Preference-based evolutionary algorithm for airport surface operations.
Journal article
Weiszer, Michal, Chen, Jun, Stewart, Paul and Zhang, Xuejun 2018. Preference-based evolutionary algorithm for airport surface operations. Transportation Research Part C: Emerging Technologies. https://doi.org/10.1016/j.trc.2018.04.008
| Authors | Weiszer, Michal, Chen, Jun, Stewart, Paul and Zhang, Xuejun |
|---|---|
| Abstract | In addition to time efficiency, minimisation of fuel consumption and related emissions has started to be considered by research on optimisation of airport surface operations as more airports face severe congestion and tightening environmental regulations. Objectives are related to economic cost which can be used as preferences to search for a region of cost efficient and Pareto optimal solutions. A multi-objective evolutionary optimisation framework with preferences is proposed in this paper to solve a complex optimisation problem integrating runway scheduling and airport ground movement problem. The evolutionary search algorithm uses modified crowding distance in the replacement procedure to take into account cost of delay and fuel price. Furthermore, uncertainty inherent in prices is reflected by expressing preferences as an interval. Preference information is used to control the extent of region of interest, which has a beneficial effect on algorithm performance. As a result, the search algorithm can achieve faster convergence and potentially better solutions. A filtering procedure is further proposed to select an evenly distributed subset of Pareto optimal solutions in order to reduce its size and help the decision maker. The computational results with data from major international hub airports show the efficiency of the proposed approach. |
In addition to time efficiency, minimisation of fuel consumption and related emissions has started to be considered by research on optimisation of airport surface operations as more airports face severe congestion and tightening environmental regulations. Objectives are related to economic cost which can be used as preferences to search for a region of cost efficient and Pareto optimal solutions. A multi-objective evolutionary optimisation framework with preferences is proposed in this paper to solve a complex optimisation problem integrating runway scheduling and airport ground movement problem. The evolutionary search algorithm uses modified crowding distance in the replacement procedure to take into account cost of delay and fuel price. Furthermore, uncertainty inherent in prices is reflected by expressing preferences as an interval. Preference information is used to control the extent of region of interest, which has a beneficial effect on algorithm performance. As a result, the search algorithm can achieve faster convergence and potentially better solutions. A filtering procedure is further proposed to select an evenly distributed subset of Pareto optimal solutions in order to reduce its size and help the decision maker. | |
| Keywords | Airports; Runway scheduling; Multiobjective optimisation |
| Year | 2018 |
| Journal | Transportation Research Part C: Emerging Technologies |
| Publisher | Elsevier |
| ISSN | 0968090X |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.trc.2018.04.008 |
| Web address (URL) | http://hdl.handle.net/10545/622704 |
| http://creativecommons.org/licenses/by/4.0/ | |
| hdl:10545/622704 | |
| Publication dates | 21 Apr 2018 |
| Publication process dates | |
| Deposited | 27 Apr 2018, 11:10 |
| Rights | Archived with thanks to Transportation Research Part C: Emerging Technologies |
| Contributors | Queen Mary University of London, University of Derby, Beihang University and National Key Laboratory of CNS/ATM |
| File | File Access Level Open |
| File | File Access Level Open |
| File | File Access Level Open |
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