Optimization of Urban Air Mobility Service Scheduling Considering Uncertain Demand and Fleet Heterogeneity

Abstract

Urban air mobility (UAM) is becoming a promising transportation mode. The consideration of heterogeneous fleets in UAM becomes imperative because of heightened cost-efficiency demands from service providers. This study proposes an optimization model for flight scheduling of heterogeneous fleets in UAM systems under demand uncertainty scenarios, exploring the impact of demand uncertainty on heterogeneous fleets. We conducted a study aimed at minimizing the generalized costs to explore the balance between service efficiency and quality. In addition, we utilized a scenario-based robust optimization framework and incorporated considerations of heterogeneous fleets to analyze their sensitivity to uncertain factors. Through a case study in the Beijing-Tianjin region of China, we found that, compared with four-seat homogeneous fleets, the implementation of heterogeneous fleets demonstrates cost reduction potentials of 9% and 5% under certain and uncertain scenarios. In addition, heterogeneous fleets exhibit high sensitivity to uncertainty, which may result in more conservative service providers receiving higher generalized costs. These findings can furnish valuable insights for service providers to deliver transportation services utilizing heterogeneous UAM systems in the future.