Of primary importance to the efficient operation and
profitability of an airline is adherence to its flight
schedule. This paper examines that segment of air
traffic control, termed traffic management adviser
(TMA), which is charged with the complex task of
scheduling arriving aircraft to available runways in a
manner that minimises delays and satisfies safety
constraints. In particular, we investigate the
effectiveness and efficiency of using genetic search
methods to support the scheduling decisions made by
TMA.
Four different genetic search methods are tested on TMA
problems suggested by recent work at the NASA Ames
Research Center. For problems of realistic size,
optimal or near-optimal assignments of aircraft to
runways are achieved in real time.
Scope and purpose. We report the application of genetic
search algorithms to solve certain complexities
associated with air traffic control. Air traffic
control is an important practical problem that is
difficult to solve by other methods because of
non-convex, non-linear, or non-analytic
characteristics.
Four genetic search algorithms are applied, with
consistent advantage being demonstrated by an algorithm
based on genetic programming functions. Good results
are achieved, with evidence that solutions can be
achieved in real time.
%0 Journal Article
%1 hansen:2004:COR
%A Hansen, James V.
%D 2004
%J Computers and Operations Research
%K Aircraft Genetic Heuristics, Scheduling algorithms, control, genetic programming, search, traffic
%N 3
%P 445--459
%R doi:10.1016/S0305-0548(02)00228-9
%T Genetic search methods in air traffic control
%U http://www.sciencedirect.com/science/article/B6VC5-480622F-4/2/468055c77aed02e9629b07b8dc6b0dbe
%V 31
%X Of primary importance to the efficient operation and
profitability of an airline is adherence to its flight
schedule. This paper examines that segment of air
traffic control, termed traffic management adviser
(TMA), which is charged with the complex task of
scheduling arriving aircraft to available runways in a
manner that minimises delays and satisfies safety
constraints. In particular, we investigate the
effectiveness and efficiency of using genetic search
methods to support the scheduling decisions made by
TMA.
Four different genetic search methods are tested on TMA
problems suggested by recent work at the NASA Ames
Research Center. For problems of realistic size,
optimal or near-optimal assignments of aircraft to
runways are achieved in real time.
Scope and purpose. We report the application of genetic
search algorithms to solve certain complexities
associated with air traffic control. Air traffic
control is an important practical problem that is
difficult to solve by other methods because of
non-convex, non-linear, or non-analytic
characteristics.
Four genetic search algorithms are applied, with
consistent advantage being demonstrated by an algorithm
based on genetic programming functions. Good results
are achieved, with evidence that solutions can be
achieved in real time.
@article{hansen:2004:COR,
abstract = {Of primary importance to the efficient operation and
profitability of an airline is adherence to its flight
schedule. This paper examines that segment of air
traffic control, termed traffic management adviser
(TMA), which is charged with the complex task of
scheduling arriving aircraft to available runways in a
manner that minimises delays and satisfies safety
constraints. In particular, we investigate the
effectiveness and efficiency of using genetic search
methods to support the scheduling decisions made by
TMA.
Four different genetic search methods are tested on TMA
problems suggested by recent work at the NASA Ames
Research Center. For problems of realistic size,
optimal or near-optimal assignments of aircraft to
runways are achieved in real time.
Scope and purpose. We report the application of genetic
search algorithms to solve certain complexities
associated with air traffic control. Air traffic
control is an important practical problem that is
difficult to solve by other methods because of
non-convex, non-linear, or non-analytic
characteristics.
Four genetic search algorithms are applied, with
consistent advantage being demonstrated by an algorithm
based on genetic programming functions. Good results
are achieved, with evidence that solutions can be
achieved in real time.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Hansen, James V.},
biburl = {https://www.bibsonomy.org/bibtex/2b32ee98d6d79261d3d894c8254647780/brazovayeye},
doi = {doi:10.1016/S0305-0548(02)00228-9},
interhash = {00e5e13c98f5b7e7cc7f1d3072a716a0},
intrahash = {b32ee98d6d79261d3d894c8254647780},
journal = {Computers and Operations Research},
keywords = {Aircraft Genetic Heuristics, Scheduling algorithms, control, genetic programming, search, traffic},
number = 3,
owner = {wlangdon},
pages = {445--459},
timestamp = {2008-06-19T17:40:57.000+0200},
title = {Genetic search methods in air traffic control},
url = {http://www.sciencedirect.com/science/article/B6VC5-480622F-4/2/468055c77aed02e9629b07b8dc6b0dbe},
volume = 31,
year = 2004
}