The emergence of connected autonomous vehicles (CAVs) is not only improving the efficiency of transportation, but also providing new opportunities for the sustainable development of transportation. Taking advantage of the energy consumption of CAVs to promote the sustainable development of transportation has attracted extensive public attention in recent years. This paper develops a mathematical approach to investigating the problem of the optimal implementation of dedicated CAV lanes while simultaneously considering economic and environmental sustainability. Specifically, the problem is described as a multi-objective bi-level programming model, in which the upper level is to minimize the system-level costs including travel time costs, CAV lane construction cost, and emission cost, whereas the lower level characterizes the multi-class network equilibrium with a heterogeneous traffic stream consisting of both human-driven vehicle (HVs) and CAVs. To address the multi-objective dedicated CAV lane implement problem, we propose an integrated solution framework that integrates a non-dominated sorting genetic algorithm II (NSGA-II) algorithm, diagonalized algorithm, and Frank–Wolfe algorithm. The NSGA-II was adopted to solve the upper-level model, i.e., hunting for the optimal CAV lanes implementation schemes. The diagonalized Frank–Wolfe (DFW) algorithm is used to cope with multi-class network equilibrium. Finally, numerical experiments were conducted to demonstrate the effectiveness of the proposed model and solution method. The experimental results show that the total travel time cost, total emission cost, and total energy consumption were decreased by about 12.03%, 10.42%, and 9.4%, respectively, in the Nguyen–Dupuis network as a result of implementing the dedicated CAV lanes.

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.