Stiverson, Paul W. (2009-05). A Study of Heuristic Approaches for Runway Scheduling for the Dallas-Fort Worth Airport. Master's Thesis. Thesis uri icon

abstract

  • Recent work in air transit efficiency has increased en-route efficiency to a point that airport efficiency is the bottleneck. With the expected expansion of air transit it will become important to get the most out of airport capacity. Departure scheduling is an area where efficiency stands to be improved, but due to the complicated nature of the problem an optimal solution is not always forthcoming. A heuristic approach can be used to find a sub-optimal take-off order in a significantly faster time than the optimal solution can be found using known methods. The aim of this research is to explore such heuristics and catalog their solution characteristics. A greedy approach as well as a k-interchange approach were developed to find improved takeoff sequences. When possible, the optimal solution was found to benchmark the performance of the heuristics, in general the heuristic solutions were within 10-15% of the optimal solution. The heuristic solutions showed improvements of up to 15% over the first-in first-out order with a running time around 4 ms.
  • Recent work in air transit efficiency has increased en-route efficiency to a point that

    airport efficiency is the bottleneck. With the expected expansion of air transit it will

    become important to get the most out of airport capacity. Departure scheduling is

    an area where efficiency stands to be improved, but due to the complicated nature

    of the problem an optimal solution is not always forthcoming. A heuristic approach

    can be used to find a sub-optimal take-off order in a significantly faster time than the

    optimal solution can be found using known methods.

    The aim of this research is to explore such heuristics and catalog their solution

    characteristics. A greedy approach as well as a k-interchange approach were developed

    to find improved takeoff sequences. When possible, the optimal solution was found to

    benchmark the performance of the heuristics, in general the heuristic solutions were

    within 10-15% of the optimal solution. The heuristic solutions showed improvements

    of up to 15% over the first-in first-out order with a running time around 4 ms.

publication date

  • May 2009