  This research addresses the crucial issue of pollution from aircraft
operations, focusing on optimizing both gate allocation and runway scheduling
simultaneously, a novel approach not previously explored. The study presents an
innovative genetic algorithm-based method for minimizing pollution from fuel
combustion during aircraft take-off and landing at airports. This algorithm
uniquely integrates the optimization of both landing gates and take-off/landing
runways, considering the correlation between engine operation time and
pollutant levels. The approach employs advanced constraint handling techniques
to manage the intricate time and resource limitations inherent in airport
operations. Additionally, the study conducts a thorough sensitivity analysis of
the model, with a particular emphasis on the mutation factor and the type of
penalty function, to fine-tune the optimization process. This dual-focus
optimization strategy represents a significant advancement in reducing
environmental impact in the aviation sector, establishing a new standard for
comprehensive and efficient airport operation management.
