Genetic search methods in air traffic control

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{hansen:2004:COR,
  author =       "James V. Hansen",
  title =        "Genetic search methods in air traffic control",
  journal =      "Computers and Operations Research",
  year =         "2004",
  volume =       "31",
  pages =        "445--459",
  keywords =     "genetic algorithms, genetic programming, Aircraft
                 traffic control, Genetic search, Heuristics,
                 Scheduling",
  number =       "3",
  URL =          "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",
}

Genetic Programming entries for James V Hansen

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