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.

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A generalized dynamic programming based algorithm and a local search heuristic are used to solve the Two Runway Departure Scheduling Problem that arises at an airport. The objective of this work is to assign the departing aircraft to one of the runways and find a departing time for each aircraft so that the overall delay is minimized subject to the timing, safety, and the ordering constraints. A reduction in the overall delay of the departing aircraft at an airport can improve the airport surface operations and aircraft scheduling. The generalized dynamic programming algorithm is an exact algorithm, and it finds the optimal solution for the two runway scheduling problem. The performance of the generalized dynamic programming algorithm is assessed by comparing its running time with a published dynamic programming algorithm for the two runway scheduling problem. The results from the generalized dynamic programming algorithm show that this algorithm runs much faster than the dynamic programming algorithm. The local search heuristic with k − exchange neighborhoods has a short running time in the order of seconds, and it finds an approximate solution. The performance of this heuristic is assessed based on the quality of the solution found by the heuristic and its running time. The results show that the solution found by the heuristic for a 25 aircraft problem has an average savings of approximately 15 percent in delays with respect to a first come-first served solution. Also, the solutions produced by a 3-opt heuristic for a 25 aircraft scheduling problem has an average quality of 8 percent with respect to the optimal solution found by the generalized dynamic programming algorithm. The heuristic can be used for both real-time and fast-time simulations of airport surface operations, and it can also provide an upper limit for an exact algorithm. Aircraft arrival scheduling problems may also be addressed using the generalized dynamic programming algorithm and the local search heuristic with slight modification to the constraints.