Aircraft fleet readiness depends on the interaction between mission demand, equipment condition, maintenance capacity, and spare-parts availability. Evaluating these factors separately can obscure how support decisions affect subsequent mission execution. This study develops a hierarchical closed-loop simulation framework for aircraft fleet operations and maintenance under resource constraints. An entity-level, event-driven model links mission execution, subsystem failures and remaining life, maintenance work orders, and spare replenishment. Explicit observation–command–state-update interfaces separate flight dispatch, maintenance triggering, and maintenance execution from physical-state updates, allowing policies to be compared using the same support processes. A computational study of eight same-type aircraft over a 30-day campaign uses 30 replications per scenario to examine resource configurations, maintenance policies, and mission demand. The results distinguish maintenance-bay and spare-parts bottlenecks and show that relieving one constraint can shift pressure to another. Under the tested resources, earlier condition-based maintenance increases maintenance activity and waiting without a conclusive improvement in mission performance. Higher demand increases sortie generation but reduces availability, mission-batch success, and demand fulfillment. The framework supports resource assessment and maintenance-policy comparison while identifying the operational consequences of mismatches between mission requirements and support capacity.

Fleet-level prognostics and health management (PHM) requires decisions that extend beyond diagnosis, remaining useful life estimation, and maintenance timing. Mission commitment, aircraft dispatch, maintenance, and spare-parts support consume the same finite sustainment capacity but operate at different temporal scales. This study investigates whether explicitly separating a mission-horizon commitment decision from day-to-day sustainment execution improves fleet-level decision quality, and under which operating regimes such a hierarchy is valuable. We develop a mission-horizon hierarchical reinforcement learning (MH-HRL) framework in which a learned weekly policy selects a portfolio of missions from a seven-day forecast and specifies a coarse sustainment posture, while a fixed daily executor implements aircraft assignment, maintenance prioritization, and replenishment decisions. Fleet evolution is represented by a next-event discrete-event simulator with stochastic mission arrivals, degradation/failure processes, repairs, and logistics lead times. Five decision policies—reactive rule-based control, rolling-horizon planning, flat PPO, a hierarchy without the learned posture signal, and the full MH-HRL policy—are evaluated over nine mission-load/support-capacity regimes using 10 paired common-random-number seeds (450 formal runs). The hierarchical policies consistently improve the composite mission-readiness-cost utility, with the full policy outperforming the rule baseline in all nine regimes after Holm correction. The benefit is not obtained by maximizing mission acceptance: the hierarchical policy accepts fewer missions but achieves higher accepted-mission success and fleet availability while substantially reducing failure-driven sustainment cost. Ablation further shows that approximately 97% of the Smart vs. Rule improvement is attributable to the weekly daily temporal decomposition rather than the additional posture signal. These results support that the principal value of hierarchy in the studied fleet-PHM setting is long-horizon commitment control, and its marginal value increases with mission pressure.