Integrated Modeling for the Optimization of Air Transport Networks Under Multiple Constraints

This paper presents an integrated multi-objective optimization model for air transport networks in the West African Economic and Monetary Union (WAEMU) region, addressing the challenges of fragmented infrastructure and regulatory constraints. By simultaneously optimizing route planning, fleet assignment, scheduling, demand management, and operational costs, the framework minimizes expenses while maximizing passenger satisfaction. The model is empirically validated using data from the World Bank and African Civil Aviation Commission, incorporating passenger traffic, socioeconomic indicators, and fleet specifications. Computational simulations using Python's PuLP library demonstrate trade-offs across scenarios, including high demand, fleet expansion, cost sensitivities, and a supplementary carbon-price sensitivity aligned with ICAO's CORSIA framework. Validation against airline benchmarks yields a mean absolute percentage error (MAPE) of 9.7% and an R^2 of 0.91 for cost estimates. Results highlight optimal fleet allocations, with wide-body aircraft preferred for high-demand routes, and show that carbon pricing shifts the Pareto frontier upward by about 2-5% under typical WAEMU operating-cost ranges. The work bridges gaps in existing literature by offering a holistic, data-driven approach tailored to African regional dynamics.