The study addresses the environmental impact of aviation by optimizing gate and runway assignments using genetic algorithms. It aims to reduce pollution levels during airport operations efficiently.
The research focuses on minimizing pollution from fuel combustion during aircraft take-off and landing at airports. It introduces a novel approach that integrates the optimization of both landing gates and runways, considering the correlation between engine operation time and pollutant levels. The study employs advanced constraint handling techniques to manage time and resource limitations inherent in airport operations. Additionally, it conducts a thorough sensitivity analysis of the model, emphasizing mutation factors and penalty functions to fine-tune the optimization process. The dual-focus optimization strategy represents a significant advancement in reducing environmental impact in the aviation sector, setting a new standard for comprehensive airport operation management.
The content also discusses key pollutants generated by aircraft engines during different flight phases near airports. It highlights the importance of optimizing LTO operations to minimize pollution effects produced by fuel combustion. The study evaluates various approaches used in the aviation industry to address capacity/demand problems at congested airports through strategic slot allocation methods.
Furthermore, it explores different methodologies such as linear programming, metaheuristics, and genetic algorithms used for airport ground movement problems, gate allocation, and runway scheduling. The research emphasizes the need for efficient solutions to minimize pollution emissions while managing air traffic congestion effectively.
Overall, the content provides valuable insights into optimizing airport operations through genetic algorithms to reduce environmental impact and enhance efficiency in aviation management.
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arxiv.org
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by Fernando Gue... at arxiv.org 03-01-2024
https://arxiv.org/pdf/2402.19222.pdfDeeper Inquiries