Reducing the delays of the departing aircraft can potentially lead to improving the efficiency of the surface operations at airports. This paper addresses a departure scheduling problem with an objective to reduce total aircraft delays subject to timing and ordering constraints. The ordering constraints model the queuing area of airports where the aircraft align themselves in the form of chains before departing. By exploiting the structure of the problem, a generalized dynamic programming approach is presented to solve the departure scheduling problem optimally. Computational results indicate that the approach presented in this paper is reasonably fast, i.e., it takes less than one tenth of a second on average to solve a 40 aircraft problem. Also, the approach produces optimal sequences whose delay is approximately 12 minutes, on average, less than the delays produced by the First Come First Serve (FCFS) sequences.

At busy airports, air traffic controllers seek to find schedules for aircraft at the runway that aim to minimize delays of the aircraft while maximizing runway throughput. In reality, finding optimal schedules by a human controller is hard to accomplish since the number of feasible schedules available for the scheduling problem is quite large. In this paper, we pose this problem as a multiobjective optimization problem, with respect to total aircraft delay and runway throughput. Using principles of multiobjective dynamic programming, we develop an algorithm to find a set of Pareto-optimal solutions that completely specify the nondominated frontier. In addition to finding these solutions, this paper provides a proof of the algorithm's correctness and gives an analysis of its performance against a baseline algorithm using the operational data for a model of the Dallas/Fort Worth International Airport.Index Terms-Aircraft scheduling, multiple objective dynamic programming, Pareto-optimality, runway scheduling.

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Aircraft movements on taxiways at busy airports often create bottlenecks. This paper introduces a mixed integer linear program to solve a Multiple Route Aircraft Taxi Scheduling Problem. The outputs of the model are in the form of optimal taxi schedules, which include routing decisions for taxiing aircraft. The model extends an existing single route formulation to include routing decisions. An efficient comparison framework compares the multi-route formulation and the single route formulation. The multi-route model is exercised for east side airport surface traffic at Dallas/Fort Worth International Airport to determine if any arrival taxi time savings can be achieved by allowing arrivals to have two taxi routes: a route that crosses an active departure runway and a perimeter route that avoids the crossing. Results indicate that the multi-route formulation yields reduced arrival taxi times over the single route formulation only when a perimeter taxiway is used. In conditions where the departure aircraft are given an optimal and fixed takeoff sequence, accumulative arrival taxi time savings in the multi-route formulation can be as high as 3.6 hours more than the single route formulation. If the departure sequence is not optimal, the multi-route formulation results in less taxi time savings made over the single route formulation, but the average arrival taxi time is significantly decreased.

This article addresses a two runway, scheduling problem that aims to assign the aircraft to the runways and find an arrival time for each aircraft such that the sum of the delays of all the aircraft is minimized subject to the timing, safety, and chain-type precedence constraints for the aircraft. An optimal algorithm is developed for the two runway, scheduling problem based on generalized dynamic programming. Computational results are presented to show that this algorithm is computationally faster than the existing dynamic programming algorithm for the two runway, scheduling problem.