AI 中文总结
该研究针对航空公司运营中 RUL 不确定性下 TA 与 MS 单独优化的问题，提出联合整合 TA、MS 与 PdM 的随机优化框架，经真实数据验证可降低运营风险。
AI 中文摘要
确保航空公司运营的可靠性、安全性与经济效率，需要维护与机队调度策略明确考虑剩余使用寿命（Remaining Useful Life, RUL）预测中的不确定性。然而，将 prognostics 不确定性整合至运营决策仍是一项重大挑战。实际运营中，尾部分配（Tail Assignment, TA）与维护调度（Maintenance Scheduling, MS）通常被单独或按顺序优化，尽管二者存在强相互依赖关系，却限制了预测健康信息的有效利用。本文提出一种统一优化框架，在带有置信区间的 RUL 下联合整合 TA、MS 与预测性维护（Predictive Maintenance, PdM）。该问题被构建为随机混合整数线性规划，通过嵌入神经网络代理模型以近似 RUL 不确定性导致的预期中断成本，开发出可扩展的求解方法。所提框架基于真实航空公司数据生成的运营场景进行评估。结果表明，与确定性及顺序方法相比，在联合规划模型中明确纳入 prognostics 不确定性可降低运营风险，即下游中断成本与航班取消数量，代价是规划成本出现适度增长。这些发现凸显了将预测性维护与运营规划紧密耦合的价值，并证明了代理辅助随机优化在面向可扩展、感知不确定性的航空公司决策中的潜力。
英文摘要
Ensuring reliability, safety, and economic efficiency in airline operations requires maintenance and fleet scheduling strategies that explicitly account for uncertainty in Remaining Useful Life (RUL) predictions. However, the integration of prognostic uncertainty into operational decision-making remains a major challenge. In practice, tail assignment (TA) and maintenance scheduling (MS) are typically optimized separately or sequentially, thereby limiting the effective use of predictive health information despite their strong interdependencies. This paper proposes a unified optimisation framework that jointly integrates TA, MS, and predictive maintenance (PdM) under RUL with confidence intervals. The problem is formulated as a stochastic mixed-integer linear program, and a scalable solution approach is developed by embedding a neural network surrogate to approximate expected disruption costs resulting from RUL uncertainty. The proposed framework is evaluated using operational scenarios derived from real-world airline data. Results show that explicitly incorporating prognostic uncertainty in a joint planning model reduces operational risk, i.e., downstream disruption costs and flight cancellations, compared to deterministic and sequential approaches, at the expense of moderate increases in planning cost. These findings highlight the value of tightly coupling predictive maintenance with operational planning and demonstrate the potential of surrogate-assisted stochastic optimisation for scalable, uncertainty-aware airline decision-making.
