Sustainable airline operations: A season-based optimization framework for flight scheduling and aircraft assignment

Global air transport networks face significant challenges in optimizing flight scheduling and aircraft assignment due to network complexity and market volatility. This study addresses the critical problem of sustainable flight schedule optimization and aircraft allocation. Taking the carbon emission into account, we construct a dual-objective optimization model aiming to maximize operational profits and minimize carbon emissions. The model scale is reduced through time segmentation and variable optimization strategies. A spatio-temporal non-dominated sorting genetic algorithm (ST-NSGA) is developed to solve the proposed model. Numerical experiments demonstrate the efficacy of our approach: the ST-NSGA delivers near-optimal solutions, with a mere 2.68 % gap compared to the Gurobi solver on tractable problem sizes. Furthermore, comparative studies show that our multi-objective strategy achieves an 11.26 % reduction in carbon emission without compromising profitability. The ST-NSGA also exhibits superior performance than other multi-objective optimization algorithm in solving large-scale instances. These findings confirm that the proposed model and algorithm significantly enhance the sustainability of flight operations while maintaining economic viability.