A Two-stage Stochastic Programming Approach for Aircraft to Ground Resource Assignment

This paper presents a two-stage stochastic optimization model for the assignment problem of aircraft to gate and charging stations in urban air mobility (UAM). We develop a rigorous mathematical model for the ground resource assignment with electric vertical takeoff and landing (eVTOL) aircraft in which the uncertain charging times and constraints of required infrastructure are captured. We employ scenario-based approaches to capture the uncertain charging times of aircraft in terms of a set of scenarios, thus reformulating the problem as a large-scale mixed integer linear program using the Python-based Pyomo modeling framework. The obtained problem, which is called deterministic equivalent form, can then be solved using the branch and cut algorithm embedded in available MILP solvers. Performance measuring criteria are also implemented to see the effectiveness of incorporating uncertainties in the ground resource assignment and indicate future improvements.