Optimization of subsidized air transport networks using electric aircraft

Abstract

Electric aircraft represent a major technological breakthrough with a promise of revolutionizing aviation systems towards more sustainable and accessible services. Prominent electric aircraft prototypes feature limited seating capacity and short ranges, which make them well-suited for efficiently operating thin routes—particularly, regional routes serving remote regions—in the near future. To capitalize on this opportunity, this paper proposes an original optimization framework in support of the strategic design of subsidized air transport networks using electric aircraft. We first develop a quadratic optimization model to disaggregate air transport demand data based on demand generation and allocation properties, resulting in refined demand estimates at the territorial scale (instead of at the airport level). We then develop an integrated bi-objective optimization model for network and fleet planning, utilizing a novel time-space-energy formulation. This model aims to balance the two primary objectives of planning subsidized air transport networks: maximizing passenger surplus and minimizing system-wide subsidization costs, while incorporating detailed modeling of demand accommodation and electric aircraft operations. To address large-scale problems, we develop a solution approach involving reformulation and a tailored binary relaxation scheme. By considering a real-world case study of Sweden, we demonstrate the benefits of the proposed approach and highlight its major insights—in terms of route network, fleet, number of chargers, flight schedules, fleet assignment and environmental emissions—with a comparison of conventional, electric, and mixed fleets. Our results demonstrate that a complete substitution of first-generation electric aircraft may diminish consumer surplus, while a combined use of electric and conventional aircraft yields superior solutions, resulting in higher passenger surplus and reduced emissions for the same subsidy spending.