Lessons from building an automated pre-departure sequencer for airports

Daniel Karapetyan (), Jason A. D. Atkin (), Andrew J. Parkes () and Juan Castro-Gutierrez ()
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Daniel Karapetyan: University of Nottingham
Jason A. D. Atkin: University of Nottingham
Andrew J. Parkes: University of Nottingham
Juan Castro-Gutierrez: University of Nottingham

Annals of Operations Research, 2017, vol. 252, issue 2, No 11, 435-453

Abstract: Abstract Commercial airports are under increasing pressure to comply with the Eurocontrol collaborative decision making (CDM) initiative, to ensure that information is passed between stakeholders, integrate automated decision support or make predictions. These systems can also aid effective operations beyond the airport by communicating scheduling decisions to other relevant parties, such as Eurocontrol, for passing on to downstream airports and enabling overall airspace improvements. One of the major CDM components is aimed at producing the target take-off times and target startup-approval times, i.e. scheduling when the aircraft should push back from the gates and start their engines and when they will take off. For medium-sized airports, a common choice for this is a “pre-departure sequencer” (PDS). In this paper, we describe the design and requirements challenges which arose during our development of a PDS system for medium sized international airports. Firstly, the scheduling problem is highly dynamic and event driven. Secondly, it is important to end-users that the system be predictable and, as far as possible, transparent in its operation, with decisions that can be explained. Thirdly, users can override decisions, and this information has to be taken into account. Finally, it is important that the system is as fair as possible for all users of the airport, and the interpretation of this is considered here. Together, these factors have influenced the design of the PDS system which has been built to work within an existing large system which is being used at many airports.

Keywords: Automated decision support; Scheduling; Aviation; Airport ground operations; Modelling user preferences; Collaborative decision making (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s10479-015-1960-z

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