Vertiport Planning for Urban Aerial Mobility: An Adaptive Discretization Approach
优化了城市空中交通系统中垂直起降场的数量、位置和容量，提出一种自适应离散化算法，收敛到1%最优性差距，发现高容量枢纽站集中服务长途出行最有利，且盈利能力对网络规划和客户期望高度敏感。
Problem definition: Electric vertical-takeoff-and-landing (eVTOL) vehicles enable urban aerial mobility (UAM). This paper optimizes the number, locations, and capacities of vertiports in UAM systems while capturing interdependencies between strategic vertiport deployment, tactical operations, and passenger demand. Academic/practical relevance: The model includes a “tractable part” (based on mixed-integer second-order conic optimization) and also a nonconvex demand function. Methodology: We develop an exact algorithm that approximates nonconvex functions with piecewise constant segments, iterating between a conservative model (which yields a feasible solution) and a relaxed model (which yields a solution guarantee). We propose an adaptive discretization scheme that converges to a global optimum—because of the relaxed model. Results: Our algorithm converges to a 1% optimality gap, dominating static discretization benchmarks in terms of solution quality, runtimes, and solution guarantee. Managerial implications: We find that the most attractive structure for UAM is one that uses a few high-capacity vertiports, consolidating operations primarily to serve long-distance trips. Moreover, UAM profitability is highly sensitive to network planning optimization and to customer expectations, perhaps even more so than to vehicle specifications. Therefore, the success of UAM operations requires not only mature eVTOL technologies but also tailored analytics-based capabilities to optimize strategic planning and market-based efforts to drive customer demand. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1148 .