On modeling and optimisation of air Traffic flow management problem with en-route capacities.
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Date
2016
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Abstract
The air transportation industry in the past ten years witnessed an upsurge with the number
of passengers swelling exponentially. This development has seen a high demand in airport
and airspace usage, which consequently has an enormous strain on the aviation industry
of a given country. Although increase in airport capacity would be logical to meet this
demand, factors such as poor weather conditions and other unforeseen ones have made
it difficult if not impossible to do such. In fact there is a high probability of capacity
reduction in most of the airports and air sectors within these regions. It is no surprise
therefore that, most countries experience congestion almost on a daily basis. Congestion
interrupts activities in the air transportation network and this has dire consequences on
the air traffic control system as well as the nation's economy due to the significant costs
incurred by airlines and passengers.
This is against a background where most air tra c managers are met with the challenge
of finding optimal scheduling strategies that can minimise delay costs. Current practices
and research has shown that there is a high possibility of reducing the effects of congestion
problems on the air traffic control system as well as the total delay costs incurred to the
nearest minimum through an optimal control of
ights. Optimal control of these
ights
can either be achieved by assigning ground holding delays or air borne delays together
with any other control actions to mitigate congestion. This exposes a need for adequate
air traffic
ow management given that it plays a crucial role in alleviating delay costs.
Air Traffic Flow Management (ATFM) is defined as a set of strategic processes that reduce
air traffic delays and congestion problems. More precisely, it is the regulation of air traffic
in such a way that the available airport and airspace capacity are utilised efficiently without
been exceeded when handling traffic. The problem of managing air traffic so as to ensure
efficient and safe
ow of aircraft throughout the airspace is often referred to as the Air
Traffic Flow Management Problem (ATFMP).
This thesis provides a detailed insight on the ATFMP wherein the existing approaches,
methodologies and optimisation techniques that have been (and continue to be) used to
address the ATFMP were critically examined. Particular attention to optimisation models
on airport capacity and airspace allocation were also discussed extensively as they depict
what is obtainable in the air transportation system. Furthermore, the thesis attempted a
comprehensive and, up-to-date review which extensively fed off literature on ATFMP. The
instances in this literature were mainly derived from North America, Europe and Africa.
Having reviewed the current ATFM practices and existing optimisation models and approaches
for solving the ATFMP, the generalised basic model was extended to account for
additional modeling variations. Furthermore, deterministic integer programming formulations
were developed for reducing the air traffic delays and congestion problems based
on the sector and path-based approaches already proposed for incorporating rerouting options
into the basic ATFMP model. The formulation does not only takes into account all
the
ight phases but it also solves for optimal synthesis of other
ow management activities
including rerouting decisions,
ight cancellation and penalisation. The claims from
the basic ATFMP model was validated on artificially constructed datasets and generated
instances. The computational performance of the basic and modified ATFMP reveals that
the resulting solutions are completely integral, and an optimal solution can be obtained
within the shortest possible computational time. Thereby, affirming the fact that these
models can be used in effective decision making and efficient management of the air traffic
flow.
Description
Master of Science in Mathematics, Statistics and Computer Science. University of KwaZulu-Natal, Durban 2016.
Keywords
Theses - Computer Science.