城市空中交通方案中的飞行器调度
Scheduling Aerial Vehicles in an Urban Air Mobility Scheme
AI总结：
针对城市交通拥堵问题，本研究围绕城市空中交通方案中的飞行器调度展开，提出ILP公式与近优算法，旨在最大化服务客户并最小化能耗，同时解决大规模场景的可扩展性问题。
摘要：
人口稠密的城市面临诸多挑战，其中之一是严重的交通拥堵。近年来，大型企业和组织提出了城市空中交通（Urban Air Mobility）概念以解决该问题，且这一方案正迅速普及。这项颠覆性技术涉及可租用的飞行器（AVs），客户可使用其在大城市内的地点间出行。由于这类飞行器通常采用电池供电的电动机，该概念有望大幅减少交通拥堵并降低空气污染。本研究聚焦飞行器与客户的调度分配问题，目标是最大化服务客户数量，并通过强制飞行器以尽可能低的高度飞行来最小化其能耗。首先提出整数线性规划（ILP）公式，可离线最优求解；随后提出近优算法，通过逐个增量求解飞行器调度问题以解决可扩展性问题，从而支持包含大量地点、飞行器和客户请求的调度场景。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
