Scheduling Aerial Vehicles in an Urban Air Mobility Scheme

TL;DR

This paper addresses scheduling aerial vehicles in urban air mobility to maximize customer service and minimize energy use, proposing both an optimal ILP solution and a scalable incremental algorithm.

Contribution

It introduces a novel scheduling framework for AVs in urban air mobility, including an ILP model and a scalable incremental algorithm for large-scale scenarios.

Findings

ILP provides optimal scheduling results.

Incremental algorithm improves scalability for large problems.

Scheduling reduces energy consumption and increases serviced customers.

Abstract

Highly populated cities face several challenges, one of them being the intense traffic congestion. In recent years, the concept of Urban Air Mobility has been put forward by large companies and organizations as a way to address this problem, and this approach has been rapidly gaining ground. This disruptive technology involves aerial vehicles (AVs) for hire than can be utilized by customers to travel between locations within large cities. This concept has the potential to drastically decrease traffic congestion and reduce air pollution, since these vehicles typically use electric motors powered by batteries. This work studies the problem of scheduling the assignment of AVs to customers, having as a goal to maximize the serviced customers and minimize the energy consumption of the AVs by forcing them to fly at the lowest possible altitude. Initially, an Integer Linear Program (ILP) formulation is presented, that is solved offline and optimally, followed by a near-optimal algorithm, that solves the problem incrementally, one AV at a time, to address scalability issues, allowing scheduling in problems involving large numbers of locations, AVs, and customer requests.