Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/60041
Title: Optimisation of changi airport's future air traffic contingency management in extreme haze condition
Authors: Lo, Kar Koon
Keywords: DRNTU::Engineering::Aeronautical engineering::Air navigation
DRNTU::Engineering::Aeronautical engineering::Accidents and air safety
DRNTU::Engineering::Aeronautical engineering::Aviation
DRNTU::Engineering::Aeronautical engineering::Flight simulation
DRNTU::Engineering::Industrial engineering::Engineering management
DRNTU::Engineering::Industrial engineering::Operations research
DRNTU::Engineering::Industrial engineering::Supply chain
Issue Date: 2014
Abstract: As world air traffic grows exponentially, Singapore Changi Airport has planned ahead and geared up for challenges in years to come. Despite being well equipped with all the sophisticated technologies, air traffic movements are still affected yearly by haze from the region. Such haze reduces visibility and forces air traffic movements to slow down as part of precautionary measures to ensure the best safety policies are practiced. Such slow air traffic movements can easily cause congestion to the high volume of air traffic in the future. This final year project explores various methods to optimise future flight movements that fly in and out of Changi Airport during haze by using Microsoft Excel simulations. Two optimisation models are created to reduce all departure and arrival aircrafts’ waiting time. The first optimisation model addresses the problem by separating the flights by time evenly. The second optimisation model tackles the problem by having an alternate land and take off or a vice versa of the pattern. In addition to support the optimisation models, Chapter 2 reviews the necessary literatures such as flight statistics and wake turbulence in order to understand the various perimeters needed in the study. To understand the future flight movements, a forecast is done in Chapter 3 before using these figures in the optimisation model simulations. At the conclusion, the pros and cons of both optimisation models are examined.
URI: http://hdl.handle.net/10356/60041
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:MAE Student Reports (FYP/IA/PA/PI)

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