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.

翻译：本研究针对飞机运行造成的污染这一关键问题，聚焦于同时优化停机位分配与跑道调度，这是一种此前未被探索的创新方法。研究提出了一种基于遗传算法的创新方法，旨在最小化飞机在机场起飞与降落过程中燃料燃烧产生的污染。该算法独特地整合了停机位与起降跑道的优化，考虑了发动机运行时间与污染物水平之间的关联性。该方法采用先进的约束处理技术，以应对机场运行中固有的复杂时间与资源限制。此外，研究对模型进行了深入的敏感性分析，特别关注变异因子和惩罚函数类型，以精细调整优化过程。这种双重点优化策略代表了航空业减少环境影响方面的重大进步，为全面高效机场运行管理建立了新标准。