Robust aircraft maintenance routing problems (RAMRPs) have been extensively studied due to their significance on flight schedule reliability. In the current practice, the traditional huge A-check program is divided into multiple heterogenous small maintenance packages (with different maintenance durations and risk levels) and performed differently during the ground time between two connected flights. As the available maintenance time becomes relatively shorter, maintenance becomes a new source of disruption. Thus, the assignment of becomes more critical. Accordingly, this paper proposes a robust strategy to alleviate the disruption due to maintenance risk. The strategy is to provide each package the appropriate amount of buffer time based on the fuzzy risk assessment. Correspondingly, a robustness measure is developed to construct the RAMRP model. As its impact can be better illustrated in large scale problems, we adopt matheuristic approach. We examine 500 disruption scenarios, the results show that the proposed strategy can greatly reduce flight delays (i.e., an 82.7% reduction in the number of delayed flights) while maintaining an acceptable decrease in aircraft utilization (i.e., a 32.42% increase in average unused flying times) compared to those obtained from the traditional model. Additionally, the proposed model is further investigated from the perspectives of aircraft utilization constraints and the risk-averse attitude of airlines. Our study validates its cost-effectiveness for delay risks reduction and highlights the significance of being properly risk-averse in decision making.

中文翻译：

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具有异构飞机维护任务的稳健飞机维护路线

鲁棒飞机维修路线问题（RAMRP）由于其对航班时刻可靠性的重要性而得到了广泛的研究。在目前的实践中，传统的庞大的A-check程序被分成多个异构的小型维护包（具有不同的维护时长和风险级别），并在两个连接的航班之间的地面时间里进行不同的执行。随着可用维护时间变得相对较短，维护成为新的干扰源。因此， 的分配变得更加关键。因此，本文提出了一种稳健的策略来减轻维护风险造成的中断。该策略是根据模糊风险评估为每个包提供适当的缓冲时间。相应地，开发了稳健性度量来构建RAMRP模型。由于它的影响可以在大规模问题中更好地说明，因此我们采用数学方法。我们检查了 500 个中断场景，结果表明，所提出的策略可以大大减少航班延误（即延误航班数量减少 82.7%），同时保持飞机利用率在可接受的范围内（即平均未使用飞机增加 32.42%）飞行时间）与传统模型获得的结果进行比较。此外，从飞机使用约束和航空公司风险规避态度的角度进一步研究了所提出的模型。我们的研究验证了其降低延误风险的成本效益，并强调了在决策中适当规避风险的重要性。