Towards the bigger vision for Africa
July 6, 2010
PhD defended
November 2, 2010
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Wesonga PhD defence

I salute you all! I will be presenting a PhD defence on the 21st October, 2010 at 2pm at Makerere University, Kampala-Uganda. It is about a very interesting title ” stochastic optimisation models for air traffic management.” I would like to invite you all to attend. Below is the abstract of the thesis for defence.

Air traffic delay is not only a source of inconvenience to the aviation passenger, but also a major deterrent to the optimisation of airport utility, especially in the developing countries. Many developing countries do less to abate this otherwise seemingly invincible constraint to development. The overall objective of this study was to investigate the dynamics of air traffic delays and to develop stochastic optimisation models that mitigate delays and facilitate efficient air traffic management.

Aviation and meteorological data sources at Entebbe International Airport for the period 2004 to 2008 on daily basis were used for exploratory data analysis, modelling and simulation purposes. Exploratory data analysis involved logistic modeling for which post-logistic model analysis estimated the average probability of departure delay to be 49 percent while that for arrival delay was 36 percent. These computations were based on a delay threshold level at 60 percent which had more representative significant number of predicators of nine and ten for departure and arrival respectively. The proportion of the number of aircrafts that delay was established to follow an autoregressive integrated moving average, ARIMA (1,1,1) time series.

The stochastic frontier model estimated the average inefficiencies of aircraft operations over the period to be 15 percent and 20 percent at departure and arrival respectively. Three stochastic optimisation models were developed to create a relationship between the airport utility and the proportions of delay. The three models measure airport utility at aircraft departures, at aircraft arrivals and the third one for aggregated aircraft departures and arrivals. In this formulation, the proportion of aircraft delay was treated as a plummeting element of the airport utility. Stochastic frontier model inefficiency estimates and the post-logistic delay probability estimates were used as inputs into the stochastic optimisation models to enforce the models’ theoretical underpinning.

Model sensitivity analysis adduced that the utility level for a given time period at the airport with higher levels of inefficiency was less than the utility level with lower levels of inefficiency. Furthermore, lower estimates of probabilities for departure and arrival delay resulted into a lower operational utility level of the airport. Further analysis suggested that at this airport, proportion of daily delay is greater for aircraft departures than during arrivals. Thus to maximise airport utility over a time period, measures have to be developed to improve overall timeliness of aircraft operations so as to attract accelerated sustainable development. Therefore, more investments are required in human resources, equipment and automatic weather monitoring systems in order to reduce the likelihood of aircraft delay and the related technical inefficiency parameters.

Keywords: Arrival delay, departure delay, proportions, stochastic optimisation models

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