Airlines face the imperative of resource management to curtail costs, necessitating the solution of several optimization problems such as flight planning, fleet assignment, aircraft routing, and crew scheduling. These problems present some challenges. The first pertains to the common practice of addressing these problems independently, potentially leading to locally optimal outcomes due to their interconnected nature. The second challenge lies in the inherent uncertainty associated with parameters like demand and non-cruise time. On the other hand, airlines can employ a strategy known as codesharing, wherein they operate shared flights, in order to minimize these challenges. In this study, we introduce a novel mathematical model designed to optimize flight planning, fleet assignment, and aircraft routing decisions concurrently, while accommodating for codesharing. This model is formulated as a three-stage non-linear mixed-integer problem, with stochastic parameters representing the demand and non-cruise time. For smaller-scale problems, optimization software can effectively solve the model. However, as the number of flights increases, conventional software becomes inadequate. Moreover, considering a wide array of scenarios for stochastic parameters leads to more robust results; however, it is not enabled because of the limitations of optimization software. In this work, we introduce two new simulation-based metaheuristic algorithms for solving large-dimensional problems, collectively called “simheuristic.” These algorithms integrate the Monte Carlo simulation technique into Simulated Annealing and Cuckoo Search. We have applied these simheuristic algorithms to various problem samples of different flight sizes and scenarios. The results demonstrate the efficacy of our proposed modeling and solution approaches in efficiently addressing flight scheduling, fleet assignment, and aircraft routing problems within acceptable timeframes.

This study presents an integrated optimization framework for fleet assignment, flight scheduling, and aircraft routing under uncertainty, addressing a core challenge in airline operational planning. A three-stage stochastic mixed-integer nonlinear programming model is developed that, for the first time, simultaneously incorporates station purity constraints, codeshare agreements, and overbooking decisions. The formulation also includes realistic operational factors such as stochastic passenger demand and non-cruise times (NCT), along with adjustable cruise speeds and flexible departure time windows. To handle the computational complexity of this large-scale stochastic problem, a Sample Average Approximation (SAA) scheme is combined with two tailored metaheuristic algorithms: Simulated Annealing and Cuckoo Search. Extensive experiments on real-world flight data demonstrate that the proposed hybrid approach achieves tight optimality gaps below 0.5%, with narrow confidence intervals across all instances. Moreover, the SA-enhanced method consistently yields superior solutions compared with the CS-based variant. The results highlight the significant operational and economic benefits of jointly optimizing codeshare decisions, station purity restrictions, and overbooking policies. The proposed framework provides a scalable and robust decision-support tool for airlines seeking to enhance resource utilization, reduce operational costs, and improve service quality under uncertainty.