Optimal Siting of Sub-Urban Air Mobility (sUAM) Ground Architectures using Network Flow Formulation

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Venkatesh, Nikhil
Payan, Alexia P.
Justin, Cedric Y.
Kee, Ethan C.
Mavris, Dimitri N.
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Air Mobility (AM) operating models have steadily made their way into public conscience over the past decade due to increased research activity pioneered by large technology corporations such as Uber and Amazon. Estimates concur that there are around 250 startup businesses with 22 major players working on such technologies with over $25 billion dollars in venture capital funding in 2017[1]. Given the meteoric rise of Air Mobility as one of the leading 21st century disruptive technologies, research effort across the spectrum of functions that can make AM concepts a reality are burgeoning - ranging from vehicle design to operations planning. More specifically, research efforts within the operations planning space deal with service route identification, ground infrastructure (such as charging stations and ports) placement and others. To this effect, the present study seeks to evaluate the feasibility and tractability of a formalized optimization method towards the siting of "vertiports" - ground infrastructure that aids the embarkation and disembarkation of AM commuters - as applied to a Sub-Urban Air Mobility (sUAM) operating model. Mixed-Integer Programming (MIP) formulations offer qualified benefits over other heuristic methods and the authors are confident of their relative performance given the proven track record of such methods in solving generalized facility location problems (GFLP). In this study, two optimization problems were considered: capacitated vertiport siting, where any vertiport considered would need to adhere to capacity constraints; and uncapacitated vertiport siting, where any vertiport considered does not have any capacity limit and can service unlimited demand. Results indicate that a network flow formulation using an MIP methodology is able to adequately place vertiports for sUAM business operations to satisfy demand flows associated with home-work commute.
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