An integrated optimisation approach to airport ground operations to foster sustainability in the aviation sector. (1st November 2015) - Weiszer, Michal; Chen, Jun; Locatelli, Giorgio
Highlights: A multi-objective approach is proposed to optimise airport problems simultaneously. Different objectives include fuel consumption, previously not considered in literature. A holistic economic optimisation framework is proposed to support the decision maker. A comprehensive comparative study compares approaches found in the literature. A genetic algorithm is adopted and tested on data from an international hub airport. Abstract: With increasing air traffic, rising fuel costs and tighter environmental targets, efficient airport ground operations are one of the key aspects towards sustainable air transportation. This complex system includes elements such as ground movement, runway scheduling and ground services. Previously, these problems were treated in isolation since information, such as landing time, pushback time and aircraft ground position, are held by different stakeholders with sometimes conflicting interests and, normally, are not shared. However, as these problems are interconnected, solutions as a result of isolated optimisation may achieve the objective of one problem but fail in the objective of the other one, missing the global optimum eventually. Potentially more energy and economic costs are thus required. In order to apply a more systematic and holistic view, this paper introduces a multi-objective integrated optimisation problem incorporating the newly proposed Active Routing concept. Built with systematic perspectives, this new model combines several elements: scheduling and routing of aircraft, 4-Dimensional Trajectory (4DT) optimisation, runway scheduling and airport bus scheduling. A holistic economic optimisation framework is also included to support the decision maker to select the economically optimal solution from a Pareto front of technically optimal solutions. To solve this problem, a multi-objective genetic algorithm is adopted and tested on real data from an international hub airport. Preliminary results show that the proposed approach is able to provide a systematic framework so that airport efficiency, environmental assessment and economic analysis could all be explicitly optimised.