Robust optimisation approaches to airside operations in terminal manoeuvring areas

Abstract

The impact of sustainable growth in the traffic volume of civil aviation has significantly influenced traffic delay by outstripping capacities of air and airport traffic resources. Robust optimisation to airside operations offers a perspicacious and holistic surveillance for flight activities in a near-terminal manoeuvring area and this study focuses on the treatment of uncertainty in developing a resolution of airborne and airport traffic control. Further, the advancement of computational intelligence in operations research assures a real-world problem becomes computationally tractable. The solution quality derived from the state-of-the-art mathematical programming, meta-heuristics and matheuristics regarding convergence performance has been seen to greatly improve. This thesis contributes to the theoretical development in the research field of robust optimisation, optimisation methods and matheuristic. In the first part of the thesis, we will provide a review of meta-heuristic approaches for airside operations research. We present a comprehensive analysis of the literature review of airside operations research and meta-heuristics approach. This unifying survey synthesises the current research progress in airside operations and highlights the benefits of sophisticated modelling and an integrated approach. In the second part of the thesis, we focus on attention to constructing the robust optimisation approaches and various optimisation methods for terminal traffic flow problems considering real-world scenarios. First, we developed a robust aircraft sequencing and scheduling problem using min-max regret criterion with mixed-mode runway configuration. We aimed to alleviate the effect of aggregate delays given that the arrival and departure time of flights is not perfectly determined and the probability distribution of delay cannot be estimated in advance. Second, we proposed a novel alternative path approach for robust terminal traffic flow problem using a min-max criterion by addressing the uncertain transit time. Third, we further extended the alternative path approach for robust terminal traffic flow problem using a light robustness approach.