为降低不正常航班给航空公司带来的负面影响，提出了一个以加权成本最小为目标的考虑 多个影响因素的不正常航班飞机计划恢复模型。模型目标函数考虑的因素包括航班延误与取消、飞机维护以及航班运行过程中与飞机和机场相关的各项改变，并且在约束条件中考虑了过站时间 等因素。针对所提出的模型设计遗传算法进行求解。基于大规模航班数据设计算例对模型和算法进行验证，研究结果表明：遗传算法适用于求解所提出的模型；通过对模型求解可以得到合理的 飞机计划恢复方案；除机场关闭之外，飞机维护任务、过站时间等因素对于航班的执行具有重要影响，因此在飞机计划恢复问题中应该考虑多方面的因素。算例结果证明了模型和算法的正确性 和有效性。

In order to alleviate the negative impacts resulted from irregular flights, an aircraft schedule recovery model under irregular flights considering multiple impact factors was proposed. The objective of the model is to minimize the weighted cost. The factors considered in the objective function include flight delays and cancellations, aircraft maintenance and the changes related to aircrafts and airports during aircraft maintenance and flight operation. Moreover, other factors such as idle time were considered in constraints. The genetic algorithm was designed to solve the model. A numerical example was designed based on large-scale flight data to verify the model and algorithm. The result shows that the genetic algorithm was suitable to solve the proposed model; the reasonable schedule of aircraft schedule recovery was obtained through solving the model; in addition to airport closed, other factors such as aircraft maintenance tasks and idle time took significant impacts on flight operation. Therefore, the multiple factors should be considered in aircraft schedule recovery problem. The validity and effectiveness of the model and algorithm were demonstrated through the result of the numerical example.