Aircraft Landing Planning is challenging because the inherently limited capacity of airport runways causes bottlenecks. This type of planning involves different stakeholders (e.g., airlines, air traffic services providers, airport authorities, and passengers) and faces various uncertainties (e.g., take-off time variability, or wind speeds). This study, conducted in collaboration with the European Organization for the Safety of Air Navigation (EUROCONTROL), proposes a mathematical formulation of the problem and a simulation framework that accounts for uncertainties. We also propose different solution methods: a descent and a tabu search, as well as a mechanism for guiding restarts, to diversify the search process. These methods provide, in our simulated environment, more effective and stable solutions than the popular first-come-first-served practice regarding three objective functions (namely, delay, fuel, and landing sequence stability), which are considered lexicographically. Indeed, the average delays and fuel costs are reduced by 50% and 10%, respectively, at the cost of a small number of landing-sequence modifications, as each flight is repositioned an average of 0.5 times. Moreover, the computations can be performed quickly, which is crucial because re-optimization needs to be done online when flight information is updated.

飞机着陆规划具有挑战性，因为机场跑道固有的有限容量会导致瓶颈。这种类型的规划涉及不同的利益相关者（例如，航空公司、空中交通服务提供商、机场当局和乘客）并面临各种不确定性（例如，起飞时间可变性或风速）。这项与欧洲空中航行安全组织 (EUROCONTROL) 合作进行的研究提出了该问题的数学公式和解释不确定性的模拟框架。我们还提出了不同的解决方法：下降和禁忌搜索，以及引导重新启动的机制，以使搜索过程多样化。这些方法在我们的模拟环境中提供，在三个目标函数（即延迟、燃料和着陆序列稳定性）方面，比按字典顺序考虑的流行的先到先得做法更有效和稳定的解决方案。事实上，平均延误和燃料成本分别降低了 50% 和 10%，代价是少量着陆序列修改，因为每个航班平均重新定位 0.5 次。此外，计算可以快速执行，这一点至关重要，因为更新航班信息时需要在线进行重新优化。因为每个航班平均重新定位 0.5 次。此外，计算可以快速执行，这一点至关重要，因为更新航班信息时需要在线进行重新优化。因为每个航班平均重新定位 0.5 次。此外，计算可以快速执行，这一点至关重要，因为更新航班信息时需要在线进行重新优化。