATC) conflicts is a constrained optimization problem : the goal is to propose, for a certain number, n, of aircraft, which might be in conflict in a near future, trajectories that satisfy the separation constraints between aircraft, and minimizes the delays due to the conflict's resolution. The type of conflict resolution trajectories we use allows to split the problem in two steps: first we choose, and freeze, what we call a configuration of the problem, i.e. for each aircraft, the direction in which the aircraft is diverted, and for each pair of aircraft, which of the two aircraft passes first at the crossing point of the two aircraft trajectories. We can then compute the optimal trajectories corresponding to this configuration, by solving a simple linear optimization problem. Thus we can use an Genetic Algorithm, along with a linear optimization algorithm, such as the simplex algorithm: the elements of the population, on which the GA operates, code configurations of the problem, and are evaluated using a linear optimization program.The advantage of this approach is that we get, as well as the fitness of an element of the population, the local optima corresponding to the configuration coded by this element. The GA actually searches for the global optimum among these local optima. We applied this methods to conflicts in which up to 6 aircraft are involved, and obtained really promising results.		International audience; The resolution of Air Traffic Control (ATC) conflicts is a constrained optimization problem : the goal is to propose, for a certain number, n, of aircraft, which might be in conflict in a near future, trajectories that satisfy the separation constraints between aircraft, and minimizes the delays due to the conflict's resolution. The type of conflict resolution trajectories we use allows to split the problem in two steps: first we choose, and freeze, what we call a configuration of the problem, i.e. for each aircraft, the direction in which the aircraft is diverted, and for each pair of aircraft, which of the two aircraft passes first at the crossing point of the two aircraft trajectories. We can then compute the optimal trajectories corresponding to this configuration, by solving a simple linear optimization problem. Thus we can use an Genetic Algorithm, along with a linear optimization algorithm, such as the simplex algorithm: the elements of the population, on which the GA operates, code configurations of the problem, and are evaluated using a linear optimization program.The advantage of this approach is that we get, as well as the fitness of an element of the population, the local optima corresponding to the configuration coded by this element. The GA actually searches for the global optimum among these local optima. We applied this methods to conflicts in which up to 6 aircraft are involved, and obtained really promising results.
−
−	~~Document type: Part of book or chapter of book~~
−
−	~~== Full document ==~~
−	~~<pdf>Media:Draft_Content_393053577-beopen505-1139-document.pdf</pdf>~~


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	* [https://hal-enac.archives-ouvertes.fr/hal-01004091/file/Durand_AE1995.pdf https://hal-enac.archives-ouvertes.fr/hal-01004091/file/Durand_AE1995.pdf]		* [https://hal-enac.archives-ouvertes.fr/hal-01004091/file/Durand_AE1995.pdf https://hal-enac.archives-ouvertes.fr/hal-01004091/file/Durand_AE1995.pdf]
		+
		+	* [http://www.springerlink.com/index/pdf/10.1007/3-540-61108-8_51 http://www.springerlink.com/index/pdf/10.1007/3-540-61108-8_51],
		+	: [http://dx.doi.org/10.1007/3-540-61108-8_51 http://dx.doi.org/10.1007/3-540-61108-8_51]
		+
		+	* [https://hal-enac.archives-ouvertes.fr/hal-01004091 https://hal-enac.archives-ouvertes.fr/hal-01004091],
		+	: [https://hal-enac.archives-ouvertes.fr/hal-01004091/document https://hal-enac.archives-ouvertes.fr/hal-01004091/document],
		+	: [https://hal-enac.archives-ouvertes.fr/hal-01004091/file/Durand_AE1995.pdf https://hal-enac.archives-ouvertes.fr/hal-01004091/file/Durand_AE1995.pdf]
		+
		+	* [https://link.springer.com/chapter/10.1007%2F3-540-61108-8_51 https://link.springer.com/chapter/10.1007%2F3-540-61108-8_51],
		+	: [https://hal-enac.archives-ouvertes.fr/hal-01004091/document https://hal-enac.archives-ouvertes.fr/hal-01004091/document],
		+	: [https://dblp.uni-trier.de/db/conf/ae/ae1995.html#MedioniDA95 https://dblp.uni-trier.de/db/conf/ae/ae1995.html#MedioniDA95],
		+	: [https://hal-enac.archives-ouvertes.fr/hal-01004091 https://hal-enac.archives-ouvertes.fr/hal-01004091],
		+	: [https://academic.microsoft.com/#/detail/1755356947 https://academic.microsoft.com/#/detail/1755356947]

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