Vertiport Planning for Urban Aerial Mobility: An Adaptive Discretization Approach

Wang Kai (), Alexandre Jacquillat () and Vikrant Vaze ()
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Wang Kai: School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Alexandre Jacquillat: Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142
Vikrant Vaze: Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire 03755

Manufacturing & Service Operations Management, 2022, vol. 24, issue 6, 3215-3235

Abstract: 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.

Keywords: urban aerial mobility; network planning; adaptive discretization (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:24:y:2022:i:6:p:3215-3235

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