- author = "James V. Hansen",
- title = "Genetic search methods in air traffic control",
- journal = "Computers and Operations Research",
- year = "2004",
- volume = "31",
- number = "3",
- pages = "445--459",
- month = mar,
- keywords = "genetic algorithms, genetic programming, Aircraft traffic control, Genetic search, Heuristics, Scheduling",
- broken = "http://www.sciencedirect.com/science/article/B6VC5-480622F-4/2/468055c77aed02e9629b07b8dc6b0dbe",
-
DOI = "
doi:10.1016/S0305-0548(02)00228-9",
-
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.",
- owner = "wlangdon",
