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Masters Degrees (Computer Science)

Permanent URI for this collectionhttps://hdl.handle.net/10413/7114

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Browsing Masters Degrees (Computer Science) by Subject "Air traffic flow management."

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    On modeling and optimisation of air Traffic flow management problem with en-route capacities.
    (2016) Alochukwu, Alex Somto Arinze.; ; Adewumi, Aderemi Oluyinka.
    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.